Rainfall radar intelligent networking layout and collaborative observation method and system based on global coordination

Abstract

This invention discloses a method and system for intelligent networking and collaborative observation of rainfall measurement radar based on global coordination, belonging to the field of collaborative observation technology. The method includes: deploying rainfall measurement radar networks based on multi-source basic data to obtain basic configuration information for the radar network; identifying weather scene types and constructing weather scene state variables based on these types; adaptively simulating the weather scene types according to pre-configured scanning parameters to output a set of candidate scanning parameters; optimizing the candidate scanning parameter set according to a network performance objective function to obtain the optimal scanning parameters; triggering the collaborative observation control process when the weather scene state variables meet preset transition conditions; and determining and distributing the optimal scanning parameters to participating radar stations based on the user operation mode identifier. This scheme can effectively achieve intelligent and efficient rainfall measurement radar networking and collaborative observation.

Description

Technical Field

[0001] This invention relates to the field of collaborative observation technology, and in particular to a method and system for intelligent networking layout and collaborative observation of rainfall radar based on global coordination. Background Technology

[0002] As a core piece of meteorological detection equipment, rain-measuring radar has become a key technological means for heavy rain monitoring and disaster early warning. To improve the coverage and reliability of regional precipitation monitoring, existing technologies mostly adopt a "multi-radar networking" mode, which involves deploying multiple rain-measuring radars within a province or river basin. By using methods such as data mosaicking and reflectivity synthesis, the detection blind spots of a single radar are compensated for, thereby improving the overall effect of precipitation monitoring.

[0003] However, existing rain-measuring radar networking technology still has significant limitations: First, network layout relies on manual experience and lacks scientific planning for overall coverage. Existing sites are prone to forming monitoring blind spots due to insufficient terrain adaptability, and the selection of sites to be built lacks multi-source data support and intelligent optimization. Second, simulation operation only uses fixed parameters and cannot dynamically adapt to different weather scenarios such as clear sky / light rain and heavy rainfall, making it difficult to predict network effectiveness. Third, equipment coordination is insufficient, with each radar operating independently in a fixed mode, making it impossible to achieve intensive observation of key areas and reasonable allocation of resources during heavy rainfall. Fourth, equipment status monitoring is lagging, and fault response relies on manual troubleshooting, which easily interrupts monitoring continuity. Summary of the Invention

[0004] To address the aforementioned technical issues, this invention provides a method and system for intelligent networking layout and collaborative observation of rainfall measurement radar based on holistic planning, in order to meet the operational needs of refined and intelligent precipitation monitoring in intelligent networking layout and collaborative observation technology for rainfall measurement radar.

[0005] In a first aspect, the present invention provides a method for intelligent networking layout and collaborative observation of rainfall measuring radar based on global coordination, the method comprising the following steps:

[0006] Based on multi-source basic data, a rain-measuring radar network layout is constructed to obtain basic configuration information for the radar network. Weather scene types are identified, and weather scene state variables are constructed according to the weather scene types. Adaptive simulation is performed on the weather scene types according to pre-configured scanning parameters to output a set of candidate scanning parameters. The set of candidate scanning parameters is optimized according to the network performance objective function to obtain the optimal scanning parameters. When the weather scene state variables meet the preset transition conditions, the collaborative observation control process is triggered. Based on the user operation mode identifier, the optimal scanning parameters are determined and sent to the radar stations participating in the network to achieve collaborative observation of the radar network.

[0007] Based on the first aspect of the approach, precise site selection driven by multi-source data and intelligent algorithm optimization are used to achieve a comprehensive layout of rain-measuring radar stations, effectively solving the problem of blind site selection in traditional methods; clarifying the collaborative relationships and coverage of stations, improving the coverage ratio of areas with heavy rainfall, and laying the foundation for high-precision monitoring.

[0008] In some possible implementations, the radar network layout for rainfall measurement is determined based on multi-source basic data to obtain basic configuration information for the radar network. This includes: constructing a basic database for radar network layout based on multi-source basic data; performing coarse and fine selection of radar sites based on the basic database, saving radar sites that meet monitoring requirements as candidate radar sites; constructing a radar site network layout optimization model based on existing radar sites and candidate radar sites to obtain site combination schemes that meet the optimization objectives; the optimization model uses whether a radar site is included in the network as the decision variable, monitoring coverage and data redundancy as constraints, and minimizing the construction cost of radar sites as the optimization objective; and selecting site combinations that can form a collaborative network relationship through the maximum collaborative distance, displaying the basic configuration information of the site combinations on a visual interface.

[0009] In some possible implementations, weather scene type identification includes: collecting echo data from various radars; retrieving rainfall intensity from the echo data; calculating the average rainfall intensity within the area; if the average rainfall intensity is greater than a preset threshold, it is determined to be a heavy rainfall weather scene; otherwise, it is determined to be a clear sky / light rain weather scene.

[0010] In some possible implementations, the method further includes: constructing the weather scene state variable S. (k) When the weather scene type is determined to be a heavy rainfall weather scene, S (k) =1, when the weather scene type is determined to be a clear / light rain weather scene. (k) =0; where k represents the kth period.

[0011] In some possible implementations, the method further includes: optimizing the candidate scanning parameter set according to the network performance objective function to obtain the optimal scanning parameters, satisfying the following formula:

[0012] in, These are the optimal scanning parameters, used for collaborative observation and control. For the candidate scan parameter set, Let k represent the network performance objective function, where k represents the k-th period and i represents the i-th radar station.

[0013] In some possible implementations, the method further includes: the network performance objective function satisfies the following formula:

[0014] in, This indicates the spatial coverage index for the current period. This indicates the frequency of time updates or the timeliness of observations. These represent the equipment workload or resource consumption indicators, where α, β, and γ are the corresponding weighting coefficients.

[0015] In some possible implementations, the preset transition condition is that the weather state variable changes from 0 to 1; when the weather scene state variable meets the preset transition condition, the collaborative observation control process is triggered; based on the user's operation mode identifier, the optimal scanning parameters are determined to be sent to the radar stations participating in the network to achieve collaborative observation of the radar network, including: when the weather scene state variable changes from 0 to 1, a collaborative observation start instruction message is sent to the user, and after the user confirms, the operation mode identifier is set to 1, and the optimal scanning parameters are sent to the radar stations participating in the network.

[0016] In some possible implementations, radar sites are coarsely selected and finely selected based on a basic database. Radar sites that meet the monitoring requirements are saved as candidate radar sites. This includes the following steps: Based on the basic database, coarsely selected radar sites that meet the monitoring requirements are determined within the target area; a preset neighborhood is determined with the coarsely selected radar sites as the center, and several candidate sampling points are generated within the neighborhood; the finely selected radar sites are determined according to the spatial attributes and constraints of the sampling points; the obstruction rate of the finely selected radar sites is calculated, and sites with obstruction rates exceeding a preset threshold are removed to obtain candidate radar sites that meet the monitoring requirements.

[0017] In some possible implementations, the method further includes constraining the total scan time of each radar within a scan cycle, wherein the total scan time of the i-th radar in the k-th cycle satisfies the following formula:

[0018] in, Let be the angular velocity of the i-th radar in this period. Let L be the maximum allowed scanning time, and L be the number of scanning layers.

[0019] Secondly, the present invention also provides a rain-measuring radar intelligent networking layout and collaborative observation system based on global coordination, the system comprising: The rain-measuring radar network layout module is used to lay out the rain-measuring radar network based on multi-source basic data and obtain the basic configuration information of the radar network. The scene adaptive simulation module is used to identify weather scene types, construct weather scene state variables based on the weather scene type, perform adaptive simulation of the weather scene type according to pre-configured scanning parameters, and output a set of candidate scanning parameters. The network performance evaluation module is used to optimize the candidate scanning parameter set according to the network performance objective function to obtain the optimal scanning parameters; The collaborative observation module is used to trigger the collaborative observation control process when the weather scene state variables meet the preset transition conditions; based on the user operation mode identifier, it determines the optimal scanning parameters to be sent to the radar stations participating in the network, so as to realize the collaborative observation of the radar network.

[0020] Thirdly, the present invention also provides an electronic device, comprising: Memory stores computer-executable instructions non-transiently; The processor is configured to run computer-executable instructions. The computer-executable instructions are executed by the processor to implement the above-mentioned intelligent networking layout and collaborative observation method for rain measurement radar based on global coordination.

[0021] Fourthly, the present invention also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer-executable instructions. When the computer-executable instructions are executed by the processor, the above-mentioned intelligent networking layout and collaborative observation method for rain measurement radar based on global coordination is realized.

[0022] This invention provides a method and system for intelligent networking and collaborative observation of rainfall measurement radars based on comprehensive regional planning. The method involves: deploying rainfall measurement radar networks based on multi-source basic data to obtain basic configuration information; identifying weather scene types and constructing weather scene state variables accordingly; adaptively simulating weather scene types using pre-configured scanning parameters to output a set of candidate scanning parameters; optimizing the candidate scanning parameter set based on a network performance objective function to obtain the optimal scanning parameters; triggering a collaborative observation control process when the weather scene state variables meet preset transition conditions; and determining the optimal scanning parameters to be sent to participating radar stations based on user operation mode identifiers, thus achieving collaborative observation of the radar network. Therefore, this invention solves the problems of blind layout planning, monotonous simulation operation, inefficient collaborative control, and lagging equipment support in existing technologies, effectively realizing intelligent and efficient rainfall measurement radar networking and collaborative observation. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 A flowchart illustrating a method for intelligent networking layout and collaborative observation of rain-measuring radar based on global coordination, provided in an embodiment of the present invention;

[0025] Figure 2 A schematic diagram of a rain-measuring radar intelligent networking layout and collaborative observation system based on global coordination is provided in an embodiment of the present invention;

[0026] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Figure 1 A method for intelligent networking and collaborative observation of rainfall radar based on global coordination is presented, such as... Figure 1 As shown, the method includes the following steps.

[0029] S100. Based on multi-source basic data, the rain measurement radar network layout is carried out to obtain the basic configuration information of the radar network.

[0030] S101. Construct a basic database for radar network layout based on multi-source basic data.

[0031] Specifically, the system integrates multi-source data layers, including Geographic Information System (GIS), high-precision topographic data, river network distribution, flash flood disaster points, population density, and key flood control areas, to construct a basic database for site selection and radar network layout.

[0032] S102. Based on the basic database, complete the coarse and fine selection of radar sites, and save the radar sites that meet the monitoring requirements as candidate radar sites.

[0033] S1021. Based on the basic database, select radar sites that meet the monitoring requirements within the target area.

[0034] Specifically, the preliminary selection criteria for meeting monitoring requirements include: identifying high-risk areas based on the susceptibility of flash floods and historical flash flood events; determining whether densely populated areas or critical facilities are covered, and identifying areas with high exposure; identifying areas prone to heavy rainfall based on historical rainfall statistics, distribution of short-duration extreme rainfall, and information on frequent severe convection; identifying gap areas based on the coverage contour formed by the existing radar station coverage areas, where gap areas may include coverage blank areas, weak edge areas, and / or obvious blind spots caused by single station failures (also known as areas with insufficient redundancy); and identifying small watersheds and valley areas with rapid confluence and sensitive response based on hydrological response characteristics such as watershed scale, slope, and channel morphology. When a radar station within the target area meets at least one of the above criteria, the radar station is determined as a preliminary selection station.

[0035] S1022. Determine a preset neighborhood centered on the coarsely selected radar site, generate several candidate sampling points within the neighborhood, and determine the selected radar site based on the spatial attributes and constraints of the sampling points.

[0036] Specifically, a preset neighborhood is determined centered on the coarsely selected radar site, and several candidate sampling points are generated within the neighborhood. These candidate sampling points can be formed based on road nodes, constructible open spaces, and relatively high points in the terrain. The constructability of the candidate sampling points is judged based on slope and altitude. Sites with terrain obstruction risks are eliminated based on elevation and terrain openness. The accessibility of the candidate sampling points to targets such as flash flood disaster points, waterlogging-prone areas, key cross sections, or key flood control targets is evaluated. 3-5 candidate sampling points that meet the constraints (constructability, accessibility, and acceptable obstruction risk) are selected as the final radar sites.

[0037] S1023. Calculate the obstruction rate of the selected radar sites, remove sites with obstruction rates exceeding a preset threshold, and obtain candidate radar sites that meet the monitoring requirements.

[0038] Specifically, a monitoring field of view simulation and terrain adaptability assessment are conducted on selected radar sites. Sites with a field of view obstruction rate exceeding a preset threshold are eliminated, resulting in selected radar sites that meet the requirements for monitoring field of view, effective detection radius, coverage of key areas, and terrain adaptability as candidate radar sites. Here, obstruction rate refers to the proportion of the detection space within the preset monitoring elevation angle and detection radius that is obstructed by terrain to the total detection space; obstruction rate = number or area of ​​detection units obstructed by terrain / total number or area of ​​detection units.

[0039] S103. Construct a radar site network layout optimization model based on existing radar sites and candidate radar sites to obtain a site combination scheme that meets the optimization objective. The optimization model takes whether a radar site is included in the network as the decision variable, monitoring coverage and data redundancy as constraints, and minimizing the construction cost of radar sites as the optimization objective.

[0040] Specifically, a candidate site set is constructed based on existing radar sites and alternative radar sites. Let the selection status of the i-th site in the candidate site set be xi, where xi=1 indicates that the site has been selected into the network layout, and xi=0 indicates that it has not been selected. Under the constraints of coverage and redundancy, a network layout scheme that balances monitoring efficiency and economy is obtained by minimizing the overall construction cost as the optimization objective.

[0041] The overall coverage rate is calculated based on the coverage of the selected sites; the proportion of monitoring units simultaneously covered by two or more sites is used as the data redundancy; and the overall coverage rate of no less than 90% and the data redundancy of no more than 15% are used as constraints.

[0042] As a possible implementation, a genetic algorithm or a particle swarm optimization algorithm can be used for iterative optimization to solve the problem, adjust the site combination scheme, and obtain the optimal solution that satisfies the constraints. Among them, the number of iterations of the genetic algorithm is not less than 50, and the population size of the particle swarm optimization algorithm is not less than 30.

[0043] S104. Select site combinations that can form a collaborative network relationship by filtering out the maximum collaborative distance, and display the basic configuration information of the site combinations in the visualization interface.

[0044] The maximum cooperative distance refers to the inter-station distance threshold used to determine whether radar stations can form a cooperative observation / cooperative control combination. It can be understood that if the distance between radar stations is less than the maximum cooperative distance, it indicates that a cooperative network relationship exists. Basic configuration information includes at least this cooperative network relationship, and may also include station location information, station identification information, and station coverage area.

[0045] In one possible implementation, the basic configuration information of the site combination can be displayed through two analysis modes. These analysis modes include a device selection mode and a region selection mode. In the device selection mode, a single radar site is selected, and the system displays core information such as the site's collaborative network list, surrounding obstruction status, coverage area, number of flash flood disaster points covered, and radar boundary area. In the region selection mode, a target observation area is selected on the geographic layer, and compatible devices within the area are identified and network combinations are formed. Key data such as the combination's obstruction rate, coverage of disaster points, overlapping area area, and distribution of monitoring blind spots are displayed, clearly defining the effective network range.

[0046] S200: Identify the weather scene type, construct weather scene state variables based on the weather scene type, perform adaptive simulation of the weather scene type according to the pre-configured scanning parameters, and output a set of candidate scanning parameters.

[0047] S201. Identify the weather scene type and construct weather scene state variables based on the weather scene type.

[0048] Specifically, echo data from each radar is collected; rainfall intensity is retrieved based on the echo data; the average rainfall intensity within the region is calculated, and a weather scene state variable S is constructed. (k) If the average rainfall intensity is greater than a preset threshold, it is determined to be a heavy rainfall weather scenario. (k) =1; otherwise, it is determined to be a clear / light rain weather scenario, S (k) =0. Where k represents the k-th period.

[0049] As one possible implementation, when the weather scene state variable S (k) When the value changes from 0 to 1, it is determined that the current area has entered a period of heavy rainfall, and this change serves as a trigger condition for collaborative observation.

[0050] As an optional implementation, when the weather scene state variable S (k) If the value remains at 0 for an extended period, the system will either maintain or enter the normal monitoring mode.

[0051] S202. Perform adaptive simulation of the weather scene type based on the pre-configured scanning parameters, and output a set of candidate scanning parameters.

[0052] Specifically, for heavy rainfall scenarios, pre-configured scanning parameters are used to perform two-dimensional / three-dimensional simulations to simulate the radar's coverage range and coverage pattern under the current scanning parameters; environmental information related to intelligent networking is acquired, and scanning parameters with high inertia in the current networking environment are selected from the pre-configured scanning parameters to construct a candidate scanning parameter set.

[0053] In one possible implementation, the pre-configured scanning parameters for heavy rainfall scenarios are elevation angle (θ=0.5°-3°) and angular velocity (ω=10°-15° / s). It can be understood that performing a volume scan based on these pre-configured parameters in heavy rainfall scenarios can also be described as executing a "low elevation angle + fast angle" volume scan.

[0054] As an optional implementation, for clear sky / light rain weather scenarios, pre-configured scanning parameters are used: number of scanning layers L = 8-12, elevation angle θ = 0.5°-15°, and angular velocity ω = 6° / s. It can be understood that these scanning parameters are the same as those used for volumetric scanning in conventional monitoring mode.

[0055] S300. Optimize the candidate scanning parameter set according to the network performance objective function to obtain the optimal scanning parameters.

[0056] Specifically, the candidate scanning parameter set is optimized according to the network performance objective function to obtain the optimal scanning parameters, which satisfy the following formula:

[0057] in, These are the optimal scanning parameters, used for collaborative observation and control. For the candidate scan parameter set, Let k represent the network performance objective function, where k represents the k-th period and i represents the i-th radar station.

[0058] The objective function for network performance satisfies the following formula:

[0059] in, This indicates the spatial coverage index for the current period. This indicates the frequency of time updates or the timeliness of observations. These represent the equipment workload or resource consumption indicators, where α, β, and γ are the corresponding weighting coefficients.

[0060] S400: When the weather scenario state quantity meets the preset transition conditions, the collaborative observation control process is triggered; based on the user operation mode identifier, the optimal scanning parameters are determined to be sent to the radar stations participating in the network, so as to realize the collaborative observation of the radar network.

[0061] Specifically, when the weather scene state quantity S (k) When the signal changes from 0 to 1, a collaborative observation instruction message is sent to the user. After the user confirms, the operation mode identifier is set to 1, and the optimal scanning parameters are sent to the radar stations participating in the network to achieve collaborative observation of the target area.

[0062] As an optional implementation, if the user does not confirm, or confirms that collaborative observation will not be performed, the operation mode flag will be set to 0, and the scan parameters will be directly set to... The parameters are then issued, and the system performs observation and control according to these scanning parameters.

[0063] As an optional implementation, during collaborative observation, the observation mode can be manually switched via the interface, and the system will follow the directly set scanning parameters. Perform observation and control.

[0064] As one possible implementation, the total scan time of each radar within a scan cycle is constrained, and the total scan time of the i-th radar in the k-th cycle satisfies the following formula:

[0065] in, Let be the angular velocity of the i-th radar in this period. Let L be the maximum allowed scanning time, and L be the number of scanning layers.

[0066] Furthermore, after observation and control based on the scanning parameters, the current k-th cycle is completed, and the monitoring loop of the k+1-th cycle is entered, and the process of steps S200-S400 is executed again.

[0067] It is understandable that after the scanning parameters are sent to the radar for observation and control, the process may also include data processing to obtain precipitation products, including:

[0068] The system preprocesses multi-radar observation data, such as denoising and data normalization, and then maps the data to the same spatial grid using a spatial registration algorithm to achieve uniform spatial resolution. For overlapping areas, a weighted averaging method is used to fuse multi-site observation data to suppress the impact of rain attenuation. Finally, quantitative precipitation is retrieved to generate precipitation products. It can be understood that the obtained precipitation products are high spatiotemporal resolution precipitation distribution and intensity fields.

[0069] Figure 2 This invention provides a globally integrated intelligent networking layout and collaborative observation system for rain measurement radar, comprising the following modules.

[0070] The rain-measuring radar network layout module is used to lay out the rain-measuring radar network based on multi-source basic data and obtain the basic configuration information of the radar network.

[0071] The scene adaptive simulation module is used to identify weather scene types, construct weather scene state variables based on the weather scene type, perform adaptive simulation of the weather scene type according to pre-configured scanning parameters, and output a set of candidate scanning parameters.

[0072] The network performance evaluation module is used to optimize the candidate scanning parameter set according to the network performance objective function to obtain the optimal scanning parameters;

[0073] The collaborative observation module is used to trigger the collaborative observation control process when the weather scene state variables meet the preset transition conditions; based on the user operation mode identifier, it determines the optimal scanning parameters to be sent to the radar stations participating in the network, so as to realize the collaborative observation of the radar network.

[0074] based on Figure 2 In addition to the collaborative observation system, this invention also provides a practical application process for collaborative observation, which specifically includes...

[0075] (1) Normal observation mode: The default operation is in the clear sky / light rain weather scenario, and all radars perform normal observations according to the pre-configured scanning parameters.

[0076] (2) Collaborative observation mode: When a heavy rainfall weather scene is detected, the system will pop up a window to prompt whether to enable the collaborative observation mode. If the user confirms that the collaborative observation mode is enabled, the system will issue a "low elevation angle + fast scan" command to the radar in the key area and a "multi-layer volume scan" command to other radars according to the optimal scanning parameters obtained by S300. At the same time, the total scanning time of a single station is constrained to T_i ≤ T_imax (T_imax=300s, which can be adjusted according to the equipment model) to avoid equipment overload. If the user chooses not to enable the collaborative observation mode, he / she can switch to manual mode and set the scanning range, detection frequency, sampling interval and other parameters. The system will then perform the observation according to the manual configuration.

[0077] (3) When an equipment malfunction is detected (e.g., a decrease in transmission power of ≥10% or an antenna rotation speed deviation of ≥2° / s), the system automatically triggers the fault diagnosis process:

[0078] I. Invoke the fault knowledge base to match the fault type corresponding to the abnormal parameters (such as power module failure, software parameter error); II. Generate a diagnostic report, including the cause of the fault, the scope of impact, and maintenance suggestions; III. For remotely repairable issues such as software parameter errors (such as abnormal scan cycle configuration), automatically execute parameter correction instructions to achieve rapid equipment recovery; for hardware faults (such as sensor damage), push maintenance work orders to maintenance personnel to ensure monitoring continuity.

[0079] The collaborative observation system of this invention is applied to electronic devices. Figure 3 A schematic diagram of the architecture of an electronic device suitable for implementing embodiments of the present invention is shown.

[0080] It should be noted that, Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of the present invention.

[0081] Those skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be implemented by instructions (computer programs), or by instructions (computer programs) controlling related hardware. These instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. The electronic device of this embodiment includes a storage medium and a processor, wherein the storage medium stores multiple instructions that can be loaded by the processor to execute any step of the method provided in the embodiments of the present invention.

[0082] Specifically, the storage medium and the processor are electrically connected directly or indirectly to enable data transmission or interaction. For example, these components can be electrically connected to each other via one or more signal lines. The storage medium stores computer-executable instructions that implement data access control methods, including at least one software functional module that can be stored in the storage medium in the form of software or firmware. The processor executes various functional applications and data processing by running the software program and module stored in the storage medium. The storage medium can be, but is not limited to, Random Access Memory (RAM), Read-Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc. The storage medium stores the program, and the processor executes the program after receiving the execution instructions.

[0083] Furthermore, the software programs and modules within the aforementioned storage medium may also include an operating system, which may include various software components and / or drivers for managing system tasks (e.g., memory management, storage device control, power management, etc.) and can communicate with various hardware or software components to provide an operating environment for other software components. The processor may be an integrated circuit chip with signal processing capabilities. The aforementioned processor may be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc., which can implement or execute the methods, steps, and logic flowcharts disclosed in this embodiment. The general-purpose processor may be a microprocessor or any conventional processor.

[0084] Since the instructions stored in the storage medium can execute the steps in any of the methods provided in the embodiments of the present invention, the beneficial effects of any of the methods provided in the embodiments of the present invention can be achieved, as detailed in the preceding embodiments, and will not be repeated here.

[0085] The above description is merely a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for intelligent networking layout and collaborative observation of rainfall measuring radar based on global coordination, characterized in that, The method includes: Based on multi-source basic data, the layout of the rain-measuring radar network is determined, and the basic configuration information of the radar network is obtained. Identify weather scene types, construct weather scene state variables based on the weather scene types, perform adaptive simulation on the weather scene types according to pre-configured scanning parameters, and output a set of candidate scanning parameters; The candidate scanning parameter set is optimized based on the network performance objective function to obtain the optimal scanning parameters; When the weather scenario state quantity meets the preset transition conditions, the collaborative observation control process is triggered; based on the user operation mode identifier, the optimal scanning parameters are determined and sent to the radar stations participating in the network to achieve collaborative observation of the radar network.

2. The method according to claim 1, characterized in that, Based on multi-source basic data, a rain-measuring radar network layout is constructed, yielding basic configuration information for the radar network, including: A basic database for radar network layout is constructed based on multi-source basic data; Based on the aforementioned basic database, a coarse and fine selection of radar sites is completed, and radar sites that meet the monitoring requirements are saved as candidate radar sites. Based on the existing radar sites and the candidate radar sites, a radar site network layout optimization model is constructed to obtain a site combination scheme that meets the optimization objective. The optimization model takes whether a radar site is included in the network as the decision variable, monitoring coverage and data redundancy as constraints, and minimizing the construction cost of radar sites as the optimization objective. By filtering out site combinations that can form a collaborative network relationship based on the maximum collaborative distance, the basic configuration information of the site combinations is displayed on the visualization interface.

3. The method according to claim 1, characterized in that, Identify weather scene types, including: Collect echo data from each radar; Rainfall intensity was retrieved from the echo data; Calculate the average rainfall intensity within the calculated area; If the average rainfall intensity is greater than a preset threshold, it is determined to be a heavy rainfall weather scenario; otherwise, it is determined to be a clear sky / light rain weather scenario.

4. The method according to claim 3, characterized in that, Based on the weather scene type, weather scene state variables are constructed, including: Constructing the weather scene state variable S (k) When the weather scene type is determined to be a heavy rainfall weather scene, S (k) =1, when the weather scene type is determined to be a clear sky / light rain weather scene, S (k) =0; where k represents the kth period.

5. The method according to claim 1, characterized in that, The candidate scanning parameter set is optimized based on the network performance objective function to obtain the optimal scanning parameters, which satisfy the following formula: ; in, These are the optimal scanning parameters, used for collaborative observation and control. For the candidate scan parameter set, Let k represent the network performance objective function, where k represents the k-th period and i represents the i-th radar station.

6. The method according to claim 5, characterized in that, The network performance objective function satisfies the following formula: ; in, This indicates the spatial coverage index for the current period. This indicates the frequency of time updates or the timeliness of observations. These represent the equipment workload or resource consumption indicators, where α, β, and γ are the corresponding weighting coefficients.

7. The method according to claim 1, characterized in that, The preset transition condition is that the weather state variable changes from 0 to 1; When the weather scenario state quantity meets the preset jump condition, the collaborative observation and control process is triggered; Based on the user operation mode identifier, the optimal scanning parameters are determined and sent to the radar sites participating in the network to achieve coordinated observation of the radar network, including: When the weather scene status variable changes from 0 to 1, a collaborative observation start instruction message is sent to the user. After the user confirms, the operation mode identifier is set to 1, and the optimal scanning parameters are sent to the radar sites participating in the network.

8. The method according to claim 2, characterized in that, Based on the aforementioned basic database, a coarse and fine selection of radar sites is completed, and radar sites that meet the monitoring requirements are saved as candidate radar sites. This includes the following steps: Based on the aforementioned basic database, preliminary radar sites that meet the monitoring requirements are identified within the target area; A preset neighborhood is determined with the coarsely selected radar site as the center, and several candidate sampling points are generated within the neighborhood. Based on the spatial attributes and constraints of the sampling points, the finely selected radar site is determined. The obstruction rate of the selected radar sites is calculated, and sites with obstruction rates exceeding a preset threshold are removed to obtain candidate radar sites that meet the monitoring requirements.

9. The method according to any one of claims 1-8, characterized in that, The method further includes: The total scan time of each radar within a scan cycle is constrained, and the total scan time of the i-th radar in the k-th cycle satisfies the following formula: ; in, Let be the angular velocity of the i-th radar in this period. Let L be the maximum allowed scanning time, and L be the number of scanning layers.

10. A rain-measuring radar intelligent networking layout and collaborative observation system based on global coordination, characterized in that, The system includes: The rain-measuring radar network layout module is used to lay out the rain-measuring radar network based on multi-source basic data and obtain the basic configuration information of the radar network. The scene adaptive simulation module is used to identify weather scene types, construct weather scene state variables according to the weather scene types, perform adaptive simulation on the scene types according to pre-configured scanning parameters, and output a set of candidate scanning parameters. The network performance module is used to optimize the candidate scanning parameter set according to the network performance objective function to obtain the optimal scanning parameters; The collaborative observation module is used to trigger the collaborative observation control process when the weather scene state quantity meets the preset jump condition; and to determine the optimal scanning parameters to be sent to the radar stations participating in the network according to the user operation mode identifier, so as to realize the collaborative observation of the radar network.

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