Unmanned aerial vehicle take-off and landing point optimization system and method considering rainfall influence

Abstract

The invention discloses an unmanned aerial vehicle take-off and landing point optimization system and method considering rainfall influence, and the system constructs a complete technology chain integrating rainfall monitoring, forecasting, influence analysis, dynamic evaluation and decision optimization through a rainfall monitoring module, a rainfall forecasting module, a data processing module, a take-off and landing point evaluation module and an optimization decision module. Based on the meteorological variable rainfall for the first time, quantitative and dynamic evaluation of the safety of the take-off and landing point is realized, and the problem of unmanned aerial vehicle take-off and landing safety blind areas caused by neglecting the influence of rainfall in the prior art is systematically solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle flight landing control, and more particularly to an unmanned aerial vehicle landing point optimization system and method considering rainfall influence. BACKGROUND

[0002] Most low-altitude flight tasks are performed by unmanned aerial vehicles as carriers, and the safe landing of unmanned aerial vehicles is a key step to ensure normal operation, and rainfall, a meteorological condition, has a huge impact on the landing point of unmanned aerial vehicles.

[0003] Currently, the layout of meteorological observation can basically meet the needs of unmanned aerial vehicle landing points for rainfall observation conditions, but the safety of rainfall influence on landing point facilities is still not considered.

[0004] For example, rainfall can cause the ground of the landing point to be wet and slippery, increase the sliding distance of fixed-wing (including compound-wing) unmanned aerial vehicles when landing, or reduce the stability of rotary-wing unmanned aerial vehicles, which may result in consequences such as unmanned aerial vehicles sliding off the runway, tipping over, rolling, etc.; rainwater can affect the electronic equipment of unmanned aerial vehicles, causing short circuits and other faults; in addition, low visibility during rainfall, especially short-term heavy rainfall, can also cause visual interference for operators, increasing the difficulty of landing operations.

[0005] The limited or poor drainage capacity of the landing point can also cause the hangar to be flooded, and the aircraft body and equipment on the aircraft to be damaged. If the protection capability of the electronic equipment of the landing point is not enough, once affected by rainfall, it may interfere with the two-way communication between the landing point and the unmanned aerial vehicle, and may not be able to receive rainfall information in real time or issue external alerts.

[0006] Although some existing documents mention collecting rainfall data, such as "Guangzhou Low Altitude Vertical Take-off and Landing Facility Site Selection and Construction Technical Guidelines (Trial)", but do not mention how the data should be processed after data collection and how to apply it to the meteorological risk assessment of the landing point, and do not form a complete dynamic assessment and optimization system of the landing point based on real-time rainfall monitoring and forecasting. In addition, the existing unmanned aerial vehicle hangar site selection method does not introduce rainfall, a high-frequency occurring meteorological variable that has a significant impact on the unmanned aerial vehicle landing point.

[0007] Therefore, how to develop an unmanned aerial vehicle landing point optimization scheme that can effectively respond to rainfall influence is a problem that needs to be solved by those skilled in the art. SUMMARY

[0008] In view of the above problems, the present application is proposed in order to provide an unmanned aerial vehicle landing point optimization system and method considering rainfall influence to overcome the above problems or at least partially solve the above problems.

[0009] In order to achieve the above purpose, the present application adopts the following technical scheme: In a first aspect, the embodiments of the present application provide a UAV landing site optimization system considering rainfall influence, comprising: a rainfall monitoring module configured to collect rainfall monitoring data of the landing site and surrounding areas in real time; a rainfall forecast module configured to receive rainfall forecast data of the landing site and surrounding areas; a data processing module configured to calculate rainfall intensity according to the rainfall monitoring data and the rainfall forecast data, determine rainfall influence index level, and analyze the influence degree of the indicators; a landing site evaluation module configured to dynamically score a plurality of evaluation indicators of the landing site, assign weights according to the analysis results of the influence degree of the indicators, and obtain a comprehensive evaluation level by weighting the dynamic scoring results using the weights; an optimization decision module configured to determine the safety classification of the landing site and corresponding measures according to the comprehensive evaluation level and the rainfall influence index level.

[0010] Preferably, the rainfall monitoring data includes one or more of rainfall amount, rainfall intensity, rainfall duration, rain cluster to landing site distance, rain cluster moving speed, and rain cluster moving direction obtained based on a rainfall observation station, a weather radar, or a weather satellite; The rainfall forecast data includes one or more of forecasted rainfall amount, forecasted rainfall intensity, forecasted rainfall start time, forecasted rainfall duration, and forecasted rainfall end time obtained based on a regional weather model, a refined grid, or an intelligent grid forecast.

[0011] Preferably, the data processing module comprises: a rainfall intensity calculation unit configured to determine a current first rainfall intensity according to the rainfall monitoring data and a second rainfall intensity that will affect soon according to the rainfall forecast data ; a rainfall influence index determination unit configured to determine the rainfall influence index as follows : when there is only current rainfall, ; when there is only rainfall that will affect soon, ; when both exist, ; a strength influence analysis unit configured to analyze the influence degree of each indicator according to the rainfall monitoring data and the rainfall forecast data by using a pre-trained rainfall influence model.

[0012] Preferably, when the rainfall influence index determination unit grades the rainfall influence index, the grading threshold is determined by converting the rainfall amount within a specified time period in the standard into a rainfall intensity threshold within a unit time period by using the maximum information entropy method.

[0013] Preferably, in the analysis of the influence degree of each index, the index includes the anti-skid performance, the drainage capacity, the electronic device protection capability, the facility resistance capability and / or the personnel influence degree.

[0014] Preferably, in the landing point evaluation module, the landing point is dynamically scored according to a predefined evaluation rule including a plurality of evaluation indexes and grading standards of the evaluation indexes; wherein the evaluation indexes and the corresponding evaluation items include: anti-skid performance: pavement material and process, wear and maintenance condition, water film risk; drainage capacity: drainage design and capacity, facility maintenance condition, field area slope and permeability; electronic device protection capability: device shell protection level, cable and interface protection, lightning and surge protection; facility resistance capability: structural stability and windproof design, waterproof and sealing performance, auxiliary facility fixation, personnel influence degree; personnel influence degree: personnel protective equipment, working environment and route, emergency plan and training.

[0015] Preferably, the comprehensive evaluation level includes excellent, good, qualified and poor.

[0016] Preferably, in the optimization decision module, the safety classification includes safe, basically safe and unsafe; The corresponding measures include: when the safety classification is safe, no measures are taken; when it is basically safe, it is recommended to increase inspection and start the emergency plan; when it is unsafe, it is recommended to enable the backup landing point, close the current landing point or execute personnel evacuation.

[0017] Preferably, the system further includes: an information visualization module for real-time dynamic superimposed display of rainfall monitoring data and rainfall forecast data, and display of optimization strategy information according to the decision result of the optimization decision module; a communication module for realizing data transmission between the modules of the system and external user terminals; a monitoring module for monitoring the running state of each module of the system.

[0018] In a second aspect, the embodiments of the present application provide a method for optimizing a UAV landing point considering the influence of rainfall, which applies the UAV landing point optimization system considering the influence of rainfall as described in any of the above embodiments. The method steps include: real-time collection of rainfall monitoring data of the landing point and the surrounding area; acquisition of rainfall forecast data of the landing point and the surrounding area; calculation of rainfall intensity according to the rainfall monitoring data and the rainfall forecast data, determination of the rainfall influence index level, and analysis of the influence degree of the index; The multiple evaluation indexes of the take-off and landing point are dynamically scored, weights are allocated according to index influence degree analysis results, and the comprehensive evaluation grade is obtained by weighting the dynamic scoring results by using the weights; According to the comprehensive evaluation grade and the rainfall influence index level, the safety classification of the take-off and landing point and the corresponding measures are determined.

[0019] The embodiment of the present application provides a UAV take-off and landing point optimization system and method considering rainfall influence; by constructing a complete technical chain integrating rainfall monitoring, forecasting, influence analysis, dynamic evaluation and decision optimization, the quantitative and dynamic evaluation of the safety of the take-off and landing point is realized based on rainfall as a meteorological variable for the first time, and the problem of UAV take-off safety blind area caused by ignoring the influence of rainfall in the prior art is systematically solved.

[0020] Compared with the prior art, the above technical solutions provided by the embodiment of the present application have at least the following beneficial effects: Through the data-driven closed-loop decision mechanism, the meteorological threat and the infrastructure resilience are deeply integrated; the dynamic evaluation method can truly reflect the actual influence of rainfall of different intensities on various elements of the take-off and landing point, so as to output an optimization strategy accurately matched with real-time risks; The present application can effectively prevent safety accidents such as skidding, water accumulation and electronic device failure directly caused by rainfall, and completely changes the deficiency of relying on static facility conditions or subjective experience for judgment in the past, thereby providing reliable and automated protection for the safe and efficient operation of UAVs. BRIEF DESCRIPTION OF DRAWINGS

[0021] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or the prior art description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor on the basis of the provided drawings.

[0022] Figure 1 The structure schematic diagram of the UAV take-off and landing point optimization system considering rainfall influence of the present application; Figure 2 The flowchart of the UAV take-off and landing point optimization method considering rainfall influence of the present application. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be described clearly and completely in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0024] The application provides a UAV landing point optimization system and method considering the influence of rainfall, aiming to provide a system and method integrating rainfall live dynamic perception, rainfall safety risk dynamic evaluation and intelligent optimization of the best response strategy.

[0025] The application has full-process visual monitoring capability from risk early warning to optimization decision, can effectively evaluate and avoid strong rainfall risk, solves the problems of response lag, incomplete evaluation and artificial decision in the prior art when responding to rainfall weather, and significantly improves the safety and operation reliability of the UAV landing point in the rainfall weather.

[0026] In the embodiment, the disclosed UAV landing point optimization system considering the influence of rainfall, as shown in Figure 1 is composed of the following modules: a rainfall monitoring module, a rainfall forecasting module, a data processing module, a landing point evaluation module and an optimization decision module.

[0027] In an optional embodiment, the rainfall monitoring module is used to collect rainfall monitoring data of the landing point and the surrounding area in real time; the rainfall monitoring data includes one or more of rainfall amount, rainfall intensity, rainfall duration, distance from the rain cluster to the landing point, rain cluster moving speed and rain cluster moving direction; the above data can be obtained based on a rain gauge observation station, a weather radar or a weather satellite; in some embodiments, rain gauges or rain gauge observation stations are reasonably arranged at the UAV landing point and the surrounding area to ensure that rainfall data can be accurately and timely obtained. Rain gauges or rain gauge observation stations can be installed at different positions of the landing point to comprehensively monitor the rainfall on the ground, and a single set or multiple sets of observation stations can be arranged according to the function of the landing point. Weather radar data of the UAV landing point can be received to realize monitoring of a larger range and more precise rainfall dynamics. Weather satellite data can be received to realize spatial monitoring of the moving direction and speed of the rain cluster and to provide rainfall warning for the UAV landing point earlier.

[0028] The rainfall forecasting module is used to receive rainfall forecasting data of the landing point and the surrounding area; the rainfall forecasting data includes one or more of forecasted rainfall amount, forecasted rainfall intensity, forecasted rainfall start time, forecasted rainfall duration and forecasted rainfall end time obtained based on regional weather patterns, refined grid or intelligent grid forecasting. In some embodiments, rainfall forecasting and warning information of the UAV landing point and the surrounding area is received in real time through a communication module or a dedicated line; rainfall forecasting data automatically generated by regional weather patterns, rainfall forecasting data automatically generated by special patterns such as refined intelligent grid, and rainfall forecasting data manually corrected can be received. The received data should at least include parameters such as rainfall amount, rainfall intensity, rainfall start time, rainfall duration and end time to provide three-dimensional rainfall safety protection for the UAV landing point.

[0029] In this embodiment, rainfall monitoring data and rainfall forecast data are continuously collected. The collection frequency can be adjusted according to the actual situation. Generally, the collection frequency is appropriately increased during rainy weather to ensure that the changes in rainfall can be grasped in a timely manner.

[0030] In one optional embodiment, the data processing module, connected to the rainfall monitoring module and the rainfall forecast and early warning module, is used to calculate rainfall intensity based on rainfall monitoring data and rainfall forecast data, determine the rainfall impact index level, and analyze the degree of impact of the index. In some implementation schemes, after receiving the data, the data processing module first removes abnormal data and issues an alarm for missing data; then it calculates the rainfall intensity based on the rainfall monitoring data and rainfall forecast data; rainfall intensity refers to the amount of rainfall per unit time, and for the take-off and landing points, hourly rainfall intensity is generally used to characterize the impact of rainfall.

[0031] In this embodiment, the data processing module includes: (1) Rainfall intensity calculation unit, used to determine the current first rainfall intensity based on the rainfall monitoring data. ,

[0032] In the formula, The current highest rainfall intensity (mm / h) In order to be in Accumulated rainfall (mm) over the period of time. The time interval is (h).

[0033] And determine the intensity of the second rainfall event based on the rainfall forecast data. In some implementation plans, the second rainfall intensity Also according to The calculation can be performed using either the standard calculation method or the weather radar inversion method. The weather radar inversion method estimates the instantaneous rainfall intensity over a large area by transmitting microwaves and receiving the echoes reflected by precipitation particles in the atmosphere. The calculation formula is as follows:

[0034] This represents the radar reflectivity factor, which is related to the size and concentration of precipitation particles and is used in monitoring data. A and b These are empirical parameters that vary with region and rainfall type (such as stratiform cloud precipitation and convective precipitation).

[0035] (2) Rainfall impact index determination unit, used to determine the rainfall impact index in the following manner. When only the current rainfall exists, ; when only the impending rainfall exists, ; when both exist, ; The classification criteria of the rainfall impact index are mainly based on the provisions of 12-hour rainfall in "Rainfall Grading" (GB / T 28592-2012). Light rain: 0.1-4.9 mm; moderate rain: 5.0-14.9 mm; heavy rain: 15.0-29.9 mm; heavy rain: 30.0-69.9 mm; heavy rain: 70.0-139.9 mm; and extremely heavy rain: ≥140.0 mm.

[0036] The present application considers the actual application, and converts the 12-hour maximum rainfall of each grade into the maximum hourly rainfall by using the maximum information entropy method, as the threshold value. The maximum information entropy formula is:

[0037] In the formula, is the 12-hour maximum rainfall, is the total duration (12h), is the unit time (1h), is the maximum rainfall in .

[0038] In this embodiment, the obtained threshold value is as follows: 1st level: 1.4 mm, 2nd level: 4.3 mm, 3rd level: 8.7 mm, 4th level: 20.3 mm, 5th level: 40.6 mm, 6th level: 40.7 mm, and the impact degree is divided into small (or none), medium, large, very large, extremely large, and severe impact.

[0039] (3) The intensity impact analysis unit is used to analyze the impact degree of each index according to the rainfall monitoring data and the rainfall forecast data through the pre-trained rainfall impact model. The index specifically includes: anti-skid performance, drainage capacity, electronic device protection capability, facility resistance capability, and / or personnel impact degree. The rainfall impact model can be dynamically established and optimized according to historical data or experimental results.

[0040] In an alternative embodiment, the take-off and landing point evaluation module is used to dynamically score a plurality of evaluation indexes of the take-off and landing point, assign weights according to the index impact degree analysis result, and obtain a comprehensive evaluation level by weighting the dynamic scoring result using the weights; In this embodiment, the take-off and landing point is dynamically scored according to the pre-defined evaluation rule containing a plurality of evaluation indexes and grading scoring standards; in some embodiments, the evaluation indexes and corresponding evaluation items include: Anti-skid performance: pavement material and process, wear and maintenance condition, and water film risk; Drainage capacity: drainage design and capacity, facility maintenance, site slope and permeability; Electronic equipment protection capability: equipment shell protection level, cable and interface protection, lightning and surge protection; Facility resistance: structural stability and windproof design, waterproof and sealing performance, accessory facilities fixation, personnel influence degree; Personnel influence degree: personnel protective equipment, working environment and route, emergency plan and training.

[0041] The evaluation results are divided into four levels: excellent: 90-100 points; good: 75-89 points; qualified: 60-74 points; poor: 0-59 points. The specific evaluation rules are shown in Table 1, Table 1

[0042] Further, according to the index influence degree analysis result, weights are allocated, and the dynamic score results are weighted and averaged by using the weights to obtain a comprehensive evaluation level. Preferably, the weights are allocated as 0.15, 0.2, 0.1, 0.25, and 0.2 according to the influence degree of rainfall on the landing point. The comprehensive evaluation level includes excellent: 90-100 points, strong defense capability, and extremely low risk; good: 75-89 points, better defense capability, controllable risk, and individual aspects can be optimized; qualified: 60-74 points, basic defense capability meets the standard, obvious risk exists, and needs to be rectified; and poor: 0-59 points, insufficient defense capability, high risk, and should be immediately stopped and rectified.

[0043] In an alternative embodiment, an optimization decision module is used to determine the safety classification of the landing point and the corresponding measures according to the comprehensive evaluation level and the rainfall influence index level. Referring to Table 2 in detail; Table 2

[0044] In an embodiment, measures are taken: if the evaluation result shows that the landing point has greater safety hazards in rainy weather, and there is a suitable backup landing point, the backup landing point is recommended in time; if the landing point can be improved to improve its performance in rainy weather, detailed improvement suggestions are made, such as laying anti-skid materials, increasing drainage pipes, facility reinforcement, personnel allocation, etc.; if an unsafe condition has occurred or will occur, the landing point needs to be evacuated and closed quickly, and a serious warning is issued.

[0045] To further optimize the above technical solution, the system further comprises: The information visualization module receives module information data of the rainfall monitoring module, the rainfall prediction module, the optimization decision module and the like, and displays the received information on a large screen display, dynamically displays rainfall monitoring and prediction information in real time, displays optimization strategy information according to the strategy of the data optimization decision module, and highlights rainfall warning information and high-level impact information; information with a significant prompting effect, such as the moving route of rainfall, the damage intensity of rain intensity, the prominence of coping strategies and the like, is displayed to improve decision efficiency.

[0046] The communication module is used for realizing data transmission between the modules of the system and information interaction with the unmanned aerial vehicle operator and the relevant management department by using reliable wireless communication technology; for example, the unmanned aerial vehicle operator can receive take-off and landing point information and optimization suggestions sent by the system in real time through a terminal device, and the relevant management department can make corresponding engineering modifications according to the improvement suggestions provided by the system.

[0047] The monitoring module monitors the running states of the above-mentioned seven modules, including data arrival rate, processing efficiency, evaluation timeliness, optimization strategy efficiency, visualization normality and communication smoothness, generates log records, and alarms in a timely manner in case of abnormal conditions, so as to ensure stable operation of the system.

[0048] The application introduces rainfall monitoring and prediction data into site selection decision-making, establishes a rainfall-site-equipment-personnel coupling model, and quantifies the influence threshold of different rainfall grades on the safety of take-off and landing points. When the target take-off and landing point does not meet the requirements due to excessive rainfall, the system automatically recommends available backup take-off and landing points or gives improvement schemes (anti-skid, drainage, reinforcement, personnel evacuation, etc.), forming a closed-loop decision. Through the communication module, the rainfall warning, evaluation results and optimization strategies are pushed to the take-off and landing point managers and the dispatch center in seconds, so as to reduce the probability of accidents and ensure the safety of low-altitude logistics, inspection and other tasks in the rainy season. Based on the same inventive concept, the application also provides a method for optimizing a take-off and landing point of an unmanned aerial vehicle considering the influence of rainfall, as shown in Figure 2 , the steps comprising: collecting rainfall monitoring data of the take-off and landing point and the surrounding area in real time; obtaining rainfall prediction data of the take-off and landing point and the surrounding area; calculating rainfall intensity according to the rainfall monitoring data and the rainfall prediction data, determining the rainfall influence index level, and analyzing the influence degree of the index; dynamically scoring a plurality of evaluation indexes of the take-off and landing point, assigning weights according to the analysis result of the influence degree of the index, and weighting the dynamic scoring result by using the weights to obtain a comprehensive evaluation level; determining the safety classification of the take-off and landing point and the corresponding measures according to the comprehensive evaluation level and the rainfall influence index level.

[0049] Since the method applies the unmanned aerial vehicle landing point optimization system considering the rainfall effect as described in any of the above, each step is consistent with the execution process of each module in the system, and therefore is not repeated here.

[0050] It should be noted that after the optimization measures are implemented, the conditions of the landing point and the rainfall are continuously monitored, and if it is found that the actual effect does not match the expectation or the rainfall situation has changed greatly, the optimization strategy is adjusted and improved in time.

[0051] At the same time, information visualization and monitoring processing are carried out, that is, information visualization: display rainfall conditions, evaluation results, and strategy information, real-time remind staff to pay attention, and take measures when there is an impact or danger; monitoring processing: record all steps, start the next round of monitoring after recording without exception, issue an alarm to the relevant departments or personnel and record if an exception is found, and enter the next round of monitoring after processing.

[0052] The present application can effectively reduce the adverse effects of rainfall on the unmanned aerial vehicle landing point, improve the safety and reliability of the unmanned aerial vehicle landing point in rainy weather, reduce the probability of unmanned aerial vehicle landing point accidents caused by rainfall, and protect the safety of landing point facilities, unmanned aerial vehicle equipment and related personnel. At the same time, the present application can also provide a scientific basis for the planning and construction of the unmanned aerial vehicle landing point, and promote the safe, stable and healthy development of the low-altitude industry.

[0053] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the device disclosed in the embodiments, since it corresponds to the method disclosed in the embodiments, the description is relatively simple, and the related parts can be referred to the method part.

[0054] The above description of the disclosed embodiments enables a person skilled in the art to implement or use the present application. Various modifications to the embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A system for optimizing a drone landing pad considering the effect of rainfall, the system comprising: The method comprises the following steps: a rainfall monitoring module is used to collect rainfall monitoring data of the take-off and landing point and the surrounding area in real time; a rainfall forecast module is used to receive rainfall forecast data of the take-off and landing point and the surrounding area; a data processing module is used to calculate rainfall intensity according to the rainfall monitoring data and the rainfall forecast data, determine the rainfall influence index level, and analyze the influence degree of the indicators; a take-off and landing point evaluation module is used to dynamically score a plurality of evaluation indicators of the take-off and landing point, assign weights according to the analysis result of the influence degree of the indicators, and obtain a comprehensive evaluation level by weighting the dynamic scoring result using the weights; an optimization decision module is used to determine the safety classification of the take-off and landing point and the corresponding measures according to the comprehensive evaluation level and the rainfall influence index level.

2. The UAV landing point optimization system of claim 1, wherein, The rainfall monitoring data includes one or more of rainfall amount, rainfall intensity, rainfall duration, distance from the rain cluster to the take-off and landing point, rain cluster moving speed, and rain cluster moving direction. The rainfall forecast data includes one or more of forecasted rainfall amount, forecasted rainfall intensity, forecasted rainfall start time, forecasted rainfall duration, and forecasted rainfall end time.

3. The UAV landing point optimization system of claim 1, wherein, The data processing module includes: a rainfall intensity calculation unit for determining a first current rainfall intensity from the rainfall monitoring data a second impending rainfall intensity from the rainfall forecast data ; a rainfall influence index determining unit for determining a rainfall influence index in the following manner : when only the current rainfall exists, ; when only the rainfall that will influence exists, ; when both exist, ; an intensity influence analysis unit is used to analyze the influence degree of each indicator by using a pre-trained rainfall influence model according to the rainfall monitoring data and the rainfall forecast data.

4. The UAV landing point optimization system of claim 3, wherein, When the rainfall influence index determination unit determines the rainfall influence index level, the classification threshold is determined by converting the rainfall amount level within a specified time period in the standard into a rainfall intensity threshold within a unit time period by using the maximum information entropy method.

5. The UAV landing point optimization system of claim 3, wherein, In the analysis of the influence degree of each indicator, the indicators include anti-skid performance, drainage capacity, electronic device protection capability, facility resistance capability, and / or personnel influence degree.

6. The drone landing point optimization system of claim 1, wherein, In the take-off and landing point evaluation module, the take-off and landing point is dynamically scored according to a predefined evaluation rule containing a plurality of evaluation indicators and grading scoring standards of the evaluation indicators; wherein the evaluation indicators and corresponding evaluation items include: anti-skid performance: pavement material and process, wear and maintenance condition, water film risk; drainage capacity: drainage design and capacity, facility maintenance condition, field area slope and permeability; electronic device protection capability: device shell protection level, cable and interface protection, lightning and surge protection; facility resistance capability: structural stability and windproof design, waterproof and sealing performance, auxiliary facility fixation, personnel influence degree; personnel influence degree: personnel protection equipment, working environment and route, emergency plan and training.

7. The drone landing point optimization system of claim 1, wherein, The comprehensive evaluation level includes excellent, good, qualified, and poor.

8. The drone landing spot optimization system of any one of claims 1, wherein, In the optimization decision module, the safety classification includes safe, basically safe, and unsafe; The corresponding measures include: when the safety classification is safe, no measures are taken; when it is basically safe, it is recommended to increase inspection and start the emergency plan; when it is unsafe, it is recommended to use a backup take-off and landing point, close the current take-off and landing point, or perform personnel evacuation.

9. The drone landing point optimization system of claim 1, wherein, Further comprising: an information visualization module is used to perform real-time dynamic superimposed display of the rainfall monitoring data and the rainfall forecast data, and display optimization strategy information according to the decision result of the optimization decision module; a communication module is used to realize data transmission between the modules of the system and external user terminals. A monitoring module is configured to monitor the running state of each module of the system. 10.A method for optimizing a UAV landing point considering the influence of rainfall, characterized in that, The unmanned aerial vehicle landing point optimization system considering rainfall influence according to any one of claims 1-9, the method comprising: collecting rainfall monitoring data of the landing point and the surrounding area in real time; obtaining rainfall forecast data of the landing point and the surrounding area; calculating rainfall intensity according to the rainfall monitoring data and the rainfall forecast data, determining the rainfall influence index level, and analyzing the influence degree of the index; dynamically scoring a plurality of evaluation indexes of the landing point, assigning weights according to the analysis result of the influence degree of the index, weighting the dynamic scoring result by using the weights to obtain a comprehensive evaluation level; determining the safety classification of the landing point and the corresponding measures according to the comprehensive evaluation level and the rainfall influence index level.

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