Disaster monitoring equipment throwing method based on unmanned aerial vehicle throwing and monitoring system

Abstract

The invention discloses a disaster monitoring equipment putting method based on unmanned aerial vehicle scattering and a monitoring system, and the method comprises the steps: obtaining the disaster risk levels of a plurality of cells in a target region; determining a putting strategy of monitoring equipment in at least one cell based on the disaster risk level; and throwing the monitoring equipment to the target area through the unmanned aerial vehicle based on the throwing strategy. Thus, the risk condition of the target area can be analyzed in a fine-grained mode, different putting strategies can be adopted for different risk areas in a targeted mode, and therefore data of different areas can be monitored more accurately.

Description

Technical Field

[0001] This application relates to the technical field of disaster monitoring, and particularly to a method for deploying disaster monitoring equipment based on drone throwing and a monitoring system. Background Art

[0002] For areas with extreme climates and complex terrains such as high plateaus and mountainous areas, disasters such as avalanches often occur frequently. Traditional monitoring methods mainly rely on fixed sensors manually deployed for monitoring, but the manual deployment efficiency is low and the deployment location is not accurate enough, resulting in poor monitoring effects. Summary of the Invention

[0003] Embodiments of this application provide a method for deploying disaster monitoring equipment based on drone throwing and a monitoring system to solve the technical problem of poor disaster monitoring effects.

[0004] According to the first aspect of the embodiments of this application, a method for deploying disaster monitoring equipment based on drone throwing is provided. The method includes: obtaining the disaster risk levels of multiple cells in a target area; determining the deployment strategy of monitoring equipment in at least one of the cells based on the disaster risk levels; and deploying the monitoring equipment to the target area by drone throwing based on the deployment strategy.

[0005] Further, the obtaining the disaster risk levels of multiple cells in the target area includes: dividing the target area into multiple cells based on a predetermined size; obtaining meteorological data and geographical data corresponding to each cell; and determining the disaster risk level of each cell based on the meteorological data and the geographical data.

[0006] Further, the determining the disaster risk level of each cell based on the meteorological data and the geographical data includes: calculating the disaster risk probability corresponding to each cell based on the meteorological data and the geographical data; comparing the disaster risk probability with multiple preset intervals to determine the disaster risk level of each cell; where each disaster risk level corresponds to a preset interval.

[0007] Further, the determining the deployment strategy of monitoring equipment in at least one of the cells based on the disaster risk levels includes: predicting the disaster formation path for target cells with a disaster risk level higher than a predetermined level; the disaster formation path at least includes: a disaster initiation area; and determining that the target deployment density of the monitoring equipment in the disaster initiation area of the target cell is higher than a preset density, and / or the target deployment quantity is higher than a preset quantity.

[0008] Further, the method of deploying monitoring devices to the target area by drone dropping based on the deployment strategy includes: based on the target deployment density and / or the target deployment quantity, deploying monitoring devices to at least one of the target cells by drone dropping; determining the deployment strategy for other cells except the target cells based on the monitoring data collected by the monitoring devices in the target cells; and deploying monitoring devices to the other cells by drone dropping based on the deployment strategy for the other cells.

[0009] Further, the method of deploying monitoring devices to the target area by drone dropping based on the deployment strategy includes: determining the flight path and dropping points of the drone based on at least one of the geographical data and meteorological data of the target area and the deployment strategy; and deploying monitoring devices to the target area by drone dropping based on the flight path and the dropping points.

[0010] Further, the method of deploying monitoring devices to the target area by drone dropping based on the flight path and the dropping points includes: determining the flight attitude, dropping angle, and dropping speed of the drone at each dropping point based on the flight path and the dropping points; and deploying monitoring devices to the target area by drone dropping based on the flight attitude, dropping angle, and dropping speed.

[0011] According to the second aspect of the embodiments of the present application, a monitoring system is further provided. The system includes: a drone, monitoring devices, a base station, and a monitoring platform. Among them, the drone deploys the monitoring devices by executing the method according to any one of the foregoing first aspects; the monitoring devices establish a communication connection with the base station and are used to transmit monitoring data to the monitoring platform through the base station.

[0012] Further, at least one of a temperature sensor, a displacement sensor, a pressure sensor, an inclination sensor, and a water-heat-salt sensor is provided in the monitoring device.

[0013] Further, the monitoring platform is used to predict the disaster risk of the target area based on the historical monitoring data and the current monitoring data transmitted by the monitoring devices through a predetermined model.

[0014] Further, the system includes a plurality of the monitoring devices, and the plurality of monitoring devices are networked through LoRa (Long Range). The monitoring devices are also used to adjust the communication path with the base station according to at least one of the geographical data, meteorological data, and communication quality of the target area.

[0015] A method and monitoring system for disaster monitoring equipment delivery based on drone dropping proposed in an embodiment of the present application, which obtains the disaster risk levels of multiple cells in a target area; determines the delivery strategies of monitoring equipment in at least one of the cells based on the disaster risk levels; and delivers the monitoring equipment to the target area by drone dropping based on the delivery strategies. In this way, for the area to be monitored, the disaster risk level corresponding to each cell can be determined by dividing the cells, so that the monitoring requirements of different positions in the target area can be determined with a finer granularity, which is conducive to flexibly setting different delivery strategies for positions with different risk levels, and there is no need for manual delivery. The monitoring equipment can be delivered to places difficult for people to reach by drone dropping, and the monitoring range is more extensive. Accurately delivering the monitoring equipment in combination with the disaster risk level can improve the accuracy and intelligence of the delivery of the monitoring equipment, avoid over-delivery of monitoring equipment in areas with low risk and insufficient delivery of monitoring equipment in areas with high risk, and thus comprehensively improve the monitoring accuracy of the target area. Description of the Drawings

[0016] The drawings described herein are used to provide a further understanding of the present application and form a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation to the present application. In the drawings: Figure 1 is a flowchart of a method for disaster monitoring equipment delivery based on drone dropping according to an embodiment of the present application; Figure 2 is a flowchart of a method for disaster monitoring equipment delivery based on drone dropping according to an embodiment of the present application; Figure 3 is a schematic diagram of a monitoring system according to an embodiment of the present application; Figure 4 is a schematic structural diagram of a monitoring equipment according to an embodiment of the present application.

[0017] Description of the Reference Numerals 1, Drone; 2, Monitoring Equipment; 3, Base Station; 4, Monitoring Platform; 21, Suspension Ring; 22, Communication Module; 23, Sensor Module; 24, Battery; 25, Buffer Layer; 26, Counterweight. Detailed Embodiments

[0018] To enable those skilled in the art to better understand the solution of this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this application. Obviously, the described embodiments are only a part of the embodiments of this application, rather than all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of this application. It should be noted that, without conflict, the embodiments in this application and the features in the embodiments can be combined with each other.

[0019] It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of this application described here can be implemented in an order other than those illustrated or described here. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, product or device including a series of steps or units does not necessarily need to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0020] In this embodiment, a method for deploying disaster monitoring devices based on drone throwing is provided. Figure 1 is a flowchart of a method for deploying disaster monitoring devices based on drone throwing according to an embodiment of this application, as Figure 1 shown, and this process includes the following steps: S10: Obtain the disaster risk levels of multiple cells in the target area; S20: Determine the deployment strategy of monitoring devices in at least one cell based on the disaster risk level; S30: Deploy monitoring devices to the target area by drone throwing based on the deployment strategy.

[0021] In this embodiment, the method for deploying disaster monitoring devices based on drone throwing can be applied to drones, or can also be applied to control devices, servers or platforms, etc. that are communicatively connected to drones to control drones. The target area can be an area where disaster situations need to be monitored. For example, the target area can be a rectangular, circular, triangular or other shaped area. Among them, disasters can refer to meteorological disasters or geological disasters, such as avalanches, or can also refer to other types of disasters such as landslides, earthquakes, mudslides, etc. Among them, geological disasters can be geological disasters occurring on slopes, etc. The monitoring devices here are disaster monitoring devices.

[0022] In one embodiment, the target area can be divided into multiple cells, and the sizes of the multiple cells can be the same or the difference range is less than a predetermined range, etc. For example, the cells can be rectangular, triangular, square or other shapes. Exemplarily, the size of the cell can be 30m×30m, etc.

[0023] In one embodiment, the disaster risk level can characterize the likelihood of a disaster occurring within a cell. For example, when the disaster is an avalanche, the disaster risk level can characterize the probability of snow instability within the cell, etc. The higher the disaster risk level, the higher the probability of a disaster occurring can be characterized.

[0024] In one embodiment, the disaster risk level can be determined based on the disaster risk probability within the cell. For example, the disaster risk probability can be the probability of a disaster occurring within the cell or the probability of an event that leads to a disaster occurring, such as the probability of snow instability, etc. The disaster risk probability can be calculated through a predetermined disaster risk probability model.

[0025] In one embodiment, the monitoring device is a disaster monitoring device, that is, a device used to monitor disaster situations. The monitoring device is used to monitor the disaster situation in the target area, for example, to generate and transmit monitoring data reflecting the disaster situation in the target area, etc. The monitoring data can be used to predict the occurrence of disasters or disaster risks in the target area. The placement strategy of the monitoring device can include, but is not limited to, at least one of the size, type, function, placement quantity, placement density, the placement spacing between each monitoring device, and the initial placement velocity, etc.

[0026] In one embodiment, there can be one drone or multiple drones. Exemplarily, when there are multiple drones, each drone can be used to place monitoring devices in one or more cells, and different drones can be used to place monitoring devices in different cells.

[0027] In one embodiment, step S30 can include: determining the flight path and placement points of the drone based on the placement strategy; and based on the flight path and placement points, using the drone to scatter and place monitoring devices in the target area.

[0028] Here, determining the flight path and placement points of the drone based on the placement strategy can include: determining the flight path and placement points of the drone based on at least one of the geographical data and meteorological data of the target area and the placement strategy.

[0029] In one embodiment, deploying monitoring devices to a target area may include: deploying monitoring devices to the target area in batches. For example, deploying monitoring devices to the target area may include: determining the deployment batches based on a deployment strategy, as well as the target cells for each batch and the corresponding target deployment strategies for the target cells; and deploying monitoring devices to the target area in batches based on the target cells for each batch and the corresponding target deployment strategies for the target cells.

[0030] In one embodiment, the target deployment strategy may include: a target deployment density and / or the target deployment quantity. Deploying monitoring devices to the target area in batches based on the target cells for each batch and the corresponding target deployment strategies for the target cells may include: in the first batch of deployment, deploying monitoring devices to at least one target cell based on the target deployment density and / or the target deployment quantity; determining the deployment strategies for other cells except the target cells based on the monitoring data collected by the monitoring devices in the target cells; and in the second batch of deployment, deploying monitoring devices to the other cells based on the deployment strategies for the other cells.

[0031] In this way, for the area to be monitored, the disaster risk level corresponding to each cell can be determined by dividing the cells, so that the monitoring requirements at different positions in the target area can be determined in a finer granularity, which is conducive to flexibly setting different deployment strategies for positions with different risk levels, and there is no need for manual deployment. By accurately deploying monitoring devices in combination with the disaster risk level, the accuracy and intelligence of the deployment of monitoring devices can be improved, avoiding the situation of deploying too many monitoring devices in areas with low risk and insufficient monitoring devices in areas with high risk, and thus comprehensively improving the monitoring accuracy of the target area.

[0032] In some embodiments, as Figure 2 shown, the above step S10 may include: S11: Divide the target area into multiple cells based on a predetermined size; S12: Obtain meteorological data and the geographical data corresponding to each cell; S13: Determine the disaster risk level of each cell based on the meteorological data and the geographical data.

[0033] In one embodiment, the predetermined size may be the size of the cell, such as 30m×30m, etc. The number of cells may be determined according to at least one of the boundary shape, area, and predetermined size of the target area. The meteorological data and the geographical data corresponding to each cell may be obtained by drone survey.

[0034] In one embodiment, the meteorological data may be the meteorological data of the target area at present and / or within a predetermined future time period. For example, the meteorological data may include at least one of the following: wind force, wind direction, illumination, rainfall, snowfall, temperature, humidity, and the duration until sunset, etc. Here, the predetermined future time period may refer to the time period between the current moment and the moment when the monitoring device is expected to be deployed.

[0035] In one embodiment, the geographical data may characterize the geological conditions corresponding to the grid cells. For example, the geographical data may include at least one of the following: slope, snow layer thickness, snow layer density, and snow layer looseness, etc. Among them, the slope may refer to the terrain slope angle.

[0036] In one embodiment, step S13 may include: calculating the disaster risk probability corresponding to each grid cell based on the meteorological data and the geographical data; comparing the disaster risk probability with a plurality of preset intervals to determine the disaster risk level of each grid cell; wherein, each disaster risk level corresponds to a preset interval.

[0037] In this way, according to the meteorological data and geographical data corresponding to each grid cell, it is possible to more accurately judge the possibility of a disaster occurring in the grid cell, thereby facilitating the accurate determination of the disaster risk level and improving the accuracy of the deployment of the monitoring device.

[0038] In some embodiments, determining the disaster risk level of each grid cell based on the meteorological data and the geographical data includes: Calculating the disaster risk probability corresponding to each grid cell based on the meteorological data and the geographical data; Comparing the disaster risk probability with a plurality of preset intervals to determine the disaster risk level of each grid cell; wherein, each disaster risk level corresponds to a preset interval.

[0039] Here, the disaster risk probability may refer to the probability of a disaster occurring or the probability of an event leading to a disaster occurring. For example, it may be the probability of snowpack instability, etc. The disaster risk probability is greater than or equal to 0 and less than or equal to 1.

[0040] In one embodiment, taking the disaster of avalanche as an example, the meteorological data may include temperature, i.e., air temperature T, and the geographical data may include slope θ, snow layer thickness h, and snow layer density ρ. The disaster risk probability P, i.e., the probability of snowpack instability , can be calculated through the snowpack instability spatial risk probability assessment model, and the formula can be expressed as , where f represents the function corresponding to the model.

[0041] In one embodiment, multiple preset intervals may represent numerical ranges of different disaster risk probabilities. For example, the number of preset intervals may be consistent with the number of disaster risk levels, and different disaster risk levels correspond to different preset intervals.

[0042] In one embodiment, determining the disaster risk level of each of the cells may refer to determining the disaster risk level corresponding to the preset interval in which the disaster risk probability of the cell is located as the disaster risk level of the cell.

[0043] Exemplarily, the disaster risk level corresponding to the first preset interval, such as P < 0.3, may be a low risk level, the disaster risk level corresponding to the second preset interval, such as 0.3 ≤ P < 0.6, may be a medium risk level, the disaster risk level corresponding to the third preset interval, such as 0.6 ≤ P < 0.9, may be a high risk level, and the disaster risk level corresponding to the fourth preset interval, such as P ≥ 0.9, may be an extremely high risk level.

[0044] Thus, based on the disaster risk probability calculated from meteorological data and geographical data, the risk of a disaster event can be accurately calculated, and then the disaster risk level can be more accurately determined according to the preset range interval, which is beneficial to improving the monitoring accuracy.

[0045] In some embodiments, the above step S20 may include: Predicting a disaster formation path based on target cells with a disaster risk level higher than a predetermined level; the disaster formation path at least includes: a disaster initiation area; Determining that the target deployment density of the monitoring devices in the disaster initiation area within the target cells is higher than a preset density, and / or the target deployment quantity is higher than a preset quantity.

[0046] Here, the disaster formation path is a possible disaster formation path, such as an avalanche path, etc. The disaster formation path may at least include a disaster initiation area, and may also include a sliding area, a deposition area, etc. For example, an avalanche path may include an avalanche initiation area, an avalanche sliding area, an avalanche deposition area, etc.

[0047] In one embodiment, step S20 may further include: determining that the deployment strategy for other cells except the target cells is not to deploy currently. Not to deploy currently may refer to not deploying in the current batch and waiting for the next batch to deploy.

[0048] In one embodiment, a disaster risk level higher than a predetermined level may refer to a disaster risk level corresponding to a disaster risk probability higher than a predetermined probability. For example, higher than a predetermined level may refer to a high risk level and an extremely high risk level, etc.

[0049] In one embodiment, the deployment strategy may include deployment density and / or deployment quantity. The disaster activation area within the target cell may refer to the area within the target cell that belongs to the disaster activation area.

[0050] In one embodiment, the deployment density of the monitoring devices within the target cell that do not belong to the disaster activation area is lower than the preset density, and / or the deployment quantity is lower than the preset quantity.

[0051] In one embodiment, the deployment strategy of the monitoring devices within other cells except the target cell may include: the deployment density is lower than the preset density, and / or the deployment quantity is lower than the preset quantity. Here, the other cells may refer to the cells with a disaster risk level lower than or equal to a predetermined level.

[0052] In this way, more monitoring devices with higher density are arranged at the positions within the disaster activation area in the cells with higher risks, so that the activation events of disasters can be monitored more accurately and timely, which is beneficial to responding to the occurrence of disasters more quickly and timely, and the monitoring effect is better.

[0053] In some embodiments, the step S30 may include: Based on the target deployment density and / or the target deployment quantity, deploy monitoring devices to at least one of the target cells by drone dropping; Based on the monitoring data collected by the monitoring devices in the target cells, determine the deployment strategies of other cells except the target cells; Based on the deployment strategies of the other cells, deploy monitoring devices to the other cells by drone dropping.

[0054] In one embodiment, based on the target deployment density and / or the target deployment quantity, deploying monitoring devices to at least one of the target cells by drone dropping may include: determining the flight path and dropping points of the drone based on at least one of the geographical data and meteorological data of at least one of the target cells and the target deployment density and / or the target deployment quantity; based on the flight path and dropping points, deploy monitoring devices to at least one of the target cells by drone dropping.

[0055] Here, based on the flight path and dropping points, deploying monitoring devices to at least one of the target cells by drone dropping may include: determining the flight attitude, dropping angle, and dropping speed of the drone at each dropping point based on the flight path and dropping points; based on the flight attitude, dropping angle, and dropping speed, deploy monitoring devices to at least one of the target cells by drone dropping.

[0056] In one embodiment, the delivery of monitoring devices to the other cells by drone dropping based on the delivery strategy of the other cells may include: determining the flight path and delivery points of the drone based on at least one of the geographical data and meteorological data of the other cells and the delivery strategy of the other cells; and delivering the monitoring devices to the other cells by drone dropping based on the flight path and delivery points.

[0057] Here, delivering the monitoring devices to the other cells by drone dropping based on the flight path and delivery points may include: determining the flight attitude, delivery angle, and delivery speed of the drone at each delivery point based on the flight path and delivery points; and delivering the monitoring devices to the other cells by drone dropping based on the flight attitude, delivery angle, and delivery speed.

[0058] In one embodiment, determining the delivery strategy of other cells except the target cell based on the monitoring data collected by the monitoring device in the target cell may refer to determining the delivery strategy of other cells except the target cell based on the monitoring data collected by the monitoring device in the target cell after the drone returns or when the drone stays above the target area.

[0059] Here, determining the delivery strategy of other cells except the target cell may be to update the delivery strategy of other cells except the target cell. Delivering the monitoring devices to the other cells by drone dropping based on the delivery strategy of the other cells may refer to delivering the monitoring devices to the other cells by drone dropping based on the updated delivery strategy.

[0060] In one embodiment, the delivery density of the monitoring devices in other cells is lower than the preset density, and / or the delivery quantity is lower than the preset quantity.

[0061] In this way, by initially delivering some monitoring devices in high-risk areas to build a basic monitoring network, and then supplementing the delivery in the remaining areas based on real-time monitoring data, the monitoring effect can be strengthened targeted.

[0062] In some embodiments, the above step S30 may include: Determining the flight path and delivery points of the drone based on at least one of the geographical data and meteorological data of the target area and the delivery strategy; Delivering the monitoring devices to the target area by drone dropping based on the flight path and delivery points.

[0063] In one embodiment, determining the flight path of the drone may refer to generating a path planning map of the drone. For example, the path planning map of the drone may be generated by the A* algorithm.

[0064] In one embodiment, a flight path includes a plurality of dropping points, and each dropping point is used to drop one or more monitoring devices.

[0065] In one embodiment, determining the flight path and dropping points of the unmanned aerial vehicle based on at least one of the geographical data and meteorological data of the target area and the dropping strategy may include: establishing an object falling dynamics model based on at least one of the geographical data and meteorological data of the target area and the dropping strategy; determining the flight path and dropping points of the unmanned aerial vehicle based on the object falling dynamics model.

[0066] Here, the object is the monitoring device, and establishing the object falling dynamics model may include the following steps: The gravitational acceleration is slightly lower than the standard gravitational acceleration at sea level in the plateau environment. Calculate the gravitational acceleration in the plateau environment through the following formula :

[0067] Wherein, is the standard gravitational acceleration on the sea surface, is the altitude of the plateau, is the radius of the earth. The air density is relatively low in the plateau environment, so the air resistance will be different. Calculate the air resistance through the following formula :

[0068] Wherein, is the speed of the object, is the drag coefficient of the object, is the cross-sectional area of the object, is the air density. During the movement of the object, the resultant force The equation can be expressed as:

[0069] Wherein, is the mass of the object.

[0070] Through these equations, the movement trajectory of the monitoring device from being dropped by the unmanned aerial vehicle to landing in the plateau environment can be simulated to ensure accurate scattering to the predetermined target position.

[0071] In one embodiment, determining the flight path and dropping points of the drone based on at least one of the geographical data and meteorological data of the target area and the dropping strategy may include: determining the number of monitoring devices to be dropped at each dropping point and the dropping point location based on at least one of the snow layer thickness, snow layer density, wind force, wind direction, temperature, and humidity of the target area, and the dropping density and / or the number of drops in the dropping strategy; determining the number of dropping points based on the number of monitoring devices to be dropped at each dropping point and the dropping point location, and determining the flight path of the drone based on the dropping point location and the number of dropping points.

[0072] In one embodiment, dropping the monitoring devices into the target area by the drone through throwing based on the flight path and dropping points may include: determining the flight attitude, dropping angle, and dropping speed at each dropping point based on the flight path and dropping points; dropping the monitoring devices into the target area by the drone through throwing based on the flight attitude, dropping angle, and dropping speed.

[0073] In one embodiment, determining the flight attitude, dropping angle, and dropping speed of the drone at each dropping point based on the flight path and dropping points may include: determining the distance between every two adjacent dropping points based on the flight path and the dropping point location, and determining the flight attitude of the drone at each dropping point based on the distance; determining the dropping angle and dropping speed of the drone at each dropping point based on the flight path, the dropping point location, and the weight of the monitoring device.

[0074] Here, the dropping speed is the initial dropping speed. The flight attitude may include being in a flight state or a stationary state, and the flight speed in the flight state or the tilt angle in the stationary state, etc. Among them, the tilt angle may refer to the tilt angle of the drone body, such as the angle with the horizontal plane, etc.

[0075] In this way, the flight strategy of the drone can be flexibly adjusted according to the dropping strategy, so as to accurately obtain the specific operation parameters such as the attitude, speed, and angle of each drop in combination with the path and dropping points, further improving the accuracy and intelligence of the dropping of the monitoring devices.

[0076] As Figure 3 shown, the present application also provides a monitoring system, and the system includes: a drone 1, a monitoring device 2, a base station 3, and a monitoring platform 4; Wherein, the drone 1 drops the monitoring device 2 by executing the method described in any one or more of the foregoing method embodiments; the monitoring device 2 establishes a communication connection with the base station 3 and is used to transmit monitoring data to the monitoring platform 4 through the base station 3.

[0077] In this embodiment, the base station 3 can upload data to the monitoring platform 4 using 4G / 5G communication technology. Among them, the base station 3 can be a relay base station, and the monitoring platform 4 can be a cloud platform or the like.

[0078] In one embodiment, the monitoring platform 4 can be used to interpret the monitoring data and process the interpreted data to determine the disaster situation in the target area. For example, dynamic modeling of the data is performed through real-time data analysis algorithms to evaluate information such as the probability of occurrence, scale, and path of the disaster. Here, the disaster situation can include whether a disaster is currently occurring, whether a disaster is likely to occur in the future, and the disaster formation path of a possible future disaster.

[0079] In one embodiment, the monitoring platform 4 further includes an emergency response module. This module can be docked with the data interfaces of multiple departments such as meteorology and geology to obtain the latest weather forecasts and geological disaster early warning information in real time. The monitoring platform 4 can also be used to send early warning information or prompt information, etc.

[0080] In this way, after the monitoring device is automatically deployed, data can be transmitted to the monitoring platform in real time through the communication between the monitoring device and the base station, which is conducive to timely early warning and response to disasters.

[0081] In some embodiments, at least one of a temperature sensor, a displacement sensor, a pressure sensor, an inclination sensor, and a hydrothermal salt sensor is provided in the monitoring device 2.

[0082] In one embodiment, as Figure 4 shown, the monitoring device 2 can include a sensor module 23, and can also include a hanging ring 21, a communication module 22, a battery 24, a buffer layer 25, and a counterweight 26, etc. Among them, the communication module 22 can be a Long Range Radio (LoRa) communication module, and the battery 24 can be a low-temperature battery. For example, when monitoring avalanche disasters, it can be a low-temperature battery to meet the requirements of the low-temperature harsh environment. The communication module 22, the sensor module 23, the battery 24, and the buffer layer 25 can be arranged between the hanging ring 21 and the counterweight 26. At least one of a temperature sensor, a displacement sensor, a pressure sensor, an inclination sensor, and a hydrothermal salt sensor can be provided in the sensor module 23.

[0083] Here, the hanging ring 21 can be used to hang on the unmanned aerial vehicle 1, and the counterweight 26 can be a counterweight block for improving the dropping effect.

[0084] In this way, by setting multiple sensors in the monitoring device 2, multi-modal monitoring data can be obtained to accurately monitor the occurrence of disasters in all aspects. Moreover, the monitoring device 2 can achieve contact monitoring of disasters such as avalanches through the above-mentioned sensors and other structures. Compared with the non-contact monitoring devices manually deployed in the related art, it can more accurately and quickly monitor the occurrence of disasters and optimize the monitoring effect.

[0085] In some embodiments, the monitoring platform 4 is used to predict the disaster risk of the target area based on the historical monitoring data and the current monitoring data transmitted by the monitoring device 2 through a predetermined model.

[0086] In one embodiment, the predetermined model can be an Artificial Intelligence (AI) model, such as an intelligent AI model for snow avalanche monitoring, etc. By continuously updating the algorithm and optimizing the data processing flow, in-depth mining and intelligent analysis of sensor data can be realized. Among them, the AI model can adopt machine learning algorithms. Based on the historical monitoring data and the current monitoring data transmitted by the monitoring device 2, predicting the disaster risk of the target area may include: learning and training based on the historical monitoring data and the current monitoring data, and predicting the disaster risk of the target area based on the trained AI model according to the current monitoring data. Thus, it is beneficial to accurately identify the early signs of disasters. At the same time, the AI model can also have the function of self-learning and optimization, and can automatically adjust the parameters of the model according to new data and feedback information to further improve the accuracy of prediction.

[0087] In some embodiments, the system includes multiple monitoring devices 2, and the multiple monitoring devices 2 are networked through LoRa; the monitoring device 2 is further used to adjust the communication path with the base station 3 according to at least one of the geographical data, meteorological data, and communication quality of the target area.

[0088] In one embodiment, multiple monitoring devices 2 can form a wireless sensor network through LoRa networking. Each monitoring device 2 uses self-organizing network technology to transmit data and relay with other adjacent monitoring devices 2 to ensure stable data transmission in complex terrains and harsh environments. The base station 3, as a data aggregation point, can receive data from multiple monitoring devices 2 through a multi-hop relay mechanism to provide guarantee for subsequent data uploading.

[0089] In one embodiment, adjusting the communication path with the base station 3 according to at least one of the geographical data, meteorological data, and communication quality of the target area may include: when the communication signal quality of at least one monitoring device on the current communication path deteriorates or a fault occurs, other monitoring devices can automatically adjust the communication path to bypass the monitoring device with deteriorated communication signal quality or a fault. This can ensure the connectivity of the network and the stability of data transmission.

[0090] In this way, through LoRa networking, rapid and stable networking among a large number of monitoring devices in the area can be achieved, which is conducive to stably transmitting monitoring data to the base station, and the communication path can be flexibly adjusted to further improve the data transmission stability. Based on this, the above monitoring system can achieve in-situ multi-parameter detection on the ground based on the design of the monitoring device 2, realize high-frequency continuity of monitoring data transmission based on LoRa networking and dynamic adjustment of the communication path, and achieve the effects of simple deployment (such as deploying in places where it is difficult for people to reach, such as high steep slopes) and simple maintenance based on the drone-dropping method. The above monitoring system has lower cost and higher reliability.

[0091] As a possible implementation manner, a dynamic avalanche disaster monitoring system and method based on a drone-dropping wireless sensor network are provided. This system consists of a drone survey module, a dropping sensor node, a self-organizing communication network, a relay base station, and a cloud data interpretation platform. The overall method flow includes: 1. Drone survey and determination of dropping points Use the drone to conduct a comprehensive topographic survey of the target area, and obtain detailed topographic data of the area, including information such as slope, aspect, and terrain undulation, through a high-precision positioning system (such as Beidou) and a topographic scanning device (such as lidar).

[0092] According to the obtained topographic data, divide the target area into multiple adjacent calculation cells (such as 30m×30m). For each calculation cell, establish a spatial risk probability assessment model for snow avalanche instability. This model calculates the instability probability by considering factors such as terrain slope, snow layer thickness, snow layer density, and meteorological conditions. The calculation formula for the snow avalanche instability probability is as follows:

[0093] According to the calculated instability probability, divide all calculation cells into four different risk levels: low risk (P<0.3), medium risk (0.3≤P<0.6), high risk (0.6≤P<0.9), and extremely high risk (P≥0.9).

[0094] For medium-risk, high-risk, and extremely high-risk areas, numerical simulations are carried out using the RAMMS model to analyze their potential avalanche paths and determine the starting area, sliding area, and accumulation area of the avalanche. This analysis helps to clarify the layout location and density of the sensors.

[0095] In high-risk and extremely high-risk areas, it is necessary to densely deploy monitoring devices 2, namely sensors, in the avalanche starting area. It is recommended that each sensor be responsible for a monitoring range of approximately 1 square kilometer to ensure that the early signs of an avalanche can be captured in a timely manner. In medium- and low-risk areas, the layout density of the sensors can be appropriately reduced.

[0096] Based on the sensor throwing points, the drone generates a path planning map using the A* algorithm. The path planning map comprehensively considers terrain obstacles and the requirements for the layout density of the sensors to ensure that the sensors can be accurately thrown into the target area. The drone flies along the predetermined path and throws sensor nodes at each predetermined position.

[0097] 2. Microstructure Design and Deployment Method of Monitoring Device 2, i.e., Thrown Sensors Nodes The node adopts a high-strength and lightweight outer shell, enabling it to provide necessary protection in harsh environments. The outer shell material is selected as aviation aluminum alloy or carbon fiber composite material, which has strong anti-drop and anti-compression capabilities and can maintain stable operation under conditions such as extreme low temperature and strong wind. The internally integrated temperature sensor can accurately measure the temperature changes in the snow layer, and the displacement sensor monitors the tiny displacements of the snow layer to capture the signs of an avalanche in a timely manner. The LoRa communication module supports long-distance and low-power communication to ensure stable data transmission between nodes even in complex mountainous environments. The lithium battery power supply system can still maintain long-term operation in low-temperature environments, ensuring that the sensor nodes continuously collect data for a long time.

[0098] This design ensures that the sensor lands stably during the high-speed throwing process and can continue to operate under extreme low temperature and harsh climate conditions.

[0099] 3. Wireless Sensor Network Networking and Data Transmission The thrown sensor nodes, i.e., monitoring devices 2, automatically form a network through the LoRa communication module to form a low-power and high-performance wireless sensor network. Each node conducts data transmission and relaying with neighboring nodes through self-organizing network technology to ensure stable data transmission in complex terrains and harsh environments. The base station 3, as the data aggregation point, receives data from multiple sensor nodes through a multi-hop relaying mechanism to provide guarantee for subsequent data uploading. In addition, the sensor nodes can dynamically adjust the network topology according to environmental changes and communication quality. For example, when the signal quality of a certain sensor node deteriorates or a fault occurs, other nodes can automatically adjust the communication path to bypass the problem node to ensure the connectivity of the network and the stability of data transmission.

[0100] 4. Data Transmission and Cloud Analysis After the data is received by the base station 3, it is uploaded to the cloud platform, i.e., the monitoring platform 4, for storage and processing using 4G / 5G communication technology. The cloud platform supports large-scale data storage and, combined with real-time data analysis algorithms, dynamically models the collected parameters such as displacement and temperature to evaluate information such as the occurrence probability, scale, and path of avalanches, thus providing a scientific basis for disaster warning and emergency response. The cloud platform also has real-time risk assessment and data visualization functions, which can intuitively display the monitoring situation of each area and provide decision-making support for the prevention and response of avalanche disasters.

[0101] In the drone scattering and sensor self-organizing network technology, in this embodiment, drones are used for the precise layout of sensor nodes, and self-organizing networks are achieved through the LoRa communication protocol. The drones are equipped with terrain scanning devices, and terrain surveys are carried out in combination with path optimization algorithms and snow avalanche instability spatial risk probability assessment models to ensure the efficient layout of sensor nodes in complex terrains, breaking through the limitations of traditional manual layout and significantly improving the monitoring coverage and accuracy. At the same time, the sensor nodes automatically form a network through the LoRa communication protocol, can optimize the network topology according to environmental changes, and ensure the stable transmission of data, especially maintaining reliable communication in harsh environments.

[0102] In the design of sensors resistant to extreme environments, the scattered sensor nodes have excellent capabilities to withstand extreme environments. The sensor housing is made of high-strength lightweight materials, with functions such as shock resistance, pressure resistance, and wind resistance, ensuring the stable operation of the device in harsh climates such as low temperatures and strong winds. The internal sensor module uses low-temperature adaptable materials, and the durability of the device is enhanced through buffer layers, counterweight blocks, and sealing designs to ensure that the sensors can adapt to the complex environments of high-risk avalanche areas.

[0103] In the drone-cloud collaborative architecture, through the collaborative work of drones and cloud platforms, an automated closed-loop monitoring system from data collection to avalanche risk assessment is constructed. The drones scatter sensor nodes to collect data and transmit it to the relay base station through the LoRa network, and then upload it to the cloud platform through 4G / 5G communication technology. The cloud platform has powerful data processing and analysis capabilities, uses machine learning algorithms to process sensor data in real time, generates avalanche risk assessment results, and issues warning signals in a timely manner. The intelligent analysis function of this platform provides a scientific decision-making basis for avalanche disaster prevention and control.

[0104] In terms of the microstructure design of the scattered sensor nodes, the sensor module 23 has further expandability and can integrate multiple types of sensors such as pressure, inclination, water heat salt, etc., so as to enrich the data dimension; the communication module 22 using LoRa technology can be replaced with wireless communication technologies such as NB-IoT and ZigBee to meet the communication scenario requirements of different situations.

[0105] When the drone is performing flight operations, its flight altitude can be adjusted in real time according to meteorological conditions. Since low temperature will have a significant negative impact on the performance of the drone battery, the flight altitude should be appropriately reduced in the extremely cold environment of the plateau to extend the flight time. At the same time, the drone can use terrain matching technology to accurately match the real-time collected terrain data with the pre-stored map to achieve precise control of the flight altitude.

[0106] During the process of sensor node scattering, the drone can use computer vision technology combined with sensors such as lidar to identify and locate the target area in real time, and then automatically adjust the throwing angle and speed to improve the success rate and accuracy of throwing. In addition, due to the particularity of avalanche disasters, a regional and phased throwing strategy can be adopted: initially, some sensor nodes are thrown in high-risk areas to build a basic monitoring network; subsequently, according to the real-time monitoring data and the development trend of the avalanche, supplementary throwing is carried out in key areas to strengthen the monitoring effect. Moreover, the collaborative operation of multiple drones can further improve the throwing efficiency and expand the monitoring range.

[0107] Through the description of the above implementation manners, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation manner. Based on such an understanding, the technical solution of the present invention, in essence, or the part that makes a contribution to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0108] The above description is only a preferred embodiment of the present application and is not intended to limit the protection scope of the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application are all included in the protection scope of the present application.

[0109] The above description is only the specific implementation manner of the present application, enabling those skilled in the art to understand or implement the present application. Various modifications to these embodiments will be obvious to those skilled in the art. 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 claimed herein.

Claims

1. A method for distributing disaster monitoring equipment based on drone scattering, characterized in that: include: Obtain disaster risk levels of multiple cells in the target area; Determining a deployment strategy for monitoring equipment in at least one of the cells based on the disaster risk level; Based on the delivery strategy, the monitoring equipment is delivered to the target area by scattering it through a drone.

2. The method according to claim 1, characterized in that The obtaining of disaster risk levels of multiple cells in the target area includes: dividing the target area into a plurality of cells based on a predetermined size; Obtaining meteorological data and geographic data corresponding to each of the cells; Based on the meteorological data and the geographic data, a disaster risk level of each of the cells is determined.

3. The method according to claim 2, characterized in that The step of determining the disaster risk level of each cell based on the meteorological data and the geographic data comprises: Calculate the disaster risk probability corresponding to each of the cells based on the meteorological data and the geographic data; The disaster risk probability is compared with a plurality of preset intervals to determine the disaster risk level of each cell; wherein each disaster risk level corresponds to one preset interval.

4. The method according to claim 1, characterized in that: The step of determining a deployment strategy of at least one monitoring device in the cell based on the disaster risk level includes: Predicting a disaster formation path based on target cells whose disaster risk levels are higher than a predetermined level; the disaster formation path at least includes: a disaster initiation area; It is determined that the target deployment density of monitoring equipment in the disaster activation area within the target cell is higher than a preset density, and / or the target deployment quantity is higher than a preset quantity.

5. The method according to claim 4, characterized in that The method of placing the monitoring equipment to the target area by scattering it through a drone based on the placement strategy includes: Based on the target delivery density and / or the target delivery quantity, the monitoring device is delivered to at least one of the target cells by scattering by a drone; Determine the delivery strategy of other cells except the target cell based on the monitoring data collected by the monitoring device in the target cell; Based on the delivery strategy of the other cells, the monitoring equipment is delivered to the other cells by scattering by drones.

6. The method according to claim 1, characterized in that The method of placing the monitoring equipment to the target area by scattering it through a drone based on the placement strategy includes: Determining a flight path and a delivery point of the drone based on at least one of the geographic data and the meteorological data of the target area and the delivery strategy; Based on the flight path and the delivery point, the monitoring equipment is dropped into the target area by the drone.

7. The method according to claim 6, characterized in that The method of placing monitoring equipment to the target area by scattering it through a drone based on the flight path and the placement point includes: Determine the flight attitude, delivery angle, and delivery speed of the UAV at each delivery point based on the flight path and delivery point; Based on the flight attitude, delivery angle and delivery speed, the monitoring equipment is dropped into the target area by the drone.

8. A monitoring system, characterized in that: The system includes: a drone, monitoring equipment, a base station and a monitoring platform; Wherein, the drone deploys the monitoring device by executing the method described in any one of claims 1 to 7; the monitoring device establishes a communication connection with the base station to transmit monitoring data to the monitoring platform through the base station.

9. The monitoring system according to claim 8, characterized in that: The monitoring device is provided with at least one of a temperature sensor, a displacement sensor, a pressure sensor, an inclination sensor and a water-heat-salt sensor.

10. The monitoring system according to claim 8, characterized in that: The monitoring platform is used to predict the disaster risk of the target area based on the historical monitoring data and current monitoring data transmitted by the monitoring equipment through a predetermined model.

11. The monitoring system according to claim 8, characterized in that: The monitoring system includes a plurality of the monitoring devices, and the plurality of the monitoring devices are networked via long-distance radio LoRa; the monitoring device is also used to adjust the communication path with the base station according to at least one of the geographical data, meteorological data and communication quality of the target area.

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