Communication method and related equipment

Abstract

The invention provides a communication method and related equipment, and relates to the technical field of communication. The method comprises the following steps: acquiring attenuation characteristics of a satellite navigation signal by network equipment; the network equipment obtains a weather prediction result based on the attenuation characteristics; the network equipment obtains weather early warning information according to the weather prediction result; and the network equipment sends the meteorological early warning information to flight equipment.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of communication, and in particular, to a communication method and related devices. BACKGROUND

[0002] The meteorological prediction in the related art has the problem of insufficient accuracy, which can easily lead to sudden accidents and cause great losses. SUMMARY

[0003] Embodiments of the present disclosure provide a communication method, which includes: a network device obtaining an attenuation feature of a satellite navigation signal; the network device obtaining a meteorological prediction result based on the attenuation feature; the network device obtaining meteorological warning information according to the meteorological prediction result; and the network device sending the meteorological warning information to a flight device. In an exemplary embodiment, the method can be executed by the network device, or the method can be executed by an apparatus (such as a chip, etc.) configured in the network device.

[0004] Embodiments of the present disclosure provide a communication method, which includes: a flight device obtaining meteorological warning information from a network device; and the flight device determining a flight decision based on the meteorological warning information. In an exemplary embodiment, the method can be executed by the flight device, or the method can be executed by an apparatus (such as a chip, etc.) configured in the flight device.

[0005] Embodiments of the present disclosure provide a communication method, which includes: a computing node obtaining an attenuation feature of a satellite navigation signal from a network device; and the computing node determining a meteorological prediction result according to the attenuation feature; and the computing node sending the meteorological prediction result to the network device. In an exemplary embodiment, the method can be executed by the computing node, or the method can be executed by an apparatus (such as a chip, etc.) configured in the computing node.

[0006] Embodiments of the present disclosure provide a network device, which includes: a first receiving processing unit configured to obtain an attenuation feature of a satellite navigation signal; the first receiving processing unit is further configured to obtain a meteorological prediction result based on the attenuation feature; the first receiving processing unit is further configured to obtain meteorological warning information according to the meteorological prediction result; and a first sending unit configured to send the meteorological warning information to a flight device.

[0007] Embodiments of the present disclosure provide a flight device, which includes: a second receiving unit configured to obtain meteorological warning information from a network device; and a second processing unit configured to determine a flight decision based on the meteorological warning information.

[0008] The embodiment of the present disclosure provides a computing node, comprising: a third receiving unit configured to acquire an attenuation feature of a satellite navigation signal from a network device; a third processing unit configured to determine a meteorological prediction result according to the attenuation feature; and a third sending unit configured to send the meteorological prediction result to the network device.

[0009] The embodiment of the present disclosure provides a communication system, comprising: the network device according to any embodiment of the present disclosure, and the flight device according to any embodiment of the present disclosure.

[0010] The embodiment of the present disclosure provides a communication device, which can comprise a module corresponding to each of the methods / operations / steps / actions described in any embodiment of the present disclosure. The module can be a hardware circuit, a software, or a combination of hardware circuit and software.

[0011] The embodiment of the present disclosure provides a communication device, comprising: a processor. The processor can implement the method in any embodiment of the present disclosure. Optionally, the communication device further comprises a memory, and the processor is coupled to the memory and is configured to execute a computer program in the memory to implement the method in any embodiment of the present disclosure. Optionally, the communication device further comprises a communication interface, and the processor is coupled to the communication interface. In the embodiment of the present disclosure, the communication interface can be a transceiver, a pin, a circuit, a bus, a module or other types of communication interfaces, which are not limited.

[0012] In an implementation manner, the communication device is a terminal (for example, a flight device). When the communication device is a terminal, the communication interface can be a transceiver or an input / output interface.

[0013] In another implementation manner, the communication device is a chip configured in a terminal. When the communication device is a chip configured in a terminal, the communication interface can be an input / output interface.

[0014] Optionally, the transceiver can be a transceiver circuit. Optionally, the input / output interface can be an input / output circuit.

[0015] In an implementation manner, the communication device is a network device and / or a computing node. When the communication device is a network device and / or a computing node, the communication interface can be a transceiver or an input / output interface.

[0016] In another implementation manner, the communication device is a chip configured in a network device and / or a computing node. When the communication device is a chip configured in a network device and / or a computing node, the communication interface can be an input / output interface.

[0017] The embodiment of the present disclosure further provides a processor, comprising: an input circuit, an output circuit and a processing circuit. The processing circuit is configured to receive a signal through the input circuit and transmit a signal through the output circuit, so that the processor executes the method in any embodiment of the present disclosure.

[0018] In the example embodiment, the processor can be one or more chips, the input circuit can be an input pin, the output circuit can be an output pin, and the processing circuit can be a transistor, a gate circuit, a flip-flop, various logic circuits and the like. The input signal received by the input circuit can be received and input by, for example but not limited to, a receiver, the signal output by the output circuit can be output to and transmitted by, for example but not limited to, a transmitter, and the input circuit and the output circuit can be the same circuit which is used as the input circuit and the output circuit at different times. The embodiment of the present disclosure does not limit the specific implementation of the processor and various circuits.

[0019] The embodiment of the present disclosure further provides a communication system, comprising at least one communication device in the embodiment of the present disclosure.

[0020] The embodiment of the present disclosure provides a communication device, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the communication method in any embodiment of the present disclosure via execution of the executable instructions.

[0021] The embodiment of the present disclosure provides a computer readable storage medium, having a computer program stored thereon, the computer program being executed by a processor to implement the communication method in any embodiment of the present disclosure.

[0022] The embodiment of the present disclosure provides a computer program product, comprising a computer program, the computer program being executed by a processor to implement the communication method in any embodiment of the present disclosure. BRIEF DESCRIPTION OF DRAWINGS

[0023] The drawings herein are incorporated into the specification and form a part of the specification, show embodiments consistent with the present disclosure, and together with the specification serve to explain the principles of the present disclosure. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and other drawings can be obtained by those skilled in the art without creative labor on the basis of these drawings.

[0024] Figure 1 A structural schematic diagram of a communication system in an embodiment of the present disclosure is shown.

[0025] Figure 2 A flowchart of a communication method in an embodiment of the present disclosure is shown.

[0026] Figure 3 FIG. 1 shows a schematic diagram of an overall architecture of a communication system in accordance with embodiments of the disclosure.

[0027] Figure 4 FIG. 2 shows a schematic diagram of a base station GPS (Global Positioning System) receiver in accordance with embodiments of the disclosure.

[0028] Figure 5 FIG. 3 shows a flowchart of another communication method in accordance with embodiments of the disclosure.

[0029] Figure 6 FIG. 4 shows a flowchart of yet another communication method in accordance with embodiments of the disclosure.

[0030] Figure 7 FIG. 5 shows a flowchart of still another communication method in accordance with embodiments of the disclosure.

[0031] Figure 8 FIG. 6 shows a flowchart of yet another communication method in accordance with embodiments of the disclosure.

[0032] Figure 9 FIG. 7 shows a schematic diagram of another communication system in accordance with embodiments of the disclosure.

[0033] Figure 10 FIG. 8 shows a schematic diagram of yet another communication system in accordance with embodiments of the disclosure.

[0034] Figure 11 FIG. 9 shows a schematic diagram of a weather sensing using a UAV in accordance with embodiments of the disclosure.

[0035] Figure 12 FIG. 10 shows a block diagram of a network device in accordance with embodiments of the disclosure.

[0036] Figure 13 FIG. 11 shows a block diagram of a flight device in accordance with embodiments of the disclosure.

[0037] Figure 14 FIG. 12 shows a block diagram of a computing node in accordance with embodiments of the disclosure.

[0038] Figure 15 FIG. 13 shows a block diagram of a communication device in accordance with embodiments of the disclosure. DETAILED DESCRIPTION

[0039] Example implementations will now be described with reference to the drawings. However, example implementations can be implemented in many different forms and should not be construed as limited to the examples set forth herein; rather, these implementations are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example implementations to those skilled in the art. The described features, structures, or characteristics can be combined in one or more implementations.

[0040] Furthermore, the accompanying drawings are merely illustrative of this disclosure, and the same reference numerals in the drawings denote the same or similar parts, thus repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0041] In this disclosure, at least one item can be described as one item or multiple items, and multiple items can be two, three, four, or more items, without limitation. " / " can indicate that the related objects are in an "or" relationship; for example, A / B can mean A or B. "And / or" can be used to describe three relationships between related objects; for example, A and / or B can represent: A alone, A and B simultaneously, and B alone, where A and B can be singular or plural. To facilitate the description of the technical solutions of this application, terms such as "first," "second," "A," or "B" can be used to distinguish technical features with the same or similar functions. These terms do not limit the quantity or execution order. Furthermore, the terms "first," "second," "A," or "B" are not necessarily different. The words “exemplary” or “for example” are used to indicate examples, illustrations, or explanations. Any design described as “exemplary” or “for example” should not be construed as being superior or more advantageous than other design options. The use of words such as “exemplary” or “for example” is intended to present the relevant concepts in a concrete manner to facilitate understanding.

[0042] The specific implementation methods of the embodiments of this disclosure will now be described in detail with reference to the accompanying drawings.

[0043] like Figure 1 As shown, the communication system architecture includes an access network (e.g., one or more of a wireless access network / mobile access network, satellite access network, etc.) and a core network (e.g., a converged core network, which refers to a core network that integrates multiple networks, such as two or more of satellite, mobile, and fixed networks). The wireless access network may include at least one wireless access network device (e.g., Figure 1 The network device 20 may also include at least one terminal device (such as...) Figure 1The terminal device 10 is connected to the network device 20 through wireless communication. The network device 20 is connected to the core network device 30 through wired or wireless communication. The core network device 30 is connected to another network device (not shown in FIG. 1) through wired or wireless communication. The terminal device 10 and the terminal device 10 can be connected to each other through wired or wireless communication. The network device 20 and the network device 20 can be connected to each other through wired or wireless communication. Figure 1 The communication system shown in FIG. 1 is only a schematic diagram. Other network devices can also be included in the communication system, such as wireless relay devices and wireless backhaul devices, which are not shown in FIG. 1. Figure 1

[0044] The network device 20 can be a base station (BS), an evolved NodeB (eNodeB), a transmission reception point (TRP), a next generation NodeB (gNB) in a 5th generation (5G) mobile communication system, a next generation NodeB in a 6th generation (6G) mobile communication system, a base station in a future mobile communication system, or an access node in a WiFi system, etc. The network device 20 can be a module or unit that completes part of the functions of a base station, for example, a central unit (CU) or a distributed unit (DU). The network device 20 can be a macro base station, a micro base station, or an indoor station, a relay node or a donor node, etc. The embodiments of the present disclosure do not limit the specific technology and specific device form adopted by the network device 20. For ease of description, the following describes the network device 20 as a base station.

[0045] The terminal device 10 can also be referred to as a terminal, a user equipment (UE), a mobile station, a mobile terminal, a communication device of a terminal user, etc. The terminal device can be widely applied to various scenarios, such as device-to-device (D2D) communication, vehicle-to-everything (V2X) communication, machine type communication (MTC), etc. to ​​The terminal device can be a mobile phone, a tablet computer, a computer with wireless transceiver function, a wearable device, a vehicle, a flight device (for example, a drone, a helicopter, an airplane, or any device with flight function, the following examples are taken from a drone, but the present disclosure is not limited thereto), a ship, a robot, a mechanical arm, a smart home device, etc. Embodiments of the present disclosure do not limit the specific technology and specific device form adopted by the terminal device.

[0046] The base station and the terminal device can be fixed in position or mobile. The base station and the terminal device can be deployed on land, including indoors or outdoors, handheld or vehicle-mounted; can also be deployed on water; can also be deployed on an airplane, a balloon, and a man-made satellite. Embodiments of the present disclosure do not limit the application scenarios of the base station and the terminal device.

[0047] The roles of the base station and the terminal device can be relative, for example, a helicopter or a drone can be configured as a mobile base station, and for those terminal devices accessing the wireless access network through the helicopter or the drone, the helicopter or the drone is a base station; but for the base station, the helicopter or the drone is a terminal device, that is, the base station and the helicopter or the drone communicate through a wireless air interface protocol. Of course, the base station and the helicopter or the drone can also communicate through an interface protocol between the base station and the base station, at this time, relative to the base station, the helicopter or the drone is also a base station. Therefore, the base station and the terminal device can be collectively referred to as a communication apparatus / communication device, Figure 1 The network device 20 in FIG. 1 can be referred to as a communication apparatus / communication device with base station function, Figure 1 The terminal device 10 in FIG. 1 can be referred to as a communication apparatus / communication device with terminal device function.

[0048] The base station and the terminal device, the base station and the base station, and the terminal device and the terminal device can communicate through a licensed spectrum, or through an unlicensed spectrum, or through both the licensed spectrum and the unlicensed spectrum; can communicate through a spectrum below 6 gigahertz (GHz), or through a spectrum above 6 GHz, or through both the spectrum below 6 GHz and the spectrum above 6 GHz. Embodiments of the present disclosure do not limit the spectrum resources used for wireless communication.

[0049] In the embodiments of the present disclosure, the functions of the base station can also be performed by a module (such as a chip) in the base station, or by a control subsystem containing the functions of the base station. The control subsystem containing the functions of the base station herein can be a control center in the above-mentioned application scenarios such as smart grid, industrial control, intelligent transportation, and smart city. The functions of the terminal device can also be performed by a module (such as a chip or a modem) in the terminal device, or by a device containing the functions of the terminal device.

[0050] The technical solutions provided by the embodiments of the present disclosure can be applied to wireless communication between communication devices. The wireless communication between communication devices can include wireless communication between a network device and a terminal device, wireless communication between network devices, and wireless communication between terminal devices. In the embodiments of the present disclosure, the term "wireless communication" can also be referred to as "communication", and the term "communication" can also be described as "data transmission", "information transmission", or "transmission".

[0051] Those skilled in the art can know that, Figure 1 The number of terminal devices and network devices in the above-mentioned system architecture is only illustrative, and any number of terminal devices and network devices can be provided according to actual needs. The embodiments of the present disclosure do not limit this.

[0052] Under the above-mentioned system architecture, the present disclosure provides a communication method, which can be executed by any communication device with information processing capability. In some embodiments, the communication method provided by the embodiments of the present disclosure can be executed by the terminal device of the above-mentioned system architecture; in other embodiments, the communication method provided by the embodiments of the present disclosure can be implemented by the network device in the above-mentioned system architecture. In still other embodiments, the communication method provided by the embodiments of the present disclosure can be implemented by the terminal device and the network device in the above-mentioned system architecture through interaction. The present disclosure relates to the fields of wireless communication and satellite communication.

[0053] As shown in Figure 2 The method provided by the embodiments of the present disclosure can include the following steps.

[0054] In S210, the network device obtains the attenuation feature of the satellite navigation signal.

[0055] Exemplarily, the network device includes a base station, and the following embodiments are exemplified by the base station, but the present disclosure is not limited thereto.

[0056] In the embodiments of the present disclosure, the attenuation characteristics of a satellite navigation signal (such as GPS, Beidou, GLONASS, and Galileo) can also be referred to as signal attenuation data or attenuation data of the satellite navigation signal, which refers to the phenomenon and law of signal power weakening in the process of the satellite navigation signal propagating from a satellite to a network device. The attenuation characteristics define how and why the signal strength is attenuated in the process of the satellite navigation signal propagating from a satellite in space to a receiver antenna of the network device, including free space attenuation, atmospheric attenuation, environmental obstruction, and multipath attenuation.

[0057] In S220, the network device obtains a weather prediction result based on the attenuation characteristics.

[0058] For example, the network device obtains the weather prediction result based on the attenuation characteristics, including: The network device sends the attenuation characteristics to a computing node, so that the computing node obtains the weather prediction result according to the attenuation characteristics; and the network device obtains the weather prediction result from the computing node.

[0059] The computing node in the embodiments of the present disclosure includes any electronic device with a data processing function. By using the computing node to process the attenuation characteristics collected by one or more network devices, the accuracy of the weather prediction result can be improved. However, the present disclosure is not limited thereto, and in other embodiments, the network device can also directly predict the weather prediction result based on the attenuation characteristics of the satellite navigation signal, so as to further reduce the delay of obtaining the weather prediction result.

[0060] In some embodiments, the computing node includes an edge computing node, and the following embodiments are all examples of the edge computing node, but the present disclosure is not limited thereto. Since the edge computing node is closer to the network device and the flying device, the delay of the network device and the flying device in obtaining the weather prediction result can be reduced.

[0061] For example, the weather prediction result includes one or more of precipitation intensity, precipitation probability, start time, precipitation range, and duration.

[0062] For example, the attenuation characteristics include one or more of a carrier phase fluctuation value, a carrier phase change rate, a signal-to-noise ratio attenuation slope, and a multipath effect change rate.

[0063] For example, the precipitation probability is determined according to one or more of the carrier phase change rate, the signal-to-noise ratio attenuation slope, and the multipath effect change rate.

[0064] Exemplarily, the method provided by the embodiment of the present disclosure further includes: collecting, by the network device, a temperature correction factor. Wherein, the precipitation probability is determined according to one or more of the carrier phase change rate, the signal-to-noise ratio attenuation slope, the multipath effect change rate, and the temperature correction factor.

[0065] In S230, the network device obtains weather warning information according to the weather prediction result.

[0066] In some embodiments, the network device can directly send the weather prediction result to the flight device as the weather warning information. In other embodiments, the network device can process the weather prediction result (for example, perform some format conversion, add some message id, etc.), obtain the weather warning information, and then send it to the flight device.

[0067] In S240, the network device sends the weather warning information to the flight device.

[0068] Exemplarily, the network device broadcasts a system information block (SIB) extension message to the flight device; the system information block extension message includes the weather warning information.

[0069] Exemplarily, the system information block extension message includes at least one of the following: a system information block type, a network device identifier, a weather type, a precipitation probability, a precipitation intensity, a duration, and a geographic coordinate code.

[0070] Exemplarily, the system information block type is indicated as a weather system information block.

[0071] Exemplarily, the weather type includes precipitation.

[0072] Exemplarily, the message id of the system information block extension message is different from that of a public warning message.

[0073] The cell broadcast PWS (Public Warning System) is a system for public warning in a mobile communication network, which sends emergency warning information to mobile terminals in a dangerous area through broadcast. The embodiment of the present disclosure uses a dedicated channel in the technical system of cell broadcast for transmission, and the SIB extension message uses a message id different from that of the public warning to distinguish.

[0074] Exemplarily, the flight device includes a drone.

[0075] In this embodiment of the disclosure, when the network device is a base station and the flight device is a drone, a weather forecasting drone flight decision system based on base station navigation signals can be provided to perform weather forecasting based on satellite navigation signals received by the base station, and to assist the drone in making flight decisions based on the weather forecasts.

[0076] This disclosure pertains to the field of wireless communication and terminal technology.

[0077] In low-altitude economic scenarios, various types of drones are needed in areas such as logistics, urban transportation, environmental protection, and urban governance. Drones flying at low altitudes are susceptible to weather conditions, necessitating the receipt of micro-scale weather forecasts to make flight decisions.

[0078] like Figure 11 As shown, the sensors deployed on drones can detect temperature, air pressure, and wind speed, but current drone flight safety still faces the following core challenges: (1) Insufficient accuracy of meteorological forecasts; traditional meteorological radar has a spatial resolution >1. With an update cycle of more than 10 minutes, it cannot capture microscale meteorological changes (such as sudden showers).

[0079] (2) Limitations of UAV meteorological perception: unable to detect precipitation, detection range <50m, and susceptible to flight interference.

[0080] (3) The solutions in the related technologies have defects: First, due to cloud interference, the delay of meteorological satellites in meteorological detection is high (>30 minutes); second, the ground meteorological stations are sparsely deployed (spacing >20km); and third, radar echoes are severely blocked by urban buildings.

[0081] Statistics show that the crash rate of small drones caused by sudden rainstorms is as high as 80%, while the range of logistics drones increases by 35% by avoiding severe weather.

[0082] This disclosure belongs to the interdisciplinary field of UAV control and weather forecasting, specifically relating to a system and method for providing real-time flight decision support for UAVs by utilizing navigation signals from mobile communication base stations to achieve ultra-local (because the coverage radius of a base station is limited, the satellite receiver data of the base station (including satellite navigation signals and their attenuation characteristics) can represent the weather forecast data (i.e., weather forecast results) within the coverage area of ​​the base station) weather forecasting, and broadcasting it through 5G air interface system information (such as SIB extended messages).

[0083] In some embodiments, a mobile communication base station GPS receiver (also referred to as a base station GPS receiver) is utilized as a distributed weather sensor network to achieve a hundred-meter weather prediction through navigation information attenuation analysis (i.e., analyzing the attenuation characteristics of satellite navigation signals), and the weather prediction results and / or weather warning information are broadcast in real time to the UAV in flight through SIB extension messages.

[0084] Exemplarily, the satellite navigation signal can be used to predict the amount of precipitation. Exemplarily, the satellite navigation signal can be used to estimate the probability of precipitation. Exemplarily, some characteristics of the satellite navigation signal (such as multipath effect, signal-to-noise ratio change, etc.) are used to indirectly infer the precipitation condition.

[0085] Exemplarily, the base station can periodically broadcast SIB extension messages. Exemplarily, the SIB extension message uses a special message id. Exemplarily, the base station and the UAV can make an agreement on which message id is used for broadcasting weather prediction results and / or weather warning information, and ordinary user terminals (such as mobile phones, etc.) will not receive and decode the cell broadcast information dedicated to weather prediction.

[0086] In the field of mobile communication (especially in LTE and 5G NR technology), SIB is a collective term for a series of broadcast messages. These messages are periodically broadcast by the network (base station, such as eNodeB or gNB) to all terminals (such as mobile phones) within the coverage range. Its main role is to tell the terminal how to access the network, how to use network services, and related information about surrounding cells. The terminal must first decode and receive these SIB messages before attempting to register with the network. The embodiments of the present disclosure extend the SIB message so that it can be used for interaction between the base station and the UAV about weather prediction results and / or weather warning information.

[0087] Exemplarily, the GPS receiver (or more generally, the "GNSS receiver (Global Navigation Satellite System)", as it may include Beidou, GLONASS, etc.) installed on the base station (especially the communication base station) is used to receive signals from navigation satellites, which are referred to as satellite navigation signals.

[0088] Exemplarily, the GPS receiver receives microwave radio signals from multiple (e.g., four or more) GPS satellites through an antenna. These signals contain two key pieces of information: navigation messages, which contain information such as satellite orbital parameters (ephemeris), clock correction amounts, system status, etc., which the receiver uses to calculate the exact position of each satellite; and ranging codes, which are a kind of pseudo-random noise code, and the receiver calculates the transmission time of the signal from the satellite to the receiver by comparing the time difference between the received code and the same code generated by itself.

[0089] The attenuation (or "delay") of GPS signals is directly related to atmospheric conditions (temperature, pressure, humidity, etc.) along the signal propagation path. By resolving this delay, the real-time state of the atmosphere can be inferred, enabling high-precision weather prediction. The signals transmitted by GPS satellites need to pass through the Earth's atmosphere before reaching the ground-based receivers. The atmosphere has two major effects on the propagation of the signals: ionospheric delay, which causes refraction of the radio signals, resulting in a change in the speed of propagation (delay), the impact of which is inversely proportional to the square of the signal frequency. Dual-frequency or multi-frequency GPS receivers can use the difference in delay of signals of different frequencies to accurately calculate and eliminate most of the ionospheric error. Therefore, for meteorological applications, the main concern is the tropospheric delay. It consists of two parts: dry delay, caused by dry air (mainly nitrogen, oxygen), which is mainly dependent on the temperature and pressure of the atmosphere, changes relatively slowly and is predictable; wet delay: caused by water vapor in the atmosphere O). High-precision GPS receivers at base stations can calculate the total propagation time of signals from satellites to receivers with extremely high accuracy. By comparing it with the theoretical propagation time in a vacuum, the total tropospheric delay value can be obtained. By installing a meteorological sensor (measuring temperature, pressure) next to the base station, the model value of the dry delay can be calculated very accurately. Total delay - dry delay = wet delay. In this way, a crucial parameter, the precipitable water content, is obtained. The size of the wet delay directly reflects the total content of atmospheric water vapor along the signal path.

[0090] The reason why the embodiments of the present disclosure can achieve a prediction of hundreds of meters is that a distributed communication base station network is used. The distribution density of mobile communication base stations (especially in urban areas) is very high, possibly reaching several or even more per square kilometer. This forms a natural, ultra-high-density atmospheric water vapor sensor network. Traditional weather stations may have only one every few tens of kilometers, and sounding stations release balloons only twice a day, completely unable to capture the rapid changes of water vapor on a small scale. Base stations can work continuously and provide water vapor data updates every minute or even higher frequency. And there is no need to deploy and maintain thousands of dedicated weather stations, only need to add data resolution and upload functions to the existing base station GPS receivers.

[0091] Weather forecasting, especially forecasts of severe weather such as short-duration heavy rainfall, thunderstorms, and dense fog, largely depends on the accuracy of the description of the initial atmospheric state, particularly the three-dimensional distribution of water vapor. For example, real-time, high-density "precipitable water" data from thousands of base stations can be input into weather forecasting models. These massive amounts of real data are used to refine the models' original estimates of the initial atmospheric state (temperature, pressure, humidity fields), bringing them closer to reality. A weather forecasting model that starts operating from a more realistic initial state will have improved accuracy in its predictions, especially for short-term nowcasts within the next 0-12 hours. It can more accurately simulate local convection, water vapor convergence, and other processes, thus predicting which areas (on a scale of hundreds of meters to kilometers) are likely to experience precipitation and when.

[0092] Figure 2 This is a framework diagram of the technical solution provided in the embodiments of this disclosure. Figure 2 As shown, satellite system 301 sends satellite navigation signals to base station GPS receiver 302. Base station GPS receiver 302 obtains signal attenuation data 303 based on the received satellite navigation signals. Base station GPS receiver 302 sends signal attenuation data 303 to edge computing node 304. Edge computing node 304 processes signal attenuation data 303 through weather prediction engine 305 to obtain weather prediction results. Exemplarily, weather prediction engine 305 can predict weather prediction results through precipitation probability model 306. Exemplarily, the prediction parameters included in the weather prediction results include precipitation intensity, precipitation range, duration, etc. After obtaining the weather prediction results, the base station SIB broadcasts 307 to send the weather prediction results and / or weather warning information to UAV communication module 308. The UAV uses flight decision engine 309 to process weather warning information and make decision outputs, including but not limited to: route adjustment, altitude change, emergency landing, etc.

[0093] For example, the system components include: (1) Signal acquisition layer: including base station GPS receiver or base station GPS receiver 302. For example... Figure 4 As shown, the base station GPS receiver 302 includes an antenna 3021, a low-noise amplifier 3022, a downconverter 3023, an ADC (Analog-to-Digital Converter) 3024, and a signal processor 3025.

[0094] For example, the attenuation characteristics (acquisition parameters) of the satellite navigation signal obtained after being received and processed by the base station GPS receiver 302 include, but are not limited to: (a) Carrier Phase Derivative (CPD). CPD refers to the degree of short-term, high-frequency fluctuation of the satellite carrier signal phase tracked by the GPS receiver. It can be expressed as a statistical value (such as standard deviation) of the high-frequency variation part of the carrier phase observation value within a period of time (for example, a few minutes).

[0095] (b) Signal-to-Noise Ratio Attenuation Slope (SNR Slope). ). refers to the rate (slope) of attenuation of the carrier-to-noise ratio (abbreviated as SNR) of the GPS signal as the satellite elevation angle decreases. This signal-to-noise ratio is related to the water vapor content in the atmosphere, and the water vapor content in the atmosphere and raindrops also affect the carrier-to-noise ratio of the GPS signal.

[0096] (c) Multi-path Effect Change Rate (Multi-path Slope). ). refers to the short-term change rate or variance of the error caused by the multi-path effect. The multi-path effect refers to the fact that in addition to receiving the signal directly from the satellite, the receiver antenna also receives the signal reflected by surrounding objects (such as the ground, buildings, water surface). These reflected signals superimpose with the direct signals, causing interference. The water vapor content in the atmosphere also affects this index.

[0097] (2) Edge computing layer: contains edge computing nodes, which can also be called edge nodes. The edge node can be a node deployed in the campus network, close to the base station around the UAV flight route, and the operation can be performed without passing through the edge node, such as directly by the base station.

[0098] Exemplarily, the precipitation probability can be calculated by the following formula: Precipitation probability = (1) In the above formula (1), , , , are weight coefficients of , , , , which are real numbers greater than or equal to 0 and less than or equal to 1. When estimating the precipitation probability by utilizing various characteristics of satellite navigation signals, the weight coefficients are adjusted to construct the precipitation probability model. The range of the weight coefficients can be set. The weight coefficients can be obtained through artificial intelligence (AI) training. The principle is to estimate the water vapor content in the atmosphere to predict the precipitation probability.

[0099] It should be noted that the present disclosure is not limited to predicting the precipitation probability by using the above formula. In other embodiments, the precipitation probability can be predicted by a pre-trained AI model.

[0100] Wherein, is the carrier phase rate of change, which can be used to detect abnormal fluctuations in carrier phase (e.g. > 0.5 mm for weather warning). is the rate of change or difference form of CPD. CPD (Carrier Phase Fluctuation Value) refers to the rate of change of carrier phase over time. It can be the rate of change of carrier phase relative to a reference point (such as a certain known time point or starting point), with units of meters per second (m / s). In high-precision positioning and time synchronization, CPD is used to measure and correct phase changes to improve accuracy and stability. (Carrier Phase Rate of Change) refers to the rate of change of CPD, i.e. the value of CPD changing over time. It describes the change of CPD over a certain period of time, with units of meters per second (m / s), the same as CPD. By monitoring , the trend and abnormal situation of CPD over time can be identified and analyzed, especially in monitoring phenomena such as multipath effects.

[0101] Exemplarily, a high-precision GPS receiver is used to collect signals from navigation satellites. By solving the carrier phase observation value, the carrier phase data is obtained. For each observation period, the instantaneous value of the carrier phase is solved. For example, by using methods such as Precise Point Positioning (PPP) or Differential GPS (DGPS). Select two reference time points (e.g. starting time point and ending time point), calculate the carrier phase difference value between the two time points. Calculate the rate of change of carrier phase over time, i.e. CPD.

[0102] Exemplarily, obtain , select a fixed time interval (e.g. every 1 second or 5 seconds), calculate the CPD change value in this time interval. Calculate the CPD change between two consecutive time points.

[0103] is the SNR attenuation gradient, which can be in units of dB / s.

[0104] Also known as multipath interference variance rate of change. represents the rate of change of multipath interference variance, i.e. the speed of variance changing over time. This indicator can help quantify the dynamic changes of multipath effects, especially under precipitation conditions, as the increase in ground reflection can significantly increase the multipath interference variance, leading to increase.

[0105] represents the temperature correction factor (from network equipment sensors, such as base station sensors). Exemplarily, it can be the temperature in the air detected by the base station tower.

[0106] Exemplarily, the base station GPS receiver receives radio frequency signals from multiple GPS satellites. These signals are tracked, decoded and processed to generate a series of raw observations, including: carrier phase observations; pseudorange observations; signal-to-noise ratio (SNR) observations. And CPD, and are obtained by using these raw observations and performing calculations and analyses through specific algorithm models. This process is completed by post-processing software or algorithm modules embedded in the receiver. The carrier phase itself is very smooth. Its dramatic fluctuations are mainly disturbed by irregular changes in the refractive index of the signal propagation path. Such irregular changes are mainly caused by atmospheric turbulence, and the intensity of turbulence is closely related to the water vapor content, temperature gradient, etc. in the atmosphere. Therefore, CPD is a sensitive indicator of atmospheric turbulence activity. High CPD values usually indicate strong atmospheric turbulence, which may indicate the occurrence of unstable weather conditions, such as thunderstorm precursors, strong convection, etc. From high elevation angles to low elevation angles, the path length of GPS signals penetrating the atmosphere increases (from "vertical" through the atmosphere to "oblique" through the atmosphere). The longer the path, the greater the signal absorption and refraction by the atmosphere (especially dry air and water vapor in the troposphere), resulting in a decrease in SNR. quantifies the degree of this attenuation. The greater the slope, the stronger the attenuation of the signal by the atmosphere (especially water vapor). has a good correlation with the total atmospheric water vapor content along the signal path. The steeper the slope, the higher the water vapor content in the atmosphere, which can be used to invert the atmospheric humidity conditions. Multipath error itself is one of the main noise sources of GPS measurements. focuses on the dynamic part of this error. Because the multipath error caused by static environments (such as fixed buildings) is relatively stable, while the multipath error caused by dynamic surfaces (such as wind-waving water, swaying trees, moving vehicles) is rapidly changing. Therefore, high values can indirectly reflect changes in the antenna's surrounding environment, especially changes in the ground surface conditions. High may indicate strong changes in the ground surface near the antenna.

[0107] Exemplarily, satellite navigation signal meteorological inversion can be performed by the following formula: (2) where represents the carrier phase change, i.e. CPD in the above. represents the water vapor density. represents the absolute temperature, i.e. the temperature correction factor in the above.

[0108] Exemplarily, the edge computing node outputs the meteorological prediction result to the base station in the following output format: { "cell_id": 50123, / / base station identifier, base station ID "pred_type": "precipitation", / / prediction type: precipitation falling to the ground from the atmosphere, such as rain, snow, hail, etc. "intensity": 3, / / precipitation intensity "probability": 0.87, / / precipitation probability "start_time": 1620450000, / / start time, i.e., the time when the precipitation starts "duration": 300, / / duration, in seconds "affected_area": [ / / precipitation range {"lat": 31.23, "lng": 121.47, "radius": 150}, / / lat represents latitude, lng represents longitude, and radius represents the radius range of a circular area with (lat, lng) as the center point {"lat": 31.24, "lng": 121.48, "radius": 200} ] } Exemplarily, the precipitation intensity can be set to 1-5 levels, but the present disclosure is not limited thereto and can be divided according to actual needs.

[0109] Exemplarily, different weight values are obtained through AI training to obtain the predicted precipitation intensity. Exemplarily, the main factor reflecting the precipitation intensity is the signal-to-noise ratio attenuation gradient .

[0110] Exemplarily, the duration is determined according to the calculation period of the edge computing node based on the collected data. Assuming that the calculation period is 300 seconds, the duration is considered to be 300 seconds, but the present disclosure is not limited thereto.

[0111] Exemplarily, the precipitation range can be predicted based on the geographical deployment positions of multiple base stations, and the data of multiple base stations can be integrated. Exemplarily, the precipitation range can be predicted based on the collected data of a single base station, thereby reducing the calculation amount.

[0112] (3) Air interface broadcast layer.

[0113] In the method provided by the embodiments of the present disclosure, the SIB extension design can be as shown in Table 1: Table 1

[0114] The geographic coordinate code in Table 1 can be determined according to the precipitation range.

[0115] (4) UAV decision layer.

[0116] In the embodiments of the present disclosure, a decision matrix such as shown below can be preconfigured in the UAV: Table 2

[0117] The probability in Table 2 above refers to a precipitation probability, and a mapping relationship between precipitation intensity, precipitation probability, and corresponding flight decision is established in this way.

[0118] As shown in Figure 5 The method provided by the embodiments of the present disclosure can include the following steps.

[0119] In S510, the flight device obtains meteorological warning information from the network device.

[0120] In S520, the flight device determines a flight decision based on the meteorological warning information.

[0121] Exemplarily, the flight device obtains a broadcast system information block extension message from the network device; the system information block extension message includes the meteorological warning information.

[0122] Exemplarily, the system information block extension message includes at least one of the following: system information block type, network device identifier (such as base station identifier), weather type, precipitation probability, precipitation intensity, duration, geographic coordinate code.

[0123] Exemplarily, the flight device determines a flight decision based on the meteorological warning information, including: The flight device obtains flight decision configuration information (which can be in the form of a decision matrix, but the present disclosure is not limited thereto), including a mapping relationship between precipitation intensity, precipitation probability, and flight decision; the flight device determines a flight decision according to the meteorological warning information and the flight decision configuration information.

[0124] Exemplarily, the method provided by the embodiments of the present disclosure further includes: the flight device performs flight state detection, calculates a risk value; the flight device executes the flight decision when the risk value is greater than a risk threshold.

[0125] Exemplarily, the method provided by the embodiments of the present disclosure further includes: the flight device dynamically adjusts the risk threshold according to the remaining power and the load value.

[0126] Exemplarily, the flight decision includes one or more of route adjustment, height change, and emergency landing.

[0127] Figure 5 Other contents of the embodiments can refer to the above embodiments, which are not described here again.

[0128] Exemplarily, the method provided by the embodiments of the present disclosure can include the following steps: Step 1: The mobile communication base station collects the attenuation characteristics (for example, carrier phase fluctuation value, signal-to-noise ratio attenuation slope, and multipath interference change (for example, multipath effect change rate)) of the GPS navigation signal in real time.

[0129] Step 2: The base station uploads the collected data (the above-mentioned attenuation characteristics) to the edge computing node. The edge computing node generates a super-local precipitation prediction based on multi-base station data fusion, so as to generate a final weather prediction result.

[0130] Exemplarily, the edge computing node can fuse the multi-base station data (i.e., the weather prediction results of each base station) in one or more of the following ways: Average method: taking the average of the weather prediction results of multiple base stations as the final weather prediction result; Weighted average method: according to the accuracy and relevance of each base station, assigning weights to the weather prediction results of each base station, and then performing weighted average to obtain the final weather prediction result; Majority voting method: for the weather prediction results of multiple base stations, taking the majority decision (for example, more than half of the base stations predict precipitation, then the overall prediction is precipitation) as the final weather prediction result.

[0131] Step 3: The edge computing node selects the surrounding base stations according to the precipitation prediction area (or precipitation range), and issues the weather prediction result to the base stations.

[0132] Exemplarily, the edge computing node mainly selects the base stations within the fixed flight route range of the unmanned aerial vehicle to issue the weather prediction result, for example, the base stations within 300 meters around the flight route of the unmanned aerial vehicle.

[0133] Step 4: The base station broadcasts the weather warning information (type 0x5B) through the 5G SIB extension message.

[0134] Step 5: The unmanned aerial vehicle executes the flight strategy / decision dynamic adjustment according to the received weather warning information.

[0135] For example, for urban logistics unmanned aerial vehicle distribution: Scenario parameters: Region: XX district (3km×2km); Base station deployment: 8 5G micro base stations (interval 300-500m); Drone: logistics delivery drone (cruise altitude 120m).

[0136] The implementation process is as follows: (1) The base station GPS device (e.g., GPS receiver) collects the characteristics of the GPS navigation signal, such as the attenuation characteristics.

[0137] (2) The base station transmits the collected data, such as the attenuation characteristics, to the edge computing node device (i.e., edge computing node).

[0138] (3) The edge computing node calculates a local predicted precipitation probability of 92%.

[0139] (4) The edge computing node issues the weather prediction result to the base station.

[0140] (5) The base station device broadcasts weather warning information.

[0141] (6) The drone receives the SIB warning information, such as carrying the weather warning information through the SIB extension message.

[0142] Assume intensity level (precipitation intensity): 3 (moderate rain); Precipitation probability: 92%; Influence area: current location radius 200m.

[0143] (7) The drone flight state detection, for example, includes detection: Remaining power: 45%; Load: 2.3kg (important medicine).

[0144] (8) The drone executes the decision, for example: fly around the influence area (i.e., the predicted precipitation range) 5km.

[0145] (9) Implementation effect: avoid heavy rain area, delivery delay only increases 8 minutes. Compared with the non-warning scenario, the device (e.g., drone) damage risk is reduced by 100%.

[0146] In the embodiments of the present disclosure, on the one hand, the base station collects the attenuation characteristics (such as carrier phase fluctuation, signal-to-noise ratio attenuation slope, multipath interference change) of the GPS navigation signal, and the edge computing node generates a super-local precipitation prediction based on multi-base station data fusion, with short prediction period and fast reaction speed. On the other hand, the weather warning information (type 0x5B) is broadcasted through the 5G SIB extension message, with short broadcast period, repeatable broadcast, and small air interface overhead. In addition, the drone can dynamically adjust the flight strategy according to the received warning information to avoid damage or inability to fly.

[0147] This disclosure provides a SIB dynamic broadcast mechanism. For example, the triggering condition can be set to a precipitation probability greater than 0.7, or a rainfall level (i.e., precipitation intensity) greater than or equal to level 3. When the triggering condition is met, the base station triggers SIB dynamic broadcast, that is, broadcasts an SIB extended message.

[0148] Figure 6 This is a flight decision map for drones. (Example) Figure 6 As shown, the method provided in this disclosure embodiment may include the following steps.

[0149] In S601, SIBs are received. For example, a drone receives SIB extended messages from a base station.

[0150] In S602, determine whether it is in the affected area; if it is, execute S603; if it is not, execute S608.

[0151] For example, based on the weather warning information carried in the SIB extended message, the drone determines whether its current location or flight path overlaps or partially overlaps with the precipitation range carried in the weather warning information. If there is an overlap or partial overlap, it is determined to be within the affected area. If there is no overlap at all, it is determined to be outside the affected area.

[0152] In S603, the drone assesses its flight status.

[0153] In S604, the drone calculates the risk value based on its flight status.

[0154] In S605, the drone determines whether the risk value is greater than the threshold (i.e., the risk threshold); if it is greater, then S606 is executed; if it is not greater, then S607 is executed.

[0155] In S606, evasion is performed.

[0156] The specific evasion strategy employed by the drone can be determined based on the flight decisions established above.

[0157] In S607, warnings are recorded.

[0158] Continue monitoring in S608.

[0159] like Figure 7 As shown, the method provided in this disclosure embodiment may include the following steps.

[0160] In S701, SIB is received.

[0161] For example, a drone receives SIB extended messages from a base station.

[0162] In S702, precipitation parameters are analyzed.

[0163] For example, the drone parses one or more of the following precipitation parameters from the SIB extension message: precipitation probability, precipitation intensity, precipitation amount, duration, precipitation range, start time.

[0164] In S703, if it is determined according to the parsed precipitation parameters that the impact area.

[0165] In S704, the drone evaluates the flight state.

[0166] The drone calculates a risk value according to the flight state.

[0167] In S705, if the drone determines that the risk value is greater than a threshold value, a strategy is executed.

[0168] For example, if the precipitation intensity is 1-2 levels, the drone executes a speed reduction; if the precipitation intensity is 3 levels, the drone executes a fly-around; if the precipitation intensity is 4-5 levels, the drone executes an emergency landing.

[0169] The embodiments of the present disclosure also provide a drone decision optimization method: a three-dimensional decision matrix of precipitation intensity-probability-flight state (flight decision) is established. Exemplarily, the risk threshold value can also be dynamically adjusted according to the remaining power of the drone and the load value.

[0170] Exemplarily, data collection and preprocessing are performed first. First, relevant data is collected and processed, including: precipitation probability, precipitation intensity, remaining power (E) (which is drone sensor data), load value (V, used to evaluate the economic value or importance of each task). A risk assessment model is established to adjust the flight risk threshold value according to the remaining power of the drone, the load value, and the precipitation intensity-probability. The precipitation intensity, precipitation probability, and flight state are mapped into a three-dimensional decision matrix. The risk threshold value is dynamically adjusted according to the remaining power and the load value.

[0171] As shown in FIG. 8, Figure 8 The method provided by the embodiments of the present disclosure can include the following steps.

[0172] In S810, the computing node obtains an attenuation feature of a satellite navigation signal from a network device.

[0173] In S820, the computing node determines a meteorological prediction result according to the attenuation feature.

[0174] In S830, the computing node sends the meteorological prediction result to the network device.

[0175] Exemplarily, the computing node obtains attenuation features of multiple satellite navigation signals from multiple network devices; the computing node fuses the attenuation features of the multiple satellite navigation signals; and the computing node generates the meteorological prediction result based on the fused attenuation features of the multiple satellite navigation signals.

[0176] For example, as shown in FIG. 7, a scenario diagram of multi-base station cooperative prediction is shown, assuming that base station A, base station B and base station C (here, three base stations are taken as an example, but the number of base stations is not limited in the disclosure) all upload attenuation features to edge node 910 (i.e., edge computing node). Edge node 910 predicts the weather prediction results of each base station based on the attenuation features uploaded by each base station, and then performs data fusion to obtain three-dimensional weather map 920. The three-dimensional weather map includes information such as longitude, latitude, height or time, precipitation probability, etc. of the base station. Figure 9

[0177] Figure 8 Other contents of the embodiments can refer to the above embodiments.

[0178] The scheme provided by the embodiments of the disclosure can predict weather changes in a small scale, and the spatial resolution is reduced from 1000 meters in the traditional method to 100 meters. The meteorological warning lead time is reduced from 2 to 5 minutes in the traditional method to 30 to 90 seconds. The unmanned aerial vehicle can receive the warning information through the base station broadcast signal (such as SIB extended message), which is more targeted. The unmanned aerial vehicle can establish a three-dimensional decision matrix of precipitation intensity-probability-flight state according to the weather prediction content, dynamically adjust the risk threshold according to the remaining power and the load value, and make flight decisions.

[0179] The scheme provided by the embodiments of the disclosure can be applied to the low-altitude economy field and has a good market application scale. For example, it can be applied to scenarios such as logistics unmanned aerial vehicles. The scheme can avoid damage to the unmanned aerial vehicle and task failure caused by rain, and affect business operation. Using the scheme provided by the embodiments of the disclosure, the flight safety of the unmanned aerial vehicle can be improved, and the risk caused by weather factors can be reduced.

[0180] As shown in FIG. 8, the overall process includes that satellite 1010 (included in the satellite system described above) sends satellite navigation signals to base station GPS receiver 1020. Base station GPS receiver 1020 obtains the attenuation features of the satellite navigation signals and sends them to edge computing node 1030. Edge computing node 1030 obtains the weather prediction results by processing the attenuation features through weather prediction engine 1040, and sends the weather prediction results to the base station. The base station SIB broadcasts 1050 the weather prediction results and / or weather prediction information. Unmanned aerial vehicle communication module 1060 receives the weather prediction information and adjusts the flight strategy 1070 according to the weather warning information. Figure 10

[0181] Other contents of the embodiments can refer to the above embodiments, which will not be described here. Figure 10

[0182] ​​Further, the embodiments of the present disclosure also provide a communication system, comprising: the network device according to any of the embodiments of the present disclosure, and the flight device according to any of the embodiments of the present disclosure. Exemplarily, the communication system further comprises the computing node according to any of the embodiments of the present disclosure.

[0183] Further, the embodiments of the present disclosure also provide a communication device, comprising: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to execute the method according to any of the embodiments of the present disclosure via executing the executable instructions.

[0184] Further, the embodiments of the present disclosure also provide a computer readable storage medium, having stored thereon a computer program, the computer program being executed by a processor to implement the method according to any of the embodiments of the present disclosure.

[0185] Further, the embodiments of the present disclosure also provide a computer program product, comprising a computer program which, when executed by a processor, performs the method according to any of the embodiments of the present disclosure.

[0186] It should be noted that the above-mentioned modules / units can be executed in a computer system, such as a group of computer-executable instructions.

[0187] Those skilled in the art can understand that each aspect of the present disclosure can be implemented as a system, a method or a program product. Therefore, each aspect of the present disclosure can be specifically implemented as follows: a complete hardware embodiment, a complete software embodiment (including firmware, microcode, etc.), or an embodiment combining hardware and software aspects, which can be collectively referred to as "circuitry", "module" or "system".

[0188] The communication device 800 according to this embodiment of the present disclosure will be described below with reference to Figure 15 Figure 15 The displayed communication device 800 is only an example and should not bring any limitation to the functions and use range of the embodiments of the present disclosure.

[0189] As shown in Figure 15 The communication device 800 is in the form of a general computing device. The components of the communication device 800 can include but are not limited to: at least one processing unit 810, at least one storage unit 820, and a bus 830 connecting different system components (including the storage unit 820 and the processing unit 810).

[0190] The storage unit 820 stores program codes which can be executed by the processing unit 810, so that the processing unit 810 performs the steps according to various exemplary embodiments of the present disclosure described in the above "Exemplary Method" section of the present specification.​

[0191] Storage 820 can include a readable medium in the form of volatile memory, such as random access memory (RAM) 8201 and / or cache memory 8202, and can further include non-volatile memory, such as read only memory (ROM) 8203.

[0192] Storage 820 can also include a program / utility 8204 having a set of program modules 8205 such as an operating system, one or more application programs, other program modules, and program data, each of which can give the computing device its functionality, at least in part. Each of the operating system, one or more application programs, other program modules, and program data can include implementations of a web environment, either alone or in some combination.

[0193] Bus 830 can represent one or more of several types of bus structures, including a storage bus or bus controller, peripheral bus, graphics bus, processor or local bus using any of a variety of bus architectures.

[0194] Communication device 800 can also communicate with one or more external devices 840 such as a keyboard or pointing device, using one or more communication ports 850. Communication device 800 can also communicate with one or more devices using communication interface 860. For instance, communication device 800 can communicate with an application on another computer or with a networked device such as a printer or a router. Communication interface 860 can include any transceiver-like mechanism used to effect such communication, including a modem or network interface, for example. Communication device 800 can also include one or more antennas for transmitting signals and receiving signals. Communication device 800 can further include a display interface 870 that forwards graphics, text, and other data from the communication device 800 (or from a frame buffer not shown) to a display 880.

[0195] Those skilled in the art will readily understand that the example embodiments described herein can be implemented by software and / or by software in combination with the necessary hardware. Thus, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash disk, a mobile hard disk, or the like) or a network, and includes a number of instructions to make a computing device (which can be a personal computer, a server, a terminal device, or a network device, etc.) execute the methods according to the embodiments of the present disclosure.

[0196] In particular, according to embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer program product, which includes: a computer program, which, when executed by a processor, implements the above-mentioned communication method.

[0197] In exemplary embodiments of the present disclosure, a computer-readable storage medium is also provided, which can be a readable signal medium or a readable storage medium. The computer-readable storage medium has stored thereon a program product capable of implementing the above-mentioned method of the present disclosure. In some possible implementations, various aspects of the present disclosure can also be implemented in the form of a program product, which includes program codes for causing terminal devices / network devices to perform the steps described in the above “Exemplary Methods” section according to various exemplary embodiments of the present disclosure when the program product is run on the terminal devices / network devices.

[0198] In the present disclosure, the computer-readable storage medium can include a data signal carried in the baseband or as part of a carrier wave, in which readable program codes are borne. Such a propagated data signal can take various forms, including but not limited to electro-magnetic signal, optical signal, or any suitable combination of the above. The readable signal medium can also be any readable medium other than the readable storage medium, which can send, propagate or transmit programs for use by or in connection with an instruction execution system, apparatus or device.

[0199] It should be noted that although several modules or units of the device for action execution are mentioned in the above detailed description, such a division is not mandatory. In fact, according to embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units for embodiment.

[0200] In addition, although the steps of the method in the present disclosure are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in this specific order, or that all the shown steps must be performed to achieve the desired result. Additionally or alternatively, some steps can be omitted, multiple steps can be combined into one step, one step can be divided into multiple steps, etc.

[0201] Through the description of the above embodiments, those skilled in the art can easily understand that the example embodiments described herein can be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solutions according to the embodiments of the present disclosure can be embodied in the form of a software product. The software product can be stored in a non-volatile storage medium (which can be a CD-ROM, a U disk, a mobile hard disk, etc.) or on a network, and includes a number of instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to perform the methods according to the embodiments of the present disclosure.

[0202] Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the features disclosed herein. It is intended that the present disclosure cover any and all variations of the present disclosure that come within the scope of the present disclosure, along with their equivalents.

Claims

1. A communication method characterized by comprising: The network device obtains an attenuation feature of a satellite navigation signal. The network device obtains a weather prediction result based on the attenuation feature. The network device obtains weather warning information according to the weather prediction result. The network device sends the weather warning information to a flight device. The network device broadcasts a system information block extension message to the flight device.

2. The method of claim 1, wherein, The system information block extension message includes the weather warning information. The system information block extension message includes at least one of a system information block type, a network device identifier, a weather type, a precipitation probability, a precipitation intensity, a duration, and a geographic coordinate code.

3. The method of claim 2, wherein, The system information block type indicates a weather system information block.

4. The method of claim 3, wherein, The weather type includes precipitation.

5. The method of claim 3, wherein, The message identifier of the system information block extension message is different from that of a public warning message.

6. The method of claim 2, wherein, The network device obtains a weather prediction result based on the attenuation feature, including:

7. The method of claim 1, wherein, The network device sends the attenuation feature to a computing node, so that the computing node obtains the weather prediction result according to the attenuation feature. The network device obtains the weather prediction result from the computing node. The weather prediction result includes one or more of a precipitation intensity, a precipitation probability, a start time, a precipitation range, and a duration.

8. The method according to any one of claims 1 to 7, characterized in that, The attenuation feature includes one or more of a carrier phase fluctuation value, a carrier phase change rate, a signal-to-noise ratio attenuation slope, and a multipath effect change rate.

9. The method of claim 8, wherein, The precipitation probability is determined according to one or more of the carrier phase change rate, the signal-to-noise ratio attenuation slope, and the multipath effect change rate.

10. The method of claim 9, wherein, Also including:

11. The method of claim 10, wherein, The network device collects a temperature correction factor. The precipitation probability is determined according to one or more of the carrier phase change rate, the signal-to-noise ratio attenuation slope, the multipath effect change rate, and the temperature correction factor. The network device includes a base station.

12. The method of claim 1, wherein, The flight device includes a drone.

13. The method of claim 1, wherein, The flight device obtains weather warning information from a network device.

14. A communication method, comprising: The flight device determines a flight decision based on the weather warning information. The flight device obtains a broadcast system information block extension message from the network device. The system information block extension message includes the weather warning information.

15. The method of claim 14, wherein, The system information block extension message includes at least one of a system information block type, a network device identifier, a weather type, a precipitation probability, a precipitation intensity, a duration, and a geographic coordinate code. The flight device determines a flight decision based on the weather warning information, including:

16. The method of claim 15, wherein, The flight device obtains flight decision configuration information, which includes a mapping relationship between a precipitation intensity, a precipitation probability, and a flight decision.

17. The method of claim 14, wherein, The flight device determines a flight decision according to the weather warning information and the flight decision configuration information. Also including: The flight device performs flight state detection and calculates a risk value.

18. The method of claim 17, wherein, The flight device executes the flight decision when the risk value is greater than a risk threshold. Also including: The flight device dynamically adjusts the risk threshold according to a remaining power and a payload value.

19. The method of claim 18, wherein, The flight decision includes one or more of a route adjustment, a height change, and an emergency landing. The computing node obtains an attenuation feature of a satellite navigation signal from a network device.

20. The method of claim 14, wherein, The computing node obtains an attenuation feature of a satellite navigation signal from a network device.

21. A method of communication, comprising: ​ ​ The computing node determines a weather prediction result according to the attenuation feature; The computing node sends the weather prediction result to the network device.

22. The method of claim 21, wherein, The computing node obtains attenuation features of multiple satellite navigation signals from multiple network devices; The computing node fuses the attenuation features of multiple satellite navigation signals; The computing node generates the weather prediction result based on the fused attenuation features of multiple satellite navigation signals.

23. A network device, comprising: Comprising: A first receiving processing unit configured to obtain an attenuation feature of a satellite navigation signal; The first receiving processing unit is further configured to obtain a weather prediction result based on the attenuation feature; The first receiving processing unit is further configured to obtain weather warning information according to the weather prediction result; A first sending unit configured to send the weather warning information to a flight device.

24. A flying apparatus, comprising: Comprising: A second receiving unit configured to obtain weather warning information from a network device; A second processing unit configured to determine a flight decision based on the weather warning information.

25. A computing node, characterized in that, Comprising: A third receiving unit configured to obtain an attenuation feature of a satellite navigation signal from a network device; A third processing unit configured to determine a weather prediction result according to the attenuation feature; A third sending unit configured to send the weather prediction result to the network device.

26. A communication system, characterized by Comprising: The network device of claim 23; The flight device of claim 24.

27. A communications device, characterized by Comprising: A processor; And A memory configured to store executable instructions of the processor; Wherein the processor is configured to execute the method of any one of claims 1-13, or execute the method of any one of claims 14-20, or execute the method of any one of claims 21-22 via executing the executable instructions.

28. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the method of any one of claims 1-13, or execute the method of any one of claims 14-20, or execute the method of any one of claims 21-22.

29. A computer program product comprising a computer program which, when executed by a processor, performs the method of any one of claims 1-13, or executes the method of any one of claims 14-20, or executes the method of any one of claims 21-22.

29. A computer program product comprising a computer program which, when executed by a processor, performs the method of any one of claims 1-13, or executes the method of any one of claims 14-20, or executes the method of any one of claims 21-22.

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