Aircraft full-life-cycle safety risk assessment method and system in low-altitude Internet of Things

Abstract

The embodiment of the invention provides an aircraft full-life-cycle safety risk assessment method and system in a low-altitude Internet of Things. The aircraft full-life-cycle safety risk assessment method in the low-altitude Internet of Things comprises the steps of obtaining a current stage of an aircraft in a full flight cycle; determining a current dynamic weight distribution model according to the stage of the current flight; obtaining and inputting required input data to the current dynamic weight distribution model to obtain an output dynamic weight value, wherein the input data comprises aircraft state information, low-altitude base station information and meteorological radar information; and determining each safety risk probability of the aircraft according to the output dynamic weight value of the dynamic weight distribution model, and executing emergency measures according to each safety risk probability. According to the aircraft full-life-cycle safety risk assessment method and system in the equipment low-altitude Internet of Things, real-time obstacle avoidance of high-speed maneuvering when a low-altitude aircraft executes a task can be met.

Description

Technical Field

[0001] The present invention relates to the technical field of low-altitude flight, and specifically relates to a method and system for safety risk assessment of the entire life cycle of an aircraft in a low-altitude intelligent network. Background Art

[0002] With the rapid increase in the number of low-altitude aircraft (such as drones, eVTOLs, etc.) and the diversification of applications, traditional risk assessment methods rely on static models or manual experience and cannot respond in real time to sudden state changes of low-altitude aircraft (such as extreme weather, airspace conflicts, equipment failures, etc.), resulting in lagging risk assessment and threatening flight safety. Summary of the Invention

[0003] The purpose of the embodiments of the present invention is to provide a method and system for safety risk assessment of the entire life cycle of an aircraft in a low-altitude intelligent network, and the method and system for safety risk assessment of the entire life cycle of an aircraft in the low-altitude intelligent network can meet the real-time obstacle avoidance during the high-speed maneuver of the low-altitude aircraft when performing tasks.

[0004] To achieve the above purpose, the embodiments of the present invention provide a method for safety risk assessment of the entire life cycle of an aircraft in a low-altitude intelligent network, and the method for safety risk assessment of the entire life cycle of an aircraft in the low-altitude intelligent network includes:

[0005] Obtain the current stage of the aircraft in the entire flight cycle;

[0006] Determine the current dynamic weight allocation model according to the current stage of flight;

[0007] Obtain and input the required input data into the current dynamic weight allocation model to obtain an output dynamic weight value, where the input data includes aircraft state information, low-altitude base station information, and weather radar information;

[0008] Determine the safety risk probabilities of the aircraft according to the output dynamic weight value of the dynamic weight allocation model, and execute emergency measures according to the safety risk probabilities.

[0009] Preferably, the obtaining of the current stage of the aircraft in the entire flight cycle includes: startup stage, takeoff stage, flight stage, landing stage; among them, each stage corresponds to a dynamic weight allocation model;

[0010] The determining of the current dynamic weight allocation model according to the current stage of flight includes:

[0011] Obtain the current dynamic weight allocation model corresponding to the current stage of flight according to the current stage of flight.

[0012] Preferably, the required input data is obtained and input into the current dynamic weight allocation model to obtain an output dynamic weight value. The input data includes aircraft status information, low-altitude base station information, and weather radar information, including:

[0013] According to the input data requirements of the current dynamic weight allocation model, a connection channel is established with aircraft sensors, low-altitude base stations, and / or weather radars;

[0014] The corresponding aircraft status information, low-altitude base station information, and weather radar information are obtained through the data connection channel;

[0015] The aircraft status information, low-altitude base station information, and weather radar information are input into the current dynamic weight allocation model to obtain an output dynamic weight value.

[0016] Preferably, the implementation of emergency measures according to the safety risk probabilities includes:

[0017] Obtaining the maximum safety risk probability among the safety risk probabilities; and

[0018] When the maximum safety risk probability is greater than a preset probability threshold, the emergency measures corresponding to the maximum safety risk are implemented according to the preset.

[0019] In addition, the present invention also provides a safety risk assessment system for the full life cycle of an aircraft in a low-altitude intelligent network. The safety risk assessment system for the full life cycle of an aircraft in the low-altitude intelligent network includes:

[0020] A stage acquisition unit for acquiring the current stage of the aircraft in the full flight cycle;

[0021] A model determination unit for determining the current dynamic weight allocation model according to the current stage of flight;

[0022] An output acquisition unit for obtaining the required input data and inputting it into the current dynamic weight allocation model to obtain an output dynamic weight value. The input data includes aircraft status information, low-altitude base station information, and weather radar information;

[0023] An evaluation execution unit for determining the safety risk probabilities of the aircraft according to the output dynamic weight value of the dynamic weight allocation model and implementing emergency measures according to the safety risk probabilities.

[0024] Preferably, the acquisition of the current stage of the aircraft in the full flight cycle includes: startup stage, takeoff stage, flight stage, landing stage; among them, each stage corresponds to a dynamic weight allocation model;

[0025] The model determination unit determining the current dynamic weight allocation model according to the current stage of flight includes:

[0026] Obtain the current dynamic weight allocation model corresponding to the current flight stage according to the current flight stage.

[0027] Preferably, the output acquisition unit is used for:

[0028] Establish a connection channel with the aircraft sensor, low-altitude base station, and / or weather radar according to the input data requirements of the current dynamic weight allocation model;

[0029] Obtain the corresponding aircraft status information, low-altitude base station information, and weather radar information through the data connection channel;

[0030] Input the aircraft status information, low-altitude base station information, and weather radar information into the current dynamic weight allocation model to obtain the output dynamic weight value.

[0031] Preferably, the evaluation execution unit is used for:

[0032] Obtain the maximum safety risk probability among the safety risk probabilities; and

[0033] When the maximum safety risk probability is greater than the preset probability threshold, execute according to the emergency measure corresponding to the maximum safety risk preset.

[0034] In addition, the present invention also provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to cause the machine to execute the above-mentioned method for evaluating the safety risks of the entire life cycle of an aircraft in a low-altitude intelligent network.

[0035] In addition, the present invention also provides a processor for running a program, wherein the program, when run, is used to execute: the above-mentioned method for evaluating the safety risks of the entire life cycle of an aircraft in a low-altitude intelligent network.

[0036] Through the above technical solutions, the lightweight model reduces the computing power consumption of the airborne terminal and supports local deployment of low-computing-power devices (such as small unmanned aircraft); through dynamic weight allocation, the detection sensitivity of key risk parameters (such as battery overload) is improved, and the accident response time is shortened. The model has a higher risk recognition accuracy for complex scenarios (such as thunderstorm weather + multi-aircraft collaboration), with a significant improvement compared to the static model; the false alarm rate is reduced, and the impact of invalid control instructions on flight efficiency is reduced.

[0037] Other features and advantages of the embodiments of the present invention will be described in detail in the subsequent specific implementation part. Brief Description of the Drawings

[0038] The accompanying drawings are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation to the embodiments of the present invention. In the accompanying drawings:

[0039] Figure 1 is a flowchart illustrating a method for safety risk assessment of the entire life cycle of an aircraft in a low-altitude intelligent networking of the present invention.

[0040] Figure 2 is a block diagram of the modules of a safety risk assessment system for the entire life cycle of an aircraft in a low-altitude intelligent networking of the present invention. Specific Embodiments

[0041] The following details the specific embodiments of the embodiments of the present invention in conjunction with the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the embodiments of the present invention, and are not used to limit the embodiments of the present invention.

[0042] Figure 1 A method for safety risk assessment of the entire life cycle of an aircraft in a low-altitude intelligent networking provided by the present invention, the method for safety risk assessment of the entire life cycle of an aircraft in a low-altitude intelligent networking includes:

[0043] S101, obtaining the current stage of the aircraft in the entire flight cycle; the obtaining the current stage of the aircraft in the entire flight cycle includes: start-up stage, take-off stage, flight stage, landing stage; wherein, each stage corresponds to a dynamic weight allocation model. Because in the entire life working scenario of the aircraft, different flight stages are adapted to different weight allocation models, require different inputs and outputs, and then facilitate the assessment of the final safety risk causes.

[0044] S102, determining the current dynamic weight allocation model according to the current stage of flight;

[0045] According to the current stage of flight, obtaining the current dynamic weight allocation model corresponding to the current stage of flight.

[0046] S103, obtaining and inputting the required input data into the current dynamic weight allocation model to obtain an output dynamic weight value, the input data including aircraft status information, low-altitude base station information, and weather radar information;

[0047] Among them, the aircraft is equipped with built-in sensors (speed, attitude, battery status), low-altitude intelligent network base stations (5G-A integrated communication and sensing signals), satellite networks (airspace occupancy information), and weather radars (sudden weather data). The present invention standardizes heterogeneous data through a spatio-temporal alignment module, eliminates timestamp differences and dimensionality interference, and generates a multi-dimensional input matrix in a unified spatio-temporal coordinate system. Determine the safety risk probabilities of the aircraft according to the output dynamic weight values of the dynamic weight allocation model, and execute emergency measures according to the safety risk probabilities. Low risk (confidence level ≥ 90%): Maintain the flight state and only record data; Medium risk (60% - 90%): Push obstacle avoidance suggestions (such as adjusting altitude, decelerating) to the operator through the low-altitude intelligent network; High risk (< 60%): Automatically trigger an emergency protocol (forcefully switch to a safe route, activate redundant power, apply for emergency landing permission).

[0048] S104. Determine the safety risk probabilities of the aircraft according to the output dynamic weight values of the dynamic weight allocation model, and execute emergency measures according to the safety risk probabilities.

[0049] Preferably, obtain and input the required input data into the current dynamic weight allocation model to obtain the output dynamic weight value. The input data includes aircraft state information, low-altitude base station information, and weather radar information, including:

[0050] Establish a connection channel with the aircraft sensors, low-altitude base stations, and / or weather radars according to the input data requirements of the current dynamic weight allocation model;

[0051] Obtain the corresponding aircraft state information, low-altitude base station information, and weather radar information through the data connection channel;

[0052] Input the aircraft state information, low-altitude base station information, and weather radar information into the current dynamic weight allocation model to obtain the output dynamic weight value.

[0053] Preferably, the execution of emergency measures according to the safety risk probabilities includes:

[0054] Obtain the maximum safety risk probability among the safety risk probabilities; and

[0055] When the maximum safety risk probability is greater than the preset probability threshold of 90%, execute according to the emergency measures corresponding to the maximum safety risk preset.

[0056] Sudden strong wind scenario: The dynamic weight allocation increases the weight of the "wind speed" parameter from the baseline of 0.3 to 0.9. The model identifies the rollover risk in a shorter time. Compared with the fixed weight model, the warning time is advanced and the obstacle avoidance success rate is improved. When the detected density of surrounding drones > 6 aircraft / km 3When the "airspace occupancy rate" weight automatically increases, the conflict prediction accuracy rate is improved.

[0057] In addition, Figure 2 Another aspect of the present invention is that the present invention further provides a safety risk assessment system for the full life cycle of an aircraft in a low-altitude intelligent network. The safety risk assessment system for the full life cycle of an aircraft in the low-altitude intelligent network includes:

[0058] A stage acquisition unit for acquiring the current stage of the aircraft in the entire flight cycle;

[0059] A model determination unit for determining the current dynamic weight allocation model according to the current stage of flight;

[0060] An output acquisition unit for acquiring and inputting the required input data into the current dynamic weight allocation model to obtain an output dynamic weight value. The input data includes aircraft state information, low-altitude base station information, and meteorological radar information;

[0061] An evaluation execution unit for determining the safety risk probabilities of the aircraft according to the output dynamic weight value of the dynamic weight allocation model, and executing emergency measures according to the safety risk probabilities.

[0062] Preferably, the acquisition of the current stage of the aircraft in the entire flight cycle includes: a startup stage, a takeoff stage, a flight stage, and a landing stage; wherein, each stage corresponds to a dynamic weight allocation model;

[0063] The model determination unit determining the current dynamic weight allocation model according to the current stage of flight includes:

[0064] According to the current stage of flight, acquiring the current dynamic weight allocation model corresponding to the current stage of flight.

[0065] Preferably, the output acquisition unit is used for:

[0066] Establishing a connection channel with the aircraft sensor, low-altitude base station, and / or meteorological radar according to the input data requirements of the current dynamic weight allocation model;

[0067] Acquiring the corresponding aircraft state information, low-altitude base station information, and meteorological radar information through the data connection channel;

[0068] Inputting the aircraft state information, low-altitude base station information, and meteorological radar information into the current dynamic weight allocation model to obtain an output dynamic weight value.

[0069] Preferably, the evaluation execution unit is used for:

[0070] Acquiring the maximum safety risk probability among the safety risk probabilities; and

[0071] When the maximum safety risk probability is greater than a preset probability threshold, the emergency measures corresponding to the preset maximum safety risk are executed.

[0072] In addition, the present invention also provides a machine-readable storage medium, on which instructions are stored, and the instructions are used to cause a machine to execute the above-mentioned method for assessing the safety risks of the entire life cycle of an aircraft in a low-altitude intelligent network.

[0073] In addition, the present invention also provides a processor for running a program, wherein when the program is run, it is used to execute: the above-mentioned method for assessing the safety risks of the entire life cycle of an aircraft in a low-altitude intelligent network.

[0074] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0075] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate means for implementing the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0076] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory generate a manufactured article including instruction means, and the instruction means implements the functions specified in Figure 1 one or more flows and / or blocks Figure 1 one or more blocks.

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one process Figure 1 one process or multiple processes and / or blocks Figure 1 steps of the functions specified in one block or multiple blocks.

[0078] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and a memory.

[0079] The memory may include non-permanent memory in the computer-readable medium, in the form of random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.

[0080] Computer-readable media includes permanent and non-permanent, removable and non-removable media and can be implemented by any method or technology for information storage. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer-readable media does not include transitory media such as modulated data signals and carrier waves.

[0081] It should also be noted that the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, commodity or device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or also includes elements inherent to such process, method, commodity or device. Without further limitation, an element defined by the statement "comprising one..." does not exclude the presence of another identical element in the process, method, commodity or device comprising the element.

[0082] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems or computer program products. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware aspects. Moreover, the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0083] The above are only the embodiments of the present application and are not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. A method for assessing the safety risks of the entire life cycle of an aircraft in a low-altitude intelligent network, characterized in that, The method for assessing the safety risks of the entire life cycle of an aircraft in the low-altitude intelligent Internet of Things includes: Obtaining the current stage of the aircraft in the entire flight cycle; Determining the current dynamic weight allocation model according to the current flight stage; Obtaining and inputting the required input data into the current dynamic weight allocation model to obtain the output dynamic weight value, where the input data includes aircraft status information, low-altitude base station information, and meteorological radar information; Determining the safety risk probabilities of the aircraft according to the output dynamic weight value of the dynamic weight allocation model, and implementing emergency measures according to the safety risk probabilities.

2. The method for evaluating the safety risks of the entire life cycle of an aircraft in a low-altitude intelligent Internet of Things according to claim 1, wherein The obtaining of the current stage of the aircraft in the entire flight cycle includes: startup stage, takeoff stage, flight stage, landing stage; where each stage corresponds to a dynamic weight allocation model; The determining of the current dynamic weight allocation model according to the current flight stage includes: Obtaining the current dynamic weight allocation model corresponding to the current flight stage according to the current flight stage.

3. The method for assessing the safety risks of the entire life cycle of an aircraft in a low-altitude intelligent Internet of Things according to claim 2, wherein Obtaining and inputting the required input data into the current dynamic weight allocation model to obtain the output dynamic weight value, where the input data includes aircraft status information, low-altitude base station information, and meteorological radar information includes: Establishing a connection channel with the aircraft sensors, low-altitude base stations, and / or meteorological radars according to the input data requirements of the current dynamic weight allocation model; Obtaining the corresponding aircraft status information, low-altitude base station information, and meteorological radar information through the data connection channel; Inputting the aircraft status information, low-altitude base station information, and meteorological radar information into the current dynamic weight allocation model to obtain the output dynamic weight value.

4. The method for evaluating the safety risks of the entire life cycle of an aircraft in a low-altitude intelligent Internet of Things according to claim 1, wherein, The implementing of emergency measures according to the safety risk probabilities includes: Obtaining the maximum safety risk probability among the safety risk probabilities; and When the maximum safety risk probability is greater than the preset probability threshold, implementing the emergency measures corresponding to the maximum safety risk according to the preset.

5. An aircraft full-life cycle safety risk assessment system in a low-altitude intelligent Internet of Things, characterized in that, The system for assessing the safety risks of the entire life cycle of an aircraft in the low-altitude intelligent Internet of Things includes: A stage obtaining unit for obtaining the current stage of the aircraft in the entire flight cycle; A model determining unit for determining the current dynamic weight allocation model according to the current flight stage; An output obtaining unit for obtaining and inputting the required input data into the current dynamic weight allocation model to obtain the output dynamic weight value, where the input data includes aircraft status information, low-altitude base station information, and meteorological radar information; An evaluation implementing unit for determining the safety risk probabilities of the aircraft according to the output dynamic weight value of the dynamic weight allocation model, and implementing emergency measures according to the safety risk probabilities.

6. The aircraft full-life-cycle safety risk assessment system in the low-altitude intelligent Internet of Things according to claim 5, characterized in that, The obtaining of the current stage of the aircraft in the entire flight cycle includes: startup stage, takeoff stage, flight stage, landing stage; where each stage corresponds to a dynamic weight allocation model; The model determining unit determining the current dynamic weight allocation model according to the current flight stage includes: Obtaining the current dynamic weight allocation model corresponding to the current flight stage according to the current flight stage.

7. The aircraft full-life-cycle safety risk assessment system in the low-altitude intelligent Internet of Things according to claim 6, wherein, The output obtaining unit is used for: Establish a connection channel with the aircraft sensors, low-altitude base stations, and / or weather radars according to the input data requirements of the current dynamic weight allocation model; Obtain the corresponding aircraft status information, low-altitude base station information, and weather radar information through the data connection channel; Input the aircraft status information, low-altitude base station information, and weather radar information into the current dynamic weight allocation model to obtain the output dynamic weight value.

8. The aircraft full-life-cycle safety risk assessment system in the low-altitude intelligent Internet of Things according to claim 5, wherein The evaluation execution unit is configured to: Obtain the maximum safety risk probability among the safety risk probabilities; and When the maximum safety risk probability is greater than the preset probability threshold, execute according to the emergency measures corresponding to the maximum safety risk preset.

9. A machine-readable storage medium having instructions stored thereon, characterized in that, This instruction is used to cause the machine to execute the method for evaluating the safety risk of the entire life cycle of the aircraft in the low-altitude intelligent network described in any one of claims 1-4 above.

10. A processor, characterized in that, For running a program, wherein, when the program is run, it is used to execute: the method for evaluating the safety risk of the entire life cycle of the aircraft in the low-altitude intelligent network described in any one of claims 1-4.

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