CN113449238B

CN113449238B - Unmanned aerial vehicle safe operation method and device, electronic equipment and storage medium

Info

Publication number: CN113449238B
Application number: CN202010212382.XA
Authority: CN
Inventors: 马凡
Original assignee: Fengyi Technology Shenzhen Co ltd
Current assignee: Fengyi Technology Shenzhen Co ltd
Priority date: 2020-03-24
Filing date: 2020-03-24
Publication date: 2023-04-25
Anticipated expiration: 2040-03-24
Also published as: CN113449238A

Abstract

The application discloses a safe operation method and device of an unmanned aerial vehicle, electronic equipment and a storage medium. The unmanned aerial vehicle safe operation method comprises the following steps: acquiring performance state parameters of the unmanned aerial vehicle in the current state; acquiring an environment complexity parameter of the unmanned aerial vehicle flight environment in the current state; acquiring task complexity parameters of a current task to be completed of the unmanned aerial vehicle; determining flight risk parameters of the unmanned aerial vehicle according to the performance state parameters, the environment complexity parameters and the task complexity parameters; and determining the current flight strategy of the unmanned aerial vehicle according to the flight risk parameters. According to the unmanned aerial vehicle flight risk control method and system, the acquired performance state parameters, environment complexity parameters and task complexity parameters are utilized to determine flight risk parameters of unmanned aerial vehicle operation, and then the current flight strategy of the unmanned aerial vehicle can be determined according to the flight risk parameters, so that flight safety early warning of unmanned aerial vehicle operation is realized, and the risk of unmanned aerial vehicle operation is reduced.

Description

Unmanned aerial vehicle safe operation method and device, electronic equipment and storage medium

Technical Field

The application relates to the technical field of unmanned aerial vehicles, in particular to a safe operation method and device of an unmanned aerial vehicle, electronic equipment and a storage medium.

Background

Unmanned aerial vehicle, abbreviated as "unmanned aerial vehicle", is an unmanned aerial vehicle that operates with a radio remote control device and a self-contained program control device. Unmanned aerial vehicle has been widely used for express transportation, geological exploration, video aerial photography, rescue and relief work and the like because of the characteristics of small size, high controllability, strong battlefield viability and the like, and the problem of unmanned aerial vehicle safe operation becomes a problem to be solved urgently in the unmanned aerial vehicle industry.

Under normal conditions, unmanned aerial vehicle manufacturers can only manage and control unmanned aerial vehicle quality in research and development and production links, and when unmanned aerial vehicles are delivered, no perfect and harsh test links exist, and running risk problems are transferred to clients or users in most cases. However, the unmanned aerial vehicle industry itself does not have a national standard, and the civil aviation bureau is in a state of holding the unmanned aerial vehicle industry, and does not go out of the strict acceptance and delivery standard. Meanwhile, most of unmanned aerial vehicle safe operation control stays in a theoretical research stage, actual execution difficulty is high, quality control of unmanned aerial vehicles by all unmanned aerial vehicle manufacturers is completely relied on, and an effective constraint effect cannot be achieved.

Therefore, a set of early warning system for effectively controlling the safe operation of the unmanned aerial vehicle is needed in the unmanned aerial vehicle industry, and development of the unmanned aerial vehicle industry is not limited.

Disclosure of Invention

The application provides an unmanned aerial vehicle safe operation method, aims at solving the technical problem that the unmanned aerial vehicle safe operation is not effectively managed and controlled so as to reduce unmanned aerial vehicle operation risk in the prior art.

In one aspect, the present application provides a method for safely operating a unmanned aerial vehicle, the method comprising:

acquiring performance state parameters of the unmanned aerial vehicle in the current state;

acquiring environment complexity parameters of the unmanned aerial vehicle flight environment in the current state, wherein the environment complexity parameters are calculated based on various environment data and corresponding preset weight coefficients in the current state of the unmanned aerial vehicle;

acquiring task complexity parameters of a current task to be completed of the unmanned aerial vehicle;

determining flight risk parameters of the unmanned aerial vehicle according to the performance state parameters, the environment complexity parameters and the task complexity parameters;

and determining the current flight strategy of the unmanned aerial vehicle according to the flight risk parameters.

In some embodiments of the present application, the acquiring a performance state parameter of the unmanned aerial vehicle in the current state includes:

acquiring unmanned aerial vehicle performance indexes of the unmanned aerial vehicle, wherein the unmanned aerial vehicle performance indexes comprise voyage and endurance, cruising speed, wind resistance and three-proofing level;

Acquiring limit test indexes of the unmanned aerial vehicle, wherein the limit test indexes comprise a endurance limit, a temperature limit, a wind speed limit, a rainfall sand dust limit and a load limit;

and determining the performance state parameters of the unmanned aerial vehicle in the current state according to the unmanned aerial vehicle performance index of the unmanned aerial vehicle and the limit test index of the unmanned aerial vehicle.

In some embodiments of the present application, the acquiring the environmental complexity parameter of the unmanned aerial vehicle flight environment in the current state includes:

acquiring weather data of the unmanned aerial vehicle flight environment in the current state, wherein the environment data comprise wind speed, temperature and humidity, visibility and other preset environment data;

according to the environmental data of the unmanned aerial vehicle flight environment in the current state, evaluating weather parameters of the unmanned aerial vehicle flight environment in the current state;

and determining the environment complexity parameter of the unmanned aerial vehicle flight environment in the current state according to the weather parameter.

In some embodiments of the present application, the estimating weather parameters of the unmanned aerial vehicle flight environment in the current state according to the environmental data of the unmanned aerial vehicle flight environment in the current state includes:

converting weather data of the unmanned aerial vehicle flight environment in the current state into corresponding weather data weights according to the corresponding relation of preset weather data and weights thereof, wherein the weather data weights comprise wind speed weights, visibility weights, temperature and humidity weights and other environment data weights;

Acquiring a preset wind speed weight coefficient, a preset visibility weight coefficient, a preset temperature and humidity weight coefficient and a preset weather data weight coefficient;

and calculating weather parameters of the unmanned aerial vehicle flight environment in the current state according to the wind speed weight, the visibility weight, the temperature and humidity weight and other environment data weight, and a preset wind speed weight coefficient, a preset visibility weight coefficient, a preset temperature and humidity weight coefficient and a preset other weather data weight coefficient.

In some embodiments of the present application, the determining, according to the weather parameter, an environmental complexity parameter of the unmanned aerial vehicle flight environment in a current state includes:

and calculating the environment complexity parameter of the unmanned aerial vehicle flight environment in the current state according to the weather parameter.

In some embodiments of the present application, the obtaining a task complexity parameter of a task currently to be completed by the unmanned aerial vehicle includes:

acquiring the range, load, route conditions and flight frequency of the current task to be completed of the unmanned aerial vehicle;

acquiring weight coefficients corresponding to the range, load, route conditions and flight frequency of the unmanned aerial vehicle respectively;

and calculating task complexity parameters of the current task to be completed of the unmanned aerial vehicle according to the range, load, route conditions, flight frequency and corresponding weight coefficients of the current task to be completed of the unmanned aerial vehicle.

In some embodiments of the present application, calculating, according to a range, a load, a route condition, a flight frequency, and respective weight coefficients of a task to be completed currently by the unmanned aerial vehicle, a task complexity parameter of the task to be completed currently by the unmanned aerial vehicle includes:

calculating the course task parameters of the current task to be completed of the unmanned aerial vehicle according to the course of the current task to be completed of the unmanned aerial vehicle and the weight coefficient corresponding to the course of the unmanned aerial vehicle;

calculating load task parameters of the current task to be completed of the unmanned aerial vehicle according to the load of the current task to be completed of the unmanned aerial vehicle and a weight coefficient corresponding to the load of the unmanned aerial vehicle;

calculating the route condition task parameters of the current task to be completed of the unmanned aerial vehicle according to the route condition of the current task to be completed of the unmanned aerial vehicle and the weight coefficient corresponding to the route condition of the unmanned aerial vehicle;

calculating flight frequency task parameters of the unmanned aerial vehicle to be completed currently according to the flight frequency of the unmanned aerial vehicle to be completed currently and the weight coefficient corresponding to the flight frequency of the unmanned aerial vehicle;

and summing the range task parameter, the load task parameter, the route condition task parameter and the flight frequency task parameter to obtain a task complexity parameter of a task to be completed currently by the unmanned aerial vehicle.

In some embodiments of the present application, the determining the flight risk parameter of the unmanned aerial vehicle according to the performance state parameter, the environmental complexity parameter and the task complexity parameter includes:

determining the flight complexity parameter of the unmanned aerial vehicle according to the environment complexity parameter and the task complexity parameter;

determining a flight risk output strategy of the unmanned aerial vehicle according to the performance state parameters, wherein the flight risk output strategy comprises a corresponding relation between a flight complexity parameter and a flight risk;

and determining the flight risk parameters of the unmanned aerial vehicle according to the flight risk output strategy and the flight complexity parameters.

In another aspect, the present application provides a safe operation device for an unmanned aerial vehicle, the device comprising:

the first acquisition unit is used for acquiring performance state parameters of the unmanned aerial vehicle in the current state;

the second acquisition unit is used for acquiring environment complexity parameters of the unmanned aerial vehicle flight environment in the current state, wherein the environment complexity parameters are calculated based on various environment data and corresponding preset weight coefficients in the current state of the unmanned aerial vehicle;

the third acquisition unit is used for acquiring task complexity parameters of a task to be completed currently of the unmanned aerial vehicle;

The first determining unit is used for determining flight risk parameters of the unmanned aerial vehicle according to the performance state parameters, the environment complexity parameters and the task complexity parameters;

and the second determining unit is used for determining the current flight strategy of the unmanned aerial vehicle according to the flight risk parameters.

In some embodiments of the present application, the first obtaining unit is specifically configured to:

In some embodiments of the present application, the second obtaining unit is specifically configured to:

In some embodiments of the present application, the third obtaining unit is specifically configured to:

In some embodiments of the present application, the first determining unit is specifically configured to:

In another aspect, the present application further provides an electronic device, including:

one or more processors;

a memory; and

one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the unmanned aerial vehicle safe operation method.

In another aspect, the present application further provides a computer readable storage medium having stored thereon a computer program, the computer program being loaded by a processor to perform the steps of the method for safe operation of a drone.

According to the embodiment of the application, the performance state parameters of the unmanned aerial vehicle in the current state are obtained; acquiring an environment complexity parameter of the unmanned aerial vehicle flight environment in the current state; acquiring task complexity parameters of a current task to be completed of the unmanned aerial vehicle; determining flight risk parameters of the unmanned aerial vehicle according to the performance state parameters, the environment complexity parameters and the task complexity parameters; and determining the current flight strategy of the unmanned aerial vehicle according to the flight risk parameters. According to the unmanned aerial vehicle flight risk parameter control method, under the condition that the existing unmanned aerial vehicle can only rely on the quality control of each unmanned aerial vehicle manufacturer on the unmanned aerial vehicle and cannot effectively monitor the unmanned aerial vehicle operation, the acquired performance state parameter, environment complexity parameter and task complexity parameter are utilized to determine the flight risk parameter of the unmanned aerial vehicle operation, the performance state of the unmanned aerial vehicle is comprehensively considered due to the flight parameters, the current flight environment of the unmanned aerial vehicle and the complexity of the current task of the unmanned aerial vehicle are comprehensively considered, and the environment complexity parameter corresponding to the current flight environment of the unmanned aerial vehicle is obtained by calculating various environment data and corresponding preset weight coefficients, so that the current flight strategy of the unmanned aerial vehicle is determined based on the flight risk parameter, the flight safety early warning of the unmanned aerial vehicle operation can be fully realized, and the risk of the unmanned aerial vehicle operation is reduced.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

Fig. 1 is a schematic view of a scenario of a safe operation system of an unmanned aerial vehicle provided by an embodiment of the present invention;

fig. 2 is a schematic flow chart of an embodiment of a method for safely operating a unmanned aerial vehicle according to the present invention;

FIG. 3 is a flow chart of one embodiment of step 202 provided in an embodiment of the present invention;

FIG. 4 is a flow chart of one embodiment of step 203 provided in an embodiment of the present invention;

fig. 5 is a schematic structural diagram of an embodiment of a safe operation device of a unmanned aerial vehicle provided in an embodiment of the present invention;

fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to fall within the scope of the invention.

In the description that follows, embodiments of the invention will be described with reference to steps and symbols performed by one or more computers, unless otherwise indicated. Thus, these steps and operations will be referred to in several instances as being performed by a computer, which as referred to herein performs operations that include processing units by the computer that represent electronic signals that represent data in a structured form. This operation transforms the data or maintains it in place in the computer's memory system, which may reconfigure or otherwise alter the computer's operation in a manner well known to those skilled in the art. The data structure maintained by the data is the physical location of the memory, which has specific characteristics defined by the data format. However, the principles of the present invention are described in the foregoing text and are not meant to be limiting, and one skilled in the art will recognize that various steps and operations described below may also be implemented in hardware.

The term "module" or "unit" as used herein may be considered a software object executing on the computing system. The various components, modules, engines, and services described herein may be viewed as implementing objects on the computing system. The apparatus and methods described herein are preferably implemented in software, but may of course also be implemented in hardware, all within the scope of the invention.

As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. The term "and/or" as used herein includes all or any element and all combination of one or more of the associated listed items.

The embodiment of the invention provides a safe operation method and device of an unmanned aerial vehicle, electronic equipment and a storage medium.

Referring to fig. 1, fig. 1 is a schematic view of a scenario of an unmanned aerial vehicle safe operation system provided by an embodiment of the present invention, where the unmanned aerial vehicle safe operation system may include an unmanned aerial vehicle 100 and an electronic device 200, where the unmanned aerial vehicle 100 is connected to the electronic device 20 through a network, an unmanned aerial vehicle parameter acquisition device is disposed in the unmanned aerial vehicle 100, and an unmanned aerial vehicle safe operation device is integrated in the electronic device 200, such as the electronic device in fig. 1, and the unmanned aerial vehicle 100 may perform data interaction with the electronic device 200.

In the embodiment of the invention, the electronic device 200 is mainly used for acquiring the performance state parameters of the unmanned aerial vehicle in the current state; acquiring environment complexity parameters of the unmanned aerial vehicle flight environment in the current state, wherein the environment complexity parameters are calculated based on various environment data and corresponding preset weight coefficients in the current state of the unmanned aerial vehicle; acquiring task complexity parameters of a current task to be completed of the unmanned aerial vehicle; determining flight risk parameters of the unmanned aerial vehicle according to the performance state parameters, the environment complexity parameters and the task complexity parameters; and determining the current flight strategy of the unmanned aerial vehicle according to the flight risk parameters.

In the embodiment of the present invention, the electronic device 200 may be an independent electronic device, or may be an electronic device network or an electronic device cluster formed by electronic devices, for example, the electronic device 200 described in the embodiment of the present invention includes, but is not limited to, a computer, a server, a network host, a single network electronic device, a plurality of network electronic device sets, or a cloud electronic device formed by a plurality of electronic devices. The Cloud electronic device is composed of a large number of electronic devices based on Cloud Computing (Cloud Computing), for example, a large number of servers form a Cloud server. In embodiments of the present invention, communication between the electronic device and the user terminal may be achieved by any communication means, including, but not limited to, mobile communication based on the third generation partnership project (3rd Generation Partnership Project,3GPP), long term evolution (Long Term Evolution, LTE), worldwide interoperability for microwave access (Worldwide Interoperability for Microwave Access, wiMAX), or computer network communication based on the TCP/IP protocol family (TCP/IP Protocol Suite, TCP/IP), user datagram protocol (User Datagram Protocol, UDP), etc.

It will be understood by those skilled in the art that the application environment shown in fig. 1 is merely an application scenario of the present application, and is not limited to the application scenario of the present application, and other application environments may also include more or fewer unmanned aerial vehicles than those shown in fig. 1, or an electronic device network connection relationship, for example, only 1 electronic device and 2 unmanned aerial vehicles are shown in fig. 1, and it will be understood that the unmanned aerial vehicle safe operation system may also include one or more other unmanned aerial vehicles connected to the electronic device network, which is not limited herein.

In addition, as shown in fig. 1, the unmanned aerial vehicle safe operation system may further include a memory 300, configured to store unmanned aerial vehicle data, such as unmanned aerial vehicle state parameters collected by the unmanned aerial vehicle parameter collecting device, weather data, and an up-to-date performance state of the unmanned aerial vehicle.

It should be noted that, the schematic view of the scenario of the unmanned aerial vehicle safe operation system shown in fig. 1 is only an example, and the unmanned aerial vehicle safe operation system and scenario described in the embodiments of the present invention are for more clearly describing the technical solution of the embodiments of the present invention, and do not constitute a limitation to the technical solution provided by the embodiments of the present invention, and as a person of ordinary skill in the art can know that, along with the evolution of the unmanned aerial vehicle safe operation system and the appearance of a new service scenario, the technical solution provided by the embodiments of the present invention is equally applicable to similar technical problems.

The following describes in detail specific embodiments.

In the embodiment of the present invention, description will be made from the perspective of a safe operation device of an unmanned aerial vehicle, which may be integrated in the electronic apparatus 200 in particular.

Firstly, the embodiment of the invention provides a safe operation method of an unmanned aerial vehicle, which comprises the following steps: acquiring performance state parameters of the unmanned aerial vehicle in the current state; acquiring an environment complexity parameter of the unmanned aerial vehicle flight environment in the current state; acquiring task complexity parameters of a current task to be completed of the unmanned aerial vehicle; determining flight risk parameters of the unmanned aerial vehicle according to the performance state parameters, the environment complexity parameters and the task complexity parameters; and determining the current flight strategy of the unmanned aerial vehicle according to the flight risk parameters.

According to the method, under the condition that the existing unmanned aerial vehicle can only rely on the quality control of each unmanned aerial vehicle manufacturer on the unmanned aerial vehicle and cannot effectively monitor the unmanned aerial vehicle operation, the acquired performance state parameters, environment complexity parameters and task complexity parameters are utilized to determine the flight risk parameters of the unmanned aerial vehicle operation, and then the current flight strategy of the unmanned aerial vehicle can be determined according to the flight risk parameters, so that the flight safety early warning of the unmanned aerial vehicle operation is realized, and the risk of the unmanned aerial vehicle operation is reduced.

Referring to fig. 2, a flow chart of an embodiment of a method for safely operating a unmanned aerial vehicle according to an embodiment of the present invention is shown, where the method for safely operating a unmanned aerial vehicle includes:

201. and acquiring performance state parameters of the unmanned aerial vehicle in the current state.

Specifically, the acquiring the performance state parameter of the unmanned aerial vehicle in the current state includes: acquiring unmanned aerial vehicle performance indexes of the unmanned aerial vehicle, wherein the unmanned aerial vehicle performance indexes comprise voyage and endurance, cruising speed, wind resistance and three-proofing level; acquiring limit test indexes of the unmanned aerial vehicle, wherein the limit test indexes comprise a endurance limit, a temperature limit, a wind speed limit, a rainfall sand dust limit and a load limit; and determining the performance state parameters of the unmanned aerial vehicle in the current state according to the unmanned aerial vehicle performance index of the unmanned aerial vehicle and the limit test index of the unmanned aerial vehicle.

In some embodiments of the present invention, the performance state parameters of the drone include three dimensional data:

1. the unmanned aerial vehicle performance indexes comprise various index data, voyage and navigation time, cruising speed, wind resistance, three-proofing level and other performance indexes, and the unmanned aerial vehicle performance indexes can be directly provided by the unmanned aerial vehicle enterprises, so that the unmanned aerial vehicle performance indexes can be directly obtained.

2. The limit test data, namely the limit test index of the unmanned aerial vehicle, needs to be fully verified in a laboratory or a test field to obtain the safe use boundary of the unmanned aerial vehicle of the model, and specifically can comprise the following parameters:

endurance limit: under standard load, under different wind speeds (upwind), unmanned aerial vehicle voyage endurance, for example, upwind 10m/s,8m/s,6m/s (upwind, downwind, crosswind influence effect is different), the more detailed the data is, the more accurate the different voyage endurance is.

Temperature limit: mainly aims at the battery and the avionics system, and can work normally under the high and low temperature conditions.

Wind speed limit: mainly comprises a maximum take-off wind speed and a maximum cruising wind-resistant wind speed.

Rain sand limit: the novel waterproof and waterproof structure mainly aims at the three-proofing characteristic of the structure, and can normally fly under the condition of different rainfall and dust grades.

Load limit: the method is not applicable to standard load unmanned aerial vehicles, and is mainly used for testing the logistics unmanned aerial vehicles (each time the weight of the loaded cargoes is inconsistent) under the condition of no wind, for example, the loading is 10kg,8kg,6kg and 4kg under different conditions, and the voyage is time. The more detailed the data, the more accurate the model. If load is coupled with wind speed, the complexity increases.

In addition to the above-described drone performance and test data, in some embodiments of the present invention, it is also desirable to know the drone health status, e.g., the drone health status may include the drone currently being in a life cycle and the drone current performance status, where the drone current performance status is elaborated in the following embodiments, and the current drone being in that stage of the life cycle is as follows in some specific scenarios:

For example, the service life of the structural system of the unmanned aerial vehicle is 3000h, the power system is 500h, the unmanned aerial vehicle has flown for the first time, the flight time and the flight mileage at present, and the unmanned aerial vehicle A, B detects whether the maintenance of the size of the automobile is normal or not, and whether the structural power type parts are replaced or not. Recording the occurrence of historical faults, and judging whether major faults occur before; whether there is a preventive maintenance record; if the operation task is performed before, the check before the flight and the check after the flight are abnormal. Whether a limit condition is exceeded, etc.

In order to facilitate understanding, the performance states of the unmanned aerial vehicle can be respectively four stages of young (stage I), middle-aged (stage II), middle-aged (stage III) and elderly (stage IV) in the embodiment of the invention.

In this embodiment, the four stages of young (stage i), young (stage ii), young (stage iii), young (stage iv), and old (stage iv) may be distinguished according to one or more of a service life duration, a flight mileage, a maintenance record, a fault record number, a major fault number, a performance maintenance number, a flight inspection abnormality number, a limit condition number, and the like. The following is an example of the service life duration, and four stages of young (stage i), old (stage ii), middle-aged (stage iii) and old (stage iv) are illustrated:

Unmanned aerial vehicle performance state	Young people (level I)	Zhuang year (level II)	Middle-aged (III level)	Old people (IV level)
					Duration of service life (t)	t is less than or equal to 1 year	1＜t≤2	1＜t≤3	3＜t

It should be noted that the above parameters are only examples, and examples only exemplify the service life duration of a single unmanned aerial vehicle, and in an actual application scenario, the performance state of the unmanned aerial vehicle may be calculated to obtain a weight value after weighting various parameters, and the weight value is calculated based on the corresponding relation between the weight value and the performance state of the unmanned aerial vehicle.

For example, x=a ₁ x ₁ +。。。+a _n x _n Wherein n is greater than or equal to 2, X is the unmanned aerial vehicle performance state weight value, xn is an unmanned aerial vehicle performance state index, such as service life duration, mileage, maintenance record, fault record times, major fault times, performance maintenance times, flight inspection abnormal times, limit working condition times, or subsequent embodimentsThe unmanned aerial vehicle performance state parameter, a _n The preset weight coefficient corresponding to the unmanned aerial vehicle performance state index Xn can be preset after test calculation and is directly used subsequently.

After the performance state weight value of the unmanned aerial vehicle is obtained through calculation, the performance state of the unmanned aerial vehicle can be determined through the performance state weight value of the unmanned aerial vehicle and the performance state weight table of the unmanned aerial vehicle, and in a specific embodiment, the performance state of the unmanned aerial vehicle can be shown in the following table:

Unmanned plane performance state weight (X)	Unmanned aerial vehicle performance state
		X≥F1	Young people (level I)
F2≥X＞F1	Zhuang year (level II)
		F3≥X＞F2	Middle-aged (III level)
F4≥X＞F3	Old people (IV level)

Wherein, F1, F2, F3, F4 are positive values, and F1 is less than F2 and F3 is less than F4, wherein the values of F1, F2, F3, F4 can be adjusted in specific application scenes, and the invention is not limited herein.

202. And acquiring the environment complexity parameter of the unmanned aerial vehicle flight environment in the current state.

In the present application, unmanned aerial vehicle environments may be classified, for example, into weather environments, geographical environments, road surface environments, humane environments, biological environments, and induced environments.

The weather environment comprises wind speed and direction, temperature and humidity, air pressure and illumination intensity, radiation intensity, rainfall, sand dust, fog and lightning stroke, the wind speed and direction directly influences the range and the endurance of the unmanned aerial vehicle, the temperature and humidity have influence on all electric components, condensation and icing can be formed when the temperature and humidity reach a certain range, flight safety is directly influenced, the air pressure directly influences the working efficiency of a power system of the unmanned aerial vehicle, the illumination intensity generates thermal effects on different surfaces, the electric components work in a high-temperature state, power consumption and efficiency are influenced, the radiation intensity directly influences the composite structure of the unmanned aerial vehicle, the rainfall, sand dust and fog are directly related to the three-proofing performance of the unmanned aerial vehicle, and the lightning stroke directly forms fatal threat to the unmanned aerial vehicle in a signal receiving and transmitting state in high air.

The geographical environment comprises longitude and latitude, altitude, topography hydrology, vegetation soil and the like, and the pavement environment comprises road distribution, electric power facility distribution, public place area, living area, emergency landing place and the like. The unmanned aerial vehicle route planning avoids the areas as far as possible, and if the unmanned aerial vehicle route planning cannot avoid all the areas, the unmanned aerial vehicle route planning gives early warning through the special areas and focuses attention.

The human environment comprises work and rest time, personnel flow, folk custom and the like.

Biological environment includes birds and forest, belongs to potential safety hazard, influences unmanned aerial vehicle flight safety.

The induced environment includes battery radiation, and at high voltage power stations, high voltage wires, communication base stations, radar areas may cause interference to unmanned aerial vehicle communication links.

Meanwhile, the system also comprises the route complexity caused by the geographic environment and other environmental factors, and can be divided into the operation environment complexity, for example, the system can perform different unmanned aerial vehicle flight operations such as winding flight or climbing due to the need of mountain turning and route obstacle avoidance, and the unmanned aerial vehicle range distance and the unmanned aerial vehicle flight quantity caused by the selection of the take-off and landing points.

In the embodiment of the application, the whole evaluation of the unmanned aerial vehicle operation environment is actually a process of establishing a specific operation scene evaluation, theoretical support is provided for forming a replicable and generalized commercialization mode of the unmanned aerial vehicle, for example, if the unmanned aerial vehicle can safely operate (how long or how many times the unmanned aerial vehicle can operate without faults or accidents) under the environment, the unmanned aerial vehicle can also safely operate at different places similar to the environment, and thus, the unmanned aerial vehicle can be popularized to a larger area only by selecting a representative place in the unmanned aerial vehicle operation area.

The environmental information can be classified into two types, namely, fixed data such as land surface information of landforms, hydrology, vegetation, soil and the like, the information hardly changes with time once the operation place of the unmanned aerial vehicle is confirmed, and the information is similar to information of road traffic, personnel flow and the like, has obvious tidal performance, can be predicted and estimated approximately, and is also calculated as the fixed data. The other type is real-time data, such as weather data, which needs to be recorded and stored in real time and cannot be accurately predicted and estimated.

Based on the environmental classification information, the environmental complexity parameter can be used for representing the environmental condition of the unmanned aerial vehicle during operation.

In one embodiment of the present invention, as shown in fig. 3, the obtaining the environmental complexity parameter of the unmanned aerial vehicle flight environment in the current state may include:

301. and acquiring weather data of the unmanned aerial vehicle flight environment in the current state, wherein the environmental data comprise wind speed, temperature and humidity, visibility and other preset environmental data.

The preset other environmental data may include at least one of a geographic environment, a road surface environment, a human environment, a biological environment, and an induced environment.

302. And evaluating weather parameters of the unmanned aerial vehicle flight environment in the current state according to the environment data of the unmanned aerial vehicle flight environment in the current state.

Specifically, the estimating, according to the environmental data of the unmanned aerial vehicle flight environment in the current state, the weather parameter of the unmanned aerial vehicle flight environment in the current state may include:

(1) According to the corresponding relation of preset weather data and weights thereof, converting the weather data of the unmanned aerial vehicle flight environment in the current state into corresponding weather data weights, wherein the weather data weights comprise wind speed weights, visibility weights, temperature and humidity weights and other environment data weights.

Specifically, the preset weather data and the corresponding relation of the weights thereof comprise each type of weather data and the corresponding type of weather data weight relation, for example, the corresponding relation of wind speed data and wind speed weight, the corresponding relation of visibility data and visibility weight, the corresponding relation of temperature and humidity data and temperature and humidity weight, and the corresponding relation of other environment data and other environment data weight.

The other environmental data may determine what type of environmental parameters are included before participating in the calculation, for example, one or more of a geographic environment, a road surface environment, a humane environment, a biological environment, and an induced environment, and at this time, a correspondence between the other environmental data and the weight of the other environmental data may be preset.

In the embodiment of the invention, the weight and the weight coefficient occupied by each weather data can be preset, different manufacturers and different models are different, and the following table 1 is taken as an example for explanation, and the specific steps are as follows:

(2) And acquiring a preset wind speed weight coefficient, a preset visibility weight coefficient, a preset temperature and humidity weight coefficient and a preset weather data weight coefficient.

The weight coefficient corresponding to each environmental data is preset, specifically may be set according to the actual application scenario, and in one embodiment, as shown in table 1 above.

(3) And calculating weather parameters of the unmanned aerial vehicle flight environment in the current state according to the wind speed weight, the visibility weight, the temperature and humidity weight and other environment data weight, and a preset wind speed weight coefficient, a preset visibility weight coefficient, a preset temperature and humidity weight coefficient and a preset other weather data weight coefficient.

303. And determining the environment complexity parameter of the unmanned aerial vehicle flight environment in the current state according to the weather parameter.

The environment complexity parameter is calculated based on various environment data and corresponding preset weight coefficients in the current state of the unmanned aerial vehicle.

Specifically, the determining, according to the weather parameter, an environmental complexity parameter of the unmanned aerial vehicle flight environment in the current state may include: and calculating the environment complexity parameter of the unmanned aerial vehicle flight environment in the current state according to the weather parameter.

In one embodiment, the environmental complexity parameter is calculated as shown in equation (1)

Z＝VC1+tC2+vC3+rC4+sC5 (1)

Wherein Z is an environmental complexity parameter, V is wind speed, t is temperature and humidity, V is visibility, r is rain and snow amount, s is other environmental data, and C1, C2, C3, C4 and C5 are respectively wind speed, temperature and humidity, visibility, rain and snow amount and weight of other environmental data in sequence.

After determining the environmental complexity parameter, the environmental complexity may be determined from the environmental complexity parameter.

In some embodiments of the present application, the environmental complexity of the unmanned aerial vehicle flight environment includes a light level (level I), a medium level (level ii), a severe level (level iii), a severe level (level iv), and a dangerous level (level v).

In one particular embodiment, the environmental complexity may be determined from a corresponding table of environmental complexity parameters and environmental complexity, such as shown in the following table:

the following is a description of the calculations in a specific scenario:

scene 1, in a city test in plain, the temperature is 8 ℃, the air pressure is 100kpa, the air speed is 2m/s, and no rain, snow, dust and haze exists in the same day.

The environmental complexity parameter z=1×0.7+1×0.1+1×0.1+1×0.05+1×0.05=1 is calculated with reference to table 1,

thus, the environmental complexity of the day is: slight (level I)

Scene 2, in a certain city of a plateau, the temperature is 4 ℃, the air pressure is 60kpa, the air speed is 16m/s, and no rain, snow, dust and haze exists in the same day.

The environmental complexity parameter z=5×0.7+5×0.1+5×0.1+1×0.05+1×0.05=4.6 is calculated with reference to table 1,

thus, the environmental complexity of the day is: severe (class iv) due to: excessive wind speed (temperature, rain and snow and haze exceeding standard) should be closely concerned with wind speed, wind direction, electric quantity and voltage indexes.

In the embodiment of the invention, after the environment complexity is determined, the critical index is required to be extracted, and the critical index is compared with the flight state of the unmanned aerial vehicle in real time, for example, the wind speed and the wind direction are compared with the flight speed, the height, the vibration, the attitude angle and the like of the unmanned aerial vehicle, and the unmanned aerial vehicle is adjusted according to the critical environmental index so as to achieve the optimal flight state.

When the environmental complexity suddenly jumps to the serious (III level) and the severe (IV level) levels, the flight task can be primarily judged, but the risk level of unmanned aerial vehicle operation can be improved, and the operation risk can be reduced by adjusting the flight strategy in the later stage, so that the defensive capability for further deteriorating the environmental complexity is improved.

203. And acquiring task complexity parameters of a task to be completed currently of the unmanned aerial vehicle.

In the embodiment of the invention, the flight task of the unmanned aerial vehicle is provided with the difficulty level, and different unmanned aerial vehicle states are required corresponding to different difficulty levels. Different flight tasks correspond to different flight parameters, for example, the load is required to be 5kg, the flight is 10km, the route is horizontal flight, only one flight is required, and the difficulty is the lowest level; the load is 10kg, the flight is 20km, the route is required to climb for many times, the route is required to fly back and forth ten times, and the difficulty is the highest level four.

In some embodiments of the present invention, as shown in fig. 4, the obtaining a task complexity parameter of a task to be completed currently by the unmanned aerial vehicle includes:

401. and acquiring the range, load, route condition and flight frequency of the current task to be completed of the unmanned aerial vehicle.

402. And acquiring weight coefficients corresponding to the range, the load, the route condition and the flight frequency of the unmanned aerial vehicle respectively.

403. And calculating task complexity parameters of the current task to be completed of the unmanned aerial vehicle according to the range, load, route conditions, flight frequency and corresponding weight coefficients of the current task to be completed of the unmanned aerial vehicle.

The calculating the task complexity parameter of the current task to be completed according to the range, the load, the route condition, the flight frequency and the weight coefficients of the current task to be completed of the unmanned aerial vehicle comprises the following steps:

(1) Calculating the course task parameters of the current task to be completed of the unmanned aerial vehicle according to the course of the current task to be completed of the unmanned aerial vehicle and the weight coefficient corresponding to the course of the unmanned aerial vehicle;

(2) Calculating load task parameters of the current task to be completed of the unmanned aerial vehicle according to the load of the current task to be completed of the unmanned aerial vehicle and a weight coefficient corresponding to the load of the unmanned aerial vehicle;

(3) Calculating the route condition task parameters of the current task to be completed of the unmanned aerial vehicle according to the route condition of the current task to be completed of the unmanned aerial vehicle and the weight coefficient corresponding to the route condition of the unmanned aerial vehicle;

(4) Calculating flight frequency task parameters of the unmanned aerial vehicle to be completed currently according to the flight frequency of the unmanned aerial vehicle to be completed currently and the weight coefficient corresponding to the flight frequency of the unmanned aerial vehicle;

(5) And summing the range task parameter, the load task parameter, the route condition task parameter and the flight frequency task parameter to obtain a task complexity parameter of a task to be completed currently by the unmanned aerial vehicle.

In one embodiment, the task complexity parameter y=mζ+sγ+fλ+pε, ζ is a loading coefficient, S is a range, γ is a range coefficient, F is a range case, λ is a range correction coefficient, P is a flight frequency, ε is a flight correction coefficient.

In another embodiment, other task details, specifically, task complexity parameter Y, may also be added＝M ζ+Sγ+Fλ+Pε+Qω, M is the load, ζ is the load factor, S is the course, γ is the course factor, F is the course situation, λ is the course correction factor, P is the flight frequency, ε is the flight correction factor, Q is other mission details, ω is the mission degree correction factor.

The respective coefficients may be preset, for example, a mission degree correction coefficient ω, and the other mission details may be preset other mission parameters, for example, a mission time for completing a flight mission, etc.

204. And determining flight risk parameters of the unmanned aerial vehicle according to the performance state parameters, the environment complexity parameters and the task complexity parameters.

Wherein the determining the flight risk parameter of the unmanned aerial vehicle according to the performance state parameter, the environment complexity parameter and the task complexity parameter includes: determining the flight complexity parameter of the unmanned aerial vehicle according to the environment complexity parameter and the task complexity parameter; determining a flight risk output strategy of the unmanned aerial vehicle according to the performance state parameters, wherein the flight risk output strategy comprises a corresponding relation between a flight complexity parameter and a flight risk; and determining the flight risk parameters of the unmanned aerial vehicle according to the flight risk output strategy and the flight complexity parameters.

205. And determining the current flight strategy of the unmanned aerial vehicle according to the flight risk parameters.

In the embodiment of the present invention, the flight risk parameter may be a flight risk level, and specifically, the flight risk level may be: the system can bear a series of risk prompts such as environmental pressure in a short period, arrival at an early warning boundary, incapability of completing flying, severe exceeding of standard advice immediate landing and the like. Based on the flight risk parameters, a current flight strategy of the unmanned aerial vehicle is determined, such as reducing the flight speed, shortening the voyage, immediately landing, and the like.

In some embodiments of the present application, in step 201, obtaining a performance state parameter of the unmanned aerial vehicle in the current state may include: acquiring unmanned aerial vehicle state parameters acquired by the unmanned aerial vehicle parameter acquisition device before the current state (the last time before the current state); calculating preset performance parameters of the unmanned aerial vehicle according to the unmanned aerial vehicle state parameters; acquiring a weight ratio coefficient corresponding to each performance parameter in the preset performance parameters; and determining the performance state parameter of the unmanned aerial vehicle in the current state according to the preset performance parameter and the weight ratio coefficient.

Wherein, unmanned aerial vehicle parameter acquisition device can include at least one sensor, and the quantity and the type of this at least one sensor can set up as required unmanned aerial vehicle state parameter that gathers, for example, if including the driving system parameter in the unmanned aerial vehicle state parameter, then at least one sensor can include vibration sensor and the noise sensor to driving system setting, if including the driving system parameter in the unmanned aerial vehicle state parameter, then at least one sensor can include the stress strain sensor to unmanned aerial vehicle driving system setting.

It is theoretically most appropriate to collect various information by sensors as much as possible without considering costs, i.e. the more and more detailed the unmanned aerial vehicle state parameters are collected, the best is the evaluation of the unmanned aerial vehicle state. However, when the cost is too high, and the development of the civil unmanned aerial vehicle is imperfect, the development experience of the medical science and the automobile industry is not available, how to distribute and assign the maintenance of the size, how many items belong to the conventional detection, how many items belong to the depth detection, and no applicable standard is available, but for enterprises or individuals who own the unmanned aerial vehicle, consumers bear the later use cost of the unmanned aerial vehicle, parts are replaced, damaged and maintained, and the like, and the production enterprises do not take the cost of the later maintenance as the primary consideration (similar to automobiles, low-cost sales and high-cost maintenance). The enterprise with the unmanned aerial vehicle generally operates autonomously and autonomously bears later maintenance, so that the old unmanned aerial vehicle can be fully utilized as much as possible, the use and maintenance cost is reduced, and the operation risk is controlled.

Therefore, in order to reduce the cost, the method and the device can integrate various indexes of the state information of the associated unmanned aerial vehicle, extract the key index of the most core (autonomously determined according to operation experience), and achieve the multi-sensor acquisition effect by using a small number of sensors, namely, the state parameters of the unmanned aerial vehicle are state parameters corresponding to the preset core performance parameters.

The unmanned aerial vehicle described in the embodiment of the invention can be an electrically-driven light and small civil unmanned aerial vehicle, and in this case, the unmanned aerial vehicle comprises a battery. For all industrial unmanned aerial vehicles driven by electricity, it is desirable to have as long a voyage and range as possible, i.e., longer flight times and longer flight distances, while maintaining reasonable cruising speeds. The most important components of the unmanned aerial vehicle system that determine this factor are the battery and the power system.

The unmanned aerial vehicle parameter acquisition device can also comprise a processor of the unmanned aerial vehicle, and can acquire some parameters of a battery of the unmanned aerial vehicle through the processor, such as voltage, current, capacity, internal resistance, charge and discharge times and the like of the battery, and of course, the battery temperature can be acquired through a temperature sensor. That is, the unmanned plane state parameters include battery state parameters, which may include a plurality of parameters of voltage parameters, current parameters, capacity parameters, internal resistance parameters, charge and discharge times, temperature parameters, and usage status parameters, it is to be understood that, in other embodiments of the present invention, the battery state parameters may include other battery state parameters besides those listed above, such as remaining service life of the battery, manufacturer information of the battery, and the like, which are not limited herein.

Besides the battery state parameters recorded by the unmanned aerial vehicle, the battery state parameters need to be uploaded to the electronic equipment, and the electronic equipment can conduct unmanned aerial vehicle state analysis based on the battery state parameters, for example, when a large amount of battery state parameter information is accumulated to a certain degree, a battery performance state attenuation curve can be formed.

The unmanned aerial vehicle generally comprises a power system (such as a motor), and for the power system, the main factors are that the motor has the information mainly including indexes such as motor service time, motor voltage, motor current, motor rotating speed, motor vibration, motor noise and the like. The motor bears axial load and radial load simultaneously for a long time, weight reduction is considered in structural design, performance and cost are considered, the motor bearing is more easily worn relative to the motor bearing of ground equipment, a wear gap and a virtual position are easily generated, and therefore unmanned aerial vehicle is abnormal or fails.

Thus, in some embodiments of the present invention, the unmanned aerial vehicle state parameters may include power system parameters, where the power system parameters may include motor operating current, motor temperature, and motor rotational speed in the unmanned aerial vehicle, and it may be understood that in other embodiments of the present invention, the power system parameters may also include other motor parameters, for example, similar to an automobile, the longer the unmanned aerial vehicle is used, the greater the abnormal noise and wind noise may increase, and in particular, the rotating structural member may wear, loosen, the fastening structural member may have a lock out or fail condition, where motor vibration parameters (collected by a vibration sensor), noise parameters (collected by a noise sensor), and so on, and the present invention is not limited thereto.

In addition, in some embodiments of the present invention, the unmanned aerial vehicle state parameter may further include a target core component parameter, where the target core component is a core component other than a battery and a power system, for example, an unmanned aerial vehicle avionics system or an unmanned aerial vehicle pneumatic system, and may specifically be selected according to an actual scenario.

If the target core component includes an avionics system, the electronic component may be aged during use of the avionics system, and the internal resistance may be increased, so the target core component parameter may include the internal resistance in the avionics system, the current used by the avionics system, and the power consumption of the avionics system device, which is not limited in this embodiment.

If the target core component includes a pneumatic system, for an unmanned aerial vehicle pneumatic system, the main parameters relate to a pneumatic profile, a propeller airfoil, a fixed wing airfoil, a tail wing airfoil, and the like, so that the target core component parameters may include parameters such as a pneumatic profile, a propeller airfoil, a fixed wing airfoil, a tail wing airfoil, and the like, and may be specifically acquired by using a stress-strain sensor.

In addition, when calculating the preset performance parameter of the unmanned aerial vehicle according to the unmanned aerial vehicle state parameter, wherein the preset performance parameter corresponds to the unmanned aerial vehicle state parameter, for example, if the unmanned aerial vehicle state parameter comprises the battery state parameter, the preset performance parameter comprises the battery performance parameter, and if the unmanned aerial vehicle state parameter comprises the power system parameter, the preset performance parameter comprises the power system performance parameter; if the unmanned aerial vehicle state parameter includes a target core component parameter, the preset performance parameter includes a target core component performance parameter, and specifically, a pneumatic system performance parameter.

In some embodiments of the invention, the unmanned aerial vehicle state parameters include battery state parameters, power system parameters, and target core component parameters; at this time, the calculating the preset performance parameter of the unmanned aerial vehicle according to the unmanned aerial vehicle state parameter includes:

(1) And determining the battery performance parameters of the unmanned aerial vehicle according to the battery state parameters.

Specifically, the battery state parameters may include a plurality of parameters among a voltage parameter, a current parameter, a capacity parameter, an internal resistance parameter, a charge/discharge number, a temperature parameter, and a use condition parameter; at this time, the determining, according to the battery state parameter, the battery performance parameter of the unmanned aerial vehicle includes: respectively calculating the weight duty ratio of the parameters according to the parameters; and calculating the battery performance parameters of the unmanned aerial vehicle according to the weight duty ratio of the parameters.

(2) And determining the performance parameters of the power system of the unmanned aerial vehicle according to the parameters of the power system.

Specifically, the power system parameters may include a motor working current, a motor temperature, and a motor rotation speed in the unmanned aerial vehicle; according to the power system parameters, determining the power system performance parameters of the unmanned aerial vehicle comprises: calculating the weight of the motor working current according to the motor working current and a preset motor working current interval; calculating motor temperature weight according to the motor temperature and a preset motor temperature interval; calculating motor rotation speed weight according to the motor rotation speed and a preset motor rotation speed interval; and determining the power system performance parameters of the unmanned aerial vehicle according to the motor working current weight, the motor temperature weight and the motor temperature weight.

In a specific embodiment, the unmanned aerial vehicle motor current working range is 5 A+/-2A, namely 3A-7A, the motor current index weight is 0.4, the motor rotating speed range is 2000 rad/s+/-200 rad/s, namely 1800-2200rad/s, the motor rotating speed index weight is 0.3, the motor temperature range is 50-60 ℃, and the motor temperature index weight is 0.3. Assuming that the current motor current is 6A, the motor rotation speed is 2100rad/s, and the motor temperature is 55 ℃, the calculation mode is as follows:

motor temperature weight: tem= (55-50)/(60-50) =0.5;

motor current weight: a= (6-3)/(7-3) =0.75;

motor speed weight: v= (2100-1800)/(2200-1800) =0.75;

the performance parameters of the power system of the unmanned aerial vehicle, namely the comprehensive weight ratio, wherein the comprehensive weight ratio S=0.5×0.3+0.75×0.4+0.75×0.3=0.675, specifically, 0.5 is the optimal state of the power system, 0-0.5 data is smaller, the whole is located in a lower deviation interval, 0.5-1 data is larger, the whole is located in an upper deviation interval, and the power system is not in the optimal state.

(3) And determining the performance parameters of the target core component of the unmanned aerial vehicle according to the parameters of the target core component.

If the target core component comprises an avionics system, the target core component parameters may include internal resistance in the avionics system, avionics system power consumption, and avionics system equipment power consumption. At this time, determining the target core component performance parameter of the unmanned aerial vehicle according to the target core component parameter may include:

Calculating the weight of the internal resistance in the avionics system according to the internal resistance in the avionics system and the initial internal resistance in the avionics system; calculating the weight of the avionics system power consumption current according to the avionics system power consumption current and a preset avionics system power consumption current interval; calculating the weight of the power consumption of the avionics system equipment according to the power consumption of the avionics system equipment and a preset power consumption interval of the avionics system equipment; and determining the performance parameters of the target core component of the unmanned aerial vehicle according to the weight of the internal resistance in the avionics system, the weight of the power consumption current of the avionics system and the weight of the power consumption of the avionics system equipment.

The unmanned aerial vehicle has the advantages that the performance of the unmanned aerial vehicle is reduced along with the lengthening of the service time, the unmanned aerial vehicle is similar to an automobile, the longer the service time is, the higher the oil consumption is, the more serious the abrasion of a tire and a brake pad is, the longer the braking distance is, the poorer the charging and discharging performance of a storage battery is, and the like. For unmanned aerial vehicles, various performance indexes are attenuated. The specific and most obvious index is the battery capacity, the actual capacity is phi' assuming that the battery capacity is phi, the service time (the number of charge and discharge cycles of the battery) is t, and the attenuation coefficient is lambda ₁ (as the service time is prolonged, the less the energy storage of the battery is, the larger the internal resistance is, the less the discharge capacity is), the temperature (environment) correction parameter is mu (different environment temperatures, different battery charging and discharging effects are achieved), and the historical state correction parameter is epsilon ₁ (whether the battery is charged or discharged without the specified operation, the history of the battery is abnormal or faulty, and the battery is overcharged or overdischarged) with an estimated formula of Φ' =Φμε ₁ (100％-λ ₁ t-2)。

Wherein the temperature (environment) correction parameter is related to the current unmanned aerial vehicle operation environment temperature, the historical state correction parameter is related to the historical use condition of the battery, and the attenuation coefficient is related to the battery performance of each different brand. Similarly, the longer the motor is used, the more serious the bearing wear, and under the same working condition, the state performance is reduced, the output power is reduced, and the efficiency is reduced. Assuming that the normal rated output power is Q, the actual output power is Q', and the attenuation coefficient is lambda ₂ (normal decay according to actual test), the state correction parameter is ε ₂ (history state is abnormal or fault, whether maintenance record exists) according to motors in different positions, a position correction coefficient p (the parameter is related to the structural form of the unmanned aerial vehicle) is also required to be added, for example, when the unmanned aerial vehicle is a four-rotor unmanned aerial vehicle, when the unmanned aerial vehicle flies forward at a certain attitude angle, the power output of the four motors is different, the power output of the six-rotor motor and the eight-rotor motor is also different, the motors are similar to front wheels and rear wheels of a front-drive automobile, and the front wheels bear load, turn and travel, and are more worn than the rear wheels.

Therefore, the actual output power estimation formula of the unmanned aerial vehicle may be Q' =qλ ₂ ε ₂ +p, wherein Q is the normal rated output power, Q' is the actual output power, lambda ₂ For the attenuation coefficient (normal attenuation from actual test), ε ₂ Parameters (historical status anomalies or faults, whether maintenance records are present) are corrected for status.

Therefore, according to the above situation, the weight ratio coefficient corresponding to each performance parameter in the preset performance parameters can be obtained.

In some embodiments of the present invention, the determining, according to the preset performance parameter and the weight ratio coefficient, a performance state parameter of the unmanned aerial vehicle in a current state may include:

(1) And determining the comprehensive performance parameters corresponding to each performance parameter in the preset performance parameters according to the preset performance parameters and the weight ratio coefficients.

(2) And obtaining an attenuation coefficient corresponding to the preset performance parameter.

Normally, all coefficients are dynamically changing, e.g. attenuation coefficients, the longer the service time, the more severe the ageing and the more severe the attenuation. In the test stage, a curve of each performance index changing along with time can be formed in advance, and a function can be fitted, so that attenuation coefficients, state correction coefficients and position correction coefficients of each stage are obtained. Specifically, the obtaining the weight ratio coefficient corresponding to each performance parameter in the preset performance parameters includes: acquiring test data of preset performance parameters of the test unmanned aerial vehicle in target time; forming each performance curve corresponding to the preset performance parameter based on the test data; and determining attenuation coefficients corresponding to each of the preset performance parameters according to the performance curves.

(3) And determining the performance state grade of the unmanned aerial vehicle according to the comprehensive performance parameters corresponding to each performance parameter and the attenuation coefficient corresponding to the preset performance parameter.

Wherein, the determining the performance state level of the unmanned aerial vehicle according to the comprehensive performance parameter corresponding to each performance parameter and the attenuation coefficient corresponding to the preset performance parameter includes: calculating effective performance parameters of each performance parameter according to the comprehensive performance parameters corresponding to each performance parameter and the attenuation coefficient corresponding to each performance parameter; calculating the sum of the effective performance parameters of each performance parameter; and determining the performance state grade of the unmanned aerial vehicle according to the sum value.

In a specific embodiment, as shown in the following table 1, assuming that O is a preset performance level parameter of another unmanned aerial vehicle, the battery weight ratio coefficient is T1, the motor weight ratio coefficient is T2, the weight ratio coefficient of the performance level parameter is T3, the comprehensive weight ratio in the current state is S, and the performance level l1= (Φ' t1+qt2+ot3) S of the unmanned aerial vehicle, specifically, L1 may be divided into one, two, three, four and five stages according to the result.

TABLE 1

In the above embodiment, the determined performance status (e.g., the performance level L1) of the unmanned aerial vehicle may prompt the operator whether to repair and maintain the unmanned aerial vehicle, or may evaluate whether the current unmanned aerial vehicle may complete the current flight task, for example, determine the flight risk parameter of the unmanned aerial vehicle in step 204 in the embodiment of the present invention.

In order to better implement the unmanned aerial vehicle safe operation method provided by the embodiment of the invention, the embodiment of the invention also provides a device based on the unmanned aerial vehicle safe operation method, wherein the meaning of nouns is the same as that in the unmanned aerial vehicle safe operation method, and specific implementation details can be referred to the description in the method embodiment.

Referring to fig. 5, fig. 5 is a schematic structural diagram of a safe operation device for an unmanned aerial vehicle according to an embodiment of the present invention, where the safe operation device 500 for an unmanned aerial vehicle may include:

a first obtaining unit 501, configured to obtain a performance state parameter of the unmanned aerial vehicle in a current state;

the second obtaining unit 502 is configured to obtain an environmental complexity parameter of the flight environment of the unmanned aerial vehicle in the current state, where the environmental complexity parameter is obtained by calculating based on multiple environmental data and corresponding preset weight coefficients in the current state of the unmanned aerial vehicle;

a third obtaining unit 503, configured to obtain a task complexity parameter of a task to be completed currently by the unmanned aerial vehicle;

a first determining unit 504, configured to determine a flight risk parameter of the unmanned aerial vehicle according to the performance state parameter, the environmental complexity parameter, and the task complexity parameter;

A second determining unit 505, configured to determine a current flight policy of the unmanned aerial vehicle according to the flight risk parameter.

According to the embodiment of the application, the performance state parameters of the unmanned aerial vehicle in the current state are acquired through the first acquisition unit 501; the second obtaining unit 502 obtains an environmental complexity parameter of the unmanned aerial vehicle flight environment in the current state; a third obtaining unit 503 obtains a task complexity parameter of a task to be completed currently by the unmanned aerial vehicle; the first determining unit 504 determines a flight risk parameter of the unmanned aerial vehicle according to the performance state parameter, the environmental complexity parameter and the task complexity parameter; the second determining unit 505 determines the current flight strategy of the unmanned aerial vehicle according to the flight risk parameter. According to the method, under the condition that the existing unmanned aerial vehicle can only rely on the quality control of each unmanned aerial vehicle manufacturer on the unmanned aerial vehicle and cannot effectively monitor the unmanned aerial vehicle operation, the acquired performance state parameters, environment complexity parameters and task complexity parameters are utilized to determine the flight risk parameters of the unmanned aerial vehicle operation, and then the current flight strategy of the unmanned aerial vehicle can be determined according to the flight risk parameters, so that the flight safety early warning of the unmanned aerial vehicle operation is realized, and the risk of the unmanned aerial vehicle operation is reduced.

The embodiment of the invention also provides an electronic device, as shown in fig. 6, which shows a schematic structural diagram of the electronic device according to the embodiment of the invention, specifically:

the electronic device may include one or more processing cores 'processors 601, one or more computer-readable storage media's memory 602, power supply 603, and input unit 604, among other components. It will be appreciated by those skilled in the art that the electronic device structure shown in fig. 6 is not limiting of the electronic device and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components. Wherein:

the processor 601 is a control center of the electronic device, connects various parts of the entire electronic device using various interfaces and lines, and performs various functions of the electronic device and processes data by running or executing software programs and/or modules stored in the memory 602, and calling data stored in the memory 602, thereby performing overall monitoring of the electronic device. Optionally, the processor 601 may include one or more processing cores; preferably, the processor 601 may integrate an application processor and a modem processor, wherein the application processor primarily processes operating storage media, user interfaces, application programs, etc., and the modem processor primarily processes wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 601.

The memory 602 may be used to store software programs and modules, and the processor 601 may execute various functional applications and data processing by executing the software programs and modules stored in the memory 602. The memory 602 may mainly include a storage program area and a storage data area, wherein the storage program area may store a storage medium, an application program (such as a sound playing function, an image playing function, etc.) required for operating at least one function, and the like; the storage data area may store data created according to the use of the electronic device, etc. In addition, the memory 602 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory 602 may also include a memory controller to provide access to the memory 602 by the processor 601.

The electronic device further comprises a power supply 603 for powering the various components, preferably the power supply 603 can be logically connected to the processor 601 via a power management storage medium, whereby the functions of managing charging, discharging, and power consumption are performed via the power management storage medium. The power supply 603 may also include one or more of any components, such as a direct current or alternating current power supply, a rechargeable storage medium, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.

The electronic device may further comprise an input unit 604, which input unit 604 may be used for receiving input digital or character information and for generating keyboard, mouse, joystick, optical or trackball signal inputs in connection with user settings and function control.

Although not shown, the electronic device may further include a display unit or the like, which is not described herein. In particular, in this embodiment, the processor 601 in the electronic device loads executable files corresponding to the processes of one or more application programs into the memory 602 according to the following instructions, and the processor 601 executes the application programs stored in the memory 602, so as to implement various functions as follows:

acquiring performance state parameters of the unmanned aerial vehicle in the current state; acquiring an environment complexity parameter of the unmanned aerial vehicle flight environment in the current state; acquiring task complexity parameters of a current task to be completed of the unmanned aerial vehicle; determining flight risk parameters of the unmanned aerial vehicle according to the performance state parameters, the environment complexity parameters and the task complexity parameters; and determining the current flight strategy of the unmanned aerial vehicle according to the flight risk parameters.

Those of ordinary skill in the art will appreciate that all or a portion of the steps of the various methods of the above embodiments may be performed by instructions, or by instructions controlling associated hardware, which may be stored in a computer-readable storage medium and loaded and executed by a processor.

To this end, an embodiment of the present invention provides a computer readable storage medium having stored thereon a computer program that is loaded by a processor to perform the steps of any of the unmanned aerial vehicle safe operation methods provided by the embodiment of the present invention. For example, the loading of the computer program by the processor may perform the steps of:

The specific implementation of each operation above may be referred to the previous embodiments, and will not be described herein.

Wherein the computer-readable storage medium may comprise: read Only Memory (ROM), random access Memory (RAM, random Access Memory), magnetic or optical disk, and the like.

The steps in any one of the unmanned aerial vehicle safe operation methods provided by the embodiments of the present invention can be executed due to the computer program stored in the computer readable storage medium, so that the beneficial effects that any one of the unmanned aerial vehicle safe operation methods provided by the embodiments of the present invention can be achieved, and detailed descriptions of the previous embodiments are omitted herein.

The above describes in detail a method, an apparatus, an electronic device and a storage medium for safe operation of an unmanned aerial vehicle provided by the embodiments of the present invention, and specific examples are applied to describe the principles and embodiments of the present invention, where the description of the above embodiments is only for helping to understand the method and core ideas of the present invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in light of the ideas of the present invention, the present description should not be construed as limiting the present invention.

Claims

1. A method of safe operation of a drone, the method comprising:

Determining a current flight strategy of the unmanned aerial vehicle according to the flight risk parameters, wherein the flight strategy comprises: the flying speed is reduced, the voyage is shortened, and the landing is immediately carried out;

wherein the determining the flight risk parameter of the unmanned aerial vehicle according to the performance state parameter, the environment complexity parameter and the task complexity parameter includes:

2. The method for safely operating the unmanned aerial vehicle according to claim 1, wherein the step of obtaining the performance state parameter of the unmanned aerial vehicle in the current state comprises the steps of:

3. The method for safely operating the unmanned aerial vehicle according to claim 1, wherein the acquiring the environmental complexity parameter of the unmanned aerial vehicle flight environment in the current state comprises:

4. The unmanned aerial vehicle safe operation method according to claim 3, wherein the evaluating weather parameters of the unmanned aerial vehicle flight environment in the current state according to the environmental data of the unmanned aerial vehicle flight environment in the current state comprises:

5. The method for safely operating the unmanned aerial vehicle according to claim 1, wherein the obtaining the task complexity parameter of the task currently to be completed by the unmanned aerial vehicle comprises:

6. The method for safely operating the unmanned aerial vehicle according to claim 5, wherein the calculating the task complexity parameter of the unmanned aerial vehicle to be currently completed according to the range, the load, the route condition, the flight frequency and the weight coefficient of each of the unmanned aerial vehicle to be currently completed comprises:

7. An unmanned aerial vehicle safe operation device, characterized in that the device comprises:

determining flight risk parameters of the unmanned aerial vehicle according to the flight risk output strategy and the flight complexity parameters;

The second determining unit is configured to determine, according to the flight risk parameter, a current flight policy of the unmanned aerial vehicle, where the flight policy includes: reduce the flying speed, shorten the voyage, and immediately drop.

8. An electronic device, the electronic device comprising:

one or more processors;

a memory; and

one or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the processor to implement the unmanned aerial vehicle safe operation method of any of claims 1 to 6.

9. A computer readable storage medium, having stored thereon a computer program, the computer program being loaded by a processor to perform the steps of the unmanned aerial vehicle safe operation method of any of claims 1 to 6.