WO2024067137A1

WO2024067137A1 - Intelligent endurance management method and system for unmanned aircraft, and medium

Info

Publication number: WO2024067137A1
Application number: PCT/CN2023/118884
Authority: WO
Inventors: 胡华智; 陈皓东; 宋晨晖; 刘畅
Original assignee: 亿航智能设备（广州）有限公司
Priority date: 2022-09-29
Filing date: 2023-09-14
Publication date: 2024-04-04
Also published as: CN115542934A

Abstract

An intelligent endurance management method and system for an unmanned aircraft, and a medium. The method comprises: acquiring performance state information of an unmanned aircraft and performing dynamic self-inspection to obtain dynamic self-inspection data; acquiring endurance deviation loss data according to an extracted endurance parameter, and storing same in endurance setting parameters, so as to update a flight dynamic database; according to the dynamic self-inspection data, extracting a navigation state and a voyage interference value of the unmanned aircraft, and obtaining endurance data in a navigation condition state; and according to the endurance data, performing task control and recall setting. In this way, on the basis of intelligent control technology, determination and task control are performed according to navigation information and endurance data of an unmanned aircraft, thereby realizing an intelligent management and control technique of assessing an endurance state by means of processing information parameters of the unmanned aircraft so as to perform task deployment, and thus improving the intelligence and precision of endurance safety management over the unmanned aircraft.

Description

A method, system and medium for intelligent endurance management of unmanned aerial vehicle

Technical Field

The present invention relates to the technical field of intelligent control and unmanned aerial vehicle management, and in particular to an intelligent endurance management method, system and medium for an unmanned aerial vehicle.

Background technique

At present, unmanned aerial vehicle technology is gradually maturing, the unmanned aerial vehicle market is expanding, and the era of consumer-grade micro-drones and civil unmanned aerial vehicles has arrived. However, the current technology for unmanned aerial vehicles lacks a systematic, comprehensive and intelligent management system, and no intelligent and reasonable technical management standards have been formed. With the rapid development of the unmanned aerial vehicle market, the support and optimization of its intelligent technology are becoming increasingly important.

The existing endurance management and task allocation for unmanned aerial vehicles are mostly controlled and assigned based on the remaining power, flight range and mission distance, but lack intelligent control technology that can combine the airworthiness status parameters and endurance information of the unmanned aerial vehicle obtained by real-time monitoring with the task list information for intelligent analysis and dynamic monitoring to adjust the task status. At present, conventional technologies and management methods lack systematicity, mobility and intelligence. There is a lack of technical means for the scientific control and reasonable operation of unmanned aerial vehicles, which is not conducive to the reasonable adaptability development of unmanned aerial vehicles. In addition, in the management process of unmanned aerial vehicles, too many interference factors affect the sensitivity and accuracy of judgment.

In view of the above problems, effective technical solutions are urgently needed.

Summary of the invention

The purpose of the embodiments of the present invention is to provide an intelligent endurance management method, system and medium for unmanned aerial vehicles, which can judge the endurance status and mission execution status of the unmanned aerial vehicle based on the endurance information and status information of the unmanned aerial vehicle, perform mission control and recall of the unmanned aerial vehicle, and improve the accuracy of status monitoring and mission management of the endurance flight safety of the unmanned aerial vehicle.

The embodiment of the present invention also provides an intelligent endurance management method for an unmanned aerial vehicle, comprising: Next steps:

Acquiring performance status information of the unmanned aerial vehicle and performing dynamic self-inspection to obtain dynamic self-inspection data, and extracting endurance parameters of the unmanned aerial vehicle;

Acquire the endurance loss data of the unmanned aerial vehicle according to the endurance parameter and store it in the endurance setting parameter to update the flight dynamic database of the unmanned aerial vehicle;

Extracting the navigation status and range interference value of the unmanned aerial vehicle according to the dynamic self-test data, and obtaining the endurance data of the unmanned aerial vehicle under the navigation condition state through a preset discrimination algorithm;

Mission control and recall settings are performed on the unmanned aerial vehicle according to the endurance data.

Optionally, in the intelligent endurance management method for an unmanned aerial vehicle described in an embodiment of the present invention, the step of obtaining the performance status information of the unmanned aerial vehicle and performing a dynamic self-check to obtain dynamic self-check data, and extracting the endurance parameter of the unmanned aerial vehicle includes:

collating and obtaining performance status information based on the status information of the unmanned aerial vehicle within a preset mission time period;

Performing a dynamic self-check operation through a preset dynamic self-check according to the performance status information;

Acquire the self-inspection result of the dynamic self-inspection operation as dynamic self-inspection data;

The endurance parameter of the unmanned aerial vehicle is extracted from the dynamic self-test data according to a preset type identification factor.

Optionally, in the intelligent flight endurance management method for an unmanned aerial vehicle described in an embodiment of the present invention, the step of obtaining the flight endurance partial loss data of the unmanned aerial vehicle according to the flight endurance parameter and storing it in the flight endurance setting parameter to update the flight dynamic database of the unmanned aerial vehicle includes:

Obtaining the endurance deviation data of the unmanned aerial vehicle by calculating and processing the obtained endurance parameter through a preset range deviation algorithm;

The endurance loss data is added to the endurance setting parameters in the form of attribute data to update the flight dynamics database of the unmanned aerial vehicle.

Optionally, in the intelligent flight management method for an unmanned aerial vehicle described in an embodiment of the present invention, the navigation status and range interference value of the unmanned aerial vehicle are extracted according to the dynamic self-test data, and the navigation status of the unmanned aerial vehicle under the navigation condition state is obtained by a preset discrimination algorithm. The flight time data of the aircraft includes:

Extracting the navigation status and range interference value of the unmanned aerial vehicle as discrimination factors according to the dynamic self-check data;

The endurance data of the unmanned aerial vehicle under the navigation condition state is obtained by calculating according to the discrimination factor through a preset discrimination algorithm.

Optionally, in the intelligent flight endurance management method for an unmanned aerial vehicle according to an embodiment of the present invention, performing mission control and recall setting for the unmanned aerial vehicle according to the flight endurance data includes:

Comparing the endurance data of the unmanned aerial vehicle with the mission range threshold in the mission list, and selecting a second mission to command and dispatch the unmanned aerial vehicle according to the threshold comparison result;

A recall instruction is set for the unmanned aerial vehicle according to the return time of the second task, and a designated maintenance and charging position of the unmanned aerial vehicle is preset according to the return time.

Optionally, the intelligent flight endurance management method for an unmanned aerial vehicle according to an embodiment of the present invention further includes:

extracting dynamic feature information of the unmanned aerial vehicle and task information of the second task in real time as identification factors;

determining whether the unmanned aerial vehicle meets the operational requirements of the second task through the identification factor;

If the job requirements are met, the task switching response mechanism is not triggered;

If the operation requirements cannot be met, the task conversion response mechanism is triggered, and the third task is extracted through the task list according to the real-time endurance data of the unmanned aerial vehicle and the pre-positioning information is sent to the unmanned aerial vehicle in response to the pre-positioning.

When the unmanned aerial vehicle receives the pre-positioning information and responds to the pre-positioning, a dynamic self-check is performed on the unmanned aerial vehicle;

Obtaining real-time airworthiness information and whole aircraft dynamic information of the unmanned aerial vehicle;

determining whether the energy information and power information of the real-time airworthiness information meet the operation information of the third mission plan, and if not, issuing a recall command to the unmanned aerial vehicle for a recall response;

It is determined whether the path information and the cruise attitude information of the whole machine dynamic information meet the operation information of the third task plan. If not, a recall command is issued to the unmanned aerial vehicle for a recall response.

In a second aspect, an embodiment of the present invention provides an intelligent flight endurance management system for an unmanned aerial vehicle, the system comprising: a memory and a processor, the memory comprising a program of an intelligent flight endurance management method for an unmanned aerial vehicle, and when the program of the intelligent flight endurance management method for an unmanned aerial vehicle is executed by the processor, the following steps are implemented:

Optionally, in the intelligent endurance management system for unmanned aerial vehicles described in the embodiment of the present invention, the step of obtaining the performance status information of the unmanned aerial vehicle and performing a dynamic self-check to obtain dynamic self-check data, and extracting the endurance parameter of the unmanned aerial vehicle includes:

In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium, wherein the computer The computer-readable storage medium includes an intelligent flight endurance management method program for an unmanned aerial vehicle. When the intelligent flight endurance management method program for an unmanned aerial vehicle is executed by a processor, the steps of the intelligent flight endurance management method for an unmanned aerial vehicle as described in any one of the above items are implemented.

As can be seen from the above, an intelligent endurance management method, system and medium for unmanned aerial vehicles provided by an embodiment of the present invention obtains performance status information of the unmanned aerial vehicle and performs dynamic self-inspection to obtain dynamic self-inspection data, obtains endurance deviation data according to the extracted endurance parameters and stores it in the endurance setting parameters to update the flight dynamic database, extracts the navigation status and range interference value of the unmanned aerial vehicle according to the dynamic self-inspection data and obtains the endurance data under the navigation conditions, and performs task control and recall settings according to the endurance data; thereby, the navigation information and endurance data of the unmanned aerial vehicle are judged and task controlled based on intelligent control technology, and intelligent management and control technology is implemented to allocate tasks according to the processing of information parameters of the unmanned aerial vehicle and the evaluation of the endurance status, thereby improving the intelligence and accuracy of the endurance safety management of the unmanned aerial vehicle.

Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or be understood by implementing the embodiments of the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description, claims, and drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for use in the embodiments of the present invention are briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without creative work.

FIG1 is a flow chart of an intelligent flight management method for an unmanned aerial vehicle provided by an embodiment of the present invention;

FIG2 is a flow chart of performing dynamic self-checking and extracting endurance parameters of an intelligent endurance management method for an unmanned aerial vehicle provided by an embodiment of the present invention;

FIG. 3 is a more detailed diagram of the intelligent flight management method for an unmanned aerial vehicle provided by an embodiment of the present invention. Flowchart of the new flight dynamics database;

FIG. 4 is a schematic diagram of the structure of an intelligent endurance management system for an unmanned aerial vehicle provided in an embodiment of the present invention.

Detailed ways

The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. The components of the embodiments of the present invention generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed invention, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present invention.

It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. At the same time, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

Please refer to FIG. 1, which is a flow chart of an intelligent endurance management method for an unmanned aerial vehicle in some embodiments of the present invention. The intelligent endurance management method for an unmanned aerial vehicle is used in a terminal device, such as a computer, a mobile phone terminal, etc. The intelligent endurance management method for an unmanned aerial vehicle comprises the following steps:

S101, acquiring performance status information of the unmanned aerial vehicle and performing dynamic self-inspection to obtain dynamic self-inspection data, and extracting the endurance parameters of the unmanned aerial vehicle;

S102, obtaining the endurance loss data of the unmanned aerial vehicle according to the endurance parameter and storing it in the endurance setting parameter to update the flight dynamic database of the unmanned aerial vehicle;

S103, extracting the navigation status and range interference value of the unmanned aerial vehicle according to the dynamic self-test data, and obtaining the endurance data of the unmanned aerial vehicle under the navigation condition state through a preset discrimination algorithm;

S104: Perform mission control and recall settings on the unmanned aerial vehicle according to the endurance data.

It should be noted that, first, the performance status information of the unmanned aerial vehicle in operation, such as power information, power information, fault information, abnormal information, etc., is obtained, and a dynamic self-inspection is performed to obtain the corresponding dynamic self-inspection data, and the endurance parameters are extracted therefrom. The endurance deviation data is obtained according to the endurance parameters and stored in the endurance setting parameters to update the data in the flight dynamic database, so that the data of the unmanned aerial vehicle can be better iterated by monitoring the dynamic endurance status information, so that the flight status of each aircraft is clearer and the subsequent flight mission allocation is more reasonable. The endurance data of the unmanned aerial vehicle under navigation conditions is obtained by extracting the navigation status and range interference value of the unmanned aerial vehicle and calling the preset discrimination algorithm for processing, and the aircraft is tasked according to the endurance data. The aircraft is dispatched and allocated for task control and the recall time, route, and location are preset, so that the state information is obtained by real-time dynamic monitoring of the unmanned aerial vehicle to perform endurance state analysis and whole machine state evaluation, and then scientific task allocation and recall are performed, so that the resource utilization of the unmanned aerial vehicle is more scientific, optimized, and sustainable.

Please refer to Figure 2, which is a flow chart of the method for performing dynamic self-check and extracting endurance parameters of the intelligent endurance management method for unmanned aerial vehicles in some embodiments of the present invention. According to the embodiment of the present invention, the acquisition of the performance status information of the unmanned aerial vehicle and the dynamic self-check to obtain dynamic self-check data, and the extraction of the endurance parameters of the unmanned aerial vehicle are specifically as follows:

S201, collating and acquiring performance status information according to the status information of the unmanned aerial vehicle within a preset mission time period;

S202, performing a dynamic self-check operation through a preset dynamic self-check according to the performance status information;

S203, obtaining the self-inspection result of the dynamic self-inspection operation as dynamic self-inspection data;

S204: extracting the endurance parameter of the unmanned aerial vehicle from the dynamic self-test data according to a preset type identification factor.

It should be noted that the task time period is set according to the demand for collecting the status information of the unmanned aerial vehicle, and the status information collected by the unmanned aerial vehicle within the preset time period is used to obtain the performance status information, which includes the body integrity information, power ratio information, flight status information, power status information, etc. The dynamic self-checking operation is performed through the self-checking algorithm according to the performance status information. Industry, extract dynamic self-test data from the self-test results, and then extract and identify the endurance parameters according to the preset different categories of identification factors. The dynamic self-test formula is as follows:
R＝{σ ₁ C ₁ ,σ ₂ C ₂ , ...σ _n C _n |D _r };

Wherein, R is the self-test result, σ _i is the characteristic coefficient, C _i is the self-test response parameter, D _r is the preset response value, and n is the number of self-test response parameters. Different self-test response parameters such as endurance parameters are set to obtain different self-test results, and then different data such as body integrity data, power data, load data, electric energy data, flight status data, etc. are obtained according to the corresponding category identification factors in different self-test response parameters.

Please refer to Figure 3, which is a flow chart of updating the flight dynamic database of the intelligent endurance management method of the unmanned aerial vehicle in some embodiments of the present invention. According to the embodiment of the present invention, the endurance partial loss data of the unmanned aerial vehicle is obtained according to the endurance parameter and stored in the endurance setting parameter to update the flight dynamic database of the unmanned aerial vehicle, specifically:

S301, obtaining the range partial loss data of the unmanned aerial vehicle by calculating and processing the obtained range parameter through a preset range partial loss algorithm;

S302: adding the endurance loss data to the endurance setting parameters in the form of attribute data to update the flight dynamics database of the unmanned aerial vehicle.

It should be noted that after obtaining the endurance parameters of the unmanned aerial vehicle, the obtained endurance parameters can be compared with the preset endurance parameters, and the endurance deviation data can be calculated and processed using the range deviation algorithm, and then the flight database can be updated to make timely adjustments to the status and mission of the unmanned aerial vehicle, reasonably allocate flight missions, and optimize endurance management and resource integration;

Among them, the range loss algorithm formula is:

Λ＝S/θ；

Among them, S is the range deviation vector, x, y, z are coordinate values, θ is the yaw angle, Λ is the endurance loss data, is the vector representation of the coordinate offset, and They are the preset vector heading and the actual vector heading respectively.

According to an embodiment of the present invention, the extracting the navigation status and the range interference value of the unmanned aerial vehicle according to the dynamic self-test data, and obtaining the endurance data of the unmanned aerial vehicle under the navigation condition state through a preset discrimination algorithm, specifically comprises:

It should be noted that the endurance state of the aircraft under navigation conditions is judged based on the navigation state of the unmanned aerial vehicle extracted from the dynamic self-test data and the range interference value as the discrimination factors, so as to identify the endurance state of the unmanned aerial vehicle for task allocation. The navigation state is a state quantity that reflects the flight capability, endurance capability, and power capability of the aircraft. The range interference value is a state quantity that reflects the weather, environment, airspace, and route obstacles during the navigation process. The preset discrimination algorithm is: according to the formula _f0 = _γUr + _τKe , where _f0 is the endurance data, γ and τ are preset characteristic parameters, _Ur is the navigation state factor, and _Ke is the range interference factor.

According to an embodiment of the present invention, performing mission control and recall setting on the unmanned aerial vehicle according to the endurance data is specifically as follows:

It should be noted that a task list is formulated based on the alternative tasks, and a task range threshold is preset. A threshold comparison is performed based on the endurance data of the unmanned aerial vehicle and the task range threshold in the list, and a task in the list that meets the threshold requirement is selected as the second task adapted for the unmanned aerial vehicle. Then, corresponding instruction information is generated based on the second task and sent to the unmanned aerial vehicle for instruction dispatch. The return time of the aircraft is set according to the preset task completion time period of the second task, and a recall instruction corresponding to the return time is sent to the unmanned aerial vehicle. The recalled aircraft is inspected and charged at the maintenance and charging position designated by the unmanned aerial vehicle according to the preset return time. To improve the efficiency of aircraft operation and command, and increase the resource utilization rate of unmanned aerial vehicles.

According to an embodiment of the present invention, it also includes:

It should be noted that, in order to determine whether the state of the unmanned aerial vehicle meets the operation requirements of the second task, the identification factor _Q1 of the dynamic characteristic information of the unmanned aerial vehicle and the identification factor _Q2 of the task information of the second task are extracted in real time, and the dynamic whole machine characteristics of the unmanned aerial vehicle are determined by the identification factors to determine whether they meet the task volume requirements of the second task, wherein the determination method is:
H = {δQ ₁ +εQ ₂ |F _c };

Wherein, δ and ε are set parameters, F _c is the set discrimination parameter, and H is the output discrimination result. If the operation requirements of the second task are met, the task conversion response mechanism is not triggered. If the operation requirements cannot be met, the task conversion response mechanism is triggered. The third task is extracted through the task list according to the real-time endurance data of the unmanned aerial vehicle, and the pre-positioning information is sent to the unmanned aerial vehicle to complete the response pre-positioning for the third task. For example, δ and ε are taken as 0.65 and 0.83 respectively, and the corresponding F _c is 0.85, Q ₁ is taken as 0.6, and Q ₂ is taken as 0.7. The discrimination result is 0.65x0.6+0.83x0.7=0.971, which is greater than the set discrimination parameter F _c , that is, the task conversion response mechanism is triggered.

According to an embodiment of the present invention, it also includes:

Determine whether the energy information and power information of the real-time airworthiness information meet the third task plan If the operation information is not satisfied, a recall command is issued to the unmanned aerial vehicle for a recall response;

It should be noted that when the unmanned aerial vehicle receives the pre-positioning information of the third task and responds to the pre-positioning, the aircraft is dynamically self-checked, and the self-check is performed according to the real-time airworthiness information and the dynamic information of the whole machine of the unmanned aerial vehicle, and it is determined whether the energy information and power information of the real-time airworthiness information meet the operation information of the third task plan. If not, a recall instruction is issued for recall, that is, the energy and power conditions of the unmanned aerial vehicle are compared with the energy and power requirements of the aircraft required by the operation information of the third task, and it is determined whether the path information and cruise attitude information of the whole machine dynamic information meet the operation information of the third task plan. If not, a recall instruction is issued for recall, that is, the path adaptation condition and cruise attitude condition of the unmanned aerial vehicle are compared with the preset path requirements and cruise attitude requirements of the aircraft required by the operation information of the third task. For example, an unmanned aerial vehicle whose energy and power are lower than 80% of the required value and whose power condition is lower than 70% of the required value is recalled, and an unmanned aerial vehicle whose path adaptation degree is lower than 70% of the required value and whose cruise attitude is lower than 65% of the required value is recalled.

According to an embodiment of the present invention, it also includes:

Setting dynamic flight thresholds for unmanned aerial vehicles;

Real-time acquisition of the dynamic power status, fuselage loss value and remaining mission flight volume of the unmanned aerial vehicle;

According to the real-time acquired power status of the unmanned aerial vehicle, the fuselage loss value and the remaining mission flight volume, a preset self-checking algorithm is used to calculate and compare with the dynamic flight threshold to obtain a real-time safety response result;

Comparing the calculated real-time safety response result with the preset value of safe flight of the unmanned aerial vehicle;

If the real-time safety response result is greater than the safety flight preset value, the unmanned aerial vehicle may continue to perform the assigned mission;

If the real-time safety response result is less than the safety flight preset value, the unmanned aerial vehicle mission is terminated and the unmanned aerial vehicle is recalled.

It should be noted that in order to monitor the flight safety status of unmanned aerial vehicles in real time, a dynamic flight threshold is set according to the performance of the unmanned aerial vehicle. The dynamic flight threshold is an inherent attribute parameter of the unmanned aerial vehicle when it is produced and leaves the factory. It reflects the remaining safety margin of the unmanned aerial vehicle in real time and is a monitoring parameter for measuring the dynamic safety of the unmanned aerial vehicle. The real-time power status, fuselage loss value and remaining mission flight volume obtained by the unmanned aerial vehicle are calculated and compared with the dynamic flight threshold according to the preset self-test algorithm to obtain a real-time safety response result. The real-time safety response result is then compared with the preset value of the unmanned aerial vehicle's safe flight. If it is greater than the preset value, the unmanned aerial vehicle is safe and can continue to perform the mission. Otherwise, the unmanned aerial vehicle needs to be recalled.

According to an embodiment of the present invention, it also includes:

The battery status and fuselage loss value of the unmanned aerial vehicle to be recalled are used as the discriminating factors;

Obtaining the remaining flight path and flight trajectory deviation of the unmanned aerial vehicle;

Performing a flight path correction on the remaining flight path of the mission according to the flight trajectory deviation to obtain a correction factor for the unmanned aerial vehicle;

The discrimination factor is weightedly calculated based on the correction factor to obtain a correction value of the return time of the unmanned aerial vehicle.

It should be noted that the flight path correction is performed on the remaining flight path of the unmanned aerial vehicle mission in combination with the flight trajectory deviation to obtain the correction factor of the unmanned aerial vehicle. The discrimination factor obtained by the remaining power of the unmanned aerial vehicle and the fuselage loss value is weighted according to the correction factor to obtain the correction value of the unmanned aerial vehicle return time. The corrected return time is calculated based on the correction factor and the discrimination factor based on the following formula: _Te = (ωA+ξB) _T0 , wherein ω and ξ are predetermined proportional coefficients, A is the correction factor, B is the discrimination factor, _T0 is the original time, and _Te is the corrected time.

As shown in FIG. 4 , the present invention further discloses an intelligent endurance management system for an unmanned aerial vehicle, including a memory 41 and a processor 42. The memory includes an intelligent endurance management method program for an unmanned aerial vehicle. When the intelligent endurance management method program for an unmanned aerial vehicle is executed by the processor, the following steps are implemented:

Obtain the performance status information of the unmanned aerial vehicle and perform dynamic self-check to obtain dynamic self-check data According to the data, extracting the endurance parameters of the unmanned aerial vehicle;

According to an embodiment of the present invention, the acquisition of the performance status information of the unmanned aerial vehicle and the dynamic self-check to obtain dynamic self-check data, and the extraction of the endurance parameter of the unmanned aerial vehicle are specifically as follows:

It should be noted that the task time period is set according to the demand for collecting the status information of the unmanned aerial vehicle, and the status information collected and obtained by the unmanned aerial vehicle within the preset time period is used to obtain the performance status information, which includes the body integrity information, power ratio information, flight status information, power status information, etc. According to the performance status information, a dynamic self-inspection operation is performed through a self-inspection algorithm, and dynamic self-inspection data is extracted from the self-inspection results, and then extracted and identified according to preset different categories of identification factors to obtain the endurance parameters. Among them, the dynamic self-inspection formula is as follows:
R＝{σ ₁ C ₁ ,σ ₂ C ₂ , ...σ _n C _n |D _r };

According to an embodiment of the present invention, the obtaining of the endurance loss data of the unmanned aerial vehicle according to the endurance parameter and storing it in the endurance setting parameter to update the flight dynamic database of the unmanned aerial vehicle is specifically as follows:

Among them, the range loss algorithm formula is:

Λ＝S/θ；

Among them, S is the range deviation vector, x, y, z are coordinate values, θ is the yaw angle, Λ is the endurance deviation data, is the vector representation of the coordinate offset, and They are the preset vector heading and the actual vector heading respectively.

It should be noted that a task list is prepared based on the candidate tasks, and a task range threshold is preset. A threshold comparison is performed based on the endurance data of the unmanned aerial vehicle and the task range threshold in the list, and a task in the list that meets the threshold requirement is selected as the second adapted task of the unmanned aerial vehicle. Then, corresponding instruction information is generated based on the second task and sent to the unmanned aerial vehicle for instruction dispatch. The return time of the aircraft is set according to the preset task completion time period of the second task, and a recall instruction corresponding to the return time is sent to the unmanned aerial vehicle. The recalled aircraft is inspected and charged at the maintenance and charging position designated by the unmanned aerial vehicle according to the preset return time, so as to improve the efficiency of aircraft operation command and improve the resource utilization rate of the unmanned aerial vehicle.

According to an embodiment of the present invention, it also includes:

When the unmanned aerial vehicle receives the pre-positioning information and responds to the pre-positioning, the unmanned aerial vehicle The aircraft conducts dynamic self-checks;

According to an embodiment of the present invention, it also includes:

Setting dynamic flight thresholds for unmanned aerial vehicles;

The real-time safety response result obtained by calculation is compared with the unmanned aerial vehicle safety flight prediction result. Set the value for comparison;

According to an embodiment of the present invention, the present invention further includes:

It should be noted that the flight path correction is performed on the remaining flight path of the unmanned aerial vehicle mission in combination with the flight trajectory deviation to obtain the correction factor of the unmanned aerial vehicle, and the discrimination factor obtained by the remaining power of the unmanned aerial vehicle and the fuselage loss value is weighted according to the correction factor to obtain the correction value of the unmanned aerial vehicle return time, wherein the corrected return time is calculated based on the correction factor and the discrimination factor based on the following formula: _Te = (ωA+ξB) _T0 , wherein ω and ξ are predetermined proportional coefficients, A is the correction factor, B is the discrimination factor, _T0 is the original return time, and _Te is the corrected return time.

The third aspect of the present invention provides a readable storage medium, the readable storage medium includes an intelligent endurance management method program for an unmanned aerial vehicle, the intelligent endurance management method program for an unmanned aerial vehicle When the flight management method program is executed by the processor, the steps of the intelligent flight management method for the unmanned aerial vehicle as described in any of the above items are implemented.

The present invention discloses an intelligent flight endurance management method, system and medium for unmanned aerial vehicles. The method obtains the performance status information of the unmanned aerial vehicle and performs dynamic self-test to obtain dynamic self-test data. The flight endurance deviation data is obtained according to the extracted flight endurance parameters and stored in the flight endurance setting parameters to update the flight dynamic database. The navigation status and range interference value of the unmanned aerial vehicle are extracted according to the dynamic self-test data to obtain the flight endurance data under the navigation condition state. Task control and recall setting are performed according to the flight endurance data. Thus, the navigation information and flight endurance data of the unmanned aerial vehicle are judged and task control is performed based on the intelligent control technology, and the intelligent control technology for task allocation is realized according to the processing of the information parameters of the unmanned aerial vehicle to evaluate the flight endurance status, thereby improving the intelligence and accuracy of the flight endurance safety management of the unmanned aerial vehicle.

In the several embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

Those skilled in the art will appreciate that all or part of the steps of implementing the above method embodiments may be completed by hardware associated with program instructions, and the above program may be stored in a readable storage medium. In the storage medium, when the program is executed, the steps of the above-mentioned method embodiment are executed; and the aforementioned storage medium includes: mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk and other media that can store program codes.

Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

An intelligent flight management method for an unmanned aerial vehicle, characterized in that it comprises the following steps:

Acquiring performance status information of the unmanned aerial vehicle and performing dynamic self-inspection to obtain dynamic self-inspection data, and extracting endurance parameters of the unmanned aerial vehicle;

Acquire the endurance loss data of the unmanned aerial vehicle according to the endurance parameter and store it in the endurance setting parameter to update the flight dynamic database of the unmanned aerial vehicle;

Extracting the navigation status and range interference value of the unmanned aerial vehicle according to the dynamic self-test data, and obtaining the endurance data of the unmanned aerial vehicle under the navigation condition state through a preset discrimination algorithm;

Mission control and recall settings are performed on the unmanned aerial vehicle according to the endurance data.
The intelligent endurance management method for unmanned aerial vehicles according to claim 1 is characterized in that the step of obtaining the performance status information of the unmanned aerial vehicle and performing a dynamic self-check to obtain dynamic self-check data, and extracting the endurance parameters of the unmanned aerial vehicle comprises:

collating and obtaining performance status information based on the status information of the unmanned aerial vehicle within a preset mission time period;

Performing a dynamic self-check operation through a preset dynamic self-check according to the performance status information;

Acquire the self-inspection result of the dynamic self-inspection operation as dynamic self-inspection data;

The endurance parameter of the unmanned aerial vehicle is extracted from the dynamic self-test data according to a preset type identification factor.
The intelligent endurance management method for an unmanned aerial vehicle according to claim 2 is characterized in that the step of obtaining the endurance deflection data of the unmanned aerial vehicle according to the endurance parameter and storing it in the endurance setting parameter to update the flight dynamic database of the unmanned aerial vehicle comprises:

Obtaining the endurance deviation data of the unmanned aerial vehicle by calculating and processing the obtained endurance parameter through a preset range deviation algorithm;

The endurance loss data is added to the endurance setting parameters in the form of attribute data to update the flight dynamics database of the unmanned aerial vehicle.
The intelligent endurance management method for unmanned aerial vehicles according to claim 3 is characterized in that the step of extracting the navigation status and range interference value of the unmanned aerial vehicle according to the dynamic self-test data, and obtaining the endurance data of the unmanned aerial vehicle under the navigation condition state through a preset discrimination algorithm comprises:

Extracting the navigation status and range interference value of the unmanned aerial vehicle as discrimination factors according to the dynamic self-check data;

The endurance data of the unmanned aerial vehicle under the navigation condition state is obtained by calculating according to the discrimination factor through a preset discrimination algorithm.
The intelligent endurance management method for an unmanned aerial vehicle according to claim 4 is characterized in that the step of performing mission control and recall setting on the unmanned aerial vehicle according to the endurance data comprises:

Comparing the endurance data of the unmanned aerial vehicle with the mission range threshold in the mission list, and selecting a second mission to command and dispatch the unmanned aerial vehicle according to the threshold comparison result;

A recall instruction is set for the unmanned aerial vehicle according to the return time of the second task, and a designated maintenance and charging position of the unmanned aerial vehicle is preset according to the return time.
The intelligent flight management method for unmanned aerial vehicles according to claim 1, characterized in that it also includes:

extracting dynamic feature information of the unmanned aerial vehicle and task information of the second task in real time as identification factors;

determining whether the unmanned aerial vehicle meets the operational requirements of the second task through the identification factor;

If the job requirements are met, the task switching response mechanism is not triggered;

If the operation requirements cannot be met, the task conversion response mechanism is triggered, and the third task is extracted through the task list according to the real-time endurance data of the unmanned aerial vehicle and the pre-positioning information is sent to the unmanned aerial vehicle in response to the pre-positioning.
The intelligent flight management method for unmanned aerial vehicles according to claim 1, characterized in that it also includes:

When the unmanned aerial vehicle receives the pre-positioning information and responds to the pre-positioning, a dynamic self-check is performed on the unmanned aerial vehicle;

Obtaining real-time airworthiness information and whole aircraft dynamic information of the unmanned aerial vehicle;

determining whether the energy information and power information of the real-time airworthiness information meet the operation information of the third mission plan, and if not, issuing a recall command to the unmanned aerial vehicle for a recall response;

It is determined whether the path information and the cruise attitude information of the whole machine dynamic information meet the operation information of the third task plan. If not, a recall command is issued to the unmanned aerial vehicle for a recall response.
An intelligent flight endurance management system for an unmanned aerial vehicle, characterized in that the system comprises: a memory and a processor, wherein the memory comprises a program of an intelligent flight endurance management method for an unmanned aerial vehicle, and when the program of the intelligent flight endurance management method for an unmanned aerial vehicle is executed by the processor, the following steps are implemented:

Acquiring performance status information of the unmanned aerial vehicle and performing dynamic self-inspection to obtain dynamic self-inspection data, and extracting endurance parameters of the unmanned aerial vehicle;

Acquire the endurance loss data of the unmanned aerial vehicle according to the endurance parameter and store it in the endurance setting parameter to update the flight dynamic database of the unmanned aerial vehicle;

Extracting the navigation status and range interference value of the unmanned aerial vehicle according to the dynamic self-test data, and obtaining the endurance data of the unmanned aerial vehicle under the navigation condition state through a preset discrimination algorithm;

Mission control and recall settings are performed on the unmanned aerial vehicle according to the endurance data.
The intelligent endurance management system for unmanned aerial vehicles according to claim 8 is characterized in that the step of obtaining the performance status information of the unmanned aerial vehicle and performing a dynamic self-check to obtain dynamic self-check data, and extracting the endurance parameters of the unmanned aerial vehicle comprises:

collating and obtaining performance status information based on the status information of the unmanned aerial vehicle within a preset mission time period;

Performing a dynamic self-check operation through a preset dynamic self-check according to the performance status information;

Acquire the self-inspection result of the dynamic self-inspection operation as dynamic self-inspection data;

The endurance parameter of the unmanned aerial vehicle is extracted from the dynamic self-test data according to a preset type identification factor.
A computer-readable storage medium, characterized in that the computer-readable storage medium includes an intelligent flight endurance management method program for an unmanned aerial vehicle, and when the intelligent flight endurance management method program for an unmanned aerial vehicle is executed by a processor, the steps of the intelligent flight endurance management method for an unmanned aerial vehicle as described in any one of claims 1 to 7 are implemented.