CN-122022350-A - Self-adaptive decision-making method and system for carrying unmanned aerial vehicle

CN122022350ACN 122022350 ACN122022350 ACN 122022350ACN-122022350-A

Abstract

The invention relates to an adaptive decision-making method and system for carrying an unmanned aerial vehicle, wherein the adaptive decision-making method comprises the following steps of S1, collecting carrying data and carrying out modeling operation based on the carrying data to obtain modeling data, S2, carrying out three-level adaptive matching operation on the modeling data based on a preset carrying equipment library to obtain candidate scheme data, wherein the three-level adaptive matching operation sequentially comprises a first-level decision-making operation, a second-level decision-making operation and a third-level decision-making operation, the first-level decision-making operation judges whether carrying can be carried in a unmanned aerial vehicle carrying mode, the second-level decision-making operation carries out material type distribution analysis on carrying can be carried in the unmanned aerial vehicle carrying mode and carries out unmanned aerial vehicle carrying operation mode matching, and the third-level decision-making operation generates the candidate scheme data according to the matched unmanned aerial vehicle carrying operation mode. The invention fuses the multi-source heterogeneous data by a scientific decision method and realizes the self-adaptive matching of multiple delivery modes.

Inventors

WANG ZIYI
WAN CHENGKUAN
JING TIANQI
XIONG CHAO
BAI PU

Assignees

四川信天翁航空科技有限公司
西藏先锋绿能环保科技股份有限公司

Dates

Publication Date: 20260512
Application Date: 20260205

Claims (10)

1. The self-adaptive decision-making method carried by the unmanned aerial vehicle is characterized by comprising the following steps of: S1, collecting carrying data and carrying out modeling operation based on the carrying data to obtain modeling data; S2, performing three-level self-adaptive matching operation on modeling data based on a preset carrying equipment library to obtain candidate scheme data; The three-level self-adaptive matching operation sequentially comprises a first-level decision operation, a second-level decision operation and a third-level decision operation, wherein the first-level decision operation judges whether the unmanned aerial vehicle can be carried in a carrying mode, the second-level decision operation carries out material type distribution analysis on the condition that the unmanned aerial vehicle can be carried in the carrying mode and carries out unmanned aerial vehicle carrying operation mode matching, and the third-level decision operation generates candidate scheme data according to the matched unmanned aerial vehicle carrying operation mode.
2. The method of claim 1, wherein the first-order decision operation in step S2 is performed for weight determination and flight environment determination.
3. The method for adaptively deciding the carrying of the unmanned aerial vehicle according to claim 2, wherein the passing criteria of the weight judgment are as follows: There is no case that the single material is overweight; the overweight situation of the single-piece material exists, but the overweight situation of the single-piece material does not exist after the overweight single-piece material is disassembled and the overweight single-piece material is disassembled; and judging whether to allow flight or not according to the related data of the clearance condition in the modeling data by the flight environment judgment.
4. The method for adaptive decision-making carried by an unmanned aerial vehicle of claim 1, wherein the secondary decision-making operation in step S2 comprises the sub-steps of: s2.2.1 setting a weight threshold; s2.2.2, carrying out material type distribution analysis according to a weight threshold; s2.2.3 carrying out unmanned aerial vehicle carrying operation mode matching according to the material type distribution analysis result.
5. The method for adaptively deciding unmanned aerial vehicle carrying according to claim 4, wherein said weight threshold is a dynamic weight threshold, and said dynamic weight threshold is dynamically adjusted according to data related to altitude of landing sites in the carrying data.
6. The method of claim 4, wherein the step S2.2.2 classifies all materials by weight according to a weight threshold and performs a statistical operation according to the classification result.
7. The method of claim 6, wherein the classification result of step S2.2.2 comprises light-load materials and heavy-load materials; the statistical operation of step S2.2.2 is to calculate the weight ratio of each result in the classification result; in the step S2.2.3, the light-load unmanned aerial vehicle cluster mode is matched if the weight ratio of the light-load materials in the classification result meets the preset light-load condition, the heavy-load unmanned aerial vehicle mode is matched if the weight ratio of the heavy-load materials in the classification result meets the preset heavy-load condition, and the large-small machine type combination mode is matched if the weight ratio of the heavy-load materials in the classification result meets the preset heavy-load condition.
8. The method for adaptively deciding the carrying of the unmanned aerial vehicle according to claim 2, wherein the first-level decision operation is performed with respect to the determination of the take-off and landing mode by the weight determination and the flight environment determination; And the landing mode is set to be a landing loading mode if the landing is available for landing loading and unloading as a result of the field matching analysis, and is set to be a non-landing loading and unloading mode if the landing is not available for landing loading and unloading as a result of the field matching analysis.
9. The method for adaptively deciding the carrying of the unmanned aerial vehicle according to claim 8, wherein in the determination of the landing mode, the landing mode is set to a non-landing loading and unloading mode by the site matching analysis, and the secondary mode matching is performed after the lifting weight analysis; In the lifting weight analysis, a lifting mode is set to be a hovering lifting mode for the following two cases, otherwise, the lifting mode is set to be a third loading and unloading mode; The condition that the weight of the single material exceeds the preset upper limit of the lifting weight does not exist; There are cases where the weight of the individual supplies exceeds a preset upper limit of the lifting weight, but the overweight individual supplies can be disassembled and there is no case where the weight of the individual supplies exceeds the preset upper limit of the lifting weight after the overweight individual supplies are disassembled.
10. An adaptive decision making system carried by an unmanned aerial vehicle, applying the adaptive decision making method carried by an unmanned aerial vehicle according to any one of claims 1 to 9, comprising: The data acquisition interface module is used for receiving the carrying data; The scene digital modeling module is used for performing modeling operation based on the carrying data to obtain modeling data; The database module is used for storing a preset carrying equipment library; And the self-adaptive decision engine is used for carrying out three-level self-adaptive matching operation on the modeling data based on the carrying equipment library to obtain candidate scheme data.

Description

Self-adaptive decision-making method and system for carrying unmanned aerial vehicle Technical Field The invention relates to the technical field of unmanned aerial vehicle application, in particular to a self-adaptive decision method and system for unmanned aerial vehicle delivery. Background The current unmanned aerial vehicle technology development is rapid, and various industries gradually adopt unmanned aerial vehicle carrying modes for cargo transportation. In electric power engineering, particularly high-voltage, ultra-high voltage and ultra-high voltage transmission lines, the transmission lines are usually built in mountain areas, forest areas, canyons or river zones with complex terrains and rare trails. During the "small-traffic" phase of line construction (i.e. "last kilometer" transport), routine maintenance or fault rush repair in these areas, material transport faces significant challenges. This stage is often the "throat" link of the whole project, directly restricting project cost, construction period and safety. In the prior art, the traditional transportation mode mainly depends on manpower, animal power (mule horse) or erection of temporary cableways. However, the methods have the obvious defects that the traditional single transportation method is difficult to adapt to all complex working conditions, the manpower and animal power efficiency is low, the safety risk is high, the cableway erection period is long, and the early investment cost is high. With the development of aviation technology, light-load unmanned aerial vehicles, heavy-load unmanned aerial vehicles and helicopters are gradually applied to power construction. However, at present, in the choice of the delivery mode, there is a general lack of scientific evaluation standards and decision mechanisms, and the following technical problems mainly exist: the model selection lacks scientific basis, the existing decision mainly depends on experience of project manager, quantitative analysis is lacking, and light-load unmanned aerial vehicles (such as tens of kilograms), heavy-load unmanned aerial vehicles (such as hundreds of kilograms) or helicopters (ton levels) are suitable for any scene, so that fuzzy judgment is often also based, and cost waste of 'big maraca' or potential safety hazard of 'little maraca' are easily caused. The multi-source heterogeneous data are difficult to fuse, the field exploration data comprise various dimensions such as terrain gradient, vegetation coverage, meteorological conditions, a material list and the like, and a set of system is lacked to fuse and analyze the discrete data. Therefore, there is a need for an adaptive decision method and system for carrying unmanned aerial vehicles, which can fuse multi-source heterogeneous data by a scientific means to realize adaptive matching of multiple carrying modes. Disclosure of Invention The invention aims to overcome the defects of the prior art, provides a self-adaptive decision method and a system for carrying an unmanned aerial vehicle, fuses multi-source heterogeneous data according to a scientific decision method, and realizes self-adaptive matching of multiple carrying modes. The aim of the invention is realized by the following technical scheme: An adaptive decision-making method carried by an unmanned aerial vehicle comprises the following steps: S1, collecting carrying data and carrying out modeling operation based on the carrying data to obtain modeling data; S2, performing three-level self-adaptive matching operation on modeling data based on a preset carrying equipment library to obtain candidate scheme data; The three-level self-adaptive matching operation sequentially comprises a first-level decision operation, a second-level decision operation and a third-level decision operation, wherein the first-level decision operation judges whether the unmanned aerial vehicle can be carried in a carrying mode, the second-level decision operation carries out material type distribution analysis on the condition that the unmanned aerial vehicle can be carried in the carrying mode and carries out unmanned aerial vehicle carrying operation mode matching, and the third-level decision operation generates candidate scheme data according to the matched unmanned aerial vehicle carrying operation mode. Further, the first-level decision operation in the step S2 performs weight determination and flight environment determination. Further, the passing criteria of the weight judgment are as follows: There is no case that the single material is overweight; the overweight situation of the single-piece material exists, but the overweight situation of the single-piece material does not exist after the overweight single-piece material is disassembled and the overweight single-piece material is disassembled; and judging whether to allow flight or not according to the related data of the clearance condition in the modeling data by the flight environment judgment. Further,