Unmanned plane multi-factor decision platform

Abstract

The invention discloses an unmanned plane multi-factor decision platform comprising a data receiver, a data integration and storage database, a space analysis and operation module and an instruction forming module; according to an unmanned plane planned air seeding space scope and plant species, real time obtained information like position information, flight attitude information and sensor detection information and stable environment essential factor information obtained before flight are commonly inputted into the module; according to a preset space analysis method and decision rules, the module carries out integrated operations so as to make a flight locus, and make segmented decisions for the flight path according to suitabilities of different plant species on different environment essential factors, wherein the decision contents comprise air seeding kinds and air seeding amounts; the decision contents are converted into control instructions, and transmitted to a flight control system and a multi-chamber seed air seeding pod through a communication interface, thus allowing the flight control system to accurately control the flight locus and the flight attitudes, and enabling the multi-chamber seed air seeding pod to open the chamber corresponding to the plant species and control the air seeding amount according to the control instructions.

Description

technical field [0001] The invention relates to the technical field of unmanned aerial vehicles, in particular to a multi-factor decision-making platform for unmanned aerial vehicles. Background technique [0002] UAV aerial sowing technology can play an important role in the sowing of crops, grasslands, woodlands, etc., and its high-efficiency operations have brought technological innovation to these fields. However, in the actual sowing work, it is necessary to determine the plant species and sowing amount according to the actual sowing environmental factors (such as plot range, soil type, soil texture, soil fertility, soil water content, irrigation conditions, rainfall, terrain, etc.), and the current Some UAV aerial sowing technologies are controlled by remote control, and lack a decision-making system to determine the type and amount of sowing according to changes in environmental factors, and the decision-making system should be universal in a certain field. Contents...

AI Technical Summary

Problems solved by technology

However, in the actual sowing work, it is necessary to determine the plant species and sowing amount according to the actual sowing environmental factors (such as plot range, soil type, soil texture, soil fertility, soil water content, irrigation conditions, rainfall, terrain, etc.), and the current Some UAV aerial sowing technologies are controlled by remote control, lacking a decision-making system to determine the type and amount of sowing according to changes in environmental factors, and the decision-making system should be universal in a certain field

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