CN-121680436-B - Unmanned aerial vehicle self-adaptive scheduling method, system, equipment and storage medium

CN121680436BCN 121680436 BCN121680436 BCN 121680436BCN-121680436-B

Abstract

The embodiment of the application discloses an unmanned aerial vehicle self-adaptive scheduling method, system, equipment and storage medium, which comprise the steps of obtaining a task type, task execution time, a target task weight set, environmental parameters and a historical success rate factor, inputting the environmental parameters into a preset function to obtain an environmental safety factor, performing suitability evaluation based on corresponding weight values in the target task weight set, the environmental safety factor and the historical success rate factor to obtain a suitability evaluation value, and scheduling an unmanned aerial vehicle based on a flight route corresponding to the task execution time and the task type under the condition that the suitability evaluation value meets preset task execution conditions, so that the accuracy of the suitability evaluation is improved, and the effectiveness of the unmanned aerial vehicle in executing tasks is fully ensured.

Inventors

SONG HANGYU
LIU HENG

Assignees

深圳市奇航疆域技术有限公司
奇航智维(苏州)技术服务有限公司

Dates

Publication Date: 20260505
Application Date: 20260210

Claims (9)

1. An unmanned aerial vehicle adaptive scheduling method is characterized by comprising the following steps: acquiring a task type, task execution time, a target task weight set, environmental parameters and a historical success rate factor, and inputting the environmental parameters into a preset function to obtain an environmental safety factor; Performing suitability evaluation based on the corresponding weight value in the target task weight set, the environmental safety factor and the historical success rate factor to obtain a suitability evaluation value; Under the condition that the suitability evaluation value does not meet a preset task execution condition, determining a time range formed by the task execution time and a preset flight supplementing time threshold as a target time range, wherein the target time range comprises a plurality of time windows, determining a target time window in each time window based on a preset flight supplementing prediction function, and scheduling the unmanned aerial vehicle based on the target time window and a flight route corresponding to the task type; And under the condition that the suitability evaluation value meets a preset task execution condition, scheduling the unmanned aerial vehicle based on the task execution time and a flight route corresponding to the task type.
2. The unmanned aerial vehicle adaptive scheduling method of claim 1, wherein the preset functions comprise a wind speed safety factor function, a rainfall safety factor function, a visibility safety factor function, a temperature safety factor function, a light intensity factor function, an environmental clarity factor function, and a shadow penalty factor function, and the environmental parameters comprise wind speed, rainfall, visibility, temperature, solar radiation intensity, cloud cover, air quality index, and solar altitude.
3. The unmanned aerial vehicle adaptive scheduling method of claim 2, wherein the inputting the environmental parameter into a preset function, to obtain an environmental safety factor, comprises: Inputting the wind speed into the wind speed safety factor function to obtain a wind speed safety factor, inputting the rainfall into the rainfall safety factor function to obtain a rainfall safety factor, inputting the visibility into the visibility safety factor function to obtain a visibility safety factor, and inputting the temperature into the temperature safety factor function to obtain a temperature safety factor; And inputting the solar radiation intensity and the cloud cover into the light intensity factor function to obtain a light intensity factor, inputting the visibility and the air quality index into the environment definition factor function to obtain an environment definition factor, and inputting the solar altitude angle into the shadow penalty factor function to obtain a shadow penalty factor.
4. The unmanned aerial vehicle adaptive scheduling method of claim 2, wherein the performing suitability assessment based on the respective weight values in the target task weight set, the environmental safety factor, and the historical success rate factor comprises: Calculating a flight safety probability based on the wind speed safety factor, the rainfall safety factor, the visibility safety factor, and the temperature safety factor; Calculating a data quality expected value based on the light intensity factor, the ambient definition factor, the shadow penalty factor, and corresponding weight values in the set of target task weights; Performing weighted calculation on the data quality weight value and the data quality expected value in the target task weight set to obtain a first weighted result, performing weighted calculation on the historical success rate weight value and the historical success rate factor in the target task weight set to obtain a second weighted result, and performing suitability evaluation based on the flight safety probability, the first weighted result and the second weighted result.
5. The unmanned aerial vehicle adaptive scheduling method of claim 1, wherein the supplemental flight prediction function comprises a fitness function, a delay penalty function, a resource conflict cost function, and a time window screening function; correspondingly, the determining the target time window in each time window based on the preset fly-by-the-fly prediction function comprises the following steps: Calculating future fitness values of the time windows based on the fitness function, determining delay penalty values of the time windows based on the delay penalty function, and determining resource conflict values of the time windows based on the resource conflict cost function; Determining a target fitness value of a corresponding time window based on the future fitness value, the delay penalty value, and the resource conflict value; And comparing the target fitness values corresponding to the time windows based on the time window screening function, determining a maximum target fitness value based on a comparison result, and outputting a target time window corresponding to the maximum target fitness value.
6. The unmanned aerial vehicle adaptive scheduling method of any of claims 1-5, further comprising, after scheduling the unmanned aerial vehicle: monitoring a task execution state of the unmanned aerial vehicle, acquiring an initial task weight set under the condition that the task execution state is a failure state, and reversely adjusting a corresponding weight value in the initial task weight set according to the target task weight set; And under the condition that the task execution state is a successful state, acquiring an initial task weight set, and performing forward adjustment on a corresponding weight value in the initial task weight set according to the target task weight set.
7. An unmanned aerial vehicle adaptive scheduling system, comprising: The data acquisition module is used for acquiring task types, task execution time, target task weight sets, environment parameters and historical success rate factors; the environment safety factor determining module is used for inputting the environment parameters into a preset function to obtain environment safety factors; The suitability evaluation module is used for performing suitability evaluation based on the corresponding weight value in the target task weight set, the environmental safety factor and the historical success rate factor to obtain a suitability evaluation value; A target time window determining module, configured to determine, when the suitability evaluation value does not meet a preset task execution condition, a time range formed by the task execution time and a preset flight compensating time threshold as a target time range, where the target time range includes a plurality of time windows, determine a target time window in each of the time windows based on a preset flight compensating prediction function, and schedule the unmanned aerial vehicle based on the target time window and a flight route corresponding to the task type; and the unmanned aerial vehicle scheduling module is used for scheduling the unmanned aerial vehicle based on the task execution time and the flight route corresponding to the task type under the condition that the suitability evaluation value meets the preset task execution condition.
8. An electronic device comprising one or more processors and storage means for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the unmanned aerial vehicle adaptive scheduling method of any of claims 1-6.
9. A storage medium storing computer executable instructions which, when executed by a computer processor, are for performing the unmanned aerial vehicle adaptive scheduling method of any of claims 1-6.

Description

Unmanned aerial vehicle self-adaptive scheduling method, system, equipment and storage medium Technical Field The embodiment of the application relates to the field of control of non-electric variables, in particular to an unmanned aerial vehicle self-adaptive scheduling method, system, equipment and storage medium. Background With the deep fusion and rapid iteration of the internet of things, artificial intelligence, avionics and communication technologies, unmanned aerial vehicle technology has entered the explosive development stage of industrial-grade and industry-grade applications. The intelligent photovoltaic power generation system has the core advantages of flexibility, convenience in deployment, controllable cost, capability of breaking through the limitation of manual operation, continuous widening of application boundaries, and wide penetration into diversified industrial fields such as photovoltaic inspection, electric power inspection, surveying and mapping, disaster relief, agricultural plant protection, urban security, logistics distribution and environmental monitoring, and the like, and becomes key equipment for promoting digital transformation of various industries, improving operation and maintenance efficiency and reducing operation risks. In the related art, in the unmanned aerial vehicle scheduling process, a user generally sets a fixed task execution time in advance, and a task is issued every time the fixed task execution time is reached, or a single environmental parameter is acquired, and the environmental parameter is compared with a corresponding preset threshold value, for example, the current wind speed is acquired, the current wind speed is compared with the preset wind speed threshold value, if the current wind speed exceeds the preset wind speed threshold value, error reporting is performed, and if the current wind speed is smaller than the preset wind speed threshold value, unmanned aerial vehicle scheduling is performed immediately, and the unmanned aerial vehicle scheduling cannot be ensured by the environmental parameter comparison mode or the mode of setting the fixed task execution time. Disclosure of Invention The embodiment of the application provides an unmanned aerial vehicle self-adaptive scheduling method, system, equipment and storage medium, which solve the problem that the effectiveness of unmanned aerial vehicle flight tasks cannot be ensured when unmanned aerial vehicle scheduling is performed in the existing environment parameter comparison mode or the mode of setting fixed task execution time in the unmanned aerial vehicle scheduling process. The method has the advantages that the suitability evaluation can be carried out through the environment parameters, the weight values corresponding to various types of parameters in the environment parameters and the historical success rate factors, the influence of different factors on the suitability evaluation is comprehensively considered, the accuracy of the suitability evaluation is improved, whether the estimated task execution time can effectively complete the flight task of the unmanned aerial vehicle is judged according to the suitability evaluation value, and under the condition that the suitability meets the preset task execution condition, the unmanned aerial vehicle is scheduled at the task execution time, so that the unmanned aerial vehicle works according to the corresponding route on time, and the validity of the flight task of the unmanned aerial vehicle is fully ensured. In a first aspect, an embodiment of the present application provides an adaptive scheduling method for an unmanned aerial vehicle, including: acquiring a task type, task execution time, a target task weight set, environmental parameters and a historical success rate factor, and inputting the environmental parameters into a preset function to obtain an environmental safety factor; Performing suitability evaluation based on the corresponding weight value in the target task weight set, the environmental safety factor and the historical success rate factor to obtain a suitability evaluation value; And under the condition that the suitability evaluation value meets a preset task execution condition, scheduling the unmanned aerial vehicle based on the task execution time and a flight route corresponding to the task type. Optionally, the preset function includes a wind speed safety factor function, a rainfall safety factor function, a visibility safety factor function, a temperature safety factor function, a light intensity factor function, an environmental clarity factor function and a shadow penalty factor function, and the environmental parameters include wind speed, rainfall, visibility, temperature, solar radiation intensity, cloud cover, air quality index and solar altitude. Optionally, the inputting the environmental parameter into a preset function to obtain an environmental safety factor includes: Inputting the wind speed into t