Automatic take-off and landing method and system based on unmanned aerial vehicle field

Abstract

The invention belongs to the technical field of digital processing, and discloses an automatic take-off and landing method and system based on an unmanned aerial vehicle field. An environment data set and a hardware data set are collected and preprocessed, the environment data set comprises wind speed data, temperature data, humidity data and extreme weather data, and the hardware data set comprises endurance data, communication data and positioning precision data; processing the environment data set to obtain a flight reference value, and classifying the flight reference value to obtain a flight suggestion result; processing the flight suggestion result to obtain a flight state decision suggestion; and processing the flight state decision suggestion and the hardware data set to obtain a flight distance result, and generally, the method has the remarkable advantages of high capability of assisting in identifying the environment state, high use safety guarantee of the unmanned aerial vehicle and great effect of assisting in identifying the use state of the unmanned aerial vehicle.

Description

Technical Field

[0001] The present invention relates to the field of digital processing technology, and more specifically, to an automatic take-off and landing method and system based on an unmanned aerial vehicle airport. Background Art

[0002] Unmanned aerial vehicle, referred to as "drone", is an unmanned aircraft controlled by radio remote control equipment and self-contained program control devices, or operated completely or intermittently autonomously by an onboard computer. Drones can be divided into military and civilian use according to their application areas. In the military, drones are divided into reconnaissance aircraft and target aircraft. In the civilian field, drones + industry applications are the real rigid demand for drones. Their applications in aerial photography, agriculture, plant protection, micro selfies, express delivery, disaster relief, wildlife observation, infectious disease monitoring, surveying and mapping, news reporting, power inspections, disaster relief, film and television shooting, creating romance, etc. have greatly expanded the uses of drones themselves. Developed countries are also actively expanding industry applications and developing drone technology. With the rapid development of the drone industry in recent years, drone airports for drone takeoffs and landings have also received attention. Compared with traditional drones that operate anywhere, building drone airports that can operate for a long time, for multiple purposes and for services has also become a key industry at present.

[0003] The patent application publication number CN115469642A discloses an automatic control method and system for unmanned aerial vehicles based on the Internet of Things. The method and system obtain flight log information by reading the flight data recorder of the target unmanned aerial vehicle, construct a three-dimensional batch model, input the flight log information into the three-dimensional fitting model, and generate a three-dimensional flight modeling result by fitting. In combination with the flight mission execution progress of the target unmanned aerial vehicle, a first flight prediction result is obtained; automatic control data is obtained, spatial position information is entered, data network retrieval is performed, environmental parameter index information is retrieved, target unmanned aerial vehicle setting parameters and target unmanned aerial vehicle attitude data are input into the trained flight prediction model, and the environmental parameter index information is used as correction data to obtain a second flight prediction result. In combination with the first flight prediction result, the automatic control data of the target unmanned aerial vehicle is auxiliary corrected.

[0004] However, although the above-mentioned method and system for automatic control of drones based on the Internet of Things can achieve real-time calibration of the flight attitude of the drone to a certain extent by processing the flight data of the drone and combining it with environmental parameters, thereby improving the data accuracy of the drone flight, in the use of drones, take-off and landing are the top priorities of drones. Complex environmental factors can easily cause damage to drones during take-off and landing. Therefore, how to ensure the safety of take-off and landing of drones in drone fields has become a major problem in the use of drones.

[0005] In view of this, the present invention proposes an automatic take-off and landing method and system based on an unmanned aerial vehicle airport to solve the above problems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solutions: comprising:

[0007] The data acquisition module is used to collect and pre-process environmental data sets and hardware data sets. The environmental data sets include wind speed data, temperature data, humidity data, and extreme weather data. The hardware data sets include endurance data, communication data, and positioning accuracy data.

[0008] Furthermore, the methods of collecting the environment data set and the hardware data set include:

[0009] By installing an anemometer, the wind speed value of the target area is collected to obtain wind speed data;

[0010] By installing temperature and humidity sensors, the temperature and humidity values ​​of the target area are collected respectively to obtain temperature data and humidity data;

[0011] By connecting to the meteorological publishing platform, the extreme weather conditions in the target area are collected to obtain extreme weather data;

[0012] By reading the UAV manual, the range, communication distance and positioning accuracy of the target model UAV are collected to obtain the range data, communication data and positioning accuracy data;

[0013] The preprocessing methods include data cleaning, data denoising and data normalization;

[0014] The data processing module is used to process the environmental data set to obtain flight reference values, classify them, and obtain flight recommendation results;

[0015] Furthermore, the steps of processing the environmental data set include:

[0016] Q1: Based on the environmental data set, the environmental reference value is obtained by substituting the calculation formula: As = (Aa × A1 + Ab × A2 + Ac × A3) × Ad, where Aa is the wind speed data, Ab is the temperature data, Ac is the humidity data, Ad is the extreme weather data, and A1, A2 and A3 are the corresponding weight factors respectively;

[0017] Q2: Collect M groups of historical environmental reference values ​​as sample sets, and divide the sample sets into 70%M training sets, 15%M test sets, and 15%M validation sets;

[0018] Q3: Establish an environmental recognition model based on the training set, obtain the historical environmental reference values ​​in the training set, preset the appropriate data cluster K1, warning data cluster K2 and high-risk data cluster K3, and randomly select three data points in the training set as the center of the first cluster, representing the appropriate data cluster K1, warning data cluster K2 and high-risk data cluster K3 respectively;

[0019] Q4: By substituting into the calculation formula: Get the distance between data items, calculate the distance between the data items in the training set and the appropriate data cluster K1, warning data cluster K2 and high-risk data cluster K3, and assign the data items to the first cluster center that is closest to them, and get a new second cluster center with three data clusters, where b is the number of first cluster centers, Cb is the bth cluster, Ci is the i-th point in the training set, and Db is the mean of all data points in the bth cluster;

[0020] Q5: Calculate the mean of the second cluster center and use it as the new first cluster center for recalculation;

[0021] Q6: Repeat Q4 and Q5 until the preset number of iterations is reached, and the flight state model is output;

[0022] Q7: Input the environmental reference value into the flight state model and output the flight reference value;

[0023] Further, the classification methods include:

[0024] A flight threshold interval (D1, D2) is preset. When the flight reference value is less than D1, a high-risk signal is generated. When the flight reference value is greater than D1 and less than D2, a warning signal is generated. When the flight reference value is greater than D2, an appropriate signal is generated.

[0025] The high-risk signal contains a group of fields representing dangerous flight environments, the warning signal contains a group of fields representing poor flight environments, and the suitable signal contains a group of fields representing good flight environments;

[0026] Pack high-risk signals, warning signals and appropriate signals to obtain flight recommendation results;

[0027] Intelligent operation module, used to process the flight suggestion results and obtain flight status decision suggestions;

[0028] Furthermore, the methods for processing the flight suggestion results include:

[0029] When the flight suggestion result is a high-risk signal, a danger message is sent to the staff through the communication unit; when the flight suggestion result is a warning signal, a warning message is sent to the UAV management system through the communication unit; when the flight suggestion result is an appropriate signal, an appropriate message is sent to the UAV management system through the communication unit;

[0030] Dangerous text messages include information about extreme weather conditions in the target area, requiring staff to log in to the drone management system to stop all drones from taking off, and suggesting that staff immediately enter the site to check the status of drones and turn off the power of all drones;

[0031] The warning information includes indicating that the environment in the target area is not suitable for drone takeoff and landing, and reducing the number of drone takeoffs, flight time, flight distance and flight altitude through the drone management system;

[0032] The appropriate information includes indicating that the target area environment is suitable for drone take-off and landing, and the drone management system operates according to the preset take-off and landing schedule;

[0033] Packaging of dangerous messages, warning messages and appropriate messages to obtain flight status decision suggestions;

[0034] Intelligent flight module, used to process flight status decision suggestions and hardware data sets to obtain flight distance results;

[0035] Further, the flight state decision suggestions and hardware data sets are processed including:

[0036] When the flight status decision recommendation is a dangerous message, a stop message is generated;

[0037] When the flight status decision suggestion is a warning message, a flight warning message is generated;

[0038] When the flight status decision suggestion is appropriate information, normal information is generated;

[0039] The grounding information contains a set of fields representing a flight distance of 0;

[0040] The flight warning message contains a set of fields representing the flight distance L;

[0041] Normal information contains a set of fields representing the distance N of the aircraft;

[0042] By substituting into the calculation group: Get the values ​​of the aircraft distance L and N, where n is the number of drones, R is the endurance data, E is the communication data, min(Rp, Ep) is the smaller value of the endurance data and communication data of the pth drone, and N1 is the weight factor, which is greater than 0 and less than 1;

[0043] Pack the grounding information, aircraft warning information and normal information to get the flight distance result;

[0044] Further, S1: collecting and preprocessing an environmental data set and a hardware data set, the environmental data set includes wind speed data, temperature data, humidity data and extreme weather data, and the hardware data set includes endurance data, communication data and positioning accuracy data;

[0045] S2: Process the environmental data set to obtain flight reference values, classify them, and obtain flight recommendation results;

[0046] S3: Process the flight suggestion result to obtain a flight status decision suggestion;

[0047] S4: Process the flight status decision recommendations and hardware data set to obtain the flight distance results.

[0048] The technical effects and advantages of the automatic take-off and landing method and system based on the UAV airport of the present invention are as follows:

[0049] The present invention processes the environmental data set to obtain a flight reference value, which can effectively quantify the environmental status of the drone airport area, thereby assisting the drone management system to identify whether the drone can take off and land. The flight status decision suggestion obtained by processing the flight suggestion result can assist in completing the analysis of the environmental status, thereby effectively ensuring the safety of the drone during take-off and landing. The flight distance result obtained by processing the flight status decision suggestion and the hardware data set can provide the staff with the operating status of the drone intuitively, thereby assisting the staff to understand the use status of the drone. Generally speaking, the present invention has the significant advantages of strong auxiliary identification of environmental status, high safety protection of drone use, and large auxiliary identification of drone use status. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of an automatic take-off and landing system based on a UAV airport of the present invention;

[0051] Figure 2 The present invention is a schematic diagram of an automated take-off and landing method based on an unmanned aerial vehicle airport. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] Example 1

[0054] See also Figure 1As shown, the automatic take-off and landing system based on the UAV airport described in this embodiment includes:

[0055] The data acquisition module is used to collect and pre-process environmental data sets and hardware data sets. The environmental data sets include wind speed data, temperature data, humidity data, and extreme weather data. The hardware data sets include endurance data, communication data, and positioning accuracy data.

[0056] Furthermore, the methods of collecting the environment data set and the hardware data set include:

[0057] By installing an anemometer, the wind speed value of the target area is collected to obtain wind speed data;

[0058] By installing temperature and humidity sensors, the temperature and humidity values ​​of the target area are collected respectively to obtain temperature data and humidity data;

[0059] By connecting to the meteorological publishing platform, the extreme weather conditions in the target area are collected to obtain extreme weather data;

[0060] It should be explained that extreme weather conditions include thunderstorms, heavy rains, typhoons and other extreme weather conditions. When one or more extreme weather conditions occur in the target area, the extreme weather data value is 0, otherwise it is 1;

[0061] By reading the UAV manual, the range, communication distance and positioning accuracy of the target model UAV are collected to obtain the range data, communication data and positioning accuracy data;

[0062] The preprocessing methods include data cleaning, data denoising and data normalization;

[0063] The data processing module is used to process the environmental data set to obtain flight reference values, classify them, and obtain flight recommendation results;

[0064] Furthermore, the steps of processing the environmental data set include:

[0065] Q1: Based on the environmental data set, the environmental reference value is obtained by substituting the calculation formula: As = (Aa × A1 + Ab × A2 + Ac × A3) × Ad, where Aa is the wind speed data, Ab is the temperature data, Ac is the humidity data, Ad is the extreme weather data, and A1, A2 and A3 are the corresponding weight factors respectively;

[0066] Q2: Collect M groups of historical environmental reference values ​​as sample sets, and divide the sample sets into 70%M training sets, 15%M test sets, and 15%M validation sets;

[0067] Q3: Establish an environmental recognition model based on the training set, obtain the historical environmental reference values ​​in the training set, preset the appropriate data cluster K1, warning data cluster K2 and high-risk data cluster K3, and randomly select three data points in the training set as the center of the first cluster, representing the appropriate data cluster K1, warning data cluster K2 and high-risk data cluster K3 respectively;

[0068] Q4: By substituting into the calculation formula: Get the distance between data items, calculate the distance between the data items in the training set and the appropriate data cluster K1, warning data cluster K2 and high-risk data cluster K3, and assign the data items to the first cluster center that is closest to them, and get a new second cluster center with three data clusters, where b is the number of first cluster centers, Cb is the bth cluster, Ci is the i-th point in the training set, and Db is the mean of all data points in the bth cluster;

[0069] Q5: Calculate the mean of the second cluster center and use it as the new first cluster center for recalculation;

[0070] Q6: Repeat Q4 and Q5 until the preset number of iterations is reached, and the flight state model is output;

[0071] Q7: Input the environmental reference value into the flight state model and output the flight reference value;

[0072] Further classification methods include:

[0073] A flight threshold interval (D1, D2) is preset. When the flight reference value is less than D1, a high-risk signal is generated. When the flight reference value is greater than D1 and less than D2, a warning signal is generated. When the flight reference value is greater than D2, an appropriate signal is generated.

[0074] The high-risk signal contains a group of fields representing dangerous flight environments, the warning signal contains a group of fields representing poor flight environments, and the suitable signal contains a group of fields representing good flight environments;

[0075] Pack high-risk signals, warning signals and appropriate signals to obtain flight recommendation results;

[0076] Intelligent operation module, used to process the flight suggestion results and obtain flight status decision suggestions;

[0077] Furthermore, the methods for processing the flight suggestion results include:

[0078] When the flight suggestion result is a high-risk signal, a danger message is sent to the staff through the communication unit; when the flight suggestion result is a warning signal, a warning message is sent to the UAV management system through the communication unit; when the flight suggestion result is an appropriate signal, an appropriate message is sent to the UAV management system through the communication unit;

[0079] Dangerous text messages include information about extreme weather conditions in the target area, requiring staff to log in to the drone management system to stop all drones from taking off, and suggesting that staff immediately enter the site to check the status of drones and turn off the power of all drones;

[0080] The warning information includes indicating that the environment in the target area is not suitable for drone takeoff and landing, and reducing the number of drone takeoffs, flight time, flight distance and flight altitude through the drone management system;

[0081] The appropriate information includes indicating that the target area environment is suitable for drone take-off and landing, and the drone management system operates according to the preset take-off and landing schedule;

[0082] Packaging of dangerous messages, warning messages and appropriate messages to obtain flight status decision suggestions;

[0083] Intelligent flight module, used to process flight status decision suggestions and hardware data sets to obtain flight distance results;

[0084] Further, the flight status decision suggestions and hardware data sets are processed including:

[0085] When the flight status decision recommendation is a dangerous message, a stop message is generated;

[0086] When the flight status decision suggestion is a warning message, a flight warning message is generated;

[0087] When the flight status decision suggestion is appropriate information, normal information is generated;

[0088] The grounding information contains a set of fields representing a flight distance of 0;

[0089] The flight warning message contains a set of fields representing the flight distance L;

[0090] Normal information contains a set of fields representing the distance N of the aircraft;

[0091] By substituting into the calculation group: Get the values ​​of the aircraft distance L and N, where n is the number of drones, R is the endurance data, E is the communication data, min(Rp, Ep) is the smaller value of the endurance data and communication data of the pth drone, and N1 is the weight factor, which is greater than 0 and less than 1;

[0092] Pack the grounding information, aircraft warning information and normal information to get the flight distance result;

[0093] The beneficial effects of this embodiment are as follows: by processing the environmental data set, the flight reference value obtained can effectively quantify the environmental status of the drone airport area, thereby assisting the drone management system to identify whether the drone can take off and land; by processing the flight suggestion results, the flight status decision suggestion obtained can assist in completing the analysis of the environmental status, thereby effectively ensuring the safety of the drone during take-off and landing; by processing the flight status decision suggestion and the hardware data set, the flight distance result obtained can provide the staff with the operating status of the drone intuitively, thereby assisting the staff to understand the use status of the drone; generally speaking, the present invention has the significant advantages of strong ability to assist in identifying the environmental status, high safety assurance of drone use, and great role in assisting in identifying the use status of the drone.

[0094] Example 2

[0095] See also Figure 2 As shown, the part not described in detail in this embodiment is described in Example 1, and an automatic take-off and landing method based on a UAV airport is provided, including: S1: collecting an environmental data set and a hardware data set, and preprocessing them, the environmental data set includes wind speed data, temperature data, humidity data and extreme weather data, and the hardware data set includes endurance data, communication data and positioning accuracy data;

[0096] S2: Process the environmental data set to obtain flight reference values, classify them, and obtain flight recommendation results;

[0097] S3: Process the flight suggestion result to obtain a flight status decision suggestion;

[0098] S4: Process the flight status decision recommendations and hardware data set to obtain the flight distance results.

[0099] Example 3

[0100] The present embodiment discloses an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the operation mode of the above-mentioned method for automated take-off and landing based on an unmanned aerial vehicle airport is implemented.

[0101] Since the electronic device introduced in this embodiment is an electronic device used to implement an automated take-off and landing method based on an unmanned aerial vehicle airport in the embodiment of this application, based on the automated take-off and landing method based on an unmanned aerial vehicle airport introduced in the embodiment of this application, a person skilled in the art can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application is not described in detail here. As long as a person skilled in the art implements an electronic device used in an automated take-off and landing method based on an unmanned aerial vehicle airport in the embodiment of this application, it belongs to the scope of protection of this application.

[0102] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0103] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical users in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. An automatic take-off and landing system based on a drone airport, characterized in that: include: The data acquisition module is used to collect and pre-process environmental data sets and hardware data sets. The environmental data sets include wind speed data, temperature data, humidity data, and extreme weather data. The hardware data sets include endurance data, communication data, and positioning accuracy data. The data processing module is used to process the environmental data set to obtain flight reference values, classify them, and obtain flight recommendation results; Intelligent operation module, used to process the flight suggestion results and obtain flight status decision suggestions; The intelligent flight module is used to process the flight status decision suggestions and hardware data sets to obtain the flight distance results.

2. The automatic take-off and landing system based on the UAV airport according to claim 1 is characterized in that: Methods for collecting environmental data sets and hardware data sets include: By installing an anemometer, the wind speed value of the target area is collected to obtain wind speed data; By installing temperature and humidity sensors, the temperature and humidity values ​​of the target area are collected respectively to obtain temperature data and humidity data; By connecting to the meteorological publishing platform, the extreme weather conditions in the target area are collected to obtain extreme weather data; By reading the UAV manual, the range, communication distance and positioning accuracy of the target model UAV are collected to obtain the range data, communication data and positioning accuracy data; The preprocessing methods include data cleaning, data denoising and data normalization.

3. The automatic take-off and landing system based on the UAV airport according to claim 2 is characterized in that: The steps to process the environmental dataset include: Q1: Based on the environmental data set, the environmental reference value is obtained by substituting the calculation formula: As = (Aa × A1 + Ab × A2 + Ac × A3) × Ad, where Aa is the wind speed data, Ab is the temperature data, Ac is the humidity data, Ad is the extreme weather data, and A1, A2 and A3 are the corresponding weight factors respectively; Q2: Collect M groups of historical environmental reference values ​​as sample sets, and divide the sample sets into 70%M training sets, 15%M test sets, and 15%M validation sets; Q3: Establish an environmental recognition model based on the training set, obtain the historical environmental reference values ​​in the training set, preset the appropriate data cluster K1, warning data cluster K2 and high-risk data cluster K3, and randomly select three data points in the training set as the center of the first cluster, representing the appropriate data cluster K1, warning data cluster K2 and high-risk data cluster K3 respectively; Q4: By substituting into the calculation formula: Get the distance between data items, calculate the distance between the data items in the training set and the appropriate data cluster K1, warning data cluster K2 and high-risk data cluster K3, and assign the data items to the first cluster center that is closest to them, and get a new second cluster center with three data clusters, where b is the number of first cluster centers, Cb is the bth cluster, Ci is the i-th point in the training set, and Db is the mean of all data points in the bth cluster; Q5: Calculate the mean of the second cluster center and use it as the new first cluster center for recalculation; Q6: Repeat Q4 and Q5 until the preset number of iterations is reached, and the flight state model is output; Q7: Input the environmental reference value into the flight state model and output the flight reference value.

4. The automatic take-off and landing system based on the UAV airport according to claim 3 is characterized in that: The classification methods include: A flight threshold interval (D1, D2) is preset. When the flight reference value is less than D1, a high-risk signal is generated. When the flight reference value is greater than D1 and less than D2, a warning signal is generated. When the flight reference value is greater than D2, an appropriate signal is generated. The high-risk signal contains a group of fields representing dangerous flight environments, the warning signal contains a group of fields representing poor flight environments, and the suitable signal contains a group of fields representing good flight environments; Pack high-risk signals, warning signals and appropriate signals to obtain flight recommendation results.

5. The automatic take-off and landing system based on the UAV airport according to claim 4 is characterized in that: The methods for processing flight recommendation results include: When the flight suggestion result is a high-risk signal, a danger message is sent to the staff through the communication unit; when the flight suggestion result is a warning signal, a warning message is sent to the UAV management system through the communication unit; when the flight suggestion result is an appropriate signal, an appropriate message is sent to the UAV management system through the communication unit; Dangerous text messages include information about extreme weather conditions in the target area, requiring staff to log in to the drone management system to stop all drones from taking off, and suggesting that staff immediately enter the site to check the status of drones and turn off the power of all drones; The warning information includes indicating that the environment in the target area is not suitable for drone takeoff and landing, and reducing the number of drone takeoffs, flight time, flight distance and flight altitude through the drone management system; The appropriate information includes indicating that the target area environment is suitable for drone take-off and landing, and the drone management system operates according to the preset take-off and landing schedule; Package danger messages, warning messages and appropriate messages to get flight status decision advice.

6. The automatic take-off and landing system based on the UAV airport according to claim 5 is characterized in that: Processing of flight state decision recommendations and hardware data sets includes: When the flight status decision recommendation is a dangerous text message, a grounding message is generated; When the flight status decision suggestion is a warning message, a flight warning message is generated; When the flight status decision suggestion is appropriate information, normal information is generated; The grounding information contains a set of fields representing a flight distance of 0; The flight warning message contains a set of fields representing the flight distance L; Normal information contains a set of fields representing the distance N of the aircraft; By substituting into the calculation group: Get the values ​​of aircraft distance L and N, where n is the number of drones, R is the endurance data, E is the communication data, min(Rp, Ep) is the smaller value of the endurance data and communication data of the pth drone, and N1 is the weight factor, which is greater than 0 and less than 1; Package the grounding information, aircraft warning information and normal information to get the flight distance result.

7. An automated take-off and landing method based on a drone airport, implemented according to any one of claims 1 to 6, characterized in that: S1: Collect environmental data sets and hardware data sets and perform preprocessing. The environmental data sets include wind speed data, temperature data, humidity data, and extreme weather data. The hardware data sets include endurance data, communication data, and positioning accuracy data. S2: Process the environmental data set to obtain flight reference values, classify them, and obtain flight recommendation results; S3: Process the flight suggestion result to obtain a flight status decision suggestion; S4: Process the flight status decision recommendations and hardware data set to obtain the flight distance results.

Images