CN118538062A - Multi-factor aircraft take-off decision method and device and electronic equipment - Google Patents

Abstract

The present application provides a multi-factor aircraft takeoff decision method, device and electronic equipment, which relates to the field of data processing. In this method, the takeoff weight, wing reference area, no-wind stall speed and real-time takeoff thrust of the target aircraft are obtained; the total drag coefficient of the target aircraft, the real-time wind speed of the takeoff area and the real-time air density of the takeoff area are obtained; the effective stall speed of the target aircraft is calculated according to the no-wind stall speed and the real-time wind speed of the takeoff area; the takeoff distance of the target aircraft is calculated based on the takeoff weight, wing reference area and real-time takeoff thrust of the target aircraft, and the total drag coefficient of the target aircraft, the effective stall speed of the target aircraft and the real-time air density of the takeoff area; if it is determined that the takeoff distance of the target aircraft is less than the preset takeoff distance threshold, the takeoff decision corresponding to the target aircraft is determined to be allowed to take off from the takeoff area. Implementing the technical solution provided by the present application is convenient for improving decision-making efficiency.

Description

A multi-factor aircraft takeoff decision method, device and electronic equipment

Technical Field

The present application relates to the technical field of data processing, and in particular to a multi-factor aircraft takeoff decision method, device and electronic equipment.

Background Art

In the current aircraft takeoff decision-making process, the technical challenges mainly focus on data collection and processing. Traditional decision-making methods usually require the collection of a large amount of multi-dimensional information such as flight data, weather information, airport conditions, and aircraft status, and then analyze and compare them one by one. This decision-making process is not only time-consuming and labor-intensive, but also may not be able to make accurate decisions in time when faced with emergencies or complex situations, which in turn affects flight safety and efficiency.

Specifically, existing aircraft takeoff decision systems often rely on manual or semi-automatic data processing methods. The system needs to extract information from multiple data sources, such as pilot reports, ground weather station observation data, airport tower instructions, etc., and then integrate, screen and compare the data. This process not only involves a large amount of data calculation and storage, but also requires human participation to interpret and judge the data. It is precisely because of the increased subjectivity and uncertainty of decision-making that the decision-making efficiency is greatly reduced.

Therefore, a multi-factor aircraft takeoff decision method, device and electronic equipment are urgently needed.

Summary of the invention

The present application provides a multi-factor aircraft takeoff decision method, device and electronic equipment to improve decision-making efficiency.

In a first aspect of the present application, a multi-factor aircraft takeoff decision method is provided, the method comprising: obtaining an operating parameter group for a target aircraft, the operating parameter group comprising the takeoff weight, wing reference area, no-wind stall speed and real-time takeoff thrust of the target aircraft; obtaining an influencing parameter group corresponding to a takeoff area of the target aircraft, the influencing parameter group comprising a total drag coefficient of the target aircraft, a real-time wind speed of the takeoff area and a real-time air density of the takeoff area; calculating an effective stall speed of the target aircraft according to the no-wind stall speed and the real-time wind speed of the takeoff area; calculating a takeoff distance of the target aircraft based on the takeoff weight, the wing reference area and the real-time takeoff thrust of the target aircraft, and the total drag coefficient, the effective stall speed of the target aircraft and the real-time air density of the takeoff area; judging a size relationship between the takeoff distance of the target aircraft and a preset takeoff distance threshold; if it is determined that the takeoff distance of the target aircraft is less than the preset takeoff distance threshold, determining that the takeoff decision corresponding to the target aircraft is to allow takeoff from the takeoff area.

By adopting the above technical solution, firstly, the decision method is based on accurate operating parameters and influencing parameters, including takeoff weight, wing reference area, no-wind stall speed, real-time takeoff thrust, total drag coefficient, real-time wind speed and real-time air density. These parameters can fully reflect the performance of the aircraft and the actual situation of the takeoff environment, thereby improving the accuracy and reliability of decision-making. Secondly, by calculating the effective stall speed and takeoff distance, the system can more accurately evaluate the takeoff capability of the aircraft under specific conditions. The effective stall speed takes into account the influence of the real-time wind speed, making the calculation closer to the actual flight situation; while the calculation of the takeoff distance comprehensively considers factors such as the aircraft's takeoff weight, thrust, and air density in the takeoff area, so as to more accurately determine whether the aircraft can take off safely within a limited runway length. In addition, the decision method also introduces the concept of a preset takeoff distance threshold. By comparing with the takeoff distance, the system can quickly make a decision to determine whether the aircraft is allowed to take off. This method not only improves the efficiency of decision-making, but also enables rapid response in emergency situations to ensure flight safety. Finally, the entire decision-making process is based on objective data and calculations, which reduces the interference of human factors, reduces the subjectivity and uncertainty of decision-making, and integrates the complex decision-making process into judging whether the take-off distance meets the threshold requirements. This helps to improve the consistency and repeatability of decisions, making aircraft take-off decisions more scientific and standardized, and improving decision-making efficiency.

Optionally, the take-off distance of the target aircraft is calculated based on the take-off weight of the target aircraft, the wing reference area, the real-time take-off thrust, the total drag coefficient of the target aircraft, the effective stall speed of the target aircraft, and the real-time air density of the take-off area, and is specifically calculated using the following calculation formula:

;

Wherein, D is the take-off distance of the target aircraft, T is the real-time take-off thrust of the target aircraft, μ is the total drag coefficient of the target aircraft, W is the take-off weight of the target aircraft, S is the wing reference area of the target aircraft, ρ is the real-time air density of the take-off area, and V is the effective stall speed of the target aircraft.

By adopting the above technical solution, firstly, the method provides a quantitative and accurate way to calculate the take-off distance of an aircraft. By using a specific calculation formula, it comprehensively considers multiple factors such as the take-off weight of the aircraft, the reference area of the wing, the real-time take-off thrust, the total drag coefficient, the effective stall speed, and the real-time air density of the take-off area. These factors are all key factors affecting the take-off performance of the aircraft. By incorporating them into the calculation formula, the take-off capability of the aircraft under specific conditions can be more accurately evaluated. Secondly, this calculation method improves the objectivity and scientificity of decision-making. Traditional decision-making methods often rely on experience or rough estimates, and it is difficult to ensure the accuracy and consistency of decisions. The decision-making method based on the calculation formula determines the take-off distance of the aircraft through objective data and calculations, reduces the interference of human factors, and makes the decision more objective and scientific. In addition, the method also improves the efficiency of decision-making. Traditional decision-making methods require the collection of a large amount of data and complex manual analysis, which is time-consuming and labor-intensive. Using this calculation formula, only the relevant parameters need to be input into the formula to quickly calculate the take-off distance, which greatly simplifies the decision-making process and improves the efficiency of decision-making.

Optionally, the total drag coefficient of the target aircraft is calculated using the following calculation formula:

;

Wherein, μ is the total drag coefficient of the target aircraft, _CD is the zero-lift drag coefficient of the target aircraft, _CS is the lift coefficient of the target aircraft, _AR is the wing aspect ratio of the target aircraft, and e is the wing efficiency factor of the target aircraft.

By adopting the above technical solution, firstly, the total drag coefficient of the aircraft can be calculated more accurately through this formula. The total drag coefficient is one of the important indicators for evaluating aircraft performance, which is directly related to the energy consumption, flight speed and take-off distance of the aircraft. The traditional drag coefficient calculation method may be too simplified or inaccurate, while the formula comprehensively considers multiple parameters such as zero-lift drag coefficient, lift coefficient, wing aspect ratio and wing efficiency factor, which can more comprehensively reflect the drag characteristics of the aircraft, thereby improving the accuracy of the calculation. Secondly, the use of this formula helps to improve the accuracy and reliability of aircraft take-off decisions. In aircraft take-off decisions, it is necessary to comprehensively consider multiple factors, including the take-off weight, thrust, drag coefficient and so on of the aircraft. By using this formula to calculate the total drag coefficient, we can more accurately evaluate the take-off performance of the aircraft under specific conditions, so as to make more reasonable decisions. This helps to avoid decision-making errors caused by inaccurate calculations and improves the safety and reliability of flight. In addition, the application of this formula also helps to optimize the design of the aircraft. By understanding the influence of various parameters on the total drag coefficient, designers can adjust the shape, size and structure of the aircraft in a targeted manner to reduce the drag coefficient and improve the aerodynamic performance of the aircraft. This not only helps reduce flight energy consumption and improve flight efficiency, but also helps enhance the overall performance and competitiveness of the aircraft.

Optionally, the effective stall speed of the target aircraft is calculated according to the windless stall speed and the real-time wind speed of the take-off area, and is specifically calculated using the following calculation formula:

;

Wherein, V is the effective stall speed of the target aircraft, V _S is the windless stall speed of the target aircraft, V _W is the real-time wind speed of the take-off area, and θ is the angle between the wind direction of the take-off area and the take-off direction of the target aircraft.

By adopting the above technical solution, firstly, this method provides an accurate calculation of the stall speed of the aircraft under specific wind conditions. The windless stall speed is the speed at which the aircraft reaches the stall state in the absence of wind, and in actual flight, wind speed and wind direction are inevitable factors. By introducing the calculation of the angle between the real-time wind speed and wind direction and the take-off direction, the stall performance of the aircraft in the real flight environment can be more accurately reflected. Secondly, this calculation method helps to improve the safety and reliability of aircraft take-off decisions. The calculation of the effective stall speed is one of the key parameters for evaluating the take-off performance of the aircraft, which is directly related to the stability and safety of the aircraft during the take-off process. By considering the influence of real-time wind speed and wind direction, the stall risk of the aircraft during the take-off phase can be more accurately evaluated, so as to make safer and more reliable decisions. In addition, the calculation method also reflects the comprehensive consideration of the flight environment. It not only considers the inherent performance parameters of the aircraft (such as the windless stall speed), but also combines the real-time flight environment information (such as wind speed and wind direction). This comprehensive consideration makes the calculation results closer to the actual flight situation and improves the pertinence and practicality of the decision. Finally, the use of this calculation formula also simplifies the decision-making process. By substituting parameters such as wind speed, wind direction and angle into the formula for calculation, the effective stall speed can be quickly obtained without complex data analysis and processing. This improves the efficiency of decision-making and enables pilots or decision-making systems to make accurate judgments in a short period of time.

Optionally, obtaining the influencing parameter group of the take-off area corresponding to the target aircraft specifically includes: receiving an original parameter group sent by a sensor device, wherein the sensor device is located in the take-off area corresponding to the target aircraft; preprocessing the original parameter group to obtain the influencing parameter group, wherein the preprocessing includes denoising, filtering and normalization.

By adopting the above technical solution, firstly, by receiving the original parameter group sent by the sensor device, various environmental information of the take-off area, such as wind speed, wind direction, air density, etc., can be obtained in real time. Sensor equipment usually has the characteristics of high sensitivity and fast response, and can accurately capture the changes in the environment of the take-off area, and provide timely and accurate data support for aircraft take-off decisions. Secondly, preprocessing the original parameter group is an important link to ensure data quality and decision accuracy. The preprocessing process includes denoising, filtering and normalization processing, which can effectively remove noise and interference in the data and improve the signal-to-noise ratio and reliability of the data. At the same time, filtering processing can smooth the fluctuations of the data and reduce random errors and outliers in the data. Normalization processing can convert data of different dimensions and ranges into a unified standard form, which is convenient for subsequent data analysis and processing. Through preprocessing, a more accurate and reliable influencing parameter group can be obtained, providing a more scientific and reasonable basis for aircraft take-off decisions. This helps to reduce decision-making errors caused by inaccurate data or errors, and improve the safety and reliability of flight. In addition, the method also has the advantages of real-time and automation. The sensor device can send data in real time, and the preprocessing process can also be implemented by an automated algorithm without manual intervention. This can greatly improve the efficiency of data acquisition and processing, reduce the impact of human factors on data quality, and improve the consistency and repeatability of decision-making.

Optionally, the method also includes: if it is determined that the take-off distance of the target aircraft is greater than the preset take-off distance threshold, determining that the take-off decision corresponding to the target aircraft is not allowed to take off from the take-off area, and generating an alarm signal; displaying the alarm signal through a display device to inform the staff.

By adopting the above technical solution, firstly, by determining that the take-off distance of the target aircraft is greater than the preset take-off distance threshold, the system can accurately judge the take-off safety of the aircraft under the current conditions. This judgment is based on accurate take-off distance calculation and threshold setting, so it has high accuracy and reliability. When the take-off distance of the aircraft exceeds the safe range, the system can respond quickly to prevent the aircraft from taking off under unsuitable conditions, thereby ensuring the safety of the flight. Secondly, the system can promptly notify the staff by generating an alarm signal and displaying it on the display device. This real-time information feedback mechanism enables the staff to quickly learn the take-off status of the aircraft and take corresponding measures. The display of the alarm signal also enhances the intuitiveness and readability of the information, allowing the staff to quickly understand and respond. In addition, this take-off decision process helps to reduce human errors and negligence. Through the automated take-off distance calculation and decision-making process, the system reduces the impact of human factors on decision-making and improves the objectivity and accuracy of decision-making. At the same time, the automatic generation and display of the alarm signal also avoids safety problems caused by negligence or misjudgment of the staff. Finally, this take-off decision process also improves flight efficiency. When the take-off distance of the aircraft exceeds the safe range, the system can promptly issue an alarm signal to avoid unnecessary take-off attempts and delays. This helps reduce aircraft waiting time and operating costs, improving the overall efficiency of flying.

Optionally, the method further includes: responding to a decision intervention operation of a user, the decision intervention operation including a user instruction; generating a target takeoff decision according to the user instruction; and sending the target takeoff decision to the target aircraft.

By adopting the above technical solution, firstly, it provides the ability for direct interaction between the user and the takeoff decision system. In practical applications, due to the variability and complexity of the flight environment, sometimes the automated decision may not be able to fully adapt to all situations. At this time, the user's experience and judgment are particularly important. By allowing users to intervene in the decision, the system can combine the automated decision and the user's professional knowledge to make more comprehensive and accurate decisions. Secondly, the introduction of user decision intervention increases the flexibility and adaptability of the decision. Different users may have different flight experience and risk preferences, and they can adjust and optimize the takeoff decision according to the actual situation. This flexibility enables the system to better meet the needs of different users and improves the pertinence and practicality of the decision. In addition, by sending the target takeoff decision to the target aircraft, the system can ensure that the decision is executed in a timely and accurate manner. This helps to reduce errors and delays in the decision execution process and improve the overall efficiency of the flight. At the same time, this direct decision transmission method also reduces the levels and links of information transmission, and improves the efficiency and accuracy of information transmission.

In a second aspect of the present application, a multi-factor aircraft takeoff decision device is provided, the multi-factor aircraft takeoff decision device comprising an acquisition module and a processing module, wherein the acquisition module is used to acquire an operating parameter group for a target aircraft, the operating parameter group comprising the takeoff weight, wing reference area, windless stall speed and real-time takeoff thrust of the target aircraft; the acquisition module is also used to acquire an influencing parameter group corresponding to a takeoff area of the target aircraft, the influencing parameter group comprising the total drag coefficient of the target aircraft, the real-time wind speed of the takeoff area and the real-time air density of the takeoff area; the processing module is used to acquire an influencing parameter group corresponding to a takeoff area of the target aircraft according to the windless stall speed and the real-time wind speed of the takeoff area. The processing module is further used to calculate the take-off distance of the target aircraft based on the take-off weight of the target aircraft, the wing reference area and the real-time take-off thrust, and the total drag coefficient of the target aircraft, the effective stall speed of the target aircraft and the real-time air density of the take-off area; the processing module is further used to determine the relationship between the take-off distance of the target aircraft and a preset take-off distance threshold; the processing module is further used to determine that the take-off decision corresponding to the target aircraft is to allow take-off from the take-off area if it is determined that the take-off distance of the target aircraft is less than the preset take-off distance threshold.

In the third aspect of the present application, an electronic device is provided, which includes a processor, a memory, a user interface and a network interface, the memory is used to store instructions, the user interface and the network interface are both used to communicate with other devices, and the processor is used to execute the instructions stored in the memory so that the electronic device performs the method described above.

In a fourth aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores instructions, and when the instructions are executed, the method described above is executed.

In summary, one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

1. The decision-making method is based on precise operating parameters and influencing parameters, including takeoff weight, wing reference area, no-wind stall speed, real-time takeoff thrust, total drag coefficient, real-time wind speed and real-time air density. These parameters can fully reflect the performance of the aircraft and the actual situation of the takeoff environment, thereby improving the accuracy and reliability of decision-making. Secondly, by calculating the effective stall speed and takeoff distance, the system can more accurately evaluate the takeoff capability of the aircraft under specific conditions. The effective stall speed takes into account the influence of the real-time wind speed, making the calculation closer to the actual flight situation; while the calculation of the takeoff distance comprehensively considers factors such as the aircraft's takeoff weight, thrust, and air density in the takeoff area, so as to more accurately determine whether the aircraft can take off safely within the limited runway length. In addition, the decision-making method also introduces the concept of a preset takeoff distance threshold. By comparing with the takeoff distance, the system can quickly make a decision to determine whether the aircraft is allowed to take off. This method not only improves the efficiency of decision-making, but also enables rapid response in emergency situations to ensure flight safety. Finally, the entire decision-making process is based on objective data and calculations, which reduces the interference of human factors, reduces the subjectivity and uncertainty of decision-making, and integrates the complex decision-making process into judging whether the take-off distance meets the threshold requirements, which helps to improve the consistency and repeatability of decisions, making aircraft take-off decisions more scientific and standardized, and improving decision-making efficiency;

2. This method provides a quantitative and accurate way to calculate the take-off distance of an aircraft. By using a specific calculation formula, it comprehensively considers multiple factors such as the aircraft's take-off weight, wing reference area, real-time take-off thrust, total drag coefficient, effective stall speed, and real-time air density in the take-off area. These factors are all key factors affecting the aircraft's take-off performance. By incorporating them into the calculation formula, the aircraft's take-off capability under specific conditions can be more accurately evaluated. Secondly, this calculation method improves the objectivity and scientificity of decision-making. Traditional decision-making methods often rely on experience or rough estimates, and it is difficult to ensure the accuracy and consistency of decisions. The decision-making method based on this calculation formula determines the take-off distance of the aircraft through objective data and calculations, reduces the interference of human factors, and makes the decision more objective and scientific. In addition, this method also improves the efficiency of decision-making. Traditional decision-making methods require the collection of a large amount of data and complex manual analysis, which is time-consuming and labor-intensive. Using this calculation formula, you only need to enter the relevant parameters into the formula to quickly calculate the take-off distance, which greatly simplifies the decision-making process and improves the efficiency of decision-making.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG1 is a flow chart of a multi-factor aircraft takeoff decision method provided in an embodiment of the present application.

FIG. 2 is another flow chart of a multi-factor aircraft takeoff decision method provided in an embodiment of the present application.

FIG3 is a module diagram of a multi-factor aircraft takeoff decision device provided in an embodiment of the present application.

FIG. 4 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.

Explanation of the reference numerals: 31, acquisition module; 32, processing module; 41, processor; 42, communication bus; 43, user interface; 44, network interface; 45, memory.

DETAILED DESCRIPTION

In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the drawings in the embodiments of this specification. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments.

In the description of the embodiments of the present application, words such as "for example" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "for example" or "for example" is intended to present related concepts in a specific way.

In the description of the embodiments of the present application, the meaning of the term "multiple" refers to two or more. For example, multiple systems refer to two or more systems, and multiple screen terminals refer to two or more screen terminals. In addition, the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. The terms "include", "comprise", "have" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

In the current aircraft takeoff decision-making process, the main technical difficulties are concentrated on data collection and processing. The traditional decision-making method requires the integration of a large amount of flight data, weather information, airport operation status, and aircraft real-time status, and then a detailed analysis and comparison of these data. This decision-making mechanism is not only inefficient, but also may not be able to make accurate judgments quickly when dealing with emergencies or complex situations, which may affect the safety and efficiency of the flight.

Specifically, most of the current aircraft takeoff decision systems rely on manual or semi-automatic data processing modes. The system needs to extract information from multiple data sources, such as real-time feedback from pilots, real-time data from ground weather stations, instructions from airport towers, etc., and then integrate, screen and compare the data. This process not only involves huge data calculation and storage work, but also requires human participation in data interpretation and judgment. This approach not only increases the subjectivity and uncertainty of decision-making, but also greatly reduces the efficiency of decision-making.

In order to solve the above technical problems, the present application provides a multi-factor aircraft takeoff decision method, with reference to FIG1 , which is a flow chart of a multi-factor aircraft takeoff decision method provided by an embodiment of the present application. The decision method is applied to a server, and includes steps S110 to S160, which are as follows:

S110, obtaining an operating parameter group for the target aircraft, where the operating parameter group includes the takeoff weight, wing reference area, no-wind stall speed, and real-time takeoff thrust of the target aircraft.

Specifically, a server is a computer program or device that processes requests and provides data or services. Here, the server is the server of the corresponding tower of the airport, which is responsible for obtaining the operating parameters of the aircraft under its jurisdiction. The operating parameter group is a set of data or values related to the operation of the aircraft, which is used to describe the status or performance of the aircraft. Among them, the take-off weight is the total weight of the aircraft at take-off, including the weight of the aircraft itself, fuel, cargo and passengers. The take-off weight has an important impact on the performance of the aircraft, such as it determines the take-off distance and take-off speed required by the aircraft. The wing reference area is the effective area of the wing, which is used to calculate lift and drag. This area is an indicator of the shape and size of the wing, affecting the lift performance and aerodynamic characteristics of the aircraft. The windless stall speed is the minimum speed at which the aircraft can maintain flight without stalling in the absence of any wind. This speed is crucial to flight safety because below this speed, the aircraft may not be able to maintain flight status. The real-time take-off thrust is the thrust generated by the aircraft at take-off. This thrust is generated by the aircraft's engine to overcome the weight and drag of the aircraft so that the aircraft can take off. The real-time take-off thrust will vary according to factors such as the aircraft's take-off weight, engine status and atmospheric conditions.

For example, suppose there is an airport control tower, and this center has a server responsible for monitoring and collecting the operating data of all aircraft. When an aircraft is ready to take off, the server will obtain the aircraft's takeoff weight (for example, 150 tons), wing reference area (for example, 300 square meters), no-wind stall speed (for example, 140 knots), and real-time takeoff thrust (for example, the current thrust is 100,000 pounds). These data will help the staff of the flight control center understand the real-time status and performance of the aircraft and ensure the safe takeoff and flight of the aircraft. In general, these operating parameters obtained by the server provide key data support for aircraft operation management and safety assurance.

S120, obtaining an influencing parameter group corresponding to the take-off area of the target aircraft, where the influencing parameter group includes a total drag coefficient of the target aircraft, a real-time wind speed in the take-off area, and a real-time air density in the take-off area.

Specifically, the takeoff area corresponding to the target aircraft refers to the specific location or area where the target aircraft plans to take off, for example, the takeoff area is a specific takeoff runway. The influencing parameter group is a group of data or values related to the takeoff area, which may affect the takeoff performance and safety of the aircraft. The total drag coefficient is a quantitative indicator of the total drag encountered by the aircraft during flight. This coefficient depends on factors such as the shape, size, surface roughness of the aircraft, and flight conditions (such as speed and altitude). The smaller the total drag coefficient, the smaller the drag encountered by the aircraft during flight and the better the performance. During takeoff, the wind has a great impact on the aircraft. Real-time wind speed provides information about the size and direction of the wind, which is crucial for evaluating the stability and safety of the aircraft during takeoff. Air density affects the lift and thrust of the aircraft. At higher altitudes or in hotter weather conditions, the air density may be lower, which will affect the performance of the aircraft. Real-time air density data helps to more accurately evaluate the performance of the aircraft under specific takeoff conditions.

For example, suppose an aircraft is preparing to take off from Runway 1 of an airport. While obtaining the aircraft's operating parameters such as takeoff weight and wing reference area, the server also obtains influencing parameters related to the takeoff area. Total drag coefficient: Assumed to be 0.025, this means that the aircraft encounters relatively little resistance during takeoff, which is beneficial to the aircraft's takeoff performance. Real-time wind speed: Assumed to be 5 meters per second, the wind direction is consistent with the runway direction, which means that the wind has a certain assist effect on the aircraft's takeoff, but the pilot also needs to pay attention to controlling the stability of the aircraft. Real-time air density: Assumed to be 1.225 kilograms per cubic meter, this is a standard sea level air density value, which means that the air conditions in the takeoff area are relatively normal. The server combines these influencing parameters with the operating parameters of the target aircraft to more accurately evaluate the performance and safety of the aircraft under specific takeoff conditions. This helps to provide takeoff decisions later and ensure the safe takeoff and flight of the aircraft.

In a possible implementation, obtaining an influencing parameter group corresponding to a take-off area of a target aircraft specifically includes: receiving an original parameter group sent by a sensor device, where the sensor device is located in the take-off area corresponding to the target aircraft; preprocessing the original parameter group to obtain an influencing parameter group, where the preprocessing includes denoising, filtering, and normalization.

Specifically, the sensor equipment is a variety of measuring equipment pre-installed in the take-off area, which is used to monitor and record environmental parameters in real time, such as wind speed, wind direction, air density, etc. The original parameter group is the data directly measured by these sensor devices. They are usually unprocessed raw signals containing noise and interference. The server receives these original parameter groups through the network or other communication methods to provide a data basis for subsequent preprocessing and analysis. Preprocessing is a series of operations on the original parameter group to eliminate noise, interference and outliers, and improve the accuracy and reliability of the data. Denoising is used to remove the noise components in the original parameters, which is usually achieved through filtering algorithms, such as smoothing filtering, median filtering, etc. Denoising helps to reduce measurement errors and improve data accuracy. Filtering is used to further filter the parameters to eliminate interference such as high-frequency noise or low-frequency drift. The filtering operation helps to extract effective information from the parameters and reduce the interference of irrelevant information. Normalization converts the parameter values to a unified range or scale for subsequent comparison and analysis. Normalization helps to eliminate the dimensional differences between different parameters and make them comparable on the same scale. The parameter groups after preprocessing are the influencing parameter groups, which have eliminated noise, interference and outliers and have a uniform scale and range. These influencing parameter groups can be directly used to evaluate the takeoff performance and safety of the target aircraft and provide accurate data support for flight decision-making. In the embodiment of the present application, when the server obtains the operating parameter group, it will also be preprocessed, which will not be repeated here.

For example, suppose that a takeoff runway at an airport is equipped with multiple sensor devices for real-time monitoring of parameters such as wind speed, wind direction, and air density. When the target aircraft is ready to take off, the server begins to receive the original parameter groups sent by these sensor devices. The original parameter groups may contain fluctuations and outliers caused by factors such as environmental noise and equipment errors. In order to obtain an accurate influencing parameter group, the server preprocesses these original parameters. First, the noise components in the parameters are removed by a denoising algorithm; then, a filtering algorithm is used to further filter out high-frequency noise or low-frequency drift; finally, normalization is performed to convert all parameter values to the range of 0 to 1.

S130. Calculate the effective stall speed of the target aircraft according to the no-wind stall speed and the real-time wind speed in the take-off area.

Specifically, the server will also calculate the effective stall speed of the target aircraft based on the windless stall speed and the real-time wind speed in the take-off area.

In a possible implementation, the effective stall speed of the target aircraft is calculated based on the no-wind stall speed and the real-time wind speed in the take-off area, specifically using the following calculation formula:

;

Among them, V is the effective stall speed of the target aircraft, V _S is the windless stall speed of the target aircraft, V _W is the real-time wind speed in the take-off area, and θ is the angle between the wind direction in the take-off area and the take-off direction of the target aircraft.

Specifically, this angle describes the relationship between the direction of the wind and the direction in which the aircraft takes off. The size of the angle determines the degree of influence of the wind on the aircraft speed. For example, when the wind is consistent with the direction in which the aircraft takes off, the wind has a boosting effect on the speed of the aircraft; when the wind is perpendicular to the direction in which the aircraft takes off, the influence of the wind is mainly reflected in the crosswind. Among them, the effective stall speed is the stall speed after taking into account the influence of the wind. cosθ is the cosine value of the angle between the wind direction and the take-off direction, which is used to calculate the component of the wind in the direction in which the aircraft takes off. This component is divided by 2 because the influence of the wind is usually not completely on the aircraft, but partially. Therefore, the influence of the wind is added to the windless stall speed to obtain the effective stall speed.

For example, suppose an aircraft has a no-wind stall speed V _S of 100 knots. At takeoff, the real-time wind speed V _W in the takeoff area is 20 knots, and the angle θ between the wind direction and the takeoff direction is 30 degrees. According to the formula, the effective stall speed can be calculated to be 108.66 knots. Therefore, the effective stall speed of this aircraft under the current wind conditions is about 108.66 knots. This means that in actual flight, when the aircraft speed is lower than this value, the aircraft may stall, requiring the pilot to pay special attention and control.

S140, based on the takeoff weight, wing reference area and real-time takeoff thrust of the target aircraft, and the total drag coefficient of the target aircraft, the effective stall speed of the target aircraft and the real-time air density of the takeoff area, calculate the takeoff distance of the target aircraft.

Specifically, the server will also calculate the takeoff distance of the target aircraft based on the takeoff weight, wing reference area and real-time thrust of the target aircraft, and the total drag coefficient, effective stall speed and real-time air density of the takeoff area of the target aircraft.

In a possible implementation, the takeoff distance of the target aircraft is calculated based on the takeoff weight, wing reference area, and real-time takeoff thrust of the target aircraft, the total drag coefficient of the target aircraft, the effective stall speed of the target aircraft, and the real-time air density of the takeoff area. Specifically, the takeoff distance of the target aircraft is calculated using the following calculation formula:

;

Among them, D is the take-off distance of the target aircraft, T is the real-time take-off thrust of the target aircraft, μ is the total drag coefficient of the target aircraft, W is the take-off weight of the target aircraft, S is the wing reference area of the target aircraft, ρ is the real-time air density in the take-off area, and V is the effective stall speed of the target aircraft.

Specifically, the takeoff distance is the target that needs to be calculated, which represents the total distance required for the aircraft to take off from a standstill. The real-time takeoff thrust is the thrust generated by the aircraft engine in real time, which is used to propel the aircraft forward and accelerate. The total drag coefficient is a coefficient that combines the various resistances encountered by the aircraft during flight, which affects the acceleration performance and takeoff distance of the aircraft. The takeoff weight of the aircraft is the total weight of the aircraft and the load carried, which determines the thrust and lift required for the aircraft to take off. The wing reference area affects the lift performance of the aircraft. The larger the wing area, the greater the lift that can be generated. The air density in the takeoff area affects the lift and drag of the aircraft. The higher the air density, the greater the lift generated under the same thrust. The effective stall speed is the stall speed after taking into account the influence of wind. It is the minimum speed at which the aircraft can maintain stable flight during flight.

For example, suppose there is a civil airliner that needs to take off, the takeoff weight is 50,000 kg, the wing reference area is 200 square meters, the real-time takeoff thrust is 150,000 Newtons, the total drag coefficient is 0.03, the effective stall speed is 60 meters per second, and the real-time air density in the takeoff area is 1.225 kg/cubic meter. The server substitutes these values into the calculation formula and obtains a takeoff distance of approximately 3,405.44 meters.

In a possible implementation manner, the total drag coefficient of the target aircraft is specifically calculated using the following calculation formula:

;

Among them, μ is the total drag coefficient of the target aircraft, _CD is the zero-lift drag coefficient of the target aircraft, _CS is the lift coefficient of the target aircraft, _AR is the wing aspect ratio of the target aircraft, and e is the wing efficiency factor of the target aircraft.

Specifically, the total drag coefficient is an important parameter that describes the total drag experienced by an aircraft during flight. The smaller the drag coefficient, the smaller the drag encountered by the aircraft during flight, which is conducive to reducing energy consumption and improving flight efficiency. The zero-lift drag coefficient is the drag coefficient experienced by an aircraft when no lift is generated, which is related to factors such as the shape of the aircraft, surface roughness, and flight speed. The lift coefficient describes the efficiency of an aircraft in generating lift. Lift is the key force that enables an aircraft to take off and fly, and the lift coefficient measures the relative relationship between this force and the airflow. The aspect ratio of a wing is the ratio of the span of the wing to the mean aerodynamic chord, that is, the distance between the leading and trailing edges of the airfoil. This ratio affects the aerodynamic performance of the wing, including lift and drag. The wing efficiency factor is a parameter that measures the aerodynamic efficiency of a wing. It takes into account the wing shape, airfoil, and the interaction between the wing and the airflow, and has a direct impact on the drag coefficient.

For example, suppose there is a civil airliner with a zero-lift drag coefficient of 0.025, a lift coefficient of 0.8, a wing aspect ratio of 10, and a wing efficiency factor of 0.9. According to the above calculation formula, the total drag coefficient is about 0.047.

In a possible implementation manner, the calculation formula for the real-time air density in the take-off area is as follows:

;

Wherein, ρ _ref is the air density of the take-off area under standard atmospheric conditions, T _ref is the temperature of the take-off area under standard atmospheric conditions, and T is the real-time temperature of the take-off area.

Specifically, real-time air density refers to the actual air density in the takeoff area when the aircraft takes off. Air density is the mass of air per unit volume, which affects the lift and drag of the aircraft, thereby affecting the takeoff distance. Among them, standard atmospheric conditions refer to the state of air at sea level, under certain temperature and pressure conditions. Temperature has a direct impact on air density, because changes in temperature will cause changes in the movement of air molecules, thereby affecting the air mass per unit volume.

S150: Determine the magnitude relationship between the take-off distance of the target aircraft and a preset take-off distance threshold.

S160: If it is determined that the take-off distance of the target aircraft is less than the preset take-off distance threshold, determine that the take-off decision corresponding to the target aircraft is to allow take-off from the take-off area.

Specifically, first, the server needs to calculate the takeoff distance of the target aircraft. This distance is determined based on various parameters of the aircraft (such as takeoff weight, thrust, wing area, etc.) and environmental conditions of the takeoff area (such as air density). The calculated takeoff distance reflects the horizontal distance required for the aircraft to accelerate from a standstill to a sufficient speed to leave the ground. Among them, the preset takeoff distance threshold is a pre-set safety standard, which represents the minimum safe distance required to take off from a specific takeoff area. This threshold is determined based on factors such as runway length, obstacle distance, and safety margin, and is intended to ensure that the aircraft has sufficient safety space during takeoff. Next, the server compares the calculated takeoff distance with the preset takeoff distance threshold. This comparison process is to determine whether the takeoff distance of the aircraft meets safety requirements. Based on the comparison result, the server makes a takeoff decision. If the calculated takeoff distance is less than the preset takeoff distance threshold, that is, the takeoff distance required by the aircraft is within the runway length and safety margin, the decision is to allow the aircraft to take off from the takeoff area, which means that the aircraft can safely leave the ground and fly to the destination under current conditions. Among them, when the takeoff distance is equal to the preset takeoff distance threshold, the server can notify and display this situation and hand it over to the pilot or tower management personnel for decision.

For example, suppose the calculated takeoff distance of the target aircraft is 1800 meters, and the preset takeoff distance threshold is 1600 meters. In this case, the takeoff distance of the aircraft is greater than the threshold, so it meets the unsafe requirement, and the decision will be not to allow the aircraft to take off from the current takeoff area. However, if the calculated takeoff distance of the target aircraft is 1400 meters, and the preset takeoff distance threshold is still 1600 meters, then the takeoff distance of the aircraft is less than the threshold. According to the decision-making process, this will allow the aircraft to take off from the takeoff area because the required takeoff distance is within the safety range. This decision-making process ensures the safety of the aircraft's takeoff and avoids potential risks caused by insufficient runway length or approaching obstacles. At the same time, it also provides a clear guide for pilots and tower managers to make data-based and safe decisions. Therefore, the entire decision-making process is based on objective data and calculations, reducing the interference of human factors, reducing the subjectivity and uncertainty of decision-making, and integrating the complicated decision-making process into judging whether the takeoff distance meets the threshold requirements, which helps to improve the consistency and repeatability of decisions, making aircraft takeoff decisions more scientific and standardized, and improving decision-making efficiency.

In a possible implementation, the method further includes: if it is determined that the take-off distance of the target aircraft is greater than a preset take-off distance threshold, determining that the take-off decision corresponding to the target aircraft is not allowed to take off from the take-off area, and generating an alarm signal; displaying the alarm signal through a display device to inform the staff.

Specifically, first, the server has calculated the takeoff distance of the target aircraft and compared it with the preset takeoff distance threshold. If the takeoff distance is greater than the threshold, it means that under the current conditions, the aircraft cannot take off safely from the designated takeoff area. Therefore, the server makes a decision not to allow the aircraft to take off from the area. Once it is determined that takeoff is not allowed, the server will immediately generate an alarm signal. This alarm signal is a notification mechanism used to alert the tower staff to pay attention to the current situation and take necessary measures. The generated alarm signal can be displayed through a tower display device such as a monitoring screen or a mobile device, or the server can be linked to the dashboard of the target aircraft. In this way, not only can the staff see the alarm information immediately, but the pilot can also understand the current situation where the aircraft cannot take off. Among them, the purpose of the alarm signal is to ensure that the staff can quickly learn about the situation and take appropriate actions as needed. This may include notifying the pilot to change the takeoff plan, check the aircraft's parameter settings, evaluate the takeoff conditions, or take other safety measures, which will not be repeated here.

In a possible implementation, referring to FIG. 2 , FIG. 2 is another flow chart of a multi-factor aircraft takeoff decision method provided in an embodiment of the present application, including steps S210 to S230, the steps being as follows: S210, responding to a user's decision intervention operation, the decision intervention operation including a user instruction; S220, generating a target takeoff decision according to the user instruction; S230, sending the target takeoff decision to the target aircraft.

Specifically, the user may want to intervene in the takeoff decision identified by the server for some reason or consideration. The user is a tower staff. This intervention can be achieved through specific operations or instructions, such as pressing a button, selecting a specific option, or entering a specific command. The server can recognize and receive these operations or instructions from the user. Once the user instruction is received, the server will parse it and understand the user's intentions and requirements. Based on the parsed user instructions, the server will generate a target takeoff decision. This decision may be to allow the aircraft to take off, not to allow the aircraft to take off, or other specific instructions related to takeoff. The generated target takeoff decision will be sent to the target aircraft. After the aircraft receives this decision, the crew pilot will perform corresponding operations according to the content of the decision, such as starting the takeoff procedure, staying on standby, or performing other related operations.

For example, suppose that an airport server is automatically calculating and deciding the takeoff decision of an aircraft. According to the server's calculation, the takeoff distance of the aircraft exceeds the preset takeoff distance threshold, so the server automatically decides not to allow the aircraft to take off and generates a corresponding alarm signal. However, at this time, a tower staff member at the airport realizes that due to a sudden change in weather conditions (such as a change in wind direction), the actual takeoff conditions of the aircraft may have improved. Based on this judgment, the staff member decides to intervene in the automatic decision of the server. He selects an option of "allow takeoff" through the operation interface on the console and sends the corresponding user instruction. After receiving this user instruction, the server will immediately parse and understand the staff member's intention. Then, the server will generate a new target takeoff decision, that is, to allow the aircraft to take off, and send this decision to the target aircraft. After the aircraft receives this new takeoff decision, the pilot will start to execute the takeoff procedure and prepare for takeoff. As a result, the server can make more flexible and accurate decisions based on actual conditions, thereby ensuring the safety and efficiency of flight.

The present application also provides a multi-factor aircraft takeoff decision device, referring to FIG. 3 , which is a module schematic diagram of a multi-factor aircraft takeoff decision device provided in an embodiment of the present application. The takeoff decision device is a server, and the server includes an acquisition module 31 and a processing module 32, wherein the acquisition module 31 acquires an operating parameter group for a target aircraft, the operating parameter group includes the takeoff weight, wing reference area, windless stall speed and real-time takeoff thrust of the target aircraft; the acquisition module 31 acquires an influencing parameter group corresponding to a takeoff area of the target aircraft, the influencing parameter group includes a total drag coefficient of the target aircraft, a real-time wind speed in the takeoff area and a real-time air density in the takeoff area; the processing module 32 calculates the effective stall speed of the target aircraft according to the windless stall speed and the real-time wind speed in the takeoff area; the processing module 32 calculates the takeoff distance of the target aircraft based on the takeoff weight, wing reference area and real-time takeoff thrust of the target aircraft, and the total drag coefficient of the target aircraft, the effective stall speed of the target aircraft and the real-time air density in the takeoff area; the processing module 32 determines the size relationship between the takeoff distance of the target aircraft and a preset takeoff distance threshold; if the processing module 32 determines that the takeoff distance of the target aircraft is less than the preset takeoff distance threshold, then determines that the takeoff decision corresponding to the target aircraft is to allow takeoff from the takeoff area.

In a possible implementation, the processing module 32 calculates the takeoff distance of the target aircraft based on the takeoff weight, wing reference area, and real-time takeoff thrust of the target aircraft, the total drag coefficient of the target aircraft, the effective stall speed of the target aircraft, and the real-time air density of the takeoff area, and specifically uses the following calculation formula:

;

Among them, D is the take-off distance of the target aircraft, T is the real-time take-off thrust of the target aircraft, μ is the total drag coefficient of the target aircraft, W is the take-off weight of the target aircraft, S is the wing reference area of the target aircraft, ρ is the real-time air density in the take-off area, and V is the effective stall speed of the target aircraft.

In a possible implementation manner, the total drag coefficient of the target aircraft is specifically calculated using the following calculation formula:

;

Among them, μ is the total drag coefficient of the target aircraft, _CD is the zero-lift drag coefficient of the target aircraft, _CS is the lift coefficient of the target aircraft, _AR is the wing aspect ratio of the target aircraft, and e is the wing efficiency factor of the target aircraft.

In a possible implementation, the processing module 32 calculates the effective stall speed of the target aircraft according to the windless stall speed and the real-time wind speed in the take-off area, specifically using the following calculation formula:

;

Among them, V is the effective stall speed of the target aircraft, V _S is the windless stall speed of the target aircraft, V _W is the real-time wind speed in the take-off area, and θ is the angle between the wind direction in the take-off area and the take-off direction of the target aircraft.

In a possible implementation, the acquisition module 31 acquires the influencing parameter group of the take-off area corresponding to the target aircraft, specifically including: the acquisition module 31 receives the original parameter group sent by the sensor device, and the sensor device is located in the take-off area corresponding to the target aircraft; the processing module 32 preprocesses the original parameter group to obtain the influencing parameter group, and the preprocessing includes denoising, filtering and normalization.

In one possible implementation, if the processing module 32 determines that the take-off distance of the target aircraft is greater than a preset take-off distance threshold, it determines that the take-off decision corresponding to the target aircraft is not allowed to take off from the take-off area, and generates an alarm signal; the processing module 32 displays the alarm signal through a display device to inform the staff.

In a possible implementation, the processing module 32 responds to a user's decision intervention operation, which includes a user instruction; the processing module 32 generates a target takeoff decision according to the user instruction; and the processing module 32 sends the target takeoff decision to the target aircraft.

It should be noted that: when the device provided in the above embodiment realizes its function, only the division of the above functional modules is used as an example. In actual application, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device is divided into different functional modules to complete all or part of the functions described above. In addition, the device and method embodiments provided in the above embodiment belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

The present application also provides an electronic device, referring to Figure 4, which is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include: at least one processor 41, at least one network interface 44, a user interface 43, a memory 45, and at least one communication bus 42.

The communication bus 42 is used to realize the connection and communication between these components.

The user interface 43 may include a display screen (Display) and a camera (Camera). Optionally, the user interface 43 may also include a standard wired interface and a wireless interface.

The network interface 44 may optionally include a standard wired interface or a wireless interface (such as a WI-FI interface).

Among them, the processor 41 may include one or more processing cores. The processor 41 uses various interfaces and lines to connect various parts in the entire server, and executes various functions of the server and processes data by running or executing instructions, programs, code sets or instruction sets stored in the memory 45, and calling data stored in the memory 45. Optionally, the processor 41 can be implemented in at least one hardware form of digital signal processing (Digital Signal Processing, DSP), field programmable gate array (Field-Programmable Gate Array, FPGA), and programmable logic array (Programmable Logic Array, PLA). The processor 41 can integrate one or a combination of a central processing unit (Central Processing Unit, CPU), a graphics processing unit (Graphics Processing Unit, GPU) and a modem. Among them, the CPU mainly processes the operating system, user interface and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to process wireless communications. It can be understood that the above-mentioned modem may not be integrated into the processor 41, and it can be implemented separately through a chip.

The memory 45 may include a random access memory (RAM) or a read-only memory (ROM). Optionally, the memory 45 includes a non-transitory computer-readable storage medium. The memory 45 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 45 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned various method embodiments, etc.; the data storage area may store data involved in the above-mentioned various method embodiments, etc. The memory 45 may also be at least one storage device located away from the aforementioned processor 41. As shown in FIG. 4 , the memory 45 as a computer storage medium may include an operating system, a network communication module, a user interface module, and an application program for a multi-factor aircraft takeoff decision method.

In the electronic device shown in FIG4 , the user interface 43 is mainly used to provide an input interface for the user and obtain data input by the user; and the processor 41 can be used to call an application program storing a multi-factor aircraft takeoff decision method in the memory 45. When executed by one or more processors, the electronic device executes one or more methods in the above-mentioned embodiments.

It should be noted that, for the above-mentioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the order of the actions described, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required for the present application.

The present application also provides a computer-readable storage medium, which stores instructions. When executed by one or more processors, the electronic device executes one or more of the methods described in the above embodiments.

In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

In the several embodiments provided in this application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of units, which 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 integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, 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 solution of this embodiment.

In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a memory and includes several instructions for a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the various embodiments of the present application. The aforementioned memory includes: various media that can store program codes, such as USB flash drives, mobile hard drives, magnetic disks or optical disks.

The above is only an exemplary embodiment of the present disclosure, and the scope of the present disclosure cannot be limited thereto. That is, any equivalent changes and modifications made according to the teachings of the present disclosure are still within the scope of the present disclosure. After considering the disclosure of the specification and the truth of practice, it will be easy for those skilled in the art to think of other embodiments of the present disclosure. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary technical means in the technical field that are not recorded in the present disclosure. The description and examples are regarded as exemplary only, and the scope and spirit of the present disclosure are defined by the claims.

Claims (10)

1. A multi-factor aircraft takeoff decision method, characterized in that the method comprises: Acquire an operating parameter group for a target aircraft, the operating parameter group comprising a takeoff weight, a wing reference area, a no-wind stall speed, and a real-time takeoff thrust of the target aircraft; Acquire an influencing parameter group corresponding to the take-off area of the target aircraft, wherein the influencing parameter group includes a total drag coefficient of the target aircraft, a real-time wind speed of the take-off area, and a real-time air density of the take-off area; Calculating the effective stall speed of the target aircraft according to the no-wind stall speed and the real-time wind speed of the take-off area; Calculate the takeoff distance of the target aircraft based on the takeoff weight of the target aircraft, the wing reference area and the real-time takeoff thrust, the total drag coefficient of the target aircraft, the effective stall speed of the target aircraft and the real-time air density of the takeoff area; Determine the magnitude relationship between the take-off distance of the target aircraft and a preset take-off distance threshold; If it is determined that the take-off distance of the target aircraft is less than the preset take-off distance threshold, then the take-off decision corresponding to the target aircraft is determined to be allowing take-off from the take-off area. 2. The multi-factor aircraft takeoff decision method according to claim 1, characterized in that the takeoff distance of the target aircraft is calculated based on the takeoff weight of the target aircraft, the wing reference area and the real-time takeoff thrust, the total drag coefficient of the target aircraft, the effective stall speed of the target aircraft and the real-time air density of the takeoff area, and is specifically calculated using the following calculation formula: ; Wherein, D is the take-off distance of the target aircraft, T is the real-time take-off thrust of the target aircraft, μ is the total drag coefficient of the target aircraft, W is the take-off weight of the target aircraft, S is the wing reference area of the target aircraft, ρ is the real-time air density of the take-off area, and V is the effective stall speed of the target aircraft. 3. The multi-factor aircraft takeoff decision method according to claim 1, characterized in that the total drag coefficient of the target aircraft is calculated using the following calculation formula: ; Wherein, μ is the total drag coefficient of the target aircraft, _CD is the zero-lift drag coefficient of the target aircraft, _CS is the lift coefficient of the target aircraft, _AR is the wing aspect ratio of the target aircraft, and e is the wing efficiency factor of the target aircraft. 4. The multi-factor aircraft takeoff decision method according to claim 1, characterized in that the effective stall speed of the target aircraft is calculated based on the no-wind stall speed and the real-time wind speed of the takeoff area, and is specifically calculated using the following calculation formula: ; Wherein, V is the effective stall speed of the target aircraft, V _S is the windless stall speed of the target aircraft, V _W is the real-time wind speed of the take-off area, and θ is the angle between the wind direction of the take-off area and the take-off direction of the target aircraft. 5. The multi-factor aircraft takeoff decision method according to claim 1, characterized in that the step of obtaining the influencing parameter group of the takeoff area corresponding to the target aircraft specifically comprises: Receiving an original parameter group sent by a sensor device, wherein the sensor device is located in a take-off area corresponding to the target aircraft; The original parameter group is preprocessed to obtain the influencing parameter group, wherein the preprocessing includes denoising, filtering and normalization processing. 6. The multi-factor aircraft takeoff decision method according to claim 1, characterized in that the method further comprises: If it is determined that the take-off distance of the target aircraft is greater than the preset take-off distance threshold, determining that the take-off decision corresponding to the target aircraft is not allowed to take off from the take-off area, and generating an alarm signal; The alarm signal is displayed on a display device so that the staff can be informed. 7. The multi-factor aircraft takeoff decision method according to claim 1, characterized in that the method further comprises: In response to a decision intervention operation of a user, the decision intervention operation includes a user instruction; generating a target takeoff decision according to the user instruction; The target takeoff decision is sent to the target aircraft. 8. A multi-factor aircraft takeoff decision device, characterized in that the multi-factor aircraft takeoff decision device comprises an acquisition module (31) and a processing module (32), wherein: The acquisition module (31) is used to acquire an operating parameter group for a target aircraft, wherein the operating parameter group includes a take-off weight, a wing reference area, a no-wind stall speed, and a real-time take-off thrust of the target aircraft; The acquisition module (31) is further used to acquire an influencing parameter group corresponding to the take-off area of the target aircraft, wherein the influencing parameter group includes a total drag coefficient of the target aircraft, a real-time wind speed of the take-off area, and a real-time air density of the take-off area; The processing module (32) is used to calculate the effective stall speed of the target aircraft according to the windless stall speed and the real-time wind speed of the take-off area; The processing module (32) is further used to calculate the take-off distance of the target aircraft based on the take-off weight of the target aircraft, the wing reference area and the real-time take-off thrust, the total drag coefficient of the target aircraft, the effective stall speed of the target aircraft and the real-time air density of the take-off area; The processing module (32) is further used to determine the magnitude relationship between the take-off distance of the target aircraft and a preset take-off distance threshold; The processing module (32) is further configured to determine that the take-off decision corresponding to the target aircraft is to allow take-off from the take-off area if it is determined that the take-off distance of the target aircraft is less than the preset take-off distance threshold. 9. An electronic device, characterized in that the electronic device comprises a processor (41), a memory (45), a user interface (43) and a network interface (44), the memory (45) being used to store instructions, the user interface (43) and the network interface (44) being used to communicate with other devices, and the processor (41) being used to execute the instructions stored in the memory (45) so that the electronic device executes the method according to any one of claims 1 to 7. 10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores instructions, and when the instructions are executed, the method according to any one of claims 1 to 7 is executed.