Internet-of-things-based management system and method for transmission of airship data

Abstract

An Internet-of-Things-based management system and method for transmission of airship data. In the method, a state prediction module performs comprehensive calculation on multi-source data on the basis of the positive and negative correlation of the multi-source data, and then generates an airship index, and predicts the flight state of an airship by means of a comparison result between the airship index and an anomaly threshold value; when it is predicted that the flight state of the airship is poor, a remote control platform automatically takes over a flight control module, and an airship base station operator controls the use of the flight control module by means of the remote control platform; when it is predicted that the flight state of the airship is poor, the remote control platform first searches for the nearest airship base station to the airship; and if the airship base station meets an airship landing condition, the airship base station operator can control the airship to land at the nearest base station. When an airship flies, multi-source data is effectively analyzed to predict the flight state of the airship, so that the airship can be controlled and managed in a timely manner before the airship is abnormal, thereby ensuring the safe use of the airship.

Description

An airship data transmission management system and method based on the Internet of Things Technical Field

[0001] The present invention relates to the technical field of data management systems, and in particular to an airship data transmission management system and method based on the Internet of Things. Background Art

[0002] An airship is a lightweight aircraft that is usually inflated with gas to maintain buoyancy. It can float in the atmosphere and fly at relatively low speeds. Airships are often used for missions that require long-term hovering or cruising. An airship data transmission management system is a system specifically designed to manage and monitor data transmission and communications of airships (also known as balloons or floating aircraft). This system has a wide range of uses in various applications.

[0003] The existing technology has the following deficiencies:

[0004] During flight, an airship automatically cruises along its route primarily through the pilot's control. However, in existing technologies, management systems typically only issue alerts when they detect an anomaly in the airship. The management system has no predictive control over airship anomalies. When an anomaly occurs, it indicates that the airship may be unable to support flight. This leaves little time for emergency operations, which can easily lead to the airship crashing and pose a significant safety hazard.

[0005] Summary of the Invention

[0006] The purpose of the present invention is to provide an airship data transmission management system and method based on the Internet of Things to address the shortcomings of the background technology.

[0007] In order to achieve the above-mentioned object, the present invention provides the following technical solutions: an airship data transmission management system based on the Internet of Things, comprising a flight control module, a data acquisition module, a state prediction module, a communication module and a remote control platform;

[0008] Flight control module: used to control the flight management of the airship. The flight control module is operated by the pilot or controlled by the remote control platform. It wakes up the data acquisition module after the airship is in flight.

[0009] Data acquisition module: During the flight of the airship, it collects multi-source data related to the airship in real time and pre-processes the multi-source data;

[0010] State prediction module: This module generates an airship index by comprehensively calculating the positive and negative relationship of multi-source data. The airship's flight state is predicted by comparing the airship index with the abnormal threshold.

[0011] Communication module: used for transmitting prediction results, and transmitting the prediction results to the remote control platform based on the Internet of Things technology;

[0012] Remote control platform: When the airship's flight status is predicted to be poor, it automatically takes over the flight control module and searches for the airship base station closest to the airship.

[0013] Preferably, the data acquisition module collects multi-source data related to the airship in real time, the multi-source data including airship data, flight data and environmental data. The airship data includes the discrete degree of the pressure difference between the inside and outside of the aircraft and the propulsion system state coefficient. The flight data includes the trajectory deviation coefficient and the abnormal flight rate. The environmental data includes the environmental impact regression coefficient.

[0014] Preferably, the state prediction module generates the airship index ft after comprehensively calculating the multi-source data based on the positive and negative relationship of the multi-source data. z , the calculation expression is:

[0015] Where yx h is the environmental impact regression coefficient, pl f is the trajectory deviation coefficient, yc f is the abnormal flight rate, qc t is the discrete degree of pressure difference between inside and outside the aircraft, zj t is the propulsion system state coefficient, α and β are the proportional coefficients of the dispersion degree of the pressure difference between the inside and outside of the aircraft and the propulsion system state coefficient, respectively, γ is the proportional coefficient of the trajectory deviation coefficient and the abnormal flight rate, and δ is the proportional coefficient of the environmental impact regression coefficient. α, β, γ, and δ are all greater than 0.

[0016] Preferably, the state prediction module obtains the airship index ft z After the value, the airship index ft z The value is compared with the abnormal threshold. If the airship index ft z Value < abnormal threshold, the airship's flight status is predicted to be poor. If the airship index ft z If the value is ≥ the abnormal threshold, the airship is predicted to be in good flight condition.

[0017] Preferably, the discrete degree of pressure difference between the inside and outside of the aircraft qc t The calculation expression is:

[0018] Where i = {1, 2, 3, ..., n}, n represents the number of sampling points set on the aircraft, n is a positive integer, Q i represents the internal and external pressure difference at the i-th sampling point, μ represents the average internal and external pressure difference, and

[0019] Preferably, the propulsion system state coefficient zj t The calculation expression is:

[0020] Preferably, the trajectory deviation coefficient pl f The calculation expression of pl is: f =∑(yj-(a*xj+b)) / m;

[0021] Where (xj, yj) is the actual trajectory point during the airship flight, m ​​is the number of actual trajectory points obtained, a is the slope, and b is the intercept.

[0022] Preferably, the abnormal flight rate yc f The calculation expression is:

[0023] Where ycs is the total duration of abnormal behavior of the airship pilot, and zsc is the flight time of the airship.

[0024] Preferably, the environmental impact regression coefficient yx h The calculation expression is:

[0025] Wherein, z is the output of the linear regression of environmental impact, and the calculation expression of the output z of the linear regression of environmental impact is: z=w0+w1hj1+w2hj2+w3hj3;

[0026] Where w0, w1, w2, and w3 are the weights of the parameters in the regression model, hj1 is the ambient wind speed, hj2 is the ambient air pressure, and hj3 is the ambient humidity.

[0027] The present invention also provides an airship data transmission management method based on the Internet of Things, the management method comprising the following steps:

[0028] S1: During the flight of the airship, the monitoring terminal collects multi-source data related to the airship in real time and pre-processes the multi-source data;

[0029] S2: The processing end generates the airship index by comprehensively calculating the multi-source data based on the positive and negative relationship of the multi-source data;

[0030] S3: The flight status of the airship is predicted by comparing the airship index with the abnormal threshold. The prediction result is transmitted to the ground control center based on the Internet of Things technology;

[0031] S4: When the airship's flight status is predicted to be poor, the ground control center automatically takes over and controls the airship's flight;

[0032] S5: The operator controls the flight of the airship through the ground control center.

[0033] In the above technical solution, the technical effects and advantages provided by the present invention are:

[0034] 1. The present invention uses a state prediction module to comprehensively calculate multi-source data based on the positive and negative proportional relationship of the multi-source data and then generates an airship index. The flight state of the airship is predicted by comparing the airship index with an abnormality threshold. When the airship's flight state is predicted to be poor, the remote control platform automatically takes over the flight control module, and the airship base station operator controls the use of the flight control module through the remote control platform. When the airship's flight state is predicted to be poor, the remote control platform first searches for the airship base station closest to the airship. If the airship base station meets the airship landing conditions, the airship base station operator controls the airship to land at the nearest base station. If there is no nearest airship base station nearby or no airship base station meets the landing conditions, the airship is controlled to return or land in an open area. The management system effectively analyzes multi-source data to predict the airship's flight state while the airship is in flight, thereby enabling timely control and management before any abnormality occurs in the airship, ensuring the safe use of the airship.

[0035] 2. The present invention uses the state prediction module to comprehensively calculate the dispersion degree of the pressure difference between the inside and outside of the aircraft, the propulsion system state coefficient, the trajectory deviation coefficient, the abnormal flight rate and the environmental impact regression coefficient to obtain the airship index ft z , not only makes the analysis more extensive and comprehensive, but also effectively improves the efficiency of data processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments described in the present invention. For ordinary technicians in this field, other drawings can also be obtained based on these drawings.

[0037] FIG1 is a system module diagram of the present invention. DETAILED DESCRIPTION

[0038] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings of 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. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0039] Example 1: Referring to FIG1 , this embodiment describes an airship data transmission management system based on the Internet of Things, including a flight control module, a data acquisition module, a state prediction module, a communication module, and a remote control platform;

[0040] Flight Control Module: This module is responsible for controlling the airship's flight, including navigation, route planning, altitude control, and emergency response. The flight control module can be operated by the pilot or by a remote control platform. Once the airship is in flight, the flight control module wakes up the data acquisition module. The flight control module collaborates with the airship's sensors and communication equipment to obtain position, status, and flight data. It also communicates with the control center at the ground station to receive flight missions and adjust the flight path.

[0041] The flight control module first obtains the current navigation information, including the aircraft's position, speed, heading, and altitude. Based on this navigation information, the flight control module plans the flight route. Taking into account the flight mission, flight path, target points, and obstacle avoidance information, the flight control module calculates the required control inputs to guide the aircraft along the planned route.

[0042] The flight control module monitors the aircraft's altitude information, typically using sensors such as a pressure sensor, altimeter, or GPS to obtain altitude data. Based on the set altitude target and navigation information, the flight control module controls the aircraft's ascent and descent to maintain the desired flight altitude.

[0043] The flight control module has an emergency response function to deal with unexpected situations such as abnormal weather, wind shear, and mechanical failure. In the event of an emergency, the flight control module can take a series of measures, such as changing the heading, altitude, or speed, to ensure the safety of the aircraft;

[0044] When the flight control module determines that specific data needs to be obtained, it will wake up the data acquisition module, which can include various sensors, such as meteorological sensors, image sensors, temperature sensors, etc., to obtain environmental data or aircraft internal status information;

[0045] The flight control module works with various sensors on the airship to obtain necessary data, such as wind speed, temperature, air pressure, acceleration, attitude, etc. The data provided by these sensors helps the flight control module make real-time flight decisions and adjustments;

[0046] The flight control module communicates with the control center at the ground station to receive flight missions, adjust the flight path, or receive real-time navigation instructions. Communication can also be used to report the status and data of the aircraft to the ground operator to monitor the operating status of the aircraft in real time.

[0047] Data acquisition module: During the flight of the airship, it collects multi-source data related to the airship in real time, pre-processes the multi-source data, and sends the multi-source data to the state prediction module based on the Internet of Things;

[0048] The collected raw data usually needs to be preprocessed to ensure the accuracy and usability of the data. Preprocessing includes steps such as data denoising, filtering, correction, coordinate transformation, etc. to eliminate noise and errors and convert the data into a format suitable for analysis and modeling;

[0049] In some cases, the data acquisition module needs to integrate data from different sensors and devices to provide more comprehensive information. Data integration can help build a more comprehensive picture of the aircraft status.

[0050] State prediction module: The airship index is generated by comprehensively calculating the multi-source data based on the positive and negative relationship of the multi-source data. The flight state of the airship is predicted by comparing the airship index with the abnormal threshold. The prediction result is sent to the remote control platform through the communication module.

[0051] Communication module: This module is responsible for data transmission and uses IoT technology (such as satellite communication, mobile network, radio communication, etc.) to transmit data to the remote control platform;

[0052] The communication module selects an appropriate communication method based on the current communication conditions and available communication channels. The communication methods may include:

[0053] Satellite communication: Long-distance communication via satellite connection, suitable for flights in remote areas or over the ocean; Mobile network: Utilizes ground mobile network base stations for communication, suitable for areas with mobile network signals within coverage; Radio communication: Uses radio waves for communication, suitable for short-distance communication or areas without network coverage;

[0054] Depending on the communication method selected, the communication module attempts to establish a connection with the remote control platform, which may include establishing a satellite link, connecting to a mobile network, or establishing a radio communication connection;

[0055] Once the connection is established, the communication module begins to transmit the prepared data to the remote control platform. The data can be transmitted in real-time streaming or batch processing, depending on the communication method and data volume. Once all data is successfully transmitted to the remote control platform, the communication module will send a confirmation signal to indicate that the data transmission is complete.

[0056] Remote control platform: When the airship's flight status is predicted to be poor, the flight control module is automatically taken over, and the airship base station operator controls the use of the flight control module through the remote control platform. When the airship's flight status is predicted to be poor, the remote control platform first searches for the airship base station closest to the airship. If the airship base station meets the airship landing conditions, the airship base station operator controls the airship to land at the nearest base station. If there is no nearest airship base station nearby or no airship base station meets the landing conditions, the airship is controlled to return home or land in an open area.

[0057] Once a poor flight status or emergency is detected, the system can trigger a remote takeover procedure, taking over the flight control module from the pilot and handing control to the remote control platform;

[0058] The control platform first tries to find the airship base station closest to the airship, which can be achieved through the airship's location information and the base station's location information;

[0059] The control platform operator evaluates the conditions of the nearest airship base station to determine whether landing conditions are met. The conditions may include wind speed, weather conditions, runway conditions, etc. If the base station meets the landing conditions, the operator can choose to control the airship to land at the base station;

[0060] If there is no available airship base station near the airship or no base station meets the landing conditions, the operator needs to take further measures. The remote control platform can choose to control the airship to return to a safe location or land in an open area to minimize risks.

[0061] Once the next action is decided, the remote control platform operator can control the flight control module accordingly through the remote control platform, which may include changing the aircraft's heading, altitude, speed, and thrust.

[0062] The present application generates an airship index by comprehensively calculating multi-source data based on the positive and negative proportional relationship of the multi-source data through a state prediction module, and predicts the flight state of the airship by comparing the airship index with the abnormal threshold. When the flight state of the airship is predicted to be poor, the remote control platform automatically takes over the flight control module, and the airship base station operator controls the use of the flight control module through the remote control platform. When the flight state of the airship is predicted to be poor, the remote control platform first searches for the airship base station closest to the airship. If the airship base station meets the airship landing conditions, the airship base station operator controls the airship to land at the nearest base station. If there is no nearest airship base station around the airship or no airship base station meets the landing conditions, the airship is controlled to return or land in an open area. When the airship is in flight, the management system effectively analyzes multi-source data to predict the flight state of the airship, so that timely control and management can be carried out before the airship becomes abnormal, ensuring the safe use of the airship.

[0063] Example 2: During the flight of the airship, the data acquisition module collects multi-source data related to the airship in real time, pre-processes the multi-source data, and then sends the multi-source data to the state prediction module based on the Internet of Things;

[0064] During the flight, the data acquisition module collects multi-source data related to the airship in real time. The multi-source data includes airship data, flight data, and environmental data. The airship data includes the dispersion degree of the pressure difference between the inside and outside of the aircraft and the propulsion system state coefficient. The flight data includes the trajectory deviation coefficient and abnormal flight rate. The environmental data includes the environmental impact regression coefficient.

[0065] in:

[0066] The calculation logic for the dispersion of the pressure difference between the inside and outside of the aircraft, the propulsion system state coefficient, the trajectory deviation coefficient, the abnormal flight rate, and the environmental impact regression coefficient is as follows:

[0067] The dispersion degree of the pressure difference between the inside and outside of the aircraft qc t The calculation expression is:

[0068] Where i = {1, 2, 3, ..., n}, n represents the number of sampling points set on the aircraft, n is a positive integer, Q i represents the internal and external pressure difference at the i-th sampling point, μ represents the average internal and external pressure difference, and

[0069] The larger the dispersion value of the internal and external pressure difference of the aircraft, the greater the fluctuation of the internal and external pressure difference in various areas of the aircraft. In this case, the aircraft may have problems such as leakage or damage. To prevent further leakage and damage, the airship needs to be controlled and managed in a timely manner.

[0070] The greater the fluctuation in the internal and external pressure differences in various areas of the aircraft, the more likely the aircraft is leaking or damaged, which can cause the airship to:

[0071] 1) Leakage in the airbag or casing: If the airbag or casing of the aircraft leaks, it will cause fluctuations in air pressure differences; this may affect the buoyancy and stability of the aircraft;

[0072] 2) Sealing system failure: The aircraft's sealing system may fail, causing gas leakage or external air pressure to enter the aircraft;

[0073] 3) Structural damage: Structural components of the aircraft, such as the frame or support structure, may be damaged, resulting in gas leaks or air pressure fluctuations.

[0074] Propulsion system state coefficient zj t The calculation expression is:

[0075] Where V e is the jet velocity at the nozzle outlet of the airship propulsion system, Δm represents the fuel mass flow rate, and Δt represents the monitoring time;

[0076] Propulsion system state coefficient zj t The larger the value, the more stable the thrust state of the propulsion system of the airship is. The state coefficient zj of the propulsion system is t The smaller the value, the more unstable the thrust state of the airship's propulsion system is, which can lead to the following problems:

[0077] 1) Flight instability: Instability in the thrust state may make it difficult for the airship to maintain balance and stability during flight; this may manifest as shaking, oscillation, or unstable flight posture of the aircraft;

[0078] 2) Altitude control issues: Unstable thrust conditions may make it difficult for the airship to maintain the desired altitude; the aircraft may become unstable and have difficulty maintaining altitude, which may lead to dangerous altitude changes;

[0079] 3) Navigation difficulties: Unstable thrust conditions may negatively impact the navigation and route control of the aircraft; the airship may deviate from the planned route or fail to follow the intended route;

[0080] 4) Reduced fuel efficiency: Unstable thrust conditions may result in reduced fuel efficiency because more fuel is required to maintain flight, which may shorten flight time and increase operating costs;

[0081] 5) Aircraft damage: Unstable thrust conditions may cause additional stress on the airship's structure or propulsion system, potentially leading to mechanical failure or structural damage.

[0082] Trajectory deviation coefficient pl f The calculation expression of pl is: f =Σ(yj-(a*xj+b)) / m;

[0083] Where (xj, yj) is the actual trajectory point during the flight of the airship, m is the number of actual trajectory points obtained, a is the slope, b is the intercept, and the trajectory deviation coefficient pl is f is the average value of the sum of squares of distances from all actual trajectory points to the expected trajectory;

[0084] In order to find the trajectory deviation coefficient pl f To minimize the slope a and intercept b, we need the trajectory deviation coefficient pl f Calculate the partial derivatives of a and b respectively and set them equal to 0. By solving this system of equations, we can get the solution of slope a and intercept b;

[0085] To solve the slope a, we can use the trajectory deviation coefficient pl f Taking the partial derivative of a and setting it equal to 0, we get the following equation:

[0086] To solve the intercept b, we can use the trajectory deviation coefficient pl f Taking the partial derivative of b and setting it equal to 0, we get the following equation:

[0087] By solving this system of equations, we can get the solution for slope a and intercept b;

[0088] Trajectory deviation coefficient pl f The larger the value, the more the airship deviates from its flight path, which may indicate the following problems:

[0089] 1) Navigation system failure: The airship's navigation system may malfunction or become inaccurate, causing the aircraft to be unable to fly along the intended route. This may include GPS failure, inertial navigation system problems, or navigation software errors;

[0090] 2) Flight control system problems: Failure or incorrect settings of the flight control system may cause the airship to fly unstable or fail to fly according to the planned route;

[0091] 3) Changes in weather conditions: Sudden changes in weather conditions, such as strong winds, changes in air pressure, thunderstorms, etc., may cause the airship to deviate from its original route;

[0092] 4) Mechanical failure: Mechanical components of the airship, such as the engine, control rudder, turbine, etc., may malfunction, causing unstable flight or deviation from the route;

[0093] 5) Pilot Error: Pilot error or pilot fatigue may cause the airship to deviate from its course; this may include navigation errors, flight control errors, etc.

[0094] 6) Communication problems: Communication problems between the airship and the ground control station or other aircraft may prevent the airship from receiving correct navigation instructions or flight plan updates;

[0095] 7) Fuel problems: Fuel supply problems may cause the aircraft to be unable to maintain the required flight performance and thus deviate from the route;

[0096] 8) External interference: External interference factors such as flocks of birds, drones, balloons, etc. may interfere with the flight trajectory of the airship.

[0097] Abnormal flight rate yc f The calculation expression is:

[0098] Where ycs is the total duration of the airship pilot's abnormal behavior, and zsc is the airship's flight time. The airship pilot's abnormal behavior is monitored by a camera installed inside the cockpit. Abnormal behavior includes:

[0099] 1) Fatigue or drowsiness: Monitors whether the pilot's eyes are frequently closed or his head is frequently drooped to detect fatigue or drowsiness;

[0100] 2) Distraction or absence: Detects whether the pilot frequently looks at objects other than the flight instruments, or whether he or she does not look at the flight instruments for a long time;

[0101] 3) Alcohol or drug influence: Observe the pilot for signs of dullness, blurred vision, or incoordination, which may be signs of alcohol or drug influence;

[0102] 4) Incorrect operation: Monitor whether incorrect operation occurs, such as wrong button, lever or switch operation;

[0103] 5) Loss of consciousness or syncope: Detects whether the pilot suddenly loses consciousness, syncs, or has other health problems;

[0104] 6) Abnormal attitude or violent movement: Detects whether the pilot suddenly performs abnormal operations or movements that may cause the aircraft to have an abnormal attitude;

[0105] 7) Sudden seat departure: Monitors whether the pilot leaves the cockpit at an inappropriate time, which may cause operational disruption;

[0106] 8) Abnormal emotions or mental states: Observe whether the pilot displays any abnormal emotions, such as extreme anxiety, agitation, or other abnormal emotions;

[0107] 9) Irregular operations: Detect whether the pilot violates flight regulations, procedures or operating rules;

[0108] Abnormal flight rate yc f The larger the value, the more abnormal the pilot's driving behavior is, which will affect the safe flight of the airship, for example:

[0109] 1) Risk of loss of control: Abnormal piloting behavior may cause the airship to lose control, especially at critical moments in flight; this may include sudden control inputs, erroneous operations, or uncoordinated movements;

[0110] 2) Attitude instability: Abnormal piloting behavior may cause the airship to have an unstable flight attitude, such as rolling, pitching or yaw; this may endanger the stability and safety of the flight;

[0111] 3) Deviation from the route: Abnormal behavior of the pilot may cause the airship to deviate from the planned route, causing it to approach other aircraft or ground obstacles, increasing the risk of collision;

[0112] 4) Altitude control issues: Abnormal piloting behavior may cause the airship to lose altitude control in the vertical direction, possibly causing it to fly too low or too high, increasing the risk of ground collision or mid-air collision;

[0113] 5) Reduced fuel efficiency: Irregular driving behavior may lead to wasted or inefficient use of fuel, which may shorten flight time or cause fuel exhaustion;

[0114] 6) Aircraft Damage: Abnormal operation may cause additional stress on the aircraft's structure or mechanical components, which may lead to mechanical failure, structural damage or component failure;

[0115] 7) Communication and navigation issues: Abnormal driving behavior may cause the pilot to lose effective communication with the ground control station or other aircraft, and may also affect the accuracy of navigation;

[0116] 8) Passenger safety risk: If there are passengers on the airship, abnormal driving behavior may endanger their safety, causing injury or a poor riding experience.

[0117] Environmental impact regression coefficient yx h The calculation expression is:

[0118] Wherein, z is the output of the linear regression of environmental impact, and the calculation expression of the output z of the linear regression of environmental impact is: z=w0+w1hj1+w2hj2+w3hj3;

[0119] Where w0, w1, w2, and w3 are the weights of the parameters in the regression model, hj1 is the ambient wind speed, hj2 is the ambient air pressure, and hj3 is the ambient humidity;

[0120] The output z of the linear regression of environmental impact is mapped to the range of [0,1], which represents the probability that the airship can fly smoothly. When z is positive, p is close to 1, indicating that the probability of the airship flying safely is high; when z is negative, p is close to 0, indicating that the probability of the airship flying safely is low.

[0121] The state prediction module generates the airship index ft after comprehensively calculating the multi-source data based on the positive and negative relationship of the multi-source data z , the calculation expression is:

[0122] Where yx h is the environmental impact regression coefficient, pl f is the trajectory deviation coefficient, yc f is the abnormal flight rate, qc t is the discrete degree of pressure difference between inside and outside the aircraft, zj t is the propulsion system state coefficient, α and β are the proportional coefficients of the dispersion degree of the pressure difference between the inside and outside of the aircraft and the propulsion system state coefficient, respectively, γ is the proportional coefficient of the trajectory deviation coefficient and the abnormal flight rate, and δ is the proportional coefficient of the environmental impact regression coefficient. α, β, γ, and δ are all greater than 0.

[0123] The state prediction module obtains the airship index ft z After the value, the airship index ft z The value is compared with the abnormal threshold. If the airship index ftz Value < abnormal threshold, the airship's flight status is predicted to be poor. If the airship index ft z If the value is ≥ the abnormal threshold, the airship is predicted to be in good flight condition.

[0124] This application uses the state prediction module to comprehensively calculate the discrete degree of the pressure difference between the inside and outside of the aircraft, the propulsion system state coefficient, the trajectory deviation coefficient, the abnormal flight rate and the environmental impact regression coefficient to obtain the airship index ft z , not only makes the analysis more extensive and comprehensive, but also effectively improves the efficiency of data processing.

[0125] Example 3: This example describes an airship data transmission management method based on the Internet of Things, the management method comprising the following steps:

[0126] During the flight of the airship, the monitoring end collects multi-source data related to the airship in real time and pre-processes the multi-source data. The processing end generates an airship index after comprehensively calculating the multi-source data based on the positive and negative relationship of the multi-source data. The flight status of the airship is predicted by comparing the airship index with the abnormal threshold. The prediction result is transmitted to the ground control center based on the Internet of Things technology. When the flight status of the airship is predicted to be poor, the ground control center automatically takes over and controls the flight of the airship. The operator controls the flight of the airship through the ground control center. When the flight status of the airship is predicted to be poor, the nearest airship base station to the airship is first found. If the airship base station meets the landing conditions of the airship, the operator controls the airship to land at the nearest base station. If there is no nearest airship base station around the airship or no airship base station meets the landing conditions, the airship is controlled to return or land in an open area.

[0127] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formulas are set by technicians in this field according to actual conditions.

[0128] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via wired or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0129] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0130] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0131] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0132] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0133] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0134] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, 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 interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0135] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0136] 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.

[0137] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random-access memory (RAM), a magnetic disk or an optical disk.

[0138] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. An airship data transmission management system based on the Internet of Things, characterized by: It includes flight control module, data acquisition module, state prediction module, communication module and remote control platform; Flight control module: used to control the flight management of the airship. The flight control module is operated by the pilot or the remote control platform, and wakes up the data acquisition module after the airship is in flight. Data acquisition module: During the flight of the airship, it collects multi-source data related to the airship in real time and pre-processes the multi-source data; State prediction module: This module generates an airship index by comprehensively calculating the positive and negative relationship of multi-source data. The airship's flight state is predicted by comparing the airship index with the abnormal threshold. Communication module: used for transmitting prediction results, and transmitting the prediction results to the remote control platform based on the Internet of Things technology; Remote control platform: When the airship's flight status is predicted to be poor, it automatically takes over the flight control module and searches for the closest airship base station. The state prediction module generates the airship index ft after comprehensively calculating the multi-source data based on the positive and negative relationship of the multi-source data z , the calculation expression is: Where yx h is the environmental impact regression coefficient, pl f is the trajectory deviation coefficient, yc f is the abnormal flight rate, qc t is the discrete degree of pressure difference between inside and outside the aircraft, zj t is the propulsion system state coefficient, α and β are the proportional coefficients of the dispersion degree of the pressure difference between the inside and outside of the aircraft and the propulsion system state coefficient, respectively, γ is the proportional coefficient of the trajectory deviation coefficient and the abnormal flight rate, and δ is the proportional coefficient of the environmental impact regression coefficient, and α, β, γ, and δ are all greater than 0; The discrete degree qc of the pressure difference between the inside and outside of the aircraft t The calculation expression is: Where i = {1, 2, 3, 3, ..., n}, n represents the number of sampling points set on the aircraft, n is a positive integer, Q i represents the internal and external pressure difference at the i-th sampling point, μ represents the average internal and external pressure difference, and The propulsion system state coefficient zj t The calculation expression is: Where V e is the jet velocity at the nozzle outlet of the airship propulsion system, Δm represents the fuel mass flow rate, and Δt represents the monitoring time; The trajectory deviation coefficient pl f The calculation expression is: pl f =∑(yj-(a*xj+b)) / m; Where (xj, yj) is the actual trajectory point during the airship flight, m ​​is the number of actual trajectory points obtained, a is the slope, and b is the intercept; The abnormal flight rate yc f The calculation expression is: Where ycs is the total duration of abnormal behavior of the airship pilot, and zsc is the flight time of the airship; The environmental impact regression coefficient yx h The calculation expression is: Where z is the output of the linear regression of environmental impact, and the calculation expression of the output z of the linear regression of environmental impact is: z=w0+w1hj1+w2hj2+w3hj3; Where w0, w1, w2, and w3 are the weights of the parameters in the regression model, hj1 is the ambient wind speed, hj2 is the ambient air pressure, and hj3 is the ambient humidity.

2. The airship data transmission management system based on the Internet of Things according to claim 1, characterized in that: The data acquisition module collects multi-source data related to the airship in real time, the multi-source data including airship data, flight data and environmental data. The airship data includes the discrete degree of the pressure difference between the inside and outside of the aircraft and the propulsion system state coefficient. The flight data includes the trajectory deviation coefficient and the abnormal flight rate. The environmental data includes the environmental impact regression coefficient.

3. The airship data transmission management system based on the Internet of Things according to claim 1, characterized in that: The state prediction module obtains the airship index ft z After the value, the airship index ft z The value is compared with the abnormal threshold. If the airship index ft z Value < abnormal threshold, the airship's flight status is predicted to be poor. If the airship index ft z If the value is ≥ the abnormal threshold, the airship is predicted to be in good flight condition.

4. A method for managing airship data transmission based on the Internet of Things, comprising: The management method comprises the following steps: S1: During the flight of the airship, the monitoring terminal collects multi-source data related to the airship in real time and pre-processes the multi-source data; S2: The processing end generates the airship index by comprehensively calculating the multi-source data based on the positive and negative relationship of the multi-source data; S3: The flight status of the airship is predicted by comparing the airship index with the abnormal threshold. The prediction result is transmitted to the ground control center based on the Internet of Things technology; S4: When the airship's flight status is predicted to be poor, the ground control center automatically takes over and controls the airship's flight; S5: The operator controls the flight of the airship through the ground control center.

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