Helicopter deck take-off and landing stable period state judgment method and system

Abstract

The invention belongs to the technical field of ship and ocean engineering, and discloses a helicopter deck take-off and landing stable period state judgment method and a helicopter deck take-off and landing stable period state judgment system. The method comprises the following steps: constructing a multilayer long-short term neural network prediction model Multi-LSTM-Aero based on dynamic constraints, and reading and preprocessing ship motion data and environmental wind field data; predicting a three-degree-of-freedom motion envelope of the ship; predicting environment wind field data; carrying out helicopter dynamics model calculation on a ship three-degree-of-freedom motion envelope prediction result and an environment wind field data prediction result; and on the basis of the obtained helicopter control quantity prediction result and the helicopter attitude quantity prediction result, through the set corresponding helicopter control limitation and attitude limitation, judging and calculating the deck take-off and landing stable period. According to the invention, the ability of operation execution personnel and command personnel to grasp the take-off and landing state can be effectively improved, and technical support is provided for improving the take-off and landing operation efficiency and the take-off and landing operation safety.

Description

Technical Field

[0001] The invention belongs to the technical field of ship and ocean engineering, and in particular relates to a method and system for determining the state of a helicopter deck during take-off and landing safety period. Background Art

[0002] With the increase in the demand for offshore operations, the coordinated operation of ship-borne helicopters and ships has become an indispensable mode of operation. The diversified mission requirements have put forward higher requirements for the take-off and landing capabilities of ship-borne helicopters in complex marine environments. However, due to the swaying motion of the ship, taking off and landing operations on the ship's helicopter deck has become one of the difficulties in ship-aircraft collaborative operations. The take-off and landing process of ship-borne helicopters on the moving deck is significantly different from the land-based take-off and landing operations. The movement of the ship itself and the complex external environment pose a great threat to the maneuverability of the helicopter. These environmental factors not only directly affect the take-off and landing capabilities of the helicopter, but also weaken the response speed and execution efficiency of the ship-aircraft system in emergency tasks to a certain extent. For ships sailing at sea, as the wind and wave environment changes at sea, the hull posture will also change accordingly, especially in high sea conditions, the movement amplitude of the ship is very violent. However, during the violent ship movement, there is often a period of stable state, which is usually called the ship movement stability period. For the take-off and landing operations of ship-borne helicopters, the stable period is the most suitable time segment for take-off and landing operations in high sea conditions, so these segments can also be called the take-off and landing stability period of ship-borne helicopter decks.

[0003] At present, the perception of the deck take-off and landing safety period mostly relies on the shipborne helicopter deck take-off and landing indication system, but this system can only indicate the ship's motion state at the current moment, and cannot predict the safety period state in the future, let alone the helicopter attitude in the future. Therefore, how to predict the environmental factors in the take-off and landing process through historical information, and to judge the take-off and landing attitude and control characteristics of the target helicopter through the predicted information has become a difficult problem in solving the research on the shipborne helicopter take-off and landing safety period.

[0004] Through the above analysis, the problems and defects of the existing technology are as follows: the current traditional method of judging the safe period of shipborne helicopter take-off and landing is mainly based on the light indication of the deck take-off and landing auxiliary system in conjunction with the command of the deck take-off and landing guide. This method cannot meet the needs of judging the ship's motion trend in the future and predicting the surrounding wind field environment. Therefore, it can only provide an experience-based operation safety judgment. The judgment standard is difficult to quantify, and the judgment result lacks a description of the helicopter take-off and landing characteristics, which causes many uncertainties in the shipborne helicopter take-off and landing operations. Summary of the invention

[0005] In order to overcome the problems existing in the related art, the disclosed embodiment of the present invention provides a method and system for distinguishing the state of a helicopter deck during take-off and landing stability period, and specifically relates to a method for distinguishing the state of a helicopter deck during take-off and landing stability period based on a Multi-LSTM-Aero prediction model.

[0006] The technical solution is as follows: A method for determining the state of a helicopter deck during take-off and landing safety period comprises: S1, construct a multi-layer long-term and short-term neural network prediction model Multi-LSTM-Aero based on dynamic constraints; S2, the model realizes the reading and preprocessing of ship motion data and environmental wind field data; S3, the model realizes the prediction of the three-degree-of-freedom motion envelope of the ship; S4, the model realizes the prediction of environmental wind field data; S5, using the prediction results of the three-degree-of-freedom motion envelope of the ship and the prediction results of the environmental wind field data to calculate the helicopter dynamics model within the model; S6, based on the obtained helicopter control quantity prediction results and helicopter attitude quantity prediction results, the deck take-off and landing safety period is judged and calculated by setting corresponding helicopter control restrictions and attitude restrictions.

[0007] In step S2, the model implements the reading and preprocessing of ship motion data and environmental wind field data, including: Before preprocessing the input data, set the basic parameters of the Multi-LSTM-Aero multi-layer long-term and short-term neural network prediction model based on dynamic constraints according to the needs. The basic parameters specifically include the total time of reading historical data , total duration of forecast data ; After setting the basic parameters of the model, read and preprocess the input data; take the ship motion data and environmental wind field data as input, and express them as follows: ship motion data , Environmental wind field data ;in, The timestamp of the read data. ; The data is cropped and corrected at equal time intervals to make the data frequency of all time history data the same; the required ship motion data type includes ship roll and ship pitch data; the wind field data type only includes vector wind speed data.

[0008] Furthermore, the input data is sequentially transmitted to the motion envelope prediction module for ship three-degree-of-freedom motion envelope prediction of the multi-layer long-term and short-term neural network prediction model Multi-LSTM-Aero based on dynamic constraints, the environmental wind prediction module for environmental wind field data prediction, and the helicopter dynamic constraint module for helicopter dynamic model calculation. The output of the specified length is obtained, with the helicopter control amount and the helicopter attitude amount as the output, which are expressed as: helicopter control amount 、Helicopter attitude ,in, is the time change of the predicted result compared to the historical data timestamp, , is the total duration of the predicted data.

[0009] In step S3, the model implements the prediction of the three-degree-of-freedom motion envelope of the ship, including: The data stream enters the motion envelope prediction module, extracts the envelope of the processed ship motion data, and converts the ship motion data into Transformed into motion data upper envelope and motion data envelope , and are used as the input of the LSTM model to obtain the ship motion envelope prediction results, which are respectively expressed as the envelope prediction results on the motion data Envelope forecast results under motion data .

[0010] In step S4, the model implements environmental wind field data prediction, including: After entering the environmental wind forecast module, the environmental wind field data is subjected to FFT filtering to remove the high-frequency noise of the environmental wind field data. The denoising process smoothes the wind field data to obtain , and use this as the input of the Multi-LSTM-Aero model to obtain the smoothed ambient wind forecast result .

[0011] In step S5, helicopter dynamics model calculation is performed, including: The upper envelope forecast result of the motion data, the lower envelope forecast result of the motion data, and the smoothed ambient wind forecast result obtained in the intermediate process are aligned with the timestamp and input into the dynamic constraint module; The rigid body dynamics equation is used to solve the rigid body dynamic force of the helicopter, and then the torque of the helicopter is solved by the rotational motion equation; the aerodynamic lift is solved by the lift equation in the aerodynamic equation, and the drag of the rotor power is solved by the drag equation; The calculation results of rigid body dynamics, aerodynamics and rotor dynamics in the helicopter dynamics system are integrated to obtain the pilot control variable through the force control equation; at the same time, the helicopter attitude change is calculated through the Euler angle differential equation.

[0012] Further, the rigid body dynamics equation is used to solve the dynamic force of the helicopter rigid body, including: Using the rigid body dynamics equation Solve the helicopter force condition, where: is the mass of the helicopter, is the velocity vector, Take the time derivative of the velocity vector, For air power, is gravity; The aerodynamic force is a function of the ambient wind forecast, i.e. , is the smoothed ambient wind forecast result, ; The method of solving the moment condition of the helicopter by the rotational motion equation includes: By rotating the equation of motion Solve for the moment acting on the helicopter, where is the inertia matrix, is the angular velocity vector, is the time derivative of the angular velocity vector, is the aerodynamic torque, are the remaining moments; the aerodynamic moment is a function of the ambient wind forecast result.

[0013] Further, the method of solving the aerodynamic lift by using the lift equation in the aerodynamic equation comprises: solving the aerodynamic lift by using the lift equation in the aerodynamic equation Solve for the lift force, where For lift, is the lift coefficient, is the air density, is the relative speed of the rotor, is the force-bearing area of ​​the rotor; The method of solving the drag force on the rotor power by using the drag equation includes: Through the resistance equation Solve for the resistance, where For resistance, is the drag coefficient; Using the rotor dynamics equation Solve the rotor force condition, where is the rotor torque, is the rotor thrust, is the rotor radius; The Euler angle differential equation is expressed as , They respectively represent the angular velocity components of the helicopter in the body coordinate system.

[0014] In step S6, the deck take-off and landing safety period is judged and calculated, including: After obtaining the prediction results of the helicopter control amount and the helicopter attitude amount, the helicopter control limit is set. and posture restrictions , and the helicopter control prediction results are , Helicopter attitude prediction results For comparison, if and , then it is believed that The moment is a safe moment, so as to screen out the time segments that meet the take-off and landing conditions, and then obtain more comprehensive prediction and judgment information of the take-off and landing safe period.

[0015] Another object of the present invention is to provide a helicopter deck take-off and landing safety period state determination system, the system implements the helicopter deck take-off and landing safety period state determination method, the system comprises: Data reading and preprocessing module, used for reading and preprocessing ship motion data and environmental wind field data; Motion envelope prediction module, used for predicting the three-degree-of-freedom motion envelope of ships; Environmental wind field prediction module, used for environmental wind field data prediction; The helicopter dynamics calculation module is used to calculate the helicopter dynamics model based on the prediction results of the ship's three-degree-of-freedom motion envelope and the environmental wind field data; The deck take-off and landing safety period judgment module is used to judge and calculate the deck take-off and landing safety period based on the obtained helicopter control quantity prediction results and helicopter attitude quantity prediction results, by setting the corresponding helicopter control restrictions and attitude restrictions.

[0016] In combination with all the above technical solutions, the beneficial effects of the present invention are as follows: the present invention utilizes the Multi-LSTM-Aero prediction model, takes three-degree-of-freedom ship motion data and environmental wind field data as model input, and takes the helicopter attitude and control amount in the future as model output. At the same time, the prediction model can output the three-degree-of-freedom motion envelope prediction results and the environmental wind speed prediction results through the intermediate process, and then provide the time segment that meets the take-off and landing safety requirements through the take-off and landing attitude restrictions and control restrictions of the ship-borne helicopter.

[0017] The present invention combines ship motion information, environmental wind field information and helicopter dynamics calculation model, integrates ship, ship-borne helicopter and environmental information, and constructs a more comprehensive helicopter take-off and landing safety period discrimination model, further increasing the comprehensiveness and credibility of the discrimination information.

[0018] The present invention utilizes a multi-layer neural network structure to integrate two types of multivariate information, namely ship motion and environmental wind field, and uses historical information to predict ship motion and environmental wind field information for a period of time in the future, thereby providing effective prediction data for subsequent dynamic model calculations, and further obtaining relatively rich take-off and landing status information.

[0019] Compared with the prior art, the advantages of the present invention further include: the traditional method of judging the safe period of take-off and landing of ship-borne helicopters is mainly based on the light indication of the deck take-off and landing auxiliary system in conjunction with the command of the deck take-off and landing guide. This method cannot meet the needs of ship movement trend judgment and surrounding wind field environment prediction for a period of time in the future, and cannot provide additional helicopter attitude and control information for deck take-off and landing operations, which makes the helicopter pilot face greater uncertainty factors, thereby affecting the operation efficiency and safety. This method integrates the three-degree-of-freedom motion data of the ship and the environmental wind field data, and on the basis of considering the ship and environmental factors, integrates the take-off and landing dynamics calculation formula into the prediction model, so that the model output results include the prediction of the helicopter take-off and landing attitude and control amount, and obtains the judgment result of the take-off and landing safe period by comparing with various restrictions, so as to provide a more detailed description of the take-off and landing state of the ship-borne helicopter. This method can effectively improve the ability of the operation executors and commanders to grasp the take-off and landing state, and provide technical support for improving the efficiency and safety of take-off and landing operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description, serve to explain the principles of the present disclosure; Figure 1 It is a flow chart of a method for determining the state of a helicopter deck during take-off and landing safety period provided by an embodiment of the present invention; Figure 2 It is a structural diagram of the Multi-LSTM-Aero prediction model provided by an embodiment of the present invention; Figure 3 is a diagram of the output result of the motion envelope prediction module provided by an embodiment of the present invention; Figure 4 This is a filter processing effect diagram of the environmental wind forecast module provided in an embodiment of the present invention; Figure 5 This is a graph of the output result of the environmental wind forecast module provided by an embodiment of the present invention; Figure 6 is an output result diagram of a helicopter dynamics constraint module provided by an embodiment of the present invention; Figure 7 is a diagram of ship motion data input provided by an embodiment of the present invention; Figure 8 is an input data diagram of environmental wind field data provided by an embodiment of the present invention; Fig. 9 is a diagram of the output result of the motion envelope prediction module provided by an embodiment of the present invention; Fig.10 This is a graph of the output result of the environmental wind forecast module provided by an embodiment of the present invention; Fig.11This is a graph of the prediction output results of the Multi-LSTM-Aero prediction model provided by an embodiment of the present invention; Fig.12 This is a diagram of the discrimination results of the Multi-LSTM-Aero prediction model provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0021] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific implementation disclosed below.

[0022] The innovation of the present invention is that when the traditional means of distinguishing the safe period is faced with the problem of predicting and distinguishing the take-off and landing window period of ship-borne helicopters in complex environments, there are problems such as single distinguishing information and ignored attitude of ship-borne helicopters. A multi-layer long-term and short-term neural network prediction model Multi-LSTM-Aero based on dynamic constraints constructed by the environmental motion information prediction module and the helicopter dynamics constraint module is proposed, which optimizes the take-off and landing window period distinguishing method and process, and enriches the means of obtaining take-off and landing distinguishing information. The model uses the stacking idea to combine the LSTM-based ship motion envelope prediction module with the wind speed time course prediction module to form an environment-motion information fusion prediction module, and then calculates the relative attitude of the ship and the aircraft and the helicopter control state in the target environment through the helicopter dynamics constraint module, thereby providing a richer reference for the determination of the safe period.

[0023] Embodiment 1, technical terms in the art related to the present invention: Ship six degrees of freedom motion: (1) Three degrees of freedom of a ship: roll, pitch and heave.

[0024] The bow and stern (front and back) direction of a ship is called the longitudinal direction, the left and right (left and right) direction is called the transverse direction, and the upper deck-bottom of the cabin (up and down) direction is called the vertical direction.

[0025] The translational motion in the forward and backward direction is called longitudinal swing, the translational motion in the left and right direction is called lateral swing, and the translational motion in the up and down direction is called vertical swing.

[0026] The angle change in the left and right direction is called roll, the angle change in the front and back direction is called pitch, and the angle change in the left and right directions of the bow is called pitch.

[0027] (2) Stable period of ship movement: When a ship is operating at sea, the ship's movement is relatively stable for a period of time. During this period, the probability of risk incurred during operations is relatively low.

[0028] The helicopter deck take-off and landing safety period state discrimination method provided by the embodiment of the present invention uses the ship motion history data and environmental wind field information measured by the sensors carried by the ship as input, and uses the multi-layer long-term and short-term neural network prediction model (Multi-LSTM-Aero prediction model) based on dynamic constraints that integrates the ship-borne helicopter take-off and landing dynamics model to predict the fuselage attitude estimation value and the control performance estimation value during the helicopter take-off and landing process, and can also output the ship motion envelope and environmental wind field information prediction results. Relying on the model prediction results, according to the helicopter operation restrictions and attitude restriction requirements, the deck take-off and landing safety period time segment is selected, thereby providing more abundant information reference for the ship-borne helicopter take-off and landing operations.

[0029] like Figure 1 As shown, a method for determining the state of a helicopter deck during take-off and landing safety period includes: S1, construct a multi-layer long-term and short-term neural network prediction model Multi-LSTM-Aero based on dynamic constraints; S2, the model realizes the reading and preprocessing of ship motion data and environmental wind field data; Aiming at the problem of prediction and discrimination of the safe take-off and landing period of shipborne helicopters, the present invention proposes a prediction model based on Multi-LSTM-Aero, whose structure mainly includes three modules, namely, motion envelope prediction module (ship motion envelope prediction module), environmental wind prediction module (environmental wind speed time course prediction module), and dynamic constraint module (helicopter dynamic constraint module). The structure of Multi-LSTM-Aero prediction model is as follows: Figure 2 As shown; Before preprocessing the input data, the basic model parameters need to be set according to the requirements. The basic model parameters specifically include the total time of reading historical data. , total duration of forecast data After setting the basic parameters of the model, the input data is read and preprocessed. The model takes ship motion data and environmental wind field data as input, which are expressed as follows: Ship motion data , Contains ship roll, ship pitch, ship heave data; environmental wind field data ,in, Indicates the timestamp of the read data. , Indicates the total time of reading historical data. The data is clipped and corrected at equal time intervals to ensure that the data frequency of all historical data is the same. The ship motion data type required by this model includes ship roll and ship pitch data; the wind field data type only includes vector wind speed data.

[0030] The input data is passed to the motion envelope prediction module, the ambient wind prediction module, and the helicopter dynamics constraint module in turn, and the total duration of the data is predicted according to the set model. Obtain the model output of the specified length. The model uses the helicopter control amount and the helicopter attitude amount as output, which are expressed as follows: helicopter control amount 、Helicopter attitude ;in, Indicates the total time of reading historical data. Indicates the time change of the model prediction result compared to the historical data timestamp, , Indicates the total duration of the model prediction data.

[0031] It can be understood that the prior art calculates the body sway of the helicopter when it is stationary on the deck of the ship, which belongs to the category of inertial force. The helicopter control amount and helicopter attitude amount of the present invention calculate the attitude of the helicopter in the air and the control amount of the pilot, which belongs to the category of aerodynamics and control.

[0032] S3, the model realizes the prediction of the three-degree-of-freedom motion envelope of the ship; In the model prediction stage, the data stream first enters the motion envelope prediction module, in which the present invention innovatively proposes to extract the envelope of the processed ship motion data, thereby converting the ship motion data into , transformed into the upper envelope of motion data and motion data envelope , and used as the input of LSTM model at the same time, so as to obtain the ship motion envelope prediction results, which are respectively expressed as the envelope prediction results on motion data Envelope forecast results under motion data The result is the intermediate data stream of the model, which provides data support for the subsequent dynamic constraint calculation. Among them, the output results of the motion envelope prediction module are as follows: Figure 3 shown.

[0033] S4, the model realizes the prediction of environmental wind field data; After the ambient wind data enters the ambient wind forecast module, it first undergoes an FFT filtering process to remove the high-frequency noise of the ambient wind data. The denoising process smoothes the wind data and obtains , and use this as the input of the Multi-LSTM-Aero model to obtain the smoothed ambient wind forecast result .in, Indicates the total time of reading historical data. Indicates the time change of the model prediction result compared to the historical data timestamp, , Indicates the total length of the model prediction data. This result is also the intermediate data stream of the model, providing data support for subsequent dynamic constraint calculations.

[0034] The filtering effect of the ambient wind forecast module is as follows: Figure 4 The output of the environmental wind forecast module is shown in Figure 5 shown.

[0035] S5, using the prediction results of the three-degree-of-freedom motion envelope of the ship and the prediction results of the environmental wind field data to calculate the helicopter dynamics model within the model; The motion data obtained in the intermediate process is enveloping the prediction result , envelope prediction results under motion data , smoothed ambient wind forecast result After aligning the timestamps, input them into the dynamic constraint module (helicopter dynamic constraint module), where Indicates the total time of reading historical data. Indicates the time change of the model prediction result compared to the historical data timestamp, , Indicates the total duration of the model prediction data; The invention innovatively proposes to use the rigid body dynamics equation Solve the helicopter force situation. The technical function of this formula is to integrate the helicopter dynamics constraints into the prediction model and provide a calculation basis for the final dynamics result output. is the mass of the helicopter, is the velocity vector, Take the time derivative of the velocity vector, For air power, For gravity.

[0036] The present invention innovatively proposes that the aerodynamic force is a function of the ambient wind forecast result, that is, , Represents the smoothed ambient wind forecast result , Indicates the total time of reading historical data. Indicates the time change of the model prediction result compared to the historical data timestamp, , Indicates the total duration of the model prediction data.

[0037] Then, through the rotational motion equation Solve for the moment acting on the helicopter, where is the inertia matrix, is the angular velocity vector, is the time derivative of the angular velocity vector, is the aerodynamic torque, is the remaining moment; the aerodynamic moment is a function of the ambient wind forecast result, that is, , Represents the smoothed ambient wind forecast result , Indicates the total time of reading historical data. Indicates the time change of the model prediction result compared to the historical data timestamp, , Indicates the total duration of the model prediction data.

[0038] Through the lift equation in the aerodynamic equation Solve for the lift force, where For lift, is the lift coefficient, is the air density, is the relative speed of the rotor, is the force area of ​​the rotor; through the drag equation Solve for the resistance, where For resistance, is the drag coefficient. Using the rotor dynamic equation Solve the rotor force condition, is the rotor torque, is the rotor thrust, is the rotor radius.

[0039] The calculation results of rigid body dynamics, aerodynamics and rotor dynamics in the helicopter dynamics system are integrated, and the force control equation is used to calculate the The driver's control amount is calculated. represents the control force calculation equation, which is the aerodynamic force , lift ,resistance , rotor torque The function of represents the helicopter pilot's control calculation equation, where Represents the helicopter pilot's control amount. At the same time, the helicopter attitude change is calculated by the Euler angle differential equation, which is expressed as , They respectively represent the angular velocity components of the helicopter in the body coordinate system.

[0040] The helicopter control amount prediction result is obtained by calculating the above formula , Helicopter attitude prediction results ,in, Indicates the total time of reading historical data. Indicates the time change of the model prediction result compared to the historical data timestamp, , Indicates the total duration of the model prediction data.

[0041] Among them, the helicopter dynamics constraint module outputs the following results: Figure 6 shown.

[0042] S6, based on the obtained helicopter control amount prediction results and helicopter attitude amount prediction results, by setting corresponding helicopter control restrictions and attitude restrictions, performing deck take-off and landing safety period judgment and calculation; Specifically, after obtaining the helicopter control amount prediction results and the helicopter attitude amount prediction results, the helicopter control limit is set and posture restrictions , and the helicopter control prediction results are , Helicopter attitude prediction results For comparison, if and , then it is believed that Every moment is a safe moment. Indicates the total time of reading historical data. It represents the time change of the model prediction result compared with the historical data timestamp, so as to filter out the time segments that meet the take-off and landing conditions, and then obtain more comprehensive prediction and discrimination information of the take-off and landing safety period.

[0043] Embodiment 2, the present invention provides a helicopter deck take-off and landing safety period state determination system, comprising: Data reading and preprocessing module, used for reading and preprocessing ship motion data and environmental wind field data; Motion envelope prediction module, used for predicting the three-degree-of-freedom motion envelope of ships; Environmental wind field prediction module, used for environmental wind field data prediction; The helicopter dynamics calculation module is used to calculate the helicopter dynamics model based on the prediction results of the ship's three-degree-of-freedom motion envelope and the environmental wind field data; The deck take-off and landing safety period judgment module is used to judge and calculate the deck take-off and landing safety period based on the obtained helicopter control quantity prediction results and helicopter attitude quantity prediction results, by setting the corresponding helicopter control restrictions and attitude restrictions.

[0044] To further illustrate the effects of the embodiments of the present invention, the following experiment was conducted.

[0045] Obtain ship motion data through onboard sensors or call offline data and environmental wind field data ,in Indicates the timestamp of the read data. , Indicates the total time of reading historical data. After outlier detection, the data is used as input for the Multi-LSTM-Aero prediction model. The ship motion data input data is as follows Figure 7 The input data of environmental wind field data is as shown in Figure 8 shown.

[0046] Set the total time for the prediction model to read historical data , the total duration of the model prediction data . Ship motion data ,in, Indicates the timestamp of the read data. , Indicates the total time of reading historical data. Input to the motion envelope prediction module, the model automatically extracts the ship motion envelope, which contains two upper and lower envelopes, corresponding to and converted into the upper envelope of motion data. , motion data lower envelope , the envelope data is input into the LSTM network structure embedded in the value model to predict the motion envelope. The output of the motion envelope prediction module is as follows: Fig. 9 shown.

[0047] Environmental wind field data ,in Indicates the timestamp of the read data. , Indicates the total time of reading historical data. Input to the environmental wind forecast module, after the model internal filtering process, the smoothed wind field environmental data is obtained. , and then input to the LSTM prediction module. The output of the environmental wind forecast module is as follows Fig.10 shown.

[0048] Envelope the prediction results on the motion data , envelope prediction results under motion data , smoothed ambient wind forecast result After aligning the timestamps, it is input into the dynamic constraint module, where: Indicates the total time of reading historical data. Indicates the time change of the model prediction result compared to the historical data timestamp, , Indicates the total duration of the model prediction data. Outputs the helicopter control prediction results , Helicopter attitude prediction results . Multi-LSTM-Aero prediction model prediction output results Fig.11 shown.

[0049] Set helicopter handling limits and posture restrictions , the safety status of the helicopter at each moment can be judged by the output of the prediction model. The judgment results of the Multi-LSTM-Aero prediction model are as follows Fig.12 shown.

[0050] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any modifications, equivalent substitutions and improvements made by any technician familiar with the technical field within the technical scope disclosed by the present invention and within the spirit and principles of the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for determining the stable state of a helicopter deck during take-off and landing, characterized in that: The method includes: S1, construct a multi-layer long-term and short-term neural network prediction model Multi-LSTM-Aero based on dynamic constraints; S2, the model realizes the reading and preprocessing of ship motion data and environmental wind field data; S3, the model realizes the prediction of the three-degree-of-freedom motion envelope of the ship; S4, the model realizes the prediction of environmental wind field data; S5, using the prediction results of the three-degree-of-freedom motion envelope of the ship and the prediction results of the environmental wind field data to calculate the helicopter dynamics model within the model; S6, based on the obtained helicopter control quantity prediction results and helicopter attitude quantity prediction results, the deck take-off and landing safety period is judged and calculated by setting corresponding helicopter control restrictions and attitude restrictions.

2. The method for determining the state of the helicopter deck during take-off and landing stability period according to claim 1 is characterized in that: In step S2, the model implements the reading and preprocessing of ship motion data and environmental wind field data, including: Before preprocessing the input data, set the basic parameters of the Multi-LSTM-Aero multi-layer long-term and short-term neural network prediction model based on dynamic constraints according to the needs. The basic parameters specifically include the total time of reading historical data , total duration of forecast data ; After setting the basic parameters of the model, read and preprocess the input data; take the ship motion data and environmental wind field data as input, and express them as follows: ship motion data , Environmental wind field data ;in, The timestamp of the read data. ; The data is cropped and corrected at equal time intervals to make the data frequency of all time history data the same; the required ship motion data type includes ship roll and ship pitch data; the wind field data type only includes vector wind speed data.

3. The method for determining the state of the helicopter deck during take-off and landing stability period according to claim 2 is characterized in that: The input data is sequentially transmitted to the motion envelope prediction module for ship three-degree-of-freedom motion envelope prediction of the multi-layer long-term and short-term neural network prediction model Multi-LSTM-Aero based on dynamic constraints, the environmental wind prediction module for environmental wind field data prediction, and the helicopter dynamic constraint module for helicopter dynamic model calculation. The output of the specified length is obtained, with the helicopter control amount and the helicopter attitude amount as the output, which are expressed as: helicopter control amount 、Helicopter attitude ,in, is the time change of the predicted result compared to the historical data timestamp, , is the total duration of the predicted data.

4. The method for determining the state of the helicopter deck during take-off and landing stability period according to claim 2 is characterized in that: In step S3, the model implements the prediction of the three-degree-of-freedom motion envelope of the ship, including: The data stream enters the motion envelope prediction module, extracts the envelope of the processed ship motion data, and converts the ship motion data into Transformed into motion data upper envelope and motion data envelope , and are used as the input of the LSTM model to obtain the ship motion envelope prediction results, which are respectively expressed as the envelope prediction results on the motion data Envelope prediction results under motion data .

5. The method for determining the state of the helicopter deck during take-off and landing stability period according to claim 3 is characterized in that: In step S4, the model implements environmental wind field data prediction, including: After entering the environmental wind forecast module, the environmental wind field data is subjected to FFT filtering to remove the high-frequency noise of the environmental wind field data. The denoising process smoothes the wind field data to obtain , and use this as the input of the Multi-LSTM-Aero model to obtain the smoothed ambient wind forecast result .

6. The method for determining the state of the helicopter deck during take-off and landing stability period according to claim 3 is characterized in that: In step S5, helicopter dynamics model calculation is performed, including: The upper envelope forecast result of the motion data, the lower envelope forecast result of the motion data, and the smoothed ambient wind forecast result obtained in the intermediate process are aligned with the timestamp and input into the dynamic constraint module; The rigid body dynamics equation is used to solve the rigid body dynamic force of the helicopter, and then the torque of the helicopter is solved by the rotational motion equation; the aerodynamic lift is solved by the lift equation in the aerodynamic equation, and the drag of the rotor power is solved by the drag equation; The calculation results of rigid body dynamics, aerodynamics and rotor dynamics in the helicopter dynamics system are integrated to obtain the pilot control variable through the force control equation; at the same time, the helicopter attitude change is calculated through the Euler angle differential equation.

7. The method for determining the state of the helicopter deck during take-off and landing stability period according to claim 6 is characterized in that: The method of solving the rigid body dynamics equation to obtain the dynamic force of the helicopter rigid body includes: Using the rigid body dynamics equation Solve the helicopter force condition, where: is the mass of the helicopter, is the velocity vector, Take the time derivative of the velocity vector, For air power, is gravity; The aerodynamic force is a function of the ambient wind forecast, i.e. , is the smoothed ambient wind forecast result, ; The method of solving the moment condition of the helicopter by the rotational motion equation includes: By rotating the equation of motion Solve for the moment acting on the helicopter, where is the inertia matrix, is the angular velocity vector, is the time derivative of the angular velocity vector, is the aerodynamic torque, are the remaining moments; the aerodynamic moment is a function of the ambient wind forecast result.

8. The method for determining the state of the helicopter deck during take-off and landing stability period according to claim 6, characterized in that: The method of solving the aerodynamic lift by using the lift equation in the aerodynamic equation comprises: solving the aerodynamic lift by using the lift equation in the aerodynamic equation Solve for the lift force, where For lift, is the lift coefficient, is the air density, is the relative speed of the rotor, is the force-bearing area of ​​the rotor; The method of solving the drag force on the rotor power by using the drag equation includes: Through the resistance equation Solve for the resistance, where For resistance, is the drag coefficient; Using the rotor dynamics equation Solve the rotor force condition, where is the rotor torque, is the rotor thrust, is the rotor radius; The Euler angle differential equation is expressed as , They respectively represent the angular velocity components of the helicopter in the body coordinate system.

9. The method for determining the state of the helicopter deck during take-off and landing stability period according to claim 3, characterized in that: In step S6, the deck take-off and landing safety period is judged and calculated, including: After obtaining the prediction results of the helicopter control amount and the helicopter attitude amount, the helicopter control limit is set. and posture restrictions , and the helicopter control prediction results are , Helicopter attitude prediction results For comparison, if and , then it is believed that The moment is a safe moment, so as to screen out the time segments that meet the take-off and landing conditions, and then obtain more comprehensive prediction and judgment information of the take-off and landing safe period.

10. A helicopter deck take-off and landing safety period state judgment system, characterized in that: The system implements the method for determining the state of the helicopter deck during take-off and landing stability period according to any one of claims 1 to 9, and the system comprises: Data reading and preprocessing module, used for reading and preprocessing ship motion data and environmental wind field data; Motion envelope prediction module, used for predicting the three-degree-of-freedom motion envelope of ships; Environmental wind field prediction module, used for environmental wind field data prediction; The helicopter dynamics calculation module is used to calculate the helicopter dynamics model based on the prediction results of the ship's three-degree-of-freedom motion envelope and the environmental wind field data; The deck take-off and landing safety period judgment module is used to judge and calculate the deck take-off and landing safety period based on the obtained helicopter control quantity prediction results and helicopter attitude quantity prediction results, by setting the corresponding helicopter control restrictions and attitude restrictions.

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