Method and system for predicting maintenance time of omnidirectional beacon equipment

Abstract

The invention provides a method and a system for predicting maintenance time of omnidirectional beacon equipment, relates to the technical field of aviation navigation, and is used for improving the accuracy of predicting the maintenance time of the omnidirectional beacon equipment. The method comprises the following steps: acquiring monitoring data of an omnidirectional beacon ground station and acquiring operation data of the omnidirectional beacon ground station; the omni-directional beacon receiver operating in the aircraft calculates the relative position of the operating aircraft according to the monitoring data and the operating data; determining whether the omnidirectional beacon equipment is abnormal or not based on the absolute position and the relative position of the running aircraft; if abnormity occurs, historical operation data and historical climate data of the omnidirectional beacon ground station or the omnidirectional beacon receiver in a preset time period are acquired; determining a feature vector matrix according to the historical operation data and the historical climate data of the preset time period; and inputting the feature vector matrix into an omnidirectional beacon equipment maintenance time prediction model, and predicting to obtain the maintenance time of the omnidirectional beacon equipment.

Description

Technical Field

[0001] The present application relates to the field of aviation navigation technology, and in particular to a method and system for predicting the maintenance time of an omnidirectional beacon device. Background Art

[0002] Omnidirectional beacon equipment is a ground equipment widely used in modern aviation radio navigation. It has been widely used since the 1960s. It has the advantages of high accuracy and stable indication, and is one of the reliable land-based navigation systems for civil aviation. Because most transport aircraft are currently equipped with omnidirectional beacon receivers, a certain scale of omnidirectional beacon network still needs to be maintained and newly built to support the operation of terminals and routes.

[0003] Since omnidirectional beacon equipment has relevant index requirements such as long operating life, special operating conditions and maintenance guarantee, omnidirectional beacon equipment is usually maintained when a fault occurs or at a certain interval. As a result, omnidirectional beacon equipment is only maintained when problems occur. Therefore, the maintenance efficiency of existing omnidirectional beacon equipment is low, thereby reducing the safety of aviation radio navigation. Summary of the invention

[0004] The embodiments of the present application provide a method and system for predicting the maintenance time of an omnidirectional beacon device, which are used to improve the accuracy of the maintenance time prediction of the omnidirectional beacon device.

[0005] An embodiment of the present invention provides a method for predicting the maintenance time of an omnidirectional beacon device. The method is applied to a system for predicting the maintenance time of an omnidirectional beacon device. The system includes: an omnidirectional beacon device and a monitoring device. The omnidirectional beacon device includes an omnidirectional beacon ground station and an omnidirectional beacon receiver. The omnidirectional beacon ground station is set within a preset range of an airport, and the omnidirectional beacon receiver is set in an aircraft. The method includes:

[0006] Acquiring monitoring data of the omnidirectional beacon ground station through the monitoring device, and acquiring operation data of the omnidirectional beacon ground station;

[0007] The omnidirectional beacon receiver running in the aircraft calculates the relative position of the running aircraft according to the monitoring data and the running data;

[0008] Determining whether the omnidirectional beacon device is abnormal based on the absolute position of the operating aircraft and the relative position;

[0009] If the omnidirectional beacon device is abnormal, the historical operation data of the omnidirectional beacon ground station or the omnidirectional beacon receiver in a preset time period is obtained; and the historical climate data corresponding to the preset time period;

[0010] Determine a characteristic vector matrix according to the historical operation data and historical climate data of the preset time period; each row of data of the characteristic vector matrix is ​​the historical operation data and historical climate data of the corresponding time;

[0011] The characteristic vector matrix is ​​input into the maintenance time prediction model of the omnidirectional beacon device to predict the maintenance time of the omnidirectional beacon device; the maintenance time prediction model of the omnidirectional beacon device is trained based on N consecutive days of sample data and corresponding maintenance time labels, and the sample data includes N consecutive days of historical climate data and historical operation data of the omnidirectional beacon device.

[0012] In an optional embodiment provided by the present invention, the omnidirectional beacon receiver operating in the aircraft calculates the relative position of the operating aircraft according to the monitoring data and the operating data, respectively, including:

[0013] Respectively acquiring a reference signal with a fixed phase and a variable phase signal with an angle phase continuously changing around the omnidirectional beacon ground station in the operation data and the monitoring data;

[0014] Calculating a first relative position of the operating aircraft by using a phase difference between the reference signal and the variable phase signal corresponding to the operating data;

[0015] The second relative position of the operating aircraft is calculated by the phase difference between the reference signal and the variable phase signal corresponding to the monitoring data.

[0016] In an optional embodiment provided by the present invention, the determining whether the omnidirectional beacon device is abnormal based on the absolute position and the relative position of the operating aircraft includes:

[0017] Calculating the absolute value of the data difference between the operating data and the monitoring data;

[0018] If the absolute value of the data difference is less than a second preset value, calculating a first position deviation between the first absolute position of the operating aircraft and the first relative position;

[0019] If the first position deviation is greater than a first preset value, it is determined that an abnormality occurs in the omnidirectional beacon ground station;

[0020] If the first position deviation is less than or equal to the first preset value, calculating a second position deviation between the second absolute position of the operating aircraft and the second relative position;

[0021] If the second position deviation is greater than a first preset value, it is determined that an abnormality occurs in the omnidirectional beacon receiver;

[0022] If the absolute value of the data difference is greater than or equal to a second preset value, it is determined that an interference signal appears within the preset range of the omnidirectional beacon ground station.

[0023] In an optional embodiment provided by the present invention, if the omnidirectional beacon device is abnormal, obtaining historical operation data of the omnidirectional beacon ground station or the omnidirectional beacon receiver in a preset time period includes:

[0024] If the omnidirectional beacon ground station is abnormal, obtaining historical operation data of the omnidirectional beacon ground station in a preset time period;

[0025] If the omnidirectional beacon receiver is abnormal, historical operation data of the omnidirectional beacon receiver in a preset time period is obtained.

[0026] In an optional embodiment provided by the present invention, determining the characteristic vector matrix according to the historical operation data and historical climate data of the preset time period includes:

[0027] If the omnidirectional beacon ground station is abnormal, extracting carrier forward power, carrier reverse power, upper sideband power, lower sideband power, signal spectrum from the historical operation data, and extracting temperature, relative humidity and site air pressure from the historical climate data;

[0028] The extracted carrier forward power, carrier reverse power, upper sideband power, lower sideband power, signal spectrum, and extracted temperature, relative humidity, and site air pressure are converted into feature vectors in units of days;

[0029] The transformed eigenvectors are arranged in chronological order to obtain the eigenvector matrix.

[0030] In an optional embodiment provided by the present invention, determining the characteristic vector matrix according to the historical operation data and historical climate data of the preset time period includes:

[0031] If the omnidirectional beacon receiver is abnormal, extracting power, modulation and angle from the historical operation data;

[0032] Convert the extracted power, modulation degree and angle into feature vectors;

[0033] The transformed eigenvectors are arranged in chronological order to obtain the eigenvector matrix.

[0034] In an optional embodiment provided by the present invention, the step of inputting the characteristic vector matrix into an omnidirectional beacon device maintenance time prediction model to predict the maintenance time of the omnidirectional beacon device includes:

[0035] Inputting the feature vector matrix into a first sub-prediction model, and obtaining a first data feature corresponding to the feature vector matrix and a corresponding data weight value thereof through the first sub-prediction model;

[0036] Inputting the feature vector matrix into a second sub-prediction model, and obtaining second data features corresponding to the feature vector matrix and corresponding data weight values ​​thereof through the first sub-prediction model;

[0037] The first data feature and the second data feature and their corresponding data weight values ​​are input into the omnidirectional beacon device maintenance time prediction model to predict the maintenance time of the omnidirectional beacon device.

[0038] In an optional embodiment provided by the present invention, the training process of the omnidirectional beacon device maintenance time prediction model is:

[0039] Acquire sample data and its corresponding sample labels, and determine a sample feature vector matrix according to the sample data; the sample data includes historical operation data of the omnidirectional beacon ground station and the omnidirectional beacon receiver and historical climate data;

[0040] Inputting the sample feature vector matrix into the first sub-prediction model to obtain the first sample data feature corresponding to the sample feature vector matrix and its corresponding data weight value;

[0041] Inputting the sample feature vector matrix into the second sub-prediction model to obtain second sample data features corresponding to the sample feature vector matrix and corresponding data weight values;

[0042] Inputting the first sample data feature and the second sample data feature and their corresponding data weight values ​​into the omnidirectional beacon device maintenance time prediction model to obtain a prediction label corresponding to the sample data;

[0043] Calculate the loss value of the omnidirectional beacon device maintenance time prediction model according to the prediction label and the sample label;

[0044] When the loss value is less than a preset value, the training of the maintenance time prediction model of the omnidirectional beacon device is stopped.

[0045] In an optional embodiment provided by the present invention, the first sample data feature and the second sample data feature and their corresponding data weight values ​​are input into the omnidirectional beacon device maintenance time prediction model to obtain the prediction label corresponding to the sample data, including:

[0046] Obtain a first prediction label corresponding to the sample data through the first sample data feature and its corresponding data weight value; obtain a second prediction label corresponding to the sample data through the second sample data feature and its corresponding data weight value;

[0047] Obtain a third prediction label corresponding to the sample data through the first sample data feature and the second sample data feature and their corresponding data weight values;

[0048] The prediction label corresponding to the sample data is obtained according to the first prediction label, the second prediction label and the third prediction label.

[0049] The embodiment of the present invention provides a system for predicting the maintenance time of an omnidirectional beacon device, the system comprising: an omnidirectional beacon device, a monitoring device, and a prediction device, the omnidirectional beacon device comprising an omnidirectional beacon ground station and an omnidirectional beacon receiver, the omnidirectional beacon ground station is arranged within a preset range of an airport, and the omnidirectional beacon receiver is arranged in an aircraft: the prediction device comprises:

[0050] An acquisition module, used to acquire monitoring data of the omnidirectional beacon ground station and to acquire operation data of the omnidirectional beacon ground station;

[0051] A calculation module, used for the omnidirectional beacon receiver running in the aircraft to calculate the relative position of the running aircraft according to the monitoring data and the running data;

[0052] The calculation module is further used to determine whether the omnidirectional beacon device is abnormal based on the absolute position and the relative position of the operating aircraft;

[0053] The acquisition module is further used to acquire the historical operation data of the omnidirectional beacon ground station or the omnidirectional beacon receiver in a preset time period if an abnormality occurs in the omnidirectional beacon device; and the historical climate data corresponding to the preset time period;

[0054] A determination module, used to determine a characteristic vector matrix according to the historical operation data and historical climate data of the preset time period; each row of data of the characteristic vector matrix is ​​the historical operation data and historical climate data of the corresponding time;

[0055] A prediction module is used to input the feature vector matrix into an omnidirectional beacon device maintenance time prediction model to predict the maintenance time of the omnidirectional beacon device; the omnidirectional beacon device maintenance time prediction model is trained based on N consecutive days of sample data and corresponding maintenance time labels, and the sample data includes N consecutive days of historical climate data and historical operation data of the omnidirectional beacon device.

[0056] The present invention provides a method and system for predicting the maintenance time of an omnidirectional beacon device. The system comprises: an omnidirectional beacon device, a monitoring device, and a prediction device. The omnidirectional beacon device comprises an omnidirectional beacon ground station and an omnidirectional beacon receiver. The omnidirectional beacon ground station is arranged within a preset range of an airport, and the omnidirectional beacon receiver is arranged in an aircraft. The prediction device firstly obtains the monitoring data of the omnidirectional beacon ground station and the operation data of the omnidirectional beacon ground station through the monitoring device; the omnidirectional beacon receiver operating in the aircraft respectively calculates the relative position of the operating aircraft according to the monitoring data and the operation data; determines whether the omnidirectional beacon device is abnormal based on the absolute position and relative position of the operating aircraft; if the omnidirectional beacon device is abnormal, obtains the historical operation data of the omnidirectional beacon ground station or the omnidirectional beacon receiver in a preset time period and the historical climate data corresponding to the preset time period; determines the characteristic vector matrix according to the historical operation data and the historical climate data in the preset time period; inputs the characteristic vector matrix into the maintenance time prediction model of the omnidirectional beacon device to predict the maintenance time of the omnidirectional beacon device. Compared with the existing maintenance of omnidirectional beacon equipment when a fault occurs or at certain intervals, the present application determines whether the omnidirectional beacon equipment has an abnormality based on the monitoring data and operation data of the omnidirectional beacon ground station, and when the omnidirectional beacon equipment has an abnormality, the maintenance time of the omnidirectional beacon equipment is predicted based on the historical operation data of a preset time period and the historical climate data. Thus, the maintenance time prediction of the omnidirectional beacon equipment is realized through the present application, so that the accuracy of the maintenance time of the omnidirectional beacon equipment can be improved through the present application, thereby improving the safety of aviation radio navigation. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] Figure 1 A flowchart of a method for predicting maintenance time of an omnidirectional beacon device provided in this application;

[0058] Figure 2 A flowchart of a training model for predicting maintenance time of omnidirectional beacon equipment provided in this application;

[0059] Figure 3 A structural diagram of a system for predicting maintenance time of an omnidirectional beacon device provided in this application. DETAILED DESCRIPTION

[0060] In order to better understand the above-mentioned technical scheme, the technical scheme of the embodiments of the present application is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical scheme of the embodiments of the present application, rather than limitations on the technical scheme of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments may be combined with each other.

[0061] like Figure 1As shown, this embodiment provides a method for predicting the maintenance time of an omnidirectional beacon device, and the method is applied to a system for predicting the maintenance time of an omnidirectional beacon device, the system comprising: an omnidirectional beacon device, a monitoring device, and a prediction device, the omnidirectional beacon device comprising an omnidirectional beacon ground station and an omnidirectional beacon receiver, the omnidirectional beacon ground station is arranged within a preset range of an airport, the omnidirectional beacon receiver is arranged in an aircraft, the prediction device is communicatively connected with the omnidirectional beacon device and the monitoring device, and the prediction device is used to perform the following steps:

[0062] S101: monitoring and acquiring the monitoring data of the omnidirectional beacon ground station through the monitoring device, and acquiring the operating data of the omnidirectional beacon ground station.

[0063] Among them, the monitoring equipment has passed the measurement and is within the validity period, and the error of the monitoring equipment should be less than one-third of the allowable error of the measured parameter. The monitoring equipment used includes: spectrum analyzer, omnidirectional antenna. Among them, the frequency range of the spectrum analyzer can include 108MHz-117.975MHz, RF input impedance 50Ω; the frequency range of the omnidirectional antenna can be: 108MHz-117.975MHz, input impedance 50Ω, polarization mode is horizontal polarization, direction ≤±0.5dB, etc. This embodiment does not make specific limitations on the parameters of the monitoring equipment.

[0064] Specifically, the monitoring data of the omnidirectional beacon ground station may include frequency, signal occupied bandwidth, field strength, modulation, etc. The operating data of the omnidirectional beacon ground station is the actual operating data of the omnidirectional beacon ground station. By comparing the monitoring data and the operating data, it can be determined whether there is any abnormality in the omnidirectional beacon ground station or its surroundings.

[0065] It should be noted that the omnidirectional beacon ground station in this embodiment is used for route navigation, and its operating parameters can be: 112-117.925MHz, channel spacing 50kHz, transmission power 200W, and effective range 200 nautical miles; the omnidirectional beacon receiver is used for terminal guidance of aircraft approach, and its operating parameters can be 108-112MHz, channel spacing 50kHz, transmission power 50W, and effective range 25 nautical miles.

[0066] S102, an omnidirectional beacon receiver operating in the aircraft calculates the relative position of the operating aircraft according to the monitoring data and the operating data.

[0067] In this embodiment, the omnidirectional beacon ground station sends two signals: one is a reference signal with a fixed phase, and the other is a variable phase signal that continuously changes with the angular phase around the beacon station. The omnidirectional beacon receiver in the aircraft can calculate its own position relative to the omnidirectional beacon ground station based on the phase difference of the two received signals, that is, calculate the relative position of the operating aircraft based on the monitoring data and the operating data.

[0068] In an optional embodiment provided by the present application, the omnidirectional beacon receiver operating in the aircraft calculates the relative position of the operating aircraft according to the monitoring data and the operating data, including: respectively obtaining the reference signal with a fixed phase in the operating data and the monitoring data and the variable phase signal with a continuously changing angle phase around the omnidirectional beacon ground station; calculating the first relative position of the operating aircraft by the phase difference between the reference signal and the variable phase signal corresponding to the operating data; calculating the second relative position of the operating aircraft by the phase difference between the reference signal and the variable phase signal corresponding to the monitoring data. The first relative position and the second relative position are the positions of the aircraft relative to the omnidirectional beacon ground station, so that in the subsequent steps, it is convenient to determine whether the omnidirectional beacon ground station is abnormal according to the first relative position, and determine whether the omnidirectional beacon receiver is abnormal according to the second relative position.

[0069] S103: Determine whether the omnidirectional beacon device is abnormal based on the absolute position and relative position of the operating aircraft.

[0070] In an optional embodiment provided in the present application, the determining whether the omnidirectional beacon device is abnormal based on the absolute position and the relative position of the operating aircraft includes:

[0071] S1031, calculating the absolute value of the data difference between the operating data and the monitoring data.

[0072] S1032A: If the absolute value of the data difference is less than a second preset value, then calculating a first position deviation between the first absolute position of the operating aircraft and the first relative position.

[0073] In this embodiment, if the absolute value of the data difference between the operating data and the monitoring data is less than the second preset value, it means that the vicinity of the omnidirectional beacon ground station is not subject to signal interference, that is, the external signal interference factor is eliminated. At this time, it is necessary to calculate the first position deviation between the first absolute position and the first relative position of the operating aircraft, thereby determining whether the omnidirectional beacon ground station has an operating abnormality.

[0074] S1033A1: If the first position deviation is greater than a first preset value, it is determined that an abnormality occurs in the omnidirectional beacon ground station.

[0075] The first absolute position of the operating aircraft is a position obtained by the aircraft's own positioning device, such as a position obtained by a global satellite positioning system or a Beidou positioning system.

[0076] S1033A2: If the first position deviation is less than or equal to the first preset value, calculate a second position deviation between the second absolute position of the operating aircraft and the second relative position.

[0077] In this embodiment, if the first position deviation is less than or equal to the first preset value, it means that there is no abnormality in the omnidirectional beacon ground station. At this time, it is necessary to determine whether there is an abnormality in the omnidirectional beacon receiver by calculating the second position deviation between the second absolute position and the second relative position of the running aircraft.

[0078] S1034: If the second position deviation is greater than the first preset value, it is determined that an abnormality occurs in the omnidirectional beacon receiver.

[0079] If the second position deviation is less than the first preset value, it is determined that an abnormality occurs in the omnidirectional beacon receiver.

[0080] S1032B: If the absolute value of the data difference is greater than or equal to a second preset value, it is determined that an interference signal appears within the preset range of the omnidirectional beacon ground station.

[0081] Among them, step S1032B is a parallel step of step S1032A. If the absolute value of the data difference is greater than or equal to the second preset value, it means that the area near the omnidirectional beacon ground station is subject to signal interference, thereby determining that an interference signal appears within the preset range of the omnidirectional beacon ground station.

[0082] S104: If the omnidirectional beacon device is abnormal, obtain historical operation data of the omnidirectional beacon ground station or the omnidirectional beacon receiver in a preset time period; and historical climate data corresponding to the preset time period.

[0083] Specifically, if the omnidirectional beacon ground station has an abnormality, the historical operation data of the omnidirectional beacon ground station in a preset time period is obtained; if the omnidirectional beacon receiver has an abnormality, the historical operation data of the omnidirectional beacon receiver in a preset time period is obtained.

[0084] The historical climate data include temperature, relative humidity, local air pressure, etc., which are not specifically limited in this embodiment.

[0085] S105, determining a characteristic vector matrix according to the historical operation data and historical climate data of the preset time period; each row of data of the characteristic vector matrix is ​​the historical operation data and historical climate data of the corresponding time period.

[0086] In an optional embodiment provided by the present application, the characteristic vector matrix is ​​determined based on the historical operation data and historical climate data of the preset time period, including: if the omnidirectional beacon ground station has an abnormality, the carrier forward power, carrier reverse power, upper sideband power, lower sideband power, signal spectrum are extracted from the historical operation data, and the temperature, relative humidity and site air pressure are extracted from the historical climate data; the extracted carrier forward power, carrier reverse power, upper sideband power, lower sideband power, signal spectrum and the extracted temperature, relative humidity and site air pressure are converted into characteristic vectors in units of days; the converted characteristic vectors are arranged in chronological order to obtain a characteristic vector matrix.

[0087] In an optional embodiment provided in the present application, the characteristic vector matrix is ​​determined based on the historical operating data and historical climate data of the preset time period, including: if the omnidirectional beacon receiver has an abnormality, extracting power, modulation, and angle from the historical operating data; converting the extracted power, modulation, and angle into characteristic vectors; and arranging the converted characteristic vectors in chronological order to obtain the characteristic vector matrix.

[0088] S106: Input the characteristic vector matrix into an omnidirectional beacon device maintenance time prediction model to predict the maintenance time of the omnidirectional beacon device.

[0089] The maintenance time prediction model of the omnidirectional beacon equipment is trained based on N consecutive days of sample data and corresponding maintenance time labels, wherein the sample data includes N consecutive days of historical climate data and historical operation data of the omnidirectional beacon equipment. The maintenance time label is manually marked, and is the time when the omnidirectional beacon equipment is maintained on any day after N consecutive days.

[0090] In an optional embodiment provided in the present application, the inputting of the characteristic vector matrix into the maintenance time prediction model of the omnidirectional beacon device to predict the maintenance time of the omnidirectional beacon device includes: inputting the characteristic vector matrix into a first sub-prediction model, and obtaining the first data feature corresponding to the characteristic vector matrix and the corresponding data weight value through the first sub-prediction model; inputting the characteristic vector matrix into a second sub-prediction model, and obtaining the second data feature corresponding to the characteristic vector matrix and the corresponding data weight value through the first sub-prediction model; inputting the first data feature and the second data feature and their respective corresponding data weight values ​​into the maintenance time prediction model of the omnidirectional beacon device to predict the maintenance time of the omnidirectional beacon device.

[0091] The first sub-prediction model and the second sub-prediction model are existing pre-trained neural network models, which use two network models with different structures. In this embodiment, by inputting the feature vector matrix into the first sub-prediction model and the second sub-prediction model respectively, the first data feature, the second data feature and their corresponding data weight values ​​can be obtained respectively, and the data weight value is used to indicate the importance of the corresponding data in the first data feature and the second data feature for the predicted value.

[0092] In this embodiment, the maintenance time prediction model for the omnidirectional beacon device is a pre-trained time prediction model, that is, it is obtained by training the time prediction model based on sample data composed of the first data feature, the second data feature and their corresponding data weight values, and the maintenance time corresponding to the sample data. Therefore, the maintenance time of the omnidirectional beacon device can be predicted by the maintenance time prediction model for the omnidirectional beacon device.

[0093] The embodiment of the present application provides a method for predicting the maintenance time of an omnidirectional beacon device. The prediction device first obtains the monitoring data of the omnidirectional beacon ground station and the operating data of the omnidirectional beacon ground station through the monitoring device; the omnidirectional beacon receiver operating in the aircraft calculates the relative position of the operating aircraft according to the monitoring data and the operating data; based on the absolute position and the relative position of the operating aircraft, it is determined whether the omnidirectional beacon device has an abnormality; if the omnidirectional beacon device has an abnormality, the historical operating data of the omnidirectional beacon ground station or the omnidirectional beacon receiver in a preset time period and the historical climate data corresponding to the preset time period are obtained; the characteristic vector matrix is ​​determined according to the historical operating data and the historical climate data in the preset time period; the characteristic vector matrix is ​​input into the omnidirectional beacon device maintenance time prediction model to predict the maintenance time of the omnidirectional beacon device. Compared with the existing maintenance of omnidirectional beacon equipment when a fault occurs or at certain intervals, the present application determines whether the omnidirectional beacon equipment has an abnormality based on the monitoring data and operation data of the omnidirectional beacon ground station, and when the omnidirectional beacon equipment has an abnormality, the maintenance time of the omnidirectional beacon equipment is predicted based on the historical operation data of a preset time period and the historical climate data. Thus, the maintenance time prediction of the omnidirectional beacon equipment is realized through the present application, so that the accuracy of the maintenance time of the omnidirectional beacon equipment can be improved through the present application, thereby improving the safety of aviation radio navigation.

[0094] like Figure 2 As shown, in an optional embodiment provided in the present application, the training process of the omnidirectional beacon device maintenance time prediction model is:

[0095] S201, obtaining sample data and its corresponding sample labels, and determining a sample feature vector matrix according to the sample data; the sample data includes historical operation data of the omnidirectional beacon ground station and the omnidirectional beacon receiver, and historical climate data.

[0096] Specifically, this embodiment is Figure 1 The sample feature vector matrix is ​​determined in the corresponding steps, which will not be described in detail in this embodiment.

[0097] S202: Input the sample feature vector matrix into the first sub-prediction model to obtain the first sample data features corresponding to the sample feature vector matrix and their corresponding data weight values.

[0098] S203: Input the sample feature vector matrix into the second sub-prediction model to obtain the second sample data features corresponding to the sample feature vector matrix and their corresponding data weight values.

[0099] S204: Input the first sample data feature and the second sample data feature and their corresponding data weight values ​​into the omnidirectional beacon device maintenance time prediction model to obtain a prediction label corresponding to the sample data.

[0100] Specifically, the first sample data feature and the second sample data feature and their corresponding data weight values ​​are input into the omnidirectional beacon device maintenance time prediction model to obtain the prediction label corresponding to the sample data, including:

[0101] S2041, obtaining a first prediction label corresponding to the sample data through the first sample data feature and its corresponding data weight value; obtaining a second prediction label corresponding to the sample data through the second sample data feature and its corresponding data weight value.

[0102] S2042: Obtain a third prediction label corresponding to the sample data through the first sample data feature and the second sample data feature and their corresponding data weight values.

[0103] S2043: Obtain a prediction label corresponding to the sample data according to the first prediction label, the second prediction label and the third prediction label.

[0104] Specifically, in this embodiment, the prediction label corresponding to the sample data can be determined by the following formula:

[0105]

[0106] Among them, yi is the predicted label corresponding to the i-th sample data, xij is the j-th predicted label obtained from the i-th sample data, where j = 1 to 3, aij is the predicted probability value corresponding to the predicted label xij, bij and cij are the predicted probability values ​​corresponding to the other two predicted labels, and α and β are preset constant values.

[0107] For example, the predicted label of xi1 is 1, and the corresponding predicted probability value is 70%; the predicted label of xi2 is 1, and the corresponding predicted probability value is 80%; the predicted label of xi3 is 2, and the corresponding predicted probability value is 70%. Then, by calculating the average probability of the predicted label being 1 is 75%, and the average probability of the predicted label being 2 is 70%. If β=70%, the predicted label of the sample data is 1.

[0108] S205: Calculate the loss value of the omnidirectional beacon device maintenance time prediction model according to the prediction label and the sample label.

[0109] S206: When the loss value is less than a preset value, stop training the maintenance time prediction model of the omnidirectional beacon device.

[0110] It should be understood that the order of execution of the steps in the above embodiment does not necessarily 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 embodiment of the present invention.

[0111] In one embodiment, a system for predicting the maintenance time of an omnidirectional beacon device is provided, the system comprising: an omnidirectional beacon device, a monitoring device, and a prediction device, wherein the omnidirectional beacon device comprises an omnidirectional beacon ground station and an omnidirectional beacon receiver, wherein the omnidirectional beacon ground station is arranged within a preset range of an airport, and the omnidirectional beacon receiver is arranged in an aircraft, and the prediction device corresponds one-to-one with the method for predicting the maintenance time of the omnidirectional beacon device in the above embodiment. Figure 3 As shown, the functional modules of the prediction device are described in detail as follows:

[0112] An acquisition module 31 is used to acquire monitoring data of the omnidirectional beacon ground station and to acquire operation data of the omnidirectional beacon ground station;

[0113] A calculation module 32, configured for the omnidirectional beacon receiver operating in the aircraft to respectively calculate the relative position of the operating aircraft according to the monitoring data and the operating data;

[0114] The calculation module 32 is further used to determine whether the omnidirectional beacon device is abnormal based on the absolute position and the relative position of the operating aircraft;

[0115] The acquisition module 31 is further used to acquire the historical operation data of the omnidirectional beacon ground station or the omnidirectional beacon receiver in a preset time period if an abnormality occurs in the omnidirectional beacon device; and the historical climate data corresponding to the preset time period;

[0116] A determination module 33, configured to determine a characteristic vector matrix according to the historical operation data and the historical climate data of the preset time period; each row of data of the characteristic vector matrix is ​​the historical operation data and the historical climate data of the corresponding time;

[0117] The prediction module 34 is used to input the feature vector matrix into the omnidirectional beacon equipment maintenance time prediction model to predict the maintenance time of the omnidirectional beacon equipment; the omnidirectional beacon equipment maintenance time prediction model is trained based on N consecutive days of sample data and corresponding maintenance time labels, and the sample data includes N consecutive days of historical climate data and historical operation data of the omnidirectional beacon equipment.

[0118] In an optional embodiment provided by the present invention, the calculation module 32 is specifically used for:

[0119] Respectively acquiring a reference signal with a fixed phase and a variable phase signal with an angle phase continuously changing around the omnidirectional beacon ground station in the operation data and the monitoring data;

[0120] Calculating a first relative position of the operating aircraft by using a phase difference between the reference signal and the variable phase signal corresponding to the operating data;

[0121] The second relative position of the operating aircraft is calculated by the phase difference between the reference signal and the variable phase signal corresponding to the monitoring data.

[0122] In an optional embodiment provided by the present invention, the calculation module 32 is further configured to:

[0123] Calculating the absolute value of the data difference between the operating data and the monitoring data;

[0124] If the absolute value of the data difference is less than a second preset value, calculating a first position deviation between the first absolute position of the operating aircraft and the first relative position;

[0125] If the first position deviation is greater than a first preset value, it is determined that an abnormality occurs in the omnidirectional beacon ground station;

[0126] If the first position deviation is less than or equal to the first preset value, calculating a second position deviation between the second absolute position of the operating aircraft and the second relative position;

[0127] If the second position deviation is greater than a first preset value, it is determined that an abnormality occurs in the omnidirectional beacon receiver;

[0128] If the absolute value of the data difference is greater than or equal to a second preset value, it is determined that an interference signal appears within the preset range of the omnidirectional beacon ground station.

[0129] In an optional embodiment provided by the present invention, the calculation module 32 is specifically used for:

[0130] If the omnidirectional beacon ground station is abnormal, obtaining historical operation data of the omnidirectional beacon ground station in a preset time period;

[0131] If the omnidirectional beacon receiver is abnormal, historical operation data of the omnidirectional beacon receiver in a preset time period is obtained.

[0132] In an optional embodiment provided by the present invention, the determination module 33 is specifically configured to:

[0133] If the omnidirectional beacon ground station is abnormal, extracting carrier forward power, carrier reverse power, upper sideband power, lower sideband power, signal spectrum from the historical operation data, and extracting temperature, relative humidity and site air pressure from the historical climate data;

[0134] The extracted carrier forward power, carrier reverse power, upper sideband power, lower sideband power, signal spectrum, and extracted temperature, relative humidity, and site air pressure are converted into feature vectors in units of days;

[0135] The transformed eigenvectors are arranged in chronological order to obtain the eigenvector matrix.

[0136] In an optional embodiment provided by the present invention, the determination module 33 is specifically configured to:

[0137] If the omnidirectional beacon receiver is abnormal, extracting power, modulation and angle from the historical operation data;

[0138] Convert the extracted power, modulation degree and angle into feature vectors;

[0139] The transformed eigenvectors are arranged in chronological order to obtain the eigenvector matrix.

[0140] In an optional embodiment provided by the present invention, the prediction module 34 is specifically used for:

[0141] Inputting the feature vector matrix into a first sub-prediction model, and obtaining a first data feature corresponding to the feature vector matrix and a corresponding data weight value thereof through the first sub-prediction model;

[0142] Inputting the feature vector matrix into a second sub-prediction model, and obtaining second data features corresponding to the feature vector matrix and corresponding data weight values ​​thereof through the first sub-prediction model;

[0143] The first data feature and the second data feature and their corresponding data weight values ​​are input into the omnidirectional beacon device maintenance time prediction model to predict the maintenance time of the omnidirectional beacon device.

[0144] In an optional embodiment provided by the present invention, the prediction device further includes a training module 35 for training the omnidirectional beacon device maintenance time prediction model, and the training module 35 is used to:

[0145] Acquire sample data and its corresponding sample labels, and determine a sample feature vector matrix according to the sample data; the sample data includes historical operation data of the omnidirectional beacon ground station and the omnidirectional beacon receiver and historical climate data;

[0146] Inputting the sample feature vector matrix into the first sub-prediction model to obtain the first sample data feature corresponding to the sample feature vector matrix and its corresponding data weight value;

[0147] Inputting the sample feature vector matrix into the second sub-prediction model to obtain second sample data features corresponding to the sample feature vector matrix and corresponding data weight values;

[0148] Inputting the first sample data feature and the second sample data feature and their corresponding data weight values ​​into the omnidirectional beacon device maintenance time prediction model to obtain a prediction label corresponding to the sample data;

[0149] Calculate the loss value of the omnidirectional beacon device maintenance time prediction model according to the prediction label and the sample label;

[0150] When the loss value is less than a preset value, the training of the maintenance time prediction model of the omnidirectional beacon device is stopped.

[0151] In an optional embodiment provided by the present invention, the training module 35 is specifically used for:

[0152] Obtain a first prediction label corresponding to the sample data through the first sample data feature and its corresponding data weight value; obtain a second prediction label corresponding to the sample data through the second sample data feature and its corresponding data weight value;

[0153] Obtain a third prediction label corresponding to the sample data through the first sample data feature and the second sample data feature and their corresponding data weight values;

[0154] The prediction label corresponding to the sample data is obtained according to the first prediction label, the second prediction label and the third prediction label.

[0155] For the specific definition of the prediction device, please refer to the definition of the method for predicting the maintenance time of the omnidirectional beacon device mentioned above, which will not be repeated here. Each module in the above device can be implemented in whole or in part by software, hardware and a combination thereof. Each of the above modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the operations corresponding to each of the above modules.

[0156] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0157] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A method for predicting maintenance time of an omnidirectional beacon device, characterized in that: The method is applied to a system for predicting the maintenance time of an omnidirectional beacon device, the system comprising: an omnidirectional beacon device, a monitoring device, and a prediction device, the omnidirectional beacon device comprising an omnidirectional beacon ground station and an omnidirectional beacon receiver, the omnidirectional beacon ground station is arranged within a preset range of an airport, the omnidirectional beacon receiver is arranged in an aircraft, and the prediction device is used to execute the following method: Acquiring monitoring data of the omnidirectional beacon ground station through the monitoring device, and acquiring operation data of the omnidirectional beacon ground station; The omnidirectional beacon receiver running in the aircraft calculates the relative position of the running aircraft according to the monitoring data and the running data; Determining whether the omnidirectional beacon device is abnormal based on the absolute position of the operating aircraft and the relative position; If the omnidirectional beacon device is abnormal, the historical operation data of the omnidirectional beacon ground station or the omnidirectional beacon receiver in a preset time period is obtained; and the historical climate data corresponding to the preset time period; Determine a characteristic vector matrix according to the historical operation data and historical climate data of the preset time period; each row of data of the characteristic vector matrix is ​​the historical operation data and historical climate data of the corresponding time; The characteristic vector matrix is ​​input into the maintenance time prediction model of the omnidirectional beacon device to predict the maintenance time of the omnidirectional beacon device; the maintenance time prediction model of the omnidirectional beacon device is trained based on N consecutive days of sample data and corresponding maintenance time labels, and the sample data includes N consecutive days of historical climate data and historical operation data of the omnidirectional beacon device.

2. The method according to claim 1, characterized in that The omnidirectional beacon receiver running in the aircraft calculates the relative position of the running aircraft according to the monitoring data and the running data, including: Respectively acquiring a reference signal with a fixed phase and a variable phase signal with an angle phase continuously changing around the omnidirectional beacon ground station in the operation data and the monitoring data; Calculating a first relative position of the operating aircraft by using a phase difference between the reference signal and the variable phase signal corresponding to the operating data; The second relative position of the operating aircraft is calculated by the phase difference between the reference signal and the variable phase signal corresponding to the monitoring data.

3. The method according to claim 2, characterized in that The determining whether the omnidirectional beacon device is abnormal based on the absolute position and the relative position of the operating aircraft includes: Calculating the absolute value of the data difference between the operating data and the monitoring data; If the absolute value of the data difference is less than a second preset value, calculating a first position deviation between the first absolute position of the operating aircraft and the first relative position; If the first position deviation is greater than a first preset value, it is determined that an abnormality occurs in the omnidirectional beacon ground station; If the first position deviation is less than or equal to the first preset value, calculating a second position deviation between the second absolute position of the operating aircraft and the second relative position; If the second position deviation is greater than a first preset value, it is determined that an abnormality occurs in the omnidirectional beacon receiver; If the absolute value of the data difference is greater than or equal to a second preset value, it is determined that an interference signal appears within the preset range of the omnidirectional beacon ground station.

4. The method according to claim 3, characterized in that If the omnidirectional beacon device is abnormal, obtaining historical operation data of the omnidirectional beacon ground station or the omnidirectional beacon receiver in a preset time period includes: If the omnidirectional beacon ground station is abnormal, obtaining historical operation data of the omnidirectional beacon ground station in a preset time period; If the omnidirectional beacon receiver is abnormal, historical operation data of the omnidirectional beacon receiver in a preset time period is obtained.

5. The method according to claim 4, characterized in that The determining of the characteristic vector matrix according to the historical operation data and the historical climate data of the preset time period includes: If the omnidirectional beacon ground station is abnormal, extracting carrier forward power, carrier reverse power, upper sideband power, lower sideband power, signal spectrum from the historical operation data, and extracting temperature, relative humidity and site air pressure from the historical climate data; The extracted carrier forward power, carrier reverse power, upper sideband power, lower sideband power, signal spectrum, and extracted temperature, relative humidity, and site air pressure are converted into feature vectors in units of days; The transformed eigenvectors are arranged in chronological order to obtain the eigenvector matrix.

6. The method according to claim 4, characterized in that The determining of the characteristic vector matrix according to the historical operation data and the historical climate data of the preset time period includes: If the omnidirectional beacon receiver is abnormal, extracting power, modulation and angle from the historical operation data; Convert the extracted power, modulation degree and angle into feature vectors; The transformed eigenvectors are arranged in chronological order to obtain the eigenvector matrix.

7. The method according to any one of claims 1 to 6, characterized in that: The step of inputting the characteristic vector matrix into an omnidirectional beacon device maintenance time prediction model to predict the maintenance time of the omnidirectional beacon device includes: Inputting the feature vector matrix into a first sub-prediction model, and obtaining a first data feature corresponding to the feature vector matrix and a corresponding data weight value thereof through the first sub-prediction model; Inputting the feature vector matrix into a second sub-prediction model, and obtaining second data features corresponding to the feature vector matrix and corresponding data weight values ​​thereof through the first sub-prediction model; The first data feature and the second data feature and their corresponding data weight values ​​are input into the omnidirectional beacon device maintenance time prediction model to predict the maintenance time of the omnidirectional beacon device.

8. The method according to claim 7, characterized in that The training process of the omnidirectional beacon device maintenance time prediction model is as follows: Acquire sample data and its corresponding sample labels, and determine a sample feature vector matrix according to the sample data; the sample data includes historical operation data of the omnidirectional beacon ground station and the omnidirectional beacon receiver and historical climate data; Inputting the sample feature vector matrix into the first sub-prediction model to obtain the first sample data feature corresponding to the sample feature vector matrix and its corresponding data weight value; Inputting the sample feature vector matrix into the second sub-prediction model to obtain second sample data features corresponding to the sample feature vector matrix and corresponding data weight values; Inputting the first sample data feature and the second sample data feature and their corresponding data weight values ​​into the omnidirectional beacon device maintenance time prediction model to obtain a prediction label corresponding to the sample data; Calculate the loss value of the omnidirectional beacon device maintenance time prediction model according to the prediction label and the sample label; When the loss value is less than a preset value, the training of the maintenance time prediction model of the omnidirectional beacon device is stopped.

9. The method according to claim 7, characterized in that: The step of inputting the first sample data feature and the second sample data feature and their corresponding data weight values ​​into the omnidirectional beacon device maintenance time prediction model to obtain a prediction label corresponding to the sample data includes: Obtain a first prediction label corresponding to the sample data through the first sample data feature and its corresponding data weight value; obtain a second prediction label corresponding to the sample data through the second sample data feature and its corresponding data weight value; Obtain a third prediction label corresponding to the sample data through the first sample data feature and the second sample data feature and their corresponding data weight values; The prediction label corresponding to the sample data is obtained according to the first prediction label, the second prediction label and the third prediction label.

10. A system for predicting maintenance time of omnidirectional beacon equipment, characterized in that: The system comprises: an omnidirectional beacon device, a monitoring device, and a prediction device. The omnidirectional beacon device comprises an omnidirectional beacon ground station and an omnidirectional beacon receiver. The omnidirectional beacon ground station is arranged within a preset range of an airport, and the omnidirectional beacon receiver is arranged in an aircraft. The prediction device comprises: An acquisition module, used to acquire monitoring data of the omnidirectional beacon ground station and to acquire operation data of the omnidirectional beacon ground station; A calculation module, used for the omnidirectional beacon receiver running in the aircraft to calculate the relative position of the running aircraft according to the monitoring data and the running data; The calculation module is further used to determine whether the omnidirectional beacon device is abnormal based on the absolute position and the relative position of the operating aircraft; The acquisition module is further used to acquire the historical operation data of the omnidirectional beacon ground station or the omnidirectional beacon receiver in a preset time period if an abnormality occurs in the omnidirectional beacon device; and the historical climate data corresponding to the preset time period; A determination module, used to determine a characteristic vector matrix according to the historical operation data and historical climate data of the preset time period; each row of data of the characteristic vector matrix is ​​the historical operation data and historical climate data of the corresponding time; A prediction module is used to input the feature vector matrix into an omnidirectional beacon device maintenance time prediction model to predict the maintenance time of the omnidirectional beacon device; the omnidirectional beacon device maintenance time prediction model is trained based on N consecutive days of sample data and corresponding maintenance time labels, and the sample data includes N consecutive days of historical climate data and historical operation data of the omnidirectional beacon device.

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