Data processing method and device, electronic equipment and storage medium

Abstract

The invention discloses a data processing method and device, electronic equipment and a storage medium. The method comprises the steps that first data and sensing signal data of flight equipment are acquired; wherein the first data comprises at least one of the following data: motion index data, dynamic index data and pose index data; determining the flight state of the flight equipment according to the sensing signal data; wherein the flight state comprises an air flight state and / or a ground flight state; and determining a flight stage corresponding to the flight state of the flight equipment according to the first data. Therefore, compared with the prior art that the flight stage is determined only according to the height change rate or the flight speed, the flight stage is determined according to the first data and the sensing signal data, the influence of different factors on the flight stage of the flight equipment is considered, and the accuracy, effectiveness and reliability of the determined flight stage are improved.

Description

Technical Field

[0001] The present application relates to the field of flight technology, and in particular to a data processing method and device, an electronic device and a storage medium. Background Art

[0002] The flight phase of a flight device is affected by both its own internal driving force and environmental factors, and is complex, diverse and uncertain. How to accurately determine the flight phase of a flight device is a tricky problem.

[0003] In the prior art, the flight phase is determined by the altitude change rate or other single factors, which is relatively unreliable and easily affected by data jitter. In addition, the algorithm used to determine the flight phase is generally based on all the data of the flight equipment after landing, which is not suitable for the scenario of real-time data transmission. Summary of the invention

[0004] In view of this, embodiments of the present application provide a data processing method and device, an electronic device, and a storage medium.

[0005] The technical solution of the embodiment of the present application is implemented as follows:

[0006] In a first aspect, an embodiment of the present application provides a data processing method, the method comprising:

[0007] In some embodiments, the method comprises:

[0008] Acquire first data and sensor signal data of the flight equipment; wherein the first data includes at least one of the following: motion index data, power index data and posture index data;

[0009] Determine the flight status of the flying device according to the sensor signal data; wherein the flight status includes: an air flight status and / or a ground flight status;

[0010] A flight phase corresponding to the flight state of the flying device is determined according to the first data.

[0011] In some embodiments, before determining the flight status of the flight device according to the sensor signal data, the method includes: performing a preprocessing operation on the first data to obtain second data; wherein the preprocessing operation includes: abnormal value data processing and / or null value data processing;

[0012] Determining the flight phase corresponding to the flight state of the flight device according to the first data includes: determining the flight phase corresponding to the flight state of the flight device according to the second data.

[0013] In some embodiments, the preprocessing operation on the first data to obtain the second data includes:

[0014] Determine, according to the first data from the first moment to the second moment, an estimated value corresponding to the first data at the second moment;

[0015] determining a first value according to the first data at the second moment and an estimated value corresponding to the first data at the second moment;

[0016] Determine a second value according to a standard deviation of the first data from the first moment to the second moment;

[0017] Determine whether the first data at the second moment is abnormal value data according to the first value and the second value;

[0018] If the first data at the second moment is abnormal value data, the first data at the second moment is updated according to the mean value of the first data from the first moment to the second moment to obtain the second data.

[0019] In some embodiments, the preprocessing operation on the first data to obtain the second data includes:

[0020] Determine whether the data type of the null value data in the first data is a predetermined type of data;

[0021] If the null value data is the predetermined type of data, updating the null value data according to the first two frames of non-null data of the null value data to obtain the second data;

[0022] If the null value data is not the predetermined type of data, the null value data is updated according to the data type of the null value data to obtain the second data.

[0023] In some embodiments, updating the null value data to obtain the second data according to the data type of the null value data includes one of the following:

[0024] If the data type of the null value data is discrete, updating the null value data according to the previous frame of non-null data to obtain the second data;

[0025] If the data type of the null value data is continuous, the null value data is updated according to the estimated value corresponding to the null value data to obtain the second data.

[0026] In some embodiments, the motion index data includes: corrected airspeed, pressure altitude and altitude change rate; the power index data includes: low-pressure rotor speed; the posture index data includes: flap angle and landing gear position; the determining the flight stage corresponding to the flight state of the flight device according to the first data includes:

[0027] If the flight state of the flight equipment is the aerial flight state, the flight phase corresponding to the aerial flight state of the flight equipment is determined according to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed of the flight equipment.

[0028] In some embodiments, determining the flight phase corresponding to the airborne flight state of the flight device according to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed of the flight device includes:

[0029] Within a predetermined time window, respectively determining differential variables corresponding to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed;

[0030] Determining a covariance matrix according to differential variables corresponding to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed;

[0031] Determining the Mahalanobis distance according to the covariance matrix;

[0032] If the Mahalanobis distance is greater than a predetermined threshold, it is determined that the flight device switches from a previous flight phase to a next flight phase.

[0033] In some embodiments, the power index data includes: ground speed, low-pressure rotor speed, and high-pressure rotor speed; and determining the flight phase corresponding to the flight state of the flight device according to the first data includes:

[0034] If the flight state of the flight equipment is the ground flight state, the flight phase corresponding to the ground flight state of the flight equipment is determined according to the ground speed of the flight equipment, the low-pressure rotor speed and / or the high-pressure rotor speed.

[0035] In some embodiments, determining the flight phase corresponding to the ground flight state of the flight device according to the ground speed of the flight device, the low-pressure rotor speed and / or the high-pressure rotor speed includes:

[0036] If the ground speed of the flight equipment and the high-pressure rotor speed are both first predetermined values, determining that the flight phase of the flight equipment is a ground phase;

[0037] If the ground speed of the flight equipment and the high-pressure rotor speed are both greater than the first predetermined value, determining that the flight phase of the flight equipment is a taxiing phase;

[0038] If the ground speed of the flight equipment is greater than a second predetermined value and the low-pressure rotor speed is greater than a third predetermined value, it is determined that the flight phase of the flight equipment is a rolling phase.

[0039] In a second aspect, an embodiment of the present application provides a data processing device, the device comprising:

[0040] An acquisition module, used to acquire first data and sensor signal data of the flight equipment; wherein the first data includes at least one of the following: motion index data, power index data and posture index data;

[0041] A first determination module is used to determine the flight state of the flying device according to the sensor signal data; wherein the flight state includes: an air flight state and / or a ground flight state;

[0042] The second determination module is used to determine the flight phase corresponding to the flight state of the flight equipment according to the first data.

[0043] In a third aspect, an embodiment of the present application provides an electronic device, the electronic device comprising:

[0044] a memory storing computer-readable instructions;

[0045] A processor is connected to the memory and is used to implement the data processing method provided by the first aspect by running the computer-readable instructions.

[0046] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium on which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, the data processing method provided in the first aspect above is implemented.

[0047] An embodiment of the present application provides a data processing method and device, an electronic device and a storage medium, the method comprising: acquiring first data and sensor signal data of a flight device; wherein the first data comprises at least one of the following: motion index data, power index data and posture index data; determining the flight state of the flight device according to the sensor signal data; wherein the flight state comprises: an air flight state and / or a ground flight state; and determining the flight phase corresponding to the flight state of the flight device according to the first data.

[0048] In this way, compared with the related art in which the flight phase is determined only based on the altitude change rate or the flight speed, the present application determines the flight phase based on the first data and the sensor signal data, takes into account the impact of different factors on the flight phase of the flight equipment, and improves the accuracy, effectiveness and reliability of the determined flight phase.

[0049] Furthermore, compared to the related art in which data is acquired after the flight is completed to divide the flight stages, the present application can acquire the first data and the sensor signal data in real time and divide the flight stages, which has relatively good timeliness and facilitates subsequent corresponding operations based on the flight stages. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A flowchart of a data processing method provided in an embodiment of the present application;

[0051] Figure 2 A flowchart of a data processing method provided in an embodiment of the present application;

[0052] Figure 3 A schematic diagram of a flight parameter classification system provided in an embodiment of the present application;

[0053] Figure 4 A flowchart of a data processing method provided in an embodiment of the present application;

[0054] Figure 5 A flowchart of a data processing method provided in an embodiment of the present application;

[0055] Figure 6 A schematic diagram of the structure of a data processing device provided in an embodiment of the present application;

[0056] Figure 7 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0057] The present application is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0058] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0059] The present application embodiment provides a data processing method. Figure 1 A schematic diagram of the implementation flow of a data processing method provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the method mainly includes the following steps:

[0060] Step S110: Acquire first data and sensor signal data of the flight equipment; wherein the first data includes at least one of the following: motion index data, power index data, and posture index data;

[0061] Step S120: determining the flight state of the flying device according to the sensor signal data; wherein the flight state includes: an air flight state and / or a ground flight state;

[0062] Step S130: Determine the flight phase corresponding to the flight state of the flying device according to the first data.

[0063] The data processing method involved in the embodiments of the present application can be executed by a terminal. The terminal can be any mobile terminal or a fixed terminal. The terminal can be a device that provides voice and / or data connectivity to a user. Exemplarily, the terminal can be an Internet of Things terminal, such as a sensor device, a mobile phone (or a "cellular" phone), and a computer with an Internet of Things terminal, for example, it can be a fixed, portable, pocket-sized, handheld, computer-built-in or vehicle-mounted device. Alternatively, the terminal can also be a device of an unmanned aerial vehicle. Alternatively, the terminal can also be a vehicle-mounted device, for example, it can be a driving computer with wireless communication function, or a wireless terminal of an external driving computer.

[0064] In some embodiments, the flying device may be any device having a flying function.

[0065] Exemplarily, the flying device may be a drone, a monoplane, a biplane, a multiplane, and / or a helicopter, etc.

[0066] In some embodiments, the motion index data includes at least one of the following: corrected airspeed, pressure altitude, and altitude change rate.

[0067] In some embodiments, the posture index data includes at least one of the following: flap angle and landing gear position.

[0068] In some embodiments, the power index data includes at least one of the following: ground speed, low-pressure rotor speed, and high-pressure rotor speed.

[0069] In some embodiments, the sensor signal data at least includes: main landing gear wheel load signal data. Optionally, the main landing gear wheel load signal includes: left landing gear wheel load signal data and / or right landing gear wheel load signal data.

[0070] In some embodiments, when the flight state of the flying device is a ground flight state, the flight phases include: a ground phase, a taxiing phase, and a rolling phase.

[0071] In some embodiments, when the flight status of the flying device is an airborne flight status, the flight phases include: take-off phase, climbing phase, cruising phase, descending phase, approach phase and landing phase.

[0072] In some embodiments, step S120 includes one of the following:

[0073] If it is determined that the corresponding logic values ​​of the left landing gear wheel load signal and the right landing gear wheel load signal are both predetermined values, determining that the flight state of the flight device is an airborne flight state;

[0074] If it is determined that at least one of the corresponding logic values ​​of the left landing gear wheel load signal and the right landing gear wheel load signal is not a predetermined value, the flight state of the flight equipment is determined to be a ground flight state.

[0075] Exemplarily, the predetermined value may be 1. A method for determining the flight status of a flight device is: WOWL=1&WOWR=1, wherein WOWL is the corresponding logic value of the left landing gear wheel load signal; and WOWR is the corresponding logic value of the right landing gear wheel load signal.

[0076] In this way, compared with the related art in which the flight phase is determined only based on the altitude change rate or the flight speed, the present application determines the flight phase based on the first data and the sensor signal data, takes into account the impact of different factors on the flight phase of the flight equipment, and improves the accuracy, effectiveness and reliability of the determined flight phase.

[0077] Furthermore, compared to the related art in which data is acquired after the flight is completed to divide the flight stages, the present application can acquire the first data and the sensor signal data in real time and divide the flight stages, which has relatively good timeliness and facilitates subsequent corresponding operations based on the flight stages.

[0078] In some embodiments, Figure 2 As shown, before step S120, the following steps are included:

[0079] Step S1101: preprocessing the first data to obtain second data; wherein the preprocessing operation includes: abnormal value data processing and / or null value data processing

[0080] Step S120 includes:

[0081] Step S1201: Determine the flight phase corresponding to the flight state of the flying device according to the second data.

[0082] In some embodiments, the second data is the first data after preprocessing.

[0083] In some embodiments, outlier data processing includes at least: outlier data determination, outlier data deletion and / or outlier data filling.

[0084] In some embodiments, the processing of null value data includes at least: determining null value data, deleting null value data and / or filling null value data.

[0085] In this way, by preprocessing the first data, the interference of abnormal value data and / or null value data on the determination of the flight phase can be reduced; thereby, the error can be reduced and the accuracy of the determined flight phase can be improved.

[0086] In some embodiments, step S1101 includes:

[0087] Determine, according to the first data from the first moment to the second moment, an estimated value corresponding to the first data at the second moment;

[0088] determining a first value according to the first data at the second moment and an estimated value corresponding to the first data at the second moment;

[0089] Determine a second value according to a standard deviation of the first data from the first moment to the second moment;

[0090] Determine whether the first data at the second moment is abnormal value data according to the first value and the second value;

[0091] If the first data at the second moment is abnormal value data, the first data at the second moment is updated according to the mean value of the first data from the first moment to the second moment to obtain the second data.

[0092] In some embodiments, the first time may be any time, and the second time may be any time after the first time.

[0093] In one embodiment, the frequency of acquiring the first data by the flight device is 1 second / time. The first moment may be the i-6th second; the second moment may be the i-th second. Determining the estimated value corresponding to the first data at the second moment based on the first data from the first moment to the second moment includes: analyzing the first data from the i-6th second to the i-th second according to the seven-point forward difference method to determine the estimated value corresponding to the first data at the i-th second; wherein i is a positive integer.

[0094] Exemplarily, a method for determining the estimated value corresponding to the first data at the i-th second is: Among them, x i is the first data of the i-th second; is the estimated value corresponding to the first data of the i-th second; i-1 is the first data of the i-1th second; xi-2 is the first data of the i-2th second; x i-3 is the first data of the i-3th second; x i-4 is the first data of the i-4th second; x i-5 is the first data of the i-5th second; x i-6 It is the first data of the i-6th second.

[0095] In some embodiments, determining the first value according to the first data at the second moment and the estimated value corresponding to the first data at the second moment includes:

[0096] The first value is determined according to the first data at the second moment and a residual of an estimated value corresponding to the first data at the second moment.

[0097] Exemplarily, a method for determining the first value is as follows: Among them, x i is the first data of the i-th second; is the estimated value corresponding to the first data of the i-th second; △x i is the first value.

[0098] In some embodiments, before determining the second value according to the standard deviation of the first data from the first moment to the second moment, the method further includes:

[0099] Determine a third value according to an average of the first data from the first moment to the second moment;

[0100] The standard deviation of the first data from the first moment to the second moment is determined according to the third value.

[0101] Exemplarily, a method for determining the third value is: Among them, x k is the first data of the kth second; u x is the third value.

[0102] Exemplarily, a method for determining the standard deviation of first data from a first moment to a second moment is: Among them, x k is the first data of the kth second; u x is the third value; σ x is the standard deviation of the first data from the i-6th second to the i-th second.

[0103] In some embodiments, the second value is a standard deviation of the first data from the first moment to the second moment. Determining whether the first data at the second moment is abnormal value data based on the first value and the second value includes one of the following:

[0104] If the first value is greater than a predetermined multiple of the second value, determining that the first data at the second moment is abnormal value data;

[0105] If the first value is less than or equal to a predetermined multiple of the second value, it is determined that the first data at the second moment is not abnormal value data.

[0106] In some embodiments, the predetermined multiple may be any multiple, for example, 0, 1, 2.5, 3, etc.

[0107] In some embodiments, the predetermined multiple can be determined based on historical data; or, the predetermined multiple can be determined based on an algorithm and / or model. Exemplarily, according to the Wright criterion, the predetermined multiple is determined to be 3.

[0108] Exemplarily, a method for updating the first data at the second moment to obtain the second data is: i =u x Among them, x i is the updated first data of the ith second, i.e., the second data of the ith second; u x is the third value, that is, the average value of the first data from the first moment to the second moment.

[0109] In some embodiments, if the first data at the second moment is abnormal value data, updating the first data at the second moment to obtain the second data according to the mean of the first data from the first moment to the second moment includes:

[0110] According to the mean value of the first data excluding the abnormal value data from the first moment to the second moment, the first data at the second moment is updated to obtain the second data.

[0111] Exemplarily, a method for updating the first data at the second moment to obtain the second data is: i =u' x Among them, x i is the updated first data of the ith second, i.e., the second data of the ith second; u' x is the mean of the valid data excluding the abnormal value data in the first data from the first moment to the second moment.

[0112] In some embodiments, the method further comprises:

[0113] If the first data at the second moment is not abnormal value data, it is determined not to update the first data at the second moment.

[0114] In this way, if the first data is determined to be abnormal value data, it is replaced and updated according to the estimated value corresponding to the first data, thereby reducing the interference of the abnormal value data on the flight stage judgment. In this way, the error can be further reduced, and the accuracy and reliability of the determined flight stage can be improved; thereby reducing the false alarm or false alarm caused by abnormal value data.

[0115] In some embodiments, step S1101 includes:

[0116] Determine whether the data type of the null value data in the first data is a predetermined type of data;

[0117] If the null value data is the predetermined type of data, updating the null value data according to the first two frames of non-null data of the null value data to obtain the second data;

[0118] If the null value data is not the predetermined type of data, the null value data is updated according to the data type of the null value data to obtain the second data.

[0119] In some embodiments, the predetermined type of data may be shared slot type data, which is used to indicate first data that shares the same slot when being stored and / or recorded.

[0120] In some embodiments, updating the null value data to obtain the second data according to the first two frames of non-null data of the null value data comprises:

[0121] Determine a fourth value according to a ratio of a difference between the first two frames of non-empty data and the first data interval;

[0122] Determine a fifth value according to a second data interval between the null value data and a previous frame of non-null data;

[0123] Determine a sixth value according to the product of the fourth value and the fifth value;

[0124] Determine a seventh value according to the sum of the sixth value and the non-empty data of the previous frame;

[0125] According to the seventh value, the null value data is updated to obtain the second data.

[0126] In some embodiments, the method further comprises:

[0127] A first data interval is determined.

[0128] In some embodiments, determining the first data interval comprises one of the following:

[0129] Determining a first data interval according to a data frame specification;

[0130] Determine a first data interval according to the time interval between the first two frames of non-empty data;

[0131] A first data interval is determined according to the frame number interval of the first two frames of non-empty data.

[0132] Exemplarily, the first data interval may be 4, 16, or 64, etc.

[0133] Optionally, the first frame of non-empty data in the first two frames of non-empty data is the first data at the third moment, and the second frame of non-empty data is the first data at the fourth moment; the first data interval is determined according to the difference between the fourth moment and the third moment. Exemplarily, the third moment is the mth second, and the fourth moment is the nth second, wherein m is a positive integer, n is a positive integer, and n is greater than m; then the first data interval w=nm.

[0134] Exemplarily, the frame number of the first frame of non-empty data in the first two frames of non-empty data is f m ; The frame number of the second frame of non-empty data is f n ; Then the first data interval w = f n -f m .

[0135] In some embodiments, determining the fourth value according to the ratio of the difference between the first two frames of non-empty data and the first data interval includes:

[0136] Determine an eighth value according to a difference between the second frame of data in the first two frames of non-empty data and the first frame of data in the first two frames of non-empty data;

[0137] A fourth value is determined according to a ratio of the eighth value to the first data interval.

[0138] Exemplarily, a method for determining the seventh value is: x∈S. i is the seventh value; x m The second frame of non-empty data in the first two frames of non-empty data; x n The first frame of non-empty data in the first two frames of non-empty data; f i The frame number of the null value data; f m is the frame number of the second frame of non-empty data in the first two frames of non-empty data; S is the shared slot data set; and w is the data interval of the first two frames of non-empty data.

[0139] In some embodiments, updating the null value data to obtain the second data according to the data type of the null value data includes one of the following:

[0140] If the data type of the null value data is discrete, updating the null value data according to the previous frame of non-null data to obtain the second data;

[0141] If the data type of the null value data is continuous, the null value data is updated according to the estimated value corresponding to the null value data to obtain the second data.

[0142] In some embodiments, the discrete data includes: landing gear position.

[0143] In some embodiments, the continuous data includes: low pressure rotor speed, pressure altitude, corrected airspeed, altitude change rate and / or flap angle.

[0144] Exemplarily, if the data type of the null value data is discrete, a method for updating the null value data according to the non-null data of the previous frame to obtain the second data is: i =x i-1 ,x∈D. Among them, x i is the updated data, i.e., the second data; x i-1 is the non-empty data of the previous frame; D is a discrete data set.

[0145] Exemplarily, if the data type of the null value data is continuous; a method for updating the null value data according to the estimated value corresponding to the null value data to obtain the second data is: x∈B. i is the updated data, i.e., the second data; is the estimated value corresponding to the null value data determined by the seven-point forward difference method; B is the continuous data set.

[0146] In this way, if it is determined that the first data is null value data, the null value data can be updated according to the type of the null value data to reduce the interference of the null value data on the flight stage judgment; thereby further reducing errors and improving the accuracy and reliability of the determined flight stage; thereby reducing false alarms or false alarms caused by null value data.

[0147] In some embodiments, the motion index data includes: corrected airspeed, pressure altitude and altitude change rate; the power index data includes: low-pressure rotor speed; the posture index data includes: flap angle and landing gear position; the determining the flight stage corresponding to the flight state of the flight device according to the first data includes:

[0148] If the flight state of the flight equipment is the aerial flight state, the flight phase corresponding to the aerial flight state of the flight equipment is determined according to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed of the flight equipment.

[0149] In some embodiments, determining the flight phase corresponding to the airborne flight state of the flight device according to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed of the flight device includes:

[0150] Within a predetermined time window, respectively determining differential variables corresponding to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed;

[0151] Determining a covariance matrix according to differential variables corresponding to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed;

[0152] Determining the Mahalanobis distance according to the covariance matrix;

[0153] If the Mahalanobis distance is greater than a predetermined threshold, it is determined that the flight device switches from a previous flight phase to a next flight phase.

[0154] In some implementations, the predetermined time window may be a time window of any length.

[0155] Exemplarily, the predetermined time window may be from the 10th second to the 100th second; or, the predetermined time window may be from the 1st hour to the 2nd hour.

[0156] Exemplarily, a method for determining the differential variable corresponding to the first data is: ΔY k =Y k -Y k-1 . Among them, ΔY k is the differential variable corresponding to the first data at the kth moment; Y k is the first data at the kth moment; Y k-1 is the first data at the k-1th moment. Within the predetermined time window, the data set of the differential variable corresponding to the first data is {ΔY k ,ΔY k-1 ,…,ΔY k-l+1}; The differential variable corresponding to each first data is: ΔX i =(ΔCAS i ,ΔN1 i ,ΔALT i ,...) T It should be noted that, here, the first data may be the first data before preprocessing; the first data may also be the first data after preprocessing, that is, the second data.

[0157] Exemplarily, a method for determining the covariance matrix is:

[0158]

[0159] in:

[0160]

[0161]

[0162]

[0163] Among them, ΔCAS i is the differential variable corresponding to the corrected airspeed at the i-th moment; u ΔCAS is the mean of the difference variable of the corrected airspeed; is the variance of the difference variable of the corrected airspeed; ΔALT i is the differential variable corresponding to the air pressure height at the i-th moment; u ΔALT is the mean of the difference variable of pressure altitude; is the variance of the differential variable of pressure altitude; ΔN1 i is the differential variable corresponding to the low-pressure rotor speed at the i-th moment; u ΔN1 is the mean of the differential variable of the low-pressure rotor speed; is the variance of the differential variable of the low-pressure rotor speed.

[0164] In some embodiments, the method further comprises:

[0165] Determining weights corresponding to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed, respectively, according to the analytic hierarchy process;

[0166] A weight matrix is ​​determined according to weights corresponding to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed.

[0167] In some embodiments, the method further comprises:

[0168] Determine, according to the analytic hierarchy process, a weight vector corresponding to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed;

[0169] A weight matrix is ​​determined according to weight vectors corresponding to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed.

[0170] In some embodiments, in the Analytic Hierarchy Process (AHP), the importance of different factors can be compared two by two according to Santy's 1-9 scaling method to construct a judgment matrix. Santy's 1-9 scaling method is shown in the following table:

[0171]

[0172]

[0173] Table 1

[0174] It is understood that each element in the above Table 1 exists independently. These elements are exemplarily listed in the same table, but it does not mean that all elements in the table must exist at the same time as shown in the table. The value of each element is independent of the value of any other element in Table 1. Therefore, those skilled in the art can understand that the value of each element in Table 1 is an independent embodiment.

[0175] For example, the weight vector may be a=(α1, α2, α3, α4, α5, α6) T ; The weight matrix can be A=diag(α1,α2,α3,α4,α5,α6). Among them, a is the weight vector; A is the weight matrix; α1 is the weight corresponding to the corrected airspeed; α2 is the weight corresponding to the pressure altitude; α3 is the weight corresponding to the altitude change rate; α4 is the weight corresponding to the flap angle; α5 is the weight corresponding to the landing gear position; α6 is the weight corresponding to the low-pressure rotor speed.

[0176] In some embodiments, the weight matrix may be determined before determining the flight phase; alternatively, the weight matrix may be determined during the process of determining the flight phase.

[0177] In some embodiments, the weight matrices corresponding to the first data of different flight phases may be the same or different.

[0178] In some embodiments, determining the Mahalanobis distance according to the covariance matrix includes:

[0179] The Mahalanobis distance is determined according to a weight matrix corresponding to the first data and the covariance matrix.

[0180] Exemplarily, a method for determining the Mahalanobis distance is: Where ΔX k is the differential variable corresponding to each first data; u is the mean corresponding to each first data; A is the weight matrix corresponding to each first data; Σ is the covariance matrix corresponding to each first data.

[0181] In some embodiments, the predetermined threshold may be determined based on user experience; or, the predetermined threshold may be determined based on historical data; or, the predetermined threshold may be determined based on an algorithm.

[0182] In some embodiments, the predetermined thresholds for different flight devices may be the same or different.

[0183] In some embodiments, the predetermined thresholds for different flight phases may be the same or different.

[0184] In some embodiments, the flight stage of the airborne flight state includes the 1st to the Nth stage; if the terminal determines that the Mahalanobis distance is greater than the predetermined threshold for the first time, it is determined that the flight stage of the flight equipment enters the 2nd stage from the 1st stage; if the terminal determines that the Mahalanobis distance is greater than the predetermined threshold for the second time, it is determined that the flight stage of the flight equipment enters the 3rd stage from the 2nd stage; and so on, if the terminal determines that the Mahalanobis distance is greater than the predetermined threshold for the N-1th time, it is determined that the flight stage of the flight equipment enters the Nth stage from the N-1th stage. Wherein, N is a positive integer, and N is greater than 1.

[0185] In some embodiments, the terminal determines that the current flight phase of the flight device is the first flight phase; if the terminal determines that the current Mahalanobis distance is greater than a predetermined threshold, it determines that the flight device enters the second flight phase from the first flight phase.

[0186] In this way, the flight phase of the flight equipment can be determined comprehensively, accurately and efficiently according to the impact of factors in different dimensions on the flight phase of the flight equipment, meeting the needs of real-time flight phase division and fault monitoring and diagnosis.

[0187] Furthermore, if the weight matrices corresponding to the first data of different flight phases are different, the degree of influence and / or importance of different first data on each flight phase can be accurately measured, thereby ensuring the accuracy of the Mahalanobis distance and the accuracy of dividing the flight phases according to the Mahalanobis distance; if the weight matrices corresponding to the first data of different flight phases are the same, the weight matrix only needs to be determined once before dividing the flight phase, which can save computing resources and time for determining the weight matrix, and there is no need to occupy computing time when subsequently dividing the flight phase, and the timeliness is relatively good.

[0188] In some embodiments, the power index data includes: ground speed, low-pressure rotor speed, and high-pressure rotor speed; and determining the flight phase corresponding to the flight state of the flight device according to the first data includes:

[0189] If the flight state of the flight equipment is the ground flight state, the flight phase corresponding to the ground flight state of the flight equipment is determined according to the ground speed of the flight equipment, the low-pressure rotor speed and / or the high-pressure rotor speed.

[0190] In some embodiments, determining the flight phase corresponding to the ground flight state of the flight device according to the ground speed, the low-pressure rotor speed and / or the high-pressure rotor speed of the flight device includes:

[0191] If the ground speed of the flight equipment and the high-pressure rotor speed are both first predetermined values, determining that the flight phase of the flight equipment is a ground phase;

[0192] If the ground speed of the flight equipment and the high-pressure rotor speed are both greater than the first predetermined value, determining that the flight phase of the flight equipment is a taxiing phase;

[0193] If the ground speed of the flight equipment is greater than a second predetermined value and the low-pressure rotor speed is greater than a third predetermined value, it is determined that the flight phase of the flight equipment is a rolling phase.

[0194] Exemplarily, the first predetermined value is 0; a method for determining that the flight stage of the flight equipment is the ground stage is: GS = 0 & N2 = 0. A method for determining that the flight stage of the flight equipment is the taxiing stage is: GS> 0 & N2> 0. Among them, GS is the ground speed of the flight equipment; N2 is the high-pressure rotor speed of the flight equipment.

[0195] Exemplarily, the second predetermined value is 40 meters per second (m / s); the third predetermined value is 40% of the maximum speed of the low-pressure rotor. A method for determining that the flight phase of the flight equipment is the taxiing phase is: GS>40&N1>40%. Among them, GS is the ground speed of the flight equipment; N1 is the speed of the low-pressure rotor of the flight equipment.

[0196] In this way, the ground flight status of the flight equipment is further divided into flight stages, and when a failure occurs in the flight equipment, the specific cause of the failure can be quickly and accurately determined according to the flight stage.

[0197] Three specific examples are provided below in combination with any of the above embodiments:

[0198] In an application scenario, a data processing method is provided, which is applied to a terminal, and the method includes:

[0199] 1) Sort out the data frame specifications of the model and determine the parameters of the shared slots to establish a set S.

[0200] In an optional embodiment, the common slot type parameters are assigned to a set S. The aircraft type may be the type of the flying device in the above embodiment; and the parameter may be the first data in the above embodiment.

[0201] 2) Divide the parameters into two categories: continuous and discrete to establish sets B and D.

[0202] In an optional embodiment, the continuous parameter names are assigned to set B, and / or the discrete parameter names are assigned to set D.

[0203] 3) Establish a parameter system for flight phase division and determine parameter weights.

[0204] In an optional embodiment, during the flight of the flight equipment, according to the definition and experience of the flight phases, the corrected airspeed, pressure altitude, altitude change rate and other parameters in different flight phases have different characteristics, which can be used as important reference indicators for judging the change of flight phases. According to the definition and experience of the flight phases, six parameters including low-pressure rotor speed (N1∈B), landing gear position (GEAR∈D), flap angle (FLAPS∈B), pressure altitude (ALT∈B), corrected airspeed (CAS∈B), altitude change rate (VS∈B) are selected as the judgment criteria. Since the conversion of various parameters is not completely synchronized during actual operation, it is necessary to make a comprehensive judgment based on the importance of each parameter, and determine the weight of each parameter during judgment according to the hierarchical analysis method. Figure 3 As shown, first, according to the types of the six indicators, a flight parameter classification system is established; wherein the flight parameter classification system includes: motion indicators, aircraft configuration indicators and power indicators; the motion indicators include at least one of the following: corrected airspeed, pressure altitude and altitude change rate; the aircraft configuration indicators include at least one of the following: flap angle and landing gear position; the power indicators include at least: low-pressure rotor speed; then a weight matrix is ​​established according to the weight of each parameter. Here, the aircraft configuration indicator can be the posture indicator data in the above embodiment.

[0205] In an application scenario, such as Figure 4 As shown, a data processing method is provided, which is applied to a terminal, and the method includes:

[0206] Step S410: receiving data.

[0207] In an optional embodiment, a list variable is created based on the original data transmitted by the aircraft. Due to the limitation of the onboard equipment, the frequency of receiving data is generally once every 1 second. For each subframe data received, it is necessary to ignore the parameters irrelevant to fault monitoring and quality analysis, and store the rest in the list variable. The time window length of the list variable needs to consider the common limitations of outlier detection, Mahalanobis distance calculation requirements and data frame characteristics. Here, the data can be the first data in the above embodiment.

[0208] Step S420: Determine the data type.

[0209] In an optional embodiment, based on the name of the data in the list variable, the data type set S (common slot type) / B (continuous data) / D (discrete data) is retrieved to determine whether the data type is continuous or discrete, and to determine whether the data is a common slot type.

[0210] Step S430: Determine whether it is an abnormal value. If so, execute step S440; if not, execute step S450.

[0211] In an optional embodiment, according to the data characteristics and usage scenario restrictions, seven-point forward difference is selected as a solution for identifying abnormal values ​​in real-time flight data. For each continuous data, in addition to the current data, the data of the previous 6s needs to be extracted from the list variable. The current data is estimated using the seven-point forward difference method. A method for determining the estimated value corresponding to the current data is: Among them, x i is the current data; is the estimated value corresponding to the current data; x i-1 is the data of the previous second of the current data; x i-2 The data of the first two seconds of the current data; x i-3 The data of the first three seconds of the current data; x i-4 is the first data of the i-4th second; x i-5 The data of the previous five seconds of the current data; x i-6 is the data of the first six seconds of the current data. One method to calculate the residual of the current data is: Among them, x i is the current data; is the estimated value corresponding to the current data; △x i is the residual. One way to determine the mean of seven data points is: Among them, x k is the first data of the kth second; u x is the mean. One way to determine the standard deviation of seven points of data is: Among them, x k is the data of the kth second; u x is the mean; σ x is the standard deviation. If Δx i >3σ x , determine that the current data is an abnormal value, and execute step S440; if Δx i ≤3σ x , determine that the current data is not an abnormal value, and execute step S450; wherein, △x i is the residual; σ x is the standard deviation.

[0212] Step S440: Correct and fill in outliers.

[0213] In an optional embodiment, the outliers are filled according to the mean of the valid value data except the outliers: i =u' x ; where x i is an outlier; u' x It is the mean of the valid data except the outliers in the seven data points.

[0214] Step S450: Determine whether it is a null value. If so, execute step S460.

[0215] Step S460: Determine the type of the null value and fill it.

[0216] If the type of the empty value is a shared slot type (the reason for the empty value is low data recording frequency), the current value is inferred and filled according to the data change rate of the two frames of non-empty data recording cycles before the empty value. One method to infer the current value is: x∈S. Among them, x i is the current value; x m The second frame of non-empty data in the first two frames of non-empty data; x n The first frame of non-empty data in the first two frames of non-empty data; f i The frame number of the null value data; f m is the frame number of the second frame of non-empty data in the first two frames of non-empty data; S is a common slot data set; w is the recording interval of the first two frames of non-empty data. If the type of the null value is not a common slot type (the reason for the null value is frame loss), further determine the data type of the null value; if the data type of the null value is discrete, update the null value according to the previous frame of non-empty data to obtain the updated data: x i =x i-1 ,x∈D. Among them, x i is the updated data; x i-1 is the non-empty data of the previous frame; D is a discrete data set; if the data type of the empty value is continuous, update the empty value according to the estimated value corresponding to the empty value to obtain the updated data: x∈B. i For updated data; is the estimated value corresponding to the null value determined by the seven-point forward difference method; B is a continuous data set.

[0217] Step S470: Divide the flight phases.

[0218] In an optional embodiment, the flight phases are divided into Figure 5 shown.

[0219] In an application scenario, such as Figure 5 As shown, a data processing method is provided, which is applied to a terminal, and the method includes:

[0220] Step S510: pre-process the data.

[0221] In an optional embodiment, the data preprocessing process is described in detail in Figure 4 Here, the data may be the first data in the above embodiment.

[0222] Step S520: Determine whether the aircraft is in the air. If yes, go to step S530; if no, go to step S580.

[0223] In an optional embodiment, if the following conditions are met: WOWL=1&WOWR=1; wherein WOWL is the corresponding logic value of the left landing gear wheel-loaded signal; WOWR is the corresponding logic value of the right landing gear wheel-loaded signal; it is determined to be in the air flight state, and step S530 is executed; if not met, it is determined not to be in the air flight state, and step S580 is executed.

[0224] Step S530: Obtain a differential data set.

[0225] In an optional embodiment, a method for determining the differential variable corresponding to the data is: ΔY k =Y k -Y k-1 . Among them, ΔY k is the difference variable corresponding to the data at the kth moment; Y k is the data at the kth moment; Y k-1 is the data at the k-1th moment. Within the predetermined time window, the data set of the differential variable corresponding to the data is {ΔY k ,ΔY k-1 ,…,ΔY k-l+1}; The difference variable corresponding to each data is: ΔX i =(ΔCAS i ,ΔN1 i ,ΔALT i ,...) T .

[0226] Step S540: Determine the mean and variance of each differential variable.

[0227] Step S550: Determine the covariance matrix.

[0228] In an optional embodiment, a method for determining the covariance matrix is:

[0229]

[0230] in:

[0231]

[0232]

[0233]

[0234] Among them, ΔCAS i is the differential variable corresponding to the corrected airspeed at the i-th moment; u ΔCAS is the mean of the difference variable of the corrected airspeed; is the variance of the difference variable of the corrected airspeed; ΔALT i is the differential variable corresponding to the air pressure height at the i-th moment; u ΔALT is the mean of the difference variable of pressure altitude; is the variance of the differential variable of pressure altitude; ΔN1 i is the differential variable corresponding to the low-pressure rotor speed at the i-th moment; u ΔN1 is the mean of the differential variable of the low-pressure rotor speed; is the variance of the differential variable of the low-pressure rotor speed.

[0235] Step S560: Determine the weighted Mahalanobis distance. In an optional embodiment, the data system includes: motion index data, aircraft configuration index data and power index data; wherein the motion index data includes: corrected airspeed, pressure altitude and altitude change rate; the aircraft configuration index data includes: flap angle and landing gear position; the power index data includes: low-pressure rotor speed; the hierarchical analysis method is used to determine the weight corresponding to each data in the data system and establish a weight matrix. A method for determining the Mahalanobis distance is: Where ΔX k is the differential variable corresponding to each data; u is the mean of the differential variable corresponding to each data; A is the weight matrix of the differential variable corresponding to each data; Σ is the covariance matrix of the differential variable corresponding to each data. Here, the aircraft configuration index data can be the posture index data in the above embodiment.

[0236] Step S570: Divide the flight state into flight phases.

[0237] In an optional embodiment, the flight stage of the air flight state includes the 1st to the Nth stage; if the terminal determines that the Mahalanobis distance is greater than the predetermined threshold for the first time, it is determined that the flight stage of the flight equipment enters the 2nd stage from the 1st stage; if the terminal determines that the Mahalanobis distance is greater than the predetermined threshold for the second time, it is determined that the flight stage of the flight equipment enters the 3rd stage from the 2nd stage; and so on, if the terminal determines that the Mahalanobis distance is greater than the predetermined threshold for the N-1th time, it is determined that the flight stage of the flight equipment enters the Nth stage from the N-1th stage. Wherein, N is a positive integer, and N is greater than 1.

[0238] Step S580: Divide the flight phases of the ground flight state.

[0239] Thus, compared with the related art that determines the flight phase only based on the altitude change rate or flight speed, the present application determines the flight phase based on data and wheel-borne signals, taking into account the impact of different factors on the flight phase of the flight equipment, and improving the accuracy, effectiveness and reliability of the determined flight phase. Compared with the related art that obtains data after completing the flight to divide the flight phase, the present application can obtain data and sensor signals in real time and divide the flight phase, which has relatively good timeliness and is convenient for subsequent corresponding operations based on the flight phase.

[0240] In addition, the flight status in the air and / or the flight status on the ground of the flight equipment is further divided into flight phases, so that when a failure occurs in the flight equipment, the specific cause of the failure can be quickly and accurately determined according to the flight phase.

[0241] like Figure 6 As shown, based on the same inventive concept as the data processing method provided in the above embodiment, the embodiment of the present application further provides a data processing device, the device comprising:

[0242] The acquisition module 601 is used to acquire first data and sensor signal data of the flight equipment; wherein the first data includes at least one of the following: motion index data, power index data and posture index data;

[0243] A first determination module 602 is used to determine the flight state of the flying device according to the sensor signal data; wherein the flight state includes: an air flight state and / or a ground flight state;

[0244] The second determining module 603 is used to determine the flight phase corresponding to the flight state of the flight device according to the first data.

[0245] In some embodiments, the device further comprises:

[0246] A preprocessing module, used for performing a preprocessing operation on the first data to obtain second data before determining the flight state of the flight equipment according to the sensor signal data; wherein the preprocessing operation includes: abnormal value data processing and / or null value processing;

[0247] The first determining module 602 is used to determine the flight phase corresponding to the flight state of the flight device according to the second data.

[0248] In some embodiments, the preprocessing module is used to perform the following steps:

[0249] Determine, according to the first data from the first moment to the second moment, an estimated value corresponding to the first data at the second moment;

[0250] determining a first value according to the first data at the second moment and an estimated value corresponding to the first data at the second moment;

[0251] Determine a second value according to a standard deviation of the first data from the first moment to the second moment;

[0252] Determine whether the first data at the second moment is abnormal value data according to the first value and the second value;

[0253] If the first data at the second moment is abnormal value data, the first data at the second moment is updated according to the mean value of the first data from the first moment to the second moment to obtain the second data.

[0254] In some embodiments, the preprocessing module is used to perform the following steps:

[0255] Determine whether the data type of the null value data in the first data is a predetermined type of data;

[0256] If the null value data is the predetermined type of data, updating the null value data according to the first two frames of non-null data of the null value data to obtain the second data;

[0257] If the null value data is not the predetermined type of data, the null value data is updated according to the data type of the null value data to obtain the second data.

[0258] In some embodiments, updating the null value data to obtain the second data according to the data type of the null value data includes one of the following:

[0259] If the data type of the null value data is discrete, updating the null value data according to the previous frame of non-null data to obtain the second data;

[0260] If the data type of the null value data is continuous, the null value data is updated according to the estimated value corresponding to the null value data to obtain the second data.

[0261] In some embodiments, the motion index data includes: corrected airspeed, pressure altitude and altitude change rate; the power index data includes: low-pressure rotor speed; the posture index data includes: flap angle and landing gear position; the second determination module 603 is used to determine the flight stage corresponding to the airborne flight state of the flight equipment according to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed of the flight equipment if the flight state of the flight equipment is the airborne flight state.

[0262] In some embodiments, the second determining module 603 is used to perform the following steps:

[0263] Within a predetermined time window, respectively determining differential variables corresponding to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed;

[0264] Determining a covariance matrix according to differential variables corresponding to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed;

[0265] Determining the Mahalanobis distance according to the covariance matrix;

[0266] If the Mahalanobis distance is greater than a predetermined threshold, it is determined that the flight device switches from a previous flight phase to a next flight phase.

[0267] In some embodiments, the power index data includes: ground speed, low-pressure rotor speed and high-pressure rotor speed; the second determination module 603 is used to determine the flight stage corresponding to the ground flight state of the flight equipment according to the ground speed of the flight equipment, the low-pressure rotor speed and / or the high-pressure rotor speed if the flight state of the flight equipment is the ground flight state.

[0268] In some embodiments, the second determining module is configured to perform one of the following:

[0269] If the ground speed of the flight equipment and the high-pressure rotor speed are both first predetermined values, determining that the flight phase of the flight equipment is a ground phase;

[0270] If the ground speed of the flight equipment and the high-pressure rotor speed are both greater than the first predetermined value, determining that the flight phase of the flight equipment is a taxiing phase;

[0271] If the ground speed of the flight equipment is greater than a second predetermined value and the low-pressure rotor speed is greater than a third predetermined value, it is determined that the flight phase of the flight equipment is a rolling phase.

[0272] like Figure 7 As shown, an electronic device is provided in an embodiment of the present application, and the electronic device includes:

[0273] Memory 701, used to store computer-readable instructions;

[0274] The processor 702 is connected to the memory, and is configured to implement the method provided in any of the foregoing embodiments by executing computer-readable instructions.

[0275] The memory 701 may be various types of memory, such as random access memory, read-only memory, flash memory, etc. The memory may be used for feature storage, for example, storing computer executable instructions, etc. The computer executable instructions may be various program instructions, for example, target program instructions and / or source program instructions, etc.

[0276] The processor 702 may be any type of processor, such as a central processing unit, a microprocessor, a digital signal processor, a programmable array, a digital signal processor, an application specific integrated circuit, or an image processor. The processor may be connected to the memory via a bus. The bus may be an integrated circuit bus, or the like.

[0277] like Figure 7 As shown, the electronic device may further include a network interface 703, and the network interface 703 may be used to interact with a peer device via a network.

[0278] An embodiment of the present application further provides a computer storage medium, which stores computer executable instructions. After the computer executable instructions are executed, the method provided in any of the above embodiments can be implemented.

[0279] The computer-readable storage medium provided in the embodiments of the present application may be a ROM, PROM, EPROM, EEPROM, FlashMemory, magnetic surface storage, optical disk, or CD-ROM, etc., which may store program codes.

[0280] In the above embodiments provided in the present application, it should be understood that the disclosed methods and devices can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as: multiple modules or components can be combined, or can be integrated into another system, or some features can be ignored, or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0281] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment.

[0282] In addition, all functional units in the embodiments of the present application may be integrated into one processing module, or each unit may be separately configured as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0283] It should be noted that: "first", "second", etc. are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0284] In addition, the technical solutions described in the embodiments of the present application can be combined arbitrarily without conflict.

[0285] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A data processing method, characterized in that: The method comprises: Acquire first data and sensor signal data of the flight equipment; wherein the first data includes at least one of the following: motion index data, power index data and posture index data; Determine the flight status of the flying device according to the sensor signal data; wherein the flight status includes: an air flight status and / or a ground flight status; A flight phase corresponding to the flight state of the flying device is determined according to the first data.

2. The method according to claim 1, characterized in that Before determining the flight status of the flight equipment according to the sensor signal data, the method includes: performing a preprocessing operation on the first data to obtain second data; wherein the preprocessing operation includes: abnormal value data processing and / or null value data processing; Determining the flight phase corresponding to the flight state of the flight device according to the first data includes: determining the flight phase corresponding to the flight state of the flight device according to the second data.

3. The method according to claim 2, characterized in that The preprocessing operation on the first data to obtain second data includes: Determine, according to the first data from the first moment to the second moment, an estimated value corresponding to the first data at the second moment; determining a first value according to the first data at the second moment and an estimated value corresponding to the first data at the second moment; Determine a second value according to a standard deviation of the first data from the first moment to the second moment; Determine whether the first data at the second moment is abnormal value data according to the first value and the second value; If the first data at the second moment is abnormal value data, the first data at the second moment is updated according to the mean value of the first data from the first moment to the second moment to obtain the second data.

4. The method according to claim 2, characterized in that: The preprocessing operation on the first data to obtain second data includes: Determine whether the data type of the null value data in the first data is a predetermined type of data; If the null value data is the predetermined type of data, updating the null value data according to the first two frames of non-null data of the null value data to obtain the second data; If the null value data is not the predetermined type of data, the null value data is updated according to the data type of the null value data to obtain the second data.

5. The method according to claim 4, characterized in that The updating of the null value data to obtain the second data according to the data type of the null value data comprises one of the following: If the data type of the null value data is discrete, updating the null value data according to the previous frame of non-null data to obtain the second data; If the data type of the null value data is continuous, the null value data is updated according to the estimated value corresponding to the null value data to obtain the second data.

6. The method according to any one of claims 1 to 2, characterized in that: The motion index data includes: corrected airspeed, pressure altitude and altitude change rate; the power index data includes: low-pressure rotor speed; the posture index data includes: flap angle and landing gear position; the flight phase corresponding to the flight state of the flight device is determined according to the first data, including: If the flight state of the flight equipment is the aerial flight state, the flight phase corresponding to the aerial flight state of the flight equipment is determined according to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed of the flight equipment.

7. The method according to claim 6, characterized in that Determining the flight phase corresponding to the aerial flight state of the flight device according to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed of the flight device includes: Within a predetermined time window, respectively determining differential variables corresponding to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed; Determining a covariance matrix according to differential variables corresponding to the corrected airspeed, the pressure altitude, the altitude change rate, the flap angle, the landing gear position and / or the low-pressure rotor speed; Determining the Mahalanobis distance according to the covariance matrix; If the Mahalanobis distance is greater than a predetermined threshold, it is determined that the flight device switches from a previous flight phase to a next flight phase.

8. The method according to any one of claims 1 to 2, characterized in that: The power index data includes: ground speed, low-pressure rotor speed, and high-pressure rotor speed; and determining the flight phase corresponding to the flight state of the flight device according to the first data includes: If the flight state of the flight equipment is the ground flight state, the flight phase corresponding to the ground flight state of the flight equipment is determined according to the ground speed of the flight equipment, the low-pressure rotor speed and / or the high-pressure rotor speed.

9. The method according to claim 8, characterized in that The determining, according to the ground speed of the flight equipment, the low-pressure rotor speed and / or the high-pressure rotor speed, of the flight equipment, the flight phase corresponding to the ground flight state in which the flight equipment is located comprises: If the ground speed of the flight equipment and the high-pressure rotor speed are both first predetermined values, determining that the flight phase of the flight equipment is a ground phase; If the ground speed of the flight equipment and the high-pressure rotor speed are both greater than the first predetermined value, determining that the flight phase of the flight equipment is a taxiing phase; If the ground speed of the flight equipment is greater than a second predetermined value and the low-pressure rotor speed is greater than a third predetermined value, it is determined that the flight phase of the flight equipment is a rolling phase.

10. A data processing device, characterized in that: The device comprises: An acquisition module, used to acquire first data and sensor signal data of the flight equipment; wherein the first data includes at least one of the following: motion index data, power index data and posture index data; A first determination module is used to determine the flight state of the flying device according to the sensor signal data; wherein the flight state includes: an air flight state and / or a ground flight state; The second determination module is used to determine the flight phase corresponding to the flight state of the flight equipment according to the first data.

11. An electronic device, characterized in that: include: a memory storing computer-readable instructions; A processor, connected to the memory, is used to implement the data processing method provided in any one of claims 1 to 9 by running the computer-readable instructions.

12. A computer storage medium, characterized in that: The computer storage medium stores computer executable instructions; after the computer executable instructions are executed by the processor, the data processing method described in any one of claims 1 to 9 can be implemented.

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