Navigation equipment fault detection method, device and electronic equipment

Abstract

The present application relates to a method, device and electronic device for detecting faults in navigation equipment. The method comprises: respectively obtaining the navigation information to be detected and the corresponding variance matrix of each navigation equipment and the historical diagnostic information corresponding to each navigation equipment; according to the historical diagnostic information, resetting the state initial values ​​corresponding to the first state recursor and the second state recursor in turn according to a preset alternating period; respectively obtaining the state recursive values ​​corresponding to the first state recursor and the second state recursor according to the IMU data and the corresponding state initial values; according to the preset alternating period, selecting the state recursive value of the corresponding first state recursor or the second state recursor to perform fault diagnosis on the navigation information to be detected and obtain the corresponding diagnostic information. The solution provided by the present application improves the accuracy of the state initial values ​​by resetting the state initial values ​​of the state recursors in turn, thereby effectively reducing the error rate of the diagnostic results and reducing the risk of contamination of the two state recursors.

Description

Technical Field

[0001] The present application relates to the technical field of fault diagnosis, and in particular to a navigation device fault detection method, device and electronic equipment. Background Art

[0002] In flying cars, the reliability of navigation equipment is crucial as it provides positioning data for the flight control and autopilot systems. A navigation failure could have catastrophic consequences, such as causing the vehicle to explode mid-air or collide with the ground. To ensure safety, flying cars will be equipped with multiple navigation systems, making it essential to monitor the reliability of each device.

[0003] Failure modes of navigation equipment can be broadly categorized into two main types: data flow anomalies and data source anomalies. Data flow anomalies typically include data interruptions, data non-updates, and abnormal data frequencies. These failure modes are related to the communication link and are easy to monitor and repair. Data source anomalies, on the other hand, refer to issues with the data itself, such as positioning errors or large errors. These failures can generally be divided into two categories: hard and soft. Hard failures are sudden, sudden failures that are unrelated to operating time; soft failures, on the other hand, gradually increase in severity over time. Therefore, it is crucial to promptly and accurately detect data source anomalies that represent soft failures.

[0004] In the related art, methods for detecting data source anomalies generally include observational chi-square detection and state chi-square detection. Observational chi-square detection compares the observed values ​​predicted by the system model with the actual observed values ​​obtained by the sensors to determine the fault. This method is effective for detecting sudden hard faults, but for gradual soft faults, due to the lag in fault detection, the system model is gradually affected by erroneous information, making the predicted output prone to tracking the fault and difficult to detect using residuals. State chi-square detection, which uses the system model to estimate state values ​​rather than observed values, effectively avoids this problem and is more sensitive to soft fault detection. However, because the state recursor does not update measurements, the error in the predicted state values ​​gradually increases over time, reducing the effectiveness of soft fault detection. Therefore, there is currently a lack of anomaly detection methods for soft faults on flying cars. Summary of the Invention

[0005] In order to solve or partially solve the problems existing in the related art, the present application provides a navigation device fault detection method, device and electronic equipment, which can improve the accuracy of the fault diagnosis results of the navigation device.

[0006] A first aspect of the present application provides a navigation device fault detection method, comprising:

[0007] respectively obtaining navigation information to be detected and a corresponding variance matrix of each navigation device and historical diagnostic information corresponding to each of the navigation devices;

[0008] According to the historical diagnostic information, resetting the initial state values ​​corresponding to the first state recursor and the second state recursor in turn according to a preset alternating period;

[0009] According to the IMU data and the corresponding initial state value, respectively obtain the state recursion values ​​corresponding to the first state recurser and the second state recurser;

[0010] According to the preset alternating period, the state recursion value of the corresponding first state recurser or the second state recurser is selected to perform fault diagnosis on the navigation information to be detected to obtain corresponding diagnosis information.

[0011] In some implementations, respectively acquiring navigation information to be detected and a corresponding variance matrix of each navigation device and historical diagnostic information corresponding to each navigation device includes:

[0012] According to a preset update cycle, the navigation information to be detected and the corresponding variance matrix of each navigation device are respectively obtained, and the most recent historical diagnostic information of the navigation device is selected; wherein, the navigation information includes at least one parameter information of position information, speed and attitude; and the historical diagnostic information includes whether the sensor corresponding to the historically collected parameter information is faulty.

[0013] In some implementations, resetting the initial state values ​​corresponding to the first state recurser and the second state recurser in turn according to the historical diagnostic information and in a preset alternating cycle includes:

[0014] When the detection duration is an even multiple of the preset alternation period, the state initial value of the first state recurser is reset according to the historical diagnostic information; when the detection duration is an odd multiple of the preset alternation period, the state initial value of the second state recurser is reset according to the historical diagnostic information; wherein, if the historical diagnostic information of the navigation device is that there is no fault, the state initial value is the navigation information of the corresponding navigation device; if the historical diagnostic information of the navigation device is that there is a fault, the state initial value is the state recursion value corresponding to the corresponding first state recurser or the second state recurser.

[0015] In some embodiments, obtaining state recursion values ​​corresponding to the first state recurser and the second state recurser respectively based on the IMU data and the corresponding initial state value includes:

[0016] Obtain IMU data corresponding to the IMU sensor in the navigation device; according to the strapdown inertial navigation mechanical arrangement method, according to the IMU data and the corresponding initial state value, respectively obtain state recursion values ​​corresponding to the first state recurser and the second state recurser; the state recursion values ​​include a navigation information prediction value and a corresponding variance matrix prediction value.

[0017] In some implementations, selecting the state recursion value of the corresponding first state recurser or second state recurser according to the preset alternating period to perform fault diagnosis on the navigation information to be detected includes:

[0018] When the detection duration is an even multiple of the preset alternation period, the state recursion value of the corresponding first state recurser is selected to perform fault diagnosis on the navigation information to be detected; when the detection duration is an odd multiple of the preset alternation period, the state recursion value of the corresponding second state recurser is selected to perform fault diagnosis on the navigation information to be detected.

[0019] In some implementations, selecting the state recursion value of the corresponding first state recurser or second state recurser to perform fault diagnosis on the navigation information to be detected to obtain corresponding diagnostic information includes:

[0020] According to the state recursion value of the first state recurser or the second state recurser and the navigation information to be detected and the variance matrix, the corresponding state detection quantity is obtained; when the state detection quantity is greater than or equal to a preset threshold, the diagnostic information is that the corresponding navigation device has a fault.

[0021] A second aspect of the present application provides a navigation device fault detection device, comprising:

[0022] An information acquisition module, configured to respectively acquire navigation information to be detected and a corresponding variance matrix of each navigation device and historical diagnostic information corresponding to each of the navigation devices;

[0023] An initial value resetting module, configured to reset the initial state values ​​corresponding to the first state recursor and the second state recursor in turn according to the historical diagnostic information and in accordance with a preset alternating period;

[0024] A state recursion module is used to obtain state recursion values ​​corresponding to the first state recursion device and the second state recursion device respectively according to the IMU data and the corresponding initial state value;

[0025] The fault diagnosis module is used to select the state recursion value of the corresponding first state recurser or second state recurser according to the preset alternation period to perform fault diagnosis on the navigation information to be detected and obtain corresponding diagnosis information.

[0026] In some embodiments, the initial value reset module includes a reset switch initial value setting module, and the reset switch is used to enable the initial value setting module to reset the state initial value of the first state recurser according to the historical diagnostic information when the detection duration is an even multiple of the preset alternation period; and to enable the initial value setting module to reset the state initial value of the second state recurser according to the historical diagnostic information when the detection duration is an odd multiple of the preset alternation period.

[0027] In some embodiments, the fault diagnosis module includes a verification switch and a chi-square test module, wherein:

[0028] The check switch is used to enable the card-square check module to select the state recursion value of the corresponding first state recursor to perform fault diagnosis on the navigation information to be detected when the detection time is an even multiple of the preset alternating period; and to enable the card-square check module to select the state recursion value of the corresponding second state recursor to perform fault diagnosis on the navigation information to be detected when the detection time is an odd multiple of the preset alternating period;

[0029] The chi-square test module is used to obtain the corresponding state detection quantity based on the state recursion value of the first state recurser or the second state recurser and the navigation information to be detected and the variance matrix; when the state detection quantity is greater than or equal to a preset threshold, the diagnostic information is that the corresponding navigation device has a fault.

[0030] A third aspect of the present application provides an electronic device, including:

[0031] processor; and

[0032] The memory stores executable codes thereon, and when the executable codes are executed by the processor, the processor is caused to execute the method described above.

[0033] A fourth aspect of the present application provides a computer-readable storage medium having executable code stored thereon. When the executable code is executed by a processor of an electronic device, the processor is caused to execute the method described above.

[0034] The technical solution provided by this application may have the following beneficial effects:

[0035] The navigation device fault detection method of the present application constructs two independent state recursors to perform fault diagnosis on the navigation device in turn, and by resetting the initial state values ​​of the state recursors alternately, the accumulation of recursion errors over time is avoided, the accuracy of the initial state values ​​is improved, and the error rate of the diagnosis results is effectively reduced. Moreover, by alternating the use of the two state recursors, the same state recursor needs to wait for a round before being used again, thereby reducing the risk of contamination of the two state recursors. The fault detection method of the present application, through the dual-state chi-square detection method, can be applied to the detection of one or more navigation devices at the same time, improving the detection efficiency and the real-time performance of the algorithm. It is not only applicable to the detection of hard faults, but also to the detection of soft faults.

[0036] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The above and other objects, features and advantages of the present application will become more apparent by describing in more detail the exemplary embodiments of the present application in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of the present application.

[0038] Figure 1 is a flowchart of a navigation device fault detection method shown in this application;

[0039] Figure 2 is another flowchart of the navigation device fault detection method shown in this application;

[0040] Figure 3 This is a diagram of the algorithm structure of the present application using two state recursors to perform state chi-square test on multiple navigation devices;

[0041] Figure 4 It is a structural diagram of a navigation equipment fault detection device shown in this application;

[0042] Figure 5 is another structural schematic diagram of the navigation equipment fault detection device shown in this application;

[0043] Figure 6 It is a schematic structural diagram of the electronic device shown in this application. DETAILED DESCRIPTION

[0044] The following describes embodiments of the present application in more detail with reference to the accompanying drawings. Although the accompanying drawings illustrate embodiments of the present application, it should be understood that the present application can be implemented in various forms and should not be limited by the embodiments described herein. Rather, these embodiments are provided to make the present application more thorough and complete, and to fully convey the scope of the present application to those skilled in the art.

[0045] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. As used in this application and the appended claims, the singular forms "a," "an," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

[0046] It should be understood that although the terms "first", "second", "third", etc. may be used in this application to describe various information, this information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, the meaning of "plurality" is two or more, unless otherwise clearly and specifically defined.

[0047] In related technologies, when diagnosing a data source anomaly in a navigation device, the state recurser does not have measurement updates, and the error in the predicted state quantity gradually increases over time, reducing the detection effect of soft faults and making it impossible to obtain accurate fault diagnosis results.

[0048] In response to the above problems, the present application provides a navigation device fault detection method, which can improve the accuracy of the fault diagnosis results of the navigation device.

[0049] The technical solution of this application is described in detail below with reference to the accompanying drawings.

[0050] Figure 1 It is a flowchart of the navigation device fault detection method shown in this application.

[0051] See also Figure 1 , the present application shows a navigation device fault detection method, which includes:

[0052] S110 , respectively obtaining navigation information to be detected and a corresponding variance matrix of each navigation device and historical diagnosis information corresponding to each navigation device.

[0053] In this application, the flying car is equipped with at least one navigation device, and a single navigation device includes multiple sensors, such as an IMU (Inertial Measurement Unit), a GNSS sensor, a magnetometer, a barometer, etc. The number and type of sensors installed in different navigation devices can be the same or different, and are not limited here.

[0054] In this step, the navigation information collected by the sensor of each navigation device can be obtained respectively. The navigation information includes the position information p n , speed v n and posture θ n Of course, in other embodiments, the navigation information may also include other types of parameter information depending on the type of sensor, which is not limited here. By using the combined navigation algorithm of related technologies, multiple parameter information in a single navigation device can be combined to generate navigation information X. n and the corresponding variance matrix P n , where the variance matrix is ​​used to represent the confidence of the corresponding navigation information. For example, the navigation information X n ={p n v n θ n}, n represents the nth navigation device, and n is a natural number.

[0055] Furthermore, historical diagnostic information D n Refers to whether the various sensors that have collected the corresponding parameter information in the history of the n-th navigation device are faulty. That is, the historical diagnostic information can simultaneously include the diagnostic information corresponding to multiple sensors in a single navigation device, and the diagnostic information of different sensors is represented independently of each other. When different navigation devices contain different sensors, the corresponding content of the historical diagnostic information is different. In order to facilitate the digital representation of the diagnostic information, the binary dichotomy can be used to represent the historical diagnostic information. For example, the number 0 is used to indicate that the sensor is normal, that is, there is no fault; the number 1 indicates that the sensor has a fault, and the specific numbers are only used as examples here. According to the sensor type corresponding to all the parameter information contained in the navigation information, the historical diagnostic information can be collectively represented in the same matrix according to the preset arrangement order of each sensor, that is, D n ={d pn d vn d θn} T .d pn Indicates the diagnostic information of the sensor collecting position information in the nth navigation device, d vn Indicates the diagnostic information of the sensor collecting speed in the nth navigation device, d θn Indicates the diagnostic information of the sensor collecting the attitude in the nth navigation device. For example, D1 = {0 0 1} T, which means that the sensors used to collect position information and speed in the first navigation device are normal, but the sensor used to collect attitude information is faulty.

[0056] S120 , according to historical diagnostic information, resetting the initial state values ​​corresponding to the first state recursor and the second state recursor in turn according to a preset alternating period.

[0057] In this step, two independent first and second state recursors are used to perform state chi-square testing. When the first state recursor is used, the second state recursor is not used; when the second state recursor is used, the first state recursor is not used. In addition, when using one of the state recursors for testing, the initial state value of the corresponding state recursor is reset according to a preset alternating cycle, so that the state recursor obtains the state recursion value based on the reset initial state value, thereby avoiding the problem of error accumulation over time.

[0058] In this step, the most recent historical diagnostic information of the navigation device and the currently collected navigation information to be detected and the corresponding variance matrix can be combined to obtain the state initial value of the first state recurser or the state initial value of the second state recurser respectively. In other words, based on the state initial values ​​of the first state recurser and the second state recurser being reset in turn, the state initial values ​​of the two are obtained independently. For example, when resetting the first state recurser, it is only necessary to obtain the state initial value of the first state recurser without resetting the state initial value of the second state recurser.

[0059] The state initial value includes the navigation information initial value X0 and the corresponding variance matrix initial value P0. In order to make the first state recursor and the second state recursor obtain more reliable state initial values, in some embodiments, if the historical diagnostic information of the navigation device is fault-free, the navigation information initial value X0 is the navigation information X0 of the corresponding navigation device. n If the historical diagnostic information of the navigation device is a fault, the initial value of the navigation information X0 is the navigation information prediction value X in the state recursion value corresponding to the first state recursor or the second state recursor. _ .

[0060] For example, assuming that the navigation information prediction value X of the first state recursor is _ ={p _ v _ θ _}, the navigation information initial value X0 of the first state recursor is {p0 v0θ0}, then the navigation information initial value X0 is obtained, that is, the position information initial value p0, the velocity initial value v0 and the attitude initial value θ0 are obtained respectively. Taking the position information initial value p0 as an example, first determine the d in the historical diagnostic information of the first navigation device.p1 Is it 0? If d p1 If d is 0, then p0 is equal to p1, that is, the initial value of the position information is equal to the position information to be detected of the first navigation device; if d is determined p1 If dp2 is 1, the second navigation device's historical diagnostic information is checked to see if it is 0, and so on. p1 ,d p2 ,...,d pn If both are 1, the position information prediction value p in the state recursion value of the first state recursion unit is used. _ , that is, p0=p _ . Of course, the historical diagnostic information of different navigation devices can also be obtained in other preset orders, and there is no restriction here. Similarly, after obtaining the initial value of the position information in the state initial value of the first state recurser, the initial value of the speed v0 and the initial value of the attitude θ0 are obtained respectively. Of course, there is no restriction on the order of obtaining the initial values ​​of the parameter information of the three sensors in the navigation information initial value, and the initial values ​​of the three sensor parameter information in the same group of navigation information initial values ​​in the first state recurser do not come from the same navigation device, that is, they can be selected from the parameter information of the corresponding sensors with normal diagnosis in different navigation devices.

[0061] Obviously, based on the determination of the initial values ​​of the three sensor parameter information described above, the navigation information initial value X0 of the first state recursor is obtained. Accordingly, the corresponding variance matrix initial value P0 can be calculated according to relevant techniques, which will not be described in detail here. Similarly, when it is necessary to reset the initial state value of the second state recursor, the corresponding navigation information initial value X0 and variance matrix initial value P0 can be obtained according to the above method.

[0062] S130, obtaining state recursion values ​​corresponding to the first state recurser and the second state recurser respectively according to the IMU data and the corresponding initial state value.

[0063] In this application, each navigation device includes an IMU sensor, and the IMU data includes the angular velocity and acceleration of the flying car. In this step, the state recursive value includes the navigation information prediction value X_ and the corresponding variance matrix prediction value P_.

[0064] According to the strapdown inertial navigation mechanical arrangement method, after obtaining the IMU data and the initial state value of the first state recursor, the state recursion value corresponding to the first state recursor can be calculated. Similarly, after obtaining the IMU data and the initial state value of the second state recursor, the state recursion value corresponding to the second state recursor can be calculated.

[0065] S140: According to a preset alternating cycle, select the state recursion value of the corresponding first state recurser or the second state recurser to perform fault diagnosis on the navigation information to be detected, and obtain corresponding diagnosis information.

[0066] It can be understood that when the state initial value of the first state recurser is reset in step S120, the corresponding state recursive value can be obtained in step S130; accordingly, this step S140 selects the state recursive value of the first state recursive device to perform fault diagnosis on the navigation information collected at the current time to obtain corresponding diagnostic information.

[0067] Similarly, as time goes by, when the state initial value of the second state recurser is reset in step S120 according to the preset alternation cycle, the corresponding state recursive value can be obtained in step S130; accordingly, this step S140 selects the state recursive value of the second state recurser to perform fault diagnosis on the navigation information collected at the current time to obtain corresponding diagnostic information.

[0068] It can be understood that as the detection time of the flying car continues, the initial state value of the first state recurser or the second state recurser is reset every preset alternation period, and different state recursors are selected alternately to perform fault diagnosis on the navigation information collected at the current moment, so as to obtain diagnostic information on whether a sensor in the navigation device corresponding to the current moment has a fault.

[0069] As can be seen from this example, the navigation device fault detection method of the present application constructs two independent state recursors to perform fault diagnosis on the navigation device in turn, and by resetting the initial state values ​​of the state recursors alternately, the accumulation of recursion errors over time is avoided, the accuracy of the initial state values ​​is improved, and the error rate of the diagnosis results is effectively reduced; and by alternating the use of the two state recursors, the same state recursor needs to wait for a round before being used again, thereby reducing the risk of contamination of the two state recursors. The fault detection method of the present application, through the dual-state chi-square detection method, can be applied to the detection of one or more navigation devices at the same time, improving the detection efficiency and the real-time performance of the algorithm, and is applicable not only to the detection of hard faults, but also to the detection of soft faults.

[0070] Figure 2 This is another flowchart of the navigation device fault detection method shown in this application. Figure 3 The following is a detailed description of the algorithm structure of the present application using two state recursors to perform state chi-square test on multiple navigation devices.

[0071] See also Figure 2 and Figure 3, the present application shows a navigation device fault detection method, which includes:

[0072] S210 , according to a preset update cycle, respectively obtaining navigation information to be detected and a corresponding variance matrix of each navigation device, and selecting the most recent historical diagnosis information of the navigation device.

[0073] In this step, the preset update period T u That is, the period for each navigation device to collect updated navigation information. For example, the preset update period T u It can be selected from 3ms to 10ms, such as 3ms, 5ms, 7ms, 8ms, 10ms, etc. The navigation information includes multiple sensor parameter information such as position information, speed and attitude; the historical diagnosis information includes whether the sensor corresponding to the historically collected parameter information is faulty.

[0074] In this step, every preset update period T u New navigation information corresponding to each sensor in the navigation device can be re-acquired, and each new navigation information collected can be detected according to subsequent steps.

[0075] S220, when the detection duration is an even multiple of the preset alternation period, the state initial value of the first state recurser is reset according to the historical diagnostic information; when the detection duration is an odd multiple of the preset alternation period, the state initial value of the second state recurser is reset according to the historical diagnostic information.

[0076] In this application, in order to facilitate regular alternating reset of the initial state values ​​of the first state recursor and the second state recursor, the timing can be started from the start of the two state recursors to obtain the corresponding detection time t in real time. s , T s is the preset alternating cycle. When k = 1, 3, 5, 7, 9, etc., the detection duration is the preset alternating cycle T s When it is an odd multiple of , reset the second state recursor, such as Figure 3 The reset switch shown is turned to position 2, and the second state recursor obtains the reset state initial value. When k = 2, 4, 6, 8, 10 ... etc., the detection time is the preset alternation period T s When it is an even multiple of , reset the first state recursor, Figure 3 The reset switch shown is turned to position 1, and the first state recursor obtains the reset state initial value. At other times, the reset switch is kept at position 0, that is, to avoid constantly resetting a state recursor, and only need to perform instantaneous reset at fixed integer multiples of the alternating cycle, and there is no need to reset the two state recursors at other times. In this step, the preset alternating cycle T s Set to be greater than the above preset update period Tu Optionally, when the detection time t is 0, the first state recurser and the second state recurser may each reset their state initial value once.

[0077] It should be noted that each time the state initial value is reset, the current state initial value is determined based on the most recent historical diagnostic information of each sensor of the navigation device. If the historical diagnostic information of the navigation device indicates that there is no fault, the state initial value is the navigation information of the corresponding navigation device; if the historical diagnostic information of the navigation device indicates that there is a fault, the state initial value is the state recursion value corresponding to the corresponding first state recurser or the second state recurser. Among them, the method for determining the state initial value of each state recurser is the same as the relevant introduction in the above-mentioned step S120, and will not be repeated here. It can be understood that with the continuous updating of the navigation information of step S210, the new navigation information after each update can obtain the corresponding diagnostic information according to the following steps, and the diagnostic information is the most recent historical diagnostic information corresponding to the navigation information of the next round of updates. By resetting the state initial value of each state recurser through the latest historical diagnostic information, the accuracy of the state initial value can be improved, and the error rate of subsequent diagnostic information can be reduced.

[0078] It is understood that when the state initial value does not need to be reset, that is, when the detection duration is not an integer multiple of the preset alternation period, this step is skipped and the following step S230 is directly executed. It should be noted that when the reset switch is kept in position 0, the first state recursor and the second state recursor continue to calculate with their respective current state initial values ​​to update their respective state recursive values, and the calculation will not stop due to the interruption of the reset switch.

[0079] S230, obtaining IMU data corresponding to the IMU sensor in the navigation device; according to the strapdown inertial navigation mechanical arrangement method, according to the IMU data and the corresponding initial state value, respectively obtaining the state recursion values ​​corresponding to the first state recurser and the second state recurser.

[0080] In this step, the preset update period T u Get the IMU data corresponding to the IMU sensor in each navigation device. For ease of understanding, the following will specifically introduce the strapdown inertial navigation mechanical arrangement process. Among them, the navigation information X at time t-1 t-1 ={p t-1 v t-1 θ t-1} Recursively calculate the navigation information prediction value X in the state recursive value at time t _t ={p t v t θ t} as an example.

[0081] 1. Attitude prediction value θ in navigation information t The update of is calculated as follows: (1) and (2).

[0082]

[0083]

[0084] Where q is the rotation quaternion corresponding to the attitude θ. q and θ can be freely converted. The conversion relationship is recorded in the relevant technology and will not be repeated here. n represents the navigation coordinate system, which is generally the north-east coordinate system. b is the body coordinate system, which is generally the front-right lower coordinate system. Δφ is the angular increment output from time t-1 to time t, which is generally the angular velocity in the IMU data multiplied by the preset update period T u express.

[0085] 2. Speed ​​prediction value v in navigation information t The update of is calculated as follows: (3) and (4).

[0086]

[0087]

[0088] Where v is the velocity; g is the acceleration due to gravity; is the attitude cosine matrix at time t-1, which can be converted from the attitude θ at time t-1; Δv t It is the specific force increment output from time t-1 to time t, which is usually calculated by multiplying the acceleration of the IMU data by the preset update period T u Other symbols have the same meanings as above.

[0089] 3. Position information prediction value p in navigation information t The update of is calculated as follows:

[0090]

[0091] The meanings of the symbols in formula (5) are the same as those of the same symbols mentioned above and are not repeated here.

[0092] 4. Calculate the navigation information prediction value X_ according to the above steps t After obtaining the sensor parameter information in , the corresponding variance matrix prediction value P_ can be obtained by calculating according to the following formulas (6) to (8).

[0093]

[0094]

[0095] δp n =δvn (8)

[0096] in, is the accelerometer output, w ε is the gyro angular rate white noise, w ▽ is the additive comparative white noise, ε b and ▽ b is the first-order Markov process random error of the gyroscope and accelerometer of the IMU sensor. By combining the error variance with the error propagation theorem, P t-1 Get the navigation information prediction value X_ t The variance P_ t-1 .

[0097] After repeating the above steps twice, the navigation information prediction value X1_ of the first state recurser, the navigation information prediction value X2_ of the second state recurser and their corresponding variance matrix prediction values ​​P1_, P2_ can be obtained respectively.

[0098] S240, when the detection duration is an even multiple of the preset alternation period, the state recursion value of the corresponding first state recurser is selected to perform fault diagnosis on the navigation information to be detected; when the detection duration is an odd multiple of the preset alternation period, the state recursion value of the corresponding second state recurser is selected to perform fault diagnosis on the navigation information to be detected.

[0099] The relationship between the detection duration of this step and the preset alternation period is set the same as that of the above step S220. It can be understood that after the state recursion values ​​corresponding to the first state recursion device and the second state recursion device are respectively calculated and obtained in the above step S230, as shown in FIG. Figure 3 As shown, the state recursion value of a corresponding state recurser can be continuously selected for fault diagnosis within a preset alternating cycle through the verification switch.

[0100] For example, when the detection time is an even multiple of the preset alternation period, the check switch is turned to position 1, and the state recursion values ​​X1_ and P1_ of the corresponding first state recursion device are selected to detect the navigation information X of the nth navigation device. n When the detection time is an odd multiple of the preset alternation period, the check switch is turned to position 2, and the state recursion values ​​X2_ and P2_ of the corresponding second state recursion device are selected to detect the navigation information X of the nth navigation device. n Perform troubleshooting.

[0101] S250, obtaining a corresponding state detection value according to the state recursion value of the first state recurser or the second state recurser and the navigation information to be detected and the variance matrix; when the state detection value is greater than or equal to a preset threshold, the diagnostic information is that the corresponding navigation device has a fault.

[0102] In this step, the state chi-square detection method is used to analyze the current navigation information X of each navigation device according to the state recursive values ​​X_ and P_. n Fault detection is performed, and the detection method for each set of navigation information is the same. n The detection can be regarded as the position information p of the sensors respectively. n , speed v n , posture θ n The three detection processes are basically the same. For the sake of simplicity, the following is based on the speed v n The following describes the fault detection as an example.

[0103] First, from the navigation information X to be detected n and the corresponding variance matrix P n Get speed related information from . For example, navigation information X n It is a 9x1 matrix that contains the variance matrix of position information, speed, and attitude. The relationship is as follows:

[0104]

[0105] Where T represents the transpose of the matrix.

[0106] Variance matrix P n It is a 9x9 matrix that contains position information, velocity, and attitude variance matrix. The relationship is as follows:

[0107]

[0108] Through the above relationship, we can get v n and P vn , v n Refers to the speed of the nth navigation device, P vn It refers to the velocity variance matrix of the nth navigation device.

[0109] Secondly, according to the state recursive values ​​X_ and P_, the speed prediction value v can be obtained by referring to the same method as above n and the velocity variance matrix prediction value P vn _.

[0110] Then, the state detection quantity J corresponding to the speed in the navigation information can be calculated according to the following formula (9): v .

[0111] J v =(v n -v n_ ) T (R vn_ -P vn ) -1 (v n -vn_ ) (9)

[0112] The calculated state detection quantity J is compared with the preset threshold T d Perform size comparison and preset threshold T d The value can be determined based on experience and is not limited here. d When J<T d When , it means that the sensor corresponding to the parameter information in the current navigation information is not faulty. For details, please refer to the following formula (10).

[0113]

[0114] By analogy, the state detection quantity of each parameter information in the navigation information can be obtained, and according to the state detection quantity, it can be judged whether the sensor corresponding to each parameter information in the navigation information is currently faulty, thereby obtaining the diagnostic information D of each sensor in the nth navigation device. n It can be understood that the diagnostic information can be used as the historical diagnostic information of the navigation information to be detected in the next round of update, and the fault detection of the navigation information of each navigation device in each round of update is cyclically performed according to the above steps S210 to S250.

[0115] As can be seen from this example, the navigation device fault detection method of the present application can use different state recursors to perform fault detection for multiple navigation devices according to different preset alternating cycles, and the first state recursor and the second state recursor each update the initial state value according to the preset alternating cycle, thereby avoiding the impact of error accumulation on the detection results. The more accurate initial state value is set based on historical diagnostic information, which is conducive to improving the accuracy of the detection results. This design can accurately detect navigation information of multiple navigation devices using only two state recursors, reducing the use of redundant detection equipment and meeting the real-time detection frequency requirements.

[0116] Corresponding to the aforementioned application function implementation method embodiment, the present application also provides a navigation device fault detection device, an electronic device and corresponding embodiments.

[0117] Figure 4 It is a structural diagram of the navigation equipment fault detection device shown in this application.

[0118] See also Figure 4 The navigation device fault detection device shown in this application includes an information acquisition module 410, an initial value reset module 420, a state recursion module 430 and a fault diagnosis module 440. Among them:

[0119] The information acquisition module 410 is used to respectively acquire navigation information to be detected and the corresponding variance matrix of each navigation device and historical diagnosis information corresponding to each navigation device.

[0120] The initial value resetting module 420 is used to reset the initial state values ​​corresponding to the first state recursor and the second state recursor in turn according to the historical diagnostic information and in a preset alternating cycle.

[0121] The state recursion module 430 is used to obtain the state recursion values ​​corresponding to the first state recurser and the second state recurser respectively according to the IMU data and the corresponding state initial value.

[0122] The fault diagnosis module 440 is used to select the state recursion value of the corresponding first state recurser or the second state recurser according to a preset alternation period to perform fault diagnosis on the navigation information to be detected, and obtain corresponding diagnosis information.

[0123] In one specific embodiment, the information acquisition module 410 is configured to acquire navigation information and a corresponding variance matrix for each navigation device according to a preset update cycle, and select the most recent historical diagnostic information for the navigation device. The navigation information includes at least one parameter selected from the group consisting of position, velocity, and attitude; and the historical diagnostic information includes whether the sensor that historically collected the corresponding parameter information is faulty.

[0124] See also Figure 5 In a specific embodiment, the initial value reset module 420 includes a reset switch 421 and an initial value setting module 422. The reset switch 421 is used to cause the initial value setting module 422 to reset the initial state value of the first state recursor according to historical diagnostic information when the detection duration is an even multiple of the preset alternating period; and to cause the initial value setting module 422 to reset the initial state value of the second state recursor according to historical diagnostic information when the detection duration is an odd multiple of the preset alternating period. If the historical diagnostic information of the navigation device indicates no fault, the initial state value is the navigation information of the corresponding navigation device; if the historical diagnostic information of the navigation device indicates a fault, the initial state value is the state recursion value corresponding to the corresponding first state recursor or the second state recursor.

[0125] In a specific embodiment, the state recursion module 430 is used to obtain IMU data corresponding to the IMU sensor in the navigation device; according to the strapdown inertial navigation mechanical arrangement method, according to the IMU data and the corresponding state initial value, the state recursion values ​​corresponding to the first state recurser and the second state recurser are respectively obtained; the state recursion value includes the navigation information prediction value and the corresponding variance matrix prediction value.

[0126] In a specific embodiment, the fault diagnosis module 440 includes a check switch 441 and a chi-square test module 442, wherein: the check switch 441 is configured to cause the chi-square test module 442 to select the state recursion value of the corresponding first state recursor to perform fault diagnosis on the navigation information to be detected when the detection duration is an even multiple of the preset alternation period; and to cause the chi-square test module 442 to select the state recursion value of the corresponding second state recursor to perform fault diagnosis on the navigation information to be detected when the detection duration is an odd multiple of the preset alternation period. The chi-square test module 442 is configured to obtain a corresponding state detection value based on the state recursion value of the first state recursor or the second state recursor, the navigation information to be detected, and the variance matrix; when the state detection value is greater than or equal to a preset threshold, the diagnostic information is that the corresponding navigation device has a fault.

[0127] In summary, the navigation device fault detection device of the present application can use the reset switch to reset the state initial value of the first state recurser and the state initial value of the second state recurser according to the preset alternation period, and at the same time use the verification switch to select the first state recurser or the second state recurser according to the preset alternation period to detect the currently updated navigation information of each navigation device, without limiting the number of navigation devices, thereby improving processing efficiency and achieving high accuracy of the detected diagnostic information.

[0128] Regarding the apparatus in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated again here.

[0129] Figure 6 It is a schematic structural diagram of the electronic device shown in this application.

[0130] See also Figure 6 , the electronic device 1000 includes a memory 1010 and a processor 1020.

[0131] The processor 1020 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0132] The memory 1010 may include various types of storage units, such as system memory, read-only memory (ROM), and permanent storage. ROM may store static data or instructions required by the processor 1020 or other modules of the computer. The permanent storage may be a readable and writable storage device. The permanent storage may be a non-volatile storage device that retains stored instructions and data even when the computer is powered off. In some embodiments, the permanent storage device uses a large-capacity storage device (e.g., a magnetic or optical disk, flash memory) as the permanent storage device. In other embodiments, the permanent storage device may be a removable storage device (e.g., a floppy disk, optical drive). The system memory may be a readable and writable storage device or a volatile readable and writable storage device, such as dynamic random access memory. The system memory may store some or all instructions and data required by the processor during operation. In addition, the memory 1010 may include any combination of computer-readable storage media, including various types of semiconductor memory chips (e.g., DRAM, SRAM, SDRAM, flash memory, programmable read-only memory), and magnetic disks and / or optical disks may also be used. In some embodiments, the memory 1010 may include a readable and / or writable removable storage device, such as a compact disc (CD), a read-only digital versatile disc (e.g., DVD-ROM, double-layer DVD-ROM), a read-only Blu-ray disc, an ultra-density optical disc, a flash memory card (e.g., SD card, mini SD card, Micro-SD card, etc.), a magnetic floppy disk, etc. Computer-readable storage media do not include carrier waves and transient electronic signals transmitted wirelessly or wired.

[0133] The memory 1010 stores executable codes. When the executable codes are processed by the processor 1020 , the processor 1020 may execute part or all of the above-mentioned methods.

[0134] In addition, the method according to the present application may also be implemented as a computer program or a computer program product, which includes computer program code instructions for executing some or all of the steps in the above method of the present application.

[0135] Alternatively, the present application can also be implemented as a computer-readable storage medium (or non-transitory machine-readable storage medium or machine-readable storage medium) on which executable code (or computer program or computer instruction code) is stored. When the executable code (or computer program or computer instruction code) is executed by the processor of the server (or server, etc.), the processor executes part or all of the steps of the above-mentioned method according to the present application.

[0136] The embodiments of the present application have been described above. The above description is illustrative and not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is selected to best explain the principles of the embodiments, their practical applications, or improvements to the technology in the market, or to enable other persons skilled in the art to understand the embodiments disclosed herein.

Claims

1. A navigation device fault detection method, characterized in that: include: respectively obtaining navigation information to be detected and a corresponding variance matrix of each navigation device and historical diagnostic information corresponding to each of the navigation devices; According to the historical diagnostic information, the state initial values ​​corresponding to the first state recursor and the second state recursor are reset in turn according to the preset alternation period; wherein, when the detection duration is an even multiple of the preset alternation period, the state initial value of the first state recursor is reset according to the historical diagnostic information; when the detection duration is an odd multiple of the preset alternation period, the state initial value of the second state recursor is reset according to the historical diagnostic information; wherein, if the historical diagnostic information of the navigation device is that there is no fault, the state initial value is the navigation information of the corresponding navigation device; if the historical diagnostic information of the navigation device is that there is a fault, the state initial value is the state recursion value corresponding to the corresponding first state recursor or the second state recursor; the state initial value includes the navigation information initial value and the corresponding variance matrix initial value, and the state recursion value includes the navigation information prediction value and the corresponding variance matrix prediction value; According to the IMU data and the corresponding initial state value, respectively obtain the state recursion values ​​corresponding to the first state recurser and the second state recurser; According to the preset alternation period, the state recursion value of the corresponding first state recurser or the second state recurser is selected to perform fault diagnosis on the navigation information to be detected to obtain corresponding diagnostic information; wherein, when the detection duration is an even multiple of the preset alternation period, the state recursion value of the corresponding first state recurser is selected to perform fault diagnosis on the navigation information to be detected; when the detection duration is an odd multiple of the preset alternation period, the state recursion value of the corresponding second state recurser is selected to perform fault diagnosis on the navigation information to be detected.

2. The method according to claim 1, characterized in that The step of respectively acquiring the navigation information to be detected and the corresponding variance matrix of each navigation device and the historical diagnostic information corresponding to each navigation device includes: According to a preset update cycle, respectively obtain the navigation information to be detected and the corresponding variance matrix of each navigation device, and select the most recent historical diagnostic information of the navigation device; The navigation information includes at least one parameter information of position information, speed and posture; and the historical diagnostic information includes whether the sensor corresponding to the historically collected parameter information is faulty.

3. The method according to claim 1, characterized in that The step of respectively obtaining state recursion values ​​corresponding to the first state recurser and the second state recurser according to the IMU data and the corresponding state initial value includes: Obtaining IMU data corresponding to the IMU sensor in the navigation device; According to the strapdown inertial navigation mechanical arrangement method, the state recursion values ​​corresponding to the first state recurser and the second state recurser are respectively obtained according to the IMU data and the corresponding initial state value.

4. The method according to claim 1, wherein The selecting the state recursion value of the corresponding first state recurser or the second state recurser to perform fault diagnosis on the navigation information to be detected to obtain corresponding diagnostic information includes: Acquire a corresponding state detection value according to the state recursion value of the first state recurser or the second state recurser, the navigation information to be detected, and the variance matrix; When the state detection value is greater than or equal to a preset threshold, the diagnostic information indicates that a fault occurs in the corresponding navigation device.

5. A navigation equipment fault detection device, characterized in that: include: An information acquisition module, configured to respectively acquire navigation information to be detected and a corresponding variance matrix of each navigation device and historical diagnostic information corresponding to each of the navigation devices; An initial value resetting module is used to reset the state initial values ​​corresponding to the first state recursor and the second state recursor in turn according to the preset alternation period based on the historical diagnostic information; wherein, when the detection duration is an even multiple of the preset alternation period, the state initial value of the first state recursor is reset according to the historical diagnostic information; when the detection duration is an odd multiple of the preset alternation period, the state initial value of the second state recursor is reset according to the historical diagnostic information; wherein, if the historical diagnostic information of the navigation device is that there is no fault, the state initial value is the navigation information of the corresponding navigation device; if the historical diagnostic information of the navigation device is that there is a fault, the state initial value is the state recursion value corresponding to the corresponding first state recursor or the second state recursor; the state initial value includes the navigation information initial value and the corresponding variance matrix initial value, and the state recursion value includes the navigation information prediction value and the corresponding variance matrix prediction value; A state recursion module is used to obtain state recursion values ​​corresponding to the first state recursion device and the second state recursion device respectively according to the IMU data and the corresponding initial state value; A fault diagnosis module is used to select the state recursion value of the corresponding first state recurser or the second state recurser according to the preset alternation period to perform fault diagnosis on the navigation information to be detected and obtain corresponding diagnostic information; wherein, when the detection duration is an even multiple of the preset alternation period, the state recursion value of the corresponding first state recurser is selected to perform fault diagnosis on the navigation information to be detected; when the detection duration is an odd multiple of the preset alternation period, the state recursion value of the corresponding second state recurser is selected to perform fault diagnosis on the navigation information to be detected.

6. The device according to claim 5, characterized in that: The initial value reset module includes a reset switch initial value setting module, and the reset switch is used to enable the initial value setting module to reset the state initial value of the first state recurser according to the historical diagnostic information when the detection duration is an even multiple of the preset alternation period; and to enable the initial value setting module to reset the state initial value of the second state recurser according to the historical diagnostic information when the detection duration is an odd multiple of the preset alternation period.

7. The device according to claim 5, characterized in that: The fault diagnosis module includes a verification switch and a chi-square test module, wherein: The check switch is used to enable the card-square check module to select the state recursion value of the corresponding first state recursor to perform fault diagnosis on the navigation information to be detected when the detection time is an even multiple of the preset alternating period; and to enable the card-square check module to select the state recursion value of the corresponding second state recursor to perform fault diagnosis on the navigation information to be detected when the detection time is an odd multiple of the preset alternating period; The chi-square test module is used to obtain the corresponding state detection quantity based on the state recursion value of the first state recurser or the second state recurser and the navigation information to be detected and the variance matrix; when the state detection quantity is greater than or equal to a preset threshold, the diagnostic information is that the corresponding navigation device has a fault.

8. An electronic device, characterized in that: include: processor; as well as A memory having executable codes stored thereon, which, when executed by the processor, causes the processor to execute the method according to any one of claims 1 to 4.

9. A computer-readable storage medium having executable code stored thereon, wherein when the executable code is executed by a processor of an electronic device, the processor is caused to execute the method according to any one of claims 1 to 4.

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