Navigation device fault detection method, apparatus, device, carrier and storage medium

Abstract

Embodiments of the present application provide a navigation device fault detection method, device, equipment, carrier and storage medium, wherein the method comprises: determining whether there is a normal IMU with normal hardware and a first abnormal IMU with abnormal hardware in all IMUs according to the detection data of each two IMUs belonging to different combined navigation devices; in the case that there are at least two normal IMUs, determining whether there is a second abnormal IMU with software abnormality in all normal IMUs according to the INS calculation result of each two normal IMUs; determining whether there is an abnormal GNSS navigation module in all GNSS navigation modules according to the GNSS calculation result of each two GNSS navigation modules belonging to different combined navigation devices; in the case that there is at least one abnormal device in the first abnormal IMU, or the second abnormal IMU, or the abnormal GNSS navigation module, reporting the abnormal device to the control module of the carrier.

Description

Technical Field

[0001] This invention relates to the field of computer technology, and more specifically, to a method, apparatus, device, carrier, and storage medium for detecting faults in navigation equipment. Background Technology

[0002] Navigation equipment is widely used in maritime, aviation, astronomy, hydrology, and land transportation fields to provide heading and position information to carriers carrying them, ensuring their safe operation. Therefore, navigation equipment is particularly important for the safe operation of carriers, especially aircraft, as malfunctions can easily lead to flight problems or even aircraft crashes.

[0003] Aircraft navigation equipment typically includes an inertial navigation module and a GNSS navigation module. The inertial navigation module is usually an inertial measurement unit (IMU), which is prone to failure due to its own hardware or software issues. The GNSS navigation module, on the other hand, is susceptible to interference from the external environment or spoofing signals, leading to errors. Therefore, if the aircraft continues to use faulty information when at least one of the inertial or GNSS navigation modules fails, it will obviously pose a serious threat to the aircraft's flight safety. Summary of the Invention

[0004] In view of this, in order to at least solve the technical problem in the related art that the aircraft continues to use the fault information collected by the faulty navigation equipment because it cannot know whether the navigation equipment has malfunctioned, thus affecting flight safety, the purpose of this invention is to provide a navigation equipment fault detection method, device, equipment, carrier and storage medium.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of the present invention are as follows:

[0006] A first aspect of the present invention provides a navigation device fault detection method for detecting faults in multiple integrated navigation devices mounted on a carrier, each integrated navigation device including an IMU and a GNSS navigation module; the method includes:

[0007] Based on the detection data of every two IMUs belonging to different integrated navigation devices, determine whether there is a normal IMU with normal hardware and a first abnormal IMU with abnormal hardware among all IMUs;

[0008] In the case that there are at least two normal IMUs, determine whether there is a second abnormal IMU with software abnormality among all normal IMUs based on the INS calculation results of each pair of normal IMUs;

[0009] Based on the GNSS calculation results of every two GNSS navigation modules belonging to different integrated navigation devices, determine whether there are any abnormal GNSS navigation modules among all GNSS navigation modules;

[0010] In the event of at least one abnormal device among the first abnormal IMU, the second abnormal IMU, or the abnormal GNSS navigation module, the abnormal device is reported to the control module of the carrier.

[0011] In an optional implementation, each integrated navigation device is equipped with a Kalman filter and a state monitoring filter; before reporting the abnormal device to the control module of the carrier, the method further includes:

[0012] For each integrated navigation device, the Kalman filter in the integrated navigation device outputs a corresponding current estimated state matrix based on the current INS solution result and the current GNSS solution result output by the IMU and GNSS navigation modules in the integrated navigation device at the current time, respectively; the current estimated state matrix includes estimated velocity, estimated position and estimated attitude.

[0013] For each integrated navigation device, the current observation state matrix is ​​output based on the current INS calculation result output by the IMU in the integrated navigation device at the current moment through the state monitoring filter in the integrated navigation device; the current observation state matrix includes observation velocity, observation position and observation attitude.

[0014] For each integrated navigation device, a current state error matrix is ​​calculated based on the corresponding current estimated state matrix and current observed state matrix; the current state error matrix includes the error between the estimated velocity and the observed velocity, the error between the estimated position and the observed position, and the error between the estimated attitude and the observed attitude.

[0015] For each integrated navigation device, the fault detection value of the integrated navigation device is calculated based on the corresponding current state error matrix;

[0016] Based on the fault detection value and the preset fault threshold value of each integrated navigation device, the fault detection result of each integrated navigation device is obtained; the fault detection result includes a normal result indicating that the integrated navigation device is normal, or an abnormal result indicating that the integrated navigation device is abnormal.

[0017] Based on the fault detection results of the integrated navigation device, the target abnormal device is determined from the identified abnormal devices;

[0018] Accordingly, the step of reporting the abnormal device to the control module of the carrier is adjusted to: reporting the target abnormal device to the control module of the carrier when the target abnormal device exists.

[0019] In an optional implementation, two state monitoring filters are configured; the step of outputting the corresponding current observation state matrix based on the current INS calculation result output by the IMU in the integrated navigation device at the current moment through the state monitoring filters in the integrated navigation device includes:

[0020] If the monitoring duration of the currently selected current state monitoring filter is less than half of the set monitoring cycle, discard the observation state matrix output by the current state monitoring filter based on the current INS solution result.

[0021] When the monitoring duration of the current state monitoring filter reaches half of the set monitoring period, the observation state matrix output by the current state monitoring filter based on the current INS solution result is used as the current observation state matrix, and another state monitoring filter is initialized; wherein, the monitoring duration of the other state monitoring filter after initialization is updated to 0, and its initial state matrix is ​​updated to the current state estimation matrix output by the Kalman filter at its initialization time.

[0022] When the monitoring duration of the current state monitoring filter reaches the set monitoring period, the current state monitoring rate filter is initialized, and the other state monitoring filter is used as the current state monitoring filter; wherein, the monitoring duration of the initialized current state monitoring rate filter is updated to 0, and its initial state matrix is ​​updated to the current state estimation matrix output by the Kalman filter at its initialization time.

[0023] In an optional implementation, only one state monitoring filter is configured; the step of outputting the corresponding current observation state matrix based on the current INS solution result output by the IMU in the integrated navigation device at the current moment through the state monitoring filter in the integrated navigation device includes:

[0024] If the monitoring duration of the state monitoring filter does not reach half of the set monitoring cycle, the observation state matrix output by the state monitoring filter based on the current INS solution result is discarded.

[0025] When the monitoring duration of the state monitoring filter reaches half of the set monitoring cycle, the observation state matrix output by the state monitoring filter based on the current INS solution result is taken as the current observation state matrix.

[0026] When the monitoring duration of the state monitoring filter reaches the set monitoring period, the state monitoring filter is initialized; wherein, the monitoring duration of the initialized state monitoring filter is updated to 0, and its initial state matrix is ​​updated to the current state estimation matrix output by the Kalman filter at its initialization time.

[0027] In an optional implementation, the step of determining the target abnormal device from the identified abnormal devices based on the fault detection results of the integrated navigation device includes:

[0028] For each abnormal device, if the fault detection result of the integrated navigation device to which the abnormal device belongs is normal, then the fault detection result of the abnormal device is updated to normal; if the fault detection result of the integrated navigation device to which the abnormal device belongs is abnormal, then the abnormal device is identified as the target abnormal device.

[0029] In an optional implementation, the method further includes:

[0030] For each identified normal device, if the fault detection result of the integrated navigation device to which the normal device belongs is normal, or if the fault detection result of the integrated navigation device to which the normal device belongs is abnormal and another device in the integrated navigation device to which the normal device belongs is an identified abnormal device, then the normal device is determined to be normal; if the fault detection result of the integrated navigation device to which the normal device belongs is abnormal and another device in the integrated navigation device to which the normal device belongs is not an identified abnormal device, then the normal device and the other device in the integrated navigation device to which the normal device belongs are identified as target abnormal devices.

[0031] In an optional implementation, the detection data includes acceleration and angular velocity; the step of determining the normal IMU with normal hardware and the first abnormal IMU with hardware malfunction based on the detection data of every two IMUs belonging to different combined navigation devices includes:

[0032] The normal IMU and the first abnormal IMU are obtained based on the acceleration deviation between the accelerations detected by each pair of IMUs and the set acceleration deviation threshold, and the angular velocity deviation between the angular velocities detected by each pair of IMUs and the set angular velocity deviation threshold.

[0033] Among them, the acceleration deviation and angular velocity deviation between normal IMUs are both less than their respective deviation thresholds, and at least one of the acceleration deviation or angular velocity deviation between the first abnormal IMU and any IMU is not less than the corresponding deviation threshold.

[0034] In an optional implementation, the INS calculation result includes position information, attitude information, and velocity; the step of determining whether there is a second abnormal IMU with software anomalies among all normal IMUs based on the INS calculation results of every two normal IMUs includes:

[0035] Based on the position deviation between position information and the set position deviation threshold, the attitude deviation between attitude information and the set attitude deviation threshold, and the velocity deviation between velocities and the set velocity deviation threshold in the INS calculation results of each pair of normal IMUs, determine whether there is a second abnormal IMU.

[0036] Wherein, at least one of the position deviation, attitude deviation, or velocity deviation between the second abnormal IMU and any other IMU is not less than the corresponding deviation threshold.

[0037] In an optional implementation, the GNSS calculation result includes position information, attitude information, and velocity; the step of determining whether there is an abnormal GNSS navigation module among all GNSS navigation modules based on the GNSS calculation results of every two GNSS navigation modules belonging to different combined navigation devices includes:

[0038] Based on the position deviation between position information and the set position deviation threshold, the attitude deviation between attitude information and the set attitude deviation threshold, and the velocity deviation between velocities and the set velocity deviation threshold in the GNSS calculation results of each pair of GNSS navigation modules, it is determined whether there is an abnormal GNSS navigation module.

[0039] Wherein, at least one of the position deviation, attitude deviation, or velocity deviation between the abnormal GNSS navigation module and any other GNSS navigation module is not less than the corresponding deviation threshold.

[0040] A second aspect of the present invention provides a navigation device fault detection apparatus for detecting faults in multiple integrated navigation devices mounted on a carrier, each integrated navigation device including an IMU and a GNSS navigation module; the apparatus includes:

[0041] The hard fault determination module is configured to: determine, based on the detection data of every two IMUs belonging to different integrated navigation devices, whether there is a normal IMU with normal hardware and a first abnormal IMU with hardware malfunction among all IMUs;

[0042] The soft fault determination module is configured to: in the presence of at least two normal IMUs, determine whether there is a second abnormal IMU with software faults among all normal IMUs based on the INS calculation results of each pair of normal IMUs;

[0043] The GNSS fault determination module is configured to determine whether there is an abnormal GNSS navigation module among all GNSS navigation modules based on the GNSS calculation results of every two GNSS navigation modules belonging to different combined navigation devices.

[0044] The reporting module is configured to report the abnormal device to the control module of the carrier in the event that at least one of the following abnormal devices exists: a first abnormal IMU, a second abnormal IMU, or an abnormal GNSS navigation module.

[0045] A third aspect of the present invention provides an electronic device including a processor and a memory, wherein the memory stores machine-executable instructions that can be executed by the processor, and the processor can execute the machine-executable instructions to implement the navigation device fault detection method provided in the first aspect above.

[0046] A fourth aspect of the present invention provides a carrier, comprising:

[0047] Organism;

[0048] Multiple integrated navigation devices are installed in different parts of the machine body;

[0049] The control module is used to control the operation of the machine body based on the output of the plurality of integrated navigation devices;

[0050] The fault detection module is used to execute the navigation device fault detection method provided in the first aspect above, so as to determine whether there is an abnormal device in the multiple combined navigation devices based on the output of the multiple combined navigation devices, and report to the control module if there is an abnormal device.

[0051] A fifth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the navigation device fault detection method provided in the first aspect above.

[0052] The navigation device fault detection method, apparatus, device, carrier, and storage medium provided in this invention first process the detection data of every two IMUs belonging to different integrated navigation devices to determine whether there are normal IMUs with normal hardware and first abnormal IMUs with hardware malfunction. If at least two normal IMUs are found, the INS calculation results of every two normal IMUs are used to determine whether there are second abnormal IMUs with software malfunction among all normal IMUs. This enables fault detection of both hardware and software malfunctioning IMUs, avoiding the problem of low carrier operation safety caused by only detecting hardware malfunctioning IMUs while ignoring those with software malfunctioning IMUs. Furthermore, by also determining whether there are abnormal GNSS navigation modules based on the GNSS calculation results of every two GNSS navigation modules belonging to different integrated navigation devices, fault detection of abnormal GNSS navigation modules can also be achieved. Next, if at least one of the abnormal devices (first abnormal IMU, second abnormal IMU, or abnormal GNSS navigation module) is present, the abnormal device is reported to the carrier's control module. This allows the control module to change its control decisions based on the abnormal device, for example, by using data output by normal devices instead of data output by the abnormal device, thereby improving movement safety and reliability.

[0053] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0054] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This diagram illustrates a structural block diagram of an electronic device provided by an embodiment of the present invention.

[0056] Figure 2 A flowchart of a navigation device fault detection method provided by an embodiment of the present invention is shown;

[0057] Figure 3 The diagram shows a functional block diagram of a navigation device fault detection device provided in an embodiment of the present invention. Detailed Implementation

[0058] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0059] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0060] It should be noted that relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0061] To address the technical problem in related technologies where aircraft fail to detect navigation equipment malfunctions and continue to use fault information collected by faulty navigation equipment, thus affecting flight safety, this invention provides a navigation equipment fault detection method. This method first processes the detection data from every two IMUs belonging to different integrated navigation devices to determine whether there are normal IMUs with normal hardware and first abnormal IMUs with hardware malfunctions. If at least two normal IMUs are found, the INS calculation results of every two normal IMUs are used to determine whether there are second abnormal IMUs with software malfunctions among all normal IMUs. This method can detect faults in both hardware and software malfunctions, avoiding the problem of low carrier operational safety caused by only detecting hardware malfunctions while ignoring software malfunctions. Furthermore, by also determining whether there are abnormal GNSS navigation modules based on the GNSS calculation results of every two GNSS navigation modules belonging to different integrated navigation devices, the method can also detect faults in abnormal GNSS navigation modules. Next, if at least one of the following abnormal devices exists: the first abnormal IMU, the second abnormal IMU, or the abnormal GNSS navigation module, the abnormal device is reported to the control module of the carrier. This allows the control module to change its control decisions based on the abnormal device. For example, it may use the data output by the normal device instead of the data output by the abnormal device, thereby improving motion safety and reliability.

[0062] The navigation device fault detection method provided by this invention can be applied to electronic devices. Please refer to [reference needed]. Figure 1 This is a structural block diagram of an electronic device. The electronic device 100 includes a memory 110, a processor 120, and a communication module 130. The memory 110, processor 120, and communication module 130 are electrically connected to each other directly or indirectly to realize data transmission or interaction. For example, these components can be electrically connected to each other through one or more communication buses or signal lines.

[0063] The memory is used to store programs or data. The memory may be, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Programmable Read-Only Memory (PROM), Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), etc.

[0064] The processor is used to read / write data or programs stored in memory and to perform the corresponding functions.

[0065] The communication module is used to establish communication connections between electronic devices and other communication terminals via a network, and to send and receive data via the network.

[0066] It should be understood that, Figure 1 The structure shown is only a schematic diagram of an electronic device; the electronic device may also include components that are larger than... Figure 1 The more or fewer components shown, or having the same Figure 1 The different configurations shown. Figure 1 The components shown can be implemented using hardware, software, or a combination thereof.

[0067] In some embodiments, the electronic device can be configured in a carrier, and can acquire detection data, INS calculation results, and GNSS calculation results from multiple integrated navigation devices on the carrier, and perform fault detection on the multiple integrated navigation devices based on this data. The carrier can be an aircraft or a vehicle, but is not limited to these. Each integrated navigation device includes an IMU and a GNSS navigation module.

[0068] The following combination Figure 2 The navigation device fault detection method provided in the embodiments of the present invention will be described. Figure 2 This is a flowchart of a navigation device fault detection method provided in an embodiment of the present invention. The navigation device fault detection method includes:

[0069] In step S100, based on the detection data of every two IMUs belonging to different integrated navigation devices, it is determined whether there is a normal IMU with normal hardware and a first abnormal IMU with abnormal hardware among all IMUs;

[0070] In step S200, if there are at least two normal IMUs, the system determines whether there is a second abnormal IMU with software anomaly among all normal IMUs based on the INS calculation results of each pair of normal IMUs.

[0071] In step S300, based on the GNSS calculation results of every two GNSS navigation modules belonging to different combined navigation devices, it is determined whether there are any abnormal GNSS navigation modules among all GNSS navigation modules;

[0072] In step S500, if at least one abnormal device exists among the first abnormal IMU, the second abnormal IMU, or the abnormal GNSS navigation module, the abnormal device is reported to the control module of the carrier.

[0073] To avoid the carrier using data output by abnormal devices even when multiple integrated navigation devices exist, which could reduce the carrier's safety and reliability, especially for aircraft-type carriers where reduced safety and reliability could lead to crashes, steps S100 to S500 of the navigation device fault detection method provided in this embodiment can be executed during the carrier's movement. This method can be executed by the control module or by a fault detection module communicatively connected to the control module, but is not limited to either, as long as the control module can promptly detect whether abnormal devices exist in all integrated navigation devices, and if so, which abnormal devices are present.

[0074] During step S100, a common-source detection of the IMUs can be performed first. That is, the detection data of every two IMUs belonging to different integrated navigation devices is processed to determine whether there are any normally functioning IMUs and a first abnormal IMU with hardware malfunction among all IMUs. In some embodiments, to improve the efficiency of acquiring normally functioning IMUs and the first abnormal IMU, the navigation device fault detection method provided in this embodiment of the invention offers a way to implement step S100, namely, the detection data includes acceleration and angular velocity; correspondingly, the step in step S100 above, which determines the normally functioning IMU and the first abnormal IMU with hardware malfunction based on the detection data of every two IMUs belonging to different integrated navigation devices, may include:

[0075] In step S110, the normal IMU and the first abnormal IMU are obtained based on the acceleration deviation between the accelerations detected by each pair of IMUs and a set acceleration deviation threshold, and the angular velocity deviation between the angular velocities detected by each pair of IMUs and a set angular velocity deviation threshold.

[0076] Among them, the acceleration deviation and angular velocity deviation between normal IMUs are both less than their respective deviation thresholds, and at least one of the acceleration deviation or angular velocity deviation between the first abnormal IMU and any IMU is not less than the corresponding deviation threshold.

[0077] For example, suppose there are three integrated navigation devices, each including one IMU. These three IMUs are designated A1, A2, and A3. A1 detects acceleration a1 and angular velocity w1; A2 detects acceleration a2 and angular velocity w2; and A3 detects acceleration a3 and angular velocity w3. The acceleration deviation threshold is a0, and the angular velocity deviation threshold is w0. Based on this, during step S110, the acceleration and angular velocity deviations between every two IMUs can be calculated: the acceleration and angular velocity deviations between A1 and A2 are |a1-a2| and |w1-w2|, respectively; the acceleration and angular velocity deviations between A1 and A3 are |a1-a3| and |w1-w3|, respectively; and the acceleration and angular velocity deviations between A2 and A3 are |a3-a2| and |w3-w2|, respectively.

[0078] Next, |a1-a2|, |a1-a3|, and |a3-a2| are compared with a0, and |w1-w2|, |w1-w3|, and |w3-w2| are compared with w0.

[0079] If |a1-a2|<a0, |a1-a3|<a0, |a3-a2|<a0, |w1-w2|<w0, |w1-w3|<w0, and |w3-w2|<w0, then it indicates that there is no first-abnormal IMU with hardware abnormalities. Conversely, it indicates the presence of a first abnormal IMU with hardware malfunction. Based on this, the abnormal IMU can be deduced by comparing the results. For example, if |a1-a2| < a0, |w1-w2| < w0, |a1-a3| ≥ a0, |w1-w3| ≥ w0, |a3-a2| ≥ a0 and |w3-w2| ≥ w0, then A1 and A2 are normal IMUs with normal hardware, while A3 is the first abnormal IMU. In other words, the acceleration and angular velocity deviations between normal IMUs are both less than their respective deviation thresholds, and at least one of the acceleration or angular velocity deviations between the first abnormal IMU and any other IMU is not less than its corresponding deviation threshold. Therefore, in the case of a first abnormal IMU, this principle can be used to determine which IMU is the first abnormal IMU.

[0080] Furthermore, if the above judgment reveals that there are no normal IMUs whose acceleration and angular velocity deviations are both less than their respective deviation thresholds, it indicates that at least N-1 IMUs have hardware anomalies, where N is the total number of IMUs. However, to ensure the safety of the carrier's movement, it can be assumed that all IMUs may have hardware anomalies, and all of them can be considered as the first abnormal IMUs. In this case, step S200 does not need to be executed, because the detection data of the IMUs with hardware anomalies is problematic, and the INS calculation result is based on the detection data, so the INS calculation result will also be affected by the anomaly. Therefore, step S200 can be omitted.

[0081] Conversely, if at least two normal IMUs exist, step S200 can be executed to determine whether a second abnormal IMU with software anomalies exists among all normal IMUs based on the INS calculation results of every two normal IMUs. To improve the efficiency of acquiring the second abnormal IMU, in some embodiments, the navigation device fault detection method provided by this invention offers a way to implement step S200, namely, the INS calculation results include position information, attitude information, and velocity; correspondingly, the step in step S200 above, determining whether a second abnormal IMU with software anomalies exists among all normal IMUs based on the INS calculation results of every two normal IMUs, may include:

[0082] In step S210, based on the position deviation between position information and the set position deviation threshold, the attitude deviation between attitude information and the set attitude deviation threshold, and the velocity deviation between velocities and the set velocity deviation threshold in the INS calculation results of each pair of normal IMUs, it is determined whether there is a second abnormal IMU.

[0083] Wherein, at least one of the position deviation, attitude deviation, or velocity deviation between the second abnormal IMU and any other IMU is not less than the corresponding deviation threshold.

[0084] In determining whether a second abnormal IMU exists, step S210 can be executed to determine whether a second abnormal IMU exists among all normal IMUs. The principle for determining the second abnormal IMU is similar to that for determining the first abnormal IMU, and will not be elaborated upon here.

[0085] If only two normal IMUs are identified after step S100, and assuming that only one normal IMU actually has a software anomaly, then after processing in step S210, both normal IMUs will be considered as the second abnormal IMU.

[0086] Therefore, the first abnormal IMU for hardware abnormalities and the second abnormal IMU for software abnormalities can be obtained through steps S100 and S200.

[0087] The execution order of step S100 is not important. During the execution of step S300, it is possible to determine whether there is an abnormal GNSS navigation module among all GNSS navigation modules based on the GNSS calculation results of every two GNSS navigation modules belonging to different integrated navigation devices. To improve the efficiency of obtaining abnormal GNSS navigation modules, in some embodiments, the navigation device fault detection method provided by this invention provides a way to implement step S300, namely, the GNSS calculation results include position information, attitude information, and velocity; correspondingly, the step of determining whether there is an abnormal GNSS navigation module among all GNSS navigation modules based on the GNSS calculation results of every two GNSS navigation modules belonging to different integrated navigation devices in step S300 may include:

[0088] In step S310, based on the position deviation between position information and the set position deviation threshold, the attitude deviation between attitude information and the set attitude deviation threshold, and the velocity deviation between velocities and the set velocity deviation threshold in the GNSS calculation results of each pair of GNSS navigation modules, it is determined whether there is an abnormal GNSS navigation module.

[0089] Wherein, at least one of the position deviation, attitude deviation, or velocity deviation between the abnormal GNSS navigation module and any other GNSS navigation module is not less than the corresponding deviation threshold.

[0090] In determining whether an abnormal GNSS navigation module exists, step S310 can be executed to determine whether an abnormal GNSS navigation module exists among all GNSS navigation modules. The principle for determining an abnormal GNSS navigation module is similar to that for determining the first abnormal IMU mentioned above, and will not be elaborated upon here.

[0091] The various thresholds mentioned above can be configured according to navigation accuracy requirements or experience, and the embodiments of the present invention do not limit this.

[0092] If at least one of the following devices is found to be faulty: a first faulty IMU, a second faulty IMU, or a faulty GNSS navigation module, step S500 can be executed to report all faulty devices to the carrier's control module. Thus, after the carrier is aware of all faulty devices, it can ignore their outputs, effectively avoiding any impact on the carrier's motion safety and reliability due to continued use of fault information.

[0093] As can be seen from the above, for the sake of the carrier's motion safety, when it is impossible to accurately locate which device is malfunctioning, all suspected malfunctioning devices are reported as malfunctioning devices to the control module. This indicates that the location accuracy of malfunctioning devices is low. Therefore, to solve this technical problem, in some embodiments, the navigation device fault detection method provided by this invention also provides a multi-source fault detection scheme, that is, each combined navigation device is equipped with a Kalman filter and a state monitoring filter. Correspondingly, before reporting the malfunctioning device to the control module of the carrier, that is, before executing step S500, the navigation device fault detection method provided by this invention may further include:

[0094] In step S410, for each integrated navigation device, the Kalman filter in the integrated navigation device outputs a corresponding current estimated state matrix based on the current INS solution result and the current GNSS solution result output by the IMU and GNSS navigation module in the integrated navigation device at the current time, respectively; the current estimated state matrix includes estimated velocity, estimated position and estimated attitude.

[0095] In step S420, for each integrated navigation device, the current observation state matrix is ​​output based on the current INS calculation result output by the IMU in the integrated navigation device at the current moment through the state monitoring filter in the integrated navigation device; the current observation state matrix includes observation velocity, observation position and observation attitude.

[0096] In step S430, for each integrated navigation device, a current state error matrix is ​​calculated based on the corresponding current estimated state matrix and current observed state matrix; the current state error matrix includes the error between the estimated velocity and the observed velocity, the error between the estimated position and the observed position, and the error between the estimated attitude and the observed attitude.

[0097] In step S440, for each integrated navigation device, the fault detection value of the integrated navigation device is calculated based on the corresponding current state error matrix;

[0098] In step S450, based on the fault detection value of each integrated navigation device and the preset fault threshold value, the fault detection result of each integrated navigation device is obtained; the fault detection result includes a normal result indicating that the integrated navigation device is normal, or an abnormal result indicating that the integrated navigation device is abnormal.

[0099] In step S460, based on the fault detection results of the integrated navigation device, the target abnormal device is identified from the identified abnormal devices.

[0100] Accordingly, the step of reporting the abnormal device to the control module of the carrier in step S400 above is adjusted to: reporting the target abnormal device to the control module of the carrier when the target abnormal device exists.

[0101] Step S410 is essentially a state estimation scheme commonly used in integrated navigation devices. Its purpose is to fuse data that does not pass through sensors using a Kalman filter. In this embodiment of the invention, it is used to fuse data from the IMU and GNSS navigation modules in the same integrated navigation device to filter out noise in the data and output more accurate position, attitude and velocity information.

[0102] Step S420 is essentially a state estimation scheme based on the INS calculation results of the IMU in the integrated navigation device. When both the IMU and GNSS navigation modules in the integrated navigation device are functioning normally, the current observed state matrix output by the state monitoring filter and the current estimated state matrix output by the Kalman filter in step S420 should conform to a chi-square distribution; otherwise, they will not. Therefore, the state monitoring filter and the Kalman filter can be combined to detect whether the integrated navigation device is malfunctioning.

[0103] During the execution of step S410, the current estimated state matrix at each current time step can be obtained using the following technical principles:

[0104]

[0105] In the above formula, Let I represent the estimated state matrix at time k; let K(k) represent the identity matrix; let H(k) represent the Kalman gain matrix at time k; let H(k) represent the measurement noise matrix at time k, which can be obtained through related techniques; let Φ(k / k-1) represent the state transition matrix from time k-1 to time k, which can also be obtained through related techniques. Z(k) represents the estimated state matrix at time k-1, obtained based on the estimation at time k-1, and Z(k) represents the matrix formed by the GNSS solution results input from the GNSS navigation module at time k. X(0) represents the estimated state matrix initialized at time 0, which is a Gaussian random vector that can be obtained through relevant techniques.

[0106] The Kalman gain matrix at time k can be obtained through the following technical principle:

[0107] K(k)=P G (k / k-1)H T (k)[H(k)P G (k / k-1)H T (k)+R(k)] -1

[0108] In the above formula, K(k) represents the Kalman gain matrix at time k, and P G (k / k-1) represents the mean square error at time k derived from the mean square error at time k-1, and H(k) represents the measurement noise matrix at time k. T (k) represents the transpose of H(k), and R(k) represents the measurement error matrix at time k, which can be obtained through relevant techniques.

[0109] Wherein, the mean square error P G (k / k-1) can be obtained through the following technical principle:

[0110] P G (k / k-1)

[0111] =Φ(k / k-1)P G (k-1)Φ T (k / k-1)

[0112] +Γ(k-1)Q(k-1)Γ T (k-1)

[0113] In the above formula, Φ(k / k-1) represents the state transition matrix from time k-1 to time k; P G (k-1) represents the mean square error estimated at time k-1; Φ T (k / k-1) represents the transpose of Φ(k / k-1); Γ(k-1) represents the system noise driving matrix at time k-1, which can be understood as a matrix storing noise coefficients and can be obtained through related techniques; Q(k-1) represents the system noise matrix at time k-1; Γ T (k-1) denotes the transpose of Γ(k-1).

[0114] Based on this, the mean square error P at time k is finally estimated. G (k) can be obtained through the following technical principles:

[0115] P G (k)=[IK(k)H(k)]P G (k / k-1)

[0116] The meanings of the relevant letters in the above formula can be found in the previous text and will not be repeated here. Furthermore, P... G (0) = 0, P(0) represents the initial value of the variance initialized at time 0, which can be obtained through relevant techniques.

[0117] Therefore, after executing step S410, the current estimated state matrix output by the Kalman filter can be obtained. and the mean square deviation P at the current time.G (k).

[0118] During the execution of step S420, the current observation state matrix at each current moment can be obtained through the following technical principles:

[0119]

[0120] In the above formula, Let Φ(k / k-1) represent the observation state matrix at time k, and let Φ(k / k-1) represent the state transition matrix from time k-1 to time k. This represents the observation state matrix at time k-1, estimated based on the data at time k-1. X(0) represents the observation state matrix initialized at time 0, which is a Gaussian random vector that can be obtained through relevant techniques.

[0121] Furthermore, the root mean square error at time k, ultimately estimated by the state monitoring filter, can be obtained through the following technical principle:

[0122] P F (k)=Φ(k / k-1)P F (k-1)Φ T (k / k-1)+Γ(k-1)Q(k-1)Γ T (k-1)

[0123] In the above formula, Φ(k / k-1) represents the state transition matrix from time k-1 to time k; P F (k-1) represents the mean square error estimated at time k-1; Φ T (k / k-1) represents the transpose of Φ(k / k-1); Γ(k-1) represents the system noise driving matrix at time k-1, which can be understood as a matrix storing noise coefficients and can be obtained through related techniques; Q(k-1) represents the system noise matrix at time k-1; Γ T (k-1) denotes the transpose of Γ(k-1). Where P F (0) = P(0), where P(0) represents the initial variance value initialized at time 0, which can be obtained through relevant techniques.

[0124] As can be seen, for the state monitoring filter, there is no need to calculate the Kalman gain or the predicted P. F (k / k-1).

[0125] Therefore, after executing step S420, the current observed state matrix output by the state monitoring filter can be obtained. and the mean square deviation P at the current time. F (k).

[0126] Furthermore, as the continuous operating time of the condition monitoring filter increases, its own error also increases, leading to a larger mean square error and affecting the accuracy of fault detection. Therefore, to solve this technical problem, in some embodiments, the navigation device fault detection method provided by this invention proposes two solutions:

[0127] The first configuration has two condition monitoring filters.

[0128] Accordingly, step S420 above, which involves outputting the corresponding current observation state matrix based on the current INS calculation result output by the IMU in the integrated navigation device at the current moment through the state monitoring filter in the integrated navigation device, may include:

[0129] In step S4211, if the monitoring duration of the currently selected current state monitoring filter does not reach half of the set monitoring cycle, the observation state matrix output by the current state monitoring filter based on the current INS solution result is discarded.

[0130] In step S4212, when the monitoring duration of the current state monitoring filter reaches half of the set monitoring period, the observation state matrix output by the current state monitoring filter based on the current INS solution result is used as the current observation state matrix, and another state monitoring filter is initialized; wherein, the monitoring duration of the other state monitoring filter after initialization is updated to 0, and its initial state matrix is ​​updated to the current state estimation matrix output by the Kalman filter at its initialization time;

[0131] In step S4213, when the monitoring duration of the current state monitoring filter reaches the set monitoring period, the current state monitoring rate filter is initialized, and the other state monitoring filter is used as the current state monitoring filter; wherein, the monitoring duration of the initialized current state monitoring rate filter is updated to 0, and its initial state matrix is ​​updated to the current state estimation matrix output by the Kalman filter at its initialization time.

[0132] For steps S4211 to S4213 above, it is understandable that two state monitoring filters are used to work and initialize alternately. For example, for state monitoring filters S1 and S2, assuming their monitoring periods are both 10s, and assuming that in a certain fault monitoring process, the already initialized state monitoring filter S1 is selected, since the state quantity used by state monitoring filter S1 during initialization is the estimated state quantity output by the Kalman filter at the initialization moment, the output result of the state monitoring filter in the first half of the period, i.e., the first 5s, is greatly affected by the initialized state quantity, which may affect the accuracy of the subsequent fault detection results. Therefore, the output result of the first 5s is not used for fault detection. In the last 5s, the output result of the state monitoring filter tends to be stable, so the output result of the last 5s can be used for fault detection. At the same time, to ensure that the output result of the switched state monitoring filter S2 can be immediately applied to fault detection after state monitoring filter S1 finishes a monitoring period, state monitoring filter S2 can be initialized when state monitoring filter S1 has been working for 6 seconds. Thus, after the condition monitoring filter S1 finishes working once, the condition monitoring filter S2 has been working for 5 seconds. Therefore, the output of the condition monitoring filter S2 can be used immediately for fault detection in the next second after switching.

[0133] It is evident that using two state monitoring filters to work alternately can ensure seamless fault detection, which is beneficial to improving the comprehensiveness and reliability of fault detection.

[0134] The second type: only one condition monitoring filter is configured.

[0135] Accordingly, step S420 above, which involves outputting the corresponding current observation state matrix based on the current INS calculation result output by the IMU in the integrated navigation device at the current moment through the state monitoring filter in the integrated navigation device, may include:

[0136] In step S4221, if the monitoring duration of the state monitoring filter does not reach half of the set monitoring cycle, the observation state matrix output by the state monitoring filter based on the current INS solution result is discarded.

[0137] In step S4222, when the monitoring duration of the state monitoring filter reaches half of the set monitoring cycle, the observation state matrix output by the state monitoring filter based on the current INS solution result is taken as the current observation state matrix.

[0138] In step S4223, when the monitoring duration of the state monitoring filter reaches the set monitoring period, the state monitoring filter is initialized; wherein, the monitoring duration of the initialized state monitoring filter is updated to 0, and its initial state matrix is ​​updated to the current state estimation matrix output by the Kalman filter at its initialization time.

[0139] The difference between the second and first schemes lies in the fact that the second scheme uses one condition monitoring filter instead of two. The operating principle is similar to the first scheme, but because there is only one condition monitoring filter, fault diagnosis cannot be performed for half of each monitoring cycle, resulting in a monitoring gap. The second scheme can save on component costs.

[0140] The first and second options mentioned above can be selected based on actual needs.

[0141] The current estimated state matrix is ​​obtained through any of the above embodiments. The mean square deviation P at the current time G (k) Current observation state matrix and the mean square deviation P at the current time. F Following step (k), step S430 can be executed. For each integrated navigation device, the current state error matrix is ​​calculated based on the corresponding current estimated state matrix and current observed state matrix. Taking an integrated navigation device as an example, the calculation principle of the current state error matrix is ​​as follows:

[0142] β(k)=e G (k)-e F (k)

[0143] Where β(k) represents the state error matrix at time k, e G (k) represents the estimation error of the estimated state matrix at time k, e F (k) represents the estimation error of the observation state matrix at time k.

[0144] In the above, e G (k) and e F (k) can be calculated using the following technical principles:

[0145]

[0146] Where X(k) represents the matrix composed of the INS solution results at time k. and The meaning of is as described above and will not be repeated here.

[0147] After obtaining the current state error matrix in step S430, step S440 can be executed. For each integrated navigation device, the fault detection value of the integrated navigation device is calculated based on the corresponding current state error matrix. The calculation principle is as follows:

[0148] λ(k)=β T (k)T -1 (k)β(k)

[0149] In the above formula, λ(k) represents the fault detection value at time k, β(k) represents the state error matrix at time k, and β T (k) represents the transpose of β(k), T -1 (k) represents the inverse of the variance T(k) of β(k).

[0150] The calculation principle of T(k) is as follows:

[0151] T(k)=S{β(k)β T (k)}

[0152] =S{e G (k)e G T (k)-e G (k)e F T (k)-e F (k)e G T (k)+e F (k)e F T (k)}

[0153] In the above, S{} is the variance calculation function. For the meanings of the other letters, please refer to the above description, which will not be repeated here.

[0154] Therefore, the fault detection value at the current moment can be obtained by executing step S440.

[0155] Next, step S450 can be executed to obtain the fault detection result of each integrated navigation device based on the fault detection value of each device and the preset fault threshold value. That is, assuming the fault threshold value is T... D Then the fault detection value λ(k) at the current moment is greater than T. D In the case of λ(k)≤T, it indicates that the corresponding integrated navigation device is malfunctioning; while in the case of λ(k)≤T D In this case, it indicates that the corresponding integrated navigation device is functioning normally.

[0156] Among them, T D It can be obtained through the following principle:

[0157] Since β(k) is a Gaussian random variable e G (k) and e F β(k) is a linear combination of β(k), therefore β(k) is also a Gaussian random variable with a mean of 0.

[0158] Extensive testing revealed that β(k) exhibits different behavior under malfunctioning and normal conditions of the integrated navigation equipment, as follows:

[0159] Under normal conditions of the integrated navigation equipment, the mean of β(k) is 0, and its variance satisfies T(k)=S{β(k)β T (k)}.

[0160] When the integrated navigation equipment malfunctions, the mean of β(k) is non-zero, and its variance satisfies T(k)=S{[β(k)-μ][β T β(k)]-μ}, where μ is the mean of β(k) in this case.

[0161] Therefore, the fault threshold value T can be calculated based on the two performance scenarios described above. D The numerical values ​​and the underlying calculation principles can be found in relevant technical documents.

[0162] Therefore, step S450 can be used to determine whether there are any abnormal integrated navigation devices and which integrated navigation devices are abnormal.

[0163] Next, step S460 can be executed, whereby the target abnormal device is determined from the identified abnormal devices based on the fault detection results of the integrated navigation device. It is understood that combining multi-source fault detection results and same-source fault detection results allows for the joint location of the target abnormal device, achieving precise positioning. To achieve precise positioning of the target abnormal device, in some embodiments of the navigation device fault detection method provided by this invention, an implementation scheme for step S460 is proposed. That is, the step of determining the target abnormal device from the identified abnormal devices based on the fault detection results of the integrated navigation device in step S460 may include:

[0164] In step S461, for each abnormal device, if the fault detection result of the integrated navigation device to which the abnormal device belongs is a normal result, then the fault detection result of the abnormal device is updated to normal; if the fault detection result of the integrated navigation device to which the abnormal device belongs is an abnormal result, then the abnormal device is identified as a target abnormal device.

[0165] For example, suppose that through steps S100 to S300, the abnormal devices identified in the IMU are A1, A2, and A3; the abnormal devices in the GNSS navigation module are B1 and B4; and the abnormal integrated navigation devices are C1 and C3; wherein C1 includes A1 and B1, and C3 includes A3 and B3. Based on this, it is evident that abnormal devices exist in A1, B1, A3, and B3 of the integrated navigation devices, and the identified abnormal devices are A1, A2, A3, B1, and B4. Therefore, A1 and B1 are both identified abnormal devices, and their respective integrated navigation devices C1 are also determined to be abnormal. Thus, A1 and B1 are the target abnormal devices. The integrated navigation device C2, to which A2 belongs, is determined to be normal; therefore, A2 can be excluded, and its detection result updated to normal. A3 is also a confirmed abnormal device, but B3 is a confirmed normal device, and its associated navigation system C3 is also considered abnormal. Therefore, the abnormality of C3 can be attributed to A3, making A3 the target abnormal device, while B3 is normal. As for B4, since its associated navigation system C4 is considered normal, B4 can be excluded, and its detection result updated to normal.

[0166] Therefore, by combining the fault detection results from the same source and multiple sources to further screen the identified abnormal devices, devices that are mistakenly judged as abnormal can be eliminated, and the truly abnormal devices—the target abnormal devices—can be screened out. This effectively improves the accuracy of the target abnormal device location, as well as the effectiveness and reliability of fault reporting.

[0167] In addition, the results of fault detection from the same source and multiple sources can be combined to determine whether the normal devices identified by the same source detection method are indeed normal. Therefore, in some embodiments, the navigation device fault detection method provided by this invention may further include:

[0168] In step S470, for each identified normal device, if the fault detection result of the integrated navigation device to which the normal device belongs is normal, or if the fault detection result of the integrated navigation device to which the normal device belongs is abnormal and another device in the integrated navigation device to which the normal device belongs is an identified abnormal device, then the normal device is determined to be normal; if the fault detection result of the integrated navigation device to which the normal device belongs is abnormal and another device in the integrated navigation device to which the normal device belongs is not an identified abnormal device, then the normal device and the other device in the integrated navigation device to which the normal device belongs are identified as target abnormal devices.

[0169] For example, suppose that through steps S100 to S300, the normal devices in the IMU are A1 and A3; the normal devices in the GNSS navigation module are B2 and B3; and the abnormal integrated navigation devices are C1 and C3; wherein C1 includes A1 and B1, and C3 includes A3 and B3. Based on this, it can be seen that the fault detection result of the integrated navigation device C1, to which the normal device A1 belongs, is an abnormal result, and another device B1 in the integrated navigation device C1 is an identified abnormal device. Therefore, it can be considered that the fault of the integrated navigation device C1 is caused by the abnormal device B1, and thus it can be determined that A1 is indeed a normal device.

[0170] For A3 and B3, the fault detection result of their respective integrated navigation equipment C3 is abnormal. However, neither A3 nor B3 is a known abnormal device. Therefore, it can be assumed that the fault of C3 is caused by at least one of A3 or B3. Therefore, for safety reasons, both A3 and B3 are identified as abnormal devices.

[0171] Since the fault detection result of its associated integrated navigation device C2 is normal, it can be determined that B2 is indeed a normal device.

[0172] Therefore, by combining the results of detection from the same source and the results of detection from multiple sources to screen normal devices, it is possible to more comprehensively and accurately locate faulty devices and better improve the reliability and safety of the carrier's movement.

[0173] After obtaining the target abnormal device through any of the above embodiments, step S500 can be executed to report the target abnormal device to the control module.

[0174] In addition, in some embodiments, information about the target malfunctioning device can be sent to the bound user terminal to remind relevant personnel to troubleshoot the fault or maintain the malfunctioning device.

[0175] It is worth noting that the technical features or technical solutions in any of the above embodiments of the present invention can be combined with each other, as long as there is no contradiction in the combination.

[0176] In addition, embodiments of the present invention also provide a carrier, comprising:

[0177] Organism;

[0178] Multiple integrated navigation devices are installed in different parts of the machine body;

[0179] The control module is used to control the operation of the machine body based on the output of the plurality of integrated navigation devices;

[0180] The fault detection module is used to execute the navigation device fault detection method provided in any of the above embodiments of the present invention, so as to determine whether there is an abnormal device in the plurality of combined navigation devices based on the output of the plurality of combined navigation devices, and report to the control module if there is an abnormal device.

[0181] The fault detection principle can be found in the relevant descriptions in the method embodiments, and will not be repeated here.

[0182] To perform the corresponding steps in the above method embodiments and various possible methods, an implementation of a navigation device fault detection device is given below. Optionally, the navigation device fault detection device can adopt the above-described... Figure 1 The device structure of the electronic device is shown. Further, please refer to... Figure 3 , Figure 3 This is a functional block diagram of a navigation device fault detection device provided in an embodiment of the present invention. It should be noted that the basic principle and technical effects of the navigation device fault detection device provided in this embodiment are the same as those in the above method embodiments. For the sake of brevity, any parts not mentioned in this embodiment can be referred to the corresponding content in the above method embodiments. The navigation device fault detection device 300 includes:

[0183] The hard fault determination module 310 is configured to: determine, based on the detection data of every two IMUs belonging to different combined navigation devices, whether there is a normal IMU with normal hardware and a first abnormal IMU with abnormal hardware among all IMUs;

[0184] The soft fault determination module 320 is configured to: in the presence of at least two normal IMUs, determine whether there is a second abnormal IMU with software abnormality among all normal IMUs based on the INS calculation results of each pair of normal IMUs;

[0185] The GNSS fault determination module 330 is configured to: determine whether there is an abnormal GNSS navigation module among all GNSS navigation modules based on the GNSS calculation results of every two GNSS navigation modules belonging to different combined navigation devices;

[0186] The reporting module 340 is configured to report the abnormal device to the control module of the carrier in the event that at least one of the following abnormal devices exists: a first abnormal IMU, a second abnormal IMU, or an abnormal GNSS navigation module.

[0187] In some embodiments, each integrated navigation device is equipped with a Kalman filter and a state monitoring filter. Accordingly, before the reporting module 340 reports the abnormal device to the control module of the carrier, the navigation device fault detection device 300 provided in this embodiment may further include:

[0188] The state estimation module is configured to: for each integrated navigation device, output a corresponding current estimated state matrix based on the current INS solution result and the current GNSS solution result output by the IMU and GNSS navigation module in the integrated navigation device at the current time, respectively, through the Kalman filter in the integrated navigation device; the current estimated state matrix includes estimated velocity, estimated position and estimated attitude.

[0189] The status monitoring module is configured to: for each integrated navigation device, output a corresponding current observation status matrix based on the current INS calculation result output by the IMU in the integrated navigation device at the current moment through the status monitoring filter in the integrated navigation device; the current observation status matrix includes observation velocity, observation position and observation attitude;

[0190] The multi-source error calculation module is configured to: for each integrated navigation device, calculate the current state error matrix based on the corresponding current estimated state matrix and current observed state matrix; the current state error matrix includes the error between the estimated velocity and the observed velocity, the error between the estimated position and the observed position, and the error between the estimated attitude and the observed attitude;

[0191] The fault value calculation module is configured to: for each integrated navigation device, calculate the fault detection value of the integrated navigation device based on the corresponding current state error matrix;

[0192] The fault result determination module is configured to: obtain the fault detection result of each integrated navigation device based on the fault detection value of each integrated navigation device and the preset fault threshold value; the fault detection result includes a normal result indicating that the integrated navigation device is normal, or an abnormal result indicating that the integrated navigation device is abnormal;

[0193] The target abnormal device determination module is configured to: determine the target abnormal device from the identified abnormal devices based on the fault detection results of the integrated navigation device;

[0194] Accordingly, the reporting module 340 is configured to report the target abnormal device to the control module of the carrier when the target abnormal device is present.

[0195] In some embodiments, two state monitoring filters are configured. Accordingly, the process by which the state monitoring module outputs the corresponding current observation state matrix based on the current INS calculation result output by the IMU in the integrated navigation device at the current moment, using the state monitoring filters in the integrated navigation device, is configured as follows:

[0196] If the monitoring duration of the currently selected current state monitoring filter is less than half of the set monitoring cycle, discard the observation state matrix output by the current state monitoring filter based on the current INS solution result.

[0197] When the monitoring duration of the current state monitoring filter reaches half of the set monitoring period, the observation state matrix output by the current state monitoring filter based on the current INS solution result is used as the current observation state matrix, and another state monitoring filter is initialized; wherein, the monitoring duration of the other state monitoring filter after initialization is updated to 0, and its initial state matrix is ​​updated to the current state estimation matrix output by the Kalman filter at its initialization time.

[0198] When the monitoring duration of the current state monitoring filter reaches the set monitoring period, the current state monitoring rate filter is initialized, and the other state monitoring filter is used as the current state monitoring filter; wherein, the monitoring duration of the initialized current state monitoring rate filter is updated to 0, and its initial state matrix is ​​updated to the current state estimation matrix output by the Kalman filter at its initialization time.

[0199] In other embodiments, only one state monitoring filter is configured. Accordingly, the process by which the state monitoring module outputs the corresponding current observation state matrix based on the current INS solution result output by the IMU in the integrated navigation device at the current moment, using the state monitoring filter in the integrated navigation device, is configured as follows:

[0200] If the monitoring duration of the state monitoring filter does not reach half of the set monitoring cycle, the observation state matrix output by the state monitoring filter based on the current INS solution result is discarded.

[0201] When the monitoring duration of the state monitoring filter reaches half of the set monitoring cycle, the observation state matrix output by the state monitoring filter based on the current INS solution result is taken as the current observation state matrix.

[0202] When the monitoring duration of the state monitoring filter reaches the set monitoring period, the state monitoring filter is initialized; wherein, the monitoring duration of the initialized state monitoring filter is updated to 0, and its initial state matrix is ​​updated to the current state estimation matrix output by the Kalman filter at its initialization time.

[0203] In some embodiments, the process by which the target abnormal device determination module determines the target abnormal device from the identified abnormal devices based on the fault detection results of the integrated navigation device is configured as follows:

[0204] For each abnormal device, if the fault detection result of the integrated navigation device to which the abnormal device belongs is normal, then the fault detection result of the abnormal device is updated to normal; if the fault detection result of the integrated navigation device to which the abnormal device belongs is abnormal, then the abnormal device is identified as the target abnormal device.

[0205] In some embodiments, the target abnormal device determination module is further configured to:

[0206] For each identified normal device, if the fault detection result of the integrated navigation device to which the normal device belongs is normal, or if the fault detection result of the integrated navigation device to which the normal device belongs is abnormal and another device in the integrated navigation device to which the normal device belongs is an identified abnormal device, then the normal device is determined to be normal; if the fault detection result of the integrated navigation device to which the normal device belongs is abnormal and another device in the integrated navigation device to which the normal device belongs is not an identified abnormal device, then the normal device and the other device in the integrated navigation device to which the normal device belongs are identified as target abnormal devices.

[0207] In some embodiments, the detection data includes acceleration and angular velocity. Accordingly, the process by which the hard fault determination module 310 determines the normal IMU with normal hardware and the first abnormal IMU with hardware malfunction based on the detection data of every two IMUs belonging to different integrated navigation devices is configured as follows:

[0208] The normal IMU and the first abnormal IMU are obtained based on the acceleration deviation between the accelerations detected by each pair of IMUs and the set acceleration deviation threshold, and the angular velocity deviation between the angular velocities detected by each pair of IMUs and the set angular velocity deviation threshold.

[0209] Among them, the acceleration deviation and angular velocity deviation between normal IMUs are both less than their respective deviation thresholds, and at least one of the acceleration deviation or angular velocity deviation between the first abnormal IMU and any IMU is not less than the corresponding deviation threshold.

[0210] In some embodiments, the INS calculation results include position information, attitude information, and velocity. Correspondingly, the soft fault determination module 320, based on the INS calculation results of every two normal IMUs, determines whether there is a second abnormal IMU with software anomalies among all normal IMUs, and the process is configured as follows:

[0211] Based on the position deviation between position information and the set position deviation threshold, the attitude deviation between attitude information and the set attitude deviation threshold, and the velocity deviation between velocities and the set velocity deviation threshold in the INS calculation results of every two normal IMUs, determine whether there is a second abnormal IMU.

[0212] Wherein, at least one of the position deviation, attitude deviation, or velocity deviation between the second abnormal IMU and any other IMU is not less than the corresponding deviation threshold.

[0213] In some embodiments, the GNSS calculation results include position information, attitude information, and velocity. Correspondingly, the process by which the GNSS fault determination module 330 determines whether there is an abnormal GNSS navigation module among all GNSS navigation modules based on the GNSS calculation results of every two GNSS navigation modules belonging to different integrated navigation devices is configured as follows:

[0214] Based on the position deviation between position information and the set position deviation threshold, the attitude deviation between attitude information and the set attitude deviation threshold, and the velocity deviation between velocities and the set velocity deviation threshold in the GNSS calculation results of each pair of GNSS navigation modules, it is determined whether there is an abnormal GNSS navigation module.

[0215] Wherein, at least one of the position deviation, attitude deviation, or velocity deviation between the abnormal GNSS navigation module and any other GNSS navigation module is not less than the corresponding deviation threshold.

[0216] Optionally, the above modules can be stored in the form of software or firmware. Figure 1 The memory shown is either stored in or embedded in the operating system (OS) of the electronic device, and can be... Figure 1 The processor executes the commands. Meanwhile, the data and program code required to execute these modules can be stored in memory.

[0217] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can also be implemented in other ways. The apparatus embodiments described above are merely illustrative; for example, the flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of apparatus, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than those marked in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram and / or flowchart, and combinations of blocks in block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0218] In addition, the functional modules in the various embodiments of the present invention can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0219] If the aforementioned functions are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0220] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A navigation device fault detection method, characterized by, A method for fault detection of a plurality of integrated navigation devices mounted on a carrier, each integrated navigation device comprising an IMU and a GNSS navigation module; the method comprises: determining, according to the detection data of each two IMUs belonging to different integrated navigation devices, whether there is a normal IMU with normal hardware and a first abnormal IMU with abnormal hardware among all IMUs; in the case where there are at least two normal IMUs, determining, according to the INS calculation results of each two normal IMUs, whether there is a second abnormal IMU with abnormal software among all normal IMUs; determining, according to the GNSS calculation results of each two GNSS navigation modules belonging to different integrated navigation devices, whether there is an abnormal GNSS navigation module among all GNSS navigation modules; in the case where there is at least one abnormal device among the first abnormal IMU, or the second abnormal IMU, or the abnormal GNSS navigation module, reporting the abnormal device to a control module of the carrier; each integrated navigation device is configured with a Kalman filter and a state monitoring filter; before reporting the abnormal device to the control module of the carrier, the method further comprises: for each integrated navigation device, outputting a corresponding current estimated state matrix by the Kalman filter in the integrated navigation device based on the current INS calculation result and the current GNSS calculation result output by the IMU and the GNSS navigation module in the integrated navigation device at the current time; the current estimated state matrix comprises an estimated velocity, an estimated position and an estimated attitude; for each integrated navigation device, outputting a corresponding current observation state matrix by the state monitoring filter in the integrated navigation device based on the current INS calculation result output by the IMU in the integrated navigation device at the current time; the current observation state matrix comprises an observed velocity, an observed position and an observed attitude; for each integrated navigation device, calculating a current state error matrix according to the corresponding current estimated state matrix and the current observation state matrix; the current state error matrix comprises an error between the corresponding estimated velocity and the observed velocity, an error between the corresponding estimated position and the observed position, and an error between the corresponding estimated attitude and the observed attitude; for each integrated navigation device, calculating a fault detection value of the integrated navigation device according to the corresponding current state error matrix; obtaining a fault detection result of each integrated navigation device according to the fault detection value of each integrated navigation device and a preset fault threshold value; the fault detection result comprises a normal result representing a normal integrated navigation device, or an abnormal result representing an abnormal integrated navigation device; determining a target abnormal device from the determined abnormal devices according to the fault detection result of the integrated navigation device; correspondingly, the step of reporting the abnormal device to the control module of the carrier is adjusted to: in the case where the target abnormal device exists, reporting the target abnormal device to the control module of the carrier. The state monitoring filter is configured with two; the step of outputting the corresponding current observation state matrix by the state monitoring filter in the integrated navigation device based on the current INS calculation result output by the IMU in the integrated navigation device at the current time comprises: In the case where the monitoring time length of the currently selected current state monitoring filter does not reach half of the set monitoring period, the observation state matrix output by the current state monitoring filter based on the current INS calculation result is discarded; in the case where the monitoring time length of the current state monitoring filter reaches half of the set monitoring period, the observation state matrix output by the current state monitoring filter based on the current INS calculation result is taken as the current observation state matrix, and another state monitoring filter is initialized; wherein the monitoring time length of the initialized another state monitoring filter is updated to 0, and the initial state matrix thereof is updated to the current state estimation matrix output by the Kalman filter at the initialization time thereof; in the case where the monitoring time length of the current state monitoring filter reaches the set monitoring period, the current state monitoring filter is initialized, and the another state monitoring filter is taken as the current state monitoring filter; wherein the monitoring time length of the initialized current state monitoring filter is updated to 0, and the initial state matrix thereof is updated to the current state estimation matrix output by the Kalman filter at the initialization time thereof; Alternatively, the state monitoring filter is configured with only one; the step of outputting the corresponding current observation state matrix by the state monitoring filter in the integrated navigation device based on the current INS calculation result output by the IMU in the integrated navigation device at the current time comprises: In the case where the monitoring time length of the state monitoring filter does not reach half of the set monitoring period, the observation state matrix output by the state monitoring filter based on the current INS calculation result is discarded; in the case where the monitoring time length of the state monitoring filter reaches half of the set monitoring period, the observation state matrix output by the state monitoring filter based on the current INS calculation result is taken as the current observation state matrix; in the case where the monitoring time length of the state monitoring filter reaches the set monitoring period, the state monitoring filter is initialized; wherein the monitoring time length of the initialized state monitoring filter is updated to 0, and the initial state matrix thereof is updated to the current state estimation matrix output by the Kalman filter at the initialization time thereof.

2. The method of claim 1, wherein, The step of determining the target abnormal device from the determined abnormal devices according to the fault detection result of the integrated navigation device comprises: For each abnormal device, if the fault detection result of the integrated navigation device to which the abnormal device belongs is a normal result, the fault detection result of the abnormal device is updated to normal; if the fault detection result of the integrated navigation device to which the abnormal device belongs is an abnormal result, the abnormal device is determined as the target abnormal device.

3. The method of claim 1, wherein, The method further comprises: For each normal device determined, if the fault detection result of the combined navigation device to which the normal device belongs is a normal result, or if the fault detection result of the combined navigation device to which the normal device belongs is an abnormal result and another device in the combined navigation device to which the normal device belongs is a determined abnormal device, the normal device is determined to be normal; if the fault detection result of the combined navigation device to which the normal device belongs is an abnormal result and another device in the combined navigation device to which the normal device belongs is not a determined abnormal device, the normal device and the other device in the combined navigation device to which the normal device belongs are determined to be target abnormal devices.

4. The method of claim 1, wherein, The detection data includes acceleration and angular velocity; the step of determining a normal IMU with normal hardware and a first abnormal IMU with abnormal hardware according to the detection data of each two IMUs belonging to different combined navigation devices comprises: According to the acceleration deviation between the acceleration detected by each two IMUs and the set acceleration deviation threshold, and the angular velocity deviation between the angular velocity detected by each two IMUs and the set angular velocity deviation threshold, the normal IMU and the first abnormal IMU are obtained; Wherein, the acceleration deviation and the angular velocity deviation between the normal IMU and the normal IMU are both less than the corresponding deviation threshold, and at least one of the acceleration deviation or the angular velocity deviation between the first abnormal IMU and any IMU is not less than the corresponding deviation threshold.

5. The method of claim 1, wherein, The INS solution includes position information, attitude information and velocity; The step of determining whether there is a second abnormal IMU with software exception in all normal IMUs according to the INS solution of each two normal IMUs comprises: According to the position deviation between the position information in the INS solution of each two normal IMUs and the set position deviation threshold, the attitude deviation between the attitude information and the set attitude deviation threshold, and the velocity deviation between the velocity and the set velocity deviation threshold, it is determined whether there is the second abnormal IMU; Wherein, at least one of the position deviation, the attitude deviation or the velocity deviation between the second abnormal IMU and any IMU is not less than the corresponding deviation threshold.

6. The method of claim 1, wherein, The GNSS solution includes position information, attitude information and velocity; the step of determining whether there is an abnormal GNSS navigation module in all GNSS navigation modules according to the GNSS solution of each two GNSS navigation modules belonging to different combined navigation devices comprises: According to the position deviation between the position information in the GNSS solution of each two GNSS navigation modules and the set position deviation threshold, the attitude deviation between the attitude information and the set attitude deviation threshold, and the velocity deviation between the velocity and the set velocity deviation threshold, it is determined whether there is the abnormal GNSS navigation module; Wherein, at least one of the position deviation, the attitude deviation or the velocity deviation between the abnormal GNSS navigation module and any GNSS navigation module is not less than the corresponding deviation threshold.

7. A navigation device fault detection apparatus characterized by, The device is used for fault detection of a plurality of combined navigation devices mounted on a carrier, each combined navigation device comprising an IMU and a GNSS navigation module; the device comprises: a hard fault determination module configured to determine, according to detection data of each two IMUs belonging to different combined navigation devices, whether there is a normal IMU with normal hardware and a first abnormal IMU with abnormal hardware in all IMUs; a soft fault determination module configured to, in the case that there are at least two normal IMUs, determine, according to INS calculation results of each two normal IMUs, whether there is a second abnormal IMU with software abnormality in all normal IMUs; a GNSS fault determination module configured to, according to GNSS calculation results of each two GNSS navigation modules belonging to different combined navigation devices, determine whether there is an abnormal GNSS navigation module in all GNSS navigation modules; a reporting module configured to, in the case that there is at least one abnormal device among the first abnormal IMU, or the second abnormal IMU, or the abnormal GNSS navigation module, report the abnormal device to a control module of the carrier; each combined navigation device is configured with a Kalman filter and a state monitoring filter; the device further comprises: a state estimation module configured to, for each combined navigation device, output a corresponding current estimated state matrix based on current INS calculation results and current GNSS calculation results output by the IMU and the GNSS navigation module in the combined navigation device at a current time through the Kalman filter in the combined navigation device; the current estimated state matrix comprises an estimated velocity, an estimated position and an estimated attitude; a state monitoring module configured to, for each combined navigation device, output a corresponding current observation state matrix based on current INS calculation results output by the IMU in the combined navigation device at a current time through the state monitoring filter in the combined navigation device; the current observation state matrix comprises an observed velocity, an observed position and an observed attitude; a multi-source error calculation module configured to, for each combined navigation device, calculate a current state error matrix according to the corresponding current estimated state matrix and the current observation state matrix; the current state error matrix comprises an error between the corresponding estimated velocity and the observed velocity, an error between the corresponding estimated position and the observed position, and an error between the corresponding estimated attitude and the observed attitude; a fault value calculation module configured to, for each combined navigation device, calculate a fault detection value of the combined navigation device according to the corresponding current state error matrix; a fault result determination module configured to obtain a fault detection result of each combined navigation device according to the fault detection value of each combined navigation device and a preset fault threshold value; the fault detection result comprises a normal result representing a normal combined navigation device, or an abnormal result representing an abnormal combined navigation device; a target abnormal device determination module configured to determine a target abnormal device from the determined abnormal devices according to the fault detection result of the combined navigation device. Correspondingly, the reporting module is configured to report the target abnormal device to the control module of the carrier in the case that the target abnormal device exists. The state monitoring filter is configured with two, and the state monitoring module is configured to discard the observation state matrix output by the current state monitoring filter based on the current INS calculation result in the case that the monitoring time length of the current state monitoring filter does not reach half of the set monitoring period, to take the observation state matrix output by the current state monitoring filter based on the current INS calculation result as the current observation state matrix in the case that the monitoring time length of the current state monitoring filter reaches half of the set monitoring period, and to initialize another state monitoring filter; wherein the monitoring time length of the initialized another state monitoring filter is updated to 0, and the initial state matrix thereof is updated to the current state estimation matrix output by the Kalman filter at the initialization time thereof; and to initialize the current state monitoring filter in the case that the monitoring time length of the current state monitoring filter reaches the set monitoring period, and to take the another state monitoring filter as the current state monitoring filter; wherein the monitoring time length of the initialized current state monitoring filter is updated to 0, and the initial state matrix thereof is updated to the current state estimation matrix output by the Kalman filter at the initialization time thereof. Alternatively, the state monitoring filter is configured with only one, and the state monitoring module is configured to discard the observation state matrix output by the state monitoring filter based on the current INS calculation result in the case that the monitoring time length of the state monitoring filter does not reach half of the set monitoring period, to take the observation state matrix output by the state monitoring filter based on the current INS calculation result as the current observation state matrix in the case that the monitoring time length of the state monitoring filter reaches half of the set monitoring period, and to initialize the state monitoring filter in the case that the monitoring time length of the state monitoring filter reaches the set monitoring period; wherein the monitoring time length of the initialized state monitoring filter is updated to 0, and the initial state matrix thereof is updated to the current state estimation matrix output by the Kalman filter at the initialization time thereof.

8. An electronic device, comprising: The computer program is executed by the processor to implement the method of any one of claims 1-6.

9. A vector, characterized in that, The computer program is executed by the processor to implement the method of any one of claims 1-6. The computer program is executed by the processor to implement the method of any one of claims 1-6. The computer program is executed by the processor to implement the method of any one of claims 1-6. The computer program is executed by the processor to implement the method of any one of claims 1-6. The computer program is executed by the processor to implement the method of any one of claims 1-6.

10. A computer-readable storage medium having stored thereon a computer program, characterized in that, ​

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