A sensor calibration steady-state judgment method, device, equipment and medium

Abstract

The application discloses a sensor calibration adaptive steady state judgment method and device, equipment and medium, relates to the flight test technical field, and is used for solving the technical problems that the accuracy and efficiency of the prior art adaptive steady state judgment are low. The method comprises the following steps: calculating a forgetting factor according to a preset data sampling period, a preset time block and a preset variance calculation formula; in the sensor calibration process, the forgetting factor is used for online noise estimation of sensor output data, and the instantaneous mean and variance of the sensor output data are obtained; the state type of the sensor output data is determined by performing sequential probability ratio test according to the instantaneous mean and variance of the sensor output data; wherein the state type comprises stable, unstable and continue to observe. Therefore, the application can improve the accuracy and efficiency of steady state judgment by "online noise estimation and sequential probability ratio test".

Description

Technical Field

[0001] This application relates to the field of flight test technology, and provides a sensor calibration adaptive steady-state judgment method, device, equipment and medium. Background Technology

[0002] As is well known, airborne sensors, as core sensing units in modern aviation, are widely used in flight testing. Their measurement accuracy directly affects the accuracy of critical data. In high-performance fighter jets, even small data differences can represent huge physical quantities, thus affecting the reliability and safety of aircraft performance calculations. Therefore, to ensure sensor measurement accuracy, periodic calibration is often necessary to eliminate errors caused by component aging, manufacturing deviations, etc.

[0003] In the sensor calibration process, such as Figure 1 As shown, the sensor output value changes with the physical quantity of the standard excitation source. When the physical quantity stabilizes at a certain calibration point, the sensor may require a certain amount of time to stabilize due to mechanical inertia, circuit response, or delay. If data is read before stabilization, it can easily lead to inaccurate calibration or even calculation errors. Furthermore, the calibration process for airborne sensors before installation differs from that of other professional metrology departments. The operation of other equipment in the flight test laboratory introduces time-varying noise such as temperature, vibration, and electromagnetic interference, significantly increasing the complexity of the calibration environment.

[0004] Based on this, a steady-state judgment method for sensor calibration has emerged. Currently, in the existing technology, the following two methods are mainly used for steady-state judgment: (1) Time threshold method, that is, after the physical quantity excitation change is stable, waiting for a fixed preset time is considered to be stable. This method is similar to the traditional manual calibration method, which is easy to implement and does not require calculation. However, it lacks adaptability and has low efficiency. Its application scenarios are mostly in calibration tasks with sensor characteristics and small workload; (2) Sliding window method, that is, when the difference between the maximum and minimum values ​​of the output value is lower than the preset threshold within a certain length of window, the data output is judged to be stable. This method directly focuses on the fluctuation range of the output value. The sliding window can smooth the noise influence and improve the judgment accuracy. However, its threshold selection is not universal and is sensitive to pulse interference, which can easily lead to misjudgment. Summary of the Invention

[0005] This application provides a sensor calibration adaptive steady-state judgment method, apparatus, device, and medium to solve the technical problems of low accuracy and efficiency of existing adaptive steady-state judgment.

[0006] On the one hand, an adaptive steady-state judgment method for sensor calibration is provided, which is applied in a flight test laboratory environment. The method includes: Calculate the forgetting factor based on the preset data sampling period, preset time block, and preset variance calculation formula; During the sensor calibration process, online noise estimation is performed on the sensor output data based on the forgetting factor to obtain the instantaneous mean and variance of the sensor output data; Based on the instantaneous mean and variance of the sensor output data, a sequential probability ratio test is performed to determine the state type of the sensor output data; wherein, the state type includes stable, unstable, and continue observation.

[0007] Optionally, the step of calculating the forgetting factor based on a preset data sampling period, a preset time block, and a preset variance calculation formula includes: The preset data sampling period, preset maximum time block length, total data duration, and number of collection points for each data block are set. The number of groups is obtained based on the preset data sampling period, the total data duration, and the number of collection points for each data block; The minimum time block is determined by using the average value of each data block and the preset variance calculation formula. The forgetting factor is calculated based on the preset forgetting factor calculation formula and the minimum time block.

[0008] Optionally, the preset forgetting factor calculation formula is expressed as follows:

[0009] in, Forgetting factor; The preset data sampling period; This is the smallest time block.

[0010] Optionally, the step of determining the state type of the sensor output data by performing a sequential probability ratio test based on the instantaneous mean and variance of the output signal includes: The likelihood ratio is calculated based on the preset likelihood ratio calculation formula and the instantaneous mean and variance of the output signal. Based on the likelihood ratio, determine the state type of the sensor output data.

[0011] Optionally, the preset likelihood ratio calculation formula is expressed as follows:

[0012] in, and are the instantaneous mean and standard deviation of the t-th time block, respectively; This indicates that the data is in a steady state; The sensor output data for the t-th time block; This indicates that the system is unstable; This is the minimum detectable offset coefficient.

[0013] Optionally, the step of determining the state type of the sensor output data based on the likelihood ratio includes: Based on the preset false positive rate and the preset false negative rate, a first judgment threshold and a second judgment threshold are determined; wherein, the first judgment threshold is less than the second judgment threshold; If the likelihood ratio is determined to be no greater than the first judgment threshold, the data is judged to be stable. If the likelihood ratio is determined to be between the first and second judgment thresholds, then the data is determined to be for continued observation. If the likelihood ratio is determined to be no less than the second judgment threshold, then the data is judged to be unstable.

[0014] Optionally, after determining the state type of the sensor output data based on the likelihood ratio, the method further includes: If the data is determined to be stable, the sensor output value at the current calibration point will be automatically recorded, and the control flow will be triggered to jump to the next calibration point, while resetting the decision state. If the data is determined to be unstable, an alert signal is sent to the calibration personnel, the judgment status is reset, and data acquisition at the current calibration point continues. If the data indicates that observation is required, the current decision status will be maintained, and data collection at the current calibration point will continue.

[0015] On the one hand, a sensor calibration adaptive steady-state judgment device is provided, which is applied in a flight test laboratory environment. The device includes: The forgetting factor calculation unit is used to calculate the forgetting factor based on the preset data sampling period, preset time block and preset variance calculation formula; An online noise estimation unit is used to perform online noise estimation on the sensor output data according to the forgetting factor during the sensor calibration process, and to obtain the instantaneous mean and variance of the sensor output data. The sequential probability ratio test unit is used to perform a sequential probability ratio test based on the instantaneous mean and variance of the output signal to determine the state type of the sensor output data; wherein, the state type includes stable, unstable and continue observation.

[0016] On one hand, an electronic device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any of the methods described above.

[0017] On the one hand, a storage medium is provided that stores computer program instructions thereon, which, when executed by a processor, implement any of the methods described above.

[0018] Compared with the prior art, the beneficial effects of this application are as follows: In this application, when performing adaptive steady-state judgment for sensor calibration, firstly, a forgetting factor can be calculated based on a preset data sampling period, a preset time block, and a preset variance calculation formula; then, during the sensor calibration process, online noise estimation can be performed on the sensor output data based on the forgetting factor to obtain the instantaneous mean and variance of the sensor output data; next, a sequential probability ratio test can be performed based on the instantaneous mean and variance of the output signal to determine the state type of the sensor output data; wherein, the state type includes stable, unstable, and continue observation.

[0019] Based on this, in this application, by calculating the forgetting factor in the laboratory environment to dynamically track the noise level of the sensor output data in real time, compared with the prior art, this application can significantly shorten the judgment delay while ensuring extremely high confidence, greatly improve the automation level and efficiency of airborne sensor calibration, achieve the goal of fast judgment, high accuracy and low resource consumption, and provide a practical solution for efficient automatic calibration of large-scale airborne sensors. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this application. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0021] Figure 1 A schematic diagram of a conventional steady-state calibration process provided in this application embodiment; Figure 2 This is a schematic diagram of an application scenario provided by an embodiment of this application; Figure 3 This is a schematic flowchart of a sensor calibration adaptive steady-state judgment method provided in an embodiment of this application; Figure 4 This is a schematic diagram of a sensor calibration adaptive steady-state judgment device provided in an embodiment of this application.

[0022] The diagram is labeled as follows: 10-Sensor calibration adaptive steady-state judgment device, 101-Processor, 102-Memory, 103-I / O interface, 104-Database, 40-Sensor calibration adaptive steady-state judgment device, 401-Forgetting factor calculation unit, 402-Online noise estimation unit, 403-Sequential probability ratio test unit, 404-State decision unit. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application. Unless otherwise specified, the embodiments and features in the embodiments of this application can be arbitrarily combined with each other. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than that shown here.

[0024] As is well known, airborne sensors, as core sensing units in modern aviation, are widely used in flight testing. Their measurement accuracy directly affects the accuracy of critical data. In high-performance fighter jets, even small data differences can represent huge physical quantities, thus affecting the reliability and safety of aircraft performance calculations. Therefore, to ensure sensor measurement accuracy, periodic calibration is often necessary to eliminate errors caused by component aging, manufacturing deviations, etc.

[0025] During sensor calibration, the sensor output value changes with the physical quantity of the standard excitation source. When the physical quantity stabilizes at a certain calibration point, the sensor may require a certain amount of time to stabilize due to mechanical inertia, circuit response, or delays. If data is read before stabilization, it can easily lead to inaccurate calibration or even calculation errors. Furthermore, the calibration environment for airborne sensors before installation differs from that of other professional metrology departments. The operation of other equipment in the flight test laboratory introduces time-varying noise such as temperature, vibration, and electromagnetic interference, significantly increasing the complexity of the calibration environment.

[0026] Based on this, a steady-state judgment method for sensor calibration has emerged. Currently, in the existing technology, the following two methods are mainly used for steady-state judgment: (1) Time threshold method, that is, after the physical quantity excitation change is stable, waiting for a fixed preset time is considered to be stable. This method is similar to the traditional manual calibration method, which is easy to implement and does not require calculation. However, it lacks adaptability and has low efficiency. Its application scenarios are mostly in calibration tasks with sensor characteristics and small workload; (2) Sliding window method, that is, when the difference between the maximum and minimum values ​​of the output value is lower than the preset threshold within a certain length of window, the data output is judged to be stable. This method directly focuses on the fluctuation range of the output value. The sliding window can smooth the noise influence and improve the judgment accuracy. However, its threshold selection is not universal and is sensitive to pulse interference, which can easily lead to misjudgment.

[0027] Based on this, this application provides an adaptive steady-state judgment method for sensor calibration. In this method, firstly, a forgetting factor is calculated based on a preset data sampling period, a preset time block, and a preset variance calculation formula. Then, during sensor calibration, online noise estimation is performed on the sensor output data based on the forgetting factor to obtain the instantaneous mean and variance of the sensor output data. Next, a sequential probability ratio test is performed based on the instantaneous mean and variance of the output signal to determine the state type of the sensor output data. The state type includes stable, unstable, and continued observation. Therefore, in this application, by calculating the forgetting factor under laboratory conditions to dynamically track the noise level of the sensor output data in real time, compared to existing technologies, this application can significantly shorten the judgment delay while maintaining extremely high confidence, greatly improving the automation level and efficiency of airborne sensor calibration, achieving the goals of fast judgment, high accuracy, and low resource consumption, and providing a practical solution for efficient automatic calibration of large-scale airborne sensors.

[0028] After introducing the design concept of the embodiments of this application, the following is a brief introduction to the application scenarios to which the technical solutions of the embodiments of this application can be applied. It should be noted that the application scenarios described below are only for illustrating the embodiments of this application and are not intended to limit the scope. In specific implementation, the technical solutions provided by the embodiments of this application can be flexibly applied according to actual needs.

[0029] like Figure 2 The diagram shown illustrates an application scenario provided by an embodiment of this application. This application scenario may include a sensor calibration adaptive steady-state judgment device 10.

[0030] The sensor calibration adaptive steady-state judgment device 10 can be used to perform adaptive steady-state judgment and decision-making for sensor calibration. For example, it can be a personal computer (PC), server, or laptop. The sensor calibration adaptive steady-state judgment device 10 may include one or more processors 101, memory 102, I / O interfaces 103, and database 104. Specifically, the processor 101 can be a central processing unit (CPU) or a digital processing unit, etc. The memory 102 can be volatile memory, such as random-access memory (RAM); the memory 102 can also be non-volatile memory, such as read-only memory, flash memory, hard disk drive (HDD), or solid-state drive (SSD); or the memory 102 can be any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. The memory 102 can be a combination of the above-mentioned memories. The memory 102 may store some program instructions of the sensor calibration adaptive steady-state judgment method provided in the embodiments of this application. When these program instructions are executed by the processor 101, they can be used to implement the steps of the sensor calibration adaptive steady-state judgment method provided in the embodiments of this application, so as to solve the technical problems of low accuracy and efficiency of adaptive steady-state judgment in the prior art. The database 104 may be used to store data such as sensor output data involved in the solution provided in the embodiments of this application.

[0031] In this embodiment, the sensor calibration adaptive steady-state judgment device 10 can obtain steady-state judgment instructions through the I / O interface 103. Then, the processor 101 of the sensor calibration adaptive steady-state judgment device 10 will solve the technical problems of low accuracy and efficiency of adaptive steady-state judgment in the prior art by following the program instructions of the sensor calibration adaptive steady-state judgment method provided in this embodiment in the memory 102. In addition, sensor output data and other data can be stored in the database 104.

[0032] Of course, the methods provided in the embodiments of this application are not limited to... Figure 1 The application scenarios shown can also be used in other possible scenarios, and this application embodiment does not impose any limitations. Figure 1 The functions that the various devices in the application scenarios shown can achieve will be described in subsequent method embodiments, and will not be elaborated on here. Below, the methods of the embodiments of this application will be described in conjunction with the accompanying drawings.

[0033] like Figure 3 The diagram shown is a flowchart of a sensor calibration adaptive steady-state judgment method provided in an embodiment of this application. This method can... Figure 1 The sensor calibration adaptive steady-state judgment device 10 is used to perform the process. Specifically, the process flow of this method is described below.

[0034] Step 301: Calculate the forgetting factor based on the preset data sampling period, preset time block and preset variance calculation formula.

[0035] Specifically, firstly, the preset data sampling period, the maximum length of the preset time block, the total data duration, and the number of sampling points per data block can be set. For example, assuming the sensor is an airborne sensor, the preset data sampling period (the airborne sensor output data sampling period) is Δt, and the airborne sensor output stabilization time is generally short, with a manual judgment time of about 4 seconds, so the preset time block... The maximum length (e.g., the maximum length of the Allan variance time block) is 4 seconds. The total data duration is set to 100 times the preset maximum time block length, i.e., the total data duration is 400 seconds. Then, it can be gradually increased according to 2Δt, 4Δt, 8Δt, etc., with a maximum value of 40 seconds. The number of sampling points in each data block is set to m, and the number of adjacent data steps is 1.

[0036] Then, the number of groups can be obtained based on the preset data sampling period, the total data duration, and the number of collection points for each data block. The specific number of groups, k, can be calculated using the following formula:

[0037] Next, the minimum time block can be determined by using the average value of each data block and a preset variance calculation formula; wherein, the preset variance calculation formula can be expressed as follows:

[0038] in, It is the average value of the i-th data block. Based on this, the preset variance calculation formula can be applied to different time blocks. corresponding Plot the Allan variance curve, then find the minimum point of the Allan variance curve and assign that minimum point to the corresponding... This is denoted as the minimum time block. .

[0039] Finally, the forgetting factor can be calculated based on the preset forgetting factor calculation formula and the minimum time block. The preset forgetting factor calculation formula is expressed as follows:

[0040] in, Forgetting factor; The preset data sampling period; This is the minimum time block length.

[0041] Based on this, this application can use Allan variance to distinguish and quantify different types of noise at different time scales in a laboratory environment. Then, the curve is obtained by calculating the result through a preset variance calculation formula, and the most statistically stable sensor noise is obtained through the lowest point of the curve.

[0042] Step 302: During the sensor calibration process, based on the forgetting factor, perform online noise estimation on the sensor output data to obtain the instantaneous mean and variance of the sensor output data.

[0043] Specifically, utilizing the forgetting factor The instantaneous mean of the sensor output data is estimated in real time during the calibration process. and variance Among them, the instantaneous mean and variance They can be expressed using the following formulas:

[0044]

[0045] in, This represents the sensor output data for the t-th time block.

[0046] Step 303: Based on the instantaneous mean and variance of the sensor output data, perform a sequential probability ratio test to determine the state type of the sensor output data.

[0047] The state types include stable, unstable, and continue to be observed.

[0048] Specifically, firstly, the likelihood ratio can be calculated based on the preset likelihood ratio calculation formula and the instantaneous mean and variance of the output signal; in this application, the preset likelihood ratio calculation formula is expressed as follows:

[0049] in, and are the instantaneous mean and standard deviation of the t-th time block, respectively; This indicates that the data is in a steady state. At this point, the sensor output data (also known as the observation data) for the t-th time block is displayed. Obey N ( , ); This indicates that the system is unstable; at this time, the observed data... Obey N ( , ); The minimum detectable offset coefficient is defined as the maximum permissible deviation between the sensor output value and the true value (stable value). The values ​​are directly referenced from the accuracy requirements of the flight test parameters.

[0050] Finally, the state type of the sensor output data can be determined directly based on the likelihood ratio.

[0051] Furthermore, when determining the state type of the sensor output data based on the likelihood ratio, firstly, a preset false positive rate can be used. Compared with the preset false negative rate Determine the first judgment threshold. With the second judgment threshold Among them, the first judgment threshold is less than the second judgment threshold.

[0052] Then, if the likelihood ratio is determined to be no greater than the first judgment threshold, the data is determined to be stable; that is, if If the data is stable, then the data is considered stable.

[0053] If the likelihood ratio is determined to be between the first and second judgment thresholds, then the data is determined to continue observation; that is, if If the data is negative, then it is determined that the observation should continue.

[0054] If the likelihood ratio is determined to be not less than the second judgment threshold, then the data is judged to be unstable; that is, if If so, the data is determined to be unstable.

[0055] In one possible implementation, after determining the state type of the sensor output data based on the likelihood ratio, an automatic calibration state decision can also be made.

[0056] Specifically, if the data is determined to be stable, the sensor output value at the current calibration point is automatically recorded, bound and stored with the standard source input value, and the control flow is triggered to jump to the next calibration point, while the decision state is reset.

[0057] If the data is determined to be unstable, an alert signal will be sent to the calibration personnel immediately, the judgment status will be reset, and data acquisition at the current calibration point will continue until the data stabilizes again. If the data is repeatedly determined to be unstable, the calibration personnel will be asked to confirm that the sensor is abnormal and the automatic calibration process will be stopped.

[0058] If the data indicates that further observation is needed, the current decision status will be maintained, and data collection at the current calibration point will continue until a clear decision is made.

[0059] To facilitate understanding, the "Adaptive Steady-State Judgment Method for Sensor Calibration" in this application is illustrated below with an example. Specifically, an airborne laser displacement sensor can be selected for research, with a displacement parameter accuracy of ±1mm, a displacement sensor calibration device positioning accuracy of ±0.1mm, and sensor output data sent to the airborne data acquisition unit KAM400, with 16-bit data encoding 0-65535. The experimental procedure is as follows: First, you can calibrate the parameter settings: range, step length, number of calibration points, initial position, etc. Then, you can click "Load Parameters" to automatically calculate the calibration stroke, create the original data table, and position the device to the initial position. Finally, click "Calibrate," and the software will sequentially locate each calibration point according to the calibration schedule, performing steady-state judgments and corresponding decisions for each calibration point until the calibration is complete. The calibration results are shown in Table 1 below.

[0060] Table 1

[0061] It is evident that this technical solution significantly improves the accuracy of steady-state judgment.

[0062] In summary, this application has the following advantages: (1) Since it can perform integrated adaptive judgment of dynamic noise estimation and optimal sequential verification, it solves the problem of stability judgment under non-stationary time-varying noise in the test laboratory environment and realizes the automatic calibration goal of fast test judgment speed and high accuracy.

[0063] (2) It has wide application. The method has three basic parameters set according to the technical indicators of the flight test parameters, so that the calibration results can be directly applied.

[0064] (3) Since the airborne sensors and airborne data acquisition units can be automatically calibrated together, the airborne data acquisition unit data can be directly obtained. The calibration results do not need to be converted, thus eliminating system errors.

[0065] (4) It greatly reduces the threshold for deployment, debugging and maintenance of traditional modes. At the same time, the benchmark parameters are quantifiable specific indicators, which standardizes the calibration process and significantly improves the credibility and authority of calibration data.

[0066] Based on the same inventive concept, embodiments of this application provide a sensor calibration adaptive steady-state judgment device 40, such as... Figure 4 As shown, the sensor calibration adaptive steady-state judgment device 40 includes: The forgetting factor calculation unit 401 is used to calculate the forgetting factor based on the preset data sampling period, preset time block and preset variance calculation formula; The online noise estimation unit 402 is used to perform online noise estimation on the sensor output data based on the forgetting factor, and obtain the instantaneous mean and variance of the sensor output data; The sequential probability ratio test unit 403 is used to perform a sequential probability ratio test based on the instantaneous mean and variance of the output signal to determine the state type of the sensor output data; wherein the state type includes stable, unstable and continue observation.

[0067] Optionally, the forgetting factor calculation unit 401 is used for: The preset data sampling period, preset maximum time block length, total data duration, and number of collection points for each data block are set. The number of groups is obtained based on the preset data sampling period, the total data duration, and the number of collection points for each data block; The minimum time block is determined by using the average value of each data block and the preset variance calculation formula. The forgetting factor is calculated based on the preset forgetting factor calculation formula and the minimum time block.

[0068] Optionally, the sequential probability ratio test unit 403 is also used for: The likelihood ratio is calculated based on the preset likelihood ratio calculation formula and the instantaneous mean and variance of the output signal. Based on the likelihood ratio, determine the state type of the sensor output data.

[0069] Optionally, the sequential probability ratio test unit 403 is also used for: Based on the preset false positive rate and the preset false negative rate, a first judgment threshold and a second judgment threshold are determined; wherein, the first judgment threshold is less than the second judgment threshold; If the likelihood ratio is determined to be no greater than the first judgment threshold, the data is judged to be stable. If the likelihood ratio is determined to be between the first and second judgment thresholds, then the data is determined to be for continued observation. If the likelihood ratio is determined to be no less than the second judgment threshold, then the data is judged to be unstable.

[0070] Optionally, the sensor calibration adaptive steady-state determination device 40 further includes a state decision unit 404, used for: If the data is determined to be stable, the sensor output value at the current calibration point will be automatically recorded, and the control flow will be triggered to jump to the next calibration point, while resetting the decision state. If the data is determined to be unstable, an alert signal is sent to the calibration personnel, the judgment status is reset, and data acquisition at the current calibration point continues. If the data indicates that observation is required, the current decision status will be maintained, and data collection at the current calibration point will continue.

[0071] The sensor calibration adaptive steady-state judgment device 40 can be used to perform... Figure 3 The method performed by the sensor calibration adaptive steady-state determination device in the illustrated embodiment is described above. Therefore, the functions that each functional module of the sensor calibration adaptive steady-state determination device 40 can achieve can be found by referring to [the relevant documentation / reference]. Figure 3 The embodiments shown are described in detail below.

[0072] In some possible implementations, various aspects of the methods provided in this application can also be implemented as a program product comprising program code that, when run on a computer device, causes the computer device to perform the steps of the methods according to the various exemplary embodiments of this application described above. For example, the computer device may perform actions such as... Figure 3 The method performed by the sensor calibration adaptive steady-state judgment device in the illustrated embodiment.

[0073] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks. Alternatively, if the integrated units of this application are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of software products. These computer software products are stored in a storage medium and include 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 methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0074] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0075] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A sensor calibration adaptive steady-state judgment method, characterized in that, The method, applied in a flight test laboratory environment, includes: Calculate the forgetting factor based on the preset data sampling period, preset time block, and preset variance calculation formula; During the sensor calibration process, online noise estimation is performed on the sensor output data based on the forgetting factor to obtain the instantaneous mean and variance of the sensor output data; Based on the instantaneous mean and variance of the sensor output data, a sequential probability ratio test is performed to determine the state type of the sensor output data; wherein, the state type includes stable, unstable, and continue observation.

2. The method as described in claim 1, characterized in that, The step of calculating the forgetting factor based on a preset data sampling period, a preset time block, and a preset variance calculation formula includes: The preset data sampling period, preset maximum time block length, total data duration, and number of collection points for each data block are set. The number of groups is obtained based on the preset data sampling period, the total data duration, and the number of collection points for each data block; The minimum time block is determined by using the average value of each data block and the preset variance calculation formula. The forgetting factor is calculated based on the preset forgetting factor calculation formula and the minimum time block.

3. The method as described in claim 2, characterized in that, The formula for calculating the preset forgetting factor is expressed as follows: in, Forgetting factor; The preset data sampling period; This is the smallest time block.

4. The method as described in claim 1, characterized in that, The step of determining the state type of the sensor output data by performing a sequential probability ratio test based on the instantaneous mean and variance of the output signal includes: The likelihood ratio is calculated based on the preset likelihood ratio calculation formula and the instantaneous mean and variance of the output signal. Based on the likelihood ratio, determine the state type of the sensor output data.

5. The method as described in claim 4, characterized in that, The preset likelihood ratio calculation formula is expressed as follows: in, and are the instantaneous mean and standard deviation of the t-th time block, respectively; This indicates that the data is in a steady state; The sensor output data for the t-th time block; This indicates that the system is unstable; This is the minimum detectable offset coefficient.

6. The method as described in claim 4, characterized in that, The step of determining the state type of the sensor output data based on the likelihood ratio includes: Based on the preset false positive rate and the preset false negative rate, a first judgment threshold and a second judgment threshold are determined; wherein, the first judgment threshold is less than the second judgment threshold; If the likelihood ratio is determined to be no greater than the first judgment threshold, the data is judged to be stable. If the likelihood ratio is determined to be between the first and second judgment thresholds, then the data is determined to be for continued observation. If the likelihood ratio is determined to be no less than the second judgment threshold, then the data is judged to be unstable.

7. The method as described in claim 4, characterized in that, After determining the state type of the sensor output data based on the likelihood ratio, the method further includes: If the data is determined to be stable, the sensor output value at the current calibration point will be automatically recorded, and the control flow will be triggered to jump to the next calibration point, while resetting the decision state. If the data is determined to be unstable, an alert signal is sent to the calibration personnel, the judgment status is reset, and data acquisition at the current calibration point continues. If the data indicates that observation is required, the current decision status will be maintained, and data collection at the current calibration point will continue.

8. A sensor calibration adaptive steady-state judgment device, characterized in that, The device is used in a flight test laboratory environment and includes: The forgetting factor calculation unit is used to calculate the forgetting factor based on the preset data sampling period, preset time block and preset variance calculation formula; An online noise estimation unit is used to perform online noise estimation on the sensor output data according to the forgetting factor during the sensor calibration process, and to obtain the instantaneous mean and variance of the sensor output data. The sequential probability ratio test unit is used to perform a sequential probability ratio test based on the instantaneous mean and variance of the output signal to determine the state type of the sensor output data; wherein, the state type includes stable, unstable and continue observation.

9. An electronic device, characterized in that, The device includes: Memory, used to store program instructions; A processor is configured to invoke program instructions stored in the memory and execute the method described in any one of claims 1-7 according to the obtained program instructions.

10. A storage medium, characterized in that, The storage medium stores computer-executable instructions for causing a computer to perform the method described in any one of claims 1-7.

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