Wire state monitoring IMU (Inertial Measurement Unit) drift correction method and system

Abstract

The invention discloses a wire state monitoring IMU drift correction method and system, and the method comprises the steps: obtaining a target first signal, carrying out the first judgment of the first signal, and enabling the first judgment to comprise a preset first threshold value; a first deviation is calculated according to the first judgment result, second judgment is conducted on the first deviation, and the second judgment comprises a preset second threshold value; and performing drift correction according to the second judgment result. By establishing the first compensation model, the model can be iteratively updated to adapt to the change of the first signal in the form of the correction factor, so that the drifting phenomenon of the IMU is effectively inhibited. Through the method, when the GNSS signal-to-noise ratio is high, IMU data drift can be effectively corrected, and the precision of an IMU measurement value is kept. In addition, the precision of wire state monitoring can be remarkably improved, the service life of the IMU is prolonged, the maintenance cost is reduced, and an efficient and reliable solution is provided for related fields.

Description

Technical Field

[0001] The present invention relates to the technical field of drift correction of an IMU for monitoring a conductor state, and in particular to a drift correction method and system for an IMU for monitoring a conductor state. Background Art

[0002] During long-term operation, the inertial measurement unit (IMU) often drifts due to the accumulation of its own sensor noise and system noise. This drift is difficult to detect in a short period of time, but it will continue to accumulate over time, eventually causing the system's measurement accuracy to drop significantly. In high-precision application scenarios such as wire status monitoring, the drift of IMU data will directly affect the accurate monitoring of wire position, angle, vibration and other status parameters.

[0003] INS (Inertial Navigation System) can maintain high accuracy in a short period of time, but due to the drift of IMU sensors, the system accuracy will inevitably decrease during long-term operation. How to effectively suppress IMU drift during long-term operation has become a key technical challenge for wire condition monitoring systems. Summary of the invention

[0004] In view of the above existing problems, the present invention is proposed.

[0005] Therefore, the present invention provides a method and system for IMU drift correction for wire status monitoring, which can solve the problems mentioned in the background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] In a first aspect, the present invention provides a method for drift correction of a conductor state monitoring IMU, comprising: acquiring a first target signal and performing a first judgment on the first signal, the first judgment including presetting a first threshold; calculating a first deviation based on the first judgment result, and performing a second judgment on the first deviation, the second judgment including presetting a second threshold; performing drift correction based on the second judgment result.

[0008] As a preferred solution of the wire state monitoring IMU drift correction method of the present invention, wherein: the drift correction according to the second judgment result includes:

[0009] Establishing a first compensation model, wherein the first compensation model is iterative and the correction factor is an arbitrary model of the first signal;

[0010] iterating the first compensation model according to the second judgment result;

[0011] Drift correction is performed according to the first compensation model after the first iterative operation is completed.

[0012] As a preferred solution of the wire state monitoring IMU drift correction method of the present invention, wherein: the drift correction according to the second judgment result also includes:

[0013] The second judgment result includes a first iteration instruction and a second stop instruction;

[0014] The first iteration instruction is used to perform the first compensation model iteration;

[0015] The second stop instruction is used to output the first compensation model after completing the first iterative operation.

[0016] As a preferred solution of the wire state monitoring IMU drift correction method of the present invention, wherein: the first deviation includes a first vector, a second vector and a first weight vector;

[0017] The first vector and the second vector are vectors extracted from the first signal;

[0018] The first weight vector is a preset iterable vector, and the first weight vector is used to constrain the first vector.

[0019] As a preferred solution of the wire state monitoring IMU drift correction method described in the present invention, the first compensation model includes: a preset forgetting factor function, and the forgetting factor function is used to control the influence factor of past measurements on the current estimated value.

[0020] As a preferred solution of the wire state monitoring IMU drift correction method of the present invention, wherein: the second judgment on the first deviation includes:

[0021] If the first deviation is not less than the second threshold, generating a first iteration instruction, and sending the first iteration instruction to the first compensation model;

[0022] If the first deviation is less than the second threshold, a second stop instruction is generated and sent to the first compensation model.

[0023] As a preferred solution of the IMU drift correction method for wire state monitoring of the present invention, wherein: the first signal of the target is obtained, and a first judgment is performed on the first signal, and the first judgment includes a preset first threshold value including:

[0024] Acquire a signal-to-noise ratio of the first signal;

[0025] Performing a first judgment on the signal-to-noise ratio;

[0026] If the signal-to-noise ratio is greater than the first threshold, calculating a first deviation according to the first judgment result;

[0027] If the signal-to-noise ratio is not greater than the first threshold, the target first signal is acquired again and the first judgment is performed again.

[0028] In a second aspect, the present invention provides a wire state monitoring IMU drift correction system, comprising:

[0029] A first judgment module, used for acquiring a target first signal and performing a first judgment on the first signal, wherein the first judgment includes presetting a first threshold;

[0030] a second judgment module, configured to calculate a first deviation according to the first judgment result, and perform a second judgment on the first deviation, wherein the second judgment includes presetting a second threshold;

[0031] A correction module is used to perform drift correction according to the second judgment result.

[0032] In a third aspect, the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the method described above when executing the computer program.

[0033] In a fourth aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the method described above when executed by a processor.

[0034] Compared with the prior art, the invention has the following beneficial effects: the invention proposes a drift correction method and system for IMU for wire state monitoring, obtains a first target signal, and performs a first judgment on the first signal, wherein the first judgment includes a preset first threshold value; calculates a first deviation according to the first judgment result, and performs a second judgment on the first deviation, wherein the second judgment includes a preset second threshold value; and performs drift correction according to the second judgment result. By establishing a first compensation model, the model can be iteratively updated to adapt to the change of the first signal in the form of a correction factor, thereby effectively suppressing the drift phenomenon of the IMU. In addition, by means of a preset forgetting factor function, the influence of past measurements on the current estimated value can be controlled to ensure that the model can adapt to the dynamic changes of the signal. When the first deviation exceeds the preset second threshold value, the system automatically performs an iterative operation to correct the drift; and when the first deviation is lower than the second threshold value, the system stops iterating and outputs the corrected model, thereby ensuring the accuracy and efficiency of the correction process. By means of such a method, the accuracy of wire state monitoring can be significantly improved, the service life of the IMU can be extended, and the maintenance cost can be reduced, providing an efficient and reliable solution for the relevant field. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative labor.

[0036] Figure 1 A method flow chart of a wire state monitoring IMU drift correction method and system provided by an embodiment of the present invention;

[0037] Figure 2 A detailed flow chart of a feasible embodiment of a wire state monitoring IMU drift correction method and system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0038] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0039] Example 1

[0040] Reference Figure 1-Figure 2 , which is the first embodiment of the present invention, provides a method and system for IMU drift correction for wire state monitoring, including:

[0041] There are some problems in the existing related technologies. For example, the traditional IMU (inertial measurement unit) is prone to accumulate errors during long-term operation, resulting in a decrease in the accuracy of monitoring data. In addition, due to the influence of environmental factors such as temperature changes and vibrations on the IMU, there is a deviation between the measured value output by it and the actual value. If these deviations are not effectively corrected, they will directly affect the accuracy and reliability of wire status monitoring.

[0042] The present application provides a method that can effectively solve the above-mentioned problems. Next, a plurality of embodiments will be combined to explain in detail how to implement the wire state monitoring IMU drift correction method;

[0043] Figure 1 A method flow chart of a wire state monitoring IMU drift correction method and system is shown, including:

[0044] S101, acquiring a target first signal, and performing a first judgment on the first signal, wherein the first judgment includes presetting a first threshold;

[0045] In an optional embodiment, the target first signal may be a current GNSS signal, such as a GPS signal, a GLONASS signal, a Galileo signal or a BeiDou signal. By receiving these global navigation satellite system (GNSS) signals, high-precision time and position information may be acquired.

[0046] In an optional embodiment, the signals are first detected and decoded to extract the time-synchronized signal data, which are then compared with the IMU measurements to determine if there is a deviation.

[0047] In an optional embodiment, if the detected deviation exceeds a preset first threshold, the system will trigger a correction mechanism to adjust the output of the IMU to reduce the impact of cumulative errors and environmental factors. In this way, the accuracy and reliability of wire status monitoring can be significantly improved.

[0048] In an optional embodiment, the first judgment can be a dynamic threshold judgment based on time series analysis. This method analyzes historical data and establishes a time series model, thereby dynamically adjusting the threshold to adapt to different environments and condition changes. When the detected deviation does not match the normal fluctuation range predicted by the model, the system will automatically adjust the threshold to ensure that the triggering of the correction mechanism is more accurate and timely.

[0049] It should be noted that this method not only improves the adaptability of the system, but also reduces false positives and false negatives caused by environmental changes.

[0050] In an optional embodiment, the first judgment can also be anomaly detection based on a machine learning algorithm. This embodiment uses a machine learning model to learn the relationship between signal characteristics and IMU measurements under normal conditions through training with a large amount of historical data. When real-time data deviates significantly from the normal behavior pattern predicted by the model, the system will identify the abnormal situation and trigger a correction mechanism.

[0051] It should be noted that this method can effectively identify anomalies caused by equipment aging, environmental changes or other unknown factors, thereby further improving the accuracy and robustness of the monitoring system.

[0052] In an optional embodiment, the first judgment may also be

[0053] Anomaly detection based on sensor data fusion technology. This embodiment integrates multiple sensor data, such as accelerometers, gyroscopes, and magnetometers, to comprehensively determine the drift of the IMU. Through the data fusion algorithm, the system can understand the device status more comprehensively, so that when the IMU data deviates, it can be cross-validated with other sensor information to improve the accuracy and reliability of anomaly detection.

[0054] It should be noted that this method is particularly suitable for monitoring in complex environments and can effectively reduce the risk of single sensor failure or misjudgment.

[0055] In an embodiment of the present application, a target first signal is acquired, and a first judgment is performed on the first signal. The first judgment includes a preset first threshold value including:

[0056] Acquire a signal-to-noise ratio of the first signal;

[0057] Make a first judgment on the signal-to-noise ratio;

[0058] If the signal-to-noise ratio is greater than the first threshold, calculating a first deviation according to the first judgment result;

[0059] If the signal-to-noise ratio is not greater than the first threshold, the target first signal is acquired again and the first judgment is performed again.

[0060] For example, the signal-to-noise ratio (SNR) of the current GNSS signal is detected. GNSS If SNR GNSS Greater than the set signal-to-noise ratio threshold SNR st , then the deviation e(t) between the estimated value after IMU drift compensation and the GNSS measurement vector is calculated based on the IMU measurement vector x(t) and GNSS measurement vector y(t) at the current moment;

[0061] It should be noted that the first target signal is obtained and the first signal is judged. The first judgment includes presetting the first threshold to ensure that the signal quality is within an acceptable range, thereby improving the stability and accuracy of the entire system. By setting an appropriate signal-to-noise ratio threshold, signals with large noise can be effectively filtered out, reducing the possibility of misjudgment. In addition, when the deviation threshold is set properly, it can ensure that the deviation between the estimated value after IMU drift compensation and the GNSS measurement vector remains within a reasonable range, thereby improving the accuracy of the estimated value. Such a design enables the system to provide reliable monitoring results when facing complex and changing environments.

[0062] S102, calculating a first deviation according to the first judgment result, and performing a second judgment on the first deviation, wherein the second judgment includes presetting a second threshold;

[0063] In an optional embodiment, the second judgment can be to dynamically adjust the deviation threshold based on the historical data of the deviation and the current deviation value. By analyzing the historical trend of the deviation, the system can predict the future trend of the deviation and adjust the threshold accordingly to adapt to environmental changes and fluctuations in equipment performance. For example, if the historical data shows that the deviation value has a trend of gradually increasing, the system can appropriately lower the threshold to identify and compensate for potential drift problems in advance.

[0064] In an optional embodiment, on the contrary, if the deviation value tends to be stable or decreases, the system can appropriately increase the threshold to reduce unnecessary compensation actions, thereby optimizing the overall performance. This adaptive threshold adjustment mechanism can ensure that the system can maintain high accuracy and high reliability under different working conditions.

[0065] In an optional embodiment, the second judgment may also be based on real-time data of environmental factors, such as temperature, humidity, and vibration, to dynamically adjust the deviation threshold.

[0066] It should be noted that if this method is used, the system can monitor these environmental parameters in real time and adjust the thresholds according to their changes to ensure accurate monitoring results under various environmental conditions. For example, in the case of increased temperature or increased vibration, the system may lower the threshold to prevent measurement errors caused by environmental factors. In this way, the system can adapt to different working environments more flexibly and improve the accuracy and reliability of monitoring.

[0067] In the embodiment of the present application, the first deviation includes being obtained by the first vector, the second vector and the first weight vector;

[0068] The first vector and the second vector are vectors extracted from the first signal;

[0069] The first weight vector is a preset iterable vector, and the first weight vector is used to constrain the first vector.

[0070] For example, the signal-to-noise ratio (SNR) of the current GNSS signal is detected. GNSS If SNR GNSS Greater than the set signal-to-noise ratio threshold SNR st , then the deviation e(t) between the estimated value after IMU drift compensation and the GNSS measurement vector is calculated according to the IMU measurement vector x(t) and the GNSS measurement vector y(t) at the current moment, e(t) = y(t) - x(t) * θ(t-1), where t is the time step;

[0071] Furthermore, if e(t) is less than the set deviation threshold γ, the estimated value x after IMU drift compensation is calculated based on the updated dynamic estimation weight vector θ(t) of the IMU drift. * (t+1), x * (t+1)=x(t+1)*θ(t).

[0072] In the embodiment of the present application, performing a second judgment on the first deviation includes:

[0073] If the first deviation is not less than the second threshold, generating a first iteration instruction, and sending the first iteration instruction to the first compensation model;

[0074] If the first deviation is less than the second threshold, a second stop instruction is generated and sent to the first compensation model.

[0075] In an optional embodiment, the first compensation model performs iterative calculation according to the received first iterative instruction to update the first weight vector, and repeats the above process until the first deviation is less than the second threshold. The first compensation model stops the iterative calculation according to the received second stop instruction, and outputs the final IMU drift compensated estimate. The second threshold is a preset deviation range used to determine when to stop the iterative process to ensure the accuracy and stability of the estimate.

[0076] It should be noted that the first deviation is calculated based on the first judgment result, and the second judgment is performed on the first deviation. The second judgment includes presetting the second threshold value to ensure that in the IMU drift compensation process, when the deviation of the estimated value is within an acceptable range, the iteration is stopped in time, thereby avoiding unnecessary waste of computing resources. At the same time, by setting the second threshold value, the balance between the accuracy of the estimated value and the computing efficiency can be flexibly controlled. In practical applications, the setting of the second threshold value needs to be determined according to the accuracy requirements and real-time requirements of the specific application scenario to achieve the optimal system performance.

[0077] S103, performing drift correction according to the second judgment result.

[0078] In the embodiment of the present application, performing drift correction according to the second judgment result includes:

[0079] Establishing a first compensation model, the first compensation model is iterative, and the correction factor is an arbitrary model of the first signal;

[0080] iterating the first compensation model according to the second judgment result;

[0081] Drift correction is performed according to the first compensation model after the first iterative operation is completed.

[0082] In an optional embodiment, the first compensation model can be a model based on a Kalman filter, which can effectively combine the measurement data of the IMU and the external sensor information to improve the accuracy of the estimation. The Kalman filter is a recursive filter that estimates the state of the system through two steps of prediction and update.

[0083] In another optional embodiment, in the prediction step, the state at the current moment is predicted based on the dynamic model of the system and the state estimate at the previous moment. In the update step, the predicted value is corrected using new measurement data to obtain a more accurate state estimate. In this way, the Kalman filter can handle noise and uncertainty and provide continuous and smooth estimation results. In addition, the model can also be adjusted according to actual application requirements to adapt to different dynamic environments and measurement conditions.

[0084] In an optional embodiment, the first compensation model can also be a model based on a particle filter. A particle filter is a recursive Bayesian filtering technique based on the Monte Carlo method, which represents the probability distribution through a set of random samples (particles) and approximates the posterior probability density function through resampling and weight updating. This model is particularly suitable for state estimation problems in nonlinear and non-Gaussian noise environments.

[0085] In another optional embodiment, in IMU drift correction, the particle filter is able to handle complex dynamic systems and can adapt to various nonlinear models. Through the particle filter, IMU data and other sensor information, such as GPS or visual sensor data, can be effectively fused to improve the monitoring accuracy of the wire state. In addition, the flexibility of the particle filter allows it to be customized in different application scenarios to meet specific accuracy and real-time requirements.

[0086] In the embodiment of the present application, the first compensation model includes: a preset forgetting factor function, where the forgetting factor function is used to control the influence factor of past measurements on the current estimated value.

[0087] In the embodiment of the present application, performing drift correction according to the second judgment result further includes:

[0088] The second judgment result includes a first iteration instruction and a second stop instruction;

[0089] The first iteration instruction is used to perform a first compensation model iteration;

[0090] The second stop instruction is used to output the first compensation model after completing the first iterative operation.

[0091] Exemplarily, the covariance matrix P(t) of the dynamic estimation weight vector of the IMU drift is updated using the update rule in the recursive least squares as the first compensation model:

[0092]

[0093] Where λ(t) represents the forgetting factor function, which is used to control the influence of past measurements on the current IMU estimation;

[0094] Among them, the calculation method of λ(t) is as follows:

[0095]

[0096]

[0097] Here, N represents the total number of time steps.

[0098] In another embodiment, The calculation method is,

[0099]

[0100] Where W represents the sliding window width and N represents the total number of time steps.

[0101] in, The calculation method is,

[0102]

[0103] Where Q represents the quantization noise coefficient, N represents the angle random walk coefficient, K represents the angular rate random walk coefficient, B represents the zero-article instability coefficient, R represents the rate slope coefficient, and R 0 is the baseline measurement noise covariance.

[0104] In summary, the present invention proposes a drift correction method for IMU for wire state monitoring, which obtains a first target signal, and performs a first judgment on the first signal, wherein the first judgment includes a preset first threshold value; calculates a first deviation according to the first judgment result, and performs a second judgment on the first deviation, wherein the second judgment includes a preset second threshold value; and performs drift correction according to the second judgment result. By establishing a first compensation model, the model can be iteratively updated to adapt to changes in the first signal in the form of a correction factor, thereby effectively suppressing the drift phenomenon of the IMU. In addition, by using a preset forgetting factor function, the influence of past measurements on the current estimated value can be controlled to ensure that the model can adapt to the dynamic changes of the signal. When the first deviation exceeds the preset second threshold value, the system automatically performs an iterative operation to correct the drift; and when the first deviation is lower than the second threshold value, the system stops iterating and outputs the corrected model, thereby ensuring the accuracy and efficiency of the correction process. By using such a method, the drift of IMU data can be effectively corrected when the GNSS signal-to-noise ratio is high, and the accuracy of the IMU measurement value can be maintained. The accuracy of wire state monitoring can be significantly improved, the service life of the IMU can be extended, and the maintenance cost can be reduced, thereby providing an efficient and reliable solution for the relevant field.

[0105] Example 2

[0106] In a preferred embodiment, a wire state monitoring IMU drift correction method is designed according to the above embodiment with the following steps:

[0107] Step 101, initialize the dynamic estimation weight vector θ(0) of IMU drift and the covariance matrix P(0) of the dynamic estimation weight vector of IMU drift; the dynamic estimation weight vector θ(0) of IMU drift is used to describe the current estimate of IMU drift. It can be initialized to an all-zero vector or an estimate based on historical data. The covariance matrix P(0) of the dynamic estimation weight vector of IMU drift is usually initialized to a large diagonal matrix to indicate that the estimation uncertainty of drift is high at the initial moment. The size of the elements on the diagonal can be set through experience or system prior knowledge to ensure the convergence speed and accuracy of the drift estimation.

[0108] Step 102-Step 104: Detect the signal-to-noise ratio (SNR) of the current GNSS signal GNSS , SNR GNSS Usually provided by the GNSS receiver, it represents the ratio of the strength of the currently received GNSS signal to the noise. If the value is high, it means that the GNSS signal is more reliable and can be used for IMU drift correction. GNSS Greater than the set signal-to-noise ratio threshold SNR st , then the deviation e(t) between the estimated value after IMU drift compensation and the GNSS measurement vector is calculated according to the IMU measurement vector x(t) and GNSS measurement vector y(t) at the current moment, e(t) = y(t) - x(t) * θ(t-1), where t is the time step; the IMU measurement vector contains the output of the IMU sensor at time t, usually including data from the accelerometer and gyroscope.

[0109] Step 105-Step 106, if e(t) is less than the set deviation threshold γ, jump to step 106, otherwise jump to step S04;

[0110] Step 107: Update the covariance matrix P(t) of the dynamic estimation weight vector of the IMU drift using the update rule in recursive least squares:

[0111]

[0112] Where λ(t) represents the forgetting factor function, which is used to control the influence of past measurements on the current IMU estimation;

[0113] Step 108: Update the dynamic estimation weight vector θ(t) of the current IMU drift according to the error and covariance matrix, θ(t)=θ(t-1)+P(t)x(t)e(t), and jump to step S02. This ensures that at each time step, the drift estimation can quickly respond to changes in the measured data and gradually approach the true value.

[0114] Step 108: Calculate the estimated value x after IMU drift compensation based on the updated dynamic estimation weight vector θ(t) of the IMU drift. * (t+1), x * (t+1)=x(t+1)*θ(t). This step is used to predict the estimated value of the IMU drift compensation at the next moment, ensuring that the system can correct the drift in advance and give a more accurate state estimate.

[0115] In this embodiment, λ(t) is further determined by the ratio of the system noise variance to the measurement noise variance. The calculation method of λ(t) is as follows:

[0116]

[0117] in, Represents the system noise variance of the IMU, reflecting the drift noise of the system itself due to internal physical characteristics or model inaccuracy; It represents the measurement noise variance of the IMU, reflecting the noise introduced by factors such as the measurement accuracy limitation of the sensor and external interference. By calculating λ(t) in this way, when the system noise is small (that is, the system is more stable), λ(t) will be closer to 1, indicating that it is more dependent on the current measurement value. When the measurement noise is large, λ(t) tends to decrease, indicating that the algorithm will rely more on historical data to reduce the impact of instantaneous noise.

[0118] In one embodiment, The calculation method is,

[0119]

[0120] Here, N represents the total number of time steps.

[0121] This embodiment takes all historical data points into account. 's calculation.

[0122] In another embodiment, The calculation method is,

[0123]

[0124] Where W represents the sliding window width and N represents the total number of time steps.

[0125] In this embodiment, at each time step, the IMU measurement value x(t) and the drift-corrected estimated value x are recorded. *(t), and store the error in an array or queue of finite length W. Whenever new data arrives, the new error is added to the sliding window and the earliest data in the window is removed. This ensures that the window always maintains a fixed size and only contains the errors of the latest W time steps. Based on the error values ​​stored in the sliding window, the variance formula is used to calculate the system noise variance at the current moment. The size of the sliding window W has a direct impact on the flexibility and accuracy of the noise estimation. Choosing a smaller W will make the system more sensitive to recent error changes, but may be more affected by instantaneous noise; choosing a larger W will make the variance estimate smoother, but may delay the system's response to drift. Therefore, choosing an appropriate sliding window size should be weighed according to the dynamic characteristics, computing resources, and response speed requirements of the specific application. The purpose of using a sliding window to calculate the system noise variance is to dynamically evaluate the effect of IMU drift correction through data from a recent period of time, avoid the impact of instantaneous errors at a single moment on the system noise estimate, and ensure that the system can adjust the estimation results in a timely manner when facing long-term accumulated drift. The flexibility of the sliding window makes this method suitable for handling drift correction tasks in high-frequency dynamic environments.

[0126] In order to accurately obtain the system variance of the IMU, in this embodiment, further, The calculation method is,

[0127]

[0128] Where Q represents the quantization noise coefficient, N represents the angle random walk coefficient, K represents the angular rate random walk coefficient, B represents the zero-article instability coefficient, R represents the rate slope coefficient, and R 0 is the baseline measurement noise covariance.

[0129] Example 3

[0130] This embodiment also provides a wire state monitoring IMU drift correction system, including:

[0131] A first judgment module, used for acquiring a target first signal and performing a first judgment on the first signal, wherein the first judgment includes presetting a first threshold;

[0132] A second judgment module, used for calculating the first deviation according to the first judgment result, and performing a second judgment on the first deviation, wherein the second judgment includes presetting a second threshold;

[0133] The correction module is used to perform drift correction according to the second judgment result.

[0134] The above-mentioned unit modules may be embedded in or independent of a processor in a computer device in the form of hardware, or may be stored in a memory in a computer device in the form of software, so that the processor can call and execute operations corresponding to the above-mentioned modules.

[0135] This embodiment also provides an electronic device, which includes a processor, a memory, a communication interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a busbar pressure loss load transfer analysis method is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse, etc.

[0136] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method proposed in the above embodiment is implemented.

[0137] The storage medium proposed in this embodiment and the method proposed in the above embodiment belong to the same inventive concept. The technical details not fully described in this embodiment can be referred to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0138] Through the above description of the implementation methods, the technicians in the relevant field can clearly understand that the present invention can be implemented by means of software and necessary general hardware, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ReadOnly, Memory, ROM), random access memory (RandomAccess Memory, RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform the methods of various embodiments of the present invention.

[0139] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0140] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the present application can adopt the form of complete hardware embodiments, complete software embodiments, or embodiments in combination with software and hardware. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiments of the present application can be implemented in various computer languages, for example, object-oriented programming language Java and literal scripting language JavaScript, etc.

[0141] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0142] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0143] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1The steps for the functions specified in one or more boxes.

[0144] Although the preferred embodiments of the present application have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0145] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for correcting IMU drift for wire state monitoring, characterized in that: include: Acquire a target first signal, and perform a first judgment on the first signal, wherein the first judgment includes presetting a first threshold; Calculating a first deviation according to the first judgment result, and performing a second judgment on the first deviation, wherein the second judgment includes presetting a second threshold; Drift correction is performed according to the second judgment result.

2. The method for correcting the drift of an IMU for monitoring the conductor state according to claim 1, characterized in that: The performing drift correction according to the second judgment result comprises: Establishing a first compensation model, wherein the first compensation model is iterative and the correction factor is an arbitrary model of the first signal; iterating the first compensation model according to the second judgment result; Drift correction is performed according to the first compensation model after the first iterative operation is completed.

3. The IMU drift correction method for wire state monitoring according to claim 2, characterized in that: The performing drift correction according to the second judgment result further includes: The second judgment result includes a first iteration instruction and a second stop instruction; The first iteration instruction is used to perform the first compensation model iteration; The second stop instruction is used to output the first compensation model after completing the first iterative operation.

4. The method for correcting the drift of an IMU for monitoring the conductor state according to claim 3, characterized in that: The first deviation is obtained by a first vector, a second vector and a first weight vector; The first vector and the second vector are vectors extracted from the first signal; The first weight vector is a preset iterable vector, and the first weight vector is used to constrain the first vector.

5. The IMU drift correction method for wire state monitoring as claimed in claim 4, characterized in that: The first compensation model includes: a preset forgetting factor function, wherein the forgetting factor function is used to control the influence factor of past measurements on the current estimated value.

6. The method for correcting the drift of an IMU for monitoring the conductor state according to claim 5, characterized in that: The performing a second judgment on the first deviation comprises: If the first deviation is not less than the second threshold, generating a first iteration instruction, and sending the first iteration instruction to the first compensation model; If the first deviation is less than the second threshold, a second stop instruction is generated and sent to the first compensation model.

7. The method for correcting the drift of an IMU for monitoring the conductor state according to claim 6, characterized in that: The acquiring of the target first signal and performing a first judgment on the first signal, wherein the first judgment includes presetting a first threshold value, includes: Acquire a signal-to-noise ratio of the first signal; Performing a first judgment on the signal-to-noise ratio; If the signal-to-noise ratio is greater than the first threshold, calculating a first deviation according to the first judgment result; If the signal-to-noise ratio is not greater than the first threshold, the target first signal is acquired again and the first judgment is performed again.

8. A wire state monitoring IMU drift correction system, characterized in that: include: A first judgment module, used for acquiring a target first signal and performing a first judgment on the first signal, wherein the first judgment includes presetting a first threshold; a second judgment module, configured to calculate a first deviation according to the first judgment result, and perform a second judgment on the first deviation, wherein the second judgment includes presetting a second threshold; A correction module is used to perform drift correction according to the second judgment result.

9. An electronic device, comprising: Memory and processor; The memory stores a computer program, and when the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.

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