Wire state monitoring method and system based on adaptive Kalman filtering

Abstract

The invention discloses a wire state monitoring method and system based on adaptive Kalman filtering. The method comprises the following steps: acquiring first parameter information, and performing first prediction according to the first parameter information; acquiring second parameter information, and calculating a first target coefficient according to the second parameter information; and updating the result of the first prediction according to the first target coefficient, and monitoring the wire state according to the updated result. By adopting the adaptive Kalman filtering technology, the method can effectively combine the data of the GNSS sensor and the IMU sensor so as to improve the precision and reliability of wire state monitoring. In a dynamically changing environment, a GNSS signal may be interfered, and an IMU sensor may generate a drift error. According to the invention, the filtering parameters can be dynamically adjusted through the adaptive Kalman filtering algorithm, so that accurate lead state estimation can be provided in different environments.

Description

Technical Field

[0001] The present invention relates to the technical field of wire state monitoring, and in particular to a wire state monitoring method and system based on adaptive Kalman filtering. Background Art

[0002] The location information provided by GNSS (Global Navigation Satellite System) usually has high accuracy in static environments. However, in highly dynamic environments such as line sway monitoring, GNSS signals may be affected by factors such as multipath effects and ionospheric refraction, resulting in reduced accuracy.

[0003] At the same time, IMU sensors can provide high dynamic response in a short period of time, but will accumulate drift errors over time. This means that in a high-dynamic environment such as line sway monitoring, since GNSS signals are susceptible to interference and IMUs have sensor drift, line sway monitoring will accumulate errors in long-term monitoring, resulting in reduced positioning accuracy. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] Therefore, the present invention provides a conductor state monitoring method and system based on adaptive Kalman filtering, which can solve the problems mentioned in the background technology.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a conductor state monitoring method based on adaptive Kalman filtering, comprising:

[0009] Acquire first parameter information, and perform a first prediction based on the first parameter information;

[0010] Acquire second parameter information, and calculate a first target coefficient according to the second parameter information;

[0011] The first prediction result is updated according to the first target coefficient, and the conductor state monitoring is performed according to the updated result.

[0012] As a preferred solution of the conductor state monitoring method based on adaptive Kalman filtering described in the present invention, wherein: the obtaining of the second parameter information and calculating the first target coefficient according to the second parameter information includes:

[0013] Acquire second parameter information, and calculate a first signal-to-noise ratio according to the second parameter information;

[0014] Update the first matrix and the second matrix according to the first signal-to-noise ratio;

[0015] The first target coefficient is calculated according to the updated first matrix and the second matrix.

[0016] As a preferred solution of the conductor state monitoring method based on adaptive Kalman filtering described in the present invention, the step of obtaining the first parameter information and performing the first prediction according to the first parameter information includes:

[0017] Establishing a first state prediction model;

[0018] Using the first parameter information as input of the first state prediction model;

[0019] The output of the first state prediction model includes at least a position state, a velocity state and an acceleration state.

[0020] As a preferred solution of the conductor state monitoring method based on adaptive Kalman filtering described in the present invention, wherein: the first state prediction model includes:

[0021] The first state prediction model is an arbitrary model whose input is the first parameter information and whose output is at least a position state, a velocity state and an acceleration state or which can directly or indirectly obtain relevant parameters of the position state, the velocity state and the acceleration state.

[0022] As a preferred solution of the wire state monitoring method based on adaptive Kalman filtering described in the present invention, wherein: updating the first matrix and the second matrix according to the first signal-to-noise ratio includes:

[0023] At least one of the first matrix and the second matrix is ​​represented by the first signal-to-noise ratio;

[0024] updating a first matrix or a second matrix represented by the first signal-to-noise ratio according to the first signal-to-noise ratio;

[0025] The first target coefficient is calculated according to the updated first matrix and the second matrix.

[0026] As a preferred solution of the conductor state monitoring method based on adaptive Kalman filtering described in the present invention, the obtaining of the first parameter information and the obtaining of the second parameter information are both parameter information in the same fixed time step.

[0027] As a preferred solution of the conductor state monitoring method based on adaptive Kalman filtering described in the present invention, the first matrix and the second matrix include:

[0028] The first matrix is ​​a measurement noise covariance matrix;

[0029] The second matrix is ​​a process noise covariance matrix.

[0030] In a second aspect, the present invention provides a conductor state monitoring system based on adaptive Kalman filtering, comprising:

[0031] A prediction module, used to obtain first parameter information and perform a first prediction based on the first parameter information;

[0032] A coefficient determination module, used to obtain second parameter information and calculate a first target coefficient according to the second parameter information;

[0033] A monitoring module is used to update the first prediction result according to the first target coefficient, and perform wire status monitoring according to the updated result.

[0034] In a third aspect, the present invention provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above-mentioned method when executing the computer program.

[0035] 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.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention proposes a wire state monitoring method and system based on adaptive Kalman filtering, which obtains first parameter information and performs a first prediction based on the first parameter information; obtains second parameter information and calculates a first target coefficient based on the second parameter information; updates the result of the first prediction based on the first target coefficient, and performs wire state monitoring based on the updated result. By adopting adaptive Kalman filtering technology, the present invention can effectively combine the data of GNSS and IMU sensors to improve the accuracy and reliability of wire state monitoring. In a dynamically changing environment, GNSS signals may be interfered, and IMU sensors may produce drift errors. The present invention can dynamically adjust the filtering parameters through an adaptive Kalman filtering algorithm, thereby providing accurate wire state estimation in different environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0038] Figure 1 A method flow chart of a conductor state monitoring method and system based on adaptive Kalman filtering is provided in one embodiment of the present invention. DETAILED DESCRIPTION

[0039] 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.

[0040] Example 1

[0041] Reference Figure 1 , which is the first embodiment of the present invention, and provides a conductor state monitoring method and system based on adaptive Kalman filtering, comprising:

[0042] There are some problems in the existing related technologies. For example, traditional wire status monitoring methods often rely on fixed filtering algorithms, which may lead to inaccurate monitoring results when facing complex and changeable environmental noise. In addition, these methods usually lack sufficient adaptive capabilities and cannot adjust the filtering parameters in real time to adapt to environmental changes, thus affecting the stability and reliability of the monitoring system.

[0043] The present application provides a method that can effectively solve the above-mentioned problems. Next, multiple embodiments will be combined to explain in detail how to implement the wire state monitoring method based on adaptive Kalman filtering;

[0044] Figure 1 A method flow chart of a conductor state monitoring method and system based on adaptive Kalman filtering is shown, including:

[0045] S101, obtaining first parameter information, and performing a first prediction according to the first parameter information;

[0046] In an optional embodiment, the first parameter information may be based on the acceleration and angular velocity information provided by the IMU. The acceleration and angular velocity data obtained by the IMU (Inertial Measurement Unit) can more accurately reflect the dynamic changes of the wire in space. The high-precision sensor of the IMU can capture the tiny movements of the wire in real time, thereby providing a more accurate initial state estimation for the adaptive Kalman filter algorithm.

[0047] In another optional embodiment, GPS data can be combined to further improve the accuracy and reliability of monitoring. GPS data can provide absolute position information of the wire, and combined with IMU data, comprehensive monitoring of the wire status can be achieved.

[0048] It should be noted that through this method of fusing information from different sensors, the system can more effectively filter out noise interference and ensure the accuracy of the monitoring results.

[0049] In the embodiment of the present application, the obtaining of first parameter information and performing a first prediction according to the first parameter information includes:

[0050] Establishing a first state prediction model;

[0051] Using the first parameter information as input of the first state prediction model;

[0052] The output of the first state prediction model includes at least a position state, a velocity state and an acceleration state.

[0053] In an embodiment of the present application, the first state prediction model includes:

[0054] The first state prediction model is an arbitrary model whose input is the first parameter information and whose output is at least a position state, a velocity state and an acceleration state or which can directly or indirectly obtain relevant parameters of the position state, the velocity state and the acceleration state.

[0055] In an optional embodiment, the first state prediction model may use an extended Kalman filter algorithm to adapt to the state estimation of a nonlinear system. The extended Kalman filter algorithm can handle more complex dynamic systems by linearizing nonlinear functions.

[0056] In an optional embodiment, the first state prediction model uses the data provided by the IMU sensor in combination with the GPS data to model the dynamic behavior of the wire. In this way, the model can predict the position, velocity and acceleration state of the wire under different conditions, thereby providing more accurate prediction results for real-time monitoring of the wire.

[0057] In an optional embodiment, the model can further improve the accuracy and robustness of the prediction by introducing external environmental factors, such as wind speed, temperature, etc.

[0058] In an optional embodiment, the first state prediction model may also integrate a machine learning algorithm to further optimize the prediction performance. Through the training data set, the machine learning algorithm can recognize and learn the complex patterns of the conductor's dynamic behavior, thereby providing accurate state estimation without a clear physical model.

[0059] In an optional embodiment, the first state prediction model in this embodiment can realize adaptive adjustment, dynamically update model parameters according to real-time data, so as to adapt to the impact of environmental changes and system aging. This adaptive capability enables the model to maintain high accuracy and reliability in long-term operation, providing strong technical support for continuous monitoring and maintenance of wires.

[0060] In the embodiment of the present application, the first parameter information is obtained, and the implementation steps of performing the first prediction according to the first parameter information are based on the acceleration and angular velocity information provided by the IMU, and the next state of the system is predicted by the state transfer equation, and the predicted state includes position, velocity and acceleration, wherein the state prediction model uses the state transfer equation to realize the prediction, and the specific steps are as follows:

[0061] Based on the acceleration and angular velocity information provided by the IMU, the next state of the system is predicted through the state transfer equation. The predicted state includes position, velocity and acceleration. The method is as follows:

[0062]

[0063] P k|k-1 =AP k-1|k-1 A T +Q k

[0064] in, The predicted state vector for time step k; represents the state vector at time step k-1; A represents the state transfer matrix, which is calculated based on IMU data; P k|k-1 represents the predicted state covariance matrix at time step k; P k-1|k-1 represents the state covariance matrix of time step k-1; B represents the control input matrix; u k-1 represents the control input provided by the IMU at time step k-1; Q k represents the process noise covariance matrix at time step k.

[0065] The process noise covariance matrix Q k The optimal value of is determined as follows:

[0066] 1) Determine the noise parameters using the Allan variance method and input the noise parameters into the coupling algorithm as the default configuration parameters.

[0067] 2) Destroy the equilibrium state, measure the sag deviation of the same conductor multiple times, make a difference comparison, and calculate the STD value of the difference sequence;

[0068] 3) Within the range determined by the Allan variance, traverse the angle random walk and rate random walk parameters, recalculate the deviation and record the STD of the corresponding difference;

[0069] 4) Repeat steps 2) and 3) until the minimum STD value is found and the corresponding process noise parameter is recorded and fixed;

[0070] 5) Adjust the next process parameter until the inertial navigation process noise configuration that minimizes the STD value is found.

[0071] It should be noted that obtaining the first parameter information and performing the first prediction based on the first parameter information can improve the accuracy of state estimation. Through the adaptive Kalman filter algorithm, the state transfer matrix and the process noise covariance matrix can be adjusted in real time to compensate for the uncertainty of the system model. This enables the algorithm to adapt to environmental changes and system dynamics, ensuring that stable and reliable monitoring results can be provided under various working conditions. In addition, the method can also reduce the measurement noise caused by sensor errors or external interference, and further improve the quality of monitoring data.

[0072] S102, obtaining second parameter information, and calculating a first target coefficient according to the second parameter information;

[0073] In an optional embodiment, the second parameter information may use a GNSS signal. The second parameter information obtained through the GNSS signal may further improve the positioning accuracy of the conductor position.

[0074] It should be noted that the data provided by GNSS (Global Navigation Satellite System) can help the system determine the spatial position of the wire more accurately, thereby achieving more precise dynamic tracking during the monitoring process. Combined with the adaptive Kalman filter algorithm, the system can correct the error of the GNSS signal in real time to ensure the continuity and accuracy of wire status monitoring.

[0075] In an optional embodiment, the calculation of the first target coefficient according to the second parameter information can be completed by a preset mathematical model, which can calculate the coefficients related to the wire state according to the parameter information provided by the GNSS signal, such as position, speed and time, etc. These coefficients reflect the physical properties of the wire under specific environment and working conditions, such as tension, temperature and vibration frequency, etc.

[0076] It should be noted that by updating these coefficients in real time, the system can more accurately predict and monitor the dynamic changes of the conductor, thereby providing strong data support for maintenance and management.

[0077] In an optional embodiment, the calculation of the first target coefficient according to the second parameter information can also be achieved by integrating a machine learning algorithm. The algorithm is capable of processing and analyzing a large amount of historical data and identifying patterns and trends of conductor state changes. In this way, the system can learn the typical behavior of the conductor under different environmental and load conditions, and predict future state changes accordingly.

[0078] In another optional embodiment, the machine learning algorithm can also identify and filter abnormal data of GNSS signals, reduce the impact of noise on monitoring results, and further improve the accuracy of monitoring. Through this method of combining traditional mathematical models and advanced machine learning technology, the conductor status monitoring system can provide more comprehensive and accurate monitoring data, providing guarantee for the safe operation of the power system.

[0079] In the embodiment of the present application, the obtaining of the second parameter information and calculating the first target coefficient according to the second parameter information includes:

[0080] Acquire second parameter information, and calculate a first signal-to-noise ratio according to the second parameter information;

[0081] Update the first matrix and the second matrix according to the first signal-to-noise ratio;

[0082] The first target coefficient is calculated according to the updated first matrix and the second matrix.

[0083] In an embodiment of the present application, the first target coefficient is the Kalman gain, which is the core parameter in the adaptive Kalman filter algorithm, and determines the weight distribution between the new observation data and the predicted data during the filtering process. By dynamically adjusting the Kalman gain, the system can optimize the filtering performance according to the statistical characteristics of the current measurement noise and process noise, thereby improving the accuracy and reliability of the wire state monitoring. In practical applications, the calculation of the Kalman gain needs to consider a variety of factors, including but not limited to measurement errors, model errors, and system dynamic characteristics. By accurately calculating the Kalman gain, it can be ensured that the wire state monitoring system can provide stable and accurate monitoring results in various complex environments.

[0084] In the embodiment of the present application, updating the first matrix and the second matrix according to the first signal-to-noise ratio includes:

[0085] At least one of the first matrix and the second matrix is ​​represented by the first signal-to-noise ratio;

[0086] updating a first matrix or a second matrix represented by the first signal-to-noise ratio according to the first signal-to-noise ratio;

[0087] The first target coefficient is calculated according to the updated first matrix and the second matrix.

[0088] In the embodiment of the present application, the first matrix and the second matrix include:

[0089] The first matrix is ​​a measurement noise covariance matrix;

[0090] The second matrix is ​​a process noise covariance matrix.

[0091] Exemplarily, obtaining the second parameter information and calculating the first target coefficient according to the second parameter information is to dynamically adjust the measurement noise covariance matrix and the process noise covariance matrix according to the signal-to-noise ratio of the GNSS signal, and calculate the Kalman gain according to the updated covariance matrix. The specific steps are as follows:

[0092] Dynamically adjust the measurement noise covariance matrix R according to the signal-to-noise ratio of the GNSS signal k and the process noise covariance matrix Q k The method is:

[0093]

[0094] 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 t k is the cumulative running time of IMU, R0 is the baseline measurement noise covariance, γ is the adaptive adjustment factor, SNR k is the GNSS signal-to-noise ratio at time step k, and I represents the identity matrix.

[0095] In an optional embodiment, the Kalman gain K at time step k is calculated based on the updated covariance matrix k The method is:

[0096] K k =P k|k-1 H T (HP k-1 H T +R k ) -1

[0097] Where H is the observation matrix, which describes the relationship between GNSS measurements and system status.

[0098] In the embodiment of the present application, the obtaining of the first parameter information and the obtaining of the second parameter information are both parameter information in the same fixed time step.

[0099] In an optional embodiment, the first parameter information includes output data of an IMU, and the second parameter information includes measurement data of a GNSS. The IMU output data provides continuous information about the motion state of the wire, while the GNSS measurement data provides precise information about the position of the wire. By combining these two data sources, the state of the wire can be estimated more accurately, thereby achieving real-time monitoring and analysis of the wire state.

[0100] In an optional embodiment, an adaptive Kalman filter algorithm is used to fuse IMU and GNSS data. The algorithm can dynamically adjust the filter parameters according to the noise characteristics of the measurement data, thereby improving the accuracy and robustness of the state estimation. The adaptive adjustment factor γ is used to control the response speed of the filter to noise changes, ensuring that stable and reliable monitoring results can be obtained under different environments and conditions.

[0101] It should be noted that at each time step k, the covariance matrix is ​​updated according to the IMU cumulative running time and the baseline measurement noise covariance. This step ensures that the filter can adapt to the time-varying characteristics of IMU and GNSS data, further improving the accuracy and reliability of monitoring. In this way, the status of the wire under various environmental conditions can be effectively monitored and predicted, providing a scientific basis for the maintenance and management of the wire.

[0102] It should be noted that obtaining the second parameter information and calculating the first target coefficient based on the second parameter information can more accurately reflect the dynamic changes of the conductor in actual operation. By adjusting the first target coefficient in real time, the system can respond quickly to the slight displacement and vibration of the conductor, thereby issuing an alarm in time when the conductor is in an abnormal state. In addition, this method can also reduce interference caused by environmental noise and equipment errors, ensuring the accuracy and reliability of monitoring data. Ultimately, this helps to extend the service life of the conductor, reduce maintenance costs, and improve the operating efficiency of the entire power system.

[0103] S103: Update the first prediction result according to the first target coefficient, and perform conductor status monitoring according to the updated result.

[0104] In an embodiment of the present application, the method for updating the system state estimation using GNSS measurement data is:

[0105] To make a status update:

[0106]

[0107] Update the covariance matrix:

[0108] P k|k =(IK kH)P k|k-1

[0109] in, represents the updated state estimate after time step k, P k|k represents the updated covariance matrix at time step k, and I represents the identity matrix.

[0110] In summary, the present invention proposes a wire state monitoring method based on adaptive Kalman filtering, obtaining first parameter information, and performing a first prediction based on the first parameter information; obtaining second parameter information, and calculating a first target coefficient based on the second parameter information; updating the result of the first prediction based on the first target coefficient, and performing wire state monitoring based on the updated result. By adopting adaptive Kalman filtering technology, the present invention can effectively combine the data of GNSS and IMU sensors to improve the accuracy and reliability of wire state monitoring. In a dynamically changing environment, GNSS signals may be interfered, and IMU sensors may produce drift errors. The present invention can dynamically adjust the filtering parameters through an adaptive Kalman filtering algorithm, thereby providing accurate wire state estimation in different environments.

[0111] Example 2

[0112] In a preferred embodiment, the method according to the above embodiment can be designed as follows:

[0113] S01. Based on the acceleration and angular velocity information provided by the IMU, the next state of the system is predicted through the state transfer equation. The predicted state includes position, velocity and acceleration.

[0114] S02. Dynamically adjust the measurement noise covariance matrix R according to the signal-to-noise ratio of the GNSS signal k and the process noise covariance matrix Q k ;

[0115] S03. Calculate the Kalman gain according to the updated covariance matrix;

[0116] S04. Update the system state estimate using GNSS measurement data.

[0117] It should be noted that the adaptive Kalman filter effectively solves the IMU drift problem by dynamically adjusting the noise covariance matrix, and can still provide high positioning accuracy when the GNSS signal is lost or the quality is degraded. The algorithm combines the fast response of the IMU with the long-term correction of the GNSS to ensure the stable operation of the wire monitoring system in complex environments.

[0118] Furthermore, in step S01, based on the acceleration and angular velocity information provided by the IMU, the method of predicting the next state of the system through the state transfer equation, wherein the predicted state includes position, velocity and acceleration, is:

[0119]

[0120] P k|k-1 =AP k-1|k-1 A T +Q k

[0121] in, The predicted state vector for time step k; represents the state vector at time step k-1; A represents the state transfer matrix, which is calculated based on IMU data; P k|k-1 represents the predicted state covariance matrix at time step k; P k-1|k-1 represents the state covariance matrix of time step k-1; B represents the control input matrix; u k-1 represents the control input provided by the IMU at time step k-1; Q k represents the process noise covariance matrix at time step k.

[0122] Furthermore, in step S02, the measurement noise covariance matrix R is dynamically adjusted according to the signal-to-noise ratio of the GNSS signal. k and the process noise covariance matrix Q k The method is:

[0123]

[0124] 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 t k is the cumulative running time of IMU, R0 is the baseline measurement noise covariance, γ is the adaptive adjustment factor, SNR k is the GNSS signal-to-noise ratio at time step k, and I represents the identity matrix.

[0125] In the Kalman filter, the signal-to-noise ratio is used to dynamically adjust the measurement noise covariance matrix to adapt to changes in GNSS signal quality. Specifically:

[0126] When SNR k When it is larger, it means the signal quality is good, and the measurement noise covariance matrix R k Reduced, making the system more dependent on GNSS data.

[0127] When SNR k When it is small, it means the signal quality is poor, and the measurement noise covariance matrix Rk Increase, reduce dependence on GNSS data, the system will rely more on IMU data.

[0128] Furthermore, in step S03, the Kalman gain K of time step k is calculated according to the updated covariance matrix. k The method is:

[0129] K k =P k|k-1 H T (HP k-1 H T +R k ) -1

[0130] Where H is the observation matrix, which describes the relationship between GNSS measurements and system status.

[0131] Furthermore, the method of step S04, using GNSS measurement data to update the system state estimation is:

[0132] To make a status update:

[0133]

[0134] Update the covariance matrix:

[0135] P k|k =(IK k H)P k|k-1

[0136] in, represents the updated state estimate after time step k, P k|k represents the updated covariance matrix at time step k, and I represents the identity matrix.

[0137] In an optional embodiment, a wire monitoring terminal equipped with GNSS / INS hardware collects raw observation data as the wire swings freely. The collected raw observation data is solved using a Kalman filter, and then the time-space coordinate sequence of the measured wire is solved through a smoother to reconstruct the time-varying information of the three-dimensional wire. By comparing the measured wire sag, windage and design line threshold information, the deformation deviation information of the wire can be obtained. It should be pointed out that the three-dimensional spatial position measured by the terminal is the point monitored by the terminal as the wire swings, and is not necessarily the lowest point of the wire sag. The measured three-dimensional coordinates can be used in conjunction with the wire sag monitoring method to calculate the corresponding sag and windage parameters.

[0138] In an optional embodiment, the accuracy of monitoring the sag and windage of the conductor is required to be relatively high, and centimeter-level accuracy can be satisfied. In the process of extracting noise using the Allan variance method, the noise of the extracted segment is easily contaminated by the noise of the adjacent segment, resulting in a certain deviation between the slope of the segment and the theoretical slope. There will also be errors when using the fitting method. Therefore, the parameters determined by this method are relatively rough and are not suitable for the noise parameters required by the combined solution process. Therefore, in actual use, the variance adjustment determined by the Allan method needs to be used as the final parameter. In actual conductor monitoring, the monitoring terminal can be well fixed on the conductor. Due to the terminal counterweight and the deadweight of the conductor, in the absence of strong winds, the terminal collects the three-dimensional spatial coordinates of the same conductor. Under the condition of extremely high algorithm accuracy, the conductor sag results at the detection location should have extremely high consistency. Under the idea of ​​controlling variables, the more appropriate the parameter setting, the better the consistency. Therefore, the optimal parameter determination can be verified by measured data, and the rationality of the parameter design is evaluated by repeatability through multiple independent measurements in a static state, and the optimal parameters are searched within the search range determined by the Allan variance. The design steps are as follows:

[0139] 1) Determine the noise parameters using the Allan variance method and input the noise parameters into the coupling algorithm as the default configuration parameters.

[0140] 2) Destroy the equilibrium state, measure the sag deviation of the same conductor multiple times, make a difference comparison, and calculate the STD value of the difference sequence;

[0141] 3) Within the range determined by the Allan variance, traverse the angle random walk and rate random walk parameters, recalculate the deviation and record the STD of the corresponding difference;

[0142] 4) Repeat steps 2 and 3 until the minimum STD value is found and the corresponding process noise parameter is recorded and fixed;

[0143] 5) Adjust the next process parameter until the inertial navigation process noise configuration with the minimum STD value is found, and the inertial navigation process noise configuration with the minimum STD value is used as the process noise covariance matrix Q k The optimal value of .

[0144] It should be noted that this method uses the Allan variance to determine the initial noise parameters as the default configuration, and then calculates its standard deviation (STD) by destroying the equilibrium state and measuring the sag deviation of the wire multiple times. On this basis, by traversing different angle random walk and rate random walk parameters, the STD values ​​of the deviations calculated are compared, and this process is repeated until the parameters that minimize the STD value are found, and the process noise parameters are fixed. Finally, by adjusting all process parameters, it is ensured that the inertial navigation process noise configuration that minimizes the system standard deviation is found to optimize the monitoring accuracy.

[0145] Example 3

[0146] This embodiment also provides a wire state monitoring system based on adaptive Kalman filtering, including:

[0147] A prediction module, used to obtain first parameter information and perform a first prediction based on the first parameter information;

[0148] A coefficient determination module, used to obtain second parameter information and calculate a first target coefficient according to the second parameter information;

[0149] A monitoring module is used to update the first prediction result according to the first target coefficient, and perform wire status monitoring according to the updated result.

[0150] 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.

[0151] This embodiment also provides a computer device, which can be a terminal. The computer device 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 implemented through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a wire state monitoring method based on adaptive Kalman filtering 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 button, a trackball or a touch pad set on the housing of the computer device, or an external keyboard, touch pad or mouse, etc.

[0152] This embodiment further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the following steps are implemented:

[0153] Acquire first parameter information, and perform a first prediction based on the first parameter information;

[0154] Acquire second parameter information, and calculate a first target coefficient according to the second parameter information;

[0155] The first prediction result is updated according to the first target coefficient, and the conductor state monitoring is performed according to the updated result.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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 1 The steps for the functions specified in one or more boxes.

[0161] 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.

[0162] 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 conductor state monitoring method based on adaptive Kalman filtering, characterized in that: include: Acquire first parameter information, and perform a first prediction based on the first parameter information; Acquire second parameter information, and calculate a first target coefficient according to the second parameter information; The first prediction result is updated according to the first target coefficient, and the conductor state monitoring is performed according to the updated result.

2. The wire state monitoring method based on adaptive Kalman filtering according to claim 1, characterized in that: The obtaining of the second parameter information and calculating the first target coefficient according to the second parameter information includes: Acquire second parameter information, and calculate a first signal-to-noise ratio according to the second parameter information; Update the first matrix and the second matrix according to the first signal-to-noise ratio; The first target coefficient is calculated according to the updated first matrix and the second matrix.

3. The wire state monitoring method based on adaptive Kalman filtering as claimed in claim 2, characterized in that: The acquiring first parameter information and performing a first prediction according to the first parameter information includes: Establishing a first state prediction model; Using the first parameter information as input of the first state prediction model; The output of the first state prediction model includes at least a position state, a velocity state and an acceleration state.

4. The wire state monitoring method based on adaptive Kalman filtering as claimed in claim 3, characterized in that: The first state prediction model includes: The first state prediction model is an arbitrary model whose input is the first parameter information and whose output is at least a position state, a velocity state and an acceleration state or which can directly or indirectly obtain relevant parameters of the position state, the velocity state and the acceleration state.

5. The method for monitoring the conductor state based on adaptive Kalman filtering according to claim 4, characterized in that: The updating of the first matrix and the second matrix according to the first signal-to-noise ratio comprises: At least one of the first matrix and the second matrix is ​​represented by the first signal-to-noise ratio; updating a first matrix or a second matrix represented by the first signal-to-noise ratio according to the first signal-to-noise ratio; The first target coefficient is calculated according to the updated first matrix and the second matrix.

6. The method for monitoring the conductor state based on adaptive Kalman filtering according to claim 5, characterized in that: The obtaining of the first parameter information and the obtaining of the second parameter information are both parameter information in the same fixed time step.

7. The method for monitoring the conductor state based on adaptive Kalman filtering according to claim 6, characterized in that: The first matrix and the second matrix include: The first matrix is ​​a measurement noise covariance matrix; The second matrix is ​​a process noise covariance matrix.

8. A conductor state monitoring system based on adaptive Kalman filtering, characterized in that: include: A prediction module, used to obtain first parameter information and perform a first prediction based on the first parameter information; A coefficient determination module, used to obtain second parameter information and calculate a first target coefficient according to the second parameter information; A monitoring module is used to update the first prediction result according to the first target coefficient, and perform wire status monitoring according to the updated result.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: 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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