Dynamic fusion method and system of inertial navigation data

Abstract

The invention discloses a dynamic fusion method and system for inertial navigation data, and the method comprises the steps: obtaining original inertial navigation data of different positions of a lead in real time through an inertial navigation system, and carrying out the preprocessing to obtain first data; performing time synchronization processing on inertial navigation system data at different positions in the first data to obtain time synchronization inertial navigation data; distributing a dynamic weight for each inertial navigation system based on the accuracy and the stability of the historical data of the inertial navigation system; and predicting the state of the next time step through a linear dynamic system equation, and when new time synchronization inertial navigation data arrives, updating the state estimation and the error covariance matrix by using the Kalman gain, the dynamic weight and the new time synchronization inertial navigation data so as to output a fusion data set processed by a Kalman filter. According to the invention, the influence of environmental noise and equipment errors on a monitoring result can be effectively reduced; by dynamically adjusting the weight and the fusion strategy, the method can better adapt to various complex and variable environmental conditions, and high stability is kept.

Description

Technical Field

[0001] The present invention relates to the field of power information technology processing technology, and in particular to a dynamic fusion method and system for inertial navigation data. Background Art

[0002] In the field of electric power, real-time monitoring of conductors is essential to ensure the safe and stable operation of the system. With the development of technology, inertial navigation systems (INS) have been widely used in various navigation and positioning tasks. However, in real-time monitoring of conductors, due to the influence of environmental noise, equipment errors and many other factors, the data of a single inertial navigation system may have errors and uncertainties. In order to improve data accuracy and reliability, multiple inertial navigation systems are usually installed at different locations of the conductor.

[0003] Traditional data fusion methods are mostly based on simple weighted average or voting mechanisms. Such methods may not fully consider the performance and characteristics of each inertial navigation system, and therefore lack the ability to dynamically adjust and optimize the weights and strategies of data fusion according to real-time conditions. Therefore, how to effectively fuse these data from different locations and different systems to obtain an accurate and comprehensive wire status assessment has become an urgent problem to be solved. Summary of the invention

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

[0005] Therefore, the present invention provides a dynamic fusion method and system for inertial navigation data to solve the problem that current data fusion lacks the ability of dynamic adjustment, cannot optimize the weight and strategy of data fusion according to real-time conditions, and effectively fuses these data from different locations and different systems for status assessment.

[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 dynamic fusion of inertial navigation data, comprising:

[0008] The original inertial navigation data of different positions of the wire are acquired in real time through the inertial navigation system, and the first data is obtained after preprocessing;

[0009] Performing time synchronization processing on the inertial navigation system data at different positions in the first data to obtain time-synchronized inertial navigation data;

[0010] Assign dynamic weights to each INS based on the accuracy and stability of the INS historical data;

[0011] The state of the next time step is predicted by the linear dynamic system equation. When new time-synchronous inertial navigation data arrives, the Kalman gain, dynamic weight and new time-synchronous inertial navigation data are used to update the state estimate and error covariance matrix to output a fused data set processed by the Kalman filter.

[0012] As a preferred solution of the dynamic fusion method of inertial navigation data described in the present invention, wherein:

[0013] The original inertial navigation data of different positions of the wire are obtained in real time through the inertial navigation system, including:

[0014] Set the optimization objectives and constraints of the inertial navigation system;

[0015] According to the optimization objectives and constraints, the particle swarm optimization algorithm is used to search for the optimal solution, the objective function values ​​of different system layout schemes are evaluated, and the final layout position of the system is determined according to the optimal solution;

[0016] According to the final arrangement position, an inertial navigation system is installed at the corresponding position of the wire to obtain real-time data of each position.

[0017] As a preferred solution of the dynamic fusion method of inertial navigation data described in the present invention, wherein:

[0018] The preprocessing includes: setting the window size of the moving average filter to N;

[0019] Initialize a buffer of size N to store the most recent N data points;

[0020] When a new data point arrives, add the new data point to the end of the buffer;

[0021] If the buffer is full, remove the first element of the buffer so that the buffer always contains the latest N data points;

[0022] The moving average of the latest N data points contained in the buffer is calculated to obtain the first data.

[0023] As a preferred solution of the dynamic fusion method of inertial navigation data described in the present invention, wherein:

[0024] The time synchronization process includes:

[0025] Create an empty time synchronization data list and determine the synchronization unit;

[0026] For each data set, create a mapping table from timestamp to data value;

[0027] For each time point t within the common time range, if the time point t exists in the data map of the data set, the data value is added to the position of the corresponding time point in the time synchronization data list; if it does not exist, the data value of the time point is estimated using linear interpolation, added to the time synchronization data list, and the time synchronization inertial navigation data is output.

[0028] As a preferred solution of the dynamic fusion method of inertial navigation data described in the present invention, it also includes:

[0029] Monitor the fused data set in real time and determine the running status of the wire by using a preset threshold;

[0030] If the monitored data exceeds the preset threshold, an alarm will be automatically triggered, indicating potential abnormal wire status.

[0031] As a preferred solution of the dynamic fusion method of inertial navigation data described in the present invention, wherein:

[0032] The dynamic weight is expressed as:

[0033]

[0034] Among them, w i (t-1) represents the weight of the i-th inertial navigation system at the previous time t-1; β∈(0,1) represents the forgetting factor; α∈(0,1) represents the adjustment coefficient; represents the accuracy evaluation index of the i-th inertial navigation system at time t; is the stability evaluation index of the i-th inertial navigation system at time t, N represents a constant, representing the number of data points used to calculate the accuracy and stability evaluation index, t represents the time index, representing the current moment, i represents the index of the inertial navigation system, j represents the index of the data point, and p ij represents the predicted value of the i-th inertial navigation system for the j-th data point, p ij (t) represents the predicted value of the i-th inertial navigation system for the j-th data point at time t, r ij represents the actual observation value of the jth data point for the i-th inertial navigation system, r ij (t) represents the actual observed value at time t; ij represents the output value of the ith inertial navigation system when processing the ith data point, o ii (t) represents the output value of the i-th inertial navigation system when processing the j-th data point at time t; is the average of all output values ​​of the ith inertial navigation system at time t.

[0035] As a preferred solution of the dynamic fusion method of inertial navigation data described in the present invention, wherein:

[0036] It also includes initializing relevant parameters of the Kalman filter to obtain a linear dynamic system equation, wherein the relevant parameters include: an initial state, an initial error covariance, a state transfer matrix, an observation matrix, a process noise covariance matrix, and an observation noise covariance matrix.

[0037] In a second aspect, the present invention provides a dynamic fusion system for inertial navigation data, comprising:

[0038] An acquisition module is used to acquire original inertial navigation data of different positions of the wire in real time through an inertial navigation system, and obtain first data after preprocessing;

[0039] A synchronization module, used for performing time synchronization processing on the inertial navigation system data at different positions in the first data to obtain time-synchronized inertial navigation data;

[0040] An allocation module for assigning a dynamic weight to each inertial navigation system based on the accuracy and stability of the inertial navigation system's historical data;

[0041] The fusion module is used to predict the state of the next time step through the linear dynamic system equation. When new time-synchronous inertial navigation data arrives, the Kalman gain, dynamic weight and new time-synchronous inertial navigation data are used to update the state estimate and error covariance matrix to output a fused data set processed by the Kalman filter.

[0042] In a third aspect, the present invention provides a computing device, comprising:

[0043] Memory and processor;

[0044] The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the dynamic fusion method of inertial navigation data are implemented.

[0045] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the dynamic fusion method of inertial navigation data.

[0046] Compared with the prior art, the present invention has the following beneficial effects: the present invention utilizes the historical data accuracy and stability information of each inertial navigation system to obtain more accurate fusion results, which helps to more accurately evaluate the real-time status of the wire, and promptly discover and deal with potential safety hazards. The use of Kalman filter for data fusion can effectively reduce the impact of environmental noise and equipment errors on monitoring results. At the same time, by dynamically adjusting weights and fusion strategies, the system can better adapt to various complex and changeable environmental conditions, thereby maintaining higher stability. It realizes real-time processing and fusion of data from multiple inertial navigation systems without manual intervention or post-processing, thereby improving the efficiency and response speed of real-time monitoring of wires. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] 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 work.

[0048] Figure 1 This is a schematic diagram of the overall process logic of the dynamic fusion method of inertial navigation data described in the first embodiment of the present invention. DETAILED DESCRIPTION

[0049] 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.

[0050] Example 1

[0051] Reference Figure 1 , is an embodiment of the present invention, and provides a dynamic fusion method for inertial navigation data, comprising:

[0052] S100: acquiring original inertial navigation data of different positions of the wire in real time through the inertial navigation system, and obtaining first data after preprocessing;

[0053] S200: performing time synchronization processing on the inertial navigation system data at different positions in the first data to obtain time-synchronized inertial navigation data;

[0054] S300: Assign dynamic weights to each INS based on the accuracy and stability of the INS historical data;

[0055] S400: The state of the next time step is predicted by the linear dynamic system equation. When new time-synchronous inertial navigation data arrives, the state estimation and error covariance matrix are updated using the Kalman gain, dynamic weight and new time-synchronous inertial navigation data to output a fused data set processed by the Kalman filter.

[0056] It should be noted that the inertial navigation system is the inertial navigation system (INS); the real-time data acquisition in step S100 can ensure that the monitoring system always has the latest information and can respond quickly to any changes in the state of the wire. Acquiring data from multiple locations can provide a comprehensive view of the wire, which is helpful to discover local anomalies or overall trends; the preprocessing in S200 can help remove noise, outliers or invalid data in the original data, thereby improving the data quality. Through preprocessing, data in different formats or units can be unified; in S300, dynamic weights are assigned to screen systems with better historical trust performance, so that the dynamic weights can be adjusted according to the real-time performance of each inertial navigation system, thereby improving the adaptability of the fusion method. In S400, the Kalman filter can combine previous estimates and new observations to provide more accurate state estimates and realize the cycle of prediction and correction; by continuously updating the state estimate and the error covariance matrix, the Kalman filter can effectively reduce the error in the data fusion process. The Kalman filter has good robustness to noise and incomplete observations, making the fused data more reliable.

[0057] In the embodiment of the present application, the above step S100 acquires the original inertial navigation data of different positions of the wire in real time through the inertial navigation system, including the following steps A1-A3:

[0058] A1: Set the optimization objectives and constraints of the inertial navigation system;

[0059] A2: According to the optimization objectives and constraints, the particle swarm optimization algorithm is used to search for the optimal solution, evaluate the objective function values ​​of different system layout schemes, and determine the final layout position of the system based on the optimal solution;

[0060] A3: According to the final arrangement position, install the inertial navigation system at the corresponding position of the wire to obtain real-time data of each position.

[0061] It should be noted that step A1 is mainly used to determine monitoring needs;

[0062] In one embodiment, the optimization objective may be whether to maximize the overall monitoring coverage of the conductor;

[0063] In another embodiment, the optimization goal may also be to minimize the monitoring blind area, or to optimize the data accuracy, etc.;

[0064] For example, a specific quantitative indicator is set, such as the monitoring coverage rate reaches more than 95%, or the data error rate is controlled within 5%.

[0065] In one embodiment, the constraints may be to determine the physical limitations of the inertial navigation system, such as the maximum working distance, signal reception range, etc., to determine the feasibility of the installation location. For example, some areas may not be suitable for installation due to environmental factors (such as terrain, buildings, etc.).

[0066] In another embodiment, the constraint condition may also be determining a cost budget, including equipment cost, installation cost, maintenance cost, etc., and setting minimum and maximum spacing between devices to avoid signal interference and ensure full coverage.

[0067] It should be noted that in step A2, a particle swarm algorithm is used, each particle represents a possible inertial navigation system arrangement scheme, and the position and velocity of the particle are randomly initialized; an objective function is defined to quantitatively evaluate the pros and cons of each arrangement scheme, and the fitness value of each particle is calculated using the function, that is, the objective function value corresponding to each arrangement scheme;

[0068] Specifically, let f(x) be the objective function, where x represents a particle, that is, a specific inertial navigation system configuration scheme, each configuration scheme consists of a series of position coordinates, denoted as x = [x1, x2, ..., x n ], where n is the number of inertial navigation systems, x i is the position of the i-th inertial navigation system on the wire.

[0069] The objective function can be defined as:

[0070]

[0071] Here, x represents a particle, which is a specific inertial navigation system arrangement scheme, which consists of a series of position coordinates, denoted as x = [x1, x2, ..., x n ], where n is the number of inertial navigation systems, x i is the position of the i-th inertial navigation system on the wire, α1 is a positive number representing the importance weight of coverage in the objective function, β1 is a positive number representing the importance weight of cost in the objective function, represents the sum of the lengths of all inertial navigation system coverage wires, C i is the length of the wire covered by the ith inertial navigation system, and L is the total length of the wire. It is used to calculate the coverage ratio, that is, the sum of the lengths of the wires covered by all inertial navigation systems is divided by the total length of the wires, and the result is a measure of coverage, C d represents the fixed cost of a single device, kinl and k m are proportionality constants used to convert the location characteristic (distance from the starting point) into installation cost and maintenance cost, respectively. Specifically, k inl is the proportionality constant for installation costs, k m is the proportionality constant of the maintenance cost, |x i -x st | represents the position x of the i-th inertial navigation system i With the starting point x of the wire st In each iteration, the particle position is updated according to the current position and speed of the particle, the fitness value of the updated particle is recalculated, and the speed and position of the particle are adjusted according to the fitness value and the historical best position of the individual and the group. Set the number of iterations, stop the iteration when the termination condition is reached, and output the current best arrangement plan.

[0072] It should be noted that during the specific operation of step A3, an on-site survey is required to confirm the feasibility of the installation location, check whether there is sufficient space, suitable power supply and network connection, etc., install the inertial navigation system at the corresponding position of the wire according to the optimal layout plan, ensure that the equipment is installed firmly and complies with safety regulations, debug the installed inertial navigation system to ensure its normal operation, conduct tests to verify whether the system can accurately obtain real-time data and transmit it to the data center, regularly maintain and inspect the inertial navigation system to ensure its long-term stable operation, set up a monitoring system to monitor the working status and data quality of the equipment in real time, and promptly discover and deal with potential problems.

[0073] Exemplarily, steps A1-A3 are applied to installing five inertial navigation systems on a 10 km section of power conductor, with the goal of maximizing the overall monitoring coverage of the conductor.

[0074] Step A1: Set the optimization goal to maximize the overall monitoring coverage of the five inertial navigation systems. Considering the budget and technical limitations, the minimum installation distance between each inertial navigation system is set to 1 km and the maximum installation distance is set to 3 km.

[0075] Step A2: Use the particle swarm optimization algorithm to search for the best placement of the inertial navigation system that meets these conditions. After multiple iterations, the algorithm may find a solution. For example, install the inertial navigation system at 1 km, 3.5 km, 5.5 km, 7.5 km, and 9.5 km of the wire.

[0076] Step A3: According to this optimal solution, inertial navigation systems are installed at the above positions of the wire to ensure that the monitoring range of each system can cover the entire wire to the maximum extent while meeting all constraints.

[0077] It should be noted that the inertial navigation system layout determined by the optimization algorithm in steps A1-A3 can ensure that the entire wire is effectively monitored, reducing or eliminating monitoring blind spots. Reasonable layout can reduce signal interference and errors, thereby improving the accuracy and reliability of data collected from the inertial navigation system. While meeting monitoring needs, budget and technical feasibility are also taken into account to ensure the practicality and economy of the solution.

[0078] In the embodiment of the present application, the preprocessing in the above step S100 includes the following steps B1-B5:

[0079] B1: Set the window size of the moving average filter to N;

[0080] B2: Initialize a buffer of size N to store the most recent N data points;

[0081] B3: When a new data point arrives, add the new data point to the end of the buffer;

[0082] B4: If the buffer is full, remove the first element of the buffer so that the buffer always contains the latest N data points;

[0083] B5: Calculate the moving average of the latest N data points contained in the buffer to obtain the first data.

[0084] It should be noted that step B1 determines the number of data points that the moving average filter will consider, and the window size N determines how many consecutive data points the filter will use to calculate the average value. A larger N value will make the filter smoother on the data, but the response speed will be slower; a smaller N value will be the opposite. The buffer in step B2 is used to store the most recent N data points, which will be used to calculate the moving average. The buffer can be an array, list or other data structure. In step B3, whenever a new data point arrives, it will be added to the end of the buffer. If the buffer is full (i.e., it already contains N data points), the first element of the buffer (the oldest data point) will be removed to ensure that the buffer always contains the latest N data points. Step B4 adds all the data points in the buffer and then divides it by the number of data points (i.e., N) to obtain a moving average, which reflects the central trend of the most recent N data points. The moving average calculated in step B5 can be used as the preprocessing result of the current data point for subsequent data analysis, signal processing or control system input.

[0085] For example, taking a 10-kilometer power line design of the installation plan of the inertial navigation system as an example, in order to optimize the installation location and reduce the impact of noise data on decision-making, it is necessary to smooth the environmental data along the line to obtain more accurate installation point recommendations. Step B1, select the window size N as 5, which means that the moving average of relevant parameters (such as wind speed, vibration frequency, etc.) will be calculated based on the 5 most recent data points. Step B2, initialize an array of size 5 as a buffer for storing the 5 most recent environmental data points. Step B3, whenever a new data point is obtained from the inertial navigation system or environmental monitoring device, add it to the end of the buffer and remove the oldest data point (if the buffer is full). Step B4, sum the 5 data points in the buffer and divide them by 5 to obtain the moving average of these environmental parameters. Step B5, use this moving average as the representative value of the environmental parameters at the current point to analyze and determine the best installation location of the inertial navigation system to ensure the stability and safety of the power line. In this way, the environmental conditions of each potential installation point can be more accurately evaluated to avoid misjudgments due to transient noise or abnormal data. Moving average processing helps make decisions based on a smoother and more stable data set, thereby improving the rationality and effectiveness of the inertial navigation system installation.

[0086] It should be noted that steps B1-B5 can effectively reduce the impact of random noise on the data and make the data smoother by calculating the average of multiple data points. The moving average usually reflects the true trend of the data better than a single data point, thereby improving the accuracy of subsequent analysis and decision-making. Since the average is calculated based on multiple data points, the impact of a single outlier on the result will be reduced.

[0087] In the embodiment of the present application, the time synchronization process in the above step S200 includes the following steps:

[0088] C1: Create an empty time synchronization data list and determine the synchronization unit;

[0089] C2: For each data set, create a mapping table from timestamp to data value;

[0090] C3: For each time point t within the common time range, if the time point t exists in the data map of the data set, the data value is added to the position of the corresponding time point in the time synchronization data list; if it does not exist, the data value of the time point is estimated using linear interpolation, added to the time synchronization data list, and the time synchronization inertial navigation data is output.

[0091] It should be noted that step C1 prepares an empty data structure for storing the time-synchronized data. At the same time, it is necessary to determine the unit of time synchronization, such as seconds, milliseconds, etc., which depends on the accuracy requirements and acquisition frequency of the data. Step C2 Since different data sets may have different sampling frequencies and timestamps, it is necessary to create a mapping table for each data set to associate the timestamp with the corresponding data value. This facilitates the subsequent search and interpolation of data by time. Step C3, for each time point t in the time range common to all data sets, perform the following operations: First, check whether the time point t exists in the timestamp mapping table of each data set. If the time point t exists in the timestamp mapping table of the data set, directly extract the corresponding data value. If the time point t does not exist, the data value of the time point is estimated using the linear interpolation method. Linear interpolation is to calculate the estimated value at time t based on the two actual data points closest to the time point t. The extracted or interpolated data value is added to the position of the corresponding time point in the time synchronization data list. Output the inertial navigation data after time synchronization, which now has the same timestamp and can be directly used for comparison and analysis.

[0092] Exemplarily, take the case where five inertial navigation systems are installed on a 10-kilometer power line; each system records data at a different frequency, and their timestamps may not be completely aligned. The goal is to synchronize this data so that the dynamic behavior of the entire line can be accurately analyzed. Step C1, create an empty time-synchronized data list and decide to use milliseconds as the unit of time synchronization. Step C2, for each data set collected from each inertial navigation system, create a mapping table from timestamp to data value (such as acceleration, angular velocity, etc.). Step C3, determine a common time range, such as from a specific start time to an end time. For each millisecond time point within this range, check the data mapping table of each inertial navigation system. If a system has data recorded at that time point, extract the data; if not, use the system's two most recent data points for linear interpolation to estimate the data value at that time point. Finally, integrate the data of all systems into a time-synchronized data list according to time points.

[0093] It should be noted that steps C1-C3 now align all data to the same timestamp, facilitating cross-system comparison and analysis. Linear interpolation ensures that even if some systems do not directly record data at certain points in time, a reasonable estimate can be obtained, thereby maintaining the continuity and integrity of the data. Time-synchronized data can more accurately reflect the actual dynamic behavior of power conductors.

[0094] In the embodiment of the present application, the dynamic weight in the above step S300 is expressed as:

[0095]

[0096] Among them, w i (t-1) represents the weight of the i-th inertial navigation system at the previous time t-1; β∈(0,1) represents the forgetting factor; α∈(0,1) represents the adjustment coefficient; represents the accuracy evaluation index of the i-th inertial navigation system at time t; is the stability evaluation index of the i-th inertial navigation system at time t, N represents a constant, representing the number of data points used to calculate the accuracy and stability evaluation index, t represents the time index, representing the current moment, i represents the index of the inertial navigation system, j represents the index of the data point, and p ij represents the predicted value of the i-th inertial navigation system for the j-th data point, p ij (t) represents the predicted value of the i-th inertial navigation system for the j-th data point at time t, r ij represents the actual observation value of the jth data point for the i-th inertial navigation system, r ij (t) represents the actual observed value at time t; ij represents the output value of the i-th inertial navigation system when processing the j-th data point, o ij (t) represents the output value of the i-th inertial navigation system when processing the j-th data point at time t; is the average of all output values ​​of the ith inertial navigation system at time t.

[0097] It should be noted that by setting dynamic weights, this method can adjust the weight of each inertial navigation system in real time according to its performance and accuracy, which means that the system with better performance at a certain moment will get a higher weight, thereby improving the accuracy and reliability of the overall data.

[0098] If an inertial navigation system fails or its performance degrades, its weight will automatically decrease, thereby reducing the impact on the overall data accuracy. The ability to adjust automatically enhances the fault tolerance of the system. This method considers both accuracy and stability evaluation indicators, which helps to find a balance between the two. This not only provides accurate data, but also ensures the stability of the data and avoids overall data distortion caused by abnormal fluctuations in a single data. By comprehensively considering the data and performance of multiple inertial navigation systems, data from different systems can be more effectively fused; among them, the setting of the forgetting factor and the adjustment coefficient can flexibly adapt to different application scenarios and needs. By adjusting these parameters, the speed and amplitude of weight updates can be controlled to adapt to different environments and data changes.

[0099] In an embodiment of the present application, the above-mentioned step S400 also includes initializing relevant parameters of the Kalman filter to obtain the linear dynamic system equation, and the relevant parameters include: initial state, initial error covariance, state transfer matrix, observation matrix, process noise covariance matrix and observation noise covariance matrix.

[0100] It should be noted that the initialization state is usually an estimated value, which indicates the most likely state of the system at the beginning. The main functions of the parameters are as follows:

[0101] Initial error covariance: represents the uncertainty of the initial state estimate;

[0102] State transition matrix: describes how the system state evolves over time;

[0103] Observation matrix: maps the system state to the observation space;

[0104] Process noise covariance matrix: represents the uncertainty in the system state transition process;

[0105] Observation noise covariance matrix: represents the uncertainty in the observation process.

[0106] The linear dynamic system equations predict the system state at the next time step based on the current state estimate and the state transfer matrix. When new time-synchronous inertial navigation data arrives, the Kalman gain (a factor that weighs the weight between prediction and observation) is used to combine the predicted state and the new observation data to update the state estimate. At the same time, the error covariance matrix also needs to be updated to reflect the uncertainty of the new state estimate.

[0107] For example, take a 10 km long power line as an example, along which 5 inertial navigation systems (INS1, INS2, INS3, INS4, INS5) are installed at equal distances. Each system can provide the position, velocity and acceleration data of the line. Due to environmental noise, sensor errors and calibration differences, these data have differences, and the goal is to use Kalman filters to fuse these data to obtain a more accurate state estimate.

[0108] Set the initial position of the wire to x0, x0 = 0 (the starting point of the wire), the initial velocity to (v0 = 0), and the initial acceleration to (a0 = 0). Combine these values ​​into an initial state vector T stands for matrix transpose.

[0109] The initial error covariance matrix P0 is set to represent the uncertainty of the initial state estimate. For example, a diagonal matrix can be set, where the elements on the diagonal represent the initial estimated variances of position, velocity, and acceleration, respectively.

[0110] Parameter settings, including:

[0111] The state transfer matrix F is used to describe how the wire state evolves over time. For example, for a simple linear dynamic model, the state transfer matrix may be a 3×3 matrix that takes into account the time evolution of position, velocity, and acceleration. Where Δt is the sampling time interval.

[0112] The observation matrix H maps the system state to the observation space. For each inertial navigation system, the observation matrix may be a 3×3 identity matrix because we directly observe the position, velocity and acceleration.

[0113] The process noise covariance matrix Q represents the uncertainty in the state transition process. This matrix is ​​usually set according to the system dynamics and noise characteristics.

[0114] The observation noise covariance matrix R represents the uncertainty in the observation process. For each inertial navigation system, this matrix can be set according to the accuracy of its sensors.

[0115] At each time step, using the state transition matrix F and the current state estimate X K-1 To predict the state of the next time step, and at the same time, predict the error covariance matrix; when the five inertial navigation systems provide new observation data, first calculate the Kalman gain, then use the observation data to update the state, and finally update the error covariance matrix; for the data of the five inertial navigation systems, the above Kalman filtering process can be performed separately, or an extended observation matrix and observation noise covariance matrix can be used to consider the data of all systems at the same time. The latter requires more complex calculations, but can provide more accurate state estimates.

[0116] It should be noted that the above parameter settings and fusion calculations can fuse the data of multiple inertial navigation systems to obtain a more accurate and reliable estimate of the dynamic state of the conductor. The Kalman filter can fuse the data of multiple inertial navigation systems to provide a more accurate and reliable estimate of the dynamic state of the conductor. The Kalman filter can process new observation data in real time and update the state estimate immediately. By reasonably setting the process noise and observation noise covariance matrix, the Kalman filter can effectively suppress the noise and error in the data and improve the accuracy of the state estimate.

[0117] In the embodiment of the present application, after the above step S400 is executed, steps D1-D2 are further included:

[0118] D1: Real-time monitoring of the fused data set and judging the operating status of the conductor by using preset thresholds;

[0119] D2: If the monitored data exceeds the preset threshold, an alarm will be automatically triggered, indicating potential abnormal wire status.

[0120] It should be noted that in steps D1-D2, by real-time monitoring of the fused data set, the power system operator can instantly understand the dynamic state of the conductor. It helps to discover potential problems, thereby performing preventive maintenance and reducing unexpected power outages and repair costs. When abnormalities occur in the conductor, such as excessive vibration, displacement, or other conditions that may cause damage, the system can quickly detect these abnormalities. By setting preset thresholds, the system can detect and handle problems in a timely manner before they become serious. The automatic triggering of the alarm mechanism can quickly notify the operation and maintenance personnel of the abnormal state of the conductor, thereby shortening the fault response time, reducing the impact of the fault on the power system, and improving the reliability of the overall system. Through real-time monitoring and alarm systems, the frequency of manual inspections can be reduced, while avoiding potential risks caused by failure to detect and handle problems in a timely manner.

[0121] The above is a schematic scheme of a method for dynamic fusion of inertial navigation data in this embodiment. It should be noted that the technical scheme of the system for dynamic fusion of inertial navigation data and the technical scheme of the method for dynamic fusion of inertial navigation data described above belong to the same concept, and the details of the technical scheme of the system for dynamic fusion of inertial navigation data in this embodiment that are not described in detail can all be referred to the description of the technical scheme of the method for dynamic fusion of inertial navigation data described above.

[0122] The dynamic fusion system of inertial navigation data in this embodiment includes:

[0123] An acquisition module is used to acquire original inertial navigation data of different positions of the wire in real time through an inertial navigation system, and obtain first data after preprocessing;

[0124] A synchronization module, used for performing time synchronization processing on the inertial navigation system data at different positions in the first data to obtain time-synchronized inertial navigation data;

[0125] An allocation module for assigning a dynamic weight to each inertial navigation system based on the accuracy and stability of the inertial navigation system's historical data;

[0126] The fusion module is used to predict the state of the next time step through the linear dynamic system equation. When new time-synchronous inertial navigation data arrives, the Kalman gain, dynamic weight and new time-synchronous inertial navigation data are used to update the state estimate and error covariance matrix to output a fused data set processed by the Kalman filter.

[0127] This embodiment further provides a computing device, which is applicable to the case of dynamic fusion of inertial navigation data, including:

[0128] Memory and processor; the memory is used to store computer executable instructions, and the processor is used to execute computer executable instructions to implement the dynamic fusion method of inertial navigation data proposed in the above embodiment.

[0129] This embodiment further provides a storage medium on which a computer program is stored. When the program is executed by a processor, the method for realizing dynamic fusion of inertial navigation data as proposed in the above embodiment is implemented.

[0130] The storage medium proposed in this embodiment and the method for realizing dynamic fusion of inertial navigation data 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.

[0131] 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. 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 (Random Access 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.

[0132] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. 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.

Claims

1. A dynamic fusion method for inertial navigation data, characterized in that: include: The original inertial navigation data of different positions of the wire are acquired in real time through the inertial navigation system, and the first data is obtained after preprocessing; Performing time synchronization processing on the inertial navigation system data at different positions in the first data to obtain time-synchronized inertial navigation data; Assign dynamic weights to each INS based on the accuracy and stability of the INS historical data; The state of the next time step is predicted by the linear dynamic system equation. When new time-synchronous inertial navigation data arrives, the Kalman gain, dynamic weight and new time-synchronous inertial navigation data are used to update the state estimate and error covariance matrix to output a fused data set processed by the Kalman filter.

2. The dynamic fusion method of inertial navigation data according to claim 1, characterized in that: The original inertial navigation data of different positions of the wire are obtained in real time through the inertial navigation system, including: Set the optimization objectives and constraints of the inertial navigation system; According to the optimization objectives and constraints, the particle swarm optimization algorithm is used to search for the optimal solution, the objective function values ​​of different system layout schemes are evaluated, and the final layout position of the system is determined according to the optimal solution; According to the final arrangement position, an inertial navigation system is installed at the corresponding position of the wire to obtain real-time data of each position.

3. The dynamic fusion method of inertial navigation data according to claim 2, characterized in that: The preprocessing includes: setting the window size of the moving average filter to N; Initialize a buffer of size N to store the most recent N data points; When a new data point arrives, add the new data point to the end of the buffer; If the buffer is full, remove the first element of the buffer so that the buffer always contains the latest N data points; The moving average of the latest N data points contained in the buffer is calculated to obtain the first data.

4. The dynamic fusion method of inertial navigation data as claimed in claim 3, characterized in that: The time synchronization process includes: Create an empty time synchronization data list and determine the synchronization unit; For each data set, create a mapping table from timestamp to data value; For each time point t within the common time range, if the time point t exists in the data map of the data set, the data value is added to the position of the corresponding time point in the time synchronization data list; if it does not exist, the data value of the time point is estimated using linear interpolation, added to the time synchronization data list, and the time synchronization inertial navigation data is output.

5. The dynamic fusion method of inertial navigation data according to claim 4, characterized in that: Also includes: Monitor the fused data set in real time and determine the running status of the conductor by using a preset threshold; If the monitored data exceeds the preset threshold, an alarm will be automatically triggered, indicating potential abnormal wire status.

6. The dynamic fusion method of inertial navigation data according to claim 5, characterized in that: The dynamic weight is expressed as: Among them, w i (t-1) represents the weight of the i-th inertial navigation system at the previous time t-1; β∈(0,1) represents the forgetting factor; α∈(0,1) represents the adjustment coefficient; represents the accuracy evaluation index of the i-th inertial navigation system at time t; is the stability evaluation index of the i-th inertial navigation system at time t, N represents a constant, representing the number of data points used to calculate the accuracy and stability evaluation index, t represents the time index, representing the current moment, i represents the index of the inertial navigation system, j represents the index of the data point, and p ij represents the predicted value of the i-th inertial navigation system for the j-th data point, p ij (t) represents the predicted value of the i-th inertial navigation system for the j-th data point at time t, r ij represents the actual observation value of the jth data point for the i-th inertial navigation system, r ij (t) represents the actual observed value at time t; ij represents the output value of the i-th inertial navigation system when processing the j-th data point, o ij (t) represents the output value of the i-th inertial navigation system when processing the j-th data point at time t; is the average of all output values ​​of the ith inertial navigation system at time t.

7. The method for dynamic fusion of inertial navigation data according to claim 1 or 6, characterized in that: It also includes initializing relevant parameters of the Kalman filter to obtain a linear dynamic system equation, wherein the relevant parameters include: an initial state, an initial error covariance, a state transfer matrix, an observation matrix, a process noise covariance matrix, and an observation noise covariance matrix.

8. A system using the dynamic fusion method of inertial navigation data as claimed in claim 1, characterized in that: include: An acquisition module is used to acquire raw inertial navigation data of different positions of the wire in real time through an inertial navigation system, and obtain first data after preprocessing; A synchronization module, used for performing time synchronization processing on the inertial navigation system data at different positions in the first data to obtain time-synchronized inertial navigation data; An allocation module for assigning a dynamic weight to each inertial navigation system based on the accuracy and stability of the inertial navigation system's historical data; The fusion module is used to predict the state of the next time step through the linear dynamic system equation. When new time-synchronous inertial navigation data arrives, the Kalman gain, dynamic weight and new time-synchronous inertial navigation data are used to update the state estimate and error covariance matrix to output a fused data set processed by the Kalman filter.

9. An electronic device, comprising: Memory and processor; The memory is used to store computer executable instructions, and the processor is used to execute the computer executable instructions. When the computer executable instructions are executed by the processor, the steps of the dynamic fusion method of inertial navigation data described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the steps of the method for dynamic fusion of inertial navigation data as claimed in any one of claims 1 to 7.

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