Distributed degaussing current high-reliability control method based on Kalman filtering algorithm

Abstract

The invention discloses a distributed degaussing current high-reliability control method based on a Kalman filtering algorithm, and relates to the technical field of ship degaussing, the distributed degaussing current high-reliability control method based on the Kalman filtering algorithm mainly comprises the following steps: according to the number of sections, arrangement positions and ship magnetic fields of measurement points of windings when the degaussing windings are all intact, determining the degaussing current of the ship; obtaining ampere-turn efficiency of a measurement point; establishing a state-space equation and an observation equation according to the ampere-turn efficiency of the measurement point and the ship magnetic field of the measurement point; and according to the state-space equation and the observation equation, using a Kalman filtering method to obtain the ampere turn of the residual intact degaussing coil. By implementing the distributed degaussing current high-reliability control method based on the Kalman filtering algorithm provided by the invention, the ampere-turn quantity of the remaining intact windings can be adjusted in real time, the magnetic field value of a ship is compensated by using the remaining windings, and the magnetic field protection effect is maintained.

Description

Technical Field

[0001] The present invention relates to the technical field of ship demagnetization, and more particularly to a high-reliability control method for distributed demagnetization current based on a Kalman filter algorithm. Background Art

[0002] As an inherent characteristic of ships, ships' magnetic fields become detection targets for magnetically fuzed weapons such as mines and torpedoes. With the continuous advancement of technology, mines and torpedoes have become increasingly intelligent. Failure to effectively control a ship's magnetic field will severely impact its viability. As modern ships grow larger, their principal dimensions will increase significantly, leading to more complex magnetic field characteristics and greater difficulty in compensating for them. In traditional centralized or zoned degaussing systems, multiple windings in the same direction within the same zone are connected in series. If a winding or power supply fails, the corresponding series windings will be unable to provide magnetic field compensation. To improve the comprehensive stealth performance of large surface ships, distributed degaussing systems are increasingly becoming the development direction for degaussing systems for modern large ships.

[0003] The distributed degaussing system uses an independent power supply for each winding section, adjusting the current in each winding section to achieve magnetic field compensation. Failure of one or more windings (or power supplies) can impact the ship's overall magnetic field protection. Magnetic field values at certain points or within a certain area may not be adequately compensated, leading to localized distortion and increasing the likelihood of magnetic detection. Therefore, a highly reliable distributed degaussing current control method is needed that can rapidly calculate and provide real-time feedback when a winding failure occurs. Through winding reconstruction, the ampere-turns of the remaining intact windings can be automatically adjusted, allowing compensation to be performed using the remaining windings to maintain protection effectiveness.

[0004] With the rapid development of computer technology and intelligent optimization algorithms, researchers at home and abroad have also conducted research on the application of intelligent algorithms to demagnetization current control methods. Kim ER et al. employed a control adjustment strategy based on the least squares method. However, for large data sets, the least squares method can be computationally complex, especially when the number of independent variables is large, which can result in long computation times. Madrid AV et al. applied a genetic algorithm (GA) to a ship demagnetization control method, achieving a magnetic field variance and peak value that were superior to the root mean square (RMS) value after compensation, improving the demagnetization current compensation effect. However, GA parameter setting is difficult, convergence is slow, and the results are random, increasing the uncertainty of the algorithm results. Xiao Cunlong et al. employed a binary search method to control the demagnetization winding compensation current. The binary search method is an efficient algorithm for finding specific elements in an ordered array, but its application also has some limitations and disadvantages. The binary search method requires the array to be ordered and static. If the array is unordered, it must be sorted first, which incurs additional time and space overhead. Summary of the Invention

[0005] The purpose of the present invention is to provide a high-reliability control method for distributed demagnetization current based on the Kalman filter algorithm, which can adjust the ampere-turns of the remaining intact windings in real time, use the remaining windings to compensate for the magnetic field value of the ship, and maintain the magnetic field protection effect.

[0006] The present invention provides a distributed demagnetization current high-reliability control method based on a Kalman filter algorithm, comprising the following steps: S1: Based on the number of winding sections, arrangement positions and the ship magnetic field at the measurement point when all demagnetized windings are intact, the ampere-turn efficiency at the measurement point is obtained; S2: establishing a state space equation and an observation equation according to the ampere-turn efficiency at the measurement point and the ship magnetic field at the measurement point; S3: According to the state space equation and the observation equation, the Kalman filter method is used to obtain the ampere-turns of the remaining intact degaussing coil.

[0007] Furthermore, step S1 specifically includes: obtaining the ampere-turn efficiency at the measuring point according to the number of winding sections, the arrangement position and the ship magnetic field at the measuring point when the demagnetized windings are all intact, as shown in the formula: , , , in, is the magnetic field strength generated by the straight wire in the winding in the surrounding space, is the coordinate of the measuring point, is the current in the straight wire, is the unit vector in the x-axis direction, is the unit vector along the y-axis, is the unit vector along the z-axis, is the distance from the straight wire in the winding to the measuring point, 、 and Respectively represent the magnetic field strength of the winding in the horizontal, longitudinal and vertical directions, that is, when the current of a single conductor is 1 ampere, it represents the ampere-turn efficiency of the winding; Indicates the number of segments of guiding wire contained in the winding, 、 and Respectively represent the winding The horizontal, longitudinal and vertical components of the magnetic field strength of a straight conductor.

[0008] Furthermore, step S2 specifically includes: establishing a state space equation and an observation equation according to the ampere-turn efficiency at the measurement point and the ship magnetic field at the measurement point; , , , , in, and is the state quantity, i.e., ampere-turns; n is the number of measurement points, is the process noise; is the measurement vector, is the measurement matrix, To measure noise; is the ship magnetic field at the measurement point, is the ampere-turn efficiency at the measurement point.

[0009] Furthermore, step S3 specifically includes: S31: performing a priori estimation based on the state space equation and the observation equation to obtain a priori estimated value of the state quantity; S32: Obtaining a priori error covariance matrix according to the priori estimated value of the state quantity; S33: Obtaining a Kalman gain according to the prior error covariance matrix; S34: performing a posteriori estimation based on the Kalman gain and the priori estimated value of the state quantity to obtain a posteriori estimated value of the state quantity; S35: Update the error covariance matrix according to the Kalman gain and the priori error covariance matrix.

[0010] Furthermore, step S31 specifically includes: performing a priori estimation based on the state space equation and the observation equation to obtain a priori estimated value of the state quantity, such as formula: , in, is the prior estimate at the nth iteration, is the last posterior estimate.

[0011] Furthermore, step S32 specifically includes: obtaining a priori error covariance matrix according to the priori estimated value of the state quantity, such as formula: , in, is the prior error covariance matrix, Compute the updated error covariance matrix for the last iteration, is the process noise covariance matrix.

[0012] Furthermore, step S33 specifically includes: obtaining the Kalman gain according to the prior error covariance matrix, such as formula: , in, is the Kalman gain at the nth iteration, is the transposed matrix of the measurement matrix, R is the measurement noise The covariance matrix of .

[0013] Furthermore, step S34 specifically includes: performing a posteriori estimation based on the Kalman gain to obtain a posteriori estimation value of the state quantity, such as formula: , in, is the posterior estimate at the nth iteration.

[0014] Furthermore, step S35 specifically includes: updating the error covariance matrix according to the Kalman gain and the prior error covariance matrix, such as formula: , in, is the error covariance matrix calculated for the nth iteration, and I is the identity matrix.

[0015] The present invention also provides a computer program product, comprising a computer program, which, when executed by a processor, implements the steps of the above-mentioned distributed demagnetization current high-reliability control method based on the Kalman filter algorithm.

[0016] The implementation of the distributed degaussing current high-reliability control method based on the Kalman filter algorithm provided by the present invention has the following beneficial effects: the present invention obtains the ampere-turn efficiency of the measuring point based on the number of winding sections, the arrangement position, and the ship magnetic field at the measuring point when all degaussing windings are intact; establishes a state-space equation and an observation equation based on the ampere-turn efficiency of the measuring point and the ship magnetic field at the measuring point; and obtains the ampere-turns of the remaining intact degaussing coils using the Kalman filter method based on the state-space equation and the observation equation; The present invention automatically adjusts the ampere-turns of the remaining intact windings by means of winding reconstruction, and uses the remaining windings to compensate for the magnetic field value of the ship to maintain the magnetic field protection effect; The computational complexity of the present invention is relatively low, especially for linear systems. It can quickly perform state estimation in systems that process large-scale data or have limited computing resources. The Kalman filter algorithm has strong real-time performance. The algorithm uses a recursive calculation method and only needs to use the estimated value at the previous moment and the measured value at the current moment to calculate the optimal estimated value at the current moment. It can update and process the data collected on-site in real time, and is very suitable for scenarios that require real-time feedback. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The present invention will be further described below with reference to the accompanying drawings and embodiments, in which: Figure 1 This is a flow chart of a distributed demagnetization current high-reliability control method based on a Kalman filter algorithm provided by the present invention; Figure 2 This is a schematic diagram of the magnetic field of a single straight wire provided by the present invention; Figure 3 The polygonal winding provided by the present invention; Figure 4 This is a flow chart of the Kalman filter algorithm provided by the present invention. DETAILED DESCRIPTION

[0018] In order to have a clearer understanding of the technical features, purposes and effects of the present invention, specific embodiments of the present invention are now described in detail with reference to the accompanying drawings.

[0019] Figure 1 A schematic diagram of a distributed demagnetization current high-reliability control method based on a Kalman filter algorithm of this embodiment is shown. In this embodiment, the distributed demagnetization current high-reliability control method based on a Kalman filter algorithm includes the following steps: S1: Based on the number of winding sections, arrangement positions and the ship magnetic field at the measurement point when all demagnetized windings are intact, the ampere-turn efficiency at the measurement point is obtained; In an exemplary embodiment, step S1 specifically includes: obtaining the ampere-turn efficiency at the measuring point according to the number of winding sections, the arrangement position, and the ship magnetic field at the measuring point when all demagnetized windings are intact, as shown in the formula: , , , in, is the magnetic field strength generated by the straight wire in the winding in the surrounding space, is the coordinate of the measuring point, is the current in the straight wire, is the unit vector in the x-axis direction, is the unit vector along the y-axis, is the unit vector along the z-axis, is the distance from the straight wire in the winding to the measuring point, 、 and Respectively represent the magnetic field strength of the winding in the horizontal, longitudinal and vertical directions, that is, when the current of a single conductor is 1 ampere, it represents the ampere-turn efficiency of the winding; Indicates the number of segments of guiding wire contained in the winding, 、 and Respectively represent the winding The horizontal, longitudinal and vertical components of the magnetic field strength of a straight conductor; It should be noted that when the demagnetized windings are intact and the number of winding sections and their layout are known, the ampere-turn efficiency a of the i-th section at the i-th measurement point can be derived. i,j ; and the ampere-turn efficiency of the i-th section at the j-th measurement point is ;Ship compensation magnetic field at the jth measurement point known; S2: establishing a state space equation and an observation equation according to the ampere-turn efficiency at the measurement point and the ship magnetic field at the measurement point; In an exemplary embodiment, step S2 specifically includes: establishing a state space equation and an observation equation according to the ampere-turn efficiency at the measurement point and the magnetic field of the ship at the measurement point; , , , , in, and is the state quantity, i.e., ampere-turns; n is the number of measurement points, is the process noise; is the measurement vector, is the measurement matrix, To measure noise; is the ship magnetic field at the measurement point, is the ampere-turn efficiency of the measurement point; It should be noted that, when the efficiency of each degaussing winding and the ship compensation magnetic field are known, the main problem of reconstructing the degaussing winding becomes the problem of solving the ampere-turns of the degaussing coil; is the state quantity X, is the measurement vector , is the measurement matrix , the state space equation can be established; S3: According to the state space equation and the observation equation, the Kalman filter method is used to obtain the ampere-turns of the remaining intact degaussing coils; In an exemplary embodiment, step S3 specifically includes: S31: performing a priori estimation based on the state space equation and the observation equation to obtain a priori estimated value of the state quantity; In an exemplary embodiment, step S31 specifically includes: performing a priori estimation based on the state space equation and the observation equation to obtain a priori estimated value of the state quantity, such as formula: , in, is the prior estimate at the nth iteration, is the last posterior estimate; S32: Obtaining a priori error covariance matrix according to the priori estimated value of the state quantity; In an exemplary embodiment, step S32 specifically includes: obtaining a priori error covariance matrix according to the priori estimated value of the state quantity, such as formula: , in, is the prior error covariance matrix, Compute the updated error covariance matrix for the last iteration, is the process noise covariance matrix; S33: Obtaining a Kalman gain according to the prior error covariance matrix; In an exemplary embodiment, step S33 specifically includes: obtaining the Kalman gain according to the prior error covariance matrix, such as the formula: , in, is the Kalman gain at the nth iteration, is the transposed matrix of the measurement matrix, R is the measurement noise The covariance matrix of S34: performing a posteriori estimation based on the Kalman gain and the priori estimated value of the state quantity to obtain a posteriori estimated value of the state quantity; In an exemplary embodiment, step S34 specifically includes: performing a posteriori estimation based on the Kalman gain to obtain a posteriori estimation value of the state quantity, such as formula: , in, is the posterior estimate at the nth iteration; S35: updating the error covariance matrix according to the Kalman gain and the prior error covariance matrix; In an exemplary embodiment, step S35 specifically includes: updating the error covariance matrix according to the Kalman gain and the prior error covariance matrix, such as formula: , in, is the error covariance matrix calculated for the nth iteration, and I is the identity matrix; It should be noted that, in step S3, calculation is performed by the Kalman filter algorithm (the specific steps of the Kalman filter algorithm are as follows Figure 4 As shown), the ampere-turns of the remaining windings are automatically adjusted according to the ampere-turns of the remaining intact degaussing coils, and the remaining windings are used to compensate for the magnetic field value of the ship to maintain the magnetic field protection effect; when using the Kalman filter method for calculation, the input and Repeat step S31 to step S31 to obtain the state quantity , and then calculate the remaining intact degaussing coil ampere-turns. When multiple section windings (or power supplies) are damaged, the method is similar.

[0020] As an exemplary embodiment, the above-mentioned distributed demagnetization current high-reliability control method based on the Kalman filter algorithm can also be implemented in the following manner. In this embodiment, the distributed demagnetization current high-reliability control method based on the Kalman filter algorithm (fault reconstruction) includes: When the demagnetized windings are intact and the number of winding sections, layout positions, etc. are known, the ampere-turn efficiency of the i-th section at the j-th measurement point can be obtained by equations (3)-(5): ; and the ship magnetic field at the jth measurement point known; When the efficiency of each degaussing winding and the ship compensation magnetic field are known, the main problem of degaussing winding reconstruction becomes the problem of solving the ampere-turns of the degaussing coil; is the state quantity X, is the measurement vector , A is the measurement matrix , establish the state space equations as formula (10), (11); Calculation is performed using the Kalman filter algorithm (the specific steps of the Kalman filter algorithm are as follows Figure 3 As shown in the figure, the ampere-turns of the remaining windings are automatically adjusted according to the ampere-turns of the remaining intact degaussing coils obtained by calculation, and the remaining windings are used to compensate for the magnetic field value of the ship to maintain the magnetic field protection effect.

[0021] As an exemplary embodiment, the above-mentioned distributed demagnetization current high-reliability control method based on the Kalman filter algorithm can also be implemented in the following manner. The following takes the reconstruction of the longitudinal rib winding XL of a certain type of ship model as an example to conduct a preliminary analysis of the distributed demagnetization current high-reliability control method (fault reconstruction) based on the Kalman filter algorithm; The demagnetization winding is used to offset the magnetic field of the ship. It is composed of a polygonal coil (i.e., cable) formed by sections of wire laid on the hull. When the demagnetization winding is laid along the longitudinal section of the ship, it is a transverse demagnetization winding, which is used to compensate for the transverse induced magnetic field of the ship. When the demagnetization winding is laid along the rib surface of the ship, it is a longitudinal demagnetization winding, which is used to compensate for the longitudinal induced magnetic field of the ship. When the demagnetization winding is laid horizontally along the two sides of the ship, it is a vertical demagnetization winding, which is used to compensate for the vertical induced magnetic field of the ship. The magnetic field of the demagnetization winding can be regarded as the vector sum of the magnetic fields generated by sections of energized wires. According to the magnetic field theory, if Figure 2 The coordinates of point A in the single straight wire AB are , the coordinates of point B are The magnetic field intensity generated at point K at a distance r from it can be obtained from the Biot-Savart theorem: (1) The magnetic field strength generated by a straight wire in the surrounding space is: (2) , When the straight wire is parallel to a coordinate plane or axis, the above integral formula can be simplified. For example, when the straight wire is parallel to the X-axis, formula (2) is simplified to: (3) When the straight wire is parallel to the xoy plane, equation (2) is simplified to: (4) From this we can see that as long as we know the coordinates of the endpoints of the straight wire, we can easily calculate the magnetic field strength at any point; like Figure 3 As shown, suppose the polygonal winding is composed of n straight wires, whose vertices are A, B, C, ..., and the magnetic field intensity generated at any point K in space is the vector sum of the magnetic field intensities of the straight wires that make up the polygonal winding; represents the spatial coordinates of the i-th (1≤i≤n) vertex. The magnetic field strength of the i-th conductor can be calculated using equations (3)-(4), and its vector sum can be expressed by equation (5): (5) The ampere-turns is the product of the number of coil turns and the current flowing through the coil, indicating the ability of the current to generate a magnetic field. The ampere-turns efficiency is the strength of the magnetic field generated by a single wire passing a current of 1A. Substituting I=1A into formula (5) yields the ampere-turns efficiency of the polygonal winding. Regardless of which magnetic field component the demagnetization winding in a ship is used to offset, it can be considered to be composed of several ampere-turn segments. Any ampere-turn segment can be equivalent to a polygon parallel to the coordinate plane or coordinate axis. The combination of the magnetic fields of each spatial polygon constitutes the magnetic field of the winding. Therefore, the ampere-turn efficiency of each segment winding in the distributed demagnetization system can be calculated using equations (3)-(5). The measured points are numbered in the order of port bow to stern, keel bow to stern, starboard bow to stern. Suppose there are m measuring points and n independent demagnetization sections in the ship. The ampere-turn efficiency of the i-th section at the j-th measuring point is Indicates that the ampere-turns of the i-th section are expressed as When the demagnetization windings are intact, the ship compensation magnetic field at the jth measurement point is expressed as express; Assuming that the number of winding sections and their layout are known when the demagnetized windings are intact, the ampere-turn efficiency a of the i-th section at the j-th measurement point can be obtained by equations (3)-(5): i,j ; and the ship magnetic field HZ at the jth measurement point j Given that the efficiency of each degaussing winding and the ship's magnetic field are known, the main problem of degaussing winding reconstruction becomes the problem of solving the ampere-turns of the degaussing coil. Assuming that the winding (or power supply) of the bth section on the ship needs to be reconstructed, the equation group can be listed accordingly: (6) Equation (6) can be transformed into: (7) (8) (9) In ampere-turns X i If the state quantity X is used, the state equation can be established as: (10) The number of measurement points n=1, 2, ..., N, where N is the maximum number of measurement points. is the process noise.

[0022] According to (7), the observation equation can be established: (11) The measurement vector in formula (11) , the measurement matrix , To measure noise; Combining equations (10) and (11), we can get the equation in ampere-turns: is the state space equation for the state.

[0023] According to the state space equation, the iterative steps of the Kalman filter algorithm are as follows: The first is the forecast, which includes: (1) Prior estimation: (12) in, is the prior estimate at the nth iteration, is the last posterior estimate; (2) Prior error covariance matrix: (13) in, is the prior error covariance matrix, Compute the updated error covariance matrix for the last iteration, is the process noise covariance matrix.

[0024] Then comes the correction, which includes: (3) Kalman gain: (14) in is the gain at the nth iteration, is the transposed matrix of the measurement matrix, R is the measurement noise The covariance matrix of (4) Posterior estimation: (15) in, is the posterior estimate at the nth iteration; (5) Update the error covariance matrix: (16) in, is the error covariance matrix calculated at the nth iteration, and I is the identity matrix; In summary, the steps of Kalman filter iteration are as follows: Figure 3 As shown, the following steps are included Step 1: Prior estimation: ; Step 2: Prior error covariance: ; Step 3: Kalman filter gain: ; Step 4: Posterior estimation: ; Step 5: Update error covariance: ; Then repeat Step 1 to Step 5; According to the above algorithm, input and , you can get the state quantity ; Then calculate the remaining intact degaussing coil ampere-turns; when multiple section windings (or power supplies) are damaged, the method is similar.

[0025] This embodiment provides a computer program product, including a computer program. When the computer program is executed by a processor, the computer program implements the steps of the above-mentioned distributed demagnetization current high-reliability control method based on the Kalman filter algorithm.

[0026] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the present invention and the claims, all of which are protected by the present invention.

Claims

1. A distributed demagnetization current high-reliability control method based on Kalman filter algorithm, characterized in that: The following steps are involved: S1: Based on the number of winding sections, arrangement positions and the ship magnetic field at the measurement point when all demagnetized windings are intact, the ampere-turn efficiency at the measurement point is obtained; S2: establishing a state space equation and an observation equation according to the ampere-turn efficiency at the measurement point and the ship magnetic field at the measurement point; S3: According to the state space equation and the observation equation, the Kalman filter method is used to obtain the ampere-turns of the remaining intact degaussing coil.

2. The distributed demagnetization current high-reliability control method based on the Kalman filter algorithm according to claim 1 is characterized in that: Step S1 specifically includes: obtaining the ampere-turn efficiency at the measuring point according to the number of winding sections, the arrangement position, and the ship magnetic field at the measuring point when all demagnetized windings are intact, as shown in the formula: ， ， ， in, is the magnetic field strength generated by the straight wire in the winding in the surrounding space, is the coordinate of the measuring point, is the current in the straight wire, is a unit vector in the three-dimensional Cartesian coordinate system, is the unit vector in the x-axis direction, is the unit vector along the y-axis, is the unit vector along the z-axis, is the distance from the straight wire in the winding to the measuring point, 、 and Respectively represent the magnetic field strength of the winding in the horizontal, longitudinal and vertical directions, that is, when the current of a single conductor is 1 ampere, it represents the ampere-turn efficiency of the winding; Indicates the number of segments of guiding wire contained in the winding, 、 and Respectively represent the winding The horizontal, longitudinal and vertical components of the magnetic field strength of a straight conductor.

3. The distributed demagnetization current high-reliability control method based on the Kalman filter algorithm according to claim 1 is characterized in that: Step S2 specifically includes: establishing a state space equation and an observation equation according to the ampere-turn efficiency at the measurement point and the ship magnetic field at the measurement point; ， ， ， ， in, and is the state quantity, i.e., ampere-turns; n is the number of measurement points, is the process noise; is the measurement vector, is the measurement matrix, To measure noise; is the ship magnetic field at the measurement point, is the ampere-turn efficiency at the measurement point.

4. The distributed demagnetization current high-reliability control method based on the Kalman filter algorithm according to claim 1 is characterized in that: Step S3 specifically includes: S31: performing a priori estimation based on the state space equation and the observation equation to obtain a priori estimated value of the state quantity; S32: Obtaining a priori error covariance matrix according to the priori estimated value of the state quantity; S33: Obtaining a Kalman gain according to the prior error covariance matrix; S34: performing a posteriori estimation based on the Kalman gain and the priori estimated value of the state quantity to obtain a posteriori estimated value of the state quantity; S35: Update the error covariance matrix according to the Kalman gain and the priori error covariance matrix.

5. The distributed demagnetization current high-reliability control method based on the Kalman filter algorithm according to claim 4 is characterized in that: Step S31 specifically includes: performing a priori estimation based on the state space equation and the observation equation to obtain a priori estimated value of the state quantity, such as the formula: ， in, is the prior estimate at the nth iteration, is the last posterior estimate.

6. The distributed demagnetization current high-reliability control method based on the Kalman filter algorithm according to claim 4 is characterized in that: Step S32 specifically includes: obtaining a priori error covariance matrix according to the priori estimated value of the state quantity, such as formula: ， in, is the prior error covariance matrix, Compute the updated error covariance matrix for the last iteration, is the process noise covariance matrix.

7. The distributed demagnetization current high-reliability control method based on the Kalman filter algorithm according to claim 4 is characterized in that: Step S33 specifically includes: obtaining the Kalman gain according to the prior error covariance matrix, such as the formula: ， in, is the Kalman gain at the nth iteration, is the transposed matrix of the measurement matrix, R is the measurement noise The covariance matrix of .

8. The distributed demagnetization current high-reliability control method based on the Kalman filter algorithm according to claim 4 is characterized in that: Step S34 specifically includes: performing a posteriori estimation based on the Kalman gain to obtain a posteriori estimation value of the state quantity, such as formula: ， in, is the posterior estimate at the nth iteration.

9. The distributed demagnetization current high-reliability control method based on the Kalman filter algorithm according to claim 4 is characterized in that: Step S35 specifically includes: updating the error covariance matrix according to the Kalman gain and the prior error covariance matrix, such as formula: ， in, is the error covariance matrix calculated for the nth iteration, and I is the identity matrix.

10. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the distributed demagnetization current high-reliability control method based on the Kalman filter algorithm described in any one of claims 1-x are implemented.

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