Linear motor speed uncertain disturbance rolling prediction determination method, system and device

Abstract

This invention belongs to the field of linear motor technology, specifically relating to a method, system, and device for predicting and determining the rolling motion of speed uncertainties in linear motors. It aims to solve the problem of the difficulty in quickly and accurately determining speed uncertainties in ultra-high-speed linear motors during transonic speeds. The invention includes: acquiring speed sensor measurements, performing fitting and standardization preprocessing to obtain a speed measurement value sequence V of the rolling prediction matrix; and then performing time series trend shift prediction to obtain a speed prediction value sequence V of the rolling prediction matrix. p Then, a speed rolling prediction matrix P is constructed. Based on the speed rolling prediction matrix P, a multi-period variable threshold method is used to determine whether a speed uncertainty disturbance occurs. If a speed uncertainty disturbance occurs, a speed anomaly signal is issued. This invention updates the rolling prediction matrix by calculating speed prediction values ​​in real time, constructs a judgment feature vector from the difference between multiple predicted values ​​and measured values, and determines the uncertainty disturbance based on the number of feature vectors exceeding an exponential threshold.

Description

Technical Field

[0001] This invention belongs to the field of linear motor technology, and specifically relates to a method, system and device for predicting and determining rolling motion due to speed uncertainty disturbance in linear motors. Background Technology

[0002] Ultra-high-speed long-stator linear motors possess advantages such as high thrust and high reliability, enabling rapid acceleration of large-mass objects and finding wide application in rail transportation and high-speed scientific experimental facilities. However, with increasing mover speed, ultra-high-speed long-stator linear motors are affected by non-fixed-length aerodynamic characteristics at transonic speeds, causing vehicle vibration and shaking. This leads to uncertainties affecting the speed measurement device of the ultra-high-speed linear motor, and determining these uncertainties is one of the bottleneck problems in accurately measuring the speed of ultra-high-speed linear motors.

[0003] Currently, regarding the problems of motor speed measurement and uncertain disturbance detection, patent CN114355186B discloses a diagnostic method for rotor bar breakage and speed sensor faults in an induction motor system. This method decouples faults, distinguishes different types of faults, and designs different observers for different faults. However, it is not suitable for situations where motor parameters change rapidly at ultra-high speeds. Patent CN112087173A discloses a motor speed fault detection method based on an observer. It uses a sliding mode observer to estimate the rotor current and speed of the induction motor, and then uses the residual signal value between the observer's rotor current and the actual current to determine whether the speed sensor is faulty. However, it cannot distinguish between motor speed sensor faults and stator winding inter-turn short-circuit faults. Patent CN105270955B discloses a self-diagnostic linear motor terminal forced deceleration device and fault detection method. It determines whether a fault has occurred by detecting position and comparing the signals from two speed sensors. However, the dual-sensor method is difficult to apply to ultra-high-speed uncertain disturbances. Patent CN217787134U discloses a fault detection device for a speed sensor in an automotive drive system, but it requires improvements to the speed sensor and the addition of new hardware.

[0004] To address the problem of determining speed uncertainty disturbances in transonic linear motors, this invention achieves rapid and accurate determination of uncertain disturbances by calculating and updating the rolling prediction matrix in real time, based on the difference between measured and predicted values. This not only avoids the need for observer design that relies on fixed motor parameters, but also eliminates the need for additional hardware. Summary of the Invention

[0005] To address the aforementioned problems in the prior art, namely the difficulty in quickly and accurately determining speed uncertainty disturbances in ultra-high-speed linear motors at transonic speeds, this invention provides a method for predicting and determining rolling motion during speed uncertainty disturbances in linear motors. The method includes:

[0006] Step S1: Obtain the speed sensor measurement value;

[0007] Step S2: Based on the speed sensor measurements, perform fitting and standardization preprocessing to obtain the speed measurement value sequence V of the rolling prediction matrix;

[0008] Step S3: Based on the velocity measurement value sequence V of the rolling prediction matrix, perform time series trend shift prediction to obtain the velocity prediction value sequence of the rolling prediction matrix. ;

[0009] Step S4, based on the speed measurement value sequence V and the speed prediction value sequence of the rolling prediction matrix. Construct the speed rolling prediction matrix P;

[0010] Step S5: Based on the speed rolling prediction matrix P, determine whether there is a speed uncertainty disturbance by using the multi-period variable threshold method. If there is a speed uncertainty disturbance, issue a speed anomaly signal.

[0011] Furthermore, the method for obtaining the velocity measurement sequence V of the rolling prediction matrix includes:

[0012] In the current k-th prediction cycle, the sequence of n+1 speed sensor measurement values ​​obtained from the previous n prediction cycles is denoted as the first sequence. : ; This represents the speed sensor measurement value in the current k-th prediction cycle. This represents the speed sensor measurement value during the (k-1)th prediction cycle. This represents the speed sensor measurement value during the kn-th prediction cycle;

[0013] Based on the first sequence Using the number of predicted cycles as the variable, a least-squares fit is performed to obtain the fitting matrix:

[0014] ;

[0015] in, The coefficient vector of the fitting matrix is ​​represented by m, which represents the number of terms in the fitting polynomial; E represents the residual between the actual and fitted values ​​of the sensor measurement, and T represents the transformation matrix. The terms in the coefficient vector of the fitted matrix; This term represents the residual between the actual value and the fitted value of the sensor measurement.

[0016] Based on the fitted matrix, the coefficient vector is calculated. Least square solution :

[0017] ;

[0018] Based on the coefficient vector Least square solution Calculate the first sequence Fitted sequence :

[0019] ;

[0020] in, Represents the transition matrix;

[0021] Based on the first sequence Fitted sequence Standardization is performed to obtain a sequence of velocity measurements for the rolling prediction matrix. :

[0022] ;

[0023] Where S represents the first sequence Fitted sequence The standard deviation.

[0024] Furthermore, the velocity prediction value sequence of the rolling prediction matrix ;

[0025] The velocity prediction value of the rolling prediction matrix for the current k-th prediction period. If expressed as a linear function, then the velocity prediction value of the rolling prediction matrix for the current k-th prediction period is:

[0026] ;

[0027] in, The intercept of a linear function. Let be the slope of a linear function. This represents the number of periods from the current k-th prediction period to the (k+d)-th prediction period.

[0028] Will The intercept and sum of a linear function The slope of a linear function is denoted as the smoothing coefficient.

[0029] The smoothing coefficient is calculated as follows:

[0030] ;

[0031] in, This represents a moving average. This represents the double moving average.

[0032] The moving average for:

[0033] ;

[0034] Indicates the preceding One forecast period;

[0035] The second moving average for:

[0036] ;

[0037] Speed ​​prediction value based on the rolling prediction matrix of the current k-th prediction period Using the smoothing coefficient, perform multi-step time series prediction to obtain the velocity prediction value for the current k-th prediction period. ;

[0038] ;

[0039] Where q represents the prediction step size.

[0040] Furthermore, step S4 specifically includes:

[0041] Based on the speed prediction value of the current k-th prediction period Construct a velocity rolling prediction matrix P with the velocity measurement value sequence V:

[0042] ;

[0043] This represents the d-th speed prediction value in the current k-th prediction period. Indicates the first Speed ​​measurement value for each prediction cycle.

[0044] Furthermore, determining whether a velocity uncertainty disturbance exists specifically includes:

[0045] Determine whether there is a velocity uncertainty disturbance using the multi-period variable threshold method:

[0046] ;

[0047] The This represents the disturbance judgment threshold for the (k+i)th prediction period, when the velocity prediction value of the (k+i)th rolling prediction matrix... And the velocity measurement of the (k+i)th rolling prediction matrix If the standard deviation exceeds the disturbance determination threshold, it is determined that the (k+i)th prediction period has been subject to a velocity disturbance; where H Indicates the threshold of the disturbance period;

[0048] ;

[0049] This represents the disturbance determination threshold for the (k+1)th period.

[0050] Furthermore, the method for issuing the speed anomaly signal includes:

[0051] When it is determined that a velocity disturbance was received in the (k+i)th prediction period, a count is generated through the sign function.

[0052] In another aspect, the present invention proposes a linear motor speed uncertainty disturbance rolling prediction and determination system, the system comprising:

[0053] The speed measurement module is configured to acquire speed sensor measurement values ​​through multiple speed sensors set on the transonic linear motor;

[0054] The speed measurement value sequence acquisition module is configured to perform fitting and standardization preprocessing based on the speed sensor measurement values ​​to obtain the speed measurement value sequence V of the rolling prediction matrix;

[0055] The speed prediction value sequence acquisition module is configured to perform time series trend shift prediction based on the speed measurement value sequence V of the rolling prediction matrix to obtain the speed prediction value sequence of the rolling prediction matrix. ;

[0056] The speed rolling prediction matrix construction module is configured to construct speed prediction values ​​based on the speed measurement value sequence V and the speed prediction value sequence of the rolling prediction matrix. Construct the speed rolling prediction matrix P;

[0057] The speed uncertainty disturbance determination module is configured to determine whether a speed uncertainty disturbance occurs based on the speed rolling prediction matrix P using a multi-period variable threshold method. If a speed uncertainty disturbance occurs, a speed abnormality signal is issued.

[0058] A third aspect of the present invention provides an electronic device comprising:

[0059] At least one processor; and

[0060] A memory communicatively connected to at least one of the processors; wherein,

[0061] The memory stores instructions that can be executed by the processor to implement the above-described linear motor speed uncertainty disturbance rolling prediction and determination method.

[0062] In a fourth aspect, the present invention provides a computer-readable storage medium storing computer instructions for execution by the computer to implement the above-described linear motor speed uncertainty disturbance rolling prediction and determination method.

[0063] The beneficial effects of this invention are:

[0064] (1) The present invention updates the rolling prediction matrix by calculating the speed prediction value in real time, constructs the judgment feature vector by the difference between the multi-point prediction value and the measurement value, and judges the uncertain disturbance based on the number of feature vectors exceeding the exponential threshold. Attached Figure Description

[0065] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0066] Figure 1 This is a flowchart illustrating the method for predicting and determining the rolling motion of a linear motor with uncertain speed disturbance in an embodiment of the present invention.

[0067] Figure 2 This is a schematic diagram illustrating the principle of the linear motor speed uncertainty disturbance rolling prediction and determination method in an embodiment of the present invention;

[0068] Figure 3 This is a schematic diagram of the structure of the transonic linear motor to which this invention is applied;

[0069] Figure 4 This is a schematic diagram of the simulation results of the speed and position of a transonic linear motor under sensor speed uncertainty disturbance. Detailed Implementation

[0070] The present application will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0071] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0072] To more clearly explain the linear motor speed uncertainty disturbance rolling prediction and determination method of the present invention, the following is in conjunction with... Figure 1 and Figure 2 The steps in the embodiments of the present invention will be described in detail below.

[0073] The linear motor speed uncertainty disturbance rolling prediction and determination method of the first embodiment of the present invention includes steps S1-S5, each step is described in detail below:

[0074] This embodiment is based on, as follows Figure 3 The ultra-high speed long stator linear motor with segmented power supply shown is implemented by using three converters connected in parallel in segments to supply power to the ultra-high speed linear motor. This can reduce the capacity of the converters and improve the voltage utilization rate.

[0075] Because the stator is longer than the mover, in order to reduce reactive power, improve the system power factor, and reduce power supply capacity, the long stator is divided into multiple stator segments, and power is supplied only to the stator segments near the mover. The segmented power supply linear motor drive system consists of a power supply, a power bus, a switching switch, a stator and a mover, and measuring devices such as speed sensors.

[0076] Step S1: Obtain the speed sensor measurement value;

[0077] After obtaining the measured values, this scheme includes two parts: the construction of the speed rolling prediction matrix (steps S2 to S4) and the speed uncertainty disturbance determination strategy (step S5). The rolling matrix is ​​updated by calculating the speed prediction values ​​in real time. A determination feature vector is constructed by the difference between the multi-point prediction values ​​and the measured values. Uncertain disturbances are determined based on the number of feature vectors that exceed the exponential threshold.

[0078] The construction of the speed rolling prediction matrix includes three parts: step S2, step S3, and step S4.

[0079] Step S2: Based on the speed sensor measurements, perform fitting and standardization preprocessing to obtain the speed measurement value sequence V of the rolling prediction matrix;

[0080] In this embodiment, the method for obtaining the velocity measurement sequence V of the rolling prediction matrix includes:

[0081] In the current k-th prediction cycle, the sequence of n+1 speed sensor measurement values ​​obtained from the previous n prediction cycles is denoted as the first sequence. : ; This represents the speed sensor measurement value in the current k-th prediction cycle. This represents the speed sensor measurement value during the (k-1)th prediction cycle. This represents the speed sensor measurement value during the kn-th prediction cycle;

[0082] Based on the first sequence Using the number of predicted cycles as the variable, a least-squares fit is performed to obtain the fitting matrix:

[0083] ;

[0084] in, The coefficient vector of the fitting matrix is ​​represented by m, which represents the number of terms in the fitting polynomial; E represents the residual between the actual and fitted values ​​of the sensor measurement, and T represents the transformation matrix. The terms in the coefficient vector of the fitted matrix; This term represents the residual between the actual value and the fitted value of the sensor measurement.

[0085] Based on the fitted matrix, the coefficient vector is calculated. Least square solution :

[0086] ;

[0087] Based on the coefficient vector Least square solution Calculate the first sequence Fitted sequence :

[0088] ;

[0089] in, Represents the transition matrix;

[0090] Based on the first sequence Fitted sequence Standardization is performed to obtain a sequence of velocity measurements for the rolling prediction matrix. :

[0091] ;

[0092] Where S represents the first sequence Fitted sequence The standard deviation.

[0093] Step S3: Based on the velocity measurement value sequence V of the rolling prediction matrix, perform time series trend shift prediction to obtain the velocity prediction value sequence of the rolling prediction matrix. ;

[0094] In this embodiment, the velocity prediction value sequence of the rolling prediction matrix ;

[0095] Within a hundred microseconds, the inertia of a mechanical system is usually large, and its velocity characteristics can be approximated as a linear function.

[0096] The velocity prediction value of the rolling prediction matrix for the current k-th prediction period. If expressed as a linear function, then the velocity prediction value of the rolling prediction matrix for the current k-th prediction period is:

[0097] ;

[0098] in, The intercept of a linear function. Let be the slope of a linear function. This represents the number of periods from the current k-th prediction period to the (k+d)-th prediction period.

[0099] Will The intercept and sum of a linear function The slope of a linear function is denoted as the smoothing coefficient.

[0100] The smoothing coefficient is calculated as follows:

[0101] ;

[0102] in, This represents a moving average. This represents the double moving average.

[0103] The moving average for:

[0104] ;

[0105] Indicates the preceding One forecast period;

[0106] The second moving average for:

[0107] ;

[0108] Speed ​​prediction value based on the rolling prediction matrix of the current k-th prediction period Using the smoothing coefficient, perform multi-step time series prediction to obtain the velocity prediction value for the current k-th prediction period. ;

[0109] ;

[0110] Where q represents the prediction step size.

[0111] Step S4, based on the speed measurement value sequence V and the speed prediction value sequence of the rolling prediction matrix. Construct the speed rolling prediction matrix P;

[0112] In this city's example, step S4 specifically includes:

[0113] Based on the speed prediction value of the current k-th prediction period Construct a velocity rolling prediction matrix P with the velocity measurement value sequence V:

[0114] ;

[0115] This represents the d-th speed prediction value in the current k-th prediction period. Indicates the first Speed ​​measurement value for each prediction cycle.

[0116] The first row of the matrix consists of the velocity measurement value before the k-th prediction period and the velocity prediction value of the k-th prediction period; the second row consists of the velocity measurement value before the (k+1)-th prediction period and the velocity prediction value of the (k+1)-th prediction period; the third, fourth, and fifth rows follow the same pattern, until the sixth row of the matrix, which consists only of the velocity measurement value of the (k+5)-th prediction period.

[0117] The constructed speed rolling prediction matrix is ​​updated once in each prediction period, continuously adding new speed prediction value sequences to the matrix while deleting the oldest speed prediction value sequence in the matrix.

[0118] Step S5: Based on the speed rolling prediction matrix P, determine whether there is a speed uncertainty disturbance by using the multi-period variable threshold method. If there is a speed uncertainty disturbance, issue a speed anomaly signal.

[0119] In this embodiment, determining whether a velocity uncertainty disturbance occurs specifically includes:

[0120] Determine whether there is a velocity uncertainty disturbance using the multi-period variable threshold method:

[0121] ;

[0122] The This represents the disturbance judgment threshold for the (k+i)th prediction period, when the velocity prediction value of the (k+i)th rolling prediction matrix... And the velocity measurement of the (k+i)th rolling prediction matrix If the standard deviation exceeds the disturbance determination threshold, it is determined that the (k+i)th prediction period has been subject to a velocity disturbance; where H Indicates the threshold of the disturbance period;

[0123] ;

[0124] This represents the disturbance determination threshold for the (k+1)th period.

[0125] In this embodiment, the method for issuing the speed anomaly signal includes:

[0126] When it is determined that a velocity disturbance was received in the (k+i)th prediction period, a count is generated through the sign function.

[0127] The simulation results obtained by the method described in this invention are as follows: Figure 4 As mentioned above, in Figure 4 In the Simulink simulation, to simulate the velocity uncertainty disturbance experienced by the actual system during transonic travel, a random velocity disturbance of up to 50 m / s is applied whenever the linear motor speed exceeds Mach 0.9 to simulate the transonic disturbance experienced by the velocity sensor. To simulate the noise disturbance experienced by the velocity sensor in the actual system, a short-duration disturbance with an amplitude of 5 m / s is applied at a time of 0.75 s. Since velocity uncertainty disturbances are mostly caused by velocity sensor pulse loss, the applied random disturbance is negative in direction, and the frequency and amplitude of the disturbance in the simulation are based on the velocity disturbance data from a real-world transonic experiment.

[0128] According to the simulation results shown in the figure, the rolling prediction matrix determination method proposed in this invention quickly emits a velocity anomaly signal (signal=1) when subjected to transonic uncertain disturbances, and quickly disappears (signal=0) after the transonic uncertain disturbances disappear, without misjudging the brief velocity noise at 0.75s. In contrast, the traditional single-cycle fixed threshold disturbance determination method, while correctly determining transonic uncertain disturbances, misjudges brief velocity noise signals.

[0129] Although the steps in the above embodiments are described in the above order, those skilled in the art will understand that in order to achieve the effect of this embodiment, different steps do not need to be executed in such an order. They can be executed simultaneously (in parallel) or in a reverse order. These simple variations are all within the protection scope of this invention.

[0130] The linear motor speed uncertainty disturbance rolling prediction and determination system of the second embodiment of the present invention includes:

[0131] The speed measurement module is configured to acquire speed sensor measurement values ​​through multiple speed sensors set on the transonic linear motor;

[0132] The speed measurement value sequence acquisition module is configured to perform fitting and standardization preprocessing based on the speed sensor measurement values ​​to obtain the speed measurement value sequence V of the rolling prediction matrix;

[0133] The speed prediction value sequence acquisition module is configured to perform time series trend shift prediction based on the speed measurement value sequence V of the rolling prediction matrix to obtain the speed prediction value sequence of the rolling prediction matrix. ;

[0134] The speed rolling prediction matrix construction module is configured to construct speed prediction values ​​based on the speed measurement value sequence V and the speed prediction value sequence of the rolling prediction matrix. Construct the speed rolling prediction matrix P;

[0135] The speed uncertainty disturbance determination module is configured to determine whether a speed uncertainty disturbance occurs based on the speed rolling prediction matrix P using a multi-period variable threshold method. If a speed uncertainty disturbance occurs, a speed abnormality signal is issued.

[0136] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the system described above can be found in the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0137] It should be noted that the linear motor speed uncertainty disturbance rolling prediction and determination system provided in the above embodiments is only an example of the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the various modules or steps and are not considered as an improper limitation of the present invention.

[0138] An electronic device according to a third embodiment of the present invention includes:

[0139] At least one processor; and

[0140] A memory communicatively connected to at least one of the processors; wherein,

[0141] The memory stores instructions that can be executed by the processor to implement the above-described linear motor speed uncertainty disturbance rolling prediction and determination method.

[0142] A computer-readable storage medium according to a fourth embodiment of the present invention stores computer instructions, which are executed by the computer to implement the above-described linear motor speed uncertainty disturbance rolling prediction and determination method.

[0143] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the storage device and processing device described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0144] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be placed in random access memory (RAM), main memory, read-only memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. To clearly illustrate the interchangeability of electronic hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the invention.

[0145] The terms “first”, “second”, etc., are used to distinguish similar objects, not to describe or indicate a specific order or sequence.

[0146] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus / device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent in such process, method, article, or apparatus / device.

[0147] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for predicting and determining rolling motion due to speed uncertainty disturbance in a linear motor, characterized in that, The method includes: Step S1: Obtain the speed sensor measurement value; Step S2: Based on the speed sensor measurements, perform fitting and standardization preprocessing to obtain the speed measurement value sequence V of the rolling prediction matrix; Step S3: Based on the velocity measurement value sequence V of the rolling prediction matrix, perform time series trend shift prediction to obtain the velocity prediction value sequence of the rolling prediction matrix. ; Step S4, based on the speed measurement value sequence V and the speed prediction value sequence of the rolling prediction matrix. Construct the speed rolling prediction matrix P; Step S5: Based on the speed rolling prediction matrix P, determine whether speed uncertainty disturbance occurs using the multi-period variable threshold method. If speed uncertainty disturbance occurs, issue a speed abnormality signal. The method for obtaining the velocity measurement sequence V of the rolling prediction matrix includes: In the current k-th prediction cycle, the sequence of n+1 speed sensor measurement values ​​obtained from the previous n prediction cycles is denoted as the first sequence. : ; This represents the speed sensor measurement value in the current k-th prediction cycle. This represents the speed sensor measurement value during the (k-1)th prediction cycle. This represents the speed sensor measurement value during the kn-th prediction cycle; Based on the first sequence Using the number of predicted cycles as the variable, a least-squares fit is performed to obtain the fitting matrix: ； in, The coefficient vector of the fitting matrix is ​​represented by m, which represents the number of terms in the fitting polynomial; E represents the residual between the actual and fitted values ​​of the sensor measurement, and T represents the transformation matrix. The terms in the coefficient vector of the fitted matrix; This term represents the residual between the actual value and the fitted value of the sensor measurement. Based on the fitted matrix, the coefficient vector is calculated. Least square solution : ； Based on the coefficient vector Least square solution Calculate the first sequence Fitted sequence : ； in, Represents the transition matrix; Based on the first sequence Fitted sequence Standardization is performed to obtain a sequence of velocity measurements for the rolling prediction matrix. : ； Where S represents the first sequence Fitted sequence The standard deviation.

2. The method for predicting and determining rolling motion due to uncertain speed disturbance of a linear motor according to claim 1, characterized in that, The speed prediction value sequence of the rolling prediction matrix ; The velocity prediction value of the rolling prediction matrix for the current k-th prediction period. If expressed as a linear function, then the velocity prediction value of the rolling prediction matrix for the current k-th prediction period is: ； in, The intercept of a linear function. Let be the slope of a linear function. This represents the number of periods from the current k-th prediction period to the (k+d)-th prediction period. Will The intercept and sum of a linear function The slope of a linear function is denoted as the smoothing coefficient. The smoothing coefficient is calculated as follows: ； in, This represents a moving average. This represents the double moving average. The moving average for: Indicates the preceding One forecast period; The second moving average for: Speed ​​prediction value based on the rolling prediction matrix of the current k-th prediction period Using the smoothing coefficient, perform multi-step time series prediction to obtain the velocity prediction value for the current k-th prediction period. ; ； Where d represents the prediction step size.

3. The method for predicting and determining rolling motion due to uncertain speed disturbance of a linear motor according to claim 2, characterized in that, Step S4 specifically includes: Based on the speed prediction value of the current k-th prediction period Construct a velocity rolling prediction matrix P with the velocity measurement value sequence V: ； This represents the d-th speed prediction value in the current k-th prediction period. Indicates the first Speed ​​measurement value for each prediction cycle.

4. The method for predicting and determining rolling motion due to uncertain speed disturbance of a linear motor according to claim 3, characterized in that, Determining whether a velocity uncertainty disturbance exists includes: Determine whether there is a velocity uncertainty disturbance using the multi-period variable threshold method: The This represents the disturbance determination threshold for the (k+i)th prediction period, and the velocity prediction value of the (k+i)th rolling prediction matrix. And the velocity measurement of the (k+i)th rolling prediction matrix If the standard deviation exceeds the disturbance determination threshold, it is determined that the (k+i)th prediction period has been subject to a velocity disturbance; where H Indicates the threshold of the disturbance period; ； This represents the disturbance determination threshold for the (k+1)th period.

5. The linear motor speed uncertainty disturbance rolling prediction and determination method according to claim 4, characterized in that, The method for issuing the speed anomaly signal includes: When it is determined that a velocity disturbance was received in the (k+i)th prediction period, a count is generated through the sign function.

6. A linear motor speed uncertainty disturbance rolling prediction and determination system, characterized in that, The system includes: The speed measurement module is configured to acquire speed sensor measurement values ​​through multiple speed sensors set on the transonic linear motor; The speed measurement value sequence acquisition module is configured to perform fitting and standardization preprocessing based on the speed sensor measurement values ​​to obtain the speed measurement value sequence V of the rolling prediction matrix; The speed prediction value sequence acquisition module is configured to perform time series trend shift prediction based on the speed measurement value sequence V of the rolling prediction matrix to obtain the speed prediction value sequence of the rolling prediction matrix. ; The speed rolling prediction matrix construction module is configured to construct speed prediction values ​​based on the speed measurement value sequence V and the speed prediction value sequence of the rolling prediction matrix. Construct the speed rolling prediction matrix P; The speed uncertainty disturbance determination module is configured to determine whether a speed uncertainty disturbance occurs based on the speed rolling prediction matrix P using a multi-period variable threshold method. If a speed uncertainty disturbance occurs, a speed abnormality signal is issued. The method for obtaining the velocity measurement sequence V of the rolling prediction matrix includes: In the current k-th prediction cycle, the sequence of n+1 speed sensor measurement values ​​obtained from the previous n prediction cycles is denoted as the first sequence. : ; This represents the speed sensor measurement value in the current k-th prediction cycle. This represents the speed sensor measurement value during the (k-1)th prediction cycle. This represents the speed sensor measurement value during the kn-th prediction cycle; Based on the first sequence Using the number of predicted cycles as the variable, a least-squares fit is performed to obtain the fitting matrix: ； in, The coefficient vector of the fitting matrix is ​​represented by m, which represents the number of terms in the fitting polynomial; E represents the residual between the actual and fitted values ​​of the sensor measurement, and T represents the transformation matrix. The terms in the coefficient vector of the fitted matrix; This term represents the residual between the actual value and the fitted value of the sensor measurement. Based on the fitted matrix, the coefficient vector is calculated. Least square solution : ； Based on the coefficient vector Least square solution Calculate the first sequence Fitted sequence : ； in, Represents the transition matrix; Based on the first sequence Fitted sequence Standardization is performed to obtain a sequence of velocity measurements for the rolling prediction matrix. : ； Where S represents the first sequence Fitted sequence The standard deviation.

7. An electronic device, characterized in that, include: At least one processor; as well as A memory communicatively connected to at least one of the processors; wherein, The memory stores instructions that can be executed by the processor to implement the linear motor speed uncertainty disturbance rolling prediction and determination method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that are executed by the computer to implement the linear motor speed uncertainty disturbance rolling prediction and determination method according to any one of claims 1-5.

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