A motor stator intelligent control method and system for crane

Abstract

The present invention relates to the field of crane technology, and specifically to a method and system for intelligent control of a motor stator for a crane, the method comprising: obtaining the historical motor speed of the crane, and arranging and constructing a speed sequence in chronological order; obtaining an optimized filtering algorithm based on the speed sequence; obtaining the current motor speed of the crane, filtering the current motor speed using the optimized filtering algorithm to obtain the filtered motor speed, calculating the speed difference between the filtered motor speed and the preset motor speed, and adjusting the motor speed according to the speed difference. The present invention improves the filtering effect of the crane motor speed data by calculating the noise interference degree of the motor speed data, and adaptively adjusting the difference threshold of the MVI algorithm according to the noise interference degree, thereby improving not only the accuracy of the data, but also the efficiency of the intelligent control of the motor stator for the crane.

Description

Technical Field

[0001] The present invention relates to the technical field of cranes, and more specifically, to an intelligent control method and system for a motor stator used in a crane. Background Art

[0002] The motor stator is an important part of motors such as generators and starters. For cranes, the rotating magnetic field generated by the coil on the motor stator after being energized produces relative motion with the conductor on the motor rotor, thereby generating induced current (i.e. eddy current) in the conductor. The induced current interacts with the rotating magnetic field to generate an electromagnetic force, causing the rotor to start rotating. The speed of rotation is the speed of the motor. This process provides the necessary power for the crane, so the performance and stability of the motor stator are crucial to the operation of the crane.

[0003] Prior art, such as the patent application document with publication number CN115258957A, discloses a slewing control method, a tower crane and a computer-readable storage medium. The slewing control method includes: obtaining a current command speed and a current motor speed; obtaining a boom slewing speed according to the current motor speed and a preset boom slewing speed estimation model; correcting the current command speed according to a preset correction model to obtain a current corrected command speed; adjusting the motor speed according to the current boom slewing speed and the corrected command speed; generating corresponding output frequency and voltage control signals according to the adjusted motor speed to control the motor operation.

[0004] The above slewing control method does not take into account the working environment of the crane. The motor speed collection process is susceptible to noise interference, resulting in noise in the collected data, which further causes the collected motor speed data to be inaccurate, with deviations or fluctuations. These inaccurate data will affect the accuracy and stability of the slewing control method, causing the crane to operate unstably or out of control. Summary of the invention

[0005] In order to solve the technical problem that the collected motor speed data is inaccurate, resulting in unstable operation of the crane, the present invention provides solutions in the following aspects.

[0006] In a first aspect, a method for intelligently controlling a motor stator for a crane comprises:

[0007] Get the historical motor speed of the crane and construct a speed sequence in chronological order;

[0008] A filtering algorithm optimized based on the speed sequence;

[0009] Obtaining the current motor speed of the crane, filtering the current motor speed using an optimized filtering algorithm to obtain a filtered motor speed, calculating a speed difference between the filtered motor speed and a preset motor speed, and adjusting the motor speed according to the speed difference;

[0010] The optimization process includes:

[0011] Select any historical motor speed as the target speed, calculate the mutation of the target speed; calculate the fitting error of the target speed; take the product of the mutation of the target speed and the fitting error as the noise interference degree of the target speed;

[0012] The noise interference degree is normalized to obtain a normalized noise interference degree, and the product of the normalized noise interference degree and the reference difference threshold is used as the adjusted difference threshold used when filtering the target speed.

[0013] The present invention analyzes the changing characteristics of the motor speed so that the filtering algorithm automatically adjusts the filtering parameters according to different working environments and load conditions, thereby significantly improving the filtering effect, and adjusts the motor speed according to the difference between the filtered motor speed data and the actual motor speed data to achieve more precise and stable control.

[0014] Preferably, the filtering algorithm is an MVI algorithm.

[0015] Preferably, the difference between the target speed and the average speed in a local range preset with the target speed as the center is calculated, and the difference between the target speed and the average speed in the local range is used as the sudden change of the target speed.

[0016] By taking the difference between the target speed and the average speed in the local range as the mutation, the instantaneous change of the speed can be quantified, thereby reflecting the stability or fluctuation of the speed. This quantitative analysis is of great significance for the precise control and fault diagnosis of the motor.

[0017] Preferably, the least squares method is used to perform curve fitting on the motor speed within a preset local range, and the average value of the sum of the areas enclosed by the fitting curve and the lines connecting all adjacent pairs of speeds within the local range is calculated to obtain the average area, which is used as the fitting error of the target speed.

[0018] The average area reflects the degree of deviation between the fitting curve and the actual speed data, and also indirectly reveals the noise and abnormal points in the data. When there are more noise or abnormal points in the data, the fitting curve may deviate far from the actual data points, resulting in an increase in the fitting error. Therefore, by monitoring the changes in the fitting error, the noise and abnormal points in the data can be discovered in time, and corresponding measures can be taken to deal with them.

[0019] Preferably, the noise interference degree is corrected to obtain a corrected noise interference degree, that is, the relationship is satisfied:

[0020] ; In the formula, is the noise interference degree of the corrected target speed, is the noise interference degree of the target speed before correction, is the variance of all data on the left side of the target speed within the local range of the target speed, is the variance of all data on the right side of the target speed within the local range of the target speed, is the mean of all data on the left side of the target speed within the local range of the target speed, is the mean of all data on the right side of the target speed within the local range of the target speed, It is an exponential function with the natural constant e as base.

[0021] By considering the variance and mean differences of the data on the left and right sides of the target speed, this formula helps to balance the smoothness and fit of the filtering algorithm and reduce the risks of overfitting and underfitting.

[0022] Preferably, the filtered motor speed data is used as the feedback signal of the closed-loop control system, the actual speed of the motor is collected, and the filtered motor speed data is compared with the set speed. When the actual speed is lower than the set speed, the stator voltage is increased to increase the motor speed; conversely, when the actual speed is higher than the set speed, the stator voltage is reduced to reduce the motor speed.

[0023] The filtered motor speed data is more stable and accurate. As a feedback signal, it can reduce control errors and improve the control accuracy of the system. Through closed-loop control, the system can automatically adjust the motor speed to keep it near the set value, thereby enhancing the stability of the system.

[0024] Preferably, the standard deviation of all rotation speeds within a preset local range centered on the target rotation speed is calculated, and the ratio of the standard deviation to the target rotation speed is taken as the mutation of the target rotation speed.

[0025] In a second aspect, a motor stator intelligent control system for a crane includes: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned motor stator intelligent control method for the crane is implemented.

[0026] The present invention has the following effects:

[0027] The present invention improves the filtering effect of the crane motor speed data by calculating the noise interference degree of the motor speed data and adaptively adjusting the difference threshold of the MVI algorithm according to the noise interference degree. This method not only improves the accuracy of the data, but also further improves the efficiency of the intelligent control of the motor stator used in the crane.

[0028] At the same time, combined with the closed-loop control principle to achieve precise speed control, the operating stability and control accuracy of the crane can be significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] By reading the following detailed description with reference to the accompanying drawings, the above and other objects, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-limiting manner, and the same or corresponding reference numerals represent the same or corresponding parts, wherein:

[0030] Figure 1 It is a method flow chart of steps S1 to S3 in a method for intelligent control of a motor stator for a crane according to an embodiment of the present invention.

[0031] Figure 2 It is a schematic diagram of steps S20 to S23 in a method for intelligently controlling a motor stator for a crane according to an embodiment of the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0033] The embodiment of the present invention discloses a motor stator intelligent control method for a crane, referring to Figure 1 , including steps S1 to S3, which are specifically as follows:

[0034] S1: Obtain the historical motor speed of the crane and construct a speed sequence in chronological order.

[0035] The Hall effect sensor is a magnetic sensor based on the Hall effect that can detect changes in the magnetic field. When the magnetic field strength changes, the Hall element in the sensor will generate a potential difference (Hall voltage), which can be converted into an electrical signal for output.

[0036] Magnetic markers (such as magnets) are installed on the shaft of the crane motor. These markers move as the motor rotates. Whenever the magnetic marker passes by the Hall effect sensor, the sensor detects the change in the magnetic field and generates a pulse signal.

[0037] The pulse signal generated by the Hall effect sensor can be recorded. By counting the number of pulses per unit time (such as per second), the number of rotations of the motor shaft, that is, the motor speed, can be indirectly calculated.

[0038] In the embodiment of the present invention, the acquisition frequency is set to 2 Hz, that is, data is collected every 0.5 seconds. Therefore, the number of pulses of the motor speed is recorded every 0.5 seconds, and the motor speed is calculated based on this. These historical motor speeds are arranged in chronological order to form a speed sequence.

[0039] S2: A filtering algorithm optimized based on the rotation speed sequence.

[0040] The crane motor speed data will be interfered by various noises in practical applications. The mean-value iterative filter (MVI) can effectively remove the noise in the data while retaining the real change characteristics of the data.

[0041] The general process of the MVI algorithm is:

[0042] (1) For each speed in the speed sequence, calculate the average of the speed and the two speeds before and after it, and calculate the difference between the average and the speed;

[0043] (2) The above difference values ​​corresponding to each speed are stored in a new sequence to form a difference sequence;

[0044] (3) Traverse the difference sequence and check whether each difference is greater than the set threshold. If the difference is greater than the threshold, the speed value is replaced by the average of the two speeds before and after the speed. Otherwise, no processing is required.

[0045] (4) Repeat the above steps (1), (2) and (3) for the updated speed sequence until the maximum difference in the difference sequence is less than the given threshold and the filtering is completed.

[0046] It should be noted that in the traditional mean value iteration filtering method (MVI), all data points use a fixed difference threshold to determine whether the current data point needs to be replaced with the mean of the previous and next data points. However, in practical applications, especially in the processing of crane motor speed data, due to the complexity of the working environment, the degree of noise interference to the motor speed data may vary greatly at different times. In addition, the motor speed data itself may also have natural changes due to the working characteristics of the motor. If the same threshold is used for all data points, filtering or under-filtering will occur.

[0047] Therefore, in order to process the motor speed data more accurately, a method of dynamically adjusting the threshold is adopted according to the speed data characteristics and the degree of noise interference to avoid the problems of filtering and under-filtering, so that the threshold can be adaptively adjusted to make the filtering process more refined, so as to better balance the relationship between denoising and retaining signal characteristics.

[0048] Reference Figure 2 The process of optimizing the MVI algorithm includes steps S20 to S23, which are as follows:

[0049] S20: Select any historical motor speed as the target speed and calculate the sudden change of the target speed.

[0050] Since the sensor is easily disturbed by the surrounding environment when collecting motor speed, noise exists in the collected data, and such noise appears as mutation points.

[0051] Specifically, any speed data in the speed sequence is selected as the target speed, and 2m (where m=7 is set based on experience) speed data are intercepted around the target speed to construct a local range of the target speed. The speed data in this local range is 2m+1. It should be noted that the first or last data in the speed sequence may not have enough previous or subsequent data points to form a complete window of 2m+1 data points, so for these edge data, their local ranges need to be discarded accordingly.

[0052] The average value of all speeds within the local range of the target speed is calculated, the difference between the target speed and the average speed of all speeds within the local range is calculated, and the difference between the target speed and the average speed within the local range is used as the mutation of the target speed.

[0053] Alternatively, the standard deviation of all rotation speeds within a preset local range with the target rotation speed as the center is calculated, and the ratio of the standard deviation to the target rotation speed is used as the sudden change of the target rotation speed.

[0054] Similarly, the mutation of all historical motor speeds can be obtained.

[0055] By calculating the difference between each historical motor speed and the average speed in its local range, abnormal data that is significantly different from surrounding data points can be identified.

[0056] S21: Calculate the fitting error of the target speed.

[0057] Specifically, the least squares method is used to perform curve fitting on all motor speeds within the local range of the target speed in the above S20 (with the timing sequence number within the local range of the target speed as the horizontal coordinate and the speed as the vertical coordinate) to obtain a fitting curve, where for the convenience of calculation, the number of historical motor data within the above local range is marked as n.

[0058] The coordinates of all historical motor speeds in the local range are respectively , , …, For each historical motor speed fitting value, it is expressed as , , …, .

[0059] For all the historical motor speeds in the local range, any adjacent historical motor speeds are connected with straight lines, such as connecting and Forming line segments .

[0060] For each line segment between adjacent historical motor speeds (i=1, 2, ..., n-1), calculate the points at both ends of the above fitting curve and With line segment The area between them is calculated using a geometric method. The areas of all the areas are added up and then divided by the number of historical motor speeds in the local range to obtain the average area, which is used as the fitting error of the target speed.

[0061] The quality of the fitting curve can be quantitatively evaluated by calculating the average area between the fitting curve and the adjacent historical motor speed connection line. The smaller the area, the closer the fitting curve is to the original data point, and the better the fitting effect. If the area of ​​the fitting curve and the adjacent data point connection line increases significantly in some local ranges, it may mean that the data points in these areas are interfered by anomalies or noise, which helps to identify and deal with potential problems in the data and improve the accuracy and reliability of the data.

[0062] S22: The product of the sudden change of the target speed and the fitting error is taken as the noise interference degree of the target speed.

[0063] Specifically, the product of the sudden change of the target speed and the fitting error is taken as the noise interference degree of the target speed.

[0064] When the target speed has a significant mutation, it means that the system may be affected by external interference or internal faults. This mutation can directly reflect the sensitivity of the system to external noise or internal disturbances. The fitting error can indirectly reflect the degree of noise interference. When the noise interference is large, the difference between the fitting curve and the actual data points will also increase, resulting in an increase in the fitting error. Therefore, the fitting error can be used as an important basis for evaluating the degree of noise interference.

[0065] When the target speed is abrupt, the fitting error may further increase. This is because the abruptness makes the distribution of data points more complex and irregular, increasing the difficulty of fitting. Therefore, the combined effect of abruptness and fitting error will increase the complexity of noise interference.

[0066] By multiplying the sudden change of the target speed and the fitting error, a quantitative noise interference index can be obtained. This index can intuitively reflect the fluctuation of the target speed when it is interfered by noise and the fitting effect, providing an important basis for subsequent noise processing and control.

[0067] According to the above method of calculating the noise interference degree of the target speed, the noise interference degrees corresponding to all other speeds in the speed sequence are calculated in a similar manner.

[0068] In addition, it should be noted that there are also normal data changes in the motor speed of the crane (such as motor speed adjustment or load changes), which will cause some speed data points to show sudden changes in their local range. Therefore, it is necessary to correct the noise interference degree calculated above to avoid misjudging these normally changing speed data as excessive noise interference.

[0069] Specifically, still taking the above target speed as an example, the variance and mean of all data on the left side of the target speed and the variance and mean of all data on the right side of the target speed within the local range of the target speed are calculated respectively.

[0070] The noise interference degree is corrected by combining the above variance and mean to obtain the corrected noise interference degree, which satisfies the relationship:

[0071]

[0072] In the formula, is the noise interference degree of the corrected target speed, is the noise interference degree of the target speed before correction, is the variance of all data on the left side of the target speed within the local range of the target speed, is the variance of all data on the right side of the target speed within the local range of the target speed, is the mean of all data on the left side of the target speed within the local range of the target speed, is the mean of all data on the right side of the target speed within the local range of the target speed, It is an exponential function with the natural constant e as base.

[0073] The noise interference degree is corrected by using the stability and difference of the data on both sides of the local range of the speed data point. The higher the stability of the data on both sides of the target speed and the larger the difference, the higher the possibility of normal data change, and the lower the noise interference degree.

[0074] When the stability and difference are large, the correction factor approaches 0, resulting in a lower degree of noise interference after correction. Conversely, when the stability and difference are small, the correction factor approaches 1, and the correction effect on the degree of noise interference is small.

[0075] Through the above correction method, the noise interference degree of each speed data can be corrected, so as to more accurately evaluate the real noise level of the data.

[0076] S23: normalizing the noise interference degree to obtain a normalized noise interference degree, and taking the product of the normalized noise interference degree and the reference difference threshold as the adjusted difference threshold used in filtering the target speed.

[0077] Based on the noise interference degree of all speed data calculated above, the difference threshold in the MVI algorithm is adaptively adjusted to process the speed data more accurately and avoid over-filtering or under-filtering. For data with large noise interference, the possibility of replacement is increased by reducing the difference threshold; and for normal data with small noise interference, the risk of wrong replacement is reduced by increasing the difference threshold. Such adaptive adjustment makes the filtering algorithm more flexible and effective.

[0078] Specifically, still taking the target speed as an example, the corresponding noise interference degree is normalized to obtain the normalized noise interference degree, and the product of the normalized noise interference degree and the reference difference threshold (according to experience, the reference difference threshold is 50) is used as the adjusted difference threshold used when filtering the target speed. The adjusted difference threshold satisfies the relationship:

[0079]

[0080] In the formula, The adjusted difference threshold used when filtering the target speed. is the baseline difference threshold, is the corrected noise interference level, is the normalization function.

[0081] S3: Obtain the current motor speed of the crane, filter the current motor speed using an optimized filtering algorithm to obtain the filtered motor speed, calculate the speed difference between the filtered motor speed and the preset motor speed, and adjust the motor speed according to the speed difference.

[0082] The current motor speed of the crane is collected in the same manner as the speed collection method in S1, and the current motor speed of the crane is filtered according to the general process of the MVI algorithm in S2 and the adaptive difference threshold to obtain the filtered motor speed.

[0083] Furthermore, the filtered motor speed data is used as a feedback signal of the closed-loop control system. The actual speed of the motor is collected in real time by the sensor and compared with the set speed. According to the difference between the actual speed and the set speed, the intelligent control system adjusts the size and direction of the stator voltage. When the actual speed is lower than the set speed, the stator voltage is increased to increase the motor speed; conversely, when the actual speed is higher than the set speed, the stator voltage is reduced to reduce the motor speed.

[0084] In summary, the motor speed data of the crane is filtered by adaptively adjusting the difference threshold of the MVI algorithm, and precise speed control is achieved by combining the closed-loop control principle, which can significantly improve the operation stability and control accuracy of the crane.

[0085] An embodiment of the present invention further discloses an intelligent control system for a motor stator for a crane, comprising a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an intelligent control method for a motor stator for a crane according to the present invention is implemented.

[0086] The system also includes other components familiar to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0087] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus or device. For example, a computer-readable storage medium may be any appropriate magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM (Resistive Random Access Memory), a dynamic random access memory DRAM (Dynamic Random Access Memory), a static random access memory SRAM (Static Random-Access Memory), an enhanced dynamic random access memory EDRAM (Enhanced Dynamic Random Access Memory), a high-bandwidth memory HBM (High-Bandwidth Memory), a hybrid memory cube HMC (Hybrid Memory Cube), etc., or any other medium that can be used to store the required information and can be accessed by an application, a module or both. Any such computer storage medium may be part of a device or accessible or connectable to a device.

[0088] In the description of this specification, "plurality" or "several" means at least two, such as two, three or more, etc., unless otherwise clearly and specifically defined.

[0089] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

Claims

1. A method for intelligent control of a motor stator for a crane, characterized in that: include: Get the historical motor speed of the crane and construct a speed sequence in chronological order; A filtering algorithm optimized based on the speed sequence; Obtaining the current motor speed of the crane, filtering the current motor speed using an optimized filtering algorithm to obtain a filtered motor speed, calculating a speed difference between the filtered motor speed and a preset motor speed, and adjusting the motor speed according to the speed difference; The optimization process includes: Select any historical motor speed as the target speed, calculate the mutation of the target speed; calculate the fitting error of the target speed; take the product of the mutation of the target speed and the fitting error as the noise interference degree of the target speed; The noise interference degree is normalized to obtain a normalized noise interference degree, and the product of the normalized noise interference degree and the reference difference threshold is used as the adjusted difference threshold used when filtering the target speed.

2. The method for intelligent control of a motor stator for a crane according to claim 1, characterized in that: The filtering algorithm is the MVI algorithm, and the full name of MXI is Mean-Value Iteration filter.

3. The method for intelligent control of a motor stator for a crane according to claim 2, characterized in that: The difference between the target speed and the average speed in a local range preset with the target speed as the center is calculated, and the difference between the target speed and the average speed in the local range is used as the mutation of the target speed.

4. The method for intelligently controlling a motor stator for a crane according to claim 3 is characterized in that: The least squares method is used to perform curve fitting on the motor speed within a preset local range, and the average value of the sum of the areas enclosed by the fitting curve and the lines connecting all adjacent pairs of speeds within the local range is calculated to obtain the average area, which is used as the fitting error of the target speed.

5. The method for intelligently controlling a motor stator for a crane according to claim 4, characterized in that: It also includes correcting the noise interference degree to obtain the corrected noise interference degree, that is, satisfying the relationship: ; In the formula, is the noise interference degree of the corrected target speed, is the noise interference degree of the target speed before correction, is the variance of all data on the left side of the target speed within the local range of the target speed, is the variance of all data on the right side of the target speed within the local range of the target speed, is the mean of all data on the left side of the target speed within the local range of the target speed, is the mean of all data on the right side of the target speed within the local range of the target speed, It is an exponential function with the natural constant e as base.

6. The method for intelligently controlling a motor stator for a crane according to claim 5, characterized in that: The filtered motor speed data is used as the feedback signal of the closed-loop control system. The actual speed of the motor is collected and compared with the set speed. When the actual speed is lower than the set speed, the stator voltage is increased to increase the motor speed; conversely, when the actual speed is higher than the set speed, the stator voltage is reduced to reduce the motor speed.

7. The method for intelligently controlling a motor stator for a crane according to claim 2, characterized in that: The standard deviation of all rotation speeds within a preset local range with the target rotation speed as the center is calculated, and the ratio of the standard deviation to the target rotation speed is used as the mutation of the target rotation speed.

8. An intelligent control system for a motor stator used in a crane, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the motor stator intelligent control method for a crane according to any one of claims 1 to 7 is implemented.

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