A Closed-Loop Control Method for Tower Crane Speed ​​Based on Position Detection and Resolver Data Coupling

Abstract

This invention relates to the field of mechanical control technology, specifically to a closed-loop control method for tower crane speed based on position detection and resolver data coupling. The method includes: acquiring suspension point displacement, temperature data, vibration data, motor speed, and voltage data during the tower crane's operation, across data acquisition cycles; calculating the drum radius within each data acquisition cycle and further analyzing the acceleration at the suspension point to obtain the suspension point load; calculating the first influence weight of the vibration data dimension and the second influence weight of the temperature data dimension, and combining the changing trends of the temperature, vibration, and voltage data to correct the suspension point displacement; and adjusting the tower crane's motor speed based on the difference between the corrected displacement and the suspension point displacement per unit time under the same suspension point load. This invention achieves highly sensitive, highly accurate, and highly efficient tower crane operation control.

Description

Technical Field

[0001] This invention relates to the field of mechanical control technology, specifically to a closed-loop control method for tower crane speed based on position detection and resolver data coupling. Background Technology

[0002] Tower pumping units are indispensable equipment in traditional oil extraction. Their basic components include a tower, an electric pump, and a drive motor. The tower, as the main support structure, is located above the wellhead, supporting and fixing the electric pump and drive motor. The latter is connected to the electric pump via shaft drive or a direct coupling, thus enabling the extraction and transportation of crude oil from underground to surface processing facilities. Despite their widespread use in both traditional and onshore oil fields, their operating efficiency and energy utilization still need improvement. The main challenges facing conventional tower pumping units include low energy efficiency, high maintenance costs, and limitations in handling high-production wells and high-viscosity oils. Therefore, energy saving, efficiency improvement, and intelligent control systems become particularly important.

[0003] In terms of speed control technology, existing tower-type pumping units commonly employ methods such as variable frequency speed regulation and flexible drive variable frequency speed control. However, these technologies suffer from high costs, complex implementation, and electromagnetic interference, and cannot achieve precise adjustment of the pumping unit motor speed. Summary of the Invention

[0004] To address the technical problem that existing methods for controlling tower crane speed cannot achieve precise adjustment of pumping unit motor speed, the present invention aims to provide a closed-loop control method for tower crane speed based on position detection and resolver data coupling. The specific technical solution adopted is as follows: During the operation of the tower crane, the suspension point displacement, suspension point load, temperature data, vibration data, and voltage data of the tower crane are acquired in each data acquisition cycle. Based on the correlation between temperature data and vibration data in each data acquisition cycle, the first influence weight of vibration data dimension and the second influence weight of temperature data dimension are obtained in each data acquisition cycle. Based on the first and second influence weights, and combined with the degree of change in temperature data, vibration data, and voltage data within the corresponding data acquisition cycle, the displacement of the suspension point within the data acquisition cycle is corrected to obtain the corrected displacement for each data acquisition cycle. The motor speed of the tower crane is adjusted based on the difference between the corrected displacement in each data acquisition cycle and the displacement per unit time under the same suspension load.

[0005] Preferably, the step of obtaining the first influence weight of the vibration data dimension and the second influence weight of the temperature data dimension in each data acquisition cycle based on the correlation between temperature data and vibration data in each data acquisition cycle specifically includes: For any data acquisition cycle, a temperature data sequence is constructed based on the temperature data at each moment within the data acquisition cycle, and a vibration data sequence is constructed based on the vibration data at each moment within the data acquisition cycle. Using temperature and vibration data sequences as inputs, Granger causality tests are used to obtain p-values ​​corresponding to the test results. The p-values ​​are used to characterize the correlation between temperature and vibration data. Based on the difference between the p-value and the preset significance level, the preset initial influence weight is adjusted to obtain the first influence weight and the second influence weight, respectively.

[0006] Preferably, the step of adjusting the preset initial influence weights based on the difference between the p-value and the preset significance level to obtain the first influence weight and the second influence weight specifically includes: Based on the proportion of differences between the preset significance level and the p-value corresponding to the test results, the adjustment factor is determined; the negative correlation coefficient of the preset initial influence weight and the first product of the adjustment factor are calculated, and the sum of the initial influence weight and the first product is taken as the first influence weight of the vibration data dimension. The value range of the initial influence weight is (0,1). The second influence weight of the temperature data dimension is determined based on the first influence weight of the vibration data dimension, and the sum of the first influence weight and the second influence weight is 1.

[0007] Preferably, the step of correcting the suspension point displacement within the data acquisition cycle based on the first influence weight and the second influence weight, combined with the degree of change trend of temperature data, vibration data, and voltage data within the corresponding data acquisition cycle, to obtain the corrected displacement for each data acquisition cycle, specifically includes: For any data acquisition cycle, the temperature change factor is obtained based on the degree of temperature data fluctuation within the current data acquisition cycle and the temperature data change trend between the current data acquisition cycle and adjacent data acquisition cycles. The vibration change factor is obtained based on the degree of fluctuation of vibration data within the current data acquisition cycle and the trend of vibration data change between the current data acquisition cycle and adjacent data acquisition cycles. The voltage fluctuation factor is obtained based on the distribution of voltage data fluctuations between the current data acquisition cycle and historical data acquisition cycles. Using the first influence weight and the second influence weight, the vibration change factor and the temperature change factor are weighted and summed respectively. The product of the summation result and the voltage fluctuation factor is used as the data fluctuation coefficient corresponding to the current data acquisition cycle. The data fluctuation coefficient is negatively correlated and normalized to obtain a correction factor. The product of the correction factor and the displacement of the suspension point within the data acquisition period is used as the correction displacement of the data acquisition period.

[0008] Preferably, the step of obtaining the temperature change factor based on the fluctuation level of temperature data within the current data acquisition cycle and the trend of temperature data change between the current data acquisition cycle and adjacent data acquisition cycles specifically includes: A fitted temperature line is obtained by performing linear fitting on the temperature data in the current data acquisition period and all previous data acquisition periods; the ratio of the difference between the mean of all temperature data in the current data acquisition period and the mean of all temperature data in the adjacent previous data acquisition period is calculated to obtain the temperature change rate of the current data acquisition period; the product of the absolute value of the slope of the fitted temperature line and the temperature change rate is used as the temperature change factor.

[0009] Preferably, the step of obtaining the vibration variation factor based on the fluctuation level of vibration data within the current data acquisition cycle and the trend of vibration data change between the current data acquisition cycle and adjacent data acquisition cycles specifically includes: A fitted vibration line is obtained by performing linear fitting on the vibration data in the current data acquisition period and all previous data acquisition periods; the ratio of the difference between the mean of all vibration data in the current data acquisition period and the mean of vibration data in the adjacent previous data acquisition period is calculated to obtain the vibration change rate of the current data acquisition period; the product of the absolute value of the slope of the fitted vibration line and the vibration change rate is used as the vibration change factor.

[0010] Preferably, the step of obtaining the voltage fluctuation factor based on the distribution of voltage data fluctuation levels between the current data acquisition cycle and historical data acquisition cycles specifically includes: Calculate the first variance of all voltage data within the current data acquisition period, and the second variance of all voltage data within all previous data acquisition periods; use the degree of difference between the first variance and the second variance as the voltage fluctuation factor.

[0011] Preferably, the step of adjusting the motor speed of the tower crane based on the difference between the corrected displacement in each data acquisition cycle and the displacement per unit time under the same suspension load specifically includes: When the displacement difference between the corrected displacement of a certain data acquisition cycle and the displacement of the suspension point per unit time under the same suspension point load is greater than 0, the speed of the tower crane's motor is reduced; when the displacement difference between the corrected displacement of a certain data acquisition cycle and the displacement of the suspension point per unit time under the same suspension point load is less than 0, the speed of the tower crane's motor is increased.

[0012] Preferably, the method further includes: The tower crane's motor speed is acquired during each data acquisition cycle in the operation of the tower crane; The radius of the roller in each data acquisition cycle is obtained based on the belt winding situation on the roller in each data acquisition cycle. Based on the motor speed and the drum radius, the acceleration at the suspension point is analyzed to obtain the suspension point load.

[0013] Preferably, obtaining the roller radius for each data acquisition cycle based on the belt winding situation on the roller within each data acquisition cycle specifically includes: The roller radius in each data acquisition cycle includes the roller radius at the suspension point end and the roller radius at the counterweight end; The method for obtaining the radius of the roller at the suspension point end is as follows: ; ; in, This represents the radius of the roller at the suspension point at time i. This indicates the roller radius at the bottom dead center. This indicates the radius of the roller at the top dead center. This indicates the initial radian of the roller when it is at bottom dead center. This represents the increase in radians at the i-th moment during the motion of the suspension point. This represents the initial radian at the suspension point when the point is at top dead center. Indicates belt thickness. Pi This represents the floor function. represents the moment at the top dead center, and n represents the moment at the bottom dead center.

[0014] Preferably, the method for obtaining the radius of the counterweight end roller is as follows: ; ; in, This represents the radius of the roller at the balancing end at time i. This represents the increase in radians of the counterweight at time i during the motion.

[0015] Preferably, the step of analyzing the acceleration at the suspension point based on the motor speed and the drum radius to obtain the suspension point load specifically includes: ; ; ; in, This represents the suspension load during the s-th data acquisition cycle. This indicates the total number of moments within the data collection period. Let represent the theoretical acceleration at the suspension point at time t. This represents the motor speed at time t. This represents the speed of the reducer at time t. This represents the radius of the roller at the suspension point at time t. Let represent the linear velocity at time t. Let t represent the linear velocity of the reducer at time t.

[0016] Preferably, the minimum radius of the drum around which the tower crane winds one loop of belt is 0.287 meters.

[0017] The embodiments of the present invention have at least the following beneficial effects: This invention employs a high-precision wire displacement sensor and a rotary transformer to monitor the suspension point position and motor speed in real time, and calculates the drum radius, acceleration, and total dynamic load, which helps to achieve precise control of the motor speed. Secondly, the intelligent control processor can simultaneously collect and process suspension point displacement, load, motor speed, electrical parameters, temperature, and vibration data, enabling real-time data analysis and processing, reducing noise interference. Simultaneously, based on changes in temperature, vibration data, and electrical parameters, it calculates the fluctuation coefficient and corrects the displacement, thereby enhancing the response speed of the tower crane speed adjustment, improving the tower crane's working efficiency, and achieving highly sensitive, highly accurate, and highly efficient tower crane operation control, thus improving the quality of the tower crane speed closed-loop control. Attached Figure Description

[0018] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1This is a flowchart of the steps of a closed-loop control method for tower crane speed based on position detection and resolver data coupling provided by the present invention; Figure 2 A schematic diagram of the closed-loop control system provided by this invention; Figure 3 A flowchart illustrating the steps of the method for obtaining suspension point load provided by this invention; Figure 4 A schematic diagram of a belt being wound on a roller according to the present invention; Figure 5 This is a flowchart of the steps for obtaining the first influence weight and the second influence weight provided by the present invention; Figure 6 This is a flowchart of the steps for obtaining the corrected displacement amount of the data acquisition cycle provided by the present invention; Figure 7 This is a flowchart of the execution method of the closed-loop control system provided by the present invention. Detailed Implementation

[0020] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a tower crane speed closed-loop control method based on position detection and resolver data coupling proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0021] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0022] The specific implementation scenario addressed by this invention is as follows: Speed ​​control of tower-type pumping units is crucial for improving efficiency. Traditional speed control methods often employ variable frequency speed regulation (VFD) or flexible drive VFD, but these methods have certain problems, such as electromagnetic interference, the need for complex technical support, and challenges in system stability and maintenance, leading to negative long-term effects. Therefore, a speed control method with strong anti-interference capabilities and high stability, capable of precisely adjusting the speed, is needed to improve the energy efficiency of tower-type pumping units. Based on this, the method of this invention utilizes a position sensor to acquire real-time rotor position information and adjusts the motor speed using resolver data. This method is not only lower in cost and simpler to implement, but also possesses strong anti-interference capabilities and high stability, enabling precise speed adjustment and improved energy efficiency.

[0023] The following description, in conjunction with the accompanying drawings, details a specific scheme for a closed-loop control method for tower crane speed based on position detection and resolver data coupling provided by the present invention.

[0024] Please see Figure 1 The diagram illustrates a flowchart of a closed-loop control method for tower crane speed based on position detection and resolver data coupling, according to an embodiment of the present invention. The method includes the following steps: Step S100: During each data acquisition cycle of the tower crane operation, acquire the suspension point displacement, suspension point load, temperature data, vibration data, and voltage data of the tower crane.

[0025] First, it should be noted that the resolver data coupling system in this embodiment includes data on motor speed, motor torque, reducer speed, and reducer torque. This system combines high-precision sensors and intelligent control processors to collect and process data such as suspension point displacement, load, and motor speed in real time, thereby reducing noise interference, optimizing control strategies, and improving control accuracy.

[0026] like Figure 2 As shown, the closed-loop control system consists of a temperature sensor, a vibration sensor, a guy wire displacement sensor, a load sensor, a rotary transformer, three-phase electrical parameter monitoring sensors, and an intelligent control processor. The temperature sensor is installed on the motor windings; the vibration sensor is installed on the motor housing. The guy wire displacement sensor is installed on the suspension point side to detect the corresponding displacement, i.e., the suspension point displacement, and converts it into a pulse output of corresponding precision—that is, the conversion from displacement to digital quantity. The load sensor is installed at the suspension point of the tower crane to measure the load in real time. The rotary transformer is calculated based on the frequency information set by the frequency converter installed at the motor end. The three-phase electrical parameter monitoring sensors read electrical parameters at a sampling frequency of 10 times / second. The intelligent control processor is mainly used to provide synchronization relationships for the synchronously acquired suspension point displacement, load, motor speed, and electrical parameters.

[0027] In this embodiment, a high-precision wire displacement sensor (model WPS-XL-10000-SV112-YH) is used to monitor the suspension point position in real time. It operates on a 24V DC power supply and supports 5-30V pulse power supply. It offers multiple signal output formats, including current (4-20mA), voltage (0-10V DC), resistance (0-5kΩ), and pulse (incremental / absolute) to meet diverse application requirements. The sensor boasts a high vibration resistance coefficient (50g) and elasticity coefficient (400g~500g), enabling stable operation within a temperature range of -40℃ to +80℃, and exhibiting a linear accuracy of ±0.5%FS. Its IP65 protection rating ensures reliable operation even in harsh environments. With a measurement range of 0-10 meters, the sensor accurately monitors changes in the tower crane's suspension point position, providing precise position signals to the control system.

[0028] In this embodiment, during the entire data acquisition process, a 10-second period is used as the cycle length. All data collected within each 10-second period is considered as the monitoring data within a cycle. That is, the time length of each data acquisition cycle is 10 seconds. Within each data acquisition cycle, the suspension point displacement, suspension point load, temperature data, vibration data, and voltage data are collected at each moment. The time interval between two adjacent moments is equal.

[0029] like Figure 3 As shown, the embodiment of the present invention also includes a method for obtaining the suspension point load, which can be implemented by steps S110 to S130.

[0030] Step S110: Acquire the motor speed of the tower crane during each data acquisition cycle in the operation of the tower crane.

[0031] Specifically, the motor speed can be monitored and obtained in real time by a motor speed sensor, and the motor speed can be obtained at every moment in each data acquisition cycle.

[0032] Step S120: Obtain the radius of the drum in each data acquisition cycle based on the belt winding situation on the drum in each data acquisition cycle.

[0033] The number of belt turns around the drum, or drum radius, is calculated based on the suspension point position. This is used to calculate important parameters such as suspension point velocity and acceleration. As the belt winds around the drum, the suspension point position changes, allowing us to determine the number of belt turns and thus the drum radius at that moment. Figure 4 As shown, where Where is the radius of the roller. This includes the actual radius of the belt. , The belt thickness; the roller radius is an important parameter for calculating the suspension point speed, acceleration, etc.

[0034] The roller radius in each data acquisition cycle includes the roller radius at the suspension point end and the roller radius at the counterweight end; The method for obtaining the radius of the roller at the suspension point end is as follows: ; ; in, This represents the radius of the roller at the suspension point at time i. This indicates the roller radius at the bottom dead center. This indicates the radius of the roller at the top dead center. This indicates the initial radian of the roller when it is at bottom dead center. This represents the increase in radians at the i-th moment during the motion of the suspension point. This represents the initial radian at the suspension point when the point is at top dead center. Indicates belt thickness. Pi This represents the floor function. represents the moment at the top dead center, and n represents the moment at the bottom dead center.

[0035] Starting roller radius It is determined by the roller radius Add the initial number of wraps multiplied by the belt thickness. It is obtained by determining the arc rotated by the length of the first second stroke during the upstroke. Add the rotated arc Divide by To determine if the belt has been wrapped around the waist completely, multiply the number of wraps by the number of loops. Plus the initial roller radius It is equal to the radius of the roller during movement. The belt is made of a thick neutral layer. .

[0036] Similarly, during the downstroke It is the roller radius at top dead center, using The total radius of the stroke winding minus the radius of the stroke winding around. Divide by To determine whether it has unfolded into a circle, use Subtract the number of unfolded circles and multiply by Obtain the radius of the roller during movement In most pumping units, the duration of the upstroke and downstroke is not exactly equal during operation, so a [specific feature / mechanism] is set. and Two values ​​divide the entire up and down stroke into two parts. The portion, the upward stroke accounts for Part, Downward Strike Occupies share; For any given moment in the motion.

[0037] The method for obtaining the radius of the counterweight end roller is as follows: ; ; in, This represents the radius of the roller at the balancing end at time i. This represents the increase in radians of the counterweight at time i during the motion.

[0038] The motion of the counterweight end is opposite to that of the suspension end; that is, when the suspension end makes an upstroke, the counterweight end makes a downstroke, and vice versa. Therefore, when At that time, the counterweight end makes a downward stroke, and the roller radius... Equal to the top dead center roller radius Subtract the number of unfolded circles and multiply by Similarly, when At that time, the counterweight end performs the upstroke, and the roller radius... equal to the bottom dead center roller radius Add the number of wraps and multiply by .

[0039] The simplified roller radius can be accurately calculated using the formulas for the suspension point end and the counterweight end. Based on this, it can be analyzed that when the number of winding turns changes (i.e., the diameter changes), the circumference formula of a circle can be used to determine the radius. ,in It is the diameter. This refers to the circumference. A change in diameter means the overall diameter will also change, thus increasing the circumference. This means that for the same number of motor rotations, the belt's displacement on the roller will increase. Conversely, when the motor rotates in the opposite direction, the belt's displacement on the roller will decrease for the same number of rotations.

[0040] Step S130: Based on the motor speed and the drum radius, analyze the acceleration at the suspension point to obtain the suspension point load.

[0041] The angular velocity of the motor rotor is detected by a rotary transformer. According to the angular velocity of the motor rotor The motor speed and torque can be obtained, where the reducer speed is 1 / 28 of the motor speed. The relevant calculations are well-known techniques and will not be elaborated further.

[0042] When the drum radius changes, i.e., when the diameter and torque change, the linear velocity of the suspension point also changes. When the motor continuously accelerates or frequently switches between acceleration and deceleration, the load position of the suspension point will change due to acceleration. Considering the entire stroke's running time, acceleration can be used to represent the intensity of the dynamic load on the suspension point. The sum of the squares of the accelerations over the entire running period reflects the total dynamic load borne by the suspension point throughout the entire stroke. Therefore, this relationship can be expressed by the following formula: ; ; ; in, This represents the suspension load during the s-th data acquisition cycle. This indicates the total number of moments within the data collection period. Let represent the theoretical acceleration at the suspension point at time t. This represents the motor speed at time t. This represents the speed of the reducer at time t. This represents the radius of the roller at the suspension point at time t. Let represent the linear velocity at time t. Let t represent the linear velocity of the reducer at time t.

[0043] It should be noted that due to the change in diameter, the roller radius will vary at different times. However, the roller radius must meet the following condition: the roller radius for the initial one turn of the belt must satisfy the following condition: That is, the acceleration of the motor does not exceed That is, the minimum radius of the roller with one initial wrap of belt is 0.287m.

[0044] Step S200: Based on the correlation between temperature data and vibration data in each data acquisition cycle, the first influence weight of the vibration data dimension and the second influence weight of the temperature data dimension are obtained in each data acquisition cycle.

[0045] It should be noted that the embodiments of the present invention aim to obtain the displacement difference by comparing the actual displacement with the displacement under the same load and without load change, and to adjust the motor deceleration or acceleration based on the displacement difference. However, in actual production, angular velocity will fluctuate due to changes in equipment status. This fluctuation in angular velocity caused by equipment status often has a significant lag; that is, equipment status does not directly cause changes in angular velocity, but rather gradually affects other parameters, and as the fluctuations accumulate, they affect the motor's operation, thus causing a change in angular velocity. Therefore, to avoid the lag problem in motor speed adjustment based solely on displacement difference obtained from angular velocity, it is necessary to improve the sensitivity of speed adjustment to ensure the response speed and working efficiency of the tower-type pumping unit's speed adjustment.

[0046] During tower crane operation, temperature changes, lubricant quality, and power supply stability are key factors affecting motor operation, leading to variations in angular velocity and displacement. These factors are also highly sensitive to changes in the motor's operating status. During operation, temperature changes cause a positive correlation between the resistance of the motor windings, resulting in a relatively heavier motor load. This leads to a decrease in motor performance, reducing efficiency and output torque, and consequently, decreasing speed and displacement. Similarly, a decline in lubricant quality increases friction during motor operation, further increasing the motor load and reducing speed and displacement. Simultaneously, friction also increases heat generation, further affecting temperature changes during operation.

[0047] Based on this, this embodiment performs feature analysis from two data dimensions: temperature and vibration, to quantify the impact of temperature and vibration on the operation of the tower crane, and then correct or compensate for them. For example... Figure 5 As shown, the method for obtaining the first influence weight and the second influence weight can be implemented by steps S210 to S230.

[0048] Step S210: For any data acquisition cycle, construct a temperature data sequence based on the temperature data at each moment within the data acquisition cycle, and construct a vibration data sequence based on the vibration data at each moment within the data acquisition cycle.

[0049] Specifically, this step will take any data acquisition cycle as an example. All temperature data within the data acquisition cycle will be arranged in chronological order to construct the temperature data sequence of the data acquisition cycle. Similarly, all vibration data within the data acquisition cycle will be arranged in chronological order to construct the vibration data sequence of the data acquisition cycle.

[0050] Step S220: Using the temperature data sequence and vibration data sequence as input, the Granger causality test is used to obtain the p-value corresponding to the test result. The p-value is used to characterize the correlation between the temperature data and the vibration data.

[0051] Specifically, the temperature data sequence and vibration data sequence of the data acquisition cycle are used as inputs, and the Granger causality test algorithm is used. The maximum lag order is automatically obtained through the information criterion method. The significance level is taken as the default value of 0.05 in this embodiment. The null hypothesis during the implementation of the Granger causality test algorithm is "temperature is not a Granger cause of vibration" or "vibration is not a Granger cause of temperature". Finally, the corresponding p value is output.

[0052] Granger causality tests and information criteria are well-known techniques and will not be discussed in detail here. It is understandable that the p-value is the output of the Granger causality test algorithm. Typically, the null hypothesis in this algorithm is that there is no Granger causality between the variables. A smaller p-value indicates sufficient evidence to suggest a Granger causality between the vibration and temperature data.

[0053] Step S230: Based on the difference between the p-value and the preset significance level, adjust the preset initial influence weight to obtain the first influence weight and the second influence weight respectively.

[0054] During motor operation, temperature changes may be caused by increased resistance between motor windings, or by deterioration in lubricating oil quality, leading to increased friction and vibration, and consequently, higher temperatures. In these two different scenarios, the degree of influence of temperature and vibration data on the abnormal situation should differ, and therefore the weights for adjusting displacement in these two data dimensions should also differ. When the temperature change is due to resistance, the influence weights of temperature and vibration should be similar; however, when the temperature change is caused by vibration, the influence weight of vibration data should be relatively higher. Based on this, the correlation between vibration and temperature data can be used to adaptively determine the weights corresponding to the influence of vibration data and temperature data on the abnormal displacement situation.

[0055] Specifically, an adjustment factor is determined based on the proportion of differences between the preset significance level and the p-value corresponding to the test result; the negative correlation coefficient of the preset initial influence weight and the first product of the adjustment factor are calculated, and the sum of the initial influence weight and the first product is taken as the first influence weight of the vibration data dimension, and the value range of the initial influence weight is (0,1); the second influence weight of the temperature data dimension is determined based on the first influence weight of the vibration data dimension, and the sum of the first influence weight and the second influence weight is 1.

[0056] As a concrete example, the formulas for calculating the first influence weight and the second influence weight can be expressed as follows: ; ; in, This indicates the primary influence weight of the data acquisition period in the vibration data dimension. This indicates the second most influential factor of the data acquisition period in the temperature data dimension. This indicates the preset initial influence weight. Indicates the significance level. This represents the p-value obtained from the Granger causality test. For a sign function, when When the value is less than 0, the function value is 0; otherwise, the function value is equal to 0. The value of .

[0057] It should be noted that the initial influence weight ranges from (0,1), and the recommended range is [0.4,0.6]. In this embodiment, the value is 0.5. Implementers can set it according to the specific implementation scenario.

[0058] The smaller the p-value obtained from the Granger causality test, the stronger the correlation between vibration and temperature data. In other words, there is more sufficient evidence to prove a relationship between the two data, making it more likely that the temperature change is caused by vibration, and the vibration data plays a dominant role. At this point, the significance level is greater than the p-value. The larger the value, the greater the influence weight of the corresponding dimension of vibration data. That is, the first influence weight represents the magnitude of the impact of the data change in the dimension of vibration data on the anomaly. Consequently, the more secondary the temperature data is, the smaller the influence weight of the corresponding temperature data dimension. That is, the second influence weight represents the magnitude of the impact of the data change in the dimension of temperature data on the anomaly.

[0059] When the significance level is smaller than the p-value, it indicates that there is insufficient evidence to suggest a significant correlation or causal relationship between temperature and vibration. In this case, the likelihood of temperature change being caused by resistance is higher, and the corresponding weights of the influence between temperature and vibration are closer.

[0060] Step S300: Based on the first influence weight and the second influence weight, and combined with the degree of change trend of temperature data, vibration data and voltage data within the corresponding data acquisition cycle, the displacement of the suspension point within the data acquisition cycle is corrected to obtain the corrected displacement for each data acquisition cycle.

[0061] The stability of the power supply directly affects the fluctuation of the motor's input power, thus impacting its speed. A decrease in voltage may cause a drop in motor speed, while an increase in voltage may lead to an increase in speed. Frequent fluctuations between overshoot and undershoot can affect the displacement. During motor operation, changes in temperature, vibration, and power supply stability are more sensitive to changes in angular velocity than changes in angular velocity. By combining data from these three dimensions and adjusting for changes in angular velocity, more sensitive and accurate control of the motor speed can be achieved.

[0062] In this embodiment, as Figure 6 As shown, this step will still be explained using any data acquisition cycle as an example. The method for obtaining the correction displacement of the data acquisition cycle can be implemented by steps S310 to S350.

[0063] Step S310: For any data acquisition cycle, obtain the temperature change factor based on the degree of temperature data fluctuation within the current data acquisition cycle and the temperature data change trend between the current data acquisition cycle and adjacent data acquisition cycles.

[0064] Specifically, a fitted temperature line is obtained by performing linear fitting on the temperature data in the current data acquisition period and all previous data acquisition periods; the ratio of the difference between the mean of all temperature data in the current data acquisition period and the mean of all temperature data in the adjacent previous data acquisition period is calculated to obtain the temperature change rate of the current data acquisition period; the product of the absolute value of the slope of the fitted temperature line and the temperature change rate is used as the temperature change factor.

[0065] In this embodiment, the i-th data acquisition cycle is taken as an example. That is, the i-th data acquisition cycle is regarded as the current data acquisition cycle. The fitted temperature line corresponding to the i-th data acquisition cycle is the straight line obtained by fitting all temperature data from the first data acquisition cycle to the i-th data acquisition cycle.

[0066] As a concrete example, the formula for calculating the temperature change factor corresponding to the i-th data acquisition cycle can be expressed as: , ,in, This represents the temperature change factor corresponding to the i-th data acquisition cycle. Let represent the slope of the fitted temperature line corresponding to the i-th data acquisition cycle. This represents the mean of all temperature data within the i-th data acquisition period. This represents the mean of all temperature data within the (i-1)th data acquisition period. Indicates the duration of the data collection period. This represents the growth rate of the temperature data in the i-th data acquisition cycle.

[0067] The slope value reflects the trend of temperature data changes across all previous data acquisition cycles. The growth rate reflects the increase in temperature data in the current data acquisition cycle and the adjacent data acquisition cycle. By comprehensively analyzing the data results from these two aspects, we can consider the changes in temperature data from both a holistic and local perspective. In other words, the temperature data change factor corresponding to the data acquisition cycle characterizes the changes in temperature data within the data acquisition cycle.

[0068] Step S320: Obtain the vibration change factor based on the fluctuation level of vibration data within the current data acquisition cycle and the trend of vibration data change between the current data acquisition cycle and adjacent data acquisition cycles.

[0069] Specifically, a fitted vibration line is obtained by performing linear fitting on the vibration data in the current data acquisition period and all previous data acquisition periods; the ratio of the difference between the mean of all vibration data in the current data acquisition period and the mean of vibration data in the adjacent previous data acquisition period is calculated to obtain the vibration change rate of the current data acquisition period; the product of the absolute value of the slope of the fitted vibration line and the vibration change rate is used as the vibration change factor.

[0070] For the same reason, similar to the method for obtaining the temperature change factor, in this embodiment, the fitted vibration line corresponding to the i-th data acquisition cycle is the straight line obtained by fitting all vibration data from the first data acquisition cycle to the i-th data acquisition cycle.

[0071] As a concrete example, the formula for calculating the vibration change factor corresponding to the i-th data acquisition cycle can be expressed as: , ,in, This represents the vibration change factor corresponding to the i-th data acquisition cycle. This represents the slope of the fitted vibration line corresponding to the i-th data acquisition cycle. This represents the mean of all vibration data within the i-th data acquisition period. This represents the mean of all vibration data within the (i-1)th data acquisition period. Indicates the duration of the data collection period. This represents the growth rate of vibration data during the i-th data acquisition cycle.

[0072] The slope value reflects the trend of vibration data across all previous data acquisition cycles. The growth rate reflects the growth of vibration data in the current data acquisition cycle and the adjacent data acquisition cycle. By comprehensively analyzing the data results from these two aspects, the changes in vibration data can be considered from both a holistic and local perspective. In other words, the temperature data change factor corresponding to the data acquisition cycle characterizes the changes in vibration data within the data acquisition cycle.

[0073] Step S330: Obtain the voltage fluctuation factor based on the distribution of voltage data fluctuation levels between the current data acquisition cycle and historical data acquisition cycles.

[0074] Specifically, the first variance of all voltage data within the current data acquisition period and the second variance of all voltage data within all previous data acquisition periods are calculated; the degree of difference between the first variance and the second variance is used as the voltage fluctuation factor.

[0075] In this embodiment, the i-th data acquisition cycle is used as an example for explanation. The voltage fluctuation factor corresponding to the i-th data acquisition cycle can be expressed by the formula: ,in, This represents the voltage fluctuation factor corresponding to the i-th data acquisition cycle. This represents the variance of all voltage data within the i-th data acquisition period, also known as the first variance. This represents the variance of all voltage data within all data acquisition cycles prior to the i-th data acquisition cycle, i.e., the variance of all voltage data from the first data acquisition cycle to the (i-1)-th data acquisition cycle, which is also known as the second variance.

[0076] It reflects the difference between the voltage fluctuation of the current data acquisition cycle and the voltage fluctuation of the historical data acquisition cycle. It measures the difference between the voltage data of the current data acquisition cycle and the overall fluctuation. The greater the difference, the greater the fluctuation of the voltage corresponding to the data dimension of the current data acquisition cycle, and the worse the power supply stability of the motor.

[0077] Step S340: Using the first influence weight and the second influence weight, the vibration change factor and the temperature change factor are weighted and summed respectively. The product of the summation result and the voltage fluctuation factor is used as the data fluctuation coefficient corresponding to the current data acquisition cycle.

[0078] The first influence weight represents the degree of influence of the data changes in the corresponding dimension of vibration data on the abnormal situation, and the second influence weight represents the degree of influence of the data changes in the corresponding dimension of temperature data on the abnormal situation. Therefore, by using the magnitude of the influence weight to weight the changes in each data dimension, we can more accurately integrate the data changes in the three dimensions.

[0079] Specifically, the data fluctuation coefficient corresponding to the i-th data acquisition cycle can be expressed by the formula: ,in This represents the data fluctuation coefficient corresponding to the i-th data acquisition period. This represents the first influence weight of the i-th data acquisition cycle in the vibration data dimension. This represents the second influence weight of the i-th data acquisition cycle in the temperature data dimension. This represents the temperature change factor corresponding to the i-th data acquisition cycle. This represents the vibration change factor corresponding to the i-th data acquisition cycle. This represents the voltage fluctuation factor corresponding to the i-th data acquisition cycle.

[0080] When there are certain abnormalities in the motor's operating status, it will cause abnormal fluctuations in temperature or vibration data. The temperature or vibration data generally show an increasing trend, and the growth rate between adjacent cycles is also relatively large. The larger the data fluctuation coefficient is at this time, the greater the data fluctuation coefficient will be. When the motor's power supply stability is poor, that is, when the voltage data fluctuates more severely, the corresponding data fluctuation coefficient will also be larger.

[0081] Step S350: The data fluctuation coefficient is negatively correlated and normalized to obtain a correction factor. The product of the correction factor and the displacement of the suspension point within the data acquisition period is used as the correction displacement of the data acquisition period.

[0082] It should be noted that abnormal motor operation can lead to increased motor load. Under this load, the angular velocity should be relatively small, and thus the displacement should also be small. However, because the change in angular velocity is relatively lagging, adjusting the motor speed based on the measured load and angular velocity cannot directly reflect this increased load. As a result, the displacement is generally smaller until the next speed adjustment cycle, and the data fluctuation coefficient is relatively large. Therefore, when comparing displacement, the fluctuation coefficient can be used for correction, which can improve the sensitivity of motor speed adjustment to a certain extent and reduce the displacement deviation between two speed adjustments.

[0083] In this embodiment, the displacement of the suspension point is the displacement per unit time. Taking the i-th data acquisition cycle as an example, the corrected displacement of the i-th data acquisition cycle can be expressed by the formula:

[0084] This represents the data fluctuation coefficient corresponding to the i-th data acquisition period. This represents the displacement of the suspension point during the i-th data acquisition cycle. This represents the correction displacement in the i-th data acquisition cycle. This is the normalization function.

[0085] When the motor operates abnormally, directly adjusting the speed using the suspension point displacement will result in an overall smaller displacement between two speed adjustments. Therefore, it's necessary to reduce the initially calculated suspension point displacement to increase the displacement difference between this value and the displacement per unit time when the load remains unchanged. This allows for a larger motor speed adjustment, ensuring the tower crane's operational efficiency. However, the data fluctuation coefficient is larger in this situation, resulting in a smaller corrected displacement. Conversely, when the motor operates normally, the motor load is closer to the actual measured value. Therefore, a displacement value closer to the actual load can be used for adjustment. The data fluctuation coefficient is smaller in this case, resulting in a corrected displacement value closer to the initial suspension point displacement.

[0086] By using the corrected displacement to subsequently adjust the rotational speed, the sensitivity of the rotational speed adjustment can be improved, and the response speed of the tower pumping unit's rotational speed adjustment can be enhanced, thereby ensuring the response speed and working efficiency of the tower pumping unit's rotational speed adjustment.

[0087] Step S400: Adjust the motor speed of the tower crane based on the difference between the corrected displacement in each data acquisition cycle and the displacement per unit time under the same suspension load.

[0088] In this embodiment, as Figure 7 As shown, the control system, based on the dual closed-loop SVPWM control method of speed and current loops for permanent magnet synchronous motors, incorporates displacement tracking control within a fixed time interval to achieve stroke and stroke control. The motor speed is derived from the rotor angular velocity, and after passing through two proportional-integral (PI) controllers, the voltage in the dq coordinate system is obtained. and This process is to achieve precise voltage control of the motor.

[0089] Voltage in the dq coordinate system and The voltage in the stationary two-phase coordinate system is obtained by using the inverse Park transform. and The Park inverse transformation is a coordinate transformation method, a well-known technique that will not be discussed further here. It yields the voltage in a stationary two-phase coordinate system. and Then, it is input into a three-phase inverter. The two-phase coordinate voltage can be converted into a three-phase PWM wave by the three-phase inverter to control the permanent magnet synchronous motor.

[0090] The actual electrical angle and actual speed of the motor under load are measured by sensors during operation. The measured electrical angle is then transformed using Park transform to obtain the current in the rotating coordinate system dq. and This feedback is sent to the current controller, forming a closed-loop current control process.

[0091] It should be noted that coordinate transformation operations such as the Park transformation and inverse Park transformation are used to convert the complex model of the motor in the three-phase stationary coordinate system to a rotating coordinate system that is easier to analyze and control. In the rotating coordinate system, the mathematical model of the motor can be simplified, and the adjustment and calculation of control parameters are also more convenient.

[0092] The actual rotational speed is measured by a sensor. Based on the transmission relationship between the motor and the load, the displacement per unit time is calculated. An adjustment is made based on this calculated displacement per unit time to obtain a corrected displacement. This corrected displacement is compared with the displacement per unit time under unchanged load conditions to obtain a displacement difference. The sign of this displacement difference determines whether to decelerate or accelerate the motor. It can be understood that the calculated displacement per unit time is the same as the suspension point displacement obtained in step S100. This is merely a simplified introduction to the control flow of the control system.

[0093] More specifically, when the displacement difference between the corrected displacement of a certain data acquisition cycle and the displacement of the suspension point per unit time under the same suspension point load is greater than 0, the speed of the tower crane's motor is reduced; when the displacement difference between the corrected displacement of a certain data acquisition cycle and the displacement of the suspension point per unit time under the same suspension point load is less than 0, the speed of the tower crane's motor is increased.

[0094] In summary, this embodiment employs a high-precision wire displacement sensor and a rotary transformer to monitor the suspension point position and motor speed in real time, and calculates the drum radius, acceleration, and total dynamic load, which helps to achieve precise control of the motor speed in the subsequent process. Secondly, the intelligent control processor can simultaneously collect and process suspension point displacement, load, motor speed, electrical parameters, temperature, and vibration data, enabling real-time data analysis and processing, reducing noise interference. At the same time, based on changes in temperature, vibration data, and electrical parameters, it calculates the fluctuation coefficient and corrects the displacement, thereby enhancing the response speed of the tower crane speed adjustment, improving the tower crane's working efficiency, and achieving highly sensitive, highly accurate, and highly efficient tower crane operation control, thus improving the quality of the tower crane speed closed-loop control.

[0095] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A closed-loop control method for tower crane speed based on position detection and resolver data coupling, characterized in that, The method includes the following steps: During the operation of the tower crane, the suspension point displacement, suspension point load, temperature data, vibration data, and voltage data of the tower crane are acquired in each data acquisition cycle. Based on the correlation between temperature data and vibration data in each data acquisition cycle, the first influence weight of vibration data dimension and the second influence weight of temperature data dimension are obtained in each data acquisition cycle. Based on the first and second influence weights, and combined with the degree of change in temperature data, vibration data, and voltage data within the corresponding data acquisition cycle, the displacement of the suspension point within the data acquisition cycle is corrected to obtain the corrected displacement for each data acquisition cycle. The motor speed of the tower crane is adjusted based on the difference between the corrected displacement in each data acquisition cycle and the displacement per unit time under the same suspension load. Based on the correlation between temperature and vibration data within each data acquisition cycle, the first influence weight of the vibration data dimension and the second influence weight of the temperature data dimension are obtained for each data acquisition cycle, specifically including: For any data acquisition cycle, a temperature data sequence is constructed based on the temperature data at each moment within the data acquisition cycle, and a vibration data sequence is constructed based on the vibration data at each moment within the data acquisition cycle. Using temperature and vibration data sequences as inputs, Granger causality tests are used to obtain p-values ​​corresponding to the test results. The p-values ​​are used to characterize the correlation between temperature and vibration data. Based on the difference between the p-value and the preset significance level, the preset initial influence weight is adjusted to obtain the first influence weight and the second influence weight, respectively. The step of adjusting the preset initial influence weights based on the difference between the p-value and the preset significance level to obtain the first influence weight and the second influence weight, specifically includes: Based on the proportion of differences between the preset significance level and the p-value corresponding to the test results, the adjustment factor is determined; the negative correlation coefficient of the preset initial influence weight and the first product of the adjustment factor are calculated, and the sum of the initial influence weight and the first product is taken as the first influence weight of the vibration data dimension. The value range of the initial influence weight is (0,1). The second influence weight of the temperature data dimension is determined based on the first influence weight of the vibration data dimension, and the sum of the first influence weight and the second influence weight is 1.

2. The tower crane speed closed-loop control method based on position detection and resolver data coupling according to claim 1, characterized in that, The step involves correcting the suspension point displacement within each data acquisition cycle based on the first and second influence weights, combined with the changing trends of temperature, vibration, and voltage data within the corresponding data acquisition cycle. This corrected displacement is then calculated for each data acquisition cycle, specifically including: For any data acquisition cycle, the temperature change factor is obtained based on the degree of temperature data fluctuation within the current data acquisition cycle and the temperature data change trend between the current data acquisition cycle and adjacent data acquisition cycles. The vibration change factor is obtained based on the degree of fluctuation of vibration data within the current data acquisition cycle and the trend of vibration data change between the current data acquisition cycle and adjacent data acquisition cycles. The voltage fluctuation factor is obtained based on the distribution of voltage data fluctuations between the current data acquisition cycle and historical data acquisition cycles. Using the first influence weight and the second influence weight, the vibration change factor and the temperature change factor are weighted and summed respectively. The product of the summation result and the voltage fluctuation factor is used as the data fluctuation coefficient corresponding to the current data acquisition cycle. The data fluctuation coefficient is negatively correlated and normalized to obtain a correction factor. The product of the correction factor and the displacement of the suspension point within the data acquisition period is used as the correction displacement of the data acquisition period.

3. The tower crane speed closed-loop control method based on position detection and resolver data coupling according to claim 2, characterized in that, The temperature change factor is obtained based on the fluctuation of temperature data within the current data acquisition cycle and the trend of temperature data change between the current data acquisition cycle and adjacent data acquisition cycles. Specifically, it includes: A fitted temperature line is obtained by performing linear fitting on the temperature data in the current data acquisition period and all previous data acquisition periods; the ratio of the difference between the mean of all temperature data in the current data acquisition period and the mean of all temperature data in the adjacent previous data acquisition period is calculated to obtain the temperature change rate of the current data acquisition period; the product of the absolute value of the slope of the fitted temperature line and the temperature change rate is used as the temperature change factor.

4. The tower crane speed closed-loop control method based on position detection and resolver data coupling according to claim 3, characterized in that, The vibration variation factor is obtained based on the fluctuation level of vibration data within the current data acquisition cycle and the trend of vibration data change between the current data acquisition cycle and adjacent data acquisition cycles. Specifically, it includes: A fitted vibration line is obtained by performing linear fitting on the vibration data in the current data acquisition period and all previous data acquisition periods; the ratio of the difference between the mean of all vibration data in the current data acquisition period and the mean of vibration data in the adjacent previous data acquisition period is calculated to obtain the vibration change rate of the current data acquisition period; the product of the absolute value of the slope of the fitted vibration line and the vibration change rate is used as the vibration change factor.

5. The tower crane speed closed-loop control method based on position detection and resolver data coupling according to claim 4, characterized in that, The voltage fluctuation factor is obtained based on the distribution of voltage data fluctuation levels between the current data acquisition cycle and historical data acquisition cycles. Specifically, it includes: Calculate the first variance of all voltage data within the current data acquisition period, and the second variance of all voltage data within all previous data acquisition periods; use the degree of difference between the first variance and the second variance as the voltage fluctuation factor.

6. The tower crane speed closed-loop control method based on position detection and resolver data coupling according to claim 1, characterized in that, The method of adjusting the motor speed of the tower crane based on the difference between the corrected displacement in each data acquisition cycle and the displacement per unit time under the same suspension load includes: When the displacement difference between the corrected displacement of a certain data acquisition cycle and the displacement of the suspension point per unit time under the same suspension point load is greater than 0, the speed of the tower crane's motor is reduced; when the displacement difference between the corrected displacement of a certain data acquisition cycle and the displacement of the suspension point per unit time under the same suspension point load is less than 0, the speed of the tower crane's motor is increased.

7. The tower crane speed closed-loop control method based on position detection and resolver data coupling according to claim 1, characterized in that, The method further includes: The tower crane's motor speed is acquired during each data acquisition cycle in the operation of the tower crane; The radius of the roller in each data acquisition cycle is obtained based on the belt winding situation on the roller in each data acquisition cycle. Based on the motor speed and the drum radius, the acceleration at the suspension point is analyzed to obtain the suspension point load.

8. The tower crane speed closed-loop control method based on position detection and resolver data coupling according to claim 7, characterized in that, The step of obtaining the roller radius for each data acquisition cycle based on the belt winding situation on the roller within each data acquisition cycle specifically includes: The roller radius in each data acquisition cycle includes the roller radius at the suspension point end and the roller radius at the counterweight end; The method for obtaining the radius of the roller at the suspension point end is as follows: ； ； in, This represents the radius of the roller at the suspension point at time i. This indicates the roller radius at the bottom dead center. This indicates the radius of the roller at the top dead center. This indicates the initial radian of the roller when it is at bottom dead center. This represents the increase in radians at the i-th moment during the motion of the suspension point. This represents the initial radian at the suspension point when the point is at top dead center. Indicates belt thickness. Pi This represents the floor function. represents the moment at the top dead center, and n represents the moment at the bottom dead center.

9. The tower crane speed closed-loop control method based on position detection and resolver data coupling according to claim 8, characterized in that, The method for obtaining the radius of the counterweight end roller is as follows: ； ； in, This represents the radius of the roller at the balancing end at time i. This represents the increase in radians of the counterweight at time i during the motion.

10. A closed-loop control method for tower crane speed based on position detection and resolver data coupling according to claim 9, characterized in that, The step of analyzing the acceleration at the suspension point based on the motor speed and the drum radius to obtain the suspension point load specifically includes: ； ； ； in, This represents the suspension load during the s-th data acquisition cycle. This indicates the total number of moments within the data collection period. Let represent the theoretical acceleration at the suspension point at time t. This represents the motor speed at time t. This represents the speed of the reducer at time t. This represents the radius of the roller at the suspension point at time t. Let represent the linear velocity at time t. Let t represent the linear velocity of the reducer at time t.

11. The tower crane speed closed-loop control method based on position detection and resolver data coupling according to claim 10, characterized in that, The minimum radius of the drum on which the belt is wound around the tower crane is 0.287 meters.

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