Stator power control method for a magnetic drive conveyor system and related apparatus

Abstract

The stator power control method of the magnetic drive conveying system and the related equipment provided by the embodiments of the present application, the magnetic drive conveying system comprises a magnetic drive conveying track and a mover, the magnetic drive conveying track comprises a plurality of stators, the method comprises the following steps: first, when at least one mover runs on the magnetic drive conveying track, information parameters of each stator at the current time are acquired; then, based on the information parameters, the predicted power of the stator corresponding to the adjacent time is calculated, the adjacent time being the next time of the current time; next, based on the predicted power, the allocation power unit of the corresponding stator is calculated; finally, based on the allocation power unit, power is allocated to each stator at the adjacent time, so that each stator drives the mover running on the stator based on the power allocation, the reliability of the power allocation of the stator in the magnetic drive conveying system is improved, and the contradiction between the reliability and the energy efficiency in the prior art is effectively solved.

Description

Technical Field

[0001] This application relates to the field of control technology, and in particular to a stator power control method and related equipment for magnetic drive conveyor systems. Background Technology

[0002] Magnetic drive conveyor systems are widely used in automated production lines for workpiece transfer and processing due to their high speed and precision. This system generates electromagnetic force through multiple stators on the magnetic drive conveyor track, precisely driving the movers in a non-contact manner. To ensure the system can handle various complex operating conditions, existing technologies typically employ a fixed static power supply strategy when providing power to the stators. In this case, the power supply system pre-allocates a fixed power budget sufficient for normal operation for each stator on the entire magnetic drive conveyor track. This normal operation is usually defined as a scenario where multiple movers are running on that stator, requiring simultaneous provision of thrust to support the movement of these movers.

[0003] However, when using the aforementioned static power supply strategy to distribute power among multiple stators on the magnetic drive conveyor track, if the mover density on a particular stator is too high at a certain moment, or if multiple movers accelerate rapidly in coordination, the actual peak power demand may exceed the preset static budget. This can lead to local stator overload or system undervoltage, severely affecting operational reliability and stability. Furthermore, for most of the operating time of the magnetic drive conveyor system, the power load on each stator is far below the peak value; for example, the movers are in a uniform, stationary, or sparsely distributed state. In these situations, a large amount of pre-allocated power is idle, resulting in significant energy waste and increasing the system's heat dissipation burden and operating costs. Therefore, the existing static power supply strategy suffers from unreliable stator power distribution. Summary of the Invention

[0004] This application provides a stator power control method and related equipment for a magnetic drive conveyor system, which can improve the reliability of stator power distribution, thereby improving the reliability and stability of motor operation and conveying in the magnetic drive conveyor system.

[0005] To achieve the above objectives, a first aspect of this application proposes a stator power control method for a magnetic drive conveyor system, the magnetic drive conveyor system including a magnetic drive conveyor track and a mover, the magnetic drive conveyor track including multiple stators, the method comprising:

[0006] When at least one of the moving parts is running on the magnetic drive conveyor track, the information parameters of each of the stators at the current moment are obtained;

[0007] Based on the information parameters, the predicted power of the stator corresponding to the adjacent time is calculated, where the adjacent time is the next time after the current time;

[0008] The corresponding stator power allocation unit is calculated based on the predicted power.

[0009] Based on the power allocation unit, power is allocated to each of the stators at the adjacent time, such that each of the stators drives the mover running on the stator based on the power allocation.

[0010] In some embodiments, the information parameters include the current stator power parameters of the stator at the current time and the previous stator power parameters at the previous time. The step of calculating the predicted power of the stator at the adjacent time based on the information parameters includes:

[0011] Based on the current stator power parameters and the preceding stator power parameters, multiple preset order differential calculations are performed to obtain the power differential parameters corresponding to each preset order. The current stator power parameters are obtained based on the voltage and current parameters at the current moment.

[0012] Based on multiple power difference parameters, regression calculations are performed for the adjacent time points to obtain the predicted power of each stator.

[0013] In some embodiments, the power difference parameters include first-order power difference parameters, second-order power difference parameters, and third-order power difference parameters. The step of performing regression calculations based on the multiple power difference parameters corresponding to the adjacent time moments to obtain the predicted power of each stator includes:

[0014] The first-order power term is obtained by multiplying the first-order power difference parameter by the time step.

[0015] The second-order power term is obtained by multiplying the square of the second-order power difference parameter by the square of the time step.

[0016] The third-order power term is obtained by multiplying the cube of the third-order power difference parameter with the cube of the time step.

[0017] The predicted power is obtained based on the current stator power parameters, the accumulated value of the first-order power term, the second-order power term, and the third-order power term.

[0018] In some embodiments, the information parameters include the position and velocity parameters of each mover at the current time, and the calculation of the predicted power of the stator corresponding to the adjacent time based on the information parameters includes:

[0019] Based on the position parameters and velocity parameters of each of the movers, the running mover operating on each of the stators is determined from the plurality of movers at the adjacent time;

[0020] The mover power parameters of each of the running movers are obtained, and the predicted power of each stator is obtained based on the cumulative value of the mover power parameters of all the running movers on each stator.

[0021] In some embodiments, obtaining the mover power parameter of each of the running movers and obtaining the predicted power of each stator based on the cumulative value of the mover power parameters of all the running movers on each stator includes:

[0022] Obtain the basic power parameters of each of the running actuators, and obtain the load mass and load acceleration of each of the running actuators at the current moment;

[0023] The power parameters of each running mover are obtained by multiplying the correction coefficient of each running mover, the load mass and the load acceleration, and adding the corresponding mover power parameters.

[0024] The predicted power of each stator is obtained by summing the power parameters of all the running movers on each stator.

[0025] In some embodiments, calculating the corresponding stator power allocation unit based on the predicted power includes:

[0026] Obtain the redundancy coefficient of each stator, and based on the product of the first value, the cumulative value of the redundancy coefficient, and the predicted power, obtain the redundancy allocation power of each stator.

[0027] Based on the ratio of the redundant power allocation to the minimum power unit, the power allocation unit corresponding to each stator is obtained by rounding up.

[0028] In some embodiments, after the power allocation based on the power allocation unit is performed for each of the stators at the adjacent time, the method further includes:

[0029] Obtain the mover operation requirement parameters for each of the running movers on each of the stators at the adjacent time;

[0030] When at least one of the stators drives all the moving parts to operate based on the allocated power unit, and fails to meet the moving part operation requirements parameters of all the moving parts, the stator is designated as a corrected stator.

[0031] Send the parameter adjustment request information of the modified stator, and receive the adjustment parameters in response to the parameter adjustment request information. Then, calculate the allocated power unit of the modified stator again based on the adjustment parameters. The adjustment parameters include at least one of the redundancy coefficient, correction coefficient, and mover power parameter.

[0032] To achieve the above objectives, a second aspect of this application provides a stator power control device for a magnetic drive conveyor system, the magnetic drive conveyor system including a magnetic drive conveyor track and a mover, the magnetic drive conveyor track including multiple stators, the method comprising:

[0033] The acquisition module is used to acquire information parameters of each stator at the current moment when at least one of the movers is running on the magnetic drive conveyor track;

[0034] The prediction module is used to calculate the predicted power of the stator corresponding to the adjacent time based on the information parameters, wherein the adjacent time is the next time after the current time;

[0035] The unit allocation module is used to calculate the corresponding allocated power unit of the stator based on the predicted power;

[0036] A control module is configured to allocate power to each of the stators at the adjacent time based on the allocated power unit, such that each of the stators drives the mover running on the stator based on the power allocation.

[0037] To achieve the above objectives, a third aspect of this application provides an electronic device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the stator power control method of the magnetic drive conveyor system as described in the first aspect.

[0038] To achieve the above objectives, a fourth aspect of the present application provides a storage medium, which is a computer-readable storage medium storing a computer program that, when executed by a processor, implements the stator power control method of the magnetic drive conveyor system described in the first aspect.

[0039] The stator power control method and related equipment for a magnetic drive conveyor system proposed in this application include a magnetic drive conveyor track and a mover. The magnetic drive conveyor track includes multiple stators. The method includes: first, when at least one mover is running on the magnetic drive conveyor track, acquiring information parameters of each stator at the current moment; then, based on the information parameters, calculating the predicted power of the stator at the next adjacent moment, where the adjacent moment is the next moment after the current moment; next, calculating the allocated power unit of the corresponding stator based on the predicted power; and finally, allocating power to each stator at the adjacent moment based on the allocated power unit, so that each stator drives the mover running on the stator based on the power allocation. This application embodiment acquires information parameters of each stator in real time during the operation of the mover, predicts the power demand of the next adjacent moment based on this, quantifies the predicted power into allocated power units, and performs dynamic and forward-looking power allocation for each stator accordingly. By accurately predicting and pre-allocating sufficient power, it can effectively cope with the instantaneous peak power demand in scenarios such as high-density operation of the mover or concentrated rapid acceleration, avoiding stator overload or system undervoltage, and greatly improving the operational reliability and stability of the magnetic drive conveyor system. Secondly, when the system is under low load (such as when the mover is at a constant speed or stationary), it predicts and allocates lower power accordingly, avoiding the huge energy waste caused by maintaining a high power budget for a long time under the static strategy, significantly improving the energy efficiency of the system and reducing operating costs, thereby improving the reliability of stator power allocation in the magnetic drive conveyor system, and effectively solving the contradiction between reliability and energy efficiency in the prior art.

[0040] Other features and advantages of this application will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the description, claims and drawings. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the structure of a magnetic drive conveying system provided in one embodiment of this application.

[0042] Figure 2 This is a flowchart of a stator power control method for a magnetic drive conveyor system provided in another embodiment of this application.

[0043] Figure 3 yes Figure 2 The flowchart for step 203.

[0044] Figure 4 This is a schematic diagram of a stator of a multi-load power type provided in another embodiment of this application.

[0045] Figure 5yes Figure 3 The flowchart for step 302.

[0046] Figure 6 yes Figure 2 Another flowchart for step 203.

[0047] Figure 7 yes Figure 6 Another flowchart for step 602.

[0048] Figure 8 yes Figure 2 The flowchart for step 203.

[0049] Figure 9 yes Figure 2 The flowchart for step 204.

[0050] Figure 10 This is a schematic diagram of the stator power control device of a magnetic drive conveying system provided in another embodiment of this application.

[0051] Figure 11 This is a schematic diagram of the hardware structure of an electronic device provided in another embodiment of this application. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0053] It should be noted that although functional modules are divided in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart.

[0054] 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 application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0055] Magnetic drive conveyor systems are widely used in automated production lines for workpiece transfer and processing due to their high speed and precision. This system generates electromagnetic force through multiple stators on the magnetic drive conveyor track, precisely driving the movers in a non-contact manner. To ensure the system can handle various complex operating conditions, existing technologies typically employ a fixed static power supply strategy when providing power to the stators. In this case, the power supply system pre-allocates a fixed power budget sufficient for normal operation for each stator on the entire magnetic drive conveyor track. This normal operation is usually defined as a scenario where multiple movers are running on that stator, requiring simultaneous provision of thrust to support the movement of these movers.

[0056] However, when using the aforementioned static power supply strategy to distribute power among multiple stators on the magnetic drive conveyor track, if the mover density on a particular stator is too high at a certain moment, or if multiple movers accelerate rapidly in coordination, the actual peak power demand may exceed the preset static budget. This can lead to local stator overload or system undervoltage, severely affecting operational reliability and stability. Furthermore, for most of the operating time of the magnetic drive conveyor system, the power load on each stator is far below the peak value; for example, the movers are in a uniform, stationary, or sparsely distributed state. In these situations, a large amount of pre-allocated power is idle, resulting in significant energy waste and increasing the system's heat dissipation burden and operating costs. Therefore, the existing static power supply strategy suffers from unreliable stator power distribution.

[0057] To improve the reliability of stator power allocation, thereby enhancing the reliability and stability of mover operation in a magnetic drive conveyor system, this application embodiment acquires information parameters of each stator in real time during mover operation and predicts the power demand of the next adjacent moment based on this. The predicted power is then quantified into allocation power units, and dynamic, forward-looking power allocation is performed for each stator accordingly. By accurately predicting and pre-allocating sufficient power, it effectively addresses instantaneous peak power demands in scenarios such as high-density mover operation or concentrated rapid acceleration, avoiding stator overload or system undervoltage. This significantly improves the operational reliability and stability of the magnetic drive conveyor system. Furthermore, when the system is under low load (e.g., mover at constant speed or stationary), lower power is predicted and allocated accordingly, avoiding the significant energy waste caused by maintaining a high power budget for extended periods under static strategies. This significantly improves system energy efficiency and reduces operating costs, thereby enhancing the reliability of stator power allocation in the magnetic drive conveyor system and effectively resolving the inherent contradiction between reliability and energy efficiency in existing technologies.

[0058] To better illustrate the mover operation control method for the synchronous transition track provided in this application, this embodiment first describes a magnetic levitation transport system applying the mover operation control method. (Refer to...) Figure 1 The diagram shown is a structural schematic of a magnetic drive conveying system provided in an embodiment of this application. Figure 1 As shown, a complete magnetic drive conveyor system is illustrated, with a magnetic drive conveyor track on top and multiple independently movable movers (such as...) distributed on the track. Figure 1 The movers 1 to 5 shown are composed of multiple segmented stators (such as...). Figure 1 The system consists of stators 1 and 2, as shown in the diagram, with arrows indicating the direction of rotor movement. The core of the system is the power management system located in the middle. It interacts with the power system below (containing multiple independent power sources, such as power sources 1 to 3) and the stator above to achieve dynamic control of the stator's power supply. Specifically, the power management system contains six functional modules: a monitoring module collects the real-time electrical status of the stator; a communication module and a prediction module work together to predict the future power demand of the stator based on the rotor status information (such as position and movement trend) obtained from the magnetic drive transport track; a control module acts as the decision-making center, collecting data from the monitoring, communication, and prediction modules to generate power allocation commands; a power distribution module, based on these commands, extracts and precisely allocates the required power from the power system to the designated stator (such as stator 1 or stator 2); and a safety module monitors the entire process in real time to ensure safe system operation. As can be seen from the information flow and energy flow indicated by the arrows in the diagram, by sensing the operating status of the mover on the stator in real time, and through a series of intelligent processes such as prediction, control and power distribution, the power provided by the power system is delivered to each stator on demand and dynamically, thus forming a complete closed-loop intelligent power management solution.

[0059] Based on the above-described magnetic drive conveyor system, the stator power control method of the magnetic drive conveyor system in the embodiments of this application will be described in detail below. (Refer to...) Figure 2 This is an optional flowchart of the stator power control method for the magnetic drive conveyor system provided in the embodiments of this application. Figure 2 The method may include, but is not limited to, steps 201 to 204. It is also understood that this embodiment... Figure 2 The order of steps 201 to 204 is not specifically limited; the order of steps can be adjusted or certain steps can be added or removed according to actual needs. The stator power control method for the magnetic drive conveyor system provided in this application embodiment can be applied to intelligent terminals, servers, computers, etc., connected to the magnetic drive conveyor system.

[0060] Step 201: When at least one mover is running on the magnetic drive conveyor track, obtain the information parameters of each stator at the current moment.

[0061] Step 201 will be described in detail below.

[0062] In some embodiments, when the magnetic drive conveyor system is operating to transport the movers (i.e., at least one mover is running on the magnetic drive conveyor track), the system acquires the information parameters of each stator at the current moment. These information parameters constitute the foundational data set required for subsequent power prediction and can include at least two types of information: one type consists of parameters collected in real-time by the monitoring module, reflecting the current electrical state of the stator, such as the stator's real-time voltage and current. Based on these parameters, the current stator power parameters and the power parameters of the preceding stator used for historical tracing can be calculated. The other type consists of parameters related to the mover's motion state, acquired through the communication module from the upper-level controller of the magnetic drive conveyor system, such as the position parameters, velocity parameters, acceleration parameters, and pre-set load mass and mover base power parameters of each mover currently running or about to run on the stator. This ensures that the system can comprehensively and in real-time grasp the dynamic information of each stator and its associated movers, providing a data foundation for accurate power prediction.

[0063] Step 202: Based on the information parameters, calculate the predicted power of the stator corresponding to the adjacent time.

[0064] Step 202 will be described in detail below.

[0065] In some embodiments, in order to assign appropriate power parameters to each stator, the predicted power of the stator corresponding to the adjacent time is further calculated based on the acquired information parameters. The adjacent time here specifically refers to the next control or sampling cycle of the current time, that is, a very short future time point, which reflects the predictive and forward-looking nature of the method in this application.

[0066] In the embodiments of this application, the methods for calculating the predicted power are diverse. For example, based on information parameters such as historical electrical states, the power change trend can be estimated through time series analysis or regression models (such as multinomial regression) to obtain the predicted power. Alternatively, based on information parameters such as the motion state of the movers, a physical model can be used for calculation. Specifically, this involves predicting which movers will be operating on the stator at adjacent moments, calculating the instantaneous power required by each mover based on parameters such as load mass and target acceleration, and finally summing the power demands of these movers to obtain the total predicted power of the stator. Through this step, a static power supply mode can be transformed into a dynamic prediction mode based on future demand.

[0067] The following section will describe in more detail how to estimate and calculate the predicted power of the stator.

[0068] Reference Figure 3 Based on the information parameters, the predicted power of the stator corresponding to the adjacent time is calculated, including the following steps 301 to 302.

[0069] Step 301: Based on the position and velocity parameters of each mover, determine the running mover that operates on each stator at adjacent times from among multiple movers.

[0070] Step 302: Obtain the mover power parameters of each running mover, and based on the cumulative value of the mover power parameters of all running moves on each stator, obtain the predicted power of each stator.

[0071] Steps 301 to 302 are described in detail below.

[0072] In some embodiments, the power prediction of the stator is first performed using parameters related to the motion of the movers. This process begins by determining the position and velocity parameters of each mover operating on the magnetic drive conveyor system based on information parameters. Then, the predicted position of each mover after the next extremely short time interval (i.e., the adjacent moment) is calculated. This predicted position is then compared with the physical coordinate range of each stator on the magnetic drive conveyor track to determine the corresponding running mover on each stator. That is, if the predicted position of a mover falls within the jurisdiction of a certain stator, that mover is identified as the running mover acting on that stator at that adjacent moment. Essentially, this process involves a precise spatial and temporal mapping of the mover group, aiming to filter out all relevant power consumption sources for subsequent independent power calculations for each stator.

[0073] After determining the running mover corresponding to each stator, the mover power parameter P of each running mover is further obtained. i The system calculates the predicted power of each stator based on the accumulated power parameters of all moving parts on each stator. This step, building upon the output of the previous step, performs power calculations for each stator and its corresponding set of moving parts. For each identified moving part, the system obtains its moving part power parameters. These parameters refer to the instantaneous power required by a single moving part to complete its predetermined motion (such as acceleration, constant speed, or deceleration) at adjacent moments. This value is typically calculated based on dynamic information such as the moving part's base power, load mass, and target acceleration. After obtaining the independent power parameters of all moving parts on the stator, the system algebraically sums these independent power parameter values ​​to obtain a predicted power P that comprehensively reflects the total load of the stator at adjacent moments. s .

[0074] Below is an example. Suppose a stator has N moving rotors, and each rotor has the same load power, i.e., the rotor power parameter is P. Then the predicted power of the stator is P. s =N*P.

[0075] However, since the movers and the workpieces they transport may differ, the load power between each mover will vary. It is preferable to consider the differences in mover load power. Assume there are N movers at the load terminal, and the load power (i.e., mover power parameters) of each mover is P. i This value can be calculated in advance by the user and set in the magnetic drive control device. Based on this, the predicted power P of the stator is... s The calculation formula is shown in Formula 1 below.

[0076]

[0077] To further simplify the model and account for model errors, a stator power prediction model with predicted power levels is adopted. The predicted power on the stator is divided into four levels: A) high load power, B) medium load power, C) low load power, and D) very low load power. The control module performs power allocation based on the load terminal power level. Assume the maximum load power at the load terminal is P. max The following gradation can be designed as shown in formula (2).

[0078]

[0079] Reference Figure 4 This is a schematic diagram of a stator with multiple load power types provided in an embodiment of this application. Figure 4 The diagram shows a magnetic drive conveyor track extending along the "direction of operation." This track is divided into three consecutive regions by dashed lines, representing different stators or stator groups: A, B, and C. Multiple movers, designated movers 1 through 6, operate on the track, and their distribution is uneven. Specifically, in stator region A, due to the dense distribution of movers 1, 2, and 3, it is labeled "A High-Load Power Stator," indicating that this region bears the highest power demand due to the largest number of movers. In stator region B, only a single mover 4 operates, therefore it is labeled "B Low-Load Power Stator," representing the lowest power demand scenario. In stator region C, two movers, 5 and 6, operate, with a load situation between the former two, and are labeled "C Medium-Load Power Stator." This figure visually illustrates the core problem that this invention aims to solve: the power load of different stators at the same time varies greatly due to the uneven distribution of the movers. It also vividly demonstrates that this invention can predict and classify the load levels of different stators based on the acquisition of information parameters such as the mover distribution, thereby providing a foundation for subsequent accurate and dynamic power allocation.

[0080] The load power (i.e., mover power parameter P) generated by the different motion states of each mover iThe power required for a mover to accelerate is much greater than that required for a mover in a constant velocity or at rest. The following will further describe how to determine the mover power parameters for each mover.

[0081] In the first scheme for obtaining mover power parameters provided in this application, mover power parameters P are preset under different motion states. i This data is pre-calculated and set by the user in the magnetic drive conveyor system, and the power management system obtains this data through the communication module. The mover motion state and mover power parameter P... i The comparison is as follows: {Uniform state: P} v Acceleration / deceleration state: P acc_dec At rest: P static Based on this, the motion state of the mover and the mover power parameter P i It can be represented using the mathematical relation P = f(Motion_state).

[0082] Therefore, in this case, for a stator with N moving parts (the moving parts are in the states of Motion_state1, Motion_stat2, and Motion_stateN respectively), the predicted power of the stator is as shown in the following formula (3).

[0083]

[0084] The second method for obtaining mover power parameters provided in this application will be further described below.

[0085] Reference Figure 5 The process involves obtaining the mover power parameters for each running mover and, based on the cumulative value of the mover power parameters of all running movers on each stator, obtaining the predicted power of each stator, including the following steps 501 to 503.

[0086] Step 501: Obtain the basic power parameters of each running mover, and obtain the load mass and load acceleration of each running mover at the current moment.

[0087] Step 502: Based on the product of the correction coefficient, load mass and load acceleration of each running mover, and the corresponding mover power parameter, obtain the mover power parameter of each running mover.

[0088] Step 503: Based on the cumulative value of the mover power parameters of all running movers on each stator, obtain the predicted power of each stator.

[0089] Steps 501 to 503 are described in detail below.

[0090] In some embodiments, firstly, for each stator, the fundamental power parameter P of each operating mover on that stator is determined. base This involves determining the load mass M and load acceleration A of each moving part at the current moment. The base power parameter is a pre-calculated or set reference value representing the static power consumption required for the moving part to maintain its basic operating state (e.g., merely suspending without generating thrust, or moving at a constant speed against basic friction). The load mass includes the mass of the moving part itself and the mass of the workpiece or object it carries; the load acceleration is a key dynamic parameter obtained from the upper-level motion planning controller, directly reflecting the moving part's motion intention at adjacent moments, with a positive value indicating acceleration and a negative value indicating deceleration.

[0091] In step 502 of some embodiments, the system will add the corresponding base power parameter P to the product of the correction coefficient δ of each running mover, the load mass M, and the load acceleration A. base This yields a final value that comprehensively reflects the total power demand of the mover at the adjacency moment, namely the mover power parameter P. i As shown in formula (4) below.

[0092] P i =P base +δ*M*A (4)

[0093] The correction factor δ is an empirical or experimentally calibrated factor used to compensate for differences between the model and the actual physical system, such as unmodeled friction and electromagnetic conversion efficiency.

[0094] Then, the system will obtain the predicted power of each stator based on the cumulative value of the mover power parameters of all running movers on each stator, as shown in the following formula (5).

[0095]

[0096] By performing the detailed calculation methods described in steps 501 to 503 above, unlike simple statistical prediction, the mover power can be decomposed into static basic power parameters and dynamic power components directly related to load mass and load acceleration. This allows for the accurate capture of drastic power fluctuations caused by mover acceleration and deceleration. Furthermore, the introduction of correction coefficients enables the physical model to adapt to different hardware and operating conditions, further improving the accuracy of prediction. This provides a highly reliable and robust basis for subsequent power allocation, ensuring that the system exhibits excellent stability and reliability when dealing with highly dynamic and complex loads.

[0097] By employing the prediction method based on the motion state of the mover described in steps 301 and 302 above, compared to the method that relies solely on historical power data for trend extrapolation, the calculation starts directly from the physical source of power consumption—namely, the motion demand of the mover. This allows the prediction results to accurately reflect the power fluctuations caused by high-dynamic behaviors such as rapid acceleration of multiple movers working together. Because the key parameters of these behaviors (such as acceleration) are directly incorporated into the calculation model, this prediction method has higher accuracy and robustness, and can provide a more reliable basis for subsequent power allocation, thereby ensuring the high stability and high reliability of the entire magnetic drive conveyor system under complex operating conditions.

[0098] The following section will further describe how to use the electrical state of the stator to estimate and predict power.

[0099] Reference Figure 6 Based on the information parameters, the predicted power of the stator corresponding to the adjacent time is calculated, including the following steps 601 to 602.

[0100] Step 601: Perform differential calculations of multiple preset orders based on the current stator power parameters and the previous stator power parameters to obtain the power differential parameters corresponding to each preset order. The current stator power parameters are obtained based on the voltage and current parameters at the current moment.

[0101] Step 602: Based on multiple power differential parameters, perform regression calculations for adjacent time points to obtain the predicted power of each stator.

[0102] Steps 601 to 602 are described in detail below.

[0103] The power supply, terminal real-time current, and voltage data collected by the monitoring module reflect the past and current terminal load power information. Based on this data, the future terminal load can be predicted. There are many technologies that can be used for this prediction, such as regression model algorithms, time series model algorithms, and artificial intelligence model algorithms. The following describes the use of regression model algorithms to predict the stator power.

[0104] Among the obtained information parameters, there are multiple consecutive stator current and voltage parameters corresponding to historical data sequences, namely {(I 0-N V 0-N ), (I 0-N+1 V 0-N+1 ), (I 0-N+2 V 0-N+2 ), ..., (I 0-1 V 0-1 ), (I0, V0)}, according to the principle that power equals current multiplied by voltage, i.e., P = IV, the power parameter sequence of each stator at multiple consecutive moments is {(P0-N ), (P 0-N+1 ), (P 0-N+2 ), ..., (P 0-1 ), (P0)}.

[0105] Based on this, the system uses the current stator power parameter P corresponding to the current time i in the information parameters. i The preceding stator power parameter P at the preceding time (i-1) i-1 Multiple preset-order differential calculations are performed to obtain the power differential parameters corresponding to each preset order. The current stator power parameters are obtained based on the voltage and current parameters at the current moment. The essence of this step is to extract the time-series dynamic characteristics of stator power consumption. First, the current power value, i.e., the current stator power parameters, is calculated using real-time collected voltage and current values. Next, the system combines this current power value with stored historical power data (i.e., previous stator power parameters) to form a power-time variation sequence. Then, by performing multiple preset-order differential calculations on this sequence—for example, first-order differential calculation to obtain the rate of power change, and second-order differential calculation to obtain the rate of change of power (i.e., the acceleration of change)—the dynamic trend of the power curve is quantified, and the calculation results are the power differential parameters.

[0106] It is understandable that when using a third-order polynomial regression model, the power difference parameters include the first-order power difference parameters corresponding to the first-order difference, the second-order power difference parameters corresponding to the second-order difference, and the third-order power difference parameters corresponding to the third-order difference.

[0107] The power first-order difference parameter corresponding to the first-order difference is obtained by the following formula (6).

[0108]

[0109] Where dt is the periodic interval, which is also the time step. Then the first-order difference sequence of power is:

[0110] The power second-order difference parameters corresponding to the second-order difference are obtained by the following formula (7).

[0111]

[0112] The second-order difference sequence of power is

[0113] The power third-order difference parameters corresponding to the third-order difference are obtained by the following formula (8).

[0114]

[0115] The third-order difference sequence of power is

[0116] Furthermore, the system performs regression calculations based on multiple power difference parameters to obtain the predicted power of each stator at adjacent time points. This step is the core of constructing the prediction model using the extracted dynamic features. The regression calculation here is a statistical or machine learning method that establishes a mathematical model to describe the relationship between input variables (i.e., the current stator power parameters and multiple power difference parameters) and output variables (i.e., the predicted power at adjacent time points). For example, a multinomial regression model can be used, with the current power value, the first-order difference of the power, the second-order difference, etc., as independent variables. The model calculations infer the most likely power value in the very short future (i.e., adjacent time points). Essentially, this step predicts the stator's "position" at the next time point based on information such as the "current position," "velocity," and "acceleration" of the stator's historical power curve, thus obtaining the predicted power. The following will further describe how to obtain the predicted power at adjacent time points using a third-order multinomial regression model.

[0117] Reference Figure 7 Based on multiple power differential parameters, regression calculations are performed for adjacent time intervals to obtain the predicted power of each stator, including the following steps 701 to 704.

[0118] Step 701: Obtain the first-order power term based on the product of the first-order power difference parameter and the time step.

[0119] Step 702: Obtain the second-order power term by multiplying the square of the second-order power difference parameter by the square of the time step.

[0120] Step 703: Obtain the third-order power term by multiplying the cube of the third-order power difference parameter by the cube of the time step.

[0121] Step 704: Based on the current stator power parameters, the cumulative values ​​of the first-order power term, the second-order power term, and the third-order power term, obtain the predicted power.

[0122] Steps 701 to 704 are described in detail below.

[0123] In some embodiments, the system obtains the first-order power term based on the product of the first-order power difference parameter and the time step. This step is the first step in building the regression prediction model, and its purpose is to calculate the linear component of the power change. Here, the first-order power difference parameter represents the rate of change of stator power per unit time, while the time step is the time interval between two consecutive sampling points. By multiplying these two parameters, the system can estimate the power increment caused by the current rate of change in the next time step; this increment is the first-order power term, which forms the basis for linear extrapolation of future power.

[0124] Simultaneously, the system will obtain the second-order power term based on the product of the square of the second-order power difference parameter and the square of the time step. This step introduces a nonlinear correction component into the regression model. Instead of directly using second-order differences, it constructs a quadratic term reflecting the strength of power change trends by squaring the first-order power difference parameters. This calculated result, the second-order power term, plays a crucial role in the regression model, enabling the prediction model to better fit and predict power curves with distinct curvatures where the rate of change itself is also changing, thereby improving the ability to capture nonlinear dynamics.

[0125] Similarly, the system will obtain the third-order power term based on the product of the cube of the third-order power difference parameter and the cube of the time step. This step further enhances the model's nonlinear fitting capability to handle more complex power fluctuation scenarios. By cubically calculating the first-order power difference parameters, the system constructs a higher-order correction term, namely the third-order power term. This term enables the prediction model to adapt to more drastic or inflection-point power change trends, thus providing a relatively realistic prediction result even when the power curve exhibits a complex shape, further improving the model's accuracy.

[0126] Finally, the system will obtain the predicted power based on the current stator power parameters, the cumulative value of the first-order power term, the second-order power term, and the third-order power term, as shown in the following formula (9).

[0127]

[0128] By executing the specific calculation method described in steps 701 to 704 above, and constructing a specific third-order polynomial regression model, not only is the first-order power term used for linear trend prediction, but second-order and third-order power terms are also innovatively introduced to capture and simulate complex nonlinear dynamics. Compared with simple linear extrapolation, it can more accurately predict the drastic power fluctuations caused by the complex cooperative motion of the mover. At the same time, compared with complex artificial intelligence models that require a large amount of data for training, this method has a small computational load and is simple to implement, making it very suitable for industrial control scenarios with extremely high real-time requirements. Thus, while ensuring high prediction accuracy, it also ensures the rapid response capability of the control system.

[0129] By implementing the prediction method described in steps 601 and 602 above, it is not necessary to obtain the complex physical parameters (such as mass and acceleration) and motion trajectory of each mover. Prediction can be made simply by monitoring the electrical parameters (voltage and current) of the stator itself. All factors affecting power consumption, including non-ideal effects that are difficult to describe precisely with physical models (such as friction, temperature drift, and electromagnetic interference), are implicitly reflected in historical power data and utilized through regression calculation. Therefore, this method reduces the coupling and communication requirements between systems, has stronger robustness and environmental adaptability, and provides an efficient and practical technical path for realizing dynamic power control.

[0130] Step 203: Calculate the corresponding stator power distribution unit based on the predicted power.

[0131] Step 203 will be described in detail below.

[0132] In some embodiments, after obtaining the predicted power for each stator, the corresponding allocated power unit for the stator is further calculated. In this process, the continuous predicted power values ​​are converted into discrete, manageable power resource units to facilitate subsequent precise allocation. The allocated power unit here is a quantified integer value representing the number of standardized minimum power units required to allocate to that stator. This will be described in further detail below.

[0133] Reference Figure 8 The corresponding stator power distribution unit is calculated based on the predicted power, including the following steps 801 to 802.

[0134] Step 801: Obtain the redundancy coefficient of each stator, and based on the product of the first value, the cumulative value of the redundancy coefficient, and the predicted power, obtain the redundancy allocation power of each stator.

[0135] Step 802: Based on the ratio of redundant power allocation to the minimum power unit, round up to obtain the power allocation unit corresponding to each stator.

[0136] Steps 801 to 802 are described in detail below.

[0137] In some embodiments, the system obtains the redundancy coefficient of each stator. Based on the product of the first value (i.e., 1) and the cumulative value of the redundancy coefficient with the predicted power, the redundancy allocation power P′ of each stator is obtained. i As shown in formula (10) below.

[0138] P′ i =P i *(1+φ i (10)

[0139] The core purpose of this step is to add a configurable safety margin to the predicted power value. The redundancy factor here is a preset safety factor used to handle prediction model errors or sudden load fluctuations; its magnitude can be adjusted by the user according to the stability and safety requirements of the actual operating conditions. The first value usually refers to the value 1, representing 100% of the predicted power itself. Therefore, by multiplying the predicted power by an amplification factor consisting of the sum of the first value and the redundancy factor (i.e., 1 + redundancy factor), a more robust target power value containing a safety buffer is calculated, namely, the redundant power allocation.

[0140] Then, based on the redundant power allocation P′ i With the smallest unit of power P s_power The ratio is then rounded up to obtain the distributed power unit n for each stator. i As shown in formula (11) below.

[0141]

[0142] in The symbol indicates rounding up. This step aims to convert the continuous power value obtained in the previous step into discrete, hardware-executable quantization instructions. The minimum power unit here is the smallest power resolution or increment that the power management system can physically allocate; it defines the granularity of power allocation. The system first divides the redundant allocated power by this minimum power unit to calculate the number of standard units required to meet the power demand. Then, to ensure that the final allocated power is never lower than the target value with a safety margin, the system rounds up this ratio. This rounded integer is the final output power unit that can be directly used to control the operation of the power distribution module.

[0143] By executing the calculation methods described in steps 801 and 802 above, and by introducing a redundancy coefficient, a flexible and adjustable safety buffer is provided for the system. This effectively absorbs and resists the risks caused by inaccurate predictions or sudden changes in operating conditions, greatly enhancing the robustness and reliability of the power allocation scheme. Furthermore, by quantizing the power into the smallest unit and performing rounding up, this method successfully converts a theoretical continuous power value into a discrete execution instruction that conforms to the actual hardware control logic, ensuring that the allocated power is always more than the allocated power. This ensures an absolutely sufficient power supply in practice. This series of processes makes the final power allocation decision both stable and reliable, and also practically executable.

[0144] Step 204: Based on the power allocation unit, perform power allocation for each stator at adjacent times, so that each stator drives the mover running on the stator based on the power allocation.

[0145] Step 204 will be described in detail below.

[0146] In some embodiments, after calculating the allocated power unit for each stator at the adjacent moment, further, based on the calculated allocated power unit, power is allocated to each stator at the adjacent moment, so that each stator drives the mover running on the stator based on the power allocation. Specifically, the control module in the power management system generates a dispatch command, instructing the power distribution module to dispatch electrical energy from the total power system corresponding to the number of allocated power units, and through actuators such as relays and converters, precisely deliver this power to the designated stator when the adjacent moment arrives. In this way, when the mover actually runs to the stator and performs high-power actions such as acceleration and deceleration, the stator has already received sufficient power support, thereby ensuring that the mover can operate smoothly and reliably strictly according to the predetermined plan, completing the closed-loop control from prediction to execution.

[0147] Reference Figure 9 After allocating power to each stator based on the power allocation unit at adjacent times, the stator power control method of the magnetic drive conveyor system further includes the following steps 901 to 903.

[0148] Step 901: Obtain the mover operation requirement parameters of each running mover on each stator at adjacent moments.

[0149] Step 902: When at least one stator drives all running movers based on the allocated power unit, and cannot meet the mover operation requirements parameters of all running movers, the stator is used as a correction stator.

[0150] Step 903: Send a parameter adjustment request message for the corrected stator and receive the adjustment parameters in response to the parameter adjustment request message. Then, recalculate the allocated power unit of the corrected stator based on the adjustment parameters. The adjustment parameters include at least one of the redundancy coefficient, correction coefficient, and mover power parameters.

[0151] Steps 901 to 903 are described in detail below.

[0152] In some embodiments, after completing the stator power allocation at adjacent moments, the system acquires the mover operation requirement parameters for each running mover on each stator at the adjacent moments. This step is the initial stage for verifying the power allocation effect, and its purpose is to collect benchmark data to determine whether the allocation was successful. The mover operation requirement parameters here refer to the specific performance targets set by the upper-level motion controller for each mover, such as target acceleration, target velocity, or target position, representing the ideal operating state that the mover should achieve. By acquiring these parameters, the system clarifies the quantified motion task that the mover group on each stator needs to jointly complete at adjacent moments.

[0153] When at least one stator, based on the allocated power unit, drives all moving units and fails to meet the operating parameters of all moving units, the system designates that stator as a correction stator. This step is a performance deviation detection and problem localization stage. The system compares the actual operating performance of the moving units with the moving unit operating parameter obtained in the previous step to determine whether the allocated power is sufficient. If a deviation occurs, such as the actual acceleration of a moving unit failing to reach the target acceleration, i.e., a "failure to meet" situation, the system identifies and marks the substandard stator as a correction stator. This label indicates that the stator is a weak link in the current power allocation strategy and requires targeted adjustments.

[0154] Next, the system sends a parameter adjustment request for the corrected stator and receives the adjustment parameters in response. Based on these adjustment parameters, it recalculates the allocated power unit for the corrected stator. The adjustment parameters include at least one of the following: a redundancy factor, a correction factor, and a mover power parameter. This step is the core execution stage for achieving closed-loop correction and system self-optimization. The system first sends a parameter adjustment request, which can be reported to a human-machine interface or an adaptive algorithm module. Upon receiving the adjustment parameters returned in response—for example, a larger redundancy factor to increase the safety margin, or a more accurate correction factor to calibrate the prediction model—the system immediately uses these new parameters to recalculate the power requirement specifically for the marked corrected stator, resulting in a new, more sufficient allocated power unit, which is then updated accordingly.

[0155] By implementing the feedback adjustment mechanism described in steps 901 to 903 above, the entire stator power control method is transformed from a static open-loop predictive system into a closed-loop control system capable of dynamic learning and real-time adaptation. When the initial predictive model results in insufficient power distribution due to sudden changes in operating conditions or inaccuracy of the model itself, the system no longer passively accepts performance degradation or operational failure, but can proactively detect and locate problems, and correct errors in real time by adjusting key parameters. This greatly enhances the robustness and operational reliability of the entire magnetic drive conveyor system. In addition, this mechanism also simplifies the system's debugging and maintenance because it has a certain degree of self-calibration capability, reducing the stringent requirements for the accuracy of initial parameter settings.

[0156] This application proposes a stator power control method and related equipment for a magnetic drive conveyor system. The magnetic drive conveyor system includes a magnetic drive conveyor track and movers. The magnetic drive conveyor track includes multiple stators. The method includes: First, when at least one mover is running on the magnetic drive conveyor track, acquiring information parameters of each stator at the current moment; then, based on the position and velocity parameters of each mover, determining the running mover operating on each stator at adjacent moments from the multiple movers, acquiring the basic power parameters of each running mover, and acquiring the load mass and load acceleration of each running mover at the current moment, based on the correction coefficient and load mass of each running mover... The product of the load acceleration and the corresponding mover power parameter is added to obtain the mover power parameter for each running mover. Based on the cumulative value of the mover power parameters of all running moves on each stator, the predicted power of each stator is obtained. Alternatively, multiple preset order difference calculations are performed based on the current stator power parameter and the previous stator power parameter to obtain the power difference parameter corresponding to each preset order. The current stator power parameter is obtained based on the voltage and current parameters at the current moment. The first-order power term is obtained by multiplying the first-order power difference parameter by the time step. The second-order power difference parameter is obtained by multiplying the square of the second-order power difference parameter by the square of the time step. To obtain the second-order power term, the third-order power term is obtained by multiplying the cube of the third-order power difference parameter by the cube of the time step. The predicted power is then obtained by summing the current stator power parameters, the first-order power term, the second-order power term, and the third-order power term, with the adjacent time step being the next time step. Next, the redundancy coefficient of each stator is obtained, and the redundancy allocation power of each stator is obtained by multiplying the first value, the sum of the redundancy coefficients, and the predicted power. The redundancy allocation power is then rounded up based on the ratio of the redundancy allocation power to the minimum power unit to obtain the allocated power unit for each stator. Finally, the allocated power unit is calculated based on the value of the allocated power unit at each adjacent time step. The stator performs power distribution so that each stator drives the movers running on the stator based on the power distribution. In addition, the mover operation requirement parameters of each running mover on each stator at adjacent times are obtained. When at least one stator drives all running movesrs based on the allocated power unit and cannot meet the mover operation requirement parameters of all running movesrs, the stator is designated as the correction stator, and a parameter adjustment request information for the correction stator is sent. The adjustment parameters in response to the parameter adjustment request information are received, and the allocated power unit of the correction stator is recalculated based on the adjustment parameters. The adjustment parameters include at least one of the redundancy coefficient, correction coefficient, and mover power parameters.

[0157] This application embodiment acquires information parameters of each stator in real time during the operation of the mover, predicts the power demand of the next adjacent moment based on this, quantifies the predicted power into allocated power units, and dynamically and proactively allocates power to each stator accordingly. By accurately predicting and pre-allocating sufficient power, it can effectively cope with instantaneous peak power demands in scenarios such as high-density operation of the mover or concentrated rapid acceleration, avoiding stator overload or system undervoltage, and greatly improving the operational reliability and stability of the magnetic drive conveyor system. Secondly, when the system is under low load (such as when the mover is at a constant speed or stationary), it predicts and allocates lower power accordingly, avoiding the huge energy waste caused by maintaining a high power budget for a long time under a static strategy, significantly improving the system's energy efficiency and reducing operating costs, thereby improving the reliability of stator power allocation in the magnetic drive conveyor system, effectively solving the contradiction between reliability and energy efficiency in existing technologies. Furthermore, compared to methods that rely solely on historical power data for trend extrapolation, directly from... The calculations start from the physical source of power consumption—the motion requirements of the mover—allowing the prediction results to accurately reflect the power fluctuations caused by high-dynamic behaviors such as rapid acceleration of multiple movers. Because key parameters of these behaviors (such as acceleration) are directly incorporated into the calculation model, this prediction method has higher accuracy and robustness, providing a more reliable basis for subsequent power allocation decisions. This ensures the high stability and reliability of the entire magnetic drive conveyor system under complex operating conditions. Furthermore, unlike simple statistical predictions, by decomposing the mover power into static basic power parameters and dynamic power components directly related to load mass and load acceleration, the drastic power fluctuations caused by mover acceleration and deceleration can be accurately captured. The introduction of correction coefficients allows the physical model to adapt to different hardware and operating conditions, further improving prediction accuracy. This provides an extremely reliable and robust basis for subsequent power allocation decisions, ensuring the system exhibits excellent stability and reliability when dealing with high-dynamic and complex loads.Furthermore, without needing to obtain the complex physical parameters (such as mass and acceleration) and motion trajectory of each mover, prediction can be made solely by monitoring the electrical parameters (voltage and current) of the stator itself. This allows all factors affecting power consumption, including non-ideal effects that are difficult to describe precisely with physical models (such as friction, temperature drift, and electromagnetic interference), to be implicitly reflected in historical power data and utilized through regression calculations. Therefore, this method reduces the coupling and communication requirements between systems, possesses stronger robustness and environmental adaptability, and provides an efficient and practical technical path for achieving dynamic power control. Specifically, by constructing... A specific third-order polynomial regression model is constructed, which not only utilizes the first-order power term for linear trend prediction but also innovatively introduces second- and third-order power terms to capture and simulate complex nonlinear dynamics. Compared to simple linear extrapolation, it can more accurately predict the drastic power fluctuations caused by the complex cooperative motion of the mover. Furthermore, compared to complex artificial intelligence models that require large amounts of data for training, this method has low computational cost and is simple to implement, making it very suitable for industrial control scenarios with extremely high real-time requirements. This ensures both high prediction accuracy and the rapid response capability of the control system. In addition, by introducing a redundancy coefficient, the system... This provides a flexible and adjustable safety buffer, effectively absorbing and mitigating risks caused by inaccurate predictions or sudden changes in operating conditions. This significantly enhances the robustness and reliability of the power allocation scheme. By quantizing power into its smallest unit and rounding up, this method successfully transforms a theoretically continuous power value into a discrete execution instruction that conforms to the actual hardware control logic. This ensures that the allocated power is always more than necessary, guaranteeing an absolutely sufficient power supply in practice. This series of processes makes the final power allocation decision both robust and reliable, and practically executable. Simultaneously, it integrates the entire stator power control method... From a static open-loop predictive system, it has been upgraded to a closed-loop control system capable of dynamic learning and real-time adaptation. When the initial predictive model suffers from insufficient power distribution due to sudden changes in operating conditions or inaccuracies in the model itself, the system no longer passively accepts performance degradation or operational failure. Instead, it proactively detects and locates the problem, and corrects the error in real time by adjusting key parameters. This greatly enhances the robustness and operational reliability of the entire magnetic drive conveyor system. Furthermore, this mechanism simplifies system debugging and maintenance because it possesses a certain degree of self-calibration capability, reducing the stringent requirements for the accuracy of initial parameter settings.

[0158] This application also provides a stator power control device for a magnetic drive conveyor system, which can implement the stator power control method of the above-mentioned magnetic drive conveyor system, see reference. Figure 10 The device 1000 includes:

[0159] The acquisition module 1010 is used to acquire information parameters of each stator at the current moment when at least one mover is running on the magnetic drive conveyor track.

[0160] The prediction module 1020 is used to calculate the predicted power of the stator corresponding to the adjacent time based on the information parameters, where the adjacent time is the next time after the current time.

[0161] The unit allocation module 1030 is used to calculate the corresponding stator allocation power unit based on the predicted power;

[0162] The control module 1040 is used to allocate power to each stator at adjacent times based on the allocated power unit, so that each stator drives the mover running on the stator based on the power allocation.

[0163] In some embodiments, the prediction module 1020 is further configured to:

[0164] Based on the current stator power parameters and the previous stator power parameters, multiple preset order differential calculations are performed to obtain the power differential parameters corresponding to each preset order. The current stator power parameters are obtained based on the voltage and current parameters at the current moment.

[0165] Based on multiple power difference parameters, regression calculations are performed for adjacent time intervals to obtain the predicted power of each stator.

[0166] In some embodiments, the prediction module 1020 is further configured to:

[0167] The first-order power term is obtained by multiplying the first-order power difference parameter with the time step.

[0168] The second-order power term is obtained by multiplying the square of the second-order power difference parameter by the square of the time step.

[0169] The third-order power term is obtained by multiplying the cube of the third-order power difference parameter by the cube of the time step.

[0170] The predicted power is obtained based on the accumulated values ​​of the current stator power parameters, the first-order power term, the second-order power term, and the third-order power term.

[0171] In some embodiments, the prediction module 1020 is further configured to:

[0172] Based on the position and velocity parameters of each mover, determine the running mover that operates on each stator at adjacent moments from among multiple movers;

[0173] Obtain the mover power parameters for each running mover, and based on the cumulative value of the mover power parameters of all running moves on each stator, obtain the predicted power of each stator.

[0174] In some embodiments, the prediction module 1020 is further configured to:

[0175] Obtain the basic power parameters of each running mover, as well as the load mass and load acceleration of each running mover at the current moment;

[0176] The mover power parameters for each running mover are obtained by multiplying the correction coefficient, load mass, and load acceleration for each running mover, and adding the corresponding mover power parameters.

[0177] The predicted power of each stator is obtained by summing the power parameters of all running movers on each stator.

[0178] In some embodiments, the unit allocation module 1030 is further configured to:

[0179] Obtain the redundancy coefficient of each stator, and based on the product of the first value, the cumulative value of the redundancy coefficient, and the predicted power, obtain the redundancy allocation power of each stator.

[0180] Based on the ratio of redundant power allocation to the minimum power unit, the power allocation unit corresponding to each stator is obtained by rounding up.

[0181] In some embodiments, the control module 1040 is further configured to:

[0182] Obtain the motion requirement parameters of each running mover on each stator at the adjacent time;

[0183] When at least one stator drives all moving parts based on the allocated power unit, and cannot meet the moving part operation requirements of all moving parts, the stator is used as a corrected stator.

[0184] Send a parameter adjustment request message for the corrected stator, and receive adjustment parameters in response to the parameter adjustment request message. Then, recalculate the allocated power unit of the corrected stator based on the adjustment parameters. The adjustment parameters include at least one of the redundancy coefficient, correction coefficient, and mover power parameters.

[0185] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, the specific implementation of the stator power control device of the magnetic drive conveyor system is basically the same as the specific implementation of the stator power control method of the magnetic drive conveyor system described above, and will not be repeated here.

[0186] In this embodiment, the stator power control device of the magnetic drive conveyor system acquires the information parameters of each stator in real time during the operation of the mover, predicts the power demand of the next adjacent moment based on this, quantifies the predicted power into allocated power units, and performs dynamic and forward-looking power allocation for each stator accordingly. By accurately predicting and pre-allocating sufficient power, it can effectively cope with the instantaneous peak power demand in scenarios such as high-density operation of the mover or concentrated rapid acceleration, avoiding stator overload or system undervoltage, and greatly improving the operational reliability and stability of the magnetic drive conveyor system. Secondly, when the system is under low load (such as when the mover is at a constant speed or stationary), it predicts and allocates lower power accordingly, avoiding the huge energy waste caused by maintaining a high power budget for a long time under a static strategy, significantly improving the system's energy efficiency and reducing operating costs, thereby improving the reliability of stator power allocation in the magnetic drive conveyor system, effectively solving the contradiction between reliability and energy efficiency in existing technologies; furthermore, compared to relying solely on historical power data for trend analysis... The push method directly calculates power consumption from the physical source—the motion requirements of the mover. This allows the prediction results to accurately reflect power fluctuations caused by high-dynamic behaviors such as rapid acceleration of multiple movers. Because key parameters of these behaviors (such as acceleration) are directly incorporated into the calculation model, this prediction method has higher accuracy and robustness, providing a more reliable basis for subsequent power allocation decisions. This ensures the high stability and reliability of the entire magnetic drive conveyor system under complex operating conditions. Furthermore, unlike simple statistical prediction, by decomposing the mover power into static basic power parameters and dynamic power components directly related to load mass and load acceleration, it can accurately capture the drastic power fluctuations caused by mover acceleration and deceleration. The introduction of correction coefficients allows the physical model to adapt to different hardware and operating conditions, further improving the accuracy of the prediction. This provides an extremely reliable and robust basis for subsequent power allocation decisions, ensuring the system has excellent stability and reliability when dealing with high-dynamic and complex loads.Furthermore, without needing to obtain the complex physical parameters (such as mass and acceleration) and motion trajectory of each mover, prediction can be made solely by monitoring the electrical parameters (voltage and current) of the stator itself. This allows all factors affecting power consumption, including non-ideal effects that are difficult to describe precisely with physical models (such as friction, temperature drift, and electromagnetic interference), to be implicitly reflected in historical power data and utilized through regression calculations. Therefore, this method reduces the coupling and communication requirements between systems, possesses stronger robustness and environmental adaptability, and provides an efficient and practical technical path for achieving dynamic power control. Specifically, by constructing... A specific third-order polynomial regression model is constructed, which not only utilizes the first-order power term for linear trend prediction but also innovatively introduces second- and third-order power terms to capture and simulate complex nonlinear dynamics. Compared to simple linear extrapolation, it can more accurately predict the drastic power fluctuations caused by the complex cooperative motion of the mover. Furthermore, compared to complex artificial intelligence models that require large amounts of data for training, this method has low computational cost and is simple to implement, making it very suitable for industrial control scenarios with extremely high real-time requirements. This ensures both high prediction accuracy and the rapid response capability of the control system. In addition, by introducing a redundancy coefficient, the system... This provides a flexible and adjustable safety buffer, effectively absorbing and mitigating risks caused by inaccurate predictions or sudden changes in operating conditions. This significantly enhances the robustness and reliability of the power allocation scheme. By quantizing power into its smallest unit and rounding up, this method successfully transforms a theoretically continuous power value into a discrete execution instruction that conforms to the actual hardware control logic. This ensures that the allocated power is always more than necessary, guaranteeing an absolutely sufficient power supply in practice. This series of processes makes the final power allocation decision both robust and reliable, and practically executable. Simultaneously, it integrates the entire stator power control method... From a static open-loop predictive system, it has been upgraded to a closed-loop control system capable of dynamic learning and real-time adaptation. When the initial predictive model suffers from insufficient power distribution due to sudden changes in operating conditions or inaccuracies in the model itself, the system no longer passively accepts performance degradation or operational failure. Instead, it proactively detects and locates the problem, and corrects the error in real time by adjusting key parameters. This greatly enhances the robustness and operational reliability of the entire magnetic drive conveyor system. Furthermore, this mechanism simplifies system debugging and maintenance because it possesses a certain degree of self-calibration capability, reducing the stringent requirements for the accuracy of initial parameter settings.

[0187] This application also provides an electronic device, including:

[0188] At least one memory;

[0189] At least one processor;

[0190] At least one program;

[0191] The program is stored in memory, and the processor executes at least one program to implement the stator power control method of the magnetic drive conveyor system described above in this application. The electronic device can be any smart terminal, including mobile phones, tablets, personal digital assistants (PDAs), and in-vehicle computers.

[0192] Please see Figure 11 , Figure 11 The hardware structure of an electronic device according to another embodiment is illustrated. The electronic device includes:

[0193] The processor 1101 can be implemented using a general-purpose CPU (Central Processing Unit), microprocessor, application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0194] The memory 1102 can be implemented in the form of ROM (Read-Only Memory), static storage device, dynamic storage device, or RAM (Random Access Memory). The memory 1102 can store the operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1102 and is called and executed by the processor 1101 to execute the stator power control method of the magnetic drive conveyor system of the embodiments of this application.

[0195] Input / output interface 1103 is used to implement information input and output;

[0196] The communication interface 1104 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0197] Bus 1105 transmits information between various components of the device (e.g., processor 1101, memory 1102, input / output interface 1103, and communication interface 1104);

[0198] The processor 1101, memory 1102, input / output interface 1103 and communication interface 1104 are connected to each other within the device via bus 1105.

[0199] This application embodiment also provides a storage medium, which is a computer-readable storage medium, storing a computer program that, when executed by a processor, implements the stator power control method of the magnetic drive conveyor system described above.

[0200] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0201] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0202] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0203] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0204] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0205] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0206] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0207] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. The coupling or direct coupling or communication connection between the shown or discussed units may be through some interfaces, or indirect coupling or communication connection between the apparatus or units, and may be electrical, mechanical, or other forms.

[0208] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0209] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0210] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0211] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A stator power control method for a magnetic drive conveyor system, characterized in that, The magnetic drive conveying system includes a magnetic drive conveying track and a mover, the magnetic drive conveying track includes multiple stators, and the method includes: When at least one of the moving parts is running on the magnetic drive conveyor track, acquire the information parameters of each of the stators at the current moment; Based on the information parameters, the predicted power of the stator corresponding to the adjacent time is calculated, where the adjacent time is the next time after the current time. The corresponding stator power allocation unit is calculated based on the predicted power. Based on the power allocation unit, power is allocated to each of the stators at the adjacent time, such that each of the stators drives the mover running on the stator based on the power allocation; The information parameters include the current stator power parameters of the stator at the current time and the previous stator power parameters at the previous time. The step of calculating the predicted power of the stator at the adjacent time based on the information parameters includes: Based on the current stator power parameters and the preceding stator power parameters, multiple preset order differential calculations are performed to obtain the power differential parameters corresponding to each preset order. The current stator power parameters are obtained based on the voltage and current parameters at the current moment. Based on multiple power difference parameters, regression calculations are performed for the adjacent time points to obtain the predicted power of each stator.

2. The stator power control method for the magnetic drive conveyor system according to claim 1, characterized in that, The power difference parameters include first-order power difference parameters, second-order power difference parameters, and third-order power difference parameters. The step of performing regression calculations based on multiple power difference parameters to obtain the predicted power of each stator at adjacent time points includes: The first-order power term is obtained by multiplying the first-order power difference parameter by the time step. The second-order power term is obtained by multiplying the square of the second-order power difference parameter by the square of the time step. The third-order power term is obtained by multiplying the cube of the third-order power difference parameter with the cube of the time step. The predicted power is obtained based on the current stator power parameters, the accumulated value of the first-order power term, the second-order power term, and the third-order power term.

3. The stator power control method for the magnetic drive conveyor system according to claim 1, characterized in that, The information parameters include the position and velocity parameters of each mover at the current time. The calculation of the predicted power of the stator corresponding to adjacent times based on the information parameters includes: Based on the position parameters and velocity parameters of each of the movers, determine from the plurality of movers the running mover that operates on each of the stators at the adjacent time; The mover power parameters of each of the running movers are obtained, and the predicted power of each stator is obtained based on the cumulative value of the mover power parameters of all the running movers on each stator.

4. The stator power control method for the magnetic drive conveyor system according to claim 3, characterized in that, The step of obtaining the mover power parameters of each of the running movers and, based on the cumulative value of the mover power parameters of all the running movers on each of the stators, obtaining the predicted power of each stator includes: Obtain the basic power parameters of each of the running actuators, and obtain the load mass and load acceleration of each of the running actuators at the current moment; The power parameters of each running mover are obtained by multiplying the correction coefficient of each running mover, the load mass and the load acceleration, and adding the corresponding mover power parameters. The predicted power of each stator is obtained by summing the power parameters of all the running movers on each stator.

5. The stator power control method for a magnetic drive conveyor system according to claim 1, characterized in that, The step of calculating the corresponding stator power allocation unit based on the predicted power includes: Obtain the redundancy coefficient of each stator, and based on the product of the first value, the cumulative value of the redundancy coefficient, and the predicted power, obtain the redundancy allocation power of each stator. Based on the ratio of the redundant power allocation to the minimum power unit, the power allocation unit corresponding to each stator is obtained by rounding up.

6. The stator power control method for the magnetic drive conveyor system according to claim 3, characterized in that, After the method performs power allocation for each of the stators based on the allocated power unit at the adjacent time, the method further includes: Obtain the mover operation requirement parameters for each of the running movers on each of the stators at the adjacent time; When at least one of the stators drives all the moving parts to operate based on the allocated power unit, and fails to meet the moving part operation requirements parameters of all the moving parts, the stator is designated as a corrected stator. Send the parameter adjustment request information of the modified stator, and receive the adjustment parameters in response to the parameter adjustment request information. Then, calculate the allocated power unit of the modified stator again based on the adjustment parameters. The adjustment parameters include at least one of the redundancy coefficient, correction coefficient, and mover power parameter.

7. A stator power control device for a magnetic drive conveyor system, characterized in that, The magnetic drive conveying system includes a magnetic drive conveying track and a mover, the magnetic drive conveying track includes multiple stators, and the device includes: The acquisition module is used to acquire information parameters of each stator at the current moment when at least one of the movers is running on the magnetic drive conveyor track; The prediction module is used to calculate the predicted power of the stator corresponding to the adjacent time based on the information parameters, wherein the adjacent time is the next time after the current time; The unit allocation module is used to calculate the corresponding allocated power unit of the stator based on the predicted power; A control module is configured to allocate power to each of the stators at the adjacent time based on the allocated power unit, so that each of the stators drives the mover running on the stator based on the power allocation; The information parameters include the current stator power parameters of the stator at the current time and the previous stator power parameters at the previous time. The step of calculating the predicted power of the stator at the adjacent time based on the information parameters includes: Based on the current stator power parameters and the preceding stator power parameters, multiple preset order differential calculations are performed to obtain the power differential parameters corresponding to each preset order. The current stator power parameters are obtained based on the voltage and current parameters at the current moment. Based on multiple power difference parameters, regression calculations are performed for the adjacent time points to obtain the predicted power of each stator.

8. An electronic device, characterized in that, The system includes a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the stator power control method of the magnetic drive conveyor system according to any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the stator power control method of the magnetic drive conveyor system according to any one of claims 1 to 6.

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