Rotor operation control method of magnetic drive conveying system and related equipment

Abstract

According to the rotor operation control method of the magnetic drive conveying system and the related equipment, the magnetic drive conveying system comprises a magnetic drive conveying track and a plurality of rotors, the rotors operate on the magnetic drive conveying track, and the method comprises the steps that the current load mass of each rotor is obtained, and the current operation distance between every two adjacent rotors is obtained; on the basis of the current load mass and the current operation spacing, the optimized operation speed of each rotor is calculated; calculating the control variation of each stator based on the optimized operation speed, and calculating the prediction position of each rotor at the prediction moment based on the control variation; based on the current load mass and the optimized operation speed, the operation safety distance between every two adjacent rotors is obtained through calculation; and when the predicted position represents that the predicted operation distance between every two adjacent rotors exceeds the operation safety distance, the stator is controlled based on the control variable quantity, so that operation control on the rotors is facilitated, and the safety of cooperative operation of the multiple rotors is remarkably improved.

Description

Technical Field

[0001] The present application relates to the field of control technology, and in particular to a mover operation control method and related equipment of a magnetic drive conveying system. Background Art

[0002] The use of magnetic levitation conveying technology to transport movers has a wide range of applications in industrial automation, such as assembly and packaging of goods along conveyor logistics lines, and the surface-mount manufacturing (SMT) of precision electronic components. These applications typically require the movers to operate sequentially on magnetically driven conveyor tracks, processing workpieces on them during operation. To further improve the efficiency of the processing process, appropriate operating planning parameters must be assigned to each mover to facilitate operational control.

[0003] In the prior art, before transporting multiple movers on a magnetically driven conveyor track, appropriate operational planning parameters are typically pre-programmed for each mover using relevant operational formulas, based on each mover's initial speed, initial position, and target position, combined with the mover's own operational parameters. This allows for a suitable machining process for the workpiece on the mover. However, since the workpiece on the mover inevitably undergoes mass changes after machining, the mover cannot operate completely according to the trajectory corresponding to the original operational planning parameters. Furthermore, since multiple movers are typically operating on a magnetically driven conveyor track, movers with varying trajectories can easily cause safety issues such as collisions between movers. Summary of the Invention

[0004] The embodiments of the present application provide a mover operation control method and related equipment for a magnetic drive conveying system, which can improve the safety of multiple mover operations in the magnetic drive conveying system.

[0005] To achieve the above-mentioned object, a first aspect of an embodiment of the present application provides a method for controlling the operation of a mover of a magnetic drive conveying system, wherein the magnetic drive conveying system includes a magnetic drive conveying track and a plurality of movers, the magnetic drive conveying track includes a plurality of stators, and the movers operate on the magnetic drive conveying track. The method includes:

[0006] Obtaining the current load mass of each of the movers, and obtaining the current running distance between every two adjacent movers;

[0007] Calculating an optimized operating speed of each mover based on the current load mass and the current operating distance;

[0008] Calculating a control variation of each of the stators based on the optimized operating speed, and calculating a predicted position of each of the movers at a predicted time based on the control variation;

[0009] Based on the current load mass and the optimized operating speed, a safe operating distance between each two adjacent movers is calculated;

[0010] When the predicted position indicates that the predicted operating distance between each two adjacent movers exceeds the operating safety distance, the stator is controlled based on the control variation to facilitate operation control of the movers.

[0011] In some embodiments, the calculating the optimized operating speed of each mover based on the current load mass and the current operating distance includes:

[0012] Determining a load sensitivity weight corresponding to each mover based on the current load mass of each mover;

[0013] Based on the ratio of the operating variable corresponding to each mover to the current operating distance, and then multiplied by the corresponding load-sensitive weight, an operation optimization function is obtained;

[0014] Based on the operating variable corresponding to each mover and the current load mass, a safety distance variable corresponding to each mover is obtained, and based on a numerical relationship between the current operating distance and the safety distance variable, a safety distance constraint is obtained;

[0015] generating an operation optimization model based on the operation optimization function and the safety distance constraint;

[0016] The operation optimization model is solved to obtain a solution corresponding to the operation variable of each mover as the optimized operation speed.

[0017] In some embodiments, determining the load sensitivity weight corresponding to each mover based on the current load mass of each mover includes:

[0018] Obtaining a load sensitivity factor, and obtaining a load sensitivity mass based on a product of the load sensitivity factor and the current load mass;

[0019] The load-sensing weight is obtained based on the exponential processing of the load-sensing quality.

[0020] In some embodiments, solving the operation optimization model to obtain a solution corresponding to the operation variable of each mover as the optimized operation speed includes:

[0021] Relaxing the safety distance constraint to obtain a relaxed safety distance constraint;

[0022] Converting the operation optimization model based on the safety distance relaxation constraint to obtain a penalty constraint operation model;

[0023] Based on a quadratic programming method, the penalty constraint operation model is solved to obtain a solution corresponding to the operation variable of each mover as the optimized operation speed.

[0024] In some embodiments, the calculating of the control variation of each stator based on the optimized operating speed includes:

[0025] Determine, from the plurality of stators, the stator where each mover is currently located as the corresponding control stator;

[0026] Obtaining a control cost function corresponding to each of the control stators;

[0027] The optimized operating speed of each mover is substituted into the optimized operating parameters of the control cost function of the corresponding control stator, and the control variable parameters of each control cost function are solved based on the quadratic programming method, and the control variable parameters obtained by the solution are used as the control change of each control stator.

[0028] In some embodiments, obtaining the control cost function corresponding to each of the control stators includes:

[0029] Obtaining an efficiency item weight of each of the control stators based on a ratio of the load-sensitive weight of the mover corresponding to each of the control stators to the current operating spacing;

[0030] A control cost term is obtained based on the product of the efficiency term weight and the square of the optimized operating parameter, plus the product of the smoothing term weight and the square of the control change parameter;

[0031] Based on the control cost terms of all prediction time steps, the control cost function corresponding to the control stator is obtained.

[0032] In some embodiments, the step of calculating the predicted position of each mover at the predicted time based on the control variation includes:

[0033] Based on the multiple relationship of the control period, the distance control matrix and the change control matrix are generated;

[0034] Based on the product of the current position of each mover at the current moment and the distance control matrix, plus the product of the change control matrix and the control change amount, the predicted position of each mover at the predicted moment is obtained.

[0035] In some embodiments, the calculating of the safe operating distance between each two adjacent movers based on the current load mass and the optimized operating speed includes:

[0036] The rear one of the two adjacent movers is used as a brake mover;

[0037] Obtaining a reaction distance based on a product of the optimized operating speed of the brake mover and a system response time;

[0038] A braking distance is obtained by dividing the square of the optimized operating speed of the brake mover by the maximum acceleration of the brake mover;

[0039] The operating safety distance between the two adjacent movers is obtained based on the reaction distance, the braking distance, and the safety margin.

[0040] In some embodiments, when the predicted position indicates that the predicted running distance between each two adjacent movers does not exceed the running safety distance, the method further includes:

[0041] Increasing the load sensitivity factor to obtain an updated load sensitivity factor, and updating the load sensitivity weight of each mover based on the updated load sensitivity factor to obtain an updated load sensitivity weight;

[0042] updating the optimized operating speed of each mover and the control variation of each stator based on the updated load-sensitive weight, and obtaining an updated operating safety distance based on the updated optimized operating speed and the updated load-sensitive weight;

[0043] Obtaining an updated predicted position of each mover at a predicted time based on the updated control variation;

[0044] The above parameter updating process is repeated until the predicted operating distance between each two adjacent movers represented by the updated predicted position exceeds the operating safety distance, and the stator is controlled according to the updated control change to facilitate operation control of the mover.

[0045] In some embodiments, controlling the stator based on the control variation includes:

[0046] Proportional feedback is obtained based on the product of the position difference between the predicted position and the current position of each mover and a proportional gain factor;

[0047] A differential feedback is obtained by multiplying the differential gain factor by the differential of the position difference of each mover;

[0048] Obtaining a phase offset based on the current load mass and the optimized operating speed of the mover corresponding to each stator;

[0049] Sine processing based on the phase offset and multiplying by the compensation term amplitude to obtain dynamic feedforward compensation;

[0050] Based on the accumulated value of the proportional feedback, the differential feedback and the dynamic feedforward compensation, a phase compensation current corresponding to each stator is obtained, and the corresponding stator is controlled based on the phase compensation current and the control variation.

[0051] In some embodiments, obtaining the phase offset based on the current load mass and the optimized operating speed of the mover corresponding to each stator includes:

[0052] Based on the product of thrust coefficient and rated current, the rated thrust is obtained;

[0053] Obtaining a command acceleration of each of the movers based on a difference between the optimized operating speed of each of the movers and the current operating speed at a current moment;

[0054] A thrust ratio is obtained by multiplying the current load mass of the mover corresponding to each stator by the command acceleration and dividing the product by the rated thrust;

[0055] Based on the inverse tangent processing of the thrust ratio, the phase offset corresponding to each stator is obtained.

[0056] To achieve the above-mentioned object, a second aspect of an embodiment of the present application provides a mover operation control device for a magnetic drive conveying system, wherein the magnetic drive conveying system includes a magnetic drive conveying track and a plurality of movers, wherein the magnetic drive conveying track includes a plurality of stators, and the movers run on the magnetic drive conveying track, wherein the device includes:

[0057] A data acquisition module is used to obtain the current load mass of each of the movers and the current running distance between each two adjacent movers;

[0058] A speed optimization module, configured to calculate an optimized operating speed of each mover based on the current load mass and the current operating distance;

[0059] a position prediction module, configured to calculate a control variation of each of the stators based on the optimized operating speed, and calculate a predicted position of each of the movers at a predicted moment based on the control variation;

[0060] A safety distance calculation module, configured to calculate a safe operating distance between each two adjacent movers based on the current load mass and the optimized operating speed;

[0061] An operation control module is used to control the stator based on the control variation when the predicted operation distance between each two adjacent movers represented by the predicted position exceeds the operation safety distance, so as to facilitate operation control of the movers.

[0062] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, it implements the mover operation control method of the magnetic drive conveying system as described in the first aspect.

[0063] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the mover operation control method of the magnetic drive conveying system described in the first aspect is implemented.

[0064] The embodiments of the present application propose a mover operation control method and related equipment for a magnetic drive conveying system. The magnetic drive conveying system includes a magnetic drive conveying track and multiple movers. The magnetic drive conveying track includes multiple stators. The movers run on the magnetic drive conveying track. The method includes: first, obtaining the current load mass of each mover and the current running distance between each two adjacent movers; then, based on the current load mass and the current running distance, calculating the optimized running speed of each mover; next, calculating the control change of each stator based on the optimized running speed, and calculating the predicted position of each mover at the predicted time based on the control change; secondly, calculating the running safety distance between each two adjacent movers based on the current load mass and the optimized running speed; finally, when the predicted position indicates that the predicted running distance between each two adjacent movers exceeds the running safety distance, controlling the stator based on the control change to facilitate the operation control of the mover. The embodiments of the present application address the problem that changes in the mover load lead to deviations in the running trajectory, which are prone to collisions and thus affect the processing efficiency. The current load mass of each mover and the current running distance between adjacent movers are obtained in real time, and based on these real-time information, the optimized running speed of each mover and the dynamic running safety distance that must be maintained between them are dynamically calculated. At the same time, the prediction of the future position of the mover and a safety verification mechanism are combined to ensure that the operation control of the mover is only executed when the predicted future mover spacing can meet the dynamically calculated safety distance requirement. This effectively overcomes the operational uncertainty caused by mass changes of the workpiece during the processing, actively avoids the collision risk caused by deviation of the mover's running trajectory, and significantly improves the safety, stability and adaptive adjustment capability of the coordinated operation of multiple movers to dynamic working conditions, thereby ensuring the continuity and reliability of the processing process and providing strong support for further improving the overall production efficiency and system robustness.

[0065] Other features and advantages of the present application will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present application. The purposes and other advantages of the present application can be achieved and obtained through the structures particularly pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 It is a structural schematic diagram of a magnetic drive conveying system provided in one embodiment of the present application.

[0067] Figure 2 This is a flowchart of a mover operation control method of a magnetic drive conveying system provided in another embodiment of the present application.

[0068] Figure 3 yes Figure 2 Flowchart of step 202 in FIG.

[0069] Figure 4 yes Figure 3 Flowchart of step 301 in FIG.

[0070] Figure 5 yes Figure 3 Flowchart of step 305 in FIG.

[0071] Figure 6 yes Figure 2 Flowchart of step 203 in FIG.

[0072] Figure 7 This is a schematic diagram of a control stator provided in yet another embodiment of the present application.

[0073] Figure 8 yes Figure 6 Flowchart of step 602 in FIG.

[0074] Figure 9 yes Figure 2 Another flow chart of step 203 in FIG.

[0075] Figure 10 yes Figure 2 Flowchart of step 204 in FIG.

[0076] Figure 11 This is a flowchart of parameter iterative update provided by another embodiment of the present application.

[0077] Figure 12 yes Figure 2 Flowchart of step 205 in FIG.

[0078] Figure 13 yes Figure 12 Flowchart of step 1203 in FIG.

[0079] Figure 14It is a structural schematic diagram of a mover operation control device of a magnetic drive conveying system provided in another embodiment of the present application.

[0080] Figure 15 This is a schematic diagram of the hardware structure of an electronic device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0081] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0082] It should be noted that although the functional modules are divided in the device schematic and the logical order is shown in the flowchart, in some cases, the steps shown or described can be performed in a different order than the module division in the device or the order in the flowchart.

[0083] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0084] The use of magnetic levitation conveying technology to transport movers has a wide range of applications in industrial automation, such as assembly and packaging of goods along conveyor logistics lines, and the surface-mount manufacturing (SMT) of precision electronic components. These applications typically require the movers to operate sequentially on magnetically driven conveyor tracks, processing workpieces on them during operation. To further improve the efficiency of the processing process, appropriate operating planning parameters must be assigned to each mover to facilitate operational control.

[0085] In the prior art, before transporting multiple movers on a magnetically driven conveyor track, appropriate operational planning parameters are typically pre-programmed for each mover using relevant operational formulas, based on each mover's initial speed, initial position, and target position, combined with the mover's own operational parameters. This allows for a suitable machining process for the workpiece on the mover. However, since the workpiece on the mover inevitably undergoes mass changes after machining, the mover cannot operate completely according to the trajectory corresponding to the original operational planning parameters. Furthermore, since multiple movers are typically operating on a magnetically driven conveyor track, movers with varying trajectories can easily cause safety issues such as collisions between movers.

[0086] In order to improve the safety of the operation of multiple movers in the magnetic drive conveying system, the embodiment of the present application addresses the problem that the change in the mover load causes the deviation of the operation trajectory, which is prone to collision and thus affects the processing efficiency. By obtaining the current load mass of each mover and the current operating distance between adjacent movers in real time, and dynamically calculating the optimized operating speed of each mover and the dynamic operating safety distance that must be maintained between them based on these real-time information, it also combines the prediction of the future position of the mover and the safety verification mechanism to ensure that the operation control of the mover is only executed when the predicted future mover spacing can meet the dynamically calculated safety distance requirement, so as to effectively overcome the operation uncertainty caused by the quality change of the workpiece during the processing, and actively avoid the collision risk caused by the deviation of the mover operation trajectory, significantly improving the safety, stability and adaptive adjustment capability of the coordinated operation of multiple movers to dynamic working conditions, thereby ensuring the continuity and reliability of the processing process, and providing strong support for further improving the overall production efficiency and system robustness.

[0087] In order to better illustrate the mover operation control method of the synchronous transition track provided by the embodiment of the present application, this embodiment first describes the magnetic levitation conveyor track to which the mover operation control method is applied. Figure 1 As shown in FIG, it is a structural diagram of a magnetic drive conveying system provided in an embodiment of the present application. Figure 1 As shown, the physical environment in which the mover operation control method of the present invention is applied is intuitively presented: that is, a scenario in which multiple movers operate in coordination on the same magnetic drive conveying track. The magnetic drive conveying system includes a magnetic drive conveying track and a mover, the mover operates on the magnetic drive conveying track, and the mover operates on the magnetic levitation conveying track. The magnetic drive conveying track is mainly composed of multiple stators, and electromagnetic coils are wound around these stators. By changing the electromagnetic parameters of the electromagnetic coils in each stator (including current magnitude, current direction, voltage intensity, voltage frequency, etc.), the electromagnetic field generated by the stator can be changed, thereby inducing the magnetic induction component (such as a permanent magnet) carried by the mover running on the stator to apply a variable magnetic thrust to the mover, so that the operating data of the mover (such as operating acceleration, operating speed, etc.) can be changed in real time as needed.

[0088] Based on the above magnetic drive conveying system, the following will specifically describe the mover operation control method of the magnetic drive conveying system in the embodiment of the present application. Figure 2 , which is an optional flow chart of the mover operation control method of the magnetic drive conveying system provided in an embodiment of the present application, Figure 2 The method may include but is not limited to steps 201 to 205. It is also understood that this embodiment is for Figure 2The order of steps 201 to 205 is not specifically limited, and the order of the steps can be adjusted or some steps can be reduced or added according to actual needs. The mover operation control method of the magnetic drive conveying system provided in the embodiment of the present application can be applied to smart terminals, servers, computers, etc. connected to the magnetic drive conveying track.

[0089] Step 201: Obtain the current load mass of each mover and the current running distance between every two adjacent movers.

[0090] Step 201 is described in detail below.

[0091] In some embodiments, when multiple movers are running together on a magnetic drive conveying track, the magnetic drive conveying system performs real-time perception of the system status. This specifically includes accurately obtaining the current load mass currently carried by each mover running on the magnetic drive conveying track. The front load mass refers to the mass of the workpiece carried by the mover during the processing process, which is a key parameter affecting its subsequent kinematic and dynamic characteristics. At the same time, it also includes measuring and obtaining the current running distance between each two adjacent movers in the running queue. This distance is an important basis for evaluating and ensuring the safety of system operation; and also includes the real-time position and real-time speed of the mover. These dynamic parameters obtained in real time together form the basis for subsequent optimization control decisions.

[0092] Among them, the current running distance between two adjacent movers can be used to detect the adjacent mover distance d in real time using the data obtained by the feedback ruler. i , with an accuracy of ±0.1mm; the current load mass of each mover can be measured by the embedded piezoelectric film sensor. i The real-time speed and real-time position of each mover can be fed back by the Hall array encoder. i With position x i , positioning accuracy 0.01mm.

[0093] In some embodiments, the response delay of the system itself (including sensor sampling and calculation delay) needs to be considered. The distance moved by the mover is s. rection =v*τ, where v is the average speed of the mover during the response delay, and τ is the system response time calibrated by experiments (i.e., the time from triggering the plan to the actual response of the mover motor, generally 6ms).

[0094] In addition, for each mover, its braking distance (ie, the deceleration distance under the current load) is obtained by the dynamic formula shown in the following formula (1).

[0095]

[0096] Among them, ffriction is the kinetic friction force generated when the trolley wheel moves on the guide rail, that is, f friction =μmg, F motor The electromagnetic thrust is the force output when driving the car to move, that is, F motor =k I I coil -k d I coil 2 ;k I is the linear thrust coefficient, k d is the magnetic saturation nonlinear coefficient, which is usually determined by fitting the coil current-thrust experimental curve, μ is the friction coefficient, and g is the gravity coefficient.

[0097] In addition, the error in the distance of the mover caused by the working error of the magnetic drive conveying system and the sensor noise needs to be considered, that is, the safety margin is δ margin = 3σ, where σ is the standard distance difference of the system's comprehensive error. The main function of the safety margin is to provide an additional safety buffer for the system to balance model errors (the theoretical calculation of the dynamic safety distance model may deviate from the actual situation), sensor noise (random errors in the measurement data of sensors such as laser TOF and piezoelectric film), and environmental interference (such as the impact of external factors such as track vibration and temperature changes on the motion of the mover).

[0098] In addition, the calibration of electromagnetic parameters includes: electromagnetic thrust calibration, measuring the acceleration a under different currents when the load mass m=0, fitting F motor =ma + μmg to get k I and k d ; Calibrate the friction coefficient, adjust the track inclination θ until the mover slides down at a uniform speed, and measure μ=tanθ.

[0099] Step 202: Based on the current load mass and the current running distance, the optimized running speed of each mover is calculated.

[0100] Step 202 is described in detail below.

[0101] In some embodiments, after determining the system state of the magnetically driven conveying system, the current load mass of each mover and the current operating distance between two adjacent movers are used to calculate and determine the most appropriate optimized operating speed for each mover under the current operating conditions using a preset optimization algorithm or model. This optimized operating speed aims to balance multiple objectives such as conveying efficiency, energy consumption, and operating smoothness, and is a dynamic programming of the ideal operating state of the movers, as described below.

[0102] Reference Figure 3, based on the current load mass and the current running distance, the optimized running speed of each mover is calculated, including the following steps 301 to 305.

[0103] Step 301: Determine the load sensitivity weight corresponding to each mover based on the current load mass of each mover.

[0104] Step 301 is described in detail below.

[0105] In some embodiments, in order to refine the subsequent speed optimization process, first for each mover i, according to the current load mass m i , to determine a corresponding load-sensitive weight w(m i ). It can be understood that the load-sensitive weight is an adjustable parameter that reflects the contribution or priority of the movers in different load states to the overall optimization goal in the subsequent optimization calculation, so that the optimization process can respond more finely to the specific load conditions of each mover, as described below.

[0106] Reference Figure 4 , determining the load sensitivity weight corresponding to each mover based on the current load mass of each mover, includes the following steps 401 to 402.

[0107] Step 401: Obtain a load sensitivity factor, and obtain a load sensitivity mass based on the product of the load sensitivity factor and the current load mass.

[0108] Step 402: Obtain a load-sensitivity weight based on the index processing of the load-sensitivity quality.

[0109] Steps 401 to 402 are described in detail below.

[0110] In some embodiments, a corresponding load sensitivity factor α is set for each mover. This load sensitivity factor is a scalar that defines the sensitivity or adjustment direction of the current load mass in the subsequent weight calculation. It can be customized as needed; it is also determined by energy consumption delay. Generally, when the load mass is light, the load sensitivity factor is high, and when the load mass is heavy, the load sensitivity factor is low. Subsequently, the load sensitivity factor of each mover and the corresponding current load mass m are obtained. i Perform product operation to obtain an intermediate quantity, namely the load sensitive mass αm i .

[0111] Afterwards, the load-sensing mass αm i Perform index processing to determine the load sensitive mass αm i The negative number of the exponential function is used to obtain the load sensitivity weight w(m i ) is shown in the following formula (2).

[0112]

[0113] By executing steps 401 and 402, a load-sensitive factor is introduced to perform a preliminary linear adjustment on the current load mass to obtain the load-sensitive mass. Then, a nonlinear mapping of exponential processing is performed on the load-sensitive mass to finally obtain the load-sensitive weight. This makes the calculation of the load-sensitive weight more flexible and adjustable. According to different application scenarios and optimization objectives, by adjusting the load-sensitive factor and the specific form of exponential processing, the influence of the current load mass on the load-sensitive weight can be finely controlled, thereby affecting the calculation of the subsequent optimized operating speed, so that the entire mover operation control strategy can better adapt to load changes and achieve specific performance optimization objectives.

[0114] Step 302: Based on the ratio of the operating variable corresponding to each mover to the current operating distance, multiplied by the corresponding load-sensitive weight, an operation optimization function is obtained.

[0115] Step 303: Based on the operating variables corresponding to each mover and the current load mass, a safety distance variable corresponding to each mover is obtained, and based on the numerical relationship between the current operating distance and the safety distance variable, a safety distance constraint is obtained.

[0116] Step 304: Generate an operation optimization model based on the operation optimization function and the safety distance constraint.

[0117] Step 305: Solve the operation optimization model to obtain the solution corresponding to the operation variable of each mover as the optimized operation speed.

[0118] Steps 302 to 305 are described in detail below.

[0119] In some embodiments, after obtaining the load sensitive weight w(m i ) and then further based on the operating variable v corresponding to each actuator i and the current running distance d i The ratio of the load sensitivity weight w(m i ), and then average processing is performed to obtain the overall operation optimization function as shown in the following formula (3).

[0120]

[0121] Where n is the number of movers, and the operation optimization function (3) aims to quantify the travel speed per unit spacing under different combinations of operation variables (i.e., speed variables) under the current load and spacing conditions, reflecting the local travel efficiency.

[0122] In order to ensure the safety of operation, we further calculate the operating variables corresponding to each mover (i.e., the speed variable v i ) and its current load mass m i , calculate a dynamic safety distance variable s safe (v i ,m i ) is shown in the following formula (4).

[0123]

[0124] Where τ is the system response time and 3σ is the safety margin. The safety distance variable represents the minimum theoretical distance that the mover needs to maintain with the previous or next mover to avoid collision under specific operating variables and load conditions. Subsequently, by comparing the actual current operating distance d of each mover i The corresponding safety distance variable s safe (v i ,m i ) (for example, requiring the current running distance to be no less than the safety distance variable), thereby establishing the safety distance constraint d i ≥s safe (v i ,m i These safety distance constraints are boundary conditions that must be strictly observed during the optimization process to ensure the basic safety of the multi-motor coordinated operation.

[0125] Afterwards, the operation optimization function (3) is integrated with the safety distance constraint to formally generate a structured operation optimization model as shown in the following formula (5).

[0126]

[0127] ST d i ≥s safe (v i ,m i ) (5)

[0128] Next, the generated operation optimization model (5) is solved. By using a suitable mathematical optimization algorithm (such as quadratic programming, interior point method, etc., the specific algorithm selection can be based on the specific form of the model), the system can calculate one or a group of optimal solutions of the optimization model. That is, for each mover, the specific value of its corresponding operating variable in the optimal solution is determined as its final optimized operating speed, as described below.

[0129] Reference Figure 5 , solving the operation optimization model and obtaining the solution corresponding to the operation variable of each mover as the optimized operation speed, including the following steps 501 to 503.

[0130] Step 501: Relax the safety distance constraint to obtain a relaxed safety distance constraint.

[0131] Step 502: The operation optimization model is converted based on the safety distance relaxation constraint to obtain a penalty constraint operation model.

[0132] Step 503: Solve the penalty constraint operation model based on the quadratic programming method to obtain the solution corresponding to the operation variable of each mover as the optimized operation speed.

[0133] Steps 501 to 503 are described in detail below.

[0134] In some embodiments, in order to improve the robustness and feasibility of solving the optimization model, the method first relaxes the previously established safety distance constraint to obtain a relaxed safety distance constraint as shown in the following formula (6).

[0135] max(0,s safe -d i ) 2 (6)

[0136] It is understandable that "relaxation processing" usually refers to converting strict hard constraints (for example, the spacing must be greater than or equal to a certain value) into soft constraints that can be violated to a certain extent, but when violated, corresponding penalties are introduced. Through this processing, even if the original hard constraints are difficult to meet simultaneously under certain extreme or transient conditions, the optimization problem can still find a meaningful approximate solution instead of directly declaring that there is no solution.

[0137] Then, the safety distance relaxation constraint (6) is used to transform the operation optimization model (5), thereby integrating the safety distance relaxation constraint (6) as a penalty term into the operation optimization function (5), thereby obtaining the penalty constraint operation model as shown in the following formula (7).

[0138]

[0139] Where λ is the penalty factor, which is in the order of 10^3. In this new penalty-constrained operation model (7), the optimization algorithm automatically balances the relationship between satisfying the original optimization objective and minimizing the degree of violation of the relaxed constraints in the process of seeking the optimal solution.

[0140] Next, in this embodiment, the quadratic programming method (QP) is used to solve the penalty constraint operation model (7). Specifically, the state vector is defined by using the motion variables. The objective function is rewritten as shown in the following formula (8).

[0141] J=v T Qv+c T v (8)

[0142] The relevant parameters are shown in the following formula (9).

[0143]

[0144] Afterwards, an interior point method is used to solve the problem in real time: the FPGA is used to pre-calculate the Hessian matrix Q, and the ARM core performs the iterative solution (limiting the convergence time, such as 10ms). The details are described below.

[0145] First, relevant preprocessing is performed: (1) Initializing the velocity vector v^0 (e.g., taking the current velocity value); (2) Setting the barrier function parameters (e.g., the logarithmic barrier coefficient μ) to a large value; (3) Defining slack variables to handle inequality constraints. Then, the Hessian matrix precalculation steps include (accelerated using an FPGA): (1) Precalculating the Hessian matrix of the objective function H = -2Q (a fixed value); (2) Storing H and the gradient vector g = -c.

[0146] The iterative optimization steps then include (executed by the ARM core): (1) Newton step calculation: solving the linear equation system H·Δv=-g; (2) step size selection: determining the step size α by backtracking straight line search to ensure that the constraints are not violated; (3) variable update v k +1 =v k +α·Δv; (4) Obstacle parameter update: gradually reduce μ and reduce the weight of the obstacle function.

[0147] After completing the above-mentioned interior point method solution, Hessian matrix pre-calculation, and iterative optimization, a convergence judgment step is performed to output the results.

[0148] The convergence judgment details are: check whether the gradient norm ||g|| < ε or the maximum number of iterations (such as 10ms time limit) is reached; if not, return to the above and continue iterative optimization.

[0149] In some embodiments, in FPGA, matrix operations (such as A·x, B·u) are mapped to hardware logic, and single-step prediction takes <1us to improve the running speed.

[0150] By applying quadratic programming to the penalty-constrained operating model, the system can effectively calculate the optimal solution within the model framework. In this optimal solution, the specific value of the operating variable (i.e., the speed variable) corresponding to each mover is determined as the final optimized operating speed of that mover.

[0151] By executing steps 501 to 503, the safety distance constraint is first relaxed and converted into a safety distance relaxation constraint, thereby enhancing the stability and adaptability of the model solution and avoiding the solution failure caused by strict constraints. Based on this safety distance relaxation constraint, the original model is converted into a penalty constraint operation model, so that the optimization process can impose a controllable penalty on the violation of the constraint while meeting the main goal. The quadratic programming method is then explicitly used to efficiently solve the penalty constraint operation model to obtain the optimized operation speed of each mover, ensuring the solvability of the optimization problem in a complex and dynamic environment. In addition, the efficiency of the solution and the quality of the solution are guaranteed by the mature quadratic programming method, so that the calculated optimized operation speed can take into account safety, efficiency and system stability in practical applications, providing a solid algorithm guarantee for achieving robust and high-performance mover operation control.

[0152] By executing steps 301 to 305, the importance of different load movers in the optimization target is dynamically adjusted by introducing load-sensitive weights, and an operation optimization function oriented towards local traffic efficiency is constructed. At the same time, the safety distance constraint calculated based on the real-time load and target speed is strictly followed. Finally, the optimized operation speed obtained by solving the operation optimization model can not only be personalized according to the real-time working conditions of each mover (current load mass and current operating distance), ensuring the safety of multi-motor operation and the system's responsiveness to dynamic changes, but also can maximize the overall operation efficiency and performance of the conveying system under the premise of ensuring safety.

[0153] Step 203: Calculate the control variation of each stator based on the optimized operating speed, and calculate the predicted position of each mover at the predicted time based on the control variation.

[0154] Step 203 is described in detail below.

[0155] In some embodiments, based on the obtained optimized operating speed of each mover, specific control instructions for each stator on the magnetic drive conveyor track are further derived, and the future state of the mover is predicted. The stator refers to the fixed electromagnetic drive unit that constitutes the magnetic drive conveyor track.

[0156] At this time, the control change amount that needs to be applied to the stator related to the current position of the mover is calculated based on the optimized operating speed of each mover, combined with the current state of the mover (such as position, actual speed) and the dynamic model of the system. The control change amount can be reflected as the adjustment amount of the driving current, voltage or electromagnetic force, which is a physical quantity that directly acts on the stator to drive the mover to move.

[0157] Subsequently, based on this control variation and the dynamic model of the mover, the method proactively calculates the predicted position of each mover at a predicted moment in the future.

[0158] The following first describes how to determine the control variation of each stator.

[0159] Reference Figure 6 , calculating the control variation of each stator based on the optimized operating speed, includes the following steps 601 to 603.

[0160] Step 601: From a plurality of stators, determine the stator where each mover is currently located as the corresponding control stator.

[0161] Step 602: Obtain the control cost function corresponding to each control stator.

[0162] Steps 601 to 602 are described in detail below.

[0163] In some embodiments, in order to accurately convert the optimized operating speed of the mover into the corresponding control variation of the stator, it is first necessary to clearly identify the execution unit with which each mover directly interacts with the electromagnetic field at the current moment. Figure 7 , is a schematic diagram of a control stator provided in an embodiment of the present application. Figure 7 As shown in , from the multiple stators constituting the magnetic drive conveyor track, according to the current real-time position information of each mover, one or a group of stators directly below or closest to it at the current moment that can exert effective driving force on it are identified and determined, and these determined stators are used as the control stators corresponding to the mover, as shown in Figure 7 This step ensures that the subsequent control instructions can be accurately applied to the correct physical execution unit.

[0164] After determining the control stator corresponding to each mover, this method requires determining a corresponding control cost function for each control stator. This control cost function quantifies the cost or expected benefit of achieving a specific control objective (such as tracking the desired speed, minimizing energy consumption, ensuring motion smoothness, etc.) within a certain control cycle, as described below.

[0165] Reference Figure 8 , obtaining the control cost function corresponding to each control stator, including the following steps 801 to 803.

[0166] Step 801: Based on the ratio of the load sensitivity weight of the mover corresponding to each control stator to the current operating spacing, the efficiency item weight of each control stator is obtained.

[0167] Step 802: Based on the product of the efficiency term weight and the square of the optimized operation parameter, plus the product of the smoothing term weight and the square of the control change parameter, a control cost term is obtained.

[0168] Step 803: Based on the control cost terms of all prediction time steps, obtain the control cost function corresponding to the control stator.

[0169] Steps 801 to 803 are described in detail below.

[0170] In some embodiments, in order to construct a control cost function that can effectively guide the behavior of the control stator, for each control stator, based on the ratio of the load sensitivity weight of the mover corresponding to each control stator (reflecting the influence of the mover load on the optimization target) and the current operation spacing (reflecting the operation density and potential interaction), the efficiency term weight of each control stator is obtained as β = w (m i ) / d i This makes the efficiency factor play a more important role in subsequent cost evaluation when the mover load sensitivity is high or the running distance is small.

[0171] Then, at the current time k, based on the efficiency weight β and the square of the optimized operating parameter v i 2 The product of [k], plus the smoothing weight γ and the square of the control change parameter Δu i 2 [k], we get the control cost term βv i 2 [k]+γΔu i 2 [k]. Among them, the control change parameter Δu i [k]=u i [k]-u i [k-1] is the control adjustment applied to the stator that the underlying controller needs to solve, such as the change in current or force. This is the difference between control inputs (such as click current or thrust command) at adjacent moments. It reflects the magnitude of the change in the control command and is used to measure the severity of the control action.

[0172] In order to form a complete optimization objective that can guide the behavior of the control stator in a future period of time, based on the control cost terms of all predicted time steps, the control cost function corresponding to the control stator of the i-th mover is obtained as shown in the following formula (10).

[0173]

[0174] Taking the square of the control change parameter and the optimized operating parameter and adding them to the cost function indicates that the optimization process tends to favor strategies with smaller control input changes, thereby reducing frequent starts and stops or sudden speed changes of the actuator, and minimizing mechanical shock and energy loss. The smoothing weight γ controls smoothness. A larger coefficient indicates stricter control input changes and a smoother response.

[0175] By executing steps 801 to 803, the upper-level planning information such as the load-sensitive weight of the mover and the current operating distance is effectively transmitted to the underlying control target through the calculation of the efficiency item weight, so that efficiency considerations are closely integrated with real-time working conditions. In the composition of the control cost item, both the tracking of optimized operating parameters (efficiency target) and the suppression of control change parameters (smoothness target) are taken into account, thus achieving a balance of multiple objectives. By accumulating the control cost item at all prediction time steps, the control cost function formed gives the control decision the ability to foresee the future, ensuring that the subsequent control instructions obtained based on this function can not only pursue operating efficiency, but also ensure the smoothness and stability of operation, and can dynamically adapt to changes in system status, thereby providing target guidance for achieving high-performance and high-robustness mover operation control.

[0176] Step 603: Substitute the optimized operating speed of each mover into the optimized operating parameters of the control cost function of the corresponding control stator, and solve the control variable parameters of each control cost function based on the quadratic programming method, and use the solved control variable parameters as the control variation of each control stator.

[0177] Step 603 is described in detail below.

[0178] In some embodiments, after obtaining the optimized operating speed of each mover and the control cost function (10) of the corresponding control stator, the optimized operating speed of each mover is substituted into the optimized operating parameters of the control cost function of the corresponding control stator, and the control variable parameters of each control cost function are solved based on the quadratic programming method, and the solved control variable parameters are used as the control variation Δu of each control stator. i .

[0179] By executing steps 601 to 603, the control stators that should currently drive each mover are precisely locked in, ensuring targeted control. A control cost function is then defined for each control stator to guide its behavior. Crucially, by using the optimized operating speed as the input or target of the control cost function and again applying quadratic programming to solve the control variable parameters of the control cost function, the control variation is obtained. This allows the optimized operating speed of the upper-level planning to effectively guide the specific behavior of the underlying controller. The sophisticated quadratic programming method ensures the efficiency and optimality of the control instruction calculation, ultimately achieving precise, efficient, and overall optimized drive control for each mover.

[0180] Reference Figure 9 , the predicted position of each mover at the predicted moment is calculated based on the control change, including the following steps 901 to 902.

[0181] Step 901: Generate a distance control matrix and a change control matrix based on the multiple relationship of the control period.

[0182] Step 902: Based on the product of the current position of each mover at the current moment and the distance control matrix, plus the product of the change control matrix and the control change amount, the predicted position of each mover at the predicted moment is obtained.

[0183] Steps 901 to 902 are described in detail below.

[0184] In some embodiments, in order to predict the future position of the mover based on a discrete-time control system model, the method first needs to construct a distance control matrix A and a change control matrix B related to state transition based on multiples of the control period in the magnetic drive control system as shown in the following formula (11).

[0185]

[0186] The control period is the time interval between two executions of control instructions by the controller in the magnetic drive conveying system; the distance control matrix usually describes how the current position state of the mover evolves to the state at the predicted time in the absence of external control input; and the change control matrix describes the impact of the control change applied at the current time on the state of the mover at the predicted time.

[0187] Next, the generated distance control matrix and change control matrix are used to calculate the future position of the mover based on the discretized dynamic equations and the control change corresponding to the mover under the optimized operating parameters. Specifically, for each mover, its current position at the current moment (and possible state variables such as current velocity, although the emphasis here is on position) is multiplied by the distance control matrix to obtain a basic predicted state component, which reflects the future position evolution caused by the inherent dynamic characteristics of the system. Then, the previously calculated control change to be applied to the stator corresponding to the mover is multiplied by the change control matrix to obtain a predicted state increment caused by the control input. Finally, the two product results (i.e., the basic predicted component and the control increment) are vector-added to obtain the predicted position of each mover at the preset prediction moment (e.g., six control cycles after the current moment, i.e., 6ms later), as shown in the following formula (12).

[0188] x i [k+1]=Ax i [k]+Bu i [k] (12)

[0189] By executing steps 901 and 902, a mathematical mapping relationship from the current state and control input to the future state is established by constructing a distance control matrix and a change control matrix related to the system control cycle and dynamic model. These matrices are then used to combine the current position of the actuator and the control change to be applied. The predicted position is accurately calculated in the form of a standard discrete-time state-space equation, allowing the system to pre-evaluate the possible future consequences of the instruction before actually executing the control instruction, especially the relative position relationship between the actuators, thereby providing crucial forward-looking information for subsequent safety verification and control decisions.

[0190] Step 204: Based on the current load mass and the optimized operating speed, a safe operating distance between every two adjacent movers is calculated.

[0191] Step 204 is described in detail below.

[0192] To ensure the safety of the mover's operation, the system simultaneously calculates the predicted position of each mover and, based on its current load mass and the planned optimized operating speed, accurately calculates the minimum safe distance that must be maintained between each adjacent mover, known as the safe operating distance, by taking into account factors such as the mover's braking capacity and system response delay. This safe operating distance is a dynamically changing threshold that adaptively adjusts with changes in the mover's load and target speed to ensure sufficient safety margin under various operating conditions. Details are described below.

[0193] Reference Figure 10 Based on the current load mass and the optimized operating speed, the operating safety distance between each two adjacent movers is calculated, including the following steps 1001 to 1004.

[0194] Step 1001: Of two adjacent movers, the rear mover is used as a braking mover.

[0195] Step 1002: Obtain a reaction distance based on the product of the optimized operating speed of the brake actuator and the system response time.

[0196] Step 1003: The braking distance is obtained by dividing the square of the optimized running speed of the brake mover by the maximum acceleration of the brake mover.

[0197] Step 1004: Based on the reaction distance, the braking distance, and the safety margin, a safe operating distance between two adjacent movers is obtained.

[0198] Steps 1001 to 1004 are described in detail below.

[0199] In some embodiments, in order to clearly calculate the running safety distance between each two movers, for each pair of two adjacent movers in the running queue, the mover at the back of the queue is used as the braking mover. Then, the safety distance variable s shown in the above formula (4) is used safe (v i ,m i ), and further based on the product of the optimized running speed of the brake actuator and the system response time, the reaction distance v is obtained. i τ, the reaction distance represents the distance the brake actuator continues to travel at the original optimized operating speed before it actually starts braking due to the inherent response lag of the system. And the braking distance v is obtained by dividing the square of the brake actuator's optimized operating speed by the brake actuator's maximum acceleration under the current load mass. i 2 / 2a max (m i ), which represents the distance the brake actuator needs to slide from the start of braking to complete stop (or deceleration to the same speed as the vehicle in front) when braking at its maximum acceleration (maximum braking capacity). In addition, the current load mass of the brake actuator will directly affect its maximum acceleration, so the braking distance is a quantity that changes dynamically with the load.

[0200] Finally, based on the cumulative value of the reaction distance, braking distance and safety margin, the operating safety distance s between two adjacent movers is obtained. safe .

[0201] By executing steps 1001 to 1004, the role of the brake mover is clarified, and its reaction distance (taking into account the system response time) and braking distance (taking into account the maximum acceleration under the influence of the current load mass) at the optimized operating speed are calculated respectively. Then, the safety margin is added on this basis. The final operating safety distance is a dynamic threshold that can truly reflect the safety requirements under the current working conditions. It can be adaptively adjusted according to the real-time current load mass of the mover and the optimized operating speed planned for it, providing reliable safety guarantees for subsequent operation control decisions, thereby effectively avoiding collisions between movers and improving the operation safety and reliability of the entire magnetic drive conveying system.

[0202] Step 205 : When the predicted operating distance between each two adjacent movers represented by the predicted position exceeds the operating safety distance, the stator is controlled based on the control variation to facilitate operation control of the movers.

[0203] Step 205 is described in detail below.

[0204] In some embodiments, after obtaining the predicted position x of each mover i After [k+1], when the predicted position represents the predicted running distance x between each two adjacent movers at the predicted time j [k+1]-x i [k+1] is greater than or equal to the operating safety distance s corresponding to the two movers safe , indicating that the future operating state of the mover is safe under the planned control variation. When this safety prerequisite is met, the corresponding stator is actually driven based on the control variation, ensuring accurate and safe operation of the mover. If the predicted operating distance does not meet the safety distance requirement, the system will take other adjustment measures, as described below.

[0205] Reference Figure 11 When the predicted position indicates that the predicted operating distance between each two adjacent movers does not exceed the operating safety distance, the mover operation control method of the magnetic drive conveying system further includes the following steps 1101 to 1104.

[0206] Step 1101: Increase the load sensitivity factor to obtain an updated load sensitivity factor, and update the load sensitivity weight of each mover based on the updated load sensitivity factor to obtain an updated load sensitivity weight.

[0207] Step 1102: updating the optimized operating speed of each mover and the control variation of each stator based on the updated load-sensitive weight, and obtaining an updated operating safety distance based on the updated optimized operating speed and the updated load-sensitive weight.

[0208] Step 1103: Obtain the updated predicted position of each mover at the predicted time based on the updated control variation.

[0209] Step 1104: Repeat the above parameter updating process until the predicted operating distance between each two adjacent movers represented by the updated predicted position exceeds the operating safety distance, and control the stator according to the updated control change to facilitate operation control of the movers.

[0210] Steps 1101 to 1104 are described in detail below.

[0211] In some embodiments, when the previously calculated predicted position indicates that the predicted operating distance between each two adjacent movers does not exceed the previously calculated operating safety distance, the system initiates an iterative adjustment mechanism to seek a safe control strategy. This includes increasing the load sensitivity factor α that affects the load sensitivity in the optimization objective, thereby obtaining an updated load sensitivity factor Δα. Subsequently, the load sensitivity weight w(m i ), get the updated load sensitive weight Δw(m i Increasing the load sensitivity factor usually means that in subsequent optimization calculations, the system will pay more attention to movers with heavier loads, or will be more inclined to choose a speed solution with lower energy consumption or smoother operation, which indirectly helps alleviate potential congestion or collision risks.

[0212] Next, we use the updated load-sensitive weight Δw(m i ) and the penalty constraint operation model shown in the above formula (7) to recalculate the optimized operation speed, thereby obtaining the updated optimized operation speed of each mover. At the same time, based on this updated optimized operation speed and the updated load sensitive weight Δw(m i ), the control variation corresponding to each stator is recalculated. Furthermore, due to the change in the optimized operating speed, the updated operating safety distance between each pair of adjacent movers is recalculated based on the updated optimized operating speed and the updated load-sensing weights. After obtaining the updated control variation, the system needs to again predict the future mover positions to assess the safety of the new control strategy.

[0213] The above parameter update process is then repeated, continuously adjusting the load sensitivity factor to update a series of key parameters (load sensitivity weight, optimized operating speed, control change, safe operating distance, and predicted position) until the predicted operating distance between each adjacent mover, represented by the most recently calculated updated predicted position, finally exceeds the most recently calculated updated safe operating distance. Once this safety condition is met, the iterative process terminates. At this point, actual drive control of the corresponding stator is performed based on the safety-verified updated control change obtained in the last iteration, thereby facilitating operational control of the mover.

[0214] By executing steps 1101 to 1104, by systematically increasing the load sensitivity factor and subsequently updating a series of key parameters including the load sensitivity weight, optimized operating speed, control variation, and operating safety distance, and then recalculating the predicted position and performing safety verification, the control strategy can be actively and gradually adjusted until a solution is found that meets operational requirements and ensures future operational safety. This greatly enhances the robustness and safety of the system in complex and dynamic environments, enabling the magnetic drive conveying system to perform self-correction and optimization when facing potential collision risks, ultimately achieving safe, reliable, and efficient mover operation control.

[0215] The following will further describe how to control the stator based on the control variation while suppressing thrust fluctuations caused by load changes and improving motion smoothness.

[0216] Reference Figure 12 , controlling the stator based on the control change amount, including the following steps 1201 to 1205.

[0217] Step 1201: Proportional feedback is obtained based on the product of the position difference between the predicted position and the current position of each mover and the proportional gain factor.

[0218] Step 1202: The differential feedback is obtained based on the differential of the position difference of each mover and multiplied by the differential gain factor.

[0219] Step 1203: Obtain a phase offset based on the current load mass and optimized operating speed of the mover corresponding to each stator.

[0220] Steps 1201 to 1203 are described in detail below.

[0221] In some embodiments, in order to achieve precise closed-loop control of the stator, before controlling the mover, the product of the position difference Δx between the predicted position of each mover at the predicted moment and the current position at the current moment and the proportional gain factor K is calculated. p , get proportional feedback K pΔx. The magnitude of this proportional feedback signal is proportional to the current position deviation and is designed to quickly respond and reduce the deviation.

[0222] Furthermore, in order to enhance the dynamic performance and stability of the control system, the position difference Δx of each actuator is differentiated in time to obtain the rate of change of the position difference d(Δx) / dt. Then, this differential result is combined with the preset differential gain factor K d Multiply them together to get the differential feedback K d d(Δx) / dt. This differential feedback signal is proportional to the rate of change of the position deviation and can foresee the deviation trend and intervene in advance, thereby suppressing overshoot, reducing oscillation, and improving the damping characteristics of the system.

[0223] In addition, to introduce feedforward control to compensate for known variations in system characteristics, a phase offset is calculated based on the current load mass of each stator's corresponding mover and its planned optimized operating speed. This phase offset is typically related to the phase angle of the electromagnetic force required to drive the mover. This is particularly true in drives such as AC permanent magnet synchronous motors, where changes in load and speed can significantly affect the optimal current phase required to produce the desired thrust. Therefore, this phase offset is a precalculated adjustment parameter based on the mover's real-time operating conditions, as described below.

[0224] Reference Figure 13 , based on the current load mass and optimized operating speed of the mover corresponding to each stator, a phase offset is obtained, including the following steps 1301 to 1304.

[0225] Step 1301: derive the rated thrust based on the product of the thrust coefficient and the rated current.

[0226] Step 1302: Based on the difference between the optimized operating speed of each mover and the current operating speed at the current moment, obtain the command acceleration of each mover.

[0227] Step 1303: Calculate the thrust ratio based on the product of the current load mass of the mover corresponding to each stator and the command acceleration, and then divide it by the rated thrust.

[0228] Step 1304: Based on the inverse tangent processing of the thrust ratio, the phase offset corresponding to each stator is obtained.

[0229] Steps 1301 to 1304 are described in detail below.

[0230] In some embodiments, in order to establish the reference parameters required for calculating the phase offset, the method first calculates the stator (or motor) thrust coefficient k I With rated current I nominalThe product of the stator and the maximum rated thrust F that can be generated under ideal conditions is obtained. nominal =k I I nominal .

[0231] Then, for each mover, the difference between its previously calculated optimized running speed and its current running speed at the current moment (i.e., the actual speed measured in real time by the sensor) is obtained, and the instantaneous acceleration required for each mover to reach its target speed is calculated, i.e., the command acceleration a cmd This speed difference represents how long the actuator needs to change its speed (usually within one control cycle). The command acceleration is the acceleration that the control system expects the actuator to achieve in the next moment or in the short term.

[0232] Furthermore, for each stator corresponding to the mover, the current load mass is multiplied by the command acceleration. This product is the instantaneous thrust required to drive the mover to achieve the command acceleration according to Newton's second law (F=ma). Then, the required thrust value calculated is divided by the rated thrust to obtain a thrust ratio m·a. cmd / F nominal This thrust ratio reflects the percentage or proportion of the driving force required by the mover to the maximum capacity of its drive unit under the current working conditions.

[0233] Finally, based on the thrust ratio m·a cmd / F nominal Perform inverse tangent processing (i.e., apply inverse tangent functions such as arctan or atan2) to obtain the phase offset φ(m,v) corresponding to the control stator corresponding to each mover.

[0234] By executing steps 1301 to 1304, the system's baseline rated thrust is determined by the thrust coefficient and the rated current, and the command acceleration is then calculated based on the difference between the optimized operating speed and the current operating speed. The actual required thrust is calculated in combination with the current load mass, and then compared with the rated thrust to obtain a thrust ratio. By performing inverse tangent processing on the thrust ratio, the phase offset is accurately calculated, which can dynamically reflect the current load condition and acceleration requirement of the mover, thereby guiding the underlying drive controller to adjust the distribution of the excitation current and the torque (thrust) current, or adjust the current vector angle, so as to achieve the optimal thrust output efficiency and dynamic response characteristics at different working points as much as possible. This has a significant beneficial effect on improving the energy utilization and control performance of the entire magnetic drive conveying system.

[0235] Step 1204: Perform sinusoidal processing based on the phase offset and multiply it by the compensation term amplitude to obtain dynamic feedforward compensation.

[0236] Step 1205: Based on the accumulated values ​​of proportional feedback, differential feedback, and dynamic feedforward compensation, the phase compensation current corresponding to each stator is obtained, and the corresponding stator is controlled based on the phase compensation current and the control variation.

[0237] Steps 1204 to 1205 are described in detail below.

[0238] In some embodiments, after obtaining the phase offset φ(m,v) corresponding to the control stator corresponding to each mover, the phase offset φ(m,v) is sinusoidally processed (sin(ωt+φ(m,v))) and then multiplied by the compensation term amplitude to obtain dynamic feedforward compensation ηsin(ωt+φ(m,v)). This dynamic feedforward compensation is intended to actively adjust the phase of the drive current (or other related control parameters) based on the load and desired speed of the mover to more efficiently and accurately generate the required driving force, thereby proactively responding to foreseeable disturbances or dynamic changes.

[0239] Then, based on the algebraic accumulation of proportional feedback, differential feedback and dynamic feedforward compensation, the comprehensive regulation signal corresponding to each stator is obtained, that is, the phase compensation current is shown in the following formula (13).

[0240]

[0241] Finally, the corresponding stator is controlled based on the calculated phase compensation current and the previously calculated control change representing the macro drive instruction.

[0242] By executing steps 1201 to 1205, proportional feedback and differential feedback (constituting a PD feedback controller) are used to ensure that the mover can accurately track the predicted position and quickly eliminate the deviation. At the same time, through the phase offset calculated based on the current load mass and optimized operating speed and the dynamic feedforward compensation generated by subsequent sinusoidal processing, the influence of the change in the mover working condition on the driving characteristics is actively adapted. The phase compensation current (and the control change itself) formed by combining these feedback and feedforward signals with the basic control change acts on the stator together, realizing accurate, stable and responsive closed-loop control of the mover's motion state, and effectively improving the positioning accuracy, anti-disturbance capability and overall dynamic performance of the magnetic drive conveying system in actual operation.

[0243] In some embodiments, by implementing the mover operation control method of the magnetic drive conveying system provided in this application, multiple tests have shown that when the mover load is 10 kg and the spacing is 300 mm, the traffic efficiency is increased from 65% to 89%, and the vibration amplitude is reduced from ±0.5 mm to ±0.08 mm, and under light load (2 kg) conditions, the energy consumption is reduced by 32%.

[0244] In some embodiments, compared with the prior art, the present application achieves significant improvements in the core performance of the magnetic drive conveying system, such as safety, efficiency, stability and energy consumption, through technical solutions such as multimodal sensor fusion, dynamic safety modeling, distributed predictive control and electromagnetic cooperative compensation. The specific advantages include: (1) Significant improvement in traffic efficiency: Dynamically adjust the speed based on real-time load and spacing, optimize the queue spacing utilization, and significantly shorten the transportation time. Distributed predictive control reduces central scheduling delay and improves the overall throughput of the system; (2) Significant improvement in motion stability: The electromagnetic phase compensation algorithm suppresses inertial force fluctuations, effectively reduces vibration during movement, and improves transportation stability; load adaptive control dynamically adjusts the acceleration curve to eliminate light load overshoot and heavy load lag; (3) Outstanding energy consumption optimization effect: Dynamic PWM frequency modulation strategy matches load demand, reducing energy waste under light load conditions; Real-time adaptation of electromagnetic thrust and load improves the overall energy efficiency ratio of the system; (4) Enhanced safety and robustness: The dynamic safety distance model calculates the safety threshold in real time, significantly reducing the risk of collision and emergency braking frequency; The predictive control mechanism identifies potential conflicts in advance, improving the stability of the system under dynamic disturbances.

[0245] The embodiment of the present application proposes a mover operation control method and related equipment for a magnetic drive conveying system. The magnetic drive conveying system includes a magnetic drive conveying track and multiple movers. The magnetic drive conveying track includes multiple stators. The movers run on the magnetic drive conveying track. The method includes: first, obtaining the current load mass of each mover and obtaining the current running distance between each two adjacent movers; then, obtaining a load sensitivity factor, and obtaining the load sensitive mass based on the product of the load sensitivity factor and the current load mass, obtaining the load sensitive weight based on the exponential processing of the load sensitive mass, obtaining the operation optimization function based on the ratio of the operation variable corresponding to each mover and the current operation distance, and then multiplying by the corresponding load sensitive weight, obtaining the operation optimization function, obtaining the safety distance variable corresponding to each mover based on the operation variable corresponding to each mover and the current load mass, and obtaining the safety distance constraint based on the numerical relationship between the current operation distance and the safety distance variable, generating an operation optimization model based on the operation optimization function and the safety distance constraint, solving the operation optimization model, and obtaining the solution corresponding to the operation variable of each mover as the optimized operation speed; next, from multiple stators, In the stator, the stator where each mover is currently located is determined as the corresponding control stator, and the control cost function corresponding to each control stator is obtained. The optimized operating speed of each mover is substituted into the optimized operating parameters of the control cost function of the corresponding control stator, and the control variable parameters of each control cost function are solved based on the quadratic programming method. The solved control variable parameters are used as the control variation of each control stator, and based on the multiple relationship of the control period, a distance control matrix and a change control matrix are generated. The predicted position of each mover at the predicted time is obtained based on the product of the current position of each mover at the current moment and the distance control matrix, and the product of the change control matrix and the control variation. Secondly, the rear mover of the two adjacent movers is used as the braking mover, and the reaction distance is obtained based on the product of the optimized operating speed of the braking mover and the system response time. The braking distance is obtained based on the square of the optimized operating speed of the braking mover divided by the maximum acceleration of the braking mover. The operating safety distance between the two adjacent movers is obtained based on the reaction distance, the braking distance and the safety margin.Finally, when the predicted position represents that the predicted running distance between each two adjacent movers exceeds the running safety distance, the proportional feedback is obtained based on the product of the position difference between the predicted position and the current position of each mover and the proportional gain factor. The differential feedback is obtained based on the differential of the position difference of each mover and multiplied by the differential gain factor. The phase offset is obtained based on the current load mass and optimized running speed of the mover corresponding to each stator. The sinusoidal processing of the phase offset is multiplied by the compensation item amplitude to obtain dynamic feedforward compensation. The phase compensation current corresponding to each stator is obtained based on the accumulated value of proportional feedback, differential feedback and dynamic feedforward compensation, and the corresponding stator is controlled based on the phase compensation current and the control change, so as to facilitate the operation control of the mover. When the predicted position When the predicted operating distance between each two adjacent movers does not exceed the operating safety distance, the load sensitivity factor is increased to obtain an updated load sensitivity factor, and the load sensitivity weight of each mover is updated based on the updated load sensitivity factor to obtain an updated load sensitivity weight; the optimized operating speed of each mover and the control variation of each stator are updated based on the updated load sensitivity weight, and an updated operating safety distance is obtained based on the updated optimized operating speed and the updated load sensitivity weight. The updated predicted position of each mover at the predicted time is obtained based on the updated control variation. The above parameter updating process is repeated until the updated predicted position indicates that the predicted operating distance between each two adjacent movers exceeds the operating safety distance, and the stator is controlled based on the updated control variation to facilitate operational control of the mover.

[0246] The embodiment of the present application aims to solve the problem that the load change of the mover causes the deviation of the running trajectory, which is prone to collision and thus affects the processing efficiency. It obtains the current load mass of each mover and the current running distance between adjacent movers in real time, and dynamically calculates the optimized running speed of each mover and the dynamic running safety distance that must be maintained between them based on this real-time information. At the same time, it also combines the prediction of the future position of the mover and the safety verification mechanism to ensure that the operation control of the mover is executed only when the predicted future mover spacing can meet the dynamically calculated safety distance requirement. This can effectively overcome the operation uncertainty caused by the quality change of the workpiece during the processing, actively avoid the collision risk caused by the deviation of the mover's running trajectory, and significantly improve the safety, stability and adaptive adjustment capability of the coordinated operation of multiple movers to dynamic working conditions, thereby ensuring the continuity and reliability of the processing process and providing It provides strong support for further improving the overall production efficiency and system robustness. In addition, by introducing load-sensitive weights to dynamically adjust the importance of different load movers in the optimization target, and constructing an operation optimization function oriented towards local traffic efficiency, while strictly adhering to the safety distance constraints calculated based on real-time load and target speed, and finally solving the optimized operating speed obtained by the operation optimization model, it can not only be personalized according to the real-time working conditions of each mover (current load mass and current operating distance), ensuring the safety of multi-motor operation and the system's responsiveness to dynamic changes, but also maximize the overall operating efficiency and performance of the conveying system under the premise of ensuring safety; and, accurately lock which control stators should be responsible for driving each mover at present, ensuring the targeting of the control, and then define a control cost function for each control stator to guide its behavior.The most critical thing is that by taking the optimized running speed as the input or target of the control cost function and again using the quadratic programming method to solve the control variable parameters of the control cost function, the control variation is obtained, so that the optimized running speed of the upper-level planning can effectively guide the specific behavior of the underlying controller, and the efficiency and optimality of the control instruction calculation are guaranteed by the mature quadratic programming method, and finally the drive control of each mover is achieved accurately, efficiently and in line with the overall optimization goal; in addition, by constructing the distance control matrix and the change control matrix related to the system control cycle and the dynamic model, a mathematical mapping relationship from the current state and the control input to the future state is established, and then using these matrices, the current position of the mover and the control variation to be applied are combined, and the predicted position is accurately calculated in the form of the standard discrete time state space equation, so that the system can pre-evaluate the possible future consequences of the instruction before actually executing the control instruction, especially the relative position relationship between the movers, thereby providing crucial forward-looking information for subsequent safety verification and control decisions; in addition, by systematically increasing the load sensitivity factor and updating the load sensitivity weight, By optimizing a series of key parameters, including operating speed, control variation, and operating safety distance, and then recalculating and safety-verifying the predicted position, the control strategy can be proactively and gradually adjusted until a solution is found that meets operational requirements while ensuring future operational safety. This greatly enhances the robustness and safety of the system in complex and dynamic environments, enabling the magnetic drive conveying system to self-correct and optimize when facing potential collision risks, ultimately achieving safe, reliable, and efficient mover operation control. Furthermore, proportional feedback and differential feedback (constituting a PD feedback controller) ensure that the mover can accurately track the predicted position and quickly eliminate deviations. At the same time, dynamic feedforward compensation generated by the phase offset calculated based on the current load mass and optimized operating speed and subsequent sinusoidal processing actively adapts to the impact of changes in the mover's operating conditions on the drive characteristics. These feedback and feedforward signals are combined with the basic control variation to form a phase compensation current (as well as the control variation itself), which acts together on the stator to achieve precise, stable, and responsive closed-loop control of the mover's motion state, effectively improving the positioning accuracy, anti-disturbance capability, and overall dynamic performance of the magnetic drive conveying system in actual operation.

[0247] The present application also provides a mover operation control device for a magnetic drive conveying system, which can implement the mover operation control method of the magnetic drive conveying system. Figure 14 , the apparatus 1400 comprises:

[0248] The data acquisition module 1410 is used to obtain the current load mass of each mover and the current running distance between each two adjacent movers;

[0249] The speed optimization module 1420 is used to calculate the optimized operating speed of each mover based on the current load mass and the current operating distance;

[0250] A position prediction module 1430 is configured to calculate a control variation of each stator based on the optimized operating speed, and to calculate a predicted position of each mover at a predicted time based on the control variation;

[0251] The safety distance calculation module 1440 is used to calculate the safe operating distance between each two adjacent movers based on the current load mass and the optimized operating speed;

[0252] The operation control module 1450 is configured to control the stator based on the control variation when the predicted operation distance between each two adjacent movers represented by the predicted position exceeds the operation safety distance, so as to facilitate operation control of the movers.

[0253] In some embodiments, the speed optimization module 1420 is further configured to:

[0254] Determine the load sensitivity weight corresponding to each mover based on the current load mass of each mover;

[0255] Based on the ratio of the operating variable corresponding to each mover and the current operating spacing, multiplied by the corresponding load sensitivity weight, the operation optimization function is obtained;

[0256] Based on the operating variables and current load mass corresponding to each mover, the safety distance variable corresponding to each mover is obtained, and based on the numerical relationship between the current operating distance and the safety distance variable, the safety distance constraint is obtained;

[0257] Generate an operation optimization model based on the operation optimization function and the safety distance constraint;

[0258] Solve the operation optimization model and obtain the solution corresponding to the operation variables of each mover as the optimized operation speed.

[0259] In some embodiments, the speed optimization module 1420 is further configured to:

[0260] Obtaining a load sensitivity factor, and obtaining a load sensitivity mass based on a product of the load sensitivity factor and the current load mass;

[0261] The load-sensing weight is obtained based on the exponential processing of the load-sensing quality.

[0262] In some embodiments, the speed optimization module 1420 is further configured to:

[0263] The safety distance constraint is relaxed to obtain a safety distance relaxation constraint;

[0264] The operation optimization model is transformed based on the safety distance relaxation constraint to obtain the penalty constraint operation model;

[0265] Based on the quadratic programming method, the penalty constraint operation model is solved, and the solution corresponding to the operating variables of each mover is obtained as the optimized operating speed.

[0266] In some embodiments, the location prediction module 1430 is further configured to:

[0267] From the plurality of stators, determine the stator where each mover is currently located as the corresponding control stator;

[0268] Obtain the control cost function corresponding to each control stator;

[0269] The optimized operating speed of each mover is substituted into the optimized operating parameters of the control cost function of the corresponding control stator, and the control variable parameters of each control cost function are solved based on the quadratic programming method, and the solved control variable parameters are used as the control variation of each control stator.

[0270] In some embodiments, the location prediction module 1430 is further configured to:

[0271] Based on the ratio of the load sensitivity weight of the mover corresponding to each control stator to the current operating spacing, the efficiency item weight of each control stator is obtained;

[0272] The control cost term is obtained based on the product of the efficiency term weight and the square of the optimized operating parameter, plus the product of the smoothing term weight and the square of the control change parameter;

[0273] Based on the control cost terms of all prediction time steps, the control cost function corresponding to the control stator is obtained.

[0274] In some embodiments, the location prediction module 1430 is further configured to:

[0275] Based on the multiple relationship of the control period, the distance control matrix and the change control matrix are generated;

[0276] Based on the product of the current position of each mover at the current moment and the distance control matrix, plus the product of the change control matrix and the control change amount, the predicted position of each mover at the predicted moment is obtained.

[0277] In some embodiments, the safety distance calculation module 1440 is further configured to:

[0278] Of the two adjacent movers, the one at the rear is used as a brake mover;

[0279] The reaction distance is obtained based on the product of the optimized operating speed of the brake actuator and the system response time;

[0280] The braking distance is obtained by dividing the square of the optimized operating speed of the brake mover by the maximum acceleration of the brake mover.

[0281] Based on the reaction distance, braking distance and safety margin, the operating safety distance between two adjacent movers is obtained.

[0282] In some embodiments, the operation control module 1450 is further configured to:

[0283] Increasing the load sensitivity factor to obtain an updated load sensitivity factor, and updating the load sensitivity weight of each mover based on the updated load sensitivity factor to obtain an updated load sensitivity weight;

[0284] Based on the updated load-sensitive weight, the optimized operating speed of each mover and the control change of each stator are updated, and the updated operating safety distance is obtained based on the updated optimized operating speed and the updated load-sensitive weight;

[0285] Based on the updated control variation, the updated predicted position of each actuator at the predicted time is obtained;

[0286] Repeat the above parameter updating process until the predicted running distance between each two adjacent movers represented by the updated predicted position exceeds the running safety distance, and control the stator according to the updated control change to facilitate the operation control of the mover.

[0287] In some embodiments, the operation control module 1450 is further configured to:

[0288] Proportional feedback is obtained based on the product of the position difference between the predicted position and the current position of each mover and the proportional gain factor;

[0289] Based on the differential of the position difference of each mover, multiplied by the differential gain factor, the differential feedback is obtained;

[0290] The phase offset is obtained based on the current load mass and optimized operating speed of the mover corresponding to each stator;

[0291] Based on the sinusoidal processing of the phase offset, the compensation term amplitude is multiplied to obtain dynamic feedforward compensation;

[0292] Based on the accumulated values ​​of proportional feedback, differential feedback and dynamic feedforward compensation, the phase compensation current corresponding to each stator is obtained, and the corresponding stator is controlled based on the phase compensation current and the control variation.

[0293] In some embodiments, the operation control module 1450 is further configured to:

[0294] Based on the product of thrust coefficient and rated current, the rated thrust is obtained;

[0295] Based on the difference between the optimized running speed of each mover and the current running speed at the current moment, the command acceleration of each mover is obtained;

[0296] The thrust ratio is obtained by multiplying the current load mass and the command acceleration of the mover corresponding to each stator by the rated thrust.

[0297] Based on the inverse tangent processing of the thrust ratio, the phase offset corresponding to each stator is obtained.

[0298] In the above embodiments, the description of each embodiment has its own focus. For the part that is not described in detail in a certain embodiment, the specific implementation method of the mover operation control device of the magnetic drive conveying system is basically the same as the specific implementation method of the mover operation control method of the above magnetic drive conveying system, and will not be repeated here.

[0299] In the embodiment of the present application, the mover operation control device of the magnetic drive conveying system addresses the problem that the change in the mover load causes the deviation of the operation trajectory, which is prone to collision and thus affects the processing efficiency. By obtaining the current load mass of each mover and the current operation distance between adjacent movers in real time, and dynamically calculating the optimized operation speed of each mover and the dynamic operation safety distance that must be maintained between them based on these real-time information, it also combines the prediction of the future position of the mover and the safety verification mechanism to ensure that the operation control of the mover is executed only when the predicted future mover spacing can meet the dynamically calculated safety distance requirement, so as to effectively overcome the operation uncertainty caused by the quality change of the workpiece during the processing, actively avoid the collision risk caused by the deviation of the mover operation trajectory, and significantly improve the safety, stability and adaptive adjustment capability of the coordinated operation of multiple movers to dynamic working conditions, thereby ensuring the continuity of the processing flow. The system improves the continuity and reliability, and provides strong support for further improving the overall production efficiency and system robustness. In addition, by introducing load-sensitive weights to dynamically adjust the importance of different load movers in the optimization target, and constructing an operation optimization function oriented to local traffic efficiency, while strictly adhering to the safety distance constraints calculated based on real-time load and target speed, and finally solving the optimized operation speed obtained by the operation optimization model, it can not only make personalized adjustments according to the real-time working conditions of each mover (current load mass and current operating distance), ensuring the safety of multi-motor operation and the system's responsiveness to dynamic changes, but also maximize the overall operation efficiency and performance of the conveying system under the premise of ensuring safety; and, accurately lock which control stators should be responsible for driving each mover at present, ensuring the targeting of control, and then define a control cost function for each control stator to guide its behavior.The most critical thing is that by taking the optimized running speed as the input or target of the control cost function and again using the quadratic programming method to solve the control variable parameters of the control cost function, the control variation is obtained, so that the optimized running speed of the upper-level planning can effectively guide the specific behavior of the underlying controller, and the efficiency and optimality of the control instruction calculation are guaranteed by the mature quadratic programming method, and finally the drive control of each mover is achieved accurately, efficiently and in line with the overall optimization goal; in addition, by constructing the distance control matrix and the change control matrix related to the system control cycle and the dynamic model, a mathematical mapping relationship from the current state and the control input to the future state is established, and then using these matrices, the current position of the mover and the control variation to be applied are combined, and the predicted position is accurately calculated in the form of the standard discrete time state space equation, so that the system can pre-evaluate the possible future consequences of the instruction before actually executing the control instruction, especially the relative position relationship between the movers, thereby providing crucial forward-looking information for subsequent safety verification and control decisions; in addition, by systematically increasing the load sensitivity factor and updating the load sensitivity weight, By optimizing a series of key parameters, including operating speed, control variation, and operating safety distance, and then recalculating and safety-verifying the predicted position, the control strategy can be proactively and gradually adjusted until a solution is found that meets operational requirements while ensuring future operational safety. This greatly enhances the robustness and safety of the system in complex and dynamic environments, enabling the magnetic drive conveying system to self-correct and optimize when facing potential collision risks, ultimately achieving safe, reliable, and efficient mover operation control. Furthermore, proportional feedback and differential feedback (constituting a PD feedback controller) ensure that the mover can accurately track the predicted position and quickly eliminate deviations. At the same time, dynamic feedforward compensation generated by the phase offset calculated based on the current load mass and optimized operating speed and subsequent sinusoidal processing actively adapts to the impact of changes in the mover's operating conditions on the drive characteristics. These feedback and feedforward signals are combined with the basic control variation to form a phase compensation current (as well as the control variation itself), which acts together on the stator to achieve precise, stable, and responsive closed-loop control of the mover's motion state, effectively improving the positioning accuracy, anti-disturbance capability, and overall dynamic performance of the magnetic drive conveying system in actual operation.

[0300] An embodiment of the present application further provides an electronic device, including:

[0301] at least one memory;

[0302] at least one processor;

[0303] at least one program;

[0304] The program is stored in the memory, and the processor executes the at least one program to implement the mover operation control method of the magnetic drive conveying system implemented in the present application. The electronic device can be any smart terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), an in-vehicle computer, etc.

[0305] See also Figure 15 , Figure 15 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0306] The processor 1501 may be implemented as a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is configured to execute relevant programs to implement the technical solutions provided in the embodiments of the present application.

[0307] The memory 1502 can be implemented in the form of ROM (Read Only Memory), static storage device, dynamic storage device, or RAM (Random Access Memory). The memory 1502 can store an 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 codes are stored in the memory 1502 and are called by the processor 1501 to execute the mover operation control method of the magnetic drive conveying system of the embodiment of this application;

[0308] Input / output interface 1503, used to implement information input and output;

[0309] Communication interface 1504, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0310] Bus 1505 , which transmits information between various components of the device (e.g., processor 1501 , memory 1502 , input / output interface 1503 , and communication interface 1504 );

[0311] The processor 1501 , the memory 1502 , the input / output interface 1503 and the communication interface 1504 are connected to each other in communication within the device via the bus 1505 .

[0312] An embodiment of the present application further provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the mover operation control method of the magnetic drive conveying system is implemented.

[0313] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0314] The embodiments described in the embodiments of this application are intended to more clearly illustrate 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. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0315] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0316] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0317] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0318] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0319] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0320] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0321] The units described above as separate components may or may not be physically separate, and 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 these units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0322] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0323] If the integrated unit is implemented in the form of 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 the present application, 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, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0324] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A method for controlling the operation of a mover of a magnetic drive conveying system, characterized in that: The magnetic drive conveying system includes a magnetic drive conveying track and a plurality of movers, the magnetic drive conveying track includes a plurality of stators, and the movers run on the magnetic drive conveying track. The method includes: Obtaining the current load mass of each of the movers, and obtaining the current running distance between every two adjacent movers; Calculating an optimized operating speed of each mover based on the current load mass and the current operating distance; Calculating a control variation of each of the stators based on the optimized operating speed, and calculating a predicted position of each of the movers at a predicted time based on the control variation; Based on the current load mass and the optimized operating speed, a safe operating distance between each two adjacent movers is calculated; When the predicted position indicates that the predicted operating distance between each two adjacent movers exceeds the operating safety distance, the stator is controlled based on the control variation to facilitate operation control of the movers.

2. The method for controlling the movement of a magnetic drive conveying system according to claim 1, characterized in that: The step of calculating the optimized operating speed of each mover based on the current load mass and the current operating distance includes: Determining a load sensitivity weight corresponding to each mover based on the current load mass of each mover; Based on the ratio of the operating variable corresponding to each mover to the current operating distance, and then multiplied by the corresponding load-sensitive weight, an operation optimization function is obtained; Based on the operating variable corresponding to each mover and the current load mass, a safety distance variable corresponding to each mover is obtained, and based on a numerical relationship between the current operating distance and the safety distance variable, a safety distance constraint is obtained; generating an operation optimization model based on the operation optimization function and the safety distance constraint; The operation optimization model is solved to obtain a solution corresponding to the operation variable of each mover as the optimized operation speed.

3. The method for controlling the movement of a magnetic drive conveying system according to claim 2, characterized in that: The determining the load sensitivity weight corresponding to each mover based on the current load mass of each mover includes: Obtaining a load sensitivity factor, and obtaining a load sensitivity mass based on a product of the load sensitivity factor and the current load mass; The load-sensing weight is obtained based on the exponential processing of the load-sensing quality.

4. The method for controlling the operation of a mover of a magnetic drive conveying system according to claim 2, characterized in that: Solving the operation optimization model to obtain a solution corresponding to the operation variable of each mover as the optimized operation speed includes: Relaxing the safety distance constraint to obtain a relaxed safety distance constraint; Converting the operation optimization model based on the safety distance relaxation constraint to obtain a penalty constraint operation model; Based on a quadratic programming method, the penalty constraint operation model is solved to obtain a solution corresponding to the operation variable of each mover as the optimized operation speed.

5. The mover operation control method of the magnetic drive conveying system according to claim 2 is characterized in that: The calculating of the control variation of each stator based on the optimized operating speed includes: Determine, from the plurality of stators, the stator where each mover is currently located as the corresponding control stator; Obtaining a control cost function corresponding to each of the control stators; The optimized operating speed of each mover is substituted into the optimized operating parameters of the control cost function of the corresponding control stator, and the control variable parameters of each control cost function are solved based on the quadratic programming method, and the control variable parameters obtained by the solution are used as the control change of each control stator.

6. The method for controlling the operation of a mover of a magnetic drive conveying system according to claim 5, characterized in that: The obtaining of the control cost function corresponding to each of the control stators includes: Obtaining an efficiency item weight of each of the control stators based on a ratio of the load-sensitive weight of the mover corresponding to each of the control stators to the current operating spacing; A control cost term is obtained based on the product of the efficiency term weight and the square of the optimized operating parameter, plus the product of the smoothing term weight and the square of the control change parameter; Based on the control cost terms of all prediction time steps, the control cost function corresponding to the control stator is obtained.

7. The mover operation control method of the magnetic drive conveying system according to claim 1 is characterized in that: The step of calculating the predicted position of each mover at the predicted time based on the control variation includes: Based on the multiple relationship of the control period, the distance control matrix and the change control matrix are generated; Based on the product of the current position of each mover at the current moment and the distance control matrix, plus the product of the change control matrix and the control change amount, the predicted position of each mover at the predicted moment is obtained.

8. The mover operation control method of the magnetic drive conveying system according to claim 1 is characterized in that: The step of calculating the safe operating distance between each two adjacent movers based on the current load mass and the optimized operating speed includes: The rear one of the two adjacent movers is used as a brake mover; Obtaining a reaction distance based on a product of the optimized operating speed of the brake mover and a system response time; A braking distance is obtained by dividing the square of the optimized operating speed of the brake mover by the maximum acceleration of the brake mover; The operating safety distance between the two adjacent movers is obtained based on the reaction distance, the braking distance, and the safety margin.

9. The method for controlling the operation of a mover of a magnetic drive conveying system according to claim 3, characterized in that: When the predicted position indicates that the predicted running distance between each two adjacent movers does not exceed the running safety distance, the method further includes: Increasing the load sensitivity factor to obtain an updated load sensitivity factor, and updating the load sensitivity weight of each mover based on the updated load sensitivity factor to obtain an updated load sensitivity weight; updating the optimized operating speed of each mover and the control variation of each stator based on the updated load-sensitive weight, and obtaining an updated operating safety distance based on the updated optimized operating speed and the updated load-sensitive weight; Obtaining an updated predicted position of each mover at a predicted time based on the updated control variation; The above parameter updating process is repeated until the predicted operating distance between each two adjacent movers represented by the updated predicted position exceeds the operating safety distance, and the stator is controlled according to the updated control change to facilitate operation control of the mover.

10. The mover operation control method of the magnetic drive conveying system according to claim 1, characterized in that: The controlling the stator based on the control variation includes: Obtaining proportional feedback based on the product of the position difference between the predicted position and the current position of each of the movers and a proportional gain factor; A differential feedback is obtained by multiplying the differential of the position difference of each mover by a differential gain factor; Obtaining a phase offset based on the current load mass and the optimized operating speed of the mover corresponding to each stator; Sine processing based on the phase offset and then multiplying by the compensation term amplitude to obtain dynamic feedforward compensation; Based on the accumulated value of the proportional feedback, the differential feedback and the dynamic feedforward compensation, a phase compensation current corresponding to each stator is obtained, and the corresponding stator is controlled based on the phase compensation current and the control variation.

11. The method for controlling the operation of a mover of a magnetic drive conveying system according to claim 10, characterized in that: The obtaining of the phase offset based on the current load mass and the optimized operating speed of the mover corresponding to each stator includes: Based on the product of thrust coefficient and rated current, the rated thrust is obtained; Obtaining a command acceleration of each of the movers based on a difference between the optimized operating speed of each of the movers and the current operating speed at a current moment; A thrust ratio is obtained by multiplying the current load mass of the mover corresponding to each stator by the command acceleration and dividing the product by the rated thrust; Based on the inverse tangent processing of the thrust ratio, the phase offset corresponding to each stator is obtained.

12. A mover operation control device for a magnetic drive conveying system, characterized in that: The magnetic drive conveying system includes a magnetic drive conveying track and a plurality of movers, the magnetic drive conveying track includes a plurality of stators, and the movers run on the magnetic drive conveying track. The device includes: A data acquisition module is used to obtain the current load mass of each of the movers and the current running distance between each two adjacent movers; A speed optimization module, configured to calculate an optimized operating speed of each mover based on the current load mass and the current operating distance; a position prediction module, configured to calculate a control variation of each of the stators based on the optimized operating speed, and calculate a predicted position of each of the movers at a predicted moment based on the control variation; A safety distance calculation module, configured to calculate a safe operating distance between each two adjacent movers based on the current load mass and the optimized operating speed; An operation control module is used to control the stator based on the control variation when the predicted operation distance between each two adjacent movers represented by the predicted position exceeds the operation safety distance, so as to facilitate operation control of the movers.

13. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, it implements the mover operation control method of the magnetic drive conveying system according to any one of claims 1 to 11.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the mover operation control method of the magnetic drive conveying system according to any one of claims 1 to 11 is implemented.

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