Linear motor with self-adaptive adjustment function and control method

Abstract

The invention relates to a linear motor with an adaptive adjustment function and a control method, and belongs to the technical field of linear motors. Therefore, energy consumption analysis and evaluation are carried out on the energy consumption characteristics of the linear motors within the preset time, the linear motors with normal energy consumption and the linear motors with abnormal energy consumption are obtained, and finally the energy consumption characteristics of the linear motors with abnormal energy consumption within the preset time are obtained. And performing energy consumption optimization on the linear motor according to the energy consumption characteristics of the linear motor with abnormal energy consumption within the preset time. The linear motor energy consumption prediction model is used for predicting and tracking the energy consumption characteristics of the linear motor within the preset time, and the linear motor with abnormal energy consumption can be found in time, so that energy consumption optimization is carried out, the temperature rise of the linear motor is prevented from being too high, the energy consumption is reduced, and the service life of the linear motor can be prolonged.

Description

Technical Field

[0001] The present invention relates to the technical field of linear motor control, and in particular to a linear motor with an adaptive adjustment function and a control method thereof. Background Art

[0002] There are many names for linear motors, such as linear modules, rectangular coordinate robots, linear slides, etc. They are automation upgrade units following linear guides, linear motion modules, and ball screw linear transmission mechanisms. The linear and curved motion of the load can be achieved through the combination of various units, making the automation of light loads more flexible and the positioning more accurate. The linear module was first developed and used in Germany, and the market positioning is photovoltaic equipment, loading and unloading manipulators, cutting and moving equipment, gluing equipment, patch equipment, etc. This type of manipulator can bring convenience to the equipment in this industry: fast monomer movement speed, high repeatability positioning accuracy, light body mass, small equipment space, and long life. The scope of application of linear modules has been expanding to the world. In recent years, the development of linear modules has been even faster, especially in Shenzhen, and is deeply favored by equipment manufacturers. Since its development, linear modules have been widely used in various equipment, reducing dependence on imported complete sets of equipment, and bringing more opportunities for engineers who are keen on equipment research and development and manufacturing. Linear modules are currently widely used in measurement, laser welding, laser cutting, glue coating machines, spraying machines, punching machines, glue dispensing machines, small CNC machine tools, engraving and milling machines, sample plotters, cutting machines, transfer machines, sorting machines, testing machines and applicable education. However, during the use of linear motors, due to the age or long-term abnormal use, the temperature of the linear motors will rise abnormally, which will lead to a decrease in the service life of the linear motors. Summary of the invention

[0003] The present invention overcomes the deficiencies of the prior art and provides a linear motor with an adaptive adjustment function and a control method.

[0004] To achieve the above object, the technical solution adopted by the present invention is: A first aspect of the present invention provides a control method for a linear motor with an adaptive adjustment function, comprising: Collecting electromagnetic data change characteristics and energy consumption characteristics inside the linear motor through sensors, analyzing the electromagnetic data change characteristics and energy consumption characteristics inside the linear motor, and obtaining a correlation relationship between the electromagnetic data change characteristics and the energy consumption characteristics; Building a linear motor energy consumption prediction model according to the correlation between the electromagnetic data change characteristics and the energy consumption characteristics, and predicting and tracking the energy consumption characteristics of the linear motor within a preset time through the linear motor energy consumption prediction model; By performing energy consumption analysis and evaluation on the energy consumption characteristics of the linear motor within a preset time, a linear motor with normal energy consumption and a linear motor with abnormal energy consumption are obtained; The energy consumption characteristics of the linear motor with abnormal energy consumption within a preset time are obtained, and the energy consumption of the linear motor is optimized according to the energy consumption characteristics of the linear motor with abnormal energy consumption within the preset time.

[0005] Furthermore, in this method, the electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are collected by sensors, and the electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are analyzed to obtain the correlation relationship between the electromagnetic data change characteristics and the energy consumption characteristics, specifically: The electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are collected through sensors, and the electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are counted, a data set is constructed, and the Apriori algorithm is introduced; The electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are analyzed by the Apriori algorithm, and a candidate frequent item set containing a single item is generated by scanning the data set, and it is determined whether the generated item set meets the set minimum confidence, support and lift thresholds; If a candidate frequent item set meets the threshold requirement, it is added to the frequent item set list, and higher-order candidate frequent item sets are generated based on these frequent item sets. Then, the support and confidence are judged, and higher-order candidate frequent item sets are generated iteratively until no new item sets that meet the threshold requirement can be generated. Output all frequent item sets that meet the minimum confidence, support, and lift thresholds, ultimately determine the strong association rules, and obtain the correlation between the electromagnetic data change characteristics and energy consumption characteristics.

[0006] Furthermore, in this method, a linear motor energy consumption prediction model is constructed according to the correlation between the electromagnetic data change characteristics and the energy consumption characteristics, specifically: Acquire a preset number of electromagnetic data change characteristics and energy consumption characteristics, respectively use the electromagnetic data change characteristics and energy consumption characteristics as nodes, connect the nodes based on the correlation relationship between the electromagnetic data change characteristics and the energy consumption characteristics, and construct correlation characteristics between the electromagnetic data change characteristics and the energy consumption characteristics; Constructing a linear motor energy consumption prediction model based on a deep neural network, inputting the electromagnetic data change characteristics and energy consumption characteristics into the linear motor energy consumption prediction model for training, and setting a prediction accuracy threshold; Obtaining the prediction accuracy of the linear motor energy consumption prediction model at the current timestamp, and determining whether the prediction accuracy of the linear motor energy consumption prediction model at the current timestamp is greater than the prediction accuracy threshold; If the prediction accuracy of the current timestamp linear motor energy consumption prediction model is greater than the prediction accuracy threshold, the model stops training and saves the model parameters. If the prediction accuracy of the current timestamp linear motor energy consumption prediction model is not greater than the prediction accuracy threshold, the model continues training.

[0007] Furthermore, in the present method, the linear motor energy consumption prediction model is used to predict and track the energy consumption characteristics of the linear motor within a preset time, specifically including: Acquire the linear motor electromagnetic data change characteristics within a preset time, and input the linear motor electromagnetic data change characteristics within the preset time into the linear motor energy consumption prediction model for prediction; By prediction, the energy consumption characteristics of the linear motor within a preset time are obtained, and a Markov model is introduced, and the energy consumption characteristics of the linear motor within the preset time are input into the Markov model for calculation; Calculate the state transition probability value of the energy consumption characteristic of the linear motor in each timestamp to transfer to another energy consumption characteristic, and determine whether the state transition probability value is greater than a preset state transition probability threshold; If the state transition probability value is not greater than the preset state transition probability threshold, the energy consumption characteristics of the current linear motor are maintained unchanged; if the state transition probability value is greater than the preset state transition probability threshold, the energy consumption characteristics of the current linear motor are updated to another energy consumption characteristic, and the energy consumption characteristics of the linear motor in each timestamp within the preset time are updated.

[0008] Furthermore, in the present method, by performing energy consumption analysis and evaluation on the energy consumption characteristics of the linear motor within a preset time, a linear motor with normal energy consumption and a linear motor with abnormal energy consumption are obtained, specifically including: Setting an energy consumption analysis evaluation threshold index, and determining whether the energy consumption characteristic of the linear motor within a preset time is greater than the energy consumption analysis evaluation threshold index; When the energy consumption characteristic of the linear motor within the preset time is greater than the energy consumption analysis evaluation threshold index, the corresponding linear motor is regarded as a linear motor with abnormal energy consumption and is displayed in a preset manner; When the energy consumption characteristic of the linear motor within the preset time is not greater than the energy consumption analysis evaluation threshold index, the corresponding linear motor is regarded as a linear motor with normal energy consumption and is displayed in a preset manner.

[0009] Furthermore, in the present method, energy consumption characteristics of the linear motor with abnormal energy consumption within a preset time are obtained, and energy consumption optimization of the linear motor is performed according to the energy consumption characteristics of the linear motor with abnormal energy consumption within the preset time, specifically including: Acquire energy consumption characteristics of the linear motor with abnormal energy consumption within a preset time, reinitialize and reduce the working parameters of the linear motor, and control the linear motor based on the working parameters of the linear motor; Obtaining energy consumption characteristics of the linear motor with reinitialized working parameters within a preset time, and determining whether the energy consumption characteristics of the linear motor with reinitialized working parameters within the preset time are greater than an energy consumption analysis evaluation threshold index; When the energy consumption characteristic of the linear motor of the reinitialized working parameters within the preset time is greater than the energy consumption analysis evaluation threshold index, the working parameters of the linear motor are continuously optimized again until they are no greater than the energy consumption analysis evaluation threshold index; When the energy consumption characteristics of the linear motor with the reinitialized working parameters within the preset time are not greater than the energy consumption analysis evaluation threshold index, the working parameters of the linear motor are output and controlled according to the currently optimized working parameters of the linear motor.

[0010] Furthermore, in the present method, the linear motor with adaptive adjustment function includes a housing, a stator, a mover, a plurality of temperature sensors and an electromagnetic data detection sensor.

[0011] A second aspect of the present invention provides a control system for a linear motor with an adaptive adjustment function, comprising a memory and a processor, wherein the memory comprises a control method program for the linear motor with an adaptive adjustment function, and when the control method program for the linear motor with an adaptive adjustment function is executed by the processor, any step of the control method for the linear motor with an adaptive adjustment function is implemented.

[0012] The third aspect of the present invention provides a computer-readable storage medium, comprising a control method program for a linear motor with an adaptive adjustment function. When the control method program for a linear motor with an adaptive adjustment function is executed by the processor, the steps of any one of the control methods for a linear motor with an adaptive adjustment function are implemented.

[0013] The present invention solves the defects existing in the background technology and has the following beneficial effects: The present invention collects electromagnetic data change characteristics and energy consumption characteristics inside the linear motor through a sensor, analyzes the electromagnetic data change characteristics and energy consumption characteristics inside the linear motor, obtains the correlation relationship between the electromagnetic data change characteristics and the energy consumption characteristics, and then builds a linear motor energy consumption prediction model according to the correlation relationship between the electromagnetic data change characteristics and the energy consumption characteristics, and predicts and tracks the energy consumption characteristics of the linear motor within a preset time through the linear motor energy consumption prediction model, thereby obtaining a linear motor with normal energy consumption and a linear motor with abnormal energy consumption by analyzing and evaluating the energy consumption characteristics of the linear motor within the preset time, and finally obtains the energy consumption characteristics of the linear motor with abnormal energy consumption within the preset time, and optimizes the energy consumption of the linear motor according to the energy consumption characteristics of the linear motor with abnormal energy consumption within the preset time. The present invention predicts and tracks the energy consumption characteristics of the linear motor within the preset time through the linear motor energy consumption prediction model, can timely discover the linear motor with abnormal energy consumption, thereby optimizing energy consumption, avoiding excessive heating of the linear motor and reducing energy consumption, and can improve the service life of the linear motor. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0015] Figure 1 The overall flow chart of the control method of the linear motor with adaptive adjustment function is shown; Figure 2 A partial flow chart of a control method for a linear motor with an adaptive adjustment function is shown; Figure 3 A system block diagram of a control system of a linear motor with an adaptive adjustment function is shown; Figure 4 A partial structural schematic diagram of a linear motor is shown.

[0016] In the figure: 1. Housing, 2. Stator, 3. Mover. DETAILED DESCRIPTION

[0017] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0018] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below. Example 1

[0019] like Figure 1 As shown, the first aspect of the present invention provides a control method for a linear motor with an adaptive adjustment function, comprising: S102: collecting electromagnetic data change characteristics and energy consumption characteristics inside the linear motor through sensors, analyzing the electromagnetic data change characteristics and energy consumption characteristics inside the linear motor, and obtaining a correlation relationship between the electromagnetic data change characteristics and the energy consumption characteristics; S104: constructing a linear motor energy consumption prediction model according to the correlation between the electromagnetic data change characteristics and the energy consumption characteristics, and predicting and tracking the energy consumption characteristics of the linear motor within a preset time through the linear motor energy consumption prediction model; S106: Analyzing and evaluating the energy consumption characteristics of the linear motor within a preset time to obtain a linear motor with normal energy consumption and a linear motor with abnormal energy consumption; S108: Obtain energy consumption characteristics of the linear motor with abnormal energy consumption within a preset time, and optimize the energy consumption of the linear motor according to the energy consumption characteristics of the linear motor with abnormal energy consumption within the preset time.

[0020] It should be noted that electromagnetic data includes magnetic field strength, magnetic flux data, etc. The present invention predicts and tracks the energy consumption characteristics of the linear motor within a preset time through a linear motor energy consumption prediction model, and can promptly detect linear motors with abnormal energy consumption, thereby optimizing energy consumption, avoiding excessive heating of the linear motor and reducing energy consumption, and increasing the service life of the linear motor.

[0021] Furthermore, in this method, the electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are collected by sensors, and the electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are analyzed to obtain the correlation relationship between the electromagnetic data change characteristics and the energy consumption characteristics, specifically: The electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are collected through sensors, and the electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are counted, a data set is constructed, and the Apriori algorithm is introduced; The Apriori algorithm is used to analyze the electromagnetic data change characteristics and energy consumption characteristics inside the linear motor. By scanning the data set, a candidate frequent item set containing a single item is generated to determine whether the generated item set meets the set minimum confidence, support, and lift thresholds. If a candidate frequent item set meets the threshold requirement, it is added to the frequent item set list, and higher-order candidate frequent item sets are generated based on these frequent item sets. Then, the support and confidence are judged, and higher-order candidate frequent item sets are generated iteratively until no new item sets that meet the threshold requirement can be generated. Output all frequent item sets that meet the minimum confidence, support, and lift thresholds, ultimately determine the strong association rules, and obtain the correlation between the electromagnetic data change characteristics and energy consumption characteristics.

[0022] It should be noted that, during the use process, there are coils in the motor, and the coils of the linear motor will gradually heat up during use, which will lead to excessive energy consumption of the linear motor, degrade the performance of the coils, and then reduce the service life of the linear motor. The Apriori algorithm is a frequent item set algorithm for mining association rules. Its core idea is to mine frequent item sets through two stages: candidate set generation and downward closure detection of the plot. Moreover, the algorithm has been widely used in various fields such as business and network security. The Apriori algorithm can establish the correlation relationship between the electromagnetic data change characteristics and the energy consumption characteristics, so that the system can better understand the correlation relationship between the electromagnetic data change characteristics and the energy consumption characteristics.

[0023] like Figure 2 As shown, further, in this method, a linear motor energy consumption prediction model is constructed according to the correlation between the electromagnetic data change characteristics and the energy consumption characteristics, specifically: S202: Obtain a preset number of electromagnetic data change characteristics and energy consumption characteristics, respectively use the electromagnetic data change characteristics and energy consumption characteristics as nodes, connect the nodes based on the correlation relationship between the electromagnetic data change characteristics and the energy consumption characteristics, and construct the correlation characteristics between the electromagnetic data change characteristics and the energy consumption characteristics; S204: constructing a linear motor energy consumption prediction model based on a deep neural network, inputting electromagnetic data change characteristics and energy consumption characteristics into the linear motor energy consumption prediction model for training, and setting a prediction accuracy threshold; S206: Obtain the prediction accuracy of the linear motor energy consumption prediction model at the current timestamp, and determine whether the prediction accuracy of the linear motor energy consumption prediction model at the current timestamp is greater than a prediction accuracy threshold; S208: If the prediction accuracy of the linear motor energy consumption prediction model at the current timestamp is greater than the prediction accuracy threshold, the model stops training and saves the model parameters. If the prediction accuracy of the linear motor energy consumption prediction model at the current timestamp is not greater than the prediction accuracy threshold, the model continues training.

[0024] It should be noted that by taking the electromagnetic data change characteristics and the energy consumption characteristics as nodes respectively, connecting the nodes based on the correlation relationship between the electromagnetic data change characteristics and the energy consumption characteristics, and constructing the correlation characteristics of the electromagnetic data change characteristics and the energy consumption characteristics, the model is trained through the data.

[0025] Furthermore, in the present method, the linear motor energy consumption prediction model is used to predict and track the energy consumption characteristics of the linear motor within a preset time, specifically including: Obtaining the linear motor electromagnetic data change characteristics within a preset time, and inputting the linear motor electromagnetic data change characteristics within the preset time into the linear motor energy consumption prediction model for prediction; By prediction, the energy consumption characteristics of the linear motor within a preset time are obtained, and a Markov model is introduced to input the energy consumption characteristics of the linear motor within the preset time into the Markov model for calculation; Calculate the state transition probability value of the energy consumption characteristic of the linear motor in each timestamp to transfer to another energy consumption characteristic, and determine whether the state transition probability value is greater than a preset state transition probability threshold; If the state transition probability value is not greater than the preset state transition probability threshold, the energy consumption characteristics of the current linear motor are maintained unchanged. If the state transition probability value is greater than the preset state transition probability threshold, the energy consumption characteristics of the current linear motor are updated to another energy consumption characteristic, and the energy consumption characteristics of the linear motor in each timestamp within the preset time are updated.

[0026] It should be noted that the state transition probability value of the energy consumption characteristic of the linear motor in each timestamp is transferred to another energy consumption characteristic through the Markov model. If the state transition probability value is not greater than the preset state transition probability threshold, the energy consumption characteristic of the current linear motor is maintained unchanged. If the state transition probability value is greater than the preset state transition probability threshold, the energy consumption characteristic of the current linear motor is updated to another energy consumption characteristic. The energy consumption characteristics of the linear motor in each timestamp within the preset time are updated, thereby improving the tracking accuracy of the energy consumption characteristics of the linear motor.

[0027] Furthermore, in the present method, by analyzing and evaluating the energy consumption characteristics of the linear motor within a preset time, a linear motor with normal energy consumption and a linear motor with abnormal energy consumption are obtained, specifically including: Setting an energy consumption analysis evaluation threshold index, and determining whether the energy consumption characteristic of the linear motor is greater than the energy consumption analysis evaluation threshold index within a preset time; When the energy consumption characteristic of the linear motor is greater than the energy consumption analysis evaluation threshold index within the preset time, the corresponding linear motor is regarded as a linear motor with abnormal energy consumption and is displayed in a preset manner; When the energy consumption characteristic of the linear motor is not greater than the energy consumption analysis evaluation threshold index within the preset time, the corresponding linear motor is regarded as a linear motor with normal energy consumption and is displayed in a preset manner.

[0028] Furthermore, in the present method, energy consumption characteristics of the linear motor with abnormal energy consumption within a preset time are obtained, and energy consumption of the linear motor is optimized according to the energy consumption characteristics of the linear motor with abnormal energy consumption within the preset time, specifically including: Acquire energy consumption characteristics of the linear motor with abnormal energy consumption within a preset time, reinitialize and reduce the working parameters of the linear motor, and control the linear motor based on the working parameters of the linear motor; Obtaining energy consumption characteristics of the linear motor within a preset time after the reinitialized working parameters, and determining whether the energy consumption characteristics of the linear motor within the preset time after the reinitialized working parameters are greater than an energy consumption analysis evaluation threshold indicator; When the energy consumption characteristic of the linear motor of the reinitialized working parameters within the preset time is greater than the energy consumption analysis evaluation threshold index, the working parameters of the linear motor are continuously optimized again until they are no greater than the energy consumption analysis evaluation threshold index; When the energy consumption characteristics of the linear motor with the reinitialized working parameters within the preset time are not greater than the energy consumption analysis evaluation threshold index, the working parameters of the linear motor are output and controlled according to the currently optimized working parameters of the linear motor.

[0029] It should be noted that the working parameters of the linear motor include parameters such as the motor speed and the motor voltage. This method can track the energy consumption characteristics of the linear motor within a preset time, and can promptly detect linear motors with abnormal energy consumption, thereby optimizing energy consumption, avoiding excessive heating of the linear motor, reducing energy consumption, and increasing the service life of the linear motor.

[0030] like Figure 4 As shown, further, in this method, the linear motor with adaptive adjustment function includes a housing 1, a stator 2, a mover 3, a plurality of temperature sensors (internal settings, not shown) and an electromagnetic data detection sensor (not shown). Among them, the electromagnetic data detection sensor can be a Hall effect sensor, an electromagnetic detection sensor, etc.

[0031] In addition, the method further comprises: Acquire control accuracy information of the linear motor at different operating temperatures, and construct a linear motor control accuracy prediction model based on a deep neural network, and input the control accuracy information of the linear motor at different operating temperatures into the linear motor control accuracy prediction model for training; Through training, the trained linear motor control accuracy prediction model is obtained, and the current working temperature information of the linear motor is obtained, and the current working temperature information of the linear motor is input into the trained linear motor control accuracy prediction model for prediction; By prediction, the control accuracy information of the linear motor under the current working temperature is obtained, and the control accuracy requirement information of each linear motor is obtained, and it is determined whether the control accuracy information of the linear motor under the current working temperature is greater than the control accuracy requirement information of the linear motor; When the control accuracy information of the linear motor under the current working temperature is not greater than the control accuracy requirement information of the linear motor, an early warning is issued to the corresponding linear motor, and the corresponding linear motor is marked; Acquire a linear motor whose control accuracy information at the current operating temperature is not greater than the control accuracy requirement information of the linear motor, and perform temperature control processing on the motor whose control accuracy information is not greater than the control accuracy requirement information of the linear motor until the control accuracy information at the current operating temperature is not greater than the control accuracy requirement information of the linear motor.

[0032] It should be noted that the control accuracy of the linear motor is different under different operating temperatures. This method can further improve the control rationality of the linear motor. Example 2

[0033] like Figure 3 As shown, the second aspect of the present invention provides a control system 4 of a linear motor with an adaptive adjustment function, comprising a memory 41 and a processor 42, wherein the memory 41 includes a control method program of the linear motor with an adaptive adjustment function, and when the control method program of the linear motor with an adaptive adjustment function is executed by the processor 42, the following steps are implemented: The electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are collected by sensors, and the electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are analyzed to obtain the correlation relationship between the electromagnetic data change characteristics and the energy consumption characteristics; A linear motor energy consumption prediction model is constructed according to the correlation between the electromagnetic data change characteristics and the energy consumption characteristics, and the linear motor energy consumption prediction model is used to predict and track the energy consumption characteristics of the linear motor within a preset time; By analyzing and evaluating the energy consumption characteristics of the linear motor within a preset time, a linear motor with normal energy consumption and a linear motor with abnormal energy consumption are obtained; The energy consumption characteristics of the linear motor with abnormal energy consumption within a preset time are obtained, and the energy consumption of the linear motor is optimized according to the energy consumption characteristics of the linear motor with abnormal energy consumption within the preset time.

[0034] Furthermore, in this system, the electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are collected by sensors, and the electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are analyzed to obtain the correlation relationship between the electromagnetic data change characteristics and the energy consumption characteristics, specifically: The electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are collected through sensors, and the electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are counted, a data set is constructed, and the Apriori algorithm is introduced; The Apriori algorithm is used to analyze the electromagnetic data change characteristics and energy consumption characteristics inside the linear motor. By scanning the data set, a candidate frequent item set containing a single item is generated to determine whether the generated item set meets the set minimum confidence, support, and lift thresholds. If a candidate frequent item set meets the threshold requirement, it is added to the frequent item set list, and higher-order candidate frequent item sets are generated based on these frequent item sets. Then, the support and confidence are judged, and higher-order candidate frequent item sets are generated iteratively until no new item sets that meet the threshold requirement can be generated. Output all frequent item sets that meet the minimum confidence, support, and lift thresholds, ultimately determine the strong association rules, and obtain the correlation between the electromagnetic data change characteristics and energy consumption characteristics.

[0035] Furthermore, in this system, a linear motor energy consumption prediction model is constructed based on the correlation between the electromagnetic data change characteristics and the energy consumption characteristics, specifically: Acquire a preset number of electromagnetic data change characteristics and energy consumption characteristics, respectively use the electromagnetic data change characteristics and the energy consumption characteristics as nodes, connect the nodes based on the correlation relationship between the electromagnetic data change characteristics and the energy consumption characteristics, and construct the correlation characteristics between the electromagnetic data change characteristics and the energy consumption characteristics; A linear motor energy consumption prediction model is built based on a deep neural network. The electromagnetic data change characteristics and energy consumption characteristics are input into the linear motor energy consumption prediction model for training, and a prediction accuracy threshold is set. Obtain the prediction accuracy of the linear motor energy consumption prediction model at the current timestamp, and determine whether the prediction accuracy of the linear motor energy consumption prediction model at the current timestamp is greater than a prediction accuracy threshold; If the prediction accuracy of the linear motor energy consumption prediction model at the current timestamp is greater than the prediction accuracy threshold, the model stops training and saves the model parameters. If the prediction accuracy of the linear motor energy consumption prediction model at the current timestamp is not greater than the prediction accuracy threshold, the model continues training.

[0036] Furthermore, in this system, the linear motor energy consumption prediction model is used to predict and track the energy consumption characteristics of the linear motor within a preset time, specifically including: Obtaining the linear motor electromagnetic data change characteristics within a preset time, and inputting the linear motor electromagnetic data change characteristics within the preset time into the linear motor energy consumption prediction model for prediction; By prediction, the energy consumption characteristics of the linear motor within a preset time are obtained, and a Markov model is introduced to input the energy consumption characteristics of the linear motor within the preset time into the Markov model for calculation; Calculate the state transition probability value of the energy consumption characteristic of the linear motor in each timestamp to transfer to another energy consumption characteristic, and determine whether the state transition probability value is greater than a preset state transition probability threshold; If the state transition probability value is not greater than the preset state transition probability threshold, the energy consumption characteristics of the current linear motor are maintained unchanged. If the state transition probability value is greater than the preset state transition probability threshold, the energy consumption characteristics of the current linear motor are updated to another energy consumption characteristic, and the energy consumption characteristics of the linear motor in each timestamp within the preset time are updated.

[0037] Furthermore, in the present system, by analyzing and evaluating the energy consumption characteristics of the linear motor within a preset time, a linear motor with normal energy consumption and a linear motor with abnormal energy consumption are obtained, specifically including: Setting an energy consumption analysis evaluation threshold index, and determining whether the energy consumption characteristic of the linear motor is greater than the energy consumption analysis evaluation threshold index within a preset time; When the energy consumption characteristic of the linear motor is greater than the energy consumption analysis evaluation threshold index within the preset time, the corresponding linear motor is regarded as a linear motor with abnormal energy consumption and is displayed in a preset manner; When the energy consumption characteristic of the linear motor is not greater than the energy consumption analysis evaluation threshold index within the preset time, the corresponding linear motor is regarded as a linear motor with normal energy consumption and is displayed in a preset manner.

[0038] Furthermore, in the system, the energy consumption characteristics of the linear motor with abnormal energy consumption within a preset time are obtained, and the energy consumption of the linear motor is optimized according to the energy consumption characteristics of the linear motor with abnormal energy consumption within the preset time, specifically including: Acquire energy consumption characteristics of the linear motor with abnormal energy consumption within a preset time, reinitialize and reduce the working parameters of the linear motor, and control the linear motor based on the working parameters of the linear motor; Obtaining energy consumption characteristics of the linear motor within a preset time after the reinitialized working parameters, and determining whether the energy consumption characteristics of the linear motor within the preset time after the reinitialized working parameters are greater than an energy consumption analysis evaluation threshold indicator; When the energy consumption characteristic of the linear motor of the reinitialized working parameters within the preset time is greater than the energy consumption analysis evaluation threshold index, the working parameters of the linear motor are continuously optimized again until they are no greater than the energy consumption analysis evaluation threshold index; When the energy consumption characteristics of the linear motor with the reinitialized working parameters within the preset time are not greater than the energy consumption analysis evaluation threshold index, the working parameters of the linear motor are output and controlled according to the currently optimized working parameters of the linear motor. Example 3

[0039] A third aspect of the present invention provides a computer-readable storage medium, including a control method program for a linear motor with an adaptive adjustment function. When the control method program for a linear motor with an adaptive adjustment function is executed by a processor, the technical solution of Example 1 is implemented.

[0040] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0041] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0042] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0043] A person of ordinary skill in the art can understand that: all or part of the steps of implementing the above method embodiment can be completed by hardware related to program instructions, and the aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the aforementioned storage medium includes: a mobile storage device, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, and other media that can store program codes.

[0044] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods of each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

[0045] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A control method for a linear motor with an adaptive adjustment function, characterized in that: include: Collecting electromagnetic data change characteristics and energy consumption characteristics inside the linear motor through sensors, analyzing the electromagnetic data change characteristics and energy consumption characteristics inside the linear motor, and obtaining a correlation relationship between the electromagnetic data change characteristics and the energy consumption characteristics; Building a linear motor energy consumption prediction model according to the correlation between the electromagnetic data change characteristics and the energy consumption characteristics, and predicting and tracking the energy consumption characteristics of the linear motor within a preset time through the linear motor energy consumption prediction model; By performing energy consumption analysis and evaluation on the energy consumption characteristics of the linear motor within a preset time, a linear motor with normal energy consumption and a linear motor with abnormal energy consumption are obtained; The energy consumption characteristics of the linear motor with abnormal energy consumption within a preset time are obtained, and the energy consumption of the linear motor is optimized according to the energy consumption characteristics of the linear motor with abnormal energy consumption within the preset time.

2. A control method for a linear motor with adaptive adjustment function according to claim 1, characterized in that: The electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are collected by sensors, and the electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are analyzed to obtain the correlation relationship between the electromagnetic data change characteristics and the energy consumption characteristics, specifically: The electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are collected through sensors, and the electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are counted, a data set is constructed, and the Apriori algorithm is introduced; The electromagnetic data change characteristics and energy consumption characteristics inside the linear motor are analyzed by the Apriori algorithm, and a candidate frequent item set containing a single item is generated by scanning the data set, and it is determined whether the generated item set meets the set minimum confidence, support and lift thresholds; If a candidate frequent item set meets the threshold requirement, it is added to the frequent item set list, and higher-order candidate frequent item sets are generated based on these frequent item sets. Then, the support and confidence are judged, and higher-order candidate frequent item sets are generated iteratively until no new item sets that meet the threshold requirement can be generated. Output all frequent item sets that meet the minimum confidence, support, and lift thresholds, ultimately determine the strong association rules, and obtain the correlation between the electromagnetic data change characteristics and energy consumption characteristics.

3. The control method of a linear motor with adaptive adjustment function according to claim 1, characterized in that: A linear motor energy consumption prediction model is constructed based on the correlation between the electromagnetic data change characteristics and the energy consumption characteristics, specifically: Acquire a preset number of electromagnetic data change characteristics and energy consumption characteristics, respectively use the electromagnetic data change characteristics and energy consumption characteristics as nodes, connect the nodes based on the correlation relationship between the electromagnetic data change characteristics and the energy consumption characteristics, and construct correlation characteristics between the electromagnetic data change characteristics and the energy consumption characteristics; Constructing a linear motor energy consumption prediction model based on a deep neural network, inputting the electromagnetic data change characteristics and energy consumption characteristics into the linear motor energy consumption prediction model for training, and setting a prediction accuracy threshold; Obtaining the prediction accuracy of the linear motor energy consumption prediction model at the current timestamp, and determining whether the prediction accuracy of the linear motor energy consumption prediction model at the current timestamp is greater than the prediction accuracy threshold; If the prediction accuracy of the current timestamp linear motor energy consumption prediction model is greater than the prediction accuracy threshold, the model stops training and saves the model parameters. If the prediction accuracy of the current timestamp linear motor energy consumption prediction model is not greater than the prediction accuracy threshold, the model continues training.

4. The control method of a linear motor with adaptive adjustment function according to claim 1, characterized in that: Predicting and tracking the energy consumption characteristics of the linear motor within a preset time by the linear motor energy consumption prediction model specifically includes: Acquire the linear motor electromagnetic data change characteristics within a preset time, and input the linear motor electromagnetic data change characteristics within the preset time into the linear motor energy consumption prediction model for prediction; By prediction, the energy consumption characteristics of the linear motor within a preset time are obtained, and a Markov model is introduced, and the energy consumption characteristics of the linear motor within the preset time are input into the Markov model for calculation; Calculate the state transition probability value of the energy consumption characteristic of the linear motor in each timestamp to transfer to another energy consumption characteristic, and determine whether the state transition probability value is greater than a preset state transition probability threshold; If the state transition probability value is not greater than the preset state transition probability threshold, the energy consumption characteristics of the current linear motor are maintained unchanged; if the state transition probability value is greater than the preset state transition probability threshold, the energy consumption characteristics of the current linear motor are updated to another energy consumption characteristic, and the energy consumption characteristics of the linear motor in each timestamp within the preset time are updated.

5. The control method of a linear motor with adaptive adjustment function according to claim 1, characterized in that: By performing energy consumption analysis and evaluation on the energy consumption characteristics of the linear motor within a preset time, a linear motor with normal energy consumption and a linear motor with abnormal energy consumption are obtained, specifically including: Setting an energy consumption analysis evaluation threshold index, and determining whether the energy consumption characteristic of the linear motor within a preset time is greater than the energy consumption analysis evaluation threshold index; When the energy consumption characteristic of the linear motor within the preset time is greater than the energy consumption analysis evaluation threshold index, the corresponding linear motor is regarded as a linear motor with abnormal energy consumption and is displayed in a preset manner; When the energy consumption characteristic of the linear motor within the preset time is not greater than the energy consumption analysis evaluation threshold index, the corresponding linear motor is regarded as a linear motor with normal energy consumption and is displayed in a preset manner.

6. The control method of a linear motor with adaptive adjustment function according to claim 1, characterized in that: Obtaining energy consumption characteristics of a linear motor with abnormal energy consumption within a preset time, and optimizing energy consumption of the linear motor according to the energy consumption characteristics of the linear motor with abnormal energy consumption within the preset time, specifically includes: Acquire energy consumption characteristics of the linear motor with abnormal energy consumption within a preset time, reinitialize and reduce the working parameters of the linear motor, and control the linear motor based on the working parameters of the linear motor; Obtaining energy consumption characteristics of the linear motor with reinitialized working parameters within a preset time, and determining whether the energy consumption characteristics of the linear motor with reinitialized working parameters within the preset time are greater than an energy consumption analysis evaluation threshold index; When the energy consumption characteristic of the linear motor of the reinitialized working parameters within the preset time is greater than the energy consumption analysis evaluation threshold index, the working parameters of the linear motor are continuously optimized again until they are no greater than the energy consumption analysis evaluation threshold index; When the energy consumption characteristics of the linear motor with the reinitialized working parameters within the preset time are not greater than the energy consumption analysis evaluation threshold index, the working parameters of the linear motor are output and controlled according to the currently optimized working parameters of the linear motor.

7. The control method of a linear motor with adaptive adjustment function according to claim 1, characterized in that: The linear motor with self-adaptive adjustment function comprises a housing, a stator, a mover, a plurality of temperature sensors and an electromagnetic data detection sensor.

8. A control system for a linear motor with an adaptive adjustment function, characterized in that: It comprises a memory and a processor, wherein the memory comprises a control method program for a linear motor with an adaptive adjustment function, and when the control method program for a linear motor with an adaptive adjustment function is executed by the processor, the steps of the control method for a linear motor with an adaptive adjustment function as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that: It comprises a control method program of a linear motor with an adaptive adjustment function, which, when executed by a processor, implements the steps of the control method of a linear motor with an adaptive adjustment function as described in any one of claims 1 to 6.

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