Control method of linear motor with operation monitoring and alarming functions

Abstract

The invention relates to a control method of a linear motor with operation monitoring and alarming functions, and belongs to the technical field of linear motors. The temperature characteristic data of the linear motors in the current timestamp are updated through the linear motor temperature state tracking model, so that a temperature characteristic data threshold value is set, the temperature characteristic data of the linear motors in the current timestamp are evaluated according to the temperature characteristic data threshold value, and the temperature evaluation result of each linear motor is obtained; and finally, early warning is performed according to the temperature evaluation result of each linear motor, and cooling control processing is performed on the linear motor with abnormal temperature. According to the method, temperature characteristic data with abnormal noise can be deleted by rearranging the temperature characteristics in a time sequence, so that subsequent identification and tracking precision of the model are facilitated, and monitoring and tracking of the linear motor are more reasonable.

Description

Technical Field

[0001] The present invention relates to the technical field of linear motor control, and in particular to a control method for a linear motor with an operation monitoring and alarm function. Background Art

[0002] Linear motors are called in many ways, 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 combination of various units can realize linear and curvilinear motion of loads, making the automation of light loads more flexible and the positioning more precise. However, during the use of linear motors, temperature sensors are generally used to monitor the actual temperature of the linear motors. When the temperature is too high, linear motors with temperature control functions can achieve adaptive cooling. However, due to the complex working environment of linear motors, temperature sensors are easily affected by noise, which can easily lead to unreliable data collected by temperature sensors, which will cause abnormal temperature recognition of linear motors. Summary of the invention

[0003] The present invention overcomes the shortcomings of the prior art and provides a control method for a linear motor with an operation monitoring and alarm function.

[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 having an operation monitoring and alarm function, comprising the following steps: The temperature sensor can collect the working temperature characteristics in a time series, collect data analysis on the working temperature characteristics in a time series, reorganize the temperature characteristics in a time series, and obtain the reorganized temperature characteristic data; Building a linear motor temperature state tracking model based on a deep neural network and the sorted temperature characteristic data, and updating the temperature characteristic data of the linear motor in the current timestamp through the linear motor temperature state tracking model; Setting a temperature characteristic data threshold, and evaluating the temperature characteristic data of the linear motor in the current timestamp according to the temperature characteristic data threshold, to obtain a temperature evaluation result of each linear motor; An early warning is issued according to the temperature evaluation result of each linear motor, and a temperature reduction control process is performed on the linear motor with abnormal temperature.

[0005] Furthermore, in this method, by collecting data and analyzing the working temperature characteristics in a time series, the temperature characteristics in a time series are reorganized to obtain the reorganized temperature characteristic data, specifically: Obtain noise characteristic data of the temperature sensor under each environmental characteristic data through big data, and obtain working environment characteristic data of the current temperature sensor, and perform data matching according to the working environment characteristic data of the current temperature sensor and the noise characteristic data of the temperature sensor under each environmental characteristic data; Obtain noise characteristic data under the working environment characteristic data of the current temperature sensor through data matching, obtain credibility data under each noise characteristic data through big data, and construct a knowledge graph according to the credibility data under each noise characteristic data; Input the noise characteristic data under the working environment characteristic data of the current temperature sensor into the knowledge graph for data matching, and obtain the credibility data of the current temperature sensor in the current noise characteristic data; The temperature features whose credibility data is lower than a preset credibility threshold data are deleted, the temperature features in a time series are reorganized, the reorganized temperature feature data are obtained, and the reorganized temperature feature data are output.

[0006] Furthermore, in this method, a linear motor temperature state tracking model is constructed based on a deep neural network and the sorted temperature characteristic data, specifically including: A linear motor temperature state tracking model is built based on a deep neural network, and the sorted temperature characteristic data is sorted in the order of time points to obtain temperature characteristic data based on time series, and the temperature characteristic data at each time point is used as an observation vector; Constructing a hidden Markov model, constructing an observation vector matrix based on the observation vector, inputting the observation vector matrix into the hidden Markov model for calculation, and calculating a state transition probability value of each observation vector being transferred to another observation vector; Setting a state transition probability threshold, when the state transition probability value of the observation vector transferring to another observation vector is greater than the state transition probability threshold, updating the observation vector matrix, when the state transition probability value of the observation vector transferring to another observation vector is not greater than the state transition probability threshold, maintaining the observation vector matrix unchanged; The updated observation vector matrix is ​​obtained, and the updated observation vector matrix is ​​used as the input feature of the linear motor temperature state tracking model, and the input feature is learned. When the linear motor temperature state tracking model meets the preset learning conditions, the linear motor temperature state tracking model is learned.

[0007] Furthermore, in the present method, updating the temperature characteristic data of the linear motor at the current timestamp by using the linear motor temperature state tracking model specifically includes: Acquire temperature characteristic data of the linear motor in the current time series, and input the temperature characteristic data of the linear motor in the current time series into the linear motor temperature state tracking model for observation and tracking; By observing and tracking, the temperature characteristic data of the linear motor in the current timestamp is updated, and the temperature characteristic data of the linear motor in the current timestamp is output as an output result.

[0008] Furthermore, in the present method, a temperature characteristic data threshold is set, and the temperature characteristic data of the linear motor in the current timestamp is evaluated according to the temperature characteristic data threshold, and the temperature evaluation result of each linear motor is obtained. The temperature evaluation result of each linear motor is obtained, which specifically includes: Setting a temperature characteristic data threshold, and determining whether the temperature characteristic data of the linear motor in the current timestamp is greater than the temperature characteristic data threshold; When the temperature characteristic data of the linear motor in the current timestamp is greater than the temperature characteristic data threshold, an abnormal temperature evaluation result is generated; When the temperature characteristic data of the linear motor in the current timestamp is not greater than the temperature characteristic data threshold, a normal temperature evaluation result is generated; A normal temperature evaluation result is obtained according to the abnormal temperature evaluation result or the normal temperature evaluation result.

[0009] Furthermore, in the method, an early warning is issued according to the temperature evaluation result of each linear motor, and a temperature reduction control process is performed on the linear motor with abnormal temperature, which specifically includes: When the temperature evaluation result of the linear motor is an abnormal temperature evaluation result, initializing the cooling working parameters of the linear motor, and performing a cooling process on the linear motor for a preset period of time according to the cooling working parameters of the linear motor; Acquire the real-time temperature characteristic data of the linear motor through the temperature reduction process in a preset period, and determine whether the real-time temperature characteristic data of the linear motor is still greater than the temperature characteristic data threshold; When the real-time temperature characteristic data of the linear motor is still greater than the temperature characteristic data threshold, the cooling working parameter of the linear motor is increased; when the real-time temperature characteristic data of the linear motor is not greater than the temperature characteristic data threshold, the increase is stopped; When the temperature increases to the maximum cooling working parameter of the linear motor, if the real-time temperature characteristic data of the linear motor is still greater than the temperature characteristic data threshold, an early warning is issued to the corresponding linear motor.

[0010] Furthermore, in the method, the linear motor includes a housing, a stator, a mover, and a plurality of temperature sensors installed inside the housing.

[0011] A second aspect of the present invention provides a control system for a linear motor with an operation monitoring and alarm function, comprising a memory and a processor, wherein the memory comprises a control method program for the linear motor with an operation monitoring and alarm function, and when the control method program for the linear motor with an operation monitoring and alarm function is executed by the processor, the steps of the control method for the linear motor with an operation monitoring and alarm function described in any one of items are implemented.

[0012] The third aspect of the present invention provides a computer-readable storage medium, including a control method program for a linear motor with an operation monitoring and alarm function, and when the control method program for a linear motor with an operation monitoring and alarm function is executed by a processor, any step of the control method for a linear motor with an operation monitoring and alarm function is performed.

[0013] The present invention solves the defects existing in the background technology and has the following beneficial effects: The present invention can collect working temperature characteristics in a time series through a temperature sensor, collect data analysis on working temperature characteristics in a time series, reorganize the temperature characteristics in a time series, obtain the organized temperature characteristic data, and then build a linear motor temperature state tracking model based on a deep neural network and the organized temperature characteristic data, and update the temperature characteristic data of the linear motor in the current timestamp through the linear motor temperature state tracking model, so as to set a temperature characteristic data threshold, and evaluate the temperature characteristic data of the linear motor in the current timestamp according to the temperature characteristic data threshold, obtain the temperature evaluation result of each linear motor, and finally give an early warning according to the temperature evaluation result of each linear motor, and perform cooling control processing on the linear motor with abnormal temperature. The present invention can delete the temperature characteristic data with abnormal noise by reorganizing the temperature characteristics in a time series, so as to facilitate the subsequent recognition and tracking accuracy of the model, and make the monitoring and tracking of the linear motor more reasonable. 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 the operation monitoring and alarm function is shown; Figure 2 A partial method flow chart showing a method for controlling a linear motor with a running monitoring and alarm function; Figure 3A schematic diagram of the structure 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.

[0019] like Figure 1 As shown, the first aspect of the present invention provides a control method for a linear motor having an operation monitoring and alarm function, comprising the following steps: S102: The working temperature characteristics in a time series can be collected by the temperature sensor, and the temperature characteristics in the time series are reorganized by collecting data analysis of the working temperature characteristics in the time series to obtain the reorganized temperature characteristic data; S104: constructing a linear motor temperature state tracking model based on the deep neural network and the sorted temperature characteristic data, and updating the temperature characteristic data of the linear motor in the current timestamp through the linear motor temperature state tracking model; S106: setting a temperature characteristic data threshold, and evaluating the temperature characteristic data of the linear motor in the current timestamp according to the temperature characteristic data threshold, and obtaining a temperature evaluation result of each linear motor; S108: Issue an early warning based on the temperature evaluation result of each linear motor, and perform temperature reduction control on the linear motor with abnormal temperature.

[0020] It should be noted that the present invention can delete temperature characteristic data with abnormal noise by reorganizing the temperature characteristics in a time series, which is beneficial to the subsequent recognition and tracking accuracy of the model and makes the monitoring and tracking of the linear motor more reasonable.

[0021] Furthermore, in this method, by collecting data and analyzing the working temperature characteristics in a time series, the temperature characteristics in a time series are reorganized to obtain the reorganized temperature characteristic data, specifically: Obtain noise characteristic data of the temperature sensor under each environmental characteristic data through big data, and obtain the working environment characteristic data of the current temperature sensor, and perform data matching according to the working environment characteristic data of the current temperature sensor and the noise characteristic data of the temperature sensor under each environmental characteristic data; Through data matching, the noise characteristic data under the working environment characteristic data of the current temperature sensor is obtained, and the credibility data under each noise characteristic data is obtained through big data, and a knowledge graph is constructed according to the credibility data under each noise characteristic data; Input the noise characteristic data under the working environment characteristic data of the current temperature sensor into the knowledge graph for data matching, and obtain the credibility data of the current temperature sensor in the current noise characteristic data; The temperature features whose credibility data is lower than a preset credibility threshold data are deleted, the temperature features in a time series are reorganized, the reorganized temperature feature data are obtained, and the reorganized temperature feature data are output.

[0022] It should be noted that during the data acquisition process, errors may occur in the data due to equipment limitations, human errors or other external factors. These errors may be random or systematic, but they will affect the quality of the data. Therefore, in different working environments, various environmental factors (such as temperature data, electromagnetic data) will affect the data acquisition of the temperature sensor, resulting in different noise data in different working environments. When the noise is large to a certain extent, it will affect the credibility of the data (such as the credibility of the data is 80% credible). This method can optimize data acquisition and improve the tracking and monitoring accuracy of the linear motor.

[0023] like Figure 2 As shown, further, in this method, a linear motor temperature state tracking model is constructed based on a deep neural network and the sorted temperature characteristic data, specifically including: S202: constructing a linear motor temperature state tracking model based on a deep neural network, and sorting the sorted temperature characteristic data according to the order of time points, obtaining temperature characteristic data based on a time series, and taking the temperature characteristic data at each time point as an observation vector; S204: constructing a hidden Markov model, constructing an observation vector matrix based on the observation vector, inputting the observation vector matrix into the hidden Markov model for calculation, and calculating the state transition probability value of each observation vector being transferred to another observation vector; S206: setting a state transition probability threshold, when the state transition probability value of the observation vector transferring to another observation vector is greater than the state transition probability threshold, updating the observation vector matrix, when the state transition probability value of the observation vector transferring to another observation vector is not greater than the state transition probability threshold, maintaining the observation vector matrix unchanged; S208: Obtain the updated observation vector matrix, and use the updated observation vector matrix as the input feature of the linear motor temperature state tracking model, learn the input feature, and when the linear motor temperature state tracking model meets the preset learning conditions, the linear motor temperature state tracking model learning is completed.

[0024] It should be noted that the Hidden Markov Model (HMM) is a statistical model that is used to describe a Markov process with hidden unknown parameters. The difficulty is to determine the hidden parameters of the process from the observable parameters. These parameters are then used for further analysis, such as pattern recognition.

[0025] It should be noted that the observation vector in each timestamp (the vector constructed by the temperature feature) may be transferred. By integrating the deep neural network and the hidden Markov model, the transfer of the observation vector is calculated and analyzed, and the observation vector is transferred from one observation vector to another. When the state transition probability value of the observation vector transferring to another observation vector is greater than the state transition probability threshold, the observation vector matrix is ​​updated, and the corresponding observation vector in the observation vector matrix is ​​updated to another observation vector to complete the data update.

[0026] Furthermore, in the present method, the temperature characteristic data of the linear motor at the current timestamp is updated by the linear motor temperature state tracking model, specifically including: The temperature characteristic data of the linear motor in the current time series is obtained, and the temperature characteristic data of the linear motor in the current time series is input into the linear motor temperature state tracking model for observation and tracking; through observation and tracking, the temperature characteristic data of the linear motor in the current timestamp is updated, and the temperature characteristic data of the linear motor in the current timestamp is output as an output result.

[0027] Furthermore, in the present method, a temperature characteristic data threshold is set, and the temperature characteristic data of the linear motor in the current timestamp is evaluated according to the temperature characteristic data threshold, and the temperature evaluation result of each linear motor is obtained, and the temperature evaluation result of each linear motor is obtained, specifically including: Set the temperature characteristic data threshold, and determine whether the temperature characteristic data of the linear motor at the current timestamp is greater than the temperature characteristic data threshold; When the temperature characteristic data of the linear motor at the current timestamp is greater than the temperature characteristic data threshold, an abnormal temperature evaluation result is generated; When the temperature characteristic data of the linear motor at the current timestamp is not greater than the temperature characteristic data threshold, a normal temperature evaluation result is generated; A normal temperature evaluation result is obtained based on the abnormal temperature evaluation result or the normal temperature evaluation result.

[0028] Furthermore, in the present method, an early warning is issued according to the temperature evaluation result of each linear motor, and a temperature reduction control process is performed on the linear motor with abnormal temperature, which specifically includes: When the temperature evaluation result of the linear motor is an abnormal temperature evaluation result, the cooling working parameters of the linear motor are initialized, and the cooling process of the linear motor is performed for a preset period of time according to the cooling working parameters of the linear motor; Acquire the real-time temperature characteristic data of the linear motor through the temperature reduction process in a preset period, and determine whether the real-time temperature characteristic data of the linear motor is still greater than the temperature characteristic data threshold; When the real-time temperature characteristic data of the linear motor is still greater than the temperature characteristic data threshold, the cooling working parameter of the linear motor is increased; when the real-time temperature characteristic data of the linear motor is not greater than the temperature characteristic data threshold, the increase is stopped; When the temperature increases to the maximum cooling working parameter of the linear motor, if the real-time temperature characteristic data of the linear motor is still greater than the temperature characteristic data threshold, an early warning is issued for the corresponding linear motor.

[0029] It should be noted that the temperature reduction working parameter processing includes reducing the speed of the linear motor, reducing the working voltage of the linear motor, increasing the air supply volume of the linear motor cooling system within a unit time, increasing the cooling water volume of the linear motor cooling system within a unit time, etc. This method can improve the rationality of the control of the linear motor.

[0030] like Figure 3 As shown, further, in the present method, the linear motor includes a housing 1, a stator 2, a mover 3 and a plurality of temperature sensors installed inside the housing.

[0031] In addition, the method further comprises: Acquire temperature rise characteristic data information of each linear motor during its service, and acquire maximum torque performance characteristic data of the linear motor under each temperature characteristic through big data, and acquire temperature characteristic data of the current linear motor during its current working process according to the temperature rise characteristic data information of the linear motor during its service; Acquire the maximum torque performance characteristic data of the current linear motor based on the temperature characteristic data raised to the current linear motor during the current working process and the maximum torque performance characteristic data of the linear motor under each temperature characteristic; Acquire the maximum torque performance requirement information of the current linear motor, and determine whether the maximum torque performance characteristic data of the current linear motor is greater than the maximum torque performance requirement information of the current linear motor; When the maximum torque performance characteristic data of the current linear motor is greater than the maximum torque performance requirement information of the current linear motor, an early warning is given to the current linear motor, and a stop working instruction is issued.

[0032] It should be noted that since the linear motor's working performance will be affected when it rises to different temperatures, the torque performance has a more obvious difference. Through this method, when the maximum torque performance characteristic data of the current linear motor is greater than the maximum torque performance requirement information of the current linear motor, the current linear motor can be warned and a stop working instruction can be issued. The affected linear motor can be warned in time and a stop working instruction can be issued to avoid corresponding losses.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] Those skilled in the art can understand that all or part of the steps of implementing the above method embodiments 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 embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0037] 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.

[0038] 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 operation monitoring and alarm function, characterized in that: The following steps are involved: The temperature sensor can collect the working temperature characteristics in a time series, collect data analysis on the working temperature characteristics in a time series, reorganize the temperature characteristics in a time series, and obtain the reorganized temperature characteristic data; Building a linear motor temperature state tracking model based on a deep neural network and the sorted temperature characteristic data, and updating the temperature characteristic data of the linear motor in the current timestamp through the linear motor temperature state tracking model; Setting a temperature characteristic data threshold, and evaluating the temperature characteristic data of the linear motor in the current timestamp according to the temperature characteristic data threshold, to obtain a temperature evaluation result of each linear motor; An early warning is issued according to the temperature evaluation result of each linear motor, and a temperature reduction control process is performed on the linear motor with abnormal temperature.

2. A control method for a linear motor with an operation monitoring and alarm function according to claim 1, characterized in that: By collecting and analyzing the working temperature characteristics in a time series, the temperature characteristics in a time series are reorganized to obtain the reorganized temperature characteristic data, specifically: Obtain noise characteristic data of the temperature sensor under each environmental characteristic data through big data, and obtain working environment characteristic data of the current temperature sensor, and perform data matching according to the working environment characteristic data of the current temperature sensor and the noise characteristic data of the temperature sensor under each environmental characteristic data; Obtain noise characteristic data under the working environment characteristic data of the current temperature sensor through data matching, obtain credibility data under each noise characteristic data through big data, and construct a knowledge graph according to the credibility data under each noise characteristic data; Input the noise characteristic data under the working environment characteristic data of the current temperature sensor into the knowledge graph for data matching, and obtain the credibility data of the current temperature sensor in the current noise characteristic data; The temperature features whose credibility data is lower than a preset credibility threshold data are deleted, the temperature features in a time series are reorganized, the reorganized temperature feature data are obtained, and the reorganized temperature feature data are output.

3. The control method of a linear motor with operation monitoring and alarm function according to claim 1, characterized in that: Based on the deep neural network and the sorted temperature characteristic data, a linear motor temperature state tracking model is built, including: A linear motor temperature state tracking model is built based on a deep neural network, and the sorted temperature characteristic data is sorted in the order of time points to obtain temperature characteristic data based on time series, and the temperature characteristic data at each time point is used as an observation vector; Constructing a hidden Markov model, constructing an observation vector matrix based on the observation vector, inputting the observation vector matrix into the hidden Markov model for calculation, and calculating a state transition probability value of each observation vector being transferred to another observation vector; Setting a state transition probability threshold, when the state transition probability value of the observation vector transferring to another observation vector is greater than the state transition probability threshold, updating the observation vector matrix, when the state transition probability value of the observation vector transferring to another observation vector is not greater than the state transition probability threshold, maintaining the observation vector matrix unchanged; The updated observation vector matrix is ​​obtained, and the updated observation vector matrix is ​​used as the input feature of the linear motor temperature state tracking model, and the input feature is learned. When the linear motor temperature state tracking model meets the preset learning conditions, the linear motor temperature state tracking model is learned.

4. A control method for a linear motor with an operation monitoring and alarm function according to claim 1, characterized in that: Updating the temperature characteristic data of the linear motor at the current timestamp by using the linear motor temperature state tracking model specifically includes: Acquire temperature characteristic data of the linear motor in the current time series, and input the temperature characteristic data of the linear motor in the current time series into the linear motor temperature state tracking model for observation and tracking; By observing and tracking, the temperature characteristic data of the linear motor in the current timestamp is updated, and the temperature characteristic data of the linear motor in the current timestamp is output as an output result.

5. The control method of a linear motor with operation monitoring and alarm function according to claim 1, characterized in that: Setting a temperature characteristic data threshold, and evaluating the temperature characteristic data of the linear motor in the current timestamp according to the temperature characteristic data threshold, and obtaining a temperature evaluation result of each linear motor, and obtaining a temperature evaluation result of each linear motor specifically includes: Setting a temperature characteristic data threshold, and determining whether the temperature characteristic data of the linear motor in the current timestamp is greater than the temperature characteristic data threshold; When the temperature characteristic data of the linear motor in the current timestamp is greater than the temperature characteristic data threshold, an abnormal temperature evaluation result is generated; When the temperature characteristic data of the linear motor in the current timestamp is not greater than the temperature characteristic data threshold, a normal temperature evaluation result is generated; A normal temperature evaluation result is obtained according to the abnormal temperature evaluation result or the normal temperature evaluation result.

6. A control method for a linear motor with an operation monitoring and alarm function according to claim 1, characterized in that: According to the temperature evaluation result of each linear motor, an early warning is issued, and a temperature reduction control process is performed on the linear motor with abnormal temperature, specifically including: When the temperature evaluation result of the linear motor is an abnormal temperature evaluation result, initializing the cooling working parameters of the linear motor, and performing a cooling process on the linear motor for a preset period of time according to the cooling working parameters of the linear motor; Acquire the real-time temperature characteristic data of the linear motor through the temperature reduction process in a preset period, and determine whether the real-time temperature characteristic data of the linear motor is still greater than the temperature characteristic data threshold; When the real-time temperature characteristic data of the linear motor is still greater than the temperature characteristic data threshold, the cooling working parameter of the linear motor is increased; when the real-time temperature characteristic data of the linear motor is not greater than the temperature characteristic data threshold, the increase is stopped; When the temperature increases to the maximum cooling working parameter of the linear motor, if the real-time temperature characteristic data of the linear motor is still greater than the temperature characteristic data threshold, an early warning is issued to the corresponding linear motor.

7. A control method for a linear motor with an operation monitoring and alarm function according to claim 1, characterized in that: The linear motor comprises a housing, a stator, a mover and a plurality of temperature sensors installed inside the housing.

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