A spindle thermal elongation compensation method, system and medium

Abstract

The embodiment of the present application provides a spindle thermal elongation compensation method, system and medium, the method comprising: collecting spindle operation status information, preprocessing the spindle operation status information to obtain optimization data; constructing a prediction model based on historical data, inputting the optimization data into the prediction model, and outputting spindle thermal elongation trend information; calculating the spindle operation accuracy information based on the spindle thermal elongation trend information; comparing the spindle operation accuracy information with the set accuracy requirement to obtain the spindle operation deviation rate; judging whether the spindle operation deviation rate is greater than or equal to the set deviation rate threshold; if greater than or equal to, generating compensation information, adjusting the spindle position based on the compensation information, and if less than, transmitting the spindle operation status information to the terminal in real time; using high-precision sensors and advanced algorithms, it is possible to achieve accurate monitoring and prediction of spindle thermal elongation, improve compensation accuracy, and improve the dimensional accuracy, shape accuracy and surface quality of machine tool processing.

Description

Technical Field

[0001] The present application relates to the technical field of spindle compensation, and in particular to a spindle thermal elongation compensation method, system and medium. Background Art

[0002] Spindle thermal elongation refers to the change in spindle length caused by temperature changes. During the operation of the spindle, the spindle bearing components will generate heat due to factors such as friction, transmission efficiency loss, and changes in the external environment. If this heat cannot be dissipated in time, the spindle temperature will rise, and then the length will change due to the thermal expansion and contraction characteristics of the material, that is, thermal elongation, resulting in the following disadvantages:

[0003] When processing at high speed, the spindle must be allowed to idle to warm up. The warm-up time at low speed is 5-10 minutes, and the warm-up time at high speed is more than 15 minutes, which seriously affects the processing efficiency.

[0004] Decreased dimensional accuracy: The thermal elongation of the spindle will change the actual distance between the cutting point and the workpiece surface, causing the processing size to deviate from the design value and affecting the dimensional accuracy of the workpiece.

[0005] Loss of shape accuracy: When machining complex-shaped workpieces, thermal elongation of the spindle may cause the cutting path to shift, resulting in workpiece shape errors;

[0006] Deterioration of surface quality: Changes in cutting force caused by thermal elongation may lead to increased cutting vibration, which in turn affects the surface roughness of the workpiece and reduces the surface quality. Summary of the invention

[0007] The purpose of the embodiments of the present application is to provide a spindle thermal elongation compensation method, system and medium. By adopting high-precision sensors and advanced algorithms, it is possible to accurately monitor and predict the thermal elongation of the spindle, improve the compensation accuracy, and improve the dimensional accuracy, shape accuracy and surface quality of machine tool processing.

[0008] The embodiment of the present application also provides a spindle thermal elongation compensation method, comprising:

[0009] Collect the spindle operation status information, pre-process the spindle operation status information, and obtain optimized data;

[0010] Build a prediction model based on historical data, input the optimization data into the prediction model, and output the spindle thermal elongation trend information;

[0011] Calculate the spindle running accuracy information based on the spindle thermal elongation trend information;

[0012] Compare the spindle operation accuracy information with the set accuracy requirement to obtain the spindle operation deviation rate;

[0013] Determine whether the spindle operation deviation rate is greater than or equal to a set deviation rate threshold;

[0014] If it is greater than or equal to, compensation information is generated and the spindle position is adjusted based on the compensation information. If it is less than, the spindle operation status information is transmitted to the terminal in real time.

[0015] Optionally, in the spindle thermal elongation compensation method described in the embodiment of the present application, collecting spindle operation status information, preprocessing the spindle operation status information, and obtaining optimization data specifically includes:

[0016] Based on temperature sensors and displacement sensors, real-time data collection of spindle temperature and elongation data is performed;

[0017] Normalize the temperature data to obtain temperature optimization data;

[0018] The elongation data is normalized to obtain the optimized elongation data;

[0019] Match the temperature optimization data with the elongation optimization data to obtain the elongation data corresponding to the spindle temperature data and generate a data pair;

[0020] Generate optimized data matching the spindle operation status information based on the data pair.

[0021] Optionally, in the spindle thermal elongation compensation method described in the embodiment of the present application, a prediction model is constructed based on historical data, specifically including:

[0022] Based on big data, spindle temperature data and elongation data are obtained to obtain a training set;

[0023] Iteratively train the initial model based on the training set to generate training results;

[0024] Determining whether the training result converges;

[0025] If converged, a prediction model is generated;

[0026] If it does not converge, correction information is generated, and the model parameters of the initial model are adjusted in real time according to the correction information.

[0027] Optionally, in the spindle thermal elongation compensation method described in the embodiment of the present application, the optimization data is input into the prediction model, and the spindle thermal elongation trend information is output, which specifically includes:

[0028] The optimization data is analyzed based on the prediction model to obtain the spindle thermal elongation data at different time nodes;

[0029] Analyze the spindle thermal elongation data of adjacent time nodes to obtain the change value of the spindle thermal elongation at adjacent time nodes;

[0030] The number of time nodes is set, and multiple time windows are established based on the number of time nodes. The thermal elongation of the spindle at adjacent time nodes in each time window is analyzed respectively to obtain the thermal elongation change information of the spindle in the time window.

[0031] The spindle thermal elongation change information in multiple time windows is fused to generate the spindle thermal elongation trend information.

[0032] Optionally, in the spindle thermal elongation compensation method described in the embodiment of the present application, the spindle operation accuracy information is calculated based on the spindle thermal elongation trend information, specifically including:

[0033] Obtain the spindle thermal elongation trend information, and analyze the spindle operation status information in different time windows based on the spindle thermal elongation trend information;

[0034] Compare the spindle operation status information in the same time window with the set operation status information to obtain the spindle operation accuracy information in the time window;

[0035] The spindle operation accuracy information in all time windows is integrated to obtain the final spindle operation accuracy information.

[0036] Optionally, in the spindle thermal elongation compensation method described in the embodiment of the present application, if is greater than or equal to , then generating compensation information, and adjusting the spindle position based on the compensation information, specifically includes:

[0037] Obtaining a spindle operation deviation rate, and generating first compensation information if the spindle operation deviation rate is greater than a first operation deviation rate threshold and less than a second operation deviation rate threshold;

[0038] generating a first compensation method based on the first compensation information, and performing a first position adjustment on the spindle position based on the first compensation method;

[0039] If the spindle operation deviation rate is greater than or equal to a second operation deviation rate threshold, generating second compensation information;

[0040] generating a second compensation method based on the second compensation information, and performing a second position adjustment on the spindle position based on the second compensation method;

[0041] The first operation deviation rate threshold is less than the second operation deviation rate threshold.

[0042] In a second aspect, an embodiment of the present application provides a spindle thermal elongation compensation system, the system comprising: a memory and a processor, the memory comprising a program of a spindle thermal elongation compensation method, and the program of the spindle thermal elongation compensation method is executed by the processor to implement the following steps:

[0043] Collect the spindle operation status information, pre-process the spindle operation status information, and obtain optimized data;

[0044] Build a prediction model based on historical data, input the optimization data into the prediction model, and output the spindle thermal elongation trend information;

[0045] Calculate the spindle running accuracy information based on the spindle thermal elongation trend information;

[0046] Compare the spindle operation accuracy information with the set accuracy requirement to obtain the spindle operation deviation rate;

[0047] Determine whether the spindle operation deviation rate is greater than or equal to a set deviation rate threshold;

[0048] If it is greater than or equal to, compensation information is generated and the spindle position is adjusted based on the compensation information. If it is less than, the spindle operation status information is transmitted to the terminal in real time.

[0049] Optionally, in the spindle thermal elongation compensation system described in the embodiment of the present application, the spindle operation status information is collected, and the spindle operation status information is preprocessed to obtain optimization data, specifically including:

[0050] Based on temperature sensors and displacement sensors, real-time data collection of spindle temperature and elongation data is performed;

[0051] Normalize the temperature data to obtain temperature optimization data;

[0052] The elongation data is normalized to obtain the optimized elongation data;

[0053] Match the temperature optimization data with the elongation optimization data to obtain the elongation data corresponding to the spindle temperature data and generate a data pair;

[0054] Generate optimized data matching the spindle operation status information based on the data pair.

[0055] Optionally, in the spindle thermal elongation compensation system described in the embodiment of the present application, a prediction model is constructed based on historical data, specifically including:

[0056] Based on big data, spindle temperature data and elongation data are obtained to obtain a training set;

[0057] Iteratively train the initial model based on the training set to generate training results;

[0058] Determining whether the training result converges;

[0059] If converged, a prediction model is generated;

[0060] If it does not converge, correction information is generated, and the model parameters of the initial model are adjusted in real time according to the correction information.

[0061] In a third aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium includes a spindle thermal elongation compensation method program. When the spindle thermal elongation compensation method program is executed by a processor, the steps of the spindle thermal elongation compensation method as described in any one of the above items are implemented.

[0062] As can be seen from the above, a spindle thermal elongation compensation method, system and medium provided in an embodiment of the present application collect spindle operating status information, pre-process the spindle operating status information, and obtain optimized data; construct a prediction model based on historical data, input the optimized data into the prediction model, and output spindle thermal elongation trend information; calculate the spindle operating accuracy information based on the spindle thermal elongation trend information; compare the spindle operating accuracy information with the set accuracy requirement to obtain the spindle operating deviation rate; determine whether the spindle operating deviation rate is greater than or equal to the set deviation rate threshold; if greater than or equal to, generate compensation information, and adjust the spindle position based on the compensation information; if less than, transmit the spindle operating status information to the terminal in real time; use high-precision sensors and advanced algorithms to achieve accurate monitoring and prediction of spindle thermal elongation, improve compensation accuracy, and improve dimensional accuracy, shape accuracy and surface quality of machine tool processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0064] Figure 1 A flow chart of a spindle thermal elongation compensation method provided in an embodiment of the present application;

[0065] Figure 2 A flow chart of a spindle operation status information preprocessing method of a spindle thermal elongation compensation method provided in an embodiment of the present application;

[0066] Figure 3 A flow chart of a method for constructing a prediction model for a spindle thermal elongation compensation method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0067] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0068] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0069] Please refer to Figure 1 , Figure 1 1 is a flow chart of a spindle thermal elongation compensation method in some embodiments of the present application. The spindle thermal elongation compensation method is used in a terminal device, and the spindle thermal elongation compensation method includes the following steps:

[0070] S101, collecting spindle operation status information, preprocessing the spindle operation status information, and obtaining optimized data;

[0071] S102, building a prediction model based on historical data, inputting the optimization data into the prediction model, and outputting the spindle thermal elongation trend information;

[0072] S103, calculating the spindle operation accuracy information based on the spindle thermal elongation trend information, comparing the spindle operation accuracy information with the set accuracy requirement, and obtaining the spindle operation deviation rate;

[0073] S104, determining whether the spindle operation deviation rate is greater than or equal to a set deviation rate threshold;

[0074] S105, if it is greater than or equal to, generate compensation information, and adjust the spindle position based on the compensation information; if it is less than, transmit the spindle operation status information to the terminal in real time.

[0075] It should be noted that the spindle operation status information is analyzed through the prediction model, so as to understand the trend of the spindle thermal elongation, and thus accurately compensate for the spindle thermal elongation, thereby improving the compensation accuracy.

[0076] Please refer to Figure 2 , Figure 2This is a flow chart of a spindle operation status information preprocessing method of a spindle thermal elongation compensation method in some embodiments of the present application. According to an embodiment of the present invention, the spindle operation status information is collected, and the spindle operation status information is preprocessed to obtain optimized data, specifically including:

[0077] S201, collecting temperature data and elongation data of the spindle in real time based on the temperature sensor and the displacement sensor;

[0078] S202, normalizing the temperature data to obtain temperature optimization data;

[0079] S203, normalizing the elongation data to obtain optimized elongation data;

[0080] S204, matching the temperature optimization data with the elongation optimization data to obtain the elongation data corresponding to the spindle temperature data, and generating a data pair;

[0081] S205: Generate optimized data matching the spindle operation status information based on the data pair.

[0082] It should be noted that the temperature data and elongation data of the spindle are obtained through the temperature sensor and the displacement sensor, and the temperature data and the elongation data are optimized respectively, so as to form a corresponding data pair and optimize the spindle operation status information.

[0083] Please refer to Figure 3 , Figure 3 The following is a flow chart of a prediction model construction method for a spindle thermal elongation compensation method in some embodiments of the present application. According to an embodiment of the present invention, a prediction model is constructed based on historical data, specifically including:

[0084] S301, acquiring spindle temperature data and elongation data based on big data to obtain a training set;

[0085] S302, iteratively training the initial model based on the training set to generate training results;

[0086] S303, determining whether the training result has converged;

[0087] S304, if converged, generating a prediction model;

[0088] S305: If it does not converge, then generate correction information, and adjust the model parameters of the initial model in real time according to the correction information.

[0089] It should be noted that the initial model is continuously trained through big data to obtain an accurate prediction model and improve the accuracy of the spindle thermal elongation prediction.

[0090] According to an embodiment of the present invention, the optimization data is input into the prediction model, and the spindle thermal elongation trend information is output, which specifically includes:

[0091] The optimization data is analyzed based on the prediction model to obtain the spindle thermal elongation data at different time nodes;

[0092] Analyze the spindle thermal elongation data of adjacent time nodes to obtain the change value of the spindle thermal elongation at adjacent time nodes;

[0093] The number of time nodes is set, and multiple time windows are established based on the number of time nodes. The thermal elongation of the spindle at adjacent time nodes in each time window is analyzed respectively to obtain the thermal elongation change information of the spindle in the time window.

[0094] The spindle thermal elongation change information in multiple time windows is fused to generate the spindle thermal elongation trend information.

[0095] It should be noted that by establishing multiple time windows and analyzing the thermal elongation of the spindle in each time window, the thermal elongation trend of the spindle can be accurately analyzed.

[0096] According to an embodiment of the present invention, the spindle operation accuracy information is calculated based on the spindle thermal elongation trend information, specifically including:

[0097] Obtain the spindle thermal elongation trend information, and analyze the spindle operation status information in different time windows based on the spindle thermal elongation trend information;

[0098] Compare the spindle operation status information in the same time window with the set operation status information to obtain the spindle operation accuracy information in the time window;

[0099] The spindle operation accuracy information in all time windows is integrated to obtain the final spindle operation accuracy information.

[0100] It should be noted that by analyzing the spindle operation status information, the spindle operation accuracy in multiple time windows can be integrated and analyzed to improve the analysis accuracy of the spindle thermal elongation.

[0101] According to an embodiment of the present invention, if it is greater than or equal to, compensation information is generated, and the spindle position is adjusted based on the compensation information, specifically including:

[0102] Obtaining a spindle operation deviation rate, and generating first compensation information if the spindle operation deviation rate is greater than a first operation deviation rate threshold and less than a second operation deviation rate threshold;

[0103] generating a first compensation method based on the first compensation information, and performing a first position adjustment on the spindle position based on the first compensation method;

[0104] If the spindle operation deviation rate is greater than or equal to a second operation deviation rate threshold, generating second compensation information;

[0105] generating a second compensation method based on the second compensation information, and performing a second position adjustment on the spindle position based on the second compensation method;

[0106] The first operating deviation rate threshold is less than the second operating deviation rate threshold.

[0107] It should be noted that the spindle operation deviation rate is obtained by analyzing the spindle operation status information, different spindle operation deviation rates result in different compensation methods, and different compensation methods make different adjustments to the spindle position.

[0108] In a second aspect, an embodiment of the present application provides a spindle thermal elongation compensation system, the system comprising: a memory and a processor, the memory comprising a program of a spindle thermal elongation compensation method, and when the program of the spindle thermal elongation compensation method is executed by the processor, the following steps are implemented:

[0109] Collect the spindle operation status information, pre-process the spindle operation status information, and obtain optimized data;

[0110] Build a prediction model based on historical data, input the optimization data into the prediction model, and output the spindle thermal elongation trend information;

[0111] Calculate the spindle running accuracy information based on the spindle thermal elongation trend information;

[0112] Compare the spindle operation accuracy information with the set accuracy requirement to obtain the spindle operation deviation rate;

[0113] Determine whether the spindle operation deviation rate is greater than or equal to the set deviation rate threshold;

[0114] If it is greater than or equal to, compensation information is generated and the spindle position is adjusted based on the compensation information. If it is less than, the spindle operation status information is transmitted to the terminal in real time.

[0115] It should be noted that the spindle operation status information is analyzed through the prediction model, so as to understand the trend of the spindle thermal elongation, and thus accurately compensate for the spindle thermal elongation, thereby improving the compensation accuracy.

[0116] According to an embodiment of the present invention, the spindle operation status information is collected and preprocessed to obtain optimized data, specifically including:

[0117] Based on temperature sensors and displacement sensors, real-time data collection of spindle temperature and elongation data is performed;

[0118] Normalize the temperature data to obtain temperature optimization data;

[0119] The elongation data is normalized to obtain the optimized elongation data;

[0120] Match the temperature optimization data with the elongation optimization data to obtain the elongation data corresponding to the spindle temperature data and generate a data pair;

[0121] Generate optimized data matching the spindle operation status information based on the data pair.

[0122] It should be noted that the temperature data and elongation data of the spindle are obtained through the temperature sensor and the displacement sensor, and the temperature data and the elongation data are optimized respectively, so as to form a corresponding data pair and optimize the spindle operation status information.

[0123] According to an embodiment of the present invention, a prediction model is constructed based on historical data, specifically including:

[0124] Based on big data, spindle temperature data and elongation data are obtained to obtain a training set;

[0125] Iteratively train the initial model based on the training set to generate training results;

[0126] Determine whether the training results converge;

[0127] If converged, a prediction model is generated;

[0128] If it does not converge, correction information is generated, and the model parameters of the initial model are adjusted in real time according to the correction information.

[0129] It should be noted that the initial model is continuously trained through big data to obtain an accurate prediction model and improve the accuracy of the spindle thermal elongation prediction.

[0130] According to an embodiment of the present invention, the optimization data is input into the prediction model, and the spindle thermal elongation trend information is output, which specifically includes:

[0131] The optimization data is analyzed based on the prediction model to obtain the spindle thermal elongation data at different time nodes;

[0132] Analyze the spindle thermal elongation data of adjacent time nodes to obtain the change value of the spindle thermal elongation at adjacent time nodes;

[0133] The number of time nodes is set, and multiple time windows are established based on the number of time nodes. The thermal elongation of the spindle at adjacent time nodes in each time window is analyzed respectively to obtain the thermal elongation change information of the spindle in the time window.

[0134] The spindle thermal elongation change information in multiple time windows is fused to generate the spindle thermal elongation trend information.

[0135] It should be noted that by establishing multiple time windows and analyzing the thermal elongation of the spindle in each time window, the thermal elongation trend of the spindle can be accurately analyzed.

[0136] According to an embodiment of the present invention, the spindle operation accuracy information is calculated based on the spindle thermal elongation trend information, specifically including:

[0137] Obtain the spindle thermal elongation trend information, and analyze the spindle operation status information in different time windows based on the spindle thermal elongation trend information;

[0138] Compare the spindle operation status information in the same time window with the set operation status information to obtain the spindle operation accuracy information in the time window;

[0139] The spindle operation accuracy information in all time windows is integrated to obtain the final spindle operation accuracy information.

[0140] It should be noted that by analyzing the spindle operation status information, the spindle operation accuracy in multiple time windows can be integrated and analyzed to improve the analysis accuracy of the spindle thermal elongation.

[0141] According to an embodiment of the present invention, if it is greater than or equal to, compensation information is generated, and the spindle position is adjusted based on the compensation information, specifically including:

[0142] Obtaining a spindle operation deviation rate, and generating first compensation information if the spindle operation deviation rate is greater than a first operation deviation rate threshold and less than a second operation deviation rate threshold;

[0143] generating a first compensation method based on the first compensation information, and performing a first position adjustment on the spindle position based on the first compensation method;

[0144] If the spindle operation deviation rate is greater than or equal to a second operation deviation rate threshold, generating second compensation information;

[0145] generating a second compensation method based on the second compensation information, and performing a second position adjustment on the spindle position based on the second compensation method;

[0146] The first operating deviation rate threshold is less than the second operating deviation rate threshold.

[0147] It should be noted that the spindle operation deviation rate is obtained by analyzing the spindle operation status information, different spindle operation deviation rates result in different compensation methods, and different compensation methods make different adjustments to the spindle position.

[0148] A third aspect of the present invention provides a computer-readable storage medium, which includes a spindle thermal elongation compensation method program. When the spindle thermal elongation compensation method program is executed by a processor, the steps of the spindle thermal elongation compensation method as described above are implemented.

[0149] The present invention discloses a spindle thermal elongation compensation method, system and medium, which collect spindle operation status information, pre-process the spindle operation status information to obtain optimization data; construct a prediction model based on historical data, input the optimization data into the prediction model, and output spindle thermal elongation trend information; calculate the spindle operation accuracy information based on the spindle thermal elongation trend information; compare the spindle operation accuracy information with the set accuracy requirement to obtain the spindle operation deviation rate; judge whether the spindle operation deviation rate is greater than or equal to the set deviation rate threshold; if greater than or equal to, generate compensation information, adjust the spindle position based on the compensation information, and if less than, transmit the spindle operation status information to the terminal in real time; adopt high-precision sensors and advanced algorithms, so as to realize accurate monitoring and prediction of spindle thermal elongation, improve compensation accuracy, and improve the dimensional accuracy, shape accuracy and surface quality of machine tool processing.

[0150] 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 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 devices or units can be electrical, mechanical or other forms.

[0151] 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.

[0152] 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.

[0153] A person skilled in the art can understand that all or part of the steps of the above method embodiment can be completed by hardware related to program instructions, and the above program can be stored in a readable storage medium. When the program is executed, it executes the steps of the above method embodiment; and the above storage medium includes: mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, etc. Various media that can store program codes.

[0154] Or, 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 readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product, which 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 described in 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.

Claims

1. A spindle thermal elongation compensation method, characterized in that: include: Collect the spindle operation status information, pre-process the spindle operation status information, and obtain optimized data; Build a prediction model based on historical data, input the optimization data into the prediction model, and output the spindle thermal elongation trend information; Calculate the spindle running accuracy information based on the spindle thermal elongation trend information; Compare the spindle operation accuracy information with the set accuracy requirement to obtain the spindle operation deviation rate; Determine whether the spindle operation deviation rate is greater than or equal to a set deviation rate threshold; If it is greater than or equal to, compensation information is generated and the spindle position is adjusted based on the compensation information; if it is less than, the spindle operation status information is transmitted to the terminal in real time; Collect the spindle operation status information, pre-process the spindle operation status information, and obtain optimization data, including: Based on temperature sensors and displacement sensors, real-time data collection of spindle temperature and elongation data is performed; Normalize the temperature data to obtain temperature optimization data; The elongation data is normalized to obtain the optimized elongation data; Match the temperature optimization data with the elongation optimization data to obtain the elongation data corresponding to the spindle temperature data and generate a data pair; Generate optimized data matching the spindle operation status information based on the data pair; Build a forecasting model based on historical data, including: Based on big data, spindle temperature data and elongation data are obtained to obtain a training set; Iteratively train the initial model based on the training set to generate training results; Determining whether the training result converges; If converged, a prediction model is generated; If it does not converge, correction information is generated, and model parameters of the initial model are adjusted in real time according to the correction information; The optimized data is input into the prediction model to output the spindle thermal elongation trend information, including: The optimization data is analyzed based on the prediction model to obtain the spindle thermal elongation data at different time nodes; Analyze the spindle thermal elongation data of adjacent time nodes to obtain the change value of the spindle thermal elongation at adjacent time nodes; The number of time nodes is set, and multiple time windows are established based on the number of time nodes. The thermal elongation of the spindle at adjacent time nodes in each time window is analyzed respectively to obtain the thermal elongation change information of the spindle in the time window. The spindle thermal elongation change information in multiple time windows is fused to generate the spindle thermal elongation trend information.

2. The spindle thermal elongation compensation method according to claim 1, characterized in that: The spindle operation accuracy information is calculated based on the spindle thermal elongation trend information, including: Obtain the spindle thermal elongation trend information, and analyze the spindle operation status information in different time windows based on the spindle thermal elongation trend information; Compare the spindle operation status information in the same time window with the set operation status information to obtain the spindle operation accuracy information in the time window; The spindle operation accuracy information in all time windows is integrated to obtain the final spindle operation accuracy information.

3. The spindle thermal elongation compensation method according to claim 2, characterized in that: If it is greater than or equal to, compensation information is generated and the spindle position is adjusted based on the compensation information, including: Obtaining a spindle operation deviation rate, and generating first compensation information if the spindle operation deviation rate is greater than a first operation deviation rate threshold and less than a second operation deviation rate threshold; generating a first compensation method based on the first compensation information, and performing a first position adjustment on the spindle position based on the first compensation method; If the spindle operation deviation rate is greater than or equal to a second operation deviation rate threshold, generating second compensation information; generating a second compensation method based on the second compensation information, and performing a second position adjustment on the spindle position based on the second compensation method; The first operation deviation rate threshold is less than the second operation deviation rate threshold.

4. A spindle thermal expansion compensation system, characterized in that: The system includes: a memory and a processor, wherein the memory includes a program of a spindle thermal elongation compensation method, and when the program of the spindle thermal elongation compensation method is executed by the processor, the following steps are implemented: Collect the spindle operation status information, pre-process the spindle operation status information, and obtain optimized data; Build a prediction model based on historical data, input the optimization data into the prediction model, and output the spindle thermal elongation trend information; Calculate the spindle running accuracy information based on the spindle thermal elongation trend information; Compare the spindle operation accuracy information with the set accuracy requirement to obtain the spindle operation deviation rate; Determine whether the spindle operation deviation rate is greater than or equal to a set deviation rate threshold; If it is greater than or equal to, compensation information is generated and the spindle position is adjusted based on the compensation information; if it is less than, the spindle operation status information is transmitted to the terminal in real time; Collect the spindle operation status information, pre-process the spindle operation status information, and obtain optimization data, including: Based on temperature sensors and displacement sensors, real-time data collection of spindle temperature and elongation data is performed; Normalize the temperature data to obtain temperature optimization data; The elongation data is normalized to obtain the optimized elongation data; Match the temperature optimization data with the elongation optimization data to obtain the elongation data corresponding to the spindle temperature data and generate a data pair; Generate optimized data matching the spindle operation status information based on the data pair; Build a forecasting model based on historical data, including: Based on big data, spindle temperature data and elongation data are obtained to obtain a training set; Iteratively train the initial model based on the training set to generate training results; Determining whether the training result converges; If converged, a prediction model is generated; If it does not converge, correction information is generated, and model parameters of the initial model are adjusted in real time according to the correction information; The optimized data is input into the prediction model to output the spindle thermal elongation trend information, including: The optimization data is analyzed based on the prediction model to obtain the spindle thermal elongation data at different time nodes; Analyze the spindle thermal elongation data of adjacent time nodes to obtain the change value of the spindle thermal elongation at adjacent time nodes; The number of time nodes is set, and multiple time windows are established based on the number of time nodes. The thermal elongation of the spindle at adjacent time nodes in each time window is analyzed respectively to obtain the thermal elongation change information of the spindle in the time window. The spindle thermal elongation change information in multiple time windows is fused to generate the spindle thermal elongation trend information.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a spindle thermal elongation compensation method program, and when the spindle thermal elongation compensation method program is executed by a processor, the steps of the spindle thermal elongation compensation method according to any one of claims 1 to 3 are implemented.

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