Multi-axis displacement platform temperature compensation method and system

Abstract

The present application belongs to the technical field of mechanical temperature compensation, and provides a multi-axis displacement platform temperature compensation method and system, which comprises: acquiring temperature data of each axis section monitoring point of the multi-axis displacement platform when the multi-axis displacement platform is not running, preliminarily identifying suspected high-risk areas in the axis section of the multi-axis displacement platform by deviation analysis on the temperature of each axis section monitoring point; starting the multi-axis displacement platform, presetting a monitoring period, real-time monitoring the suspected high-risk areas in the multi-axis displacement platform, and analyzing the temperature gradient change rate to identify high-risk areas in the suspected high-risk areas; according to the determined high-risk areas, combining the distance between the axis sections to perform combined analysis on the risk degree of the high-risk areas between the axis sections in the multi-axis displacement platform; the present application combines the axis section distance, the temperature gradient value and the temperature gradient change rate similarity during the combined analysis, improves the thermal management efficiency, and reduces the compensation cost and energy consumption.

Description

Technical Field

[0001] This invention belongs to the field of mechanical temperature compensation technology, specifically a temperature compensation method and system for a multi-axis displacement platform. Background Technology

[0002] Multi-axis displacement platforms have extremely high requirements for temperature stability in precision machining, semiconductor manufacturing and other scenarios. However, during operation, local temperature rise is easily generated due to factors such as friction and motor heating, which leads to uneven thermal expansion of shaft sections, reduced motion accuracy and even mechanical failure.

[0003] However, there are some defects and shortcomings in the existing technology. On the one hand, traditional methods mostly rely on static temperature measurement or a single dynamic threshold when the equipment is shut down, lacking dynamic tracking of the rate of change of temperature gradient, making it difficult to capture early thermal anomalies. On the other hand, the existing technology cannot integrate the frequency and intensity of risk occurrence, and the combination of compensation areas is based only on human experience or spatial distance, without considering the similarity of risk values, which often leads to redundant compensation.

[0004] Therefore, the present invention provides a method and system for temperature compensation of a multi-axis displacement platform. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0006] In a first aspect, the present invention provides a temperature compensation method for a multi-axis displacement platform, comprising:

[0007] Temperature data of each axis segment monitoring point of the multi-axis displacement platform is obtained when the platform is not in operation. The temperature data includes temperature. By performing deviation analysis on the temperature of each axis segment monitoring point, suspected high-risk areas in the axis segments of the multi-axis displacement platform are preliminarily identified.

[0008] Start the multi-axis displacement platform, preset the monitoring period, monitor the suspected high-risk areas in the multi-axis displacement platform in real time, and analyze the rate of change of temperature gradient in the suspected high-risk areas during operation to identify high-risk areas in the suspected high-risk areas.

[0009] Based on the identified high-risk areas, the risk level of the high-risk areas between the axis segments of the multi-axis displacement platform is analyzed in combination with the distance between the axis segments to determine whether to merge the high-risk areas for temperature compensation.

[0010] As a further aspect of the present invention, the specific process for initially identifying suspected high-risk areas in the shaft segments of a multi-axis displacement platform is as follows:

[0011] In a multi-axis displacement platform, monitoring points are uniformly set at the same distance along the axial direction. Based on any axis segment, the temperature difference between two adjacent monitoring points is processed, and then the ratio is processed with the distance between adjacent monitoring points to obtain the temperature gradient between adjacent monitoring points.

[0012] If the temperature gradient between adjacent monitoring points is greater than or equal to the temperature gradient threshold, the area between the adjacent monitoring points is recorded as a suspected high-risk area.

[0013] As a further aspect of the present invention: the specific process for identifying high-risk areas within suspected high-risk areas is as follows:

[0014] Extract all high-risk areas from the multi-axis displacement platform and analyze the rate of temperature gradient change to determine the proportion of windows with fast rate of change and the degree of temperature gradient change.

[0015] The operational risk value of a suspected high-risk area is obtained by multiplying the proportion of windows with the rate of rapid change with the value of temperature gradient change. If the value is greater than or equal to the operational risk threshold, the suspected high-risk area is recorded as a high-risk area.

[0016] As a further aspect of the present invention: the process for determining the proportion of the fast rate of change windows is as follows:

[0017] The monitoring period was divided into several monitoring time points with equal time intervals. At each monitoring time point, the temperature of two monitoring points in the suspected high-risk area of ​​the multi-axis displacement platform was collected and analyzed to determine the temperature gradient at each monitoring time point.

[0018] The temperature gradients from all monitoring time points are collected sequentially and integrated into a temperature gradient sequence. Two adjacent temperature gradients from this sequence are extracted as a time window to determine the temperature gradient sequence after removal. Based on the removed temperature gradient sequence, the temperature gradient deviation value corresponding to the time window is extracted and compared with the duration of the time window to obtain the temperature gradient change rate of the time window. If the deviation is greater than or equal to the standard value of the temperature gradient change rate, the time window is designated as a fast rate of change window. The proportion of fast rate of change windows within the time window is calculated.

[0019] As a further aspect of the present invention, the specific process for determining the temperature gradient at the monitoring time point is as follows:

[0020] Based on any monitoring time point, the absolute value of the difference between the temperatures of two monitoring points within the suspected high-risk area at that time point is processed, and then the absolute value is calculated as the ratio of this difference to the distance between the two monitoring points within the suspected high-risk area to obtain the temperature gradient at the monitoring time point.

[0021] As a further aspect of the present invention: the process for determining the temperature gradient sequence after rejection is as follows:

[0022] The temperature gradient deviation value is obtained by subtracting the previous temperature gradient from the next temperature gradient within the time window; if it is less than or equal to 0, the temperature gradient data in the temperature gradient sequence corresponding to the time window is removed.

[0023] As a further aspect of the present invention: the process for determining the degree of temperature gradient change is as follows:

[0024] Extract the temperature gradient change rate corresponding to the fast rate of change window, and subtract it from the standard value of the temperature gradient change rate to obtain the temperature gradient change rate difference of the fast rate of change window. Sum the temperature gradient change rate differences of all fast rate of change windows, take the average value, and then compare it with the standard value of the temperature gradient change rate to obtain the temperature gradient change degree value.

[0025] As a further aspect of the present invention: the specific process for determining whether to perform temperature compensation for merging high-risk areas is as follows:

[0026] Extract all axis segments containing high-risk areas from the multi-axis displacement platform and combine them into an axis segment sequence. Combine any two axis segments in the axis segment sequence and denote it as a combined axis segment. Extract any high-risk area from one axis segment in the combined axis segment and combine it with all high-risk areas of another axis segment in the combined axis segment to obtain a combined region. Obtain the distance between the points in the high-risk areas of the combined region and denote it as the axis segment spacing.

[0027] If the distance is less than the standard value of the shaft segment spacing, the combined region is recorded as a combination of closely spaced regions, and analysis is performed to determine the temperature gradient similarity value of the closely spaced combined regions.

[0028] If the temperature gradient similarity threshold is less than or equal to the threshold, then two high-risk areas in the closely spaced combined area are merged for temperature compensation.

[0029] As a further aspect of the present invention: the process for determining the temperature gradient similarity value is as follows:

[0030] Extract the temperature gradient sequence corresponding to the high-risk area in the close proximity region combination, and calculate the numerical similarity between the temperature gradient sequences corresponding to the high-risk area in the close proximity region combination using the Euclidean distance formula. This similarity is denoted as the temperature gradient similarity value.

[0031] Secondly, the present invention also provides a temperature compensation system for a multi-axis displacement platform, the system comprising:

[0032] Preliminary identification module: Acquires temperature data of each axis segment monitoring point when the multi-axis displacement platform is not running. The temperature data includes temperature. By performing deviation analysis on the temperature of each axis segment monitoring point, it preliminarily identifies suspected high-risk areas in the axis segments of the multi-axis displacement platform.

[0033] High-risk area identification module: Start the multi-axis displacement platform, preset the monitoring period, monitor the suspected high-risk areas in the multi-axis displacement platform in real time, and analyze the rate of change of temperature gradient in the suspected high-risk areas during operation to identify high-risk areas in the suspected high-risk areas.

[0034] Merging Analysis Module: Based on the identified high-risk areas, the module combines the risk level of high-risk areas between the axis segments of the multi-axis displacement platform with the distance between the axis segments to determine whether to merge the high-risk areas for temperature compensation.

[0035] The beneficial effects of this invention are as follows: Temperature data of monitoring points on each axis segment of the multi-axis displacement platform is acquired when the platform is not in operation. This temperature data includes temperature. By analyzing the temperature deviation at each axis segment monitoring point, suspected high-risk areas within the multi-axis displacement platform's axis segments are preliminarily identified. The multi-axis displacement platform is then started, and a preset monitoring period is established. Real-time monitoring of suspected high-risk areas within the multi-axis displacement platform is performed, and the rate of temperature gradient change in these suspected high-risk areas during operation is analyzed to identify high-risk areas within the suspected high-risk areas. Based on the identified high-risk areas, the risk level of high-risk areas between axis segments in the multi-axis displacement platform is combined with the distance between the axis segments for analysis. This invention analyzes and determines whether to combine high-risk areas for temperature compensation. It initially screens suspected high-risk areas through temperature gradient analysis during static cooling, and then dynamically identifies high-risk areas by combining the rate of temperature gradient change during operation, achieving a two-layer precise risk location combining static and dynamic methods. The operational risk value quantification model considers both the frequency and intensity of risk occurrence, improving the comprehensiveness of the judgment. During combined analysis, it combines shaft segment spacing, temperature gradient values, and the similarity of temperature gradient change rates to scientifically determine whether to combine compensation, improving thermal management efficiency and reducing compensation costs and energy consumption. This forms a closed-loop management system of monitoring, evaluation, and compensation, providing data support for thermal design optimization and enhancing the reliability and stability of equipment operation. Attached Figure Description

[0036] The invention will now be further described with reference to the accompanying drawings.

[0037] Figure 1 This is a flowchart illustrating the steps of a temperature compensation method for a multi-axis displacement platform according to an embodiment of the present invention.

[0038] Figure 2 This is a system block diagram of a temperature compensation system for a multi-axis displacement platform according to an embodiment of the present invention. Detailed Implementation

[0039] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0040] Example 1

[0041] Please see Figure 1 As shown in the figure, a multi-axis displacement platform temperature compensation method according to an embodiment of the present invention includes the following steps:

[0042] Step 1: Obtain temperature data of each axis segment monitoring point when the multi-axis displacement platform is not running. The temperature data includes temperature. By performing deviation analysis on the temperature of each axis segment monitoring point, we can preliminarily identify suspected high-risk areas in the axis segments of the multi-axis displacement platform.

[0043] In a multi-axis displacement platform, monitoring points are evenly set along the axial direction at the same distance. The distance between adjacent monitoring points is set according to the accuracy requirements of the multi-axis displacement platform. After the multi-axis displacement platform has completely cooled down (e.g., after standing for 12 hours), the temperature of each monitoring point is measured by a temperature sensor.

[0044] Based on any axis segment, the temperature difference between two adjacent monitoring points is calculated to obtain the temperature difference between adjacent points. The ratio of the temperature difference between adjacent points to the distance between adjacent monitoring points is calculated to obtain the temperature gradient between adjacent monitoring points.

[0045] In some embodiments, the temperature gradient of adjacent monitoring points is compared with a temperature gradient threshold. The specific comparison process is as follows:

[0046] If the temperature gradient between adjacent monitoring points is greater than or equal to the temperature gradient threshold, it indicates that the temperature change between adjacent monitoring points is large, and the area between adjacent monitoring points is recorded as a suspected high-risk area.

[0047] If the temperature gradient between adjacent monitoring points is less than the temperature gradient threshold, it indicates that the temperature change between adjacent monitoring points is small, and the area between adjacent monitoring points is recorded as a non-suspected high-risk area.

[0048] Step 2: Start the multi-axis displacement platform, preset the monitoring period, monitor the suspected high-risk areas in the multi-axis displacement platform in real time, and analyze the rate of change of temperature gradient in the suspected high-risk areas during operation to identify high-risk areas in the suspected high-risk areas.

[0049] Extract all high-risk areas from the multi-axis displacement platform, divide the monitoring period into several monitoring time points with equal time intervals, and collect the temperature of two monitoring points contained in the suspected high-risk area in the multi-axis displacement platform using a temperature sensor at each monitoring time point.

[0050] Based on any monitoring time point, the absolute value of the difference between the temperatures of two monitoring points contained in the suspected high-risk area at the monitoring time point is processed to obtain the temperature deviation value of the monitoring time point; the temperature deviation value of the monitoring time point is then processed as the ratio of the distance between the two monitoring points contained in the suspected high-risk area to obtain the temperature gradient of the monitoring time point.

[0051] The temperature gradients collected at all monitoring time points are integrated into a temperature gradient sequence {h1, h2, ..., hn}, where i = 1, 2, ..., n, and n represents the total number of monitoring time points within the monitoring period.

[0052] The temperature gradient of two adjacent monitoring time points is extracted from the temperature gradient sequence as a time window. The difference between the second temperature gradient and the first temperature gradient within the time window is calculated to obtain the temperature gradient deviation value.

[0053] Compare the temperature gradient deviation value with 0:

[0054] If the temperature gradient deviation value is greater than 0, it indicates that the temperature gradient change within the time window is on an upward trend, and the temperature gradient data in the temperature gradient sequence corresponding to the time window will be retained.

[0055] If the temperature gradient deviation is less than or equal to 0, it means that the temperature gradient change within the time window is not increasing, and the temperature gradient data in the temperature gradient sequence corresponding to the time window will be removed.

[0056] Based on the removed temperature gradient sequence, the temperature gradient deviation value corresponding to the time window is extracted, and the ratio of the temperature gradient deviation value to the duration of the time window is processed to obtain the temperature gradient change rate of the time window.

[0057] Compare the rate of change of temperature gradient within the time window with the standard value of the rate of change of temperature gradient:

[0058] If the rate of change of the temperature gradient within a time window is greater than or equal to the standard value of the rate of change of the temperature gradient, it indicates that the rate of change of the temperature gradient within the time window is relatively fast, and the time window is recorded as the fast rate of change window.

[0059] If the rate of change of the temperature gradient within a time window is less than the standard value of the rate of change of the temperature gradient, it indicates that the rate of change of the temperature gradient within the time window is slow, and the time window is recorded as a slow rate of change window.

[0060] It should be noted that the standard value of the temperature gradient change rate was set by those skilled in the art based on the historical characteristics of temperature gradient changes;

[0061] The number of windows with a fast rate of change within a statistical time window is counted, and the percentage of such windows within a time window is calculated.

[0062] Extract the temperature gradient change rate corresponding to the fast change rate window, and subtract it from the standard value of the temperature gradient change rate to obtain the temperature gradient change rate difference of the fast change rate window. Sum the temperature gradient change rate differences of all fast change rate windows, take the average value, and then compare it with the standard value of the temperature gradient change rate to obtain the temperature gradient change degree value.

[0063] The operational risk value of suspected high-risk areas is obtained by multiplying the proportion of windows with rapid rate of change with the value of temperature gradient change.

[0064] In some embodiments, the operational risk value of a suspected high-risk area is compared with an operational risk threshold. The specific comparison process is as follows:

[0065] If the operational risk value of a suspected high-risk area is greater than or equal to the operational risk threshold, then the suspected high-risk area will be recorded as a high-risk area.

[0066] If the operational risk value of a suspected high-risk area is less than the operational risk threshold, the suspected high-risk area will be recorded as a low-risk area.

[0067] Understandably, setting operational risk values ​​systematically quantifies the dynamic changes in temperature gradients to assess high-risk areas of multi-axis displacement platforms. This not only enhances the comprehensiveness of risk assessment by filtering data showing an upward trend in temperature gradients, but also considers the frequency and intensity of risk occurrences. Furthermore, it provides reliable support for subsequent assessments of whether combined temperature compensation can be applied to high-risk areas to reduce the probability of failure and improve temperature compensation efficiency. Simultaneously, it provides data support for thermal design optimization, forming a closed-loop management system of monitoring, evaluation, and improvement, significantly enhancing the reliability and stability of multi-axis displacement platform operation.

[0068] Step 3: Based on the identified high-risk areas, analyze the risk level of the high-risk areas between the axis segments of the multi-axis displacement platform, combined with the distance between the axis segments, to determine whether to merge the high-risk areas for temperature compensation.

[0069] Extract all axis segments containing high-risk areas from the multi-axis displacement platform and combine them into an axis segment sequence. Combine any two axis segments in the axis segment sequence and denote them as combined axis segments.

[0070] A combined analysis is performed on high-risk areas within the combined shaft segments to determine whether temperature compensation should be applied to these merged high-risk areas. The specific process is as follows:

[0071] Extract any high-risk area from one segment of the combined axis segment and combine it with all high-risk areas from the other segment of the combined axis segment to obtain the combined area;

[0072] The distance between points in the high-risk areas of the combined area is obtained using a laser rangefinder and recorded as the axis segment spacing.

[0073] It should be noted that the center of a high-risk area is a feature point that is determined mathematically and can represent the location of the high-risk area.

[0074] For example, suppose monitoring points are uniformly distributed on a certain axis segment of a multi-axis displacement platform, with an adjacent monitoring point spacing of d = 10 cm, and the monitoring points are numbered P1, P2, P3, ..., with coordinates x = 0 cm, x = 10 cm, x = 20 cm, ... The area between monitoring points P2 and P3 (i.e., the line segment from x = 10 cm to x = 20 cm) is a high-risk area. Then the midpoint of this high-risk area is:

[0075] Compare the shaft segment spacing with the standard value of shaft segment spacing:

[0076] If the shaft segment spacing is greater than or equal to the standard value of shaft segment spacing, the combination operation is cancelled;

[0077] If the distance between axis segments is less than the standard value for the distance between axis segments, the combined area is recorded as a combination of areas with close proximity.

[0078] It should be noted that the standard value of the axle segment spacing is set by those skilled in the art based on historical experience. When combining areas, if there are no high-risk areas remaining in the axle segment, the combination operation will be stopped; if the number of remaining high-risk areas is not 1 after the combination is completed, the combination operation will continue.

[0079] Extract temperature gradient sequences corresponding to high-risk areas in closely spaced region combinations and perform similarity analysis. Specifically:

[0080] Using the Euclidean distance formula: The numerical similarity between temperature gradient sequences corresponding to high-risk areas in a closely spaced region combination is calculated and denoted as the temperature gradient similarity value d, where x i y represents the temperature gradient at the i-th monitoring time point of the preceding high-risk area in the proximity area combination. i represents the temperature gradient at the i-th monitoring time point of the next high-risk area in the combination of close proximity areas, and n represents the total number of monitoring time points within the monitoring period;

[0081] It should be noted that the extracted temperature gradient sequence is the temperature gradient sequence after removing the temperature gradient data.

[0082] In some embodiments, the temperature gradient similarity value of closely spaced combined regions is compared with a temperature gradient similarity threshold. The specific comparison process is as follows:

[0083] If the temperature gradient similarity value of closely spaced combined regions is greater than or equal to the temperature gradient similarity threshold, it means that two high-risk regions in closely spaced combined regions can be merged for temperature compensation.

[0084] If the temperature gradient similarity value of closely spaced combined regions is less than the temperature gradient similarity threshold, it means that temperature compensation cannot be performed on two high-risk regions that are closely spaced combined regions.

[0085] The technical solution of this embodiment is as follows: Temperature data of monitoring points on each axis segment of the multi-axis displacement platform is acquired when the platform is not in operation. The temperature data includes temperature. By analyzing the temperature deviation at each axis segment monitoring point, suspected high-risk areas in the axis segments of the multi-axis displacement platform are initially identified. The multi-axis displacement platform is started, a preset monitoring period is established, and the suspected high-risk areas in the multi-axis displacement platform are monitored in real time. The rate of temperature gradient change in the suspected high-risk areas during operation is analyzed to identify high-risk areas within the suspected high-risk areas. Based on the identified high-risk areas, the risk level of the high-risk areas between axis segments in the multi-axis displacement platform is combined with the distance between the axis segments for analysis. This invention analyzes and determines whether to combine high-risk areas for temperature compensation. It initially screens suspected high-risk areas through temperature gradient analysis during static cooling, and then dynamically identifies high-risk areas by combining the rate of temperature gradient change during operation, achieving a two-layer precise risk location combining static and dynamic methods. The operational risk value quantification model considers both the frequency and intensity of risk occurrence, improving the comprehensiveness of the judgment. During combined analysis, it combines shaft segment spacing, temperature gradient values, and the similarity of temperature gradient change rates to scientifically determine whether to combine compensation, improving thermal management efficiency and reducing compensation costs and energy consumption. This forms a closed-loop management system of monitoring, evaluation, and compensation, providing data support for thermal design optimization and enhancing the reliability and stability of equipment operation.

[0086] Example 2

[0087] Please see Figure 2 As shown in the embodiment of the present invention, a temperature compensation system for a multi-axis displacement platform includes the following modules:

[0088] Preliminary identification module: Acquires temperature data of each axis segment monitoring point when the multi-axis displacement platform is not running. The temperature data includes temperature. By performing deviation analysis on the temperature of each axis segment monitoring point, it preliminarily identifies suspected high-risk areas in the axis segments of the multi-axis displacement platform.

[0089] High-risk area identification module: Start the multi-axis displacement platform, preset the monitoring period, monitor the suspected high-risk areas in the multi-axis displacement platform in real time, and analyze the rate of change of temperature gradient in the suspected high-risk areas during operation to identify high-risk areas in the suspected high-risk areas.

[0090] Merging Analysis Module: Based on the identified high-risk areas, the module combines the risk level of high-risk areas between the axis segments of the multi-axis displacement platform with the distance between the axis segments to determine whether to merge the high-risk areas for temperature compensation.

[0091] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for temperature compensation of a multi-axis displacement platform, characterized in that: include: Temperature data of each axis segment monitoring point of the multi-axis displacement platform is obtained when the platform is not in operation. The temperature data includes temperature. By performing deviation analysis on the temperature of each axis segment monitoring point, suspected high-risk areas in the axis segments of the multi-axis displacement platform are preliminarily identified. The specific process for initially identifying suspected high-risk areas in the shaft segments of a multi-axis displacement platform is as follows: In a multi-axis displacement platform, monitoring points are uniformly set at the same distance along the axial direction. Based on any axis segment, the temperature difference between two adjacent monitoring points is processed, and then the ratio is processed with the distance between adjacent monitoring points to obtain the temperature gradient between adjacent monitoring points. If the temperature gradient between adjacent monitoring points is greater than or equal to the temperature gradient threshold, the area between the adjacent monitoring points will be recorded as a suspected high-risk area. The specific process for identifying high-risk areas within suspected high-risk areas is as follows: Extract all high-risk areas from the multi-axis displacement platform and analyze the rate of temperature gradient change to determine the proportion of windows with fast rate of change and the degree of temperature gradient change. The proportion of windows with rapid rate of change is multiplied by the value of temperature gradient change to obtain the operational risk value of suspected high-risk areas; if it is greater than or equal to the operational risk threshold, the suspected high-risk area is recorded as a high-risk area. The process for determining the proportion of windows with the fastest rate of change is as follows: The monitoring period was divided into several monitoring time points with equal time intervals. At each monitoring time point, the temperature of two monitoring points in the suspected high-risk area of ​​the multi-axis displacement platform was collected and analyzed to determine the temperature gradient at each monitoring time point. The temperature gradients from all monitoring time points are collected sequentially and integrated into a temperature gradient sequence. Two adjacent temperature gradients from this sequence are extracted as a time window to determine the temperature gradient sequence after filtering out other temperature gradients. Based on this filtered sequence, the temperature gradient deviation value corresponding to each time window is extracted and compared with the duration of that time window to obtain the temperature gradient change rate. If the deviation is greater than or equal to the standard value for the temperature gradient change rate, the time window is designated as a fast rate of change window. The proportion of fast rate of change windows within each time window is then calculated. The process for determining the degree of temperature gradient change is as follows: Extract the temperature gradient change rate corresponding to the fast change rate window, and subtract it from the standard value of the temperature gradient change rate to obtain the temperature gradient change rate difference of the fast change rate window. Sum the temperature gradient change rate differences of all fast change rate windows, take the average value, and then compare it with the standard value of the temperature gradient change rate to obtain the temperature gradient change degree value. Start the multi-axis displacement platform, preset the monitoring period, monitor the suspected high-risk areas in the multi-axis displacement platform in real time, and analyze the rate of change of temperature gradient in the suspected high-risk areas during operation to identify high-risk areas in the suspected high-risk areas. Based on the identified high-risk areas, the risk level of the high-risk areas between the axis segments of the multi-axis displacement platform is analyzed in combination with the distance between the axis segments to determine whether to merge the high-risk areas for temperature compensation. The specific process for determining whether to apply temperature compensation for merging high-risk areas is as follows: Extract all axis segments containing high-risk areas from the multi-axis displacement platform and combine them into an axis segment sequence. Combine any two axis segments in the axis segment sequence and denote it as a combined axis segment. Extract any high-risk area from one axis segment in the combined axis segment and combine it with all high-risk areas of another axis segment in the combined axis segment to obtain a combined region. Obtain the distance between the points in the high-risk areas of the combined region and denote it as the axis segment spacing. If the distance is less than the standard value of the shaft segment spacing, the combined region is recorded as a combination of closely spaced regions, and analysis is performed to determine the temperature gradient similarity value of the closely spaced combined regions. If the temperature gradient similarity threshold is less than or equal to the threshold, then two high-risk areas in the closely spaced combined area are merged for temperature compensation. The process for determining the temperature gradient similarity value is as follows: Extract the temperature gradient sequence corresponding to the high-risk area in the close proximity region combination, and calculate the numerical similarity between the temperature gradient sequences corresponding to the high-risk area in the close proximity region combination using the Euclidean distance formula. This similarity is denoted as the temperature gradient similarity value.

2. The temperature compensation method for a multi-axis displacement platform according to claim 1, characterized in that: The specific process for determining the temperature gradient at the monitoring time point is as follows: Based on any monitoring time point, the absolute value of the difference between the temperatures of two monitoring points within the suspected high-risk area at that time point is processed, and then the absolute value is calculated as the ratio of this difference to the distance between the two monitoring points within the suspected high-risk area to obtain the temperature gradient at the monitoring time point.

3. The temperature compensation method for a multi-axis displacement platform according to claim 1, characterized in that: The process for determining the temperature gradient sequence after rejection is as follows: The temperature gradient deviation value is obtained by subtracting the previous temperature gradient from the next temperature gradient within the time window; if it is less than or equal to 0, the temperature gradient data in the temperature gradient sequence corresponding to the time window is removed.

4. A temperature compensation system for a multi-axis displacement platform, characterized in that, The system is used to perform the method according to any one of claims 1-3, and the system comprises: Preliminary identification module: Acquires temperature data of each axis segment monitoring point when the multi-axis displacement platform is not running. The temperature data includes temperature. By performing deviation analysis on the temperature of each axis segment monitoring point, it preliminarily identifies suspected high-risk areas in the axis segments of the multi-axis displacement platform. High-risk area identification module: Start the multi-axis displacement platform, preset the monitoring period, monitor the suspected high-risk areas in the multi-axis displacement platform in real time, and analyze the rate of change of temperature gradient in the suspected high-risk areas during operation to identify high-risk areas in the suspected high-risk areas. Merging Analysis Module: Based on the identified high-risk areas, the module combines the risk level of high-risk areas between the axis segments of the multi-axis displacement platform with the distance between the axis segments to determine whether to merge the high-risk areas for temperature compensation.

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