A method for dynamic security protection of machining data of mechanical parts

Abstract

This invention relates to the field of data processing technology, specifically to a method for dynamic security protection of machining data for mechanical parts. The method includes: acquiring machining current data sequences of mechanical parts; obtaining an initial standard current factor based on the machining current data sequences; obtaining abnormal current data segments based on the initial standard current factor; obtaining a current trend difference factor based on the abnormal current data segments; obtaining abnormal current data based on the current trend difference factor; obtaining data similarity based on the fluctuation trend differences between different abnormal current data in the abnormal current data segments; obtaining a correlated abnormal dataset based on the data similarity; obtaining a vibration anomaly factor based on the correlated abnormal dataset; and compressing the machining current data sequence based on the vibration anomaly factor. This invention makes the segmentation of current data more reasonable, improves the compression efficiency of the rotary door compression algorithm, and enhances the dynamic security of machining data.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a method for dynamic security protection of machining data for mechanical parts. Background Technology

[0002] The machining process of mechanical parts typically utilizes a cutting tool on a CNC machine tool to create friction between the parts, thus completing the machining. During machining, the friction constantly changes, causing the CNC machine tool's current data to fluctuate. To ensure the normal operation of the machining process, it is necessary to save and analyze the CNC machine tool's current data. However, due to the large volume of collected current data, compression is required to improve data security. Traditional methods use a rotary door compression algorithm to compress the current data. However, this algorithm cannot effectively distinguish between abnormal data caused by machine tool vibration and other interference factors. This results in all abnormal and normal data being compressed in the same segment, reducing compression efficiency and compressing the dynamic security of the machining data. Summary of the Invention

[0003] This invention provides a dynamic security protection method for machining data of mechanical parts to solve the existing problem: the traditional rotary door compression algorithm cannot effectively distinguish abnormal data generated by machine tool vibration and other interference factors, resulting in uniform segmentation and compression of all abnormal data and normal data, which reduces compression efficiency.

[0004] The present invention provides a method for dynamic security protection of machining data for mechanical parts, which adopts the following technical solution:

[0005] Includes the following steps:

[0006] A sequence of machining current data for several mechanical parts is collected. The sequence of machining current data contains multiple current data points, and each current data point corresponds to a collection time.

[0007] The initial standard current factor for each current data is obtained based on the difference in current data variation between different mechanical parts. The initial standard current factor is used to describe the difference between the current data and the normal current data. Based on the maximum difference in the initial standard current factor variation between the current data of different mechanical parts at the same acquisition time, several initial abnormal current data and several normal current data are selected from the current data.

[0008] The processing current data sequence is divided into several abnormal current data segments based on the initial abnormal current data and normal current data. Based on the changing trends of abnormal current data before and after different initial abnormal current data within each abnormal current data segment, a current trend difference factor is obtained for each initial abnormal current data segment. Several abnormal current data are selected from the initial abnormal current data based on the current trend difference factor. The data similarity between any two abnormal current data segments is obtained based on the fluctuation trend differences between different abnormal current data segments. The abnormal current data are then categorized based on data similarity to obtain several associated abnormal datasets. Finally, the vibration anomaly factor for each abnormal current data segment is obtained based on the distribution of different abnormal current data within the associated abnormal datasets.

[0009] The machining current data sequence is compressed based on the vibration anomaly factor.

[0010] Preferably, the method for obtaining the initial standard current factor for each current data based on the variation differences in current data between different mechanical parts includes:

[0011] Take any one mechanical part as the target mechanical part, and denote each mechanical part other than the target mechanical part as the reference mechanical part of the target mechanical part;

[0012]

[0013] In the formula, The first data in the sequence of machining current data for the target mechanical part Initial standard current factor for each current data; This indicates the sequence of machining current data for all reference mechanical parts of the target mechanical part. In the current data, the first one is related to the machining current data sequence of the target mechanical part. The number of equal current data; This indicates the number of all reference mechanical parts for the target mechanical part.

[0014] Preferably, the method for selecting several initial abnormal current data and several normal current data from the current data based on the maximum difference in the initial standard current factor among the current data of different mechanical parts at the same acquisition time includes:

[0015] The first in the sequence of machining current data for all mechanical parts From the initial standard current factors of the current data, the current data corresponding to the largest initial standard current factor is taken as the first current data in the entire processing current data sequence. Standard current data for each current data point; obtain standard current data for all current data in the processing current data sequence for each mechanical part.

[0016] For any current data in the processing current data sequence of any mechanical part, the absolute value of the difference between the standard current data and the current data is recorded as the standard current difference. The standard current difference of all current data in the processing current data sequence of the mechanical part is obtained, and all standard current differences are linearly normalized. Each normalized standard current difference is recorded as the standard current factor.

[0017] A standard current factor threshold T1 is preset. For any current data in the processing current data sequence of any mechanical part, if the standard current factor of the current data is greater than or equal to T1, then the current data is recorded as the initial abnormal current data; if the standard current factor of the current data is less than T1, then the current data is recorded as the normal current data.

[0018] Preferably, the specific method for dividing the processing current data sequence into several abnormal current data segments based on the initial abnormal current data and normal current data includes:

[0019] For any machining current data sequence of a mechanical part, a preset threshold T2 for the number of initial abnormal current data is set; in the machining current data sequence, the data segment consisting of every T2 initial abnormal current data is recorded as an abnormal current data segment.

[0020] Preferably, the specific method for obtaining the current trend difference factor for each initial abnormal current data based on the changing trend of the abnormal current data before and after different initial abnormal current data in the abnormal current data segment includes:

[0021] For any abnormal current data segment, the least squares method is used to fit all the current data in the abnormal current data segment to obtain a fitting curve. The value of each current data on the fitting curve is recorded as the fitted current value of each current data. For any initial abnormal current data in the abnormal current data segment, each initial abnormal current data before the initial abnormal current data is recorded as the preceding abnormal current data, and each initial abnormal current data after the initial abnormal current data is recorded as the following abnormal current data.

[0022]

[0023] In the formula, The initial current trend difference factor represents the initial abnormal current data. This indicates the number of all preceding abnormal current data points for the initial abnormal current data. Indicates the first One set of abnormal current data; Indicates the first Fitted current values ​​from the preceding abnormal current data; This indicates the number of all subsequent abnormal current data points corresponding to the initial abnormal current data. Indicates the first One post-abnormal current data; Indicates the first Fitted current values ​​of each post-abnormal current data; This indicates the number of all current data in the abnormal current data segment; the initial current trend difference factor of all initial abnormal current data in the abnormal current data segment is obtained, and all initial current trend difference factors are linearly normalized. Each normalized initial current trend difference factor is denoted as the current trend difference factor.

[0024] Preferably, the specific method for selecting several abnormal current data from the initial abnormal current data based on the current trend difference factor includes:

[0025] A current trend difference factor threshold T3 is preset, and the initial abnormal current data with a current trend difference factor greater than T3 are recorded as abnormal current data.

[0026] Preferably, the method for obtaining the data similarity of any two abnormal current data based on the difference in fluctuation trends between different abnormal current data in the abnormal current data segment includes:

[0027] For any two adjacent abnormal current data in any abnormal current data segment, the difference between the second abnormal current data and the first abnormal current data is recorded as the first difference, the difference between the serial numbers of the second abnormal current data and the first abnormal current data in the abnormal current data segment is recorded as the second difference, and the ratio of the first difference to the second difference is recorded as the slope of the first abnormal current data.

[0028] For any three adjacent abnormal current data in the abnormal current data segment, the absolute value of the difference in slope between the first and second abnormal current data is recorded as the preceding trend difference value of the second abnormal current data, and the absolute value of the difference in slope between the third and second abnormal current data is recorded as the following trend difference value of the second abnormal current data.

[0029]

[0030] In the formula, Indicates the first The abnormal current data and the first Data similarity of abnormal current data, ; Indicates the preset hyperparameters; Indicates the first The preceding trend difference value of each abnormal current data; Indicates the first The preceding trend difference value of each abnormal current data; Indicates the preset hyperparameters; Indicates the first One abnormal current data point; Indicates the first One abnormal current data point; Indicates the first Post-trend difference value of abnormal current data; Indicates the first Post-trend difference value of abnormal current data; Represents a symbolic function.

[0031] Preferably, the specific method for classifying abnormal current data into several associated abnormal datasets based on data similarity includes:

[0032] A similarity threshold KL is preset. Any abnormal current data in any abnormal current data segment is recorded as the target abnormal current data. Each abnormal current data other than the target abnormal current data is recorded as the reference abnormal current data of the target abnormal current data. The reference abnormal current data with a similarity equal to KL with the target abnormal current data is recorded as the associated abnormal current data of the target abnormal current data. The dataset composed of all associated abnormal current data is used as the associated abnormal dataset of the target abnormal current data.

[0033] Preferably, the specific method for obtaining the vibration anomaly factor for each abnormal current data segment based on the distribution of different abnormal current data in the associated abnormal dataset is as follows:

[0034]

[0035] In the formula, It indicates the degree of vibration abnormality in any abnormal current data segment; This represents the mean of all abnormal current data in the abnormal current data segment. This represents the mean of all current data in the abnormal current data segment. This indicates the number of all abnormal current data in the abnormal current data segment; This represents the associated abnormal dataset of all abnormal current data in the abnormal current data segment, specifically the first abnormal data. The number of times each abnormal current data point occurs; Indicates the first The number of all abnormal current data in the associated abnormal dataset of each abnormal current data is obtained; the vibration abnormality degree of all abnormal current data segments is obtained, and all vibration abnormality degrees are linearly normalized. Each normalized vibration abnormality degree is recorded as a vibration abnormality factor.

[0036] Preferably, the specific method for compressing the processing current data sequence based on the vibration anomaly factor is as follows:

[0037] Preset a vibration anomaly factor threshold For any abnormal current data segment, the first and second current data points within the segment are both designated as a dividing point. For any other abnormal current data point within the segment besides the first and second current data points... Current data;

[0038]

[0039] In the formula, Indicates the first The degree of boundary between individual current data; Indicates the preset hyperparameters; Indicates the first The slope of the first dividing point before each current data point; Indicates the first The slope of the second dividing point before the current data; The vibration anomaly factor represents the abnormal current data segment; This represents the preset threshold for the vibration anomaly factor; Indicates the first The first dividing point before the current data and the first... The maximum upslope of all current data in a data segment consisting of current data; Indicates the first The first dividing point before the current data and the first... The minimum downslope of all current data in a data segment consisting of current data;

[0040] A threshold T5 is preset for the degree of separation; current data with a degree of separation greater than T5 in the abnormal current data segment are recorded as the separation point; all separation points in the abnormal current data segment are obtained; the data segment formed between any two separation points is recorded as the data segment to be compressed; each data segment to be compressed is used as a segment, and rotating door compression is performed on all segments to obtain several compressed data segments; all compressed data segments are stored in the database.

[0041] The beneficial effects of the technical solution of this invention are as follows: An initial standard current factor is obtained based on the processing current data sequence; abnormal current data segments are obtained based on the initial standard current factor; a current trend difference factor is obtained based on the abnormal current data segments; data similarity is obtained based on the current trend difference factor; the degree of vibration anomaly is obtained based on the data similarity; and the processing current data sequence is compressed based on the degree of vibration anomaly. The initial standard current factor of this invention reflects the difference between current data and normal current data; the current trend difference factor reflects the degree to which the initial abnormal current data belongs to current data with a significant change in actual trend; and the data similarity reflects the similarity of trend changes and numerical change patterns between current data. This makes the segmentation of current data more reasonable, improves the compression efficiency of the rotating door compression algorithm, and enhances the dynamic security of the processing data. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0043] Figure 1 This is a flowchart illustrating the steps of a method for dynamic security protection of machining data for mechanical parts according to the present invention.

[0044] Figure 2 This is a schematic diagram of a lathe machining part according to the present invention. Detailed Implementation

[0045] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a dynamic security protection method for machining data of mechanical parts proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0046] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0047] The following description, in conjunction with the accompanying drawings, details a specific scheme for a dynamic safety protection method for machining data of mechanical parts provided by the present invention.

[0048] Please see Figure 1The diagram illustrates a flowchart of a method for dynamic security protection of machining data for mechanical parts according to an embodiment of the present invention. The method includes the following steps:

[0049] Step S001: Collect the machining current data sequence of several mechanical parts.

[0050] It should be noted that traditional methods use a rotary door compression algorithm to compress current data. However, this algorithm cannot effectively distinguish between abnormal data caused by machine tool vibration and other interference factors. Consequently, all abnormal and normal data are compressed in the same segment, reducing compression efficiency and the dynamic security of the machining data. Therefore, this embodiment proposes a dynamic security protection method for machining data of mechanical parts.

[0051] Specifically, to implement the dynamic safety protection method for machining data of mechanical parts proposed in this embodiment, it is first necessary to collect machining current data sequences. The specific process is as follows: Fifty mechanical parts of the same model are machined separately using a CNC machine tool. A current sensor collects the current data of the CNC machine tool during the machining of each mechanical part every second, for a total of 100 seconds. Taking any mechanical part as an example, all collected current data are arranged in ascending order of collection time, and the resulting sequence is recorded as the machining current data sequence for that mechanical part. The machining current data sequences for all mechanical parts are then obtained. The machining method for each mechanical part of the same model is consistent. It should also be noted that this embodiment does not specifically limit the number of mechanical parts, the collection interval, or the total collection time; these parameters can be determined according to the specific implementation. Please refer to [link to relevant documentation]. Figure 2 It shows a schematic diagram of machined parts, which illustrates the chuck, feed direction, mechanical parts, rotation direction, and cutting tool.

[0052] Thus, the processing current data sequence is obtained through the above method.

[0053] Step S002: Obtain the initial standard current factor for each current data based on the variation difference of current data between different mechanical parts; based on the maximum variation difference of the initial standard current factor between current data of different mechanical parts at the same acquisition time, select a number of initial abnormal current data and a number of normal current data from the current data.

[0054] It should be noted that when CNC machine tools process mechanical parts, factors such as the material of the mechanical parts themselves, the material of the cutting tools, and machine tool vibration can all interfere with the friction between the cutting tools and the mechanical parts, thereby changing the corresponding current data and generating some abnormal current data. Among these, machine tool vibration, compared to other interference factors, is usually actively generated by the machine tool during the processing and does not have a significant impact on the quality of the mechanical parts. It is a interference factor within the normal allowable range and has little practical reference value. In order to improve the safety protection efficiency of current data, this embodiment divides the abnormal data generated by machine tool vibration into data segments and performs rotary door compression on each data segment to improve compression efficiency.

[0055] It should be further noted that for mechanical parts of the same model, these mechanical parts have basically the same shape and processing method, resulting in a high degree of similarity in the changes of the corresponding current data. In this embodiment, the standard range of different processing times can be obtained based on the differences between the corresponding current data of different mechanical parts, and the initial abnormal current data can be determined based on the standard range for subsequent analysis and processing.

[0056] Specifically, any one mechanical part is designated as the target mechanical part, and every other mechanical part is designated as a reference mechanical part for the target mechanical part; the machining current data sequence of the target mechanical part is used as the reference mechanical part. Taking the current data as an example, based on the machining current data sequence of all reference mechanical parts, the first... The current data is used to obtain the first current data in the machining current data sequence of the target mechanical part. The initial standard current factor of the current data. Wherein, in the machining current data sequence of the target mechanical part... The method for calculating the initial standard current factor for each current data point is as follows:

[0057]

[0058] In the formula, This represents the first data point in the sequence of machining current data for the target mechanical part. Initial standard current factor for each current data; This indicates the number of machining current data sequences of all reference mechanical parts for the target mechanical part. In the current data, the first current data in the sequence of machining current data for the target mechanical part is... The number of equal current data; This indicates the number of all reference mechanical parts for the target mechanical part. Where the processing current data sequence of the target mechanical part is... The larger the initial standard current factor of a current data point, the closer the current data point is to the normal range of current data fluctuations.

[0059] Furthermore, the processing current data sequence of each mechanical part is obtained. The initial standard current factor of the current data is used, and the current data corresponding to the largest initial standard current factor is taken as the first current data in the entire processing current data sequence. Standard current data for each current data point is obtained. Standard current data for all current data in the processing current data sequence of each mechanical part is obtained. Taking any current data in the processing current data sequence of any mechanical part as an example, the absolute value of the difference between the standard current data and the current data is recorded as the standard current difference of the current data. The standard current difference of all current data in the processing current data sequence of the mechanical part is obtained. All standard current differences are linearly normalized, and each normalized standard current difference is recorded as the standard current factor.

[0060] Furthermore, a standard current factor threshold T1 is preset. In this embodiment, T1=0.5 is used as an example. This embodiment does not impose specific limitations, and T1 can be determined according to the specific implementation. Taking any current data in the processing current data sequence of any mechanical part as an example, if the standard current factor of the current data is greater than or equal to T1, then the current data is recorded as the initial abnormal current data; if the standard current factor of the current data is less than T1, then the current data is recorded as the normal current data. All initial abnormal current data and all normal current data in the processing current data sequence of the mechanical part are obtained, and all initial abnormal current data and all normal current data in the processing current data sequences of all mechanical parts are obtained.

[0061] Thus, by using the above method, all initial abnormal current data and all normal current data in the machining current data sequence of all mechanical parts have been obtained.

[0062] Step S003: Divide the processing current data sequence into several abnormal current data segments based on the initial abnormal current data and normal current data; obtain the current trend difference factor for each initial abnormal current data segment based on the changing trend of the abnormal current data before and after different initial abnormal current data segments; select several abnormal current data from the initial abnormal current data based on the current trend difference factor; obtain the data similarity between any two abnormal current data segments based on the fluctuation trend difference between different abnormal current data segments; classify the abnormal current data based on the data similarity to obtain several associated abnormal datasets; obtain the vibration abnormality factor for each abnormal current data segment based on the distribution of different abnormal current data in the associated abnormal datasets.

[0063] It should be noted that in practice, if the materials of the blades and mechanical parts themselves are problematic, the internal impurities in the blades and mechanical parts will not be uniformly distributed. This will cause the frictional force generated by the contact between the blades and mechanical parts to not change uniformly, resulting in significant fluctuations and irregular trends in the abnormal current data. Conversely, the vibration of a machine tool will occur at a relatively regular frequency, causing significant fluctuations in the abnormal current data, but also exhibiting a more regular trend. To better protect the current data, this embodiment analyzes the changing trends between initial abnormal current data to determine the degree of vibration abnormality in the abnormal current data for subsequent analysis and processing.

[0064] Specifically, taking the processing current data sequence of any mechanical part as an example, a preset threshold T2 for the number of initial abnormal current data is set. In this embodiment, T2=20 is used as an example, but this embodiment is not specifically limited, and T2 can be determined according to the specific implementation. In this processing current data sequence, the data segment consisting of every T2 initial abnormal current data is denoted as an abnormal current data segment. Taking any abnormal current data segment as an example, the least squares method is used to fit all the current data in the abnormal current data segment to obtain a fitting curve, and the value of each current data on the fitting curve is denoted as the fitted current value of each current data. Taking any initial abnormal current data in the abnormal current data segment as an example, each initial abnormal current data before the initial abnormal current data is denoted as the preceding abnormal current data, and each initial abnormal current data after the initial abnormal current data is denoted as the following abnormal current data. There may be multiple normal current data in each abnormal current data segment; the least squares method is a well-known technique and will not be described in detail in this embodiment. It should be noted that if the number of remaining initial abnormal current data does not meet the preset T2, the data segment consisting of the remaining initial abnormal current data will be recorded as one abnormal current data.

[0065] Furthermore, based on the fitted current values ​​of the preceding and subsequent abnormal current data of the initial abnormal current data, the initial current trend difference factor of the initial abnormal current data is obtained. The calculation method for the initial current trend difference factor of the initial abnormal current data is as follows:

[0066]

[0067] In the formula, This represents the initial current trend difference factor of the initial abnormal current data; This indicates the number of all preceding abnormal current data for this initial abnormal current data; Indicates the first One set of abnormal current data; Indicates the first Fitted current values ​​from the preceding abnormal current data; This indicates the number of all subsequent abnormal current data points for this initial abnormal current data. Indicates the first One post-abnormal current data; Indicates the first Fitted current values ​​of each post-abnormal current data; This represents the number of all current data points in the abnormal current data segment. A larger initial current trend difference factor indicates a greater difference between the initial abnormal current and its value under the ideal trend, reflecting that the initial abnormal current data is more likely to represent current data with a significant change in actual trend. The process involves obtaining the initial current trend difference factors for all initial abnormal current data points in the abnormal current data segment, linearly normalizing all initial current trend difference factors, and denoting each normalized initial current trend difference factor as the current trend difference factor.

[0068] Furthermore, a current trend difference factor threshold T3 is preset. In this embodiment, T3=0.6 is used as an example. This embodiment does not impose specific limitations, and T3 can be determined according to the specific implementation. The initial abnormal current data with a current trend difference factor greater than T3 is recorded as abnormal current data. All abnormal current data in the abnormal current data segment are obtained. Taking any two adjacent abnormal current data in the abnormal current data segment as an example, the difference between the second abnormal current data and the first abnormal current data is recorded as the first difference. The difference between the serial numbers of the second abnormal current data and the first abnormal current data in the abnormal current data segment is recorded as the second difference. The ratio of the first difference to the second difference is recorded as the slope of the first abnormal current data. The slope of all abnormal current data in the abnormal current data segment is obtained.

[0069] Furthermore, taking any three adjacent abnormal current data points in the abnormal current data segment as an example, the absolute value of the difference in slope between the first and second abnormal current data points is recorded as the preceding trend difference value of the second abnormal current data point, and the absolute value of the difference in slope between the third and second abnormal current data points is recorded as the following trend difference value of the second abnormal current data point; the preceding trend difference values ​​and following trend difference values ​​of all abnormal current data points are obtained. It should be noted that the preceding trend difference value of the first abnormal current data point in the abnormal current data segment is defaulted to 0 in this embodiment, and the following trend difference value of the last abnormal current data point in the abnormal current data segment is defaulted to 0 in this embodiment.

[0070] Furthermore, taking the first segment of this abnormal current data... The abnormal current data and the first The differences in the preceding and following trend differences among the abnormal current data are used to obtain the first... The abnormal current data and the first Data similarity of the abnormal current data. Among them, the first... The abnormal current data and the first The method for calculating the data similarity of abnormal current data is as follows:

[0071]

[0072] In the formula, Indicates the first The abnormal current data and the first Data similarity of abnormal current data, ; This represents the preset hyperparameters; in this embodiment, the preset hyperparameters are... This is used to limit the range of variation of the preceding trend difference value and the following trend difference value; Indicates the first The preceding trend difference value of each abnormal current data; Indicates the first The preceding trend difference value of each abnormal current data; This represents the preset hyperparameters; in this embodiment, the preset hyperparameters are... This is used to limit the range of variation in abnormal current data; Indicates the first One abnormal current data point; Indicates the first One abnormal current data point; Indicates the first Post-trend difference value of abnormal current data; Indicates the first Post-trend difference value of abnormal current data; Indicates restriction The condition for taking the value 1; This represents a sign function. Where if... Indicates the first The abnormal current data and the first The greater the data similarity of the abnormal current data, the more it indicates that the first abnormal current data... The abnormal current data and the first The more similar the trend and numerical variation patterns among the abnormal current data, the better.

[0073] Furthermore, any one abnormal current data point in the abnormal current data segment is designated as the target abnormal current data point. Each abnormal current data point other than the target abnormal current data point is designated as a reference abnormal current data point for the target abnormal current data point. Reference abnormal current data points with a similarity of 1 to the target abnormal current data point are designated as associated abnormal current data points for the target abnormal current data point. The dataset comprised of all associated abnormal current data points is then used as the associated abnormal dataset for the target abnormal current data point. The associated abnormal datasets for all abnormal current data points in the abnormal current data segment are then obtained. Each abnormal current data segment corresponds to multiple associated abnormal datasets.

[0074] Furthermore, the vibration anomaly factor of this abnormal current data segment is obtained based on the associated anomaly dataset of all abnormal current data in this abnormal current data segment. The calculation method for the vibration anomaly factor of this abnormal current data segment is as follows:

[0075]

[0076] In the formula, This indicates the degree of vibration abnormality in the abnormal current data segment; This represents the average of all abnormal current data in this abnormal current data segment; This represents the average value of all current data in this abnormal current data segment; This indicates the number of all abnormal current data in this abnormal current data segment; This indicates the first abnormal data set in the associated abnormal data set of all abnormal current data in this abnormal current data segment. The number of times each abnormal current data point occurs; Indicates the first The number of all abnormal current data points in the associated abnormal dataset for each abnormal current data point is determined. A higher vibration anomaly level in a given abnormal current data segment indicates that the segment is less likely to be caused by machine tool vibration. The vibration anomaly level of all abnormal current data segments is obtained, and all vibration anomaly levels are linearly normalized. Each normalized vibration anomaly level is then denoted as a vibration anomaly factor.

[0077] Thus, the vibration anomaly factors for all abnormal current data segments are obtained using the above method.

[0078] Step S004: Compress the processing current data sequence according to the vibration anomaly factor.

[0079] Specifically, a threshold value for a vibration anomaly factor is preset. In this embodiment, This example is used for illustration; no specific limitations are set in this embodiment. This can be determined based on the specific implementation; taking any abnormal current data segment as an example, the first and second current data points in the abnormal current data segment are both recorded as a dividing point, and the third current data point in the abnormal current data segment other than the first and second current data points is used as the dividing point. Taking the current data as an example, according to the first current data... The difference between the current data and other boundary points is obtained from the first... The degree of boundary of each current data point. Among them, the first... The method for calculating the boundary degree of individual current data is as follows:

[0080]

[0081] In the formula, Indicates the first The degree of boundary between individual current data; This represents the preset hyperparameters; in this embodiment, the preset hyperparameters are... , used to limit the range of slope variation; Indicates the first The slope of the first dividing point before each current data point; Indicates the first The slope of the second dividing point before the current data; This indicates the vibration anomaly factor for the abnormal current data segment; This represents the preset threshold for the vibration anomaly factor; Indicates the first The first dividing point before the current data and the first... The maximum upslope of all current data in a data segment consisting of current data; Indicates the first The first dividing point before the current data and the first... The minimum downslope of all current data within a data segment composed of current data points is used to determine the boundary degree of all current data within that abnormal current data segment. The determination of the upslope and downslope is well-known in the rotating door compression algorithm and will not be elaborated upon in this embodiment.

[0082] Furthermore, a threshold value T5 is preset, where this embodiment uses T5=0 as an example. This embodiment does not impose specific limitations, and T5 can be determined according to the specific implementation. Current data in the abnormal current data segment with a threshold value greater than T5 are recorded as threshold points. All threshold points in the abnormal current data segment are obtained. The data segment formed between any two threshold points is recorded as a data segment to be compressed. Each data segment to be compressed is treated as a sub-segment, and rotating door compression is performed on all sub-segments to obtain several compressed data segments. All compressed data segments are stored in the database. The process of compressing sub-segments is a well-known part of the rotating door compression algorithm and will not be elaborated upon in this embodiment.

[0083] This concludes the embodiment.

[0084] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for dynamic security protection of machining data for mechanical parts, characterized in that, The method includes the following steps: A sequence of machining current data for several mechanical parts is collected. The sequence of machining current data contains multiple current data points, and each current data point corresponds to a collection time. The initial standard current factor for each current data is obtained based on the difference in current data variation between different mechanical parts. The initial standard current factor is used to describe the difference between the current data and the normal current data. Based on the maximum difference in the initial standard current factor variation between the current data of different mechanical parts at the same acquisition time, several initial abnormal current data and several normal current data are selected from the current data. The processing current data sequence is divided into several abnormal current data segments based on the initial abnormal current data and normal current data. Based on the changing trends of abnormal current data before and after different initial abnormal current data within each abnormal current data segment, a current trend difference factor is obtained for each initial abnormal current data segment. Several abnormal current data are selected from the initial abnormal current data based on the current trend difference factor. The data similarity between any two abnormal current data segments is obtained based on the fluctuation trend differences between different abnormal current data segments. The abnormal current data are then categorized based on data similarity to obtain several associated abnormal datasets. Finally, the vibration anomaly factor for each abnormal current data segment is obtained based on the distribution of different abnormal current data within the associated abnormal datasets. The machining current data sequence is compressed based on the vibration anomaly factor; The specific method for compressing the processing current data sequence based on the vibration anomaly factor is as follows: Preset a vibration anomaly factor threshold For any abnormal current data segment, the first and second current data points within the segment are both designated as a dividing point. For any other abnormal current data point within the segment besides the first and second current data points... Current data; In the formula, Indicates the first The degree of boundary between individual current data; Indicates the preset hyperparameters; Indicates the first The slope of the first dividing point before each current data point; Indicates the first The slope of the second dividing point before the current data; The vibration anomaly factor represents the abnormal current data segment; This represents the preset threshold for the vibration anomaly factor; Indicates the first The first dividing point before the current data and the first... The maximum upslope of all current data in a data segment consisting of current data; Indicates the first The first dividing point before the current data and the first... The minimum downslope of all current data in a data segment consisting of current data; A threshold T5 is preset for the degree of separation; current data with a degree of separation greater than T5 in the abnormal current data segment are recorded as the separation point; all separation points in the abnormal current data segment are obtained; the data segment formed between any two separation points is recorded as the data segment to be compressed; each data segment to be compressed is used as a segment, and rotating door compression is performed on all segments to obtain several compressed data segments; all compressed data segments are stored in the database.

2. The method for dynamic security protection of machining data of mechanical parts according to claim 1, characterized in that, The specific method for obtaining the initial standard current factor for each current data based on the variation differences in current data between different mechanical parts includes: Take any one mechanical part as the target mechanical part, and denote each mechanical part other than the target mechanical part as the reference mechanical part of the target mechanical part; In the formula, The first data in the sequence of machining current data for the target mechanical part Initial standard current factor for each current data; This indicates the sequence of machining current data for all reference mechanical parts of the target mechanical part. In the current data, the first one is related to the machining current data sequence of the target mechanical part. The number of equal current data; This indicates the number of all reference mechanical parts for the target mechanical part.

3. The method for dynamic security protection of machining data of mechanical parts according to claim 1, characterized in that, The method for selecting several initial abnormal current data and several normal current data from the current data based on the maximum difference in the initial standard current factor among current data of different mechanical parts at the same acquisition time includes the following specific method: The first in the sequence of machining current data for all mechanical parts From the initial standard current factors of the current data, the current data corresponding to the largest initial standard current factor is taken as the first current data in the entire processing current data sequence. Standard current data for each current data point; obtain standard current data for all current data in the processing current data sequence for each mechanical part. For any current data in the processing current data sequence of any mechanical part, the absolute value of the difference between the standard current data and the current data is recorded as the standard current difference. The standard current difference of all current data in the processing current data sequence of the mechanical part is obtained, and all standard current differences are linearly normalized. Each normalized standard current difference is recorded as the standard current factor. A standard current factor threshold T1 is preset. For any current data in the processing current data sequence of any mechanical part, if the standard current factor of the current data is greater than or equal to T1, then the current data is recorded as the initial abnormal current data. If the standard current factor of the current data is less than T1, then the current data is recorded as normal current data.

4. The method for dynamic security protection of machining data of mechanical parts according to claim 1, characterized in that, The specific method for dividing the processing current data sequence into several abnormal current data segments based on the initial abnormal current data and normal current data includes: For any machining current data sequence of a mechanical part, a preset threshold T2 for the number of initial abnormal current data is set; in the machining current data sequence, the data segment consisting of every T2 initial abnormal current data is recorded as an abnormal current data segment.

5. The method for dynamic security protection of machining data of mechanical parts according to claim 1, characterized in that, The method for obtaining the current trend difference factor for each initial abnormal current data based on the changing trend of the abnormal current data before and after different initial abnormal current data in the abnormal current data segment includes the following specific methods: For any abnormal current data segment, the least squares method is used to fit all the current data in the abnormal current data segment to obtain a fitting curve. The value of each current data on the fitting curve is recorded as the fitted current value of each current data. For any initial abnormal current data in the abnormal current data segment, each initial abnormal current data before the initial abnormal current data is recorded as the preceding abnormal current data, and each initial abnormal current data after the initial abnormal current data is recorded as the following abnormal current data. In the formula, The initial current trend difference factor represents the initial abnormal current data. This indicates the number of all preceding abnormal current data points for the initial abnormal current data. Indicates the first One set of abnormal current data; Indicates the first Fitted current values ​​from the preceding abnormal current data; This indicates the number of all subsequent abnormal current data points corresponding to the initial abnormal current data. Indicates the first One post-abnormal current data; Indicates the first Fitted current values ​​of each post-abnormal current data; This indicates the number of all current data in the abnormal current data segment; Obtain the initial current trend difference factor of all initial abnormal current data in the abnormal current data segment, perform linear normalization on all initial current trend difference factors, and record each normalized initial current trend difference factor as the current trend difference factor.

6. The method for dynamic security protection of machining data of mechanical parts according to claim 1, characterized in that, The specific method for selecting several abnormal current data from the initial abnormal current data based on the current trend difference factor includes: A current trend difference factor threshold T3 is preset, and the initial abnormal current data with a current trend difference factor greater than T3 are recorded as abnormal current data.

7. The method for dynamic security protection of machining data of mechanical parts according to claim 1, characterized in that, The method for obtaining the data similarity of any two abnormal current data based on the difference in fluctuation trends between different abnormal current data in the abnormal current data segment includes the following specific methods: For any two adjacent abnormal current data in any abnormal current data segment, the difference between the second abnormal current data and the first abnormal current data is recorded as the first difference, the difference between the serial numbers of the second abnormal current data and the first abnormal current data in the abnormal current data segment is recorded as the second difference, and the ratio of the first difference to the second difference is recorded as the slope of the first abnormal current data. For any three adjacent abnormal current data in the abnormal current data segment, the absolute value of the difference in slope between the first and second abnormal current data is recorded as the preceding trend difference value of the second abnormal current data, and the absolute value of the difference in slope between the third and second abnormal current data is recorded as the following trend difference value of the second abnormal current data. In the formula, Indicates the first The abnormal current data and the first Data similarity of abnormal current data, ; Indicates the preset hyperparameters; Indicates the first The preceding trend difference value of each abnormal current data; Indicates the first The preceding trend difference value of each abnormal current data; Indicates the preset hyperparameters; Indicates the first One abnormal current data point; Indicates the first One abnormal current data point; Indicates the first Post-trend difference value of abnormal current data; Indicates the first Post-trend difference value of abnormal current data; Represents a symbolic function.

8. The method for dynamic security protection of machining data of mechanical parts according to claim 1, characterized in that, The specific method for classifying abnormal current data based on data similarity to obtain several associated abnormal datasets includes: A similarity threshold KL is preset. Any abnormal current data in any abnormal current data segment is recorded as the target abnormal current data. Each abnormal current data other than the target abnormal current data is recorded as the reference abnormal current data of the target abnormal current data. The reference abnormal current data with a similarity equal to KL with the target abnormal current data is recorded as the associated abnormal current data of the target abnormal current data. The dataset composed of all associated abnormal current data is used as the associated abnormal dataset of the target abnormal current data.

9. The method for dynamic security protection of machining data of mechanical parts according to claim 1, characterized in that, The method for obtaining the vibration anomaly factor for each abnormal current data segment based on the distribution of different abnormal current data in the associated abnormal dataset includes the following: In the formula, It indicates the degree of vibration abnormality in any abnormal current data segment; This represents the mean of all abnormal current data in the abnormal current data segment. This represents the mean of all current data in the abnormal current data segment. This indicates the number of all abnormal current data in the abnormal current data segment; This represents the associated abnormal dataset of all abnormal current data in the abnormal current data segment, specifically the first abnormal data. The number of times each abnormal current data point occurs; Indicates the first The number of all abnormal current data in the associated abnormal dataset of each abnormal current data is obtained; the vibration abnormality degree of all abnormal current data segments is obtained, and all vibration abnormality degrees are linearly normalized. Each normalized vibration abnormality degree is recorded as a vibration abnormality factor.

Images