A data processing method for a vacuum chamber helium leak detection device

Abstract

The present application relates to the field of data processing technology, and in particular to a data processing method for vacuum chamber helium leak detection equipment. The method comprises the steps of obtaining a helium concentration signal, filtering the helium concentration signal to obtain a preprocessed concentration signal, setting a reference point for each data point, constructing a reference point set for each data point, and calculating the credibility of the reference point in the reference point set; determining a confidence weight for each reference point based on the credibility of the reference point; multiplying each confidence weight by the preprocessed concentration data of the corresponding reference point as a local contribution value, and summing the multiple local contribution values as the final filtering result. The present application has the effect of improving the accuracy of helium concentration data processing.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a data processing method for vacuum box helium leak detection equipment. Background Art

[0002] In the field of precision manufacturing, it is often necessary to monitor and evaluate the sealing performance of products. Vacuum chamber helium leak detection equipment, as an efficient leak detection tool, is used in a variety of fields, including aerospace and automotive manufacturing. The leak detection principle of vacuum chamber helium leak detection equipment is based on the unique properties of helium: its small molecular size and rapid diffusion rate. During the product testing process, the product is placed in the vacuum chamber of the vacuum chamber helium detection equipment. A cavity is formed between the product and the vacuum chamber, and the gas in the cavity is extracted to create a vacuum environment. Helium is then introduced into the product. Under the influence of air pressure, the helium enters the vacuum chamber through the leak point. A helium concentration sensor inside the vacuum chamber collects the helium concentration information within the vacuum chamber. Analysis of this helium concentration information can be used to determine if the product is leaking. When collecting the helium concentration signal within the vacuum chamber, electromagnetic interference (from power lines, industrial equipment, or wireless signals) surrounding the equipment may affect the sensor's signal acquisition, resulting in noise in the helium concentration signal. Therefore, de-noising of the helium concentration signal is often required before analysis.

[0003] The NLM (Non-Local Means) filtering algorithm is a common filtering algorithm that can be applied not only to denoising two-dimensional images, but also to processing one-dimensional signals. During the filtering process, the algorithm first defines a search window and a reference segment based on the data point to be processed. It then searches for segments similar to the reference segment in the window and takes a weighted average of the segments based on similarity to update the value of the data point to be processed. The weight assignment depends on the magnitude of the similarity. The filtering result of a data point depends only on similar neighborhood data within a local range. This can cause noise data to have a significant impact on the filtering result, especially when the noise is concentrated or there are many outliers. This can distort the subsequent similarity measurement and may erroneously assign high weights to actually irrelevant segments, ultimately leading to inaccurate filtering results. Summary of the Invention

[0004] In order to reduce the occurrence of inaccurate filtering during data processing, the present application provides a data processing method for a vacuum chamber helium leak detection device.

[0005] This application provides a data processing method for a vacuum box helium leak detection device, which adopts the following technical solutions:

[0006] A data processing method for helium leak detection equipment in a vacuum chamber comprises the following steps: obtaining a helium concentration signal, filtering the helium concentration signal to obtain a preprocessed concentration signal, setting a reference point for each data point, constructing a reference point set for each data point, and calculating the credibility of the reference point in the reference point set; determining a confidence weight for each reference point based on the credibility of the reference point; multiplying each confidence weight by the preprocessed concentration data of the corresponding reference point as a local contribution value, and summing the multiple local contribution values as a final filtering result;

[0007] The steps of calculating the credibility of a reference point in a reference point set include: constructing a reference segment based on the reference point; constructing a neighborhood segment corresponding to the reference segment based on the position of the reference segment; calculating the noise probability of the reference segment and the noise probability of the neighborhood segment; determining a time weight based on the time interval between the data point and the corresponding reference point, taking the product of the noise probability of the helium concentration data in the reference segment, the noise probability of the neighborhood segment and the time weight as the degree of anomaly, and processing the degree of anomaly through an exponential function to obtain the credibility of the reference segment to the data point.

[0008] The beneficial effect is as follows: first, the collected helium concentration signal is initially filtered to obtain a preprocessed concentration signal. Because traditional filtering methods are affected by noise, the filtering results may be inaccurate. Therefore, in this application, the data points in the preprocessed concentration signal are further adjusted to obtain the final filtering results for each data point, thereby improving the accuracy of the final helium concentration data processing.

[0009] The process of adjusting data points in the preprocessed signal is based on their adjacent reference points. During the initial filtering of the helium concentration signal to obtain the preprocessed concentration signal, if the baseline data segment corresponding to a data point contains excessive noise, the preprocessed concentration data corresponding to that reference point will also be inaccurate. Therefore, when adjusting the preprocessed concentration data of a data point to obtain the final filtered result, the credibility of the corresponding reference point is obtained, and the reference points are weighted based on the credibility, thereby further improving the accuracy of the final filtered result calculation.

[0010] During the credibility calculation process, the probability that the helium concentration data is a noise signal is analyzed, and the credibility of the reference point is finally obtained based on the noise probability of the data in the reference segment corresponding to the reference point.

[0011] Optionally, the step of calculating the noise probability of the helium concentration data in the reference segment includes: obtaining a pressure signal in the vacuum box, calculating the noise factor of each data point in the helium concentration signal based on the pressure signal and the helium concentration signal, and taking the average of the noise factors corresponding to multiple data points in the reference segment as the noise probability of the reference segment.

[0012] The beneficial effect is that during product airtightness testing, if the product's sealing is poor, it indicates a leak. When helium leaks, it will cause changes in both the helium concentration and the pressure within the vacuum chamber. Therefore, in this method, the pressure signal within the vacuum chamber is acquired. The changes in the pressure signal and the helium concentration signal are combined to analyze the noise factor of the helium concentration signal, which represents the noise data. For each reference segment, which corresponds to multiple helium concentration data points, the average of the noise factors corresponding to these multiple helium concentration data points is used as the noise probability for that reference segment.

[0013] Optionally, the step of calculating the noise factor of each data point in the helium concentration signal based on the pressure signal and the helium concentration signal includes: for any data point in the ammonia concentration signal, constructing a comparison data segment; taking the standard deviation of all data in the comparison data segment as the overall difference, and determining the contribution of the data point corresponding to the comparison data segment to the overall difference based on the overall difference; obtaining the data ranking of the pressure data in the pressure signal corresponding to the data point in the comparison data point, taking the product of the data ranking, contribution and concentration data of the pressure data at the same time as the noise degree, and taking the normalized result of the noise degree as the noise factor.

[0014] The beneficial effect is that a noise signal typically stands out from the surrounding data. Therefore, this method constructs a comparison data segment corresponding to a data point, comparing the helium concentration data corresponding to the data point with the helium concentration data in the comparison data segment. The standard deviation of all data in the comparison data segment is calculated, representing the degree of data dispersion. The standard deviation is also based on the value of each data point. The greater the contribution of a data point to the standard deviation, the more prominent it is in the comparison data segment. Finally, the noise factor of the data point is calculated based on the contribution, pressure data, and helium concentration data corresponding to the current data point.

[0015] Optionally, the step of obtaining the data ranking of the pressure data in the pressure signal corresponding to the data point in the comparison data point includes: for any moment, obtaining multiple pressure data in the pressure signal corresponding to the comparison data segment, and arranging them from small to large based on the numerical value, and using the rank of the pressure data corresponding to the moment as the data ranking of the pressure data at the moment.

[0016] The beneficial effect is that the data in the comparison data segment is sorted to highlight the overall level of the helium concentration data of the current data point in the comparison data segment.

[0017] Optionally, the step of constructing the comparison data segment includes: determining the length of the comparison data segment , get the value centered on the current data point and with a length of The data segment is the comparison data segment.

[0018] The beneficial effect is that, based on the current data point, equal amounts of data are selected on both sides of the current data point to form a comparison data segment.

[0019] Optionally, the step of determining the contribution of a data point to the overall difference based on the overall difference includes: for any data point, obtaining the standard deviation of all data except itself in its corresponding comparison data segment as a reference difference, and taking the normalized result of the difference between the overall difference and the reference difference as the contribution of the data point to the overall difference.

[0020] The beneficial effect is that the method calculates the standard deviation of all data except the helium concentration data corresponding to the data point, and determines the contribution of the current data point to the standard deviation by comparing the difference between the two standard deviations.

[0021] Optionally, the step of constructing a neighborhood segment corresponding to the reference segment based on the position of the reference segment includes: determining a sampling time range based on the reference segment, and obtaining a preset number of data segments within the sampling time range as neighborhood segments.

[0022] Optionally, the step of setting a reference point for each data point and constructing a reference point set for each data point includes: determining the number of reference point sets , the adjacent and continuous data points on both sides data points as reference points; and the set of multiple reference points as the reference point set.

[0023] The beneficial effect is that under normal circumstances, the helium concentration signal changes linearly as a whole, so the current data point can be optimized and adjusted based on the adjacent data points. Furthermore, in this method, the data points on both sides of the current data point are used as reference points to adjust the preprocessed concentration data of the current data point.

[0024] Optionally, the step of constructing a reference segment based on a reference point includes: The data segment is used as the reference segment corresponding to the reference point.

[0025] Optionally, the step of obtaining a preset number of data segments within the sampling time range includes: obtaining multiple data segments of the same length as the reference segment, calculating the mean of the helium concentration data corresponding to the data segments, obtaining the absolute difference between the mean and the mean of the helium concentration data in the reference segment, and selecting the segment with the smallest absolute difference. data segments as neighborhood segments.

[0026] This application has the following technical effects:

[0027] The helium concentration signal is initially processed to obtain a preprocessed concentration signal. Based on the noise corresponding to each data point in the helium concentration signal, the data in the preprocessed concentration signal is adjusted again to reduce the impact of noise on the final filtering result and improve the accuracy of the helium concentration signal processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 This is a method flow chart of a data processing method for a vacuum box helium leak detection device according to an embodiment of the present application.

[0029] Figure 2 The present invention is a method flow chart of step S2 in a data processing method of a vacuum box helium leak detection device. DETAILED DESCRIPTION

[0030] The embodiment of the present application discloses a data processing method for a vacuum chamber helium leak detection device, which obtains a helium concentration signal, performs a primary filtering process on the helium concentration signal, and obtains a preprocessed concentration signal. For each data point in the concentration signal, a reference point set corresponding to each data point is constructed, and each data point is subjected to secondary optimization adjustment based on the credibility of the reference point in the reference point set to complete the final filtering of the data. The credibility in this method reflects whether the initial filtering result corresponding to the data point is reliable. Weighted filtering based on the credibility of the reference point enables the final filtering result to more truly reflect the potential trend of the data. This method effectively reduces the impact of noise on the filtering result and improves the accuracy of the data after processing.

[0031] Reference Figure 1 A data processing method for a vacuum box helium leak detection device includes steps S1 to S3.

[0032] S1: Acquire the helium concentration signal, filter the ammonia concentration signal to obtain a preprocessed concentration signal, set the reference point of each data point, and construct a reference point set for each data point.

[0033] During the product sealing test, the helium concentration signal in the vacuum box is collected by a helium sensor set in the vacuum box. In this embodiment, the collection frequency is once per second, and in other embodiments, it can be twice per second, three times per second, etc.

[0034] The NLM algorithm is used to perform initial filtering on the helium concentration signal to obtain a preprocessed concentration signal.

[0035] For any data point, construct a reference point set for that data point.

[0036] For any data point, it is optimized by its neighboring data points, so the neighboring data points are selected as reference points.

[0037] The amount of data to determine the reference point is During the initial filtering process using the NLM algorithm, a corresponding reference data segment is determined for each data point. By searching for segments similar to the reference data point in the search box, the weighted average of the segments is calculated based on the similarity to update the value of the data point to be processed. The steps of the NLM algorithm are conventional technical means in this field and will not be repeated in this embodiment. Equal to the length of the reference data segment. In this embodiment, the length of the reference data segment is 21. In the specific process of selecting reference points, for any data point, 10 data points are selected on both sides of the data point to form the reference point. Multiple reference points and the data point itself constitute a reference point set.

[0038] S2: Calculate the credibility of the reference points in the reference point set.

[0039] Reference Figure 2 The step of calculating the credibility of the reference points in the reference point set includes: step S21 to step S23.

[0040] S21: constructing a reference segment based on the reference point; and constructing a neighborhood segment corresponding to the reference segment based on the position of the reference segment.

[0041] For the credibility of any reference point, first construct a reference segment based on the reference point. Specifically: obtain a segment with the reference point as the center and length The data segment is the reference segment. It can also be understood as taking the reference point as the center and obtaining the data on both sides of the reference point. data, thereby forming a reference segment corresponding to the reference point.

[0042] Based on the position of the reference point and the length of the reference segment, it can be seen that for any data point to be processed, there are reference points, each reference point corresponds to a reference segment, and each reference segment includes the current data point (the data point to be processed).

[0043] For each reference segment corresponding to a reference point, a neighborhood segment corresponding to the reference segment is constructed.

[0044] The data segments adjacent to the reference segment are constructed as neighborhood segments of the reference segment. The neighborhood segments and the reference segment together reflect the credibility of the data within the reference segment. Specifically, a sampling time range is determined based on the reference segment, and a preset number of data segments within the sampling time range are obtained as neighborhood segments.

[0045] In this embodiment, the sampling time range is 10 minutes before and after the reference point sampling time corresponding to the reference segment. In this time period, multiple data segments of the same length as the reference segment are found, the mean of the corresponding helium concentration data in the data segment is calculated, and the absolute difference between the mean and the mean of the helium concentration data in the reference segment is obtained. The one with the smallest absolute difference is selected. data segments as neighborhood segments.

[0046] Here, the smaller the absolute difference, the more similar the mean of the helium concentration data in the reference segment and the corresponding neighboring segment is. It can also be understood that the two data segments are more similar, which can further reflect the situation of the data in the reference segment.

[0047] S22: Calculate the noise probability of the reference segment and the noise probability of the neighboring segments.

[0048] The step of calculating the noise probability of the helium concentration data in the reference segment includes: obtaining a pressure signal in the vacuum box, and calculating a noise factor of each data point in the helium concentration signal based on the pressure signal and the helium concentration signal.

[0049] For any data point in the ammonia concentration signal, a comparison data segment is constructed. The standard deviation of all data in the comparison data segment is used as the overall difference. Based on the overall difference, the contribution of the data point corresponding to the comparison data segment to the overall difference is determined. The data ranking of the pressure data in the pressure signal corresponding to the data point is obtained. The product of the data ranking, contribution, and concentration data of the pressure data at the same time is used as the noise degree. The normalized result of the noise degree is used as the noise factor. The average of the noise factors corresponding to multiple data points in the reference segment is used as the noise probability of the reference segment.

[0050] For any data point in the helium concentration signal, the relationship between the data point and the adjacent data points is analyzed. Therefore, a comparative data segment corresponding to the data point is constructed. In the process of constructing the comparative data segment, the length of the comparative data segment is first determined. , get the value centered on the current data point and with a length of The data segment is the comparison data segment. In this embodiment, the length of the comparison data segment is 31. 15 adjacent and continuous data points are selected on both sides of the data point. These 15 adjacent data points and the data point itself constitute the comparison data segment. The relationship between the helium concentrations corresponding to the data points in the comparison data segment is then analyzed.

[0051] In the comparison data segment, the standard deviation of the data in the comparison data segment represents the degree of discreteness of the data. In the process of standard deviation calculation, the value of each concentration data has a certain artifact on the final standard deviation. If the helium concentration data of a data point is larger, the gain of the standard deviation of the data in the comparison data segment is larger. This also shows that the more prominent the concentration data is among the adjacent concentration data, that is, the concentration data of the data point is much larger than the adjacent concentration data or much smaller than the adjacent concentration data, the greater the possibility that it belongs to noise. Based on this, the contribution of the data point to the whole can reflect the possibility that the data point is noise to a certain extent. Therefore, in this step, the standard deviation of the overall data in the comparison data segment is calculated, and the standard deviation is used as the overall difference. At the same time, for the comparison data segment corresponding to a data point, the standard deviation of all data points in the comparison data segment except the data point itself is calculated, and the standard deviation is used as the reference difference, and the absolute difference between the overall difference and the reference difference is used as the contribution of the data point to the overall difference.

[0052] The real helium concentration signal itself also has some fluctuations. When the data point is the peak value of the helium concentration signal, it is also possible to cause a large gain in the standard deviation of the comparison data segment corresponding to the data. Therefore, the pressure signal in the vacuum box is obtained in this application. Based on the actual scenario, when the concentration of helium changes, helium enters the inside of the vacuum box, which in turn causes the air pressure in the vacuum box to change. In the real helium concentration signal, the change in helium concentration is usually accompanied by a change in the pressure in the vacuum box. If the helium concentration of a data point is large, but the corresponding pressure data does not change at the same time, then the possibility that the helium concentration at the data point is noise is greater. Therefore, in this step, the noise probability of the data point is calculated based on the contribution, helium concentration data and pressure data in the vacuum box.

[0053] Specifically, the calculation formula for the noise probability of a data point can be expressed as:

[0054] Where, Represents the noise factor of one data point of the helium concentration signal; Indicates the concentration data corresponding to the data point; Indicates overall poor; represents the reference difference; Represents the first hyperparameter, mainly used to prevent Zero occurrence; Indicates the ranking of the pressure data corresponding to the data point at the same moment in the multiple pressure data sequences corresponding to the comparison data segment; represents the linear normalization function.

[0055] Data ranking refers to the order of the pressure data corresponding to a data point within the multiple pressure data corresponding to the comparison data segment (arranged from smallest to largest). For example, if there is a set of pressure data: 56.5, 55.5, 57.5, 59.5, 63.5, 62.5, 52.5, and the value corresponding to the current data point is 63.5, then the data points are sorted from largest to smallest as 63.5, 62.5, 59.5, 57.5, 56.5, 55.5, and 52.5. In this case, the data ranking of the current data point is 1.

[0056] In the formula, It represents the contribution of the concentration data corresponding to the data point to the overall difference. The greater the contribution, the more discrete the concentration data corresponding to the data point is, and thus the greater the possibility that the helium concentration data corresponding to the data point is noise.

[0057] Indicates the helium concentration corresponding to the data point. The larger the value, the more prominent it is in the comparison data segment, and the more likely it is to be noise data. It indicates the position of the pressure data of the data point at the same moment in the sequence of all pressure data in the comparison data segment sorted from small to large. The smaller the value, the smaller the pressure data.

[0058] S23: Determine a time weight based on the time interval between the data point and the corresponding reference point, take the product of the noise probability of the helium concentration data in the reference segment, the noise probability of the neighboring segment, and the time weight as the degree of abnormality, and process the degree of abnormality using an exponential function to obtain the credibility of the reference segment for the data point.

[0059] In the preprocessed concentration signal, one data point corresponds to multiple reference segments, each corresponding to a reference point. If the noise probability of the multiple data points in the helium concentration signal corresponding to a reference segment is relatively high, the accuracy of the filtering result based on that reference segment—that is, the data value corresponding to the corresponding data point in the preprocessed concentration signal—is lower. Furthermore, the credibility of the data value of a reference point is also affected by the data within the neighboring segments corresponding to the reference segment itself. If the noise probability of the data points in all neighboring segments corresponding to a reference segment is relatively high, this also indicates that the preprocessed concentration value of that reference point is inaccurate.

[0060] Furthermore, when rescaling the preprocessed concentration data corresponding to a data point, the farther a reference segment is from the data point, the lower its reference value. Therefore, the time interval between the data point and the reference segment should be considered when calculating the credibility. To this end, this method calculates the time weight corresponding to the reference segment based on the time interval between the data point and the reference point. Finally, the credibility of the final reference segment is calculated by combining the noise probability of the helium concentration in the reference segment, the noise probability of the helium concentration in the neighboring segments, and the time weight corresponding to the reference segment.

[0061] Specifically, the calculation formula for the credibility of the reference segment corresponding to a data point can be expressed as

[0062] Where, Indicates the data point corresponding to the credibility of the reference segments; Indicates the data point and The time interval between reference points; Represents the second hyperparameter, mainly used to prevent The situation of 0 occurs; Indicates the Noise probability of a reference segment; Indicates the The reference segment corresponds to The noise probability of a neighborhood segment; Indicates the number of neighborhood segments.

[0063] In the formula, The larger it is, the longer the sampling time interval between the data point and the reference point is, and the lower the reference value of the corresponding reference segment will be, which in turn leads to lower credibility of the reference segment. The larger the value is, the greater the mean value of the noise probability of the helium concentration data corresponding to the reference segment is. There is a lot of noise in the helium concentration data corresponding to the reference segment, which will affect the filtering of the helium concentration signal, thereby resulting in low accuracy of the pre-processed concentration data corresponding to the reference point. The larger the value is, the greater the mean noise probability of the helium concentration data in the neighborhood segment corresponding to the reference segment is, which means that the accuracy of the corresponding reference point is low.

[0064] S3: Determine the confidence weight of each reference point based on the credibility of the reference point; take the product of each confidence weight and the preprocessed concentration data of the corresponding reference point as the local contribution value, and take the sum of multiple local contribution values as the final filtering result.

[0065] For the preprocessed concentration data of a data point, it is adjusted through multiple reference points around it. Different reference points have different credibility. The weights corresponding to each reference point are determined based on the credibility, and finally the weighted average is performed to obtain the final filtering result corresponding to the data point.

[0066] For the final filtering result corresponding to any data point, the calculation formula can be expressed as:

[0067] Where, Indicates the final filtering result of the data point; Indicates the data point corresponding to the credibility of the reference points; Indicates the Pre-processed concentration data corresponding to reference points; Indicates the number of reference segments.

[0068] It represents the credibility of the reference point. The greater the credibility, the smaller the noise probability of the helium concentration data in the reference segment corresponding to the reference point, which in turn indicates that the accuracy of the pre-processed concentration data corresponding to the reference point is higher, and thus a higher weight is assigned.

[0069] Repeat the above steps to calculate the final filtering result of each data point in the helium concentration signal, obtain the final filtered signal, and monitor the air tightness of the product based on the final filtered signal. Specifically, the box plot algorithm can be used to detect anomalies in the helium concentration data. For example, 120 consecutive data points in the time series can be selected to form a box line for anomaly detection. If 5 consecutive helium concentration abnormal data exceeding the abnormal upper limit in the box line are detected in the time series, it can be judged that the equipment being leak-tested is unqualified. The box plot algorithm is a conventional technical means in this field and will not be described in this application.

[0070] An embodiment of the present application also discloses a data processing method for a vacuum box helium leak detection device, including a processor and a memory, wherein the memory stores computer program instructions. When the computer program instructions are executed by the processor, a data processing method for a vacuum box helium leak detection device according to the present application is implemented.

[0071] The above system also includes other components well known to those skilled in the art, such as a communication bus and a communication interface. The configuration and functions of these components are known in the art and will not be described in detail here.

[0072] The above are all preferred embodiments of the present application, and are not intended to limit the scope of protection of the present application. Therefore, any equivalent changes made based on the structure, shape, and principle of the present application should be included in the scope of protection of the present application.

Claims

1. A data processing method for a vacuum chamber helium leak detection device, characterized in that: The method comprises the following steps: obtaining a helium concentration signal, filtering the helium concentration signal to obtain a preprocessed concentration signal, setting a reference point for each data point, and constructing a reference point set for each data point; calculating the credibility of the reference point in the reference point set; determining a confidence weight for each reference point based on the credibility of the reference point; multiplying each confidence weight by the preprocessed concentration data of the corresponding reference point as a local contribution value, and taking the sum of multiple local contribution values as a final filtering result; The steps of calculating the credibility of a reference point in a reference point set include: constructing a reference segment based on the reference point; constructing a neighborhood segment corresponding to the reference segment based on the position of the reference segment; calculating the noise probability of the reference segment and the noise probability of the neighborhood segment; determining a time weight based on the time interval between the data point and the corresponding reference point, taking the product of the noise probability of the helium concentration data in the reference segment, the noise probability of the neighborhood segment and the time weight as the degree of anomaly, and processing the degree of anomaly through an exponential function to obtain the credibility of the reference segment to the data point.

2. The data processing method for vacuum chamber helium leak detection equipment according to claim 1, characterized in that: The step of calculating the noise probability of the helium concentration data in the reference segment includes: obtaining a pressure signal in the vacuum chamber, calculating a noise factor of each data point in the helium concentration signal based on the pressure signal and the helium concentration signal, and taking the average of the noise factors corresponding to multiple data points in the reference segment as the noise probability of the reference segment.

3. The data processing method for vacuum chamber helium leak detection equipment according to claim 2, characterized in that: The steps of calculating the noise factor of each data point in the helium concentration signal based on the pressure signal and the helium concentration signal include: for any data point in the ammonia concentration signal, constructing a comparison data segment; taking the standard deviation of all data in the comparison data segment as the overall difference, and determining the contribution of the data point corresponding to the comparison data segment to the overall difference based on the overall difference; obtaining the data ranking of the pressure data in the pressure signal corresponding to the data point in the comparison data point, taking the product of the data ranking, contribution and concentration data of the pressure data at the same time as the noise degree, and taking the normalized result of the noise degree as the noise factor.

4. The data processing method for vacuum chamber helium leak detection equipment according to claim 3, characterized in that: The step of obtaining the data ranking of the pressure data in the pressure signal corresponding to the data point in the comparison data point includes: for any moment, obtaining multiple pressure data in the pressure signal corresponding to the comparison data segment, and arranging them from small to large based on the numerical value, and using the position of the pressure data corresponding to the moment as the data ranking of the pressure data at the moment.

5. The data processing method for vacuum chamber helium leak detection equipment according to claim 3, characterized in that: The steps of constructing the comparison data segment include: determining the length of the comparison data segment , get the value centered on the current data point and with a length of The data segment is the comparison data segment.

6. The data processing method for vacuum chamber helium leak detection equipment according to claim 3, characterized in that: The step of determining the contribution of a data point to the overall difference based on the overall difference includes: for any data point, obtaining the standard deviation of all data except itself in the corresponding comparison data segment as a reference difference, and taking the normalized result of the difference between the overall difference and the reference difference as the contribution of the data point to the overall difference.

7. The data processing method for vacuum chamber helium leak detection equipment according to claim 1, characterized in that: The step of constructing a neighborhood segment corresponding to the reference segment based on the position of the reference segment includes: determining a sampling time range based on the reference segment, and obtaining a preset number of data segments within the sampling time range as neighborhood segments.

8. The data processing method for vacuum chamber helium leak detection equipment according to claim 1, characterized in that: The steps of setting reference points for each data point and constructing a reference point set for each data point include: determining the number of reference point sets , the adjacent and continuous data points on both sides data points as reference points; and the set of multiple reference points as the reference point set.

9. The data processing method for vacuum chamber helium leak detection equipment according to claim 1, characterized in that: The step of constructing a reference segment based on a reference point includes: The data segment is used as the reference segment corresponding to the reference point.

10. The data processing method for vacuum box helium leak detection equipment according to claim 7, characterized in that: The step of obtaining a preset number of data segments within a sampling time range includes: obtaining multiple data segments of the same length as the reference segment, calculating the mean of the helium concentration data corresponding to the data segments, obtaining the absolute difference between the mean and the mean of the helium concentration data in the reference segment, and selecting the one with the smallest absolute difference. data segments as neighborhood segments.

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