Helium mass spectrometer leak rate determination method fusing confidence quantification and finite state machine control

Abstract

The application discloses a helium mass spectrum leak rate determination method fusing confidence quantification and finite state machine control and relates to the technical field of vacuum leak detection. The method comprises the following steps: S1, obtaining multi-dimensional features from a leak signal subjected to multi-frequency band filtering processing; S2, if a baseline signal and environmental interference features collected in the absence of helium gas meet corresponding preset conditions, respectively, determining a preset threshold value as an initial dynamic threshold value; S3, determining the weight of each feature in the multi-dimensional features by using an analytic hierarchy process and calculating the sub-confidence of each feature in the multi-dimensional features; S4, calculating the comprehensive confidence of the multi-dimensional features, and if the comprehensive confidence is not lower than a preset value, determining the helium mass spectrum leak rate by using the leak signal and the initial dynamic threshold value; and S5, dynamically updating the initial dynamic threshold value based on the comprehensive confidence, and repeatedly executing steps S1-S5 to continuously determine the helium mass spectrum leak rate. The method improves the determination reliability, the weak leak signal capturing capacity and the like.

Description

Technical Field

[0001] This invention relates to the field of vacuum leak detection technology, and more specifically, to a helium mass spectrometry leak rate determination method that integrates confidence quantification and finite state machine control. Background Technology

[0002] Existing helium mass spectrometry leak rate determination techniques have two major limitations:

[0003] First, the judgment logic is too simplistic, relying solely on a binary comparison of "signal-threshold" to output results. This makes it impossible to quantify the reliability of the results, making it difficult for operators to judge the validity of the results in scenarios with weak signals (leakage rate < 5 × 10^-13 Pam³ / s) or strong noise (such as fluctuations in vacuum system pumping or a sudden increase in helium background concentration).

[0004] Secondly, the process control is disordered. During the leak detection process, the state transitions such as "monitoring-suspected-confirmation-correction" lack clear triggering conditions. It is easy to jump directly to the alarm state due to momentary interference, or to miss the detection due to signal attenuation.

[0005] For example, in leak detection of aerospace engine combustion chambers, ambient temperature fluctuations can cause instantaneous fluctuations of ±15% in mass spectrometry signals, resulting in a false alarm rate of 12%-18% for existing fixed threshold methods. In low-temperature packaging leak detection of semiconductor chips (-40℃), circuit temperature drift causes slow baseline shifts, and the probability of masking minute leak signals (3×10^-13 Pam³ / s) exceeds 40%. Furthermore, existing technologies lack a reliability evaluation mechanism. When the judgment result is near a critical threshold (e.g., signal = 1.02×threshold), it is impossible to distinguish between genuine micro-leakage and noise interference, requiring manual secondary verification, which reduces detection efficiency.

[0006] Therefore, it is urgent to build an integrated judgment system that includes "multi-dimensional feature analysis, confidence quantification, and state machine orderly control" to improve the intelligence and reliability of leak detection in complex scenarios. Summary of the Invention

[0007] The purpose of this invention is to provide a helium mass spectrometry leak rate determination method that integrates confidence quantification and finite state machine control, so as to solve the problems existing in the above-mentioned background art.

[0008] The above-mentioned technical objective of the present invention is achieved through the following technical solution:

[0009] Firstly, this application provides a method for determining the leak rate of helium mass spectrometry by integrating confidence quantification and finite state machine control, including the following specific steps:

[0010] S1 performs multi-band filtering on the collected leakage signal and obtains multi-dimensional features from the leakage signal after multi-band filtering. The multi-dimensional features include stability features, threshold exceedance features, trend features and environmental interference features.

[0011] S2, if the collected baseline signal without helium background and environmental interference characteristics meet the corresponding preset conditions, the preset threshold is determined as the initial dynamic threshold.

[0012] S3. The weights of each feature in the multi-dimensional features are determined by the analytic hierarchy process, and the sub-confidence of each feature in the multi-dimensional features is calculated.

[0013] S4. Calculate the comprehensive confidence of the multi-dimensional features by using the weights of each feature and the sub-confidence of each feature. If the comprehensive confidence is not lower than the preset value, determine the helium mass spectrometry leak rate by using the leak signal and the initial dynamic threshold.

[0014] S5, dynamically update the initial dynamic threshold based on the comprehensive confidence level, and repeat steps S1-S5 to continuously determine the helium mass spectrometry leak rate until the detection ends.

[0015] Based on the above technical solution, the present invention can be further improved as follows.

[0016] Furthermore, in step S2 above, the method also includes:

[0017] The baseline compensation value is obtained based on the baseline signal and the reconstructed signal obtained in the mid-frequency domain stage of multi-band filtering.

[0018] If the baseline compensation value is not lower than the initial dynamic threshold, proceed to step S3.

[0019] Furthermore, the aforementioned stability characteristics are specifically as follows: ; In the formula, Indicates stability characteristics, Indicates the first The signal amplitude at each sampling point The average value of the signal amplitude at all sampling points. This represents the total number of sampling points.

[0020] Furthermore, the aforementioned threshold exceedance feature specifically refers to: ; In the formula, For threshold exceedance features, when hour, ; This indicates the signal amplitude at the current sampling point. This represents the initial dynamic threshold, or the initial dynamic threshold after dynamic updates.

[0021] Furthermore, the aforementioned trend characteristics are specifically as follows: ;in: ; ; In the formula, It is a trend characteristic. This represents the cumulative value of the first derivative of the signal. The mean of the absolute values ​​of the second derivatives, These represent the weight coefficients of the corresponding items. These are the start time and end time of integration, respectively. Indicates signal Regarding time The first derivative, For the first The absolute value of the second derivative of the signal at each sampling point. This represents the total number of sampling points involved in the calculation.

[0022] Furthermore, the preset condition for the aforementioned baseline signal is that the signal fluctuation does not exceed 5%; the preset condition for the environmental interference characteristics is that the environmental interference characteristic is less than 0.2, and the specific environmental interference characteristics are: ; In the formula, Characterized by environmental disturbances. Indicates standard ambient temperature. ; This indicates the current ambient temperature.

[0023] Furthermore, the sub-confidence scores of each feature in the above multi-dimensional features are as follows:

[0024] Sub-confidence of stability feature : In the formula, This represents the normalized value of the stability characteristic;

[0025] Sub-confidence of features exceeding threshold : In the formula, This refers to the continuous sampling duration when the baseline compensation value is greater than the initial dynamic threshold or the dynamically updated initial dynamic threshold. Indicates the minimum effective duration. Indicates a threshold exceeding the feature;

[0026] Sub-confidence of trend features :when ,and hour, ;otherwise, In the formula, The cumulative value of the first derivative of the signal. The mean of the absolute values ​​of the second derivatives;

[0027] Sub-confidence of environmental interference characteristics : ;in, Characterized by environmental disturbances. , and when hour, .

[0028] Furthermore, the aforementioned overall confidence level is specifically as follows: ;

[0029] In the formula, Indicates the overall confidence level. Sub-confidence of stability features For the sub-confidence of the threshold exceeding the feature, Sub-confidence levels for trend features Sub-confidence levels representing environmental interference characteristics. These represent the weights of the corresponding items.

[0030] Furthermore, the above-mentioned dynamic update of the initial dynamic threshold based on the comprehensive confidence level is as follows: ; In the formula, Indicates the first The initial dynamic threshold for determining the leakage rate of helium mass spectrometry. This represents the overall confidence level of the current number of judgments.

[0031] Furthermore, the above-mentioned dynamic update of the initial dynamic threshold based on the comprehensive confidence level is as follows:

[0032] when hour, ;

[0033] when hour, ;

[0034] when hour, ;

[0035] In the formula, Indicates the first The initial dynamic threshold for determining the leakage rate of helium mass spectrometry. This represents the overall confidence level of the current number of judgments.

[0036] Secondly, this application provides a helium mass spectrometry leak rate determination system that integrates confidence quantification and finite state machine control, applicable to the helium mass spectrometry leak rate determination method that integrates confidence quantification and finite state machine control in any of the first aspects, including:

[0037] The first module is used to perform multi-band filtering on the collected leakage signal and to obtain multi-dimensional features from the leakage signal after multi-band filtering. The multi-dimensional features include stability features, threshold exceedance features, trend features and environmental interference features.

[0038] The second module is used to determine the preset threshold as the initial dynamic threshold if the collected baseline signal without helium background and environmental interference features meet the corresponding preset conditions.

[0039] The third module is used to determine the weight of each feature in the multi-dimensional features through the analytic hierarchy process and to calculate the sub-confidence of each feature in the multi-dimensional features.

[0040] The fourth module is used to calculate the comprehensive confidence of multi-dimensional features by using the weights of each feature and the sub-confidence of each feature. If the comprehensive confidence is not lower than the preset value, the helium mass spectrometry leak rate is determined by the leak signal and the initial dynamic threshold.

[0041] The fifth module is used to dynamically update the initial dynamic threshold based on the comprehensive confidence level, and to repeatedly execute the first to fourth modules to continuously determine the helium mass spectrometry leak rate until the detection ends.

[0042] Furthermore, the second module mentioned above is also used for:

[0043] The baseline compensation value is obtained based on the baseline signal and the reconstructed signal obtained in the mid-frequency domain stage of multi-band filtering.

[0044] If the baseline compensation value is not lower than the initial dynamic threshold, then proceed to the third module for processing.

[0045] Thirdly, this application provides an electronic device, including: at least one processor, at least one memory, and a data bus;

[0046] In this system, the processor and memory communicate with each other via a data bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the helium mass spectrometry leak rate determination method that combines confidence quantification and finite state machine control, as described in any of the first aspects.

[0047] Fourthly, this application provides a non-transitory computer-readable storage medium that stores computer instructions, which cause the computer to execute the helium mass spectrometry leak rate determination method that integrates confidence quantification and finite state machine control as described in any of the first aspects.

[0048] Compared with the prior art, the present invention has at least the following beneficial effects:

[0049] 1. Significantly improved reliability: The overall confidence level significantly improves the accuracy of critical state determination compared to existing technologies, while reducing the false alarm rate; 2. Ordered state control: The finite state machine avoids disordered transitions, resulting in a low state transition error rate in high-temperature environments, a substantial reduction compared to existing technologies; 3. Enhanced ability to detect weak leakage signals: For minute leaks with a leakage rate of 1×10^-13 Pam³ / s, the detection rate of existing technologies is significantly improved from 58%; 4. Expanded environmental adaptability: It can operate stably in environments ranging from -40℃ to 100℃, expanding the applicable environmental range compared to existing technologies. Attached Figure Description

[0050] The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and form part of this application, do not constitute a limitation thereof. In the drawings:

[0051] Figure 1 This is a flowchart of the determination method in an embodiment of the present invention;

[0052] Figure 2 This is a schematic diagram of the determination process in an embodiment of the present invention;

[0053] Figure 3 This is a flowchart of state transitions in an embodiment of the present invention. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations.

[0055] Therefore, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the invention without inventive effort are within the scope of protection of the invention.

[0056] It should be noted that similar labels and letters in the following figures indicate similar items. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures.

[0057] In the description of the embodiments of the present invention, "multiple" means at least two.

[0058] Example 1: Due to the high false alarm rate of the current fixed threshold method, in low-temperature packaging leak detection of semiconductor chips, circuit temperature drift causes slow baseline drift, and the probability of masking a tiny leak signal (3×10^-13 Pam³ / s) exceeds 40%. Furthermore, existing technologies lack a reliability evaluation mechanism; when the judgment result is near a critical threshold (e.g., signal = 1.02×threshold), it cannot distinguish between a genuine micro-leak and noise interference, requiring manual secondary verification, which reduces detection efficiency. Therefore, this example provides a helium mass spectrometry leak rate determination method that integrates confidence quantification and finite state machine control. Figure 1 and Figure 2 As shown, the specific steps include the following:

[0059] S1 performs multi-band filtering on the acquired leakage signal and obtains multi-dimensional features from the leakage signal after multi-band filtering. The multi-dimensional features include stability features, threshold exceedance features, trend features, and environmental interference features.

[0060] The multi-band filtering process includes time-domain processing and frequency-domain processing. The time-domain processing can use a moving average filter with a window size of 5-10 sampling points to suppress high-frequency impulse noise. The frequency-domain processing can use the db4 wavelet basis function to decompose the time-domain filtered signal into three levels and threshold the high-frequency wavelet coefficients. The soft threshold is: λ=σ×√(2lnN), where σ is the noise standard deviation and N is the number of sampling points. Then, the reconstructed signal I_recon(n) is obtained to suppress low-frequency drift noise.

[0061] In addition, a baseline signal I_base without helium background can be collected every 10-30 seconds to update the baseline compensation value in real time: Icomp (n) = I_recon (n) - I_base, so as to eliminate the influence of long-term circuit drift.

[0062] In step S1 above, the multi-dimensional features obtained include stability features, threshold exceedance features, trend features, and environmental interference features. Each feature is obtained in the following way:

[0063] The aforementioned stability characteristics are specifically as follows: ; In the formula, Indicates stability characteristics, Indicates the first The signal amplitude at each sampling point The average value of the signal amplitude at all sampling points. This represents the total number of sampling points.

[0064] Specifically, the threshold exceedance feature mentioned above is as follows: ; In the formula, For threshold exceedance features, when hour, ; This indicates the signal amplitude at the current sampling point. This represents the initial dynamic threshold, or the initial dynamic threshold after dynamic updates.

[0065] Specifically, the aforementioned trend characteristics are as follows: ;in: ; ; In the formula, It is a trend characteristic. This represents the cumulative value of the first derivative of the signal. The mean of the absolute values ​​of the second derivatives, These represent the weight coefficients of the corresponding items. These are the start time and end time of integration, respectively. Indicates signal Regarding time The first derivative, For the first The absolute value of the second derivative of the signal at each sampling point. This represents the total number of sampling points involved in the calculation.

[0066] Among them, the characteristics of environmental interference can be expressed as: ; In the formula, Characterized by environmental disturbances. Indicates standard ambient temperature. ; This indicates the current ambient temperature.

[0067] S2, if the baseline signal and environmental interference characteristics without helium background collected meet the corresponding preset conditions, the preset threshold is determined as the initial dynamic threshold.

[0068] Specifically, the preset condition for the baseline signal can be that the signal fluctuation does not exceed 5%; the preset condition for the environmental interference characteristics can be that the environmental interference characteristics are less than 0.2.

[0069] In step S2 above, the method further includes:

[0070] S21, Based on the baseline signal and the reconstructed signal obtained in the frequency domain stage of multi-band filtering, the baseline compensation value is obtained; wherein, the baseline compensation value can be obtained by the calculation formula in step S1 above.

[0071] S22, if the baseline compensation value is not lower than the initial dynamic threshold, proceed to step S3.

[0072] S3. The weights of each feature in the multi-dimensional features are determined by the analytic hierarchy process (AHP), and the sub-confidence of each feature in the multi-dimensional features is calculated.

[0073] In step S3 above, the weights of the four types of features can be determined using the Analytic Hierarchy Process (AHP). For example, the weights of each feature determined by the AHP are: stability feature weights. The threshold exceeds the feature weight. Trend feature weights Environmental interference feature weights It can also be calibrated through 1000 sets of standard leak tests to ensure that the weight is negatively correlated with the judgment error.

[0074] In the above, the sub-confidence of each feature in the multi-dimensional features can be obtained in the following way:

[0075] Sub-confidence of stability feature : In the formula, This represents the normalized value of the stability characteristic;

[0076] Sub-confidence of features exceeding threshold : In the formula, This refers to the continuous sampling duration when the baseline compensation value is greater than the initial dynamic threshold or the dynamically updated initial dynamic threshold. Indicates the minimum effective duration. express;

[0077] Sub-confidence of trend features :when ,and hour, ;otherwise, In the formula, The cumulative value of the first derivative of the signal. The mean of the absolute values ​​of the second derivatives;

[0078] Sub-confidence of environmental interference characteristics : ;in, Characterized by environmental disturbances. , and when hour, .

[0079] S4. Calculate the comprehensive confidence of the multi-dimensional features by using the weights of each feature and the sub-confidence of each feature. If the comprehensive confidence is not lower than the preset value, determine the helium mass spectrometry leak rate by using the leak signal and the initial dynamic threshold.

[0080] In step S4 above, the overall confidence level can be calculated in the following way: ; In the formula, Indicates the overall confidence level. Sub-confidence of stability features For the sub-confidence of the threshold exceeding the feature, Sub-confidence levels for trend features Sub-confidence levels representing environmental interference characteristics. These represent the weights of the corresponding items; it should be noted that... The overall confidence level can be divided into three levels: high confidence. Zhongzhixin Low confidence .

[0081] S5, dynamically update the initial dynamic threshold based on the comprehensive confidence level, and repeat steps S1-S5 to continuously determine the helium mass spectrometry leak rate until the detection ends.

[0082] In step S5 above, the initial dynamic threshold can be dynamically updated based on the comprehensive confidence level in the following way: ; In the formula, Indicates the first The initial dynamic threshold for determining the leakage rate of helium mass spectrometry. This represents the overall confidence level of the current number of judgments.

[0083] In step S5 above, the initial dynamic threshold can also be dynamically updated based on the comprehensive confidence level in the following way:

[0084] when hour, ;

[0085] when hour, ;

[0086] when hour, ;

[0087] In the formula, Indicates the first The initial dynamic threshold for determining the leakage rate of helium mass spectrometry. This represents the overall confidence level of the current number of judgments.

[0088] Specifically, when performing steps S1-S5, it can also be described in the following form: First, six states can be defined, namely initialization state S0, baseline stabilization state S1, normal monitoring state S2, suspected leakage state S3, confirmed leakage state S4, and interference correction state S5; such as Figure 3 As shown, the triggering conditions, state actions, and transition conditions for leaving each state are as follows:

[0089] Initialization state S0 is triggered by the following conditions: the leak detector is powered on and the detection module passes the self-test; the state action is: it can collect a 5s baseline signal and initialize the dynamic threshold T=T0; the transition condition is: if the baseline signal fluctuation is <5% and the environmental parameters are stable (E<0.2), then it enters the baseline stable state S1.

[0090] Baseline stable state S1 is triggered by the condition that the initialization state S0 transition condition is met; the state action is to update the baseline signal I_base every 5 seconds and calculate the initial dynamic threshold; the transition condition is that the baseline update is completed, signal acquisition is started and normal monitoring state S2 is entered.

[0091] Normal monitoring state S2 is triggered when: the baseline stability state S1 transition condition is met, or the interference correction state S5 correction is completed, the overall confidence level is ≥0.8 and the baseline compensation value is ≤initial dynamic threshold; the state actions are as follows: 1. If the baseline compensation value is >initial dynamic threshold and the overall confidence level is ≥0.5, then enter the suspected leakage state S3; 2. If the environmental interference characteristic is >0.8, then enter the interference correction state S5; 3. After detection ends, return to the initialization state S0.

[0092] Suspected Leakage State S3 is triggered by the following conditions: in Normal Monitoring State S2, the baseline compensation value is greater than the initial dynamic threshold, and the overall confidence level is ≥0.5. The state action is to extend the sampling frequency to 200Hz and encrypt the calculation of the overall confidence level (once every 100ms). The transition conditions are: 1. If t≥t0 and the overall confidence level is ≥0.8, then enter the confirmed leak state S4; 2. If t<t0 or the overall confidence level<0.5, then enter the normal monitoring state S2; 3. If the environmental interference characteristic is >0.8, then enter the interference correction state S5.

[0093] The leak status S4 is confirmed, and the trigger condition is: in the suspected leak status S3, t≥t0 and the overall confidence level≥0.8; the status action is: output a leak alarm (which can be achieved through audible and visual alarm + RS485 signal) and calculate the leak rate; the transition conditions are: 1. If the detection ends, it enters the initialization status S0; 2. If it is manually reset, it enters the normal monitoring status S2.

[0094] Interference correction state S5 is triggered when the environmental interference characteristic is >0.8 in normal monitoring state S2 or suspected leakage state S3, or when the mean value of the second derivative is >2 mV / s². The state action is to start the temperature drift compensation algorithm, that is, to recalculate the new baseline compensation value and recalibrate the baseline. The transition condition is when the temperature interference is eliminated (e.g., environmental interference characteristic ≤0.5) and the normalized value of the stability characteristic is <0.3, then it enters normal monitoring state S2.

[0095] Example 2: This application provides a helium mass spectrometry leak rate determination system that integrates confidence quantification and finite state machine control, applied to the helium mass spectrometry leak rate determination method that integrates confidence quantification and finite state machine control in Example 1, including:

[0096] The first module is used to perform multi-band filtering on the collected leakage signal and to obtain multi-dimensional features from the leakage signal after multi-band filtering. The multi-dimensional features include stability features, threshold exceedance features, trend features and environmental interference features.

[0097] The second module is used to determine the preset threshold as the initial dynamic threshold if the collected baseline signal without helium background and environmental interference features meet the corresponding preset conditions.

[0098] The third module is used to determine the weight of each feature in the multi-dimensional features through the analytic hierarchy process and to calculate the sub-confidence of each feature in the multi-dimensional features.

[0099] The fourth module is used to calculate the comprehensive confidence of multi-dimensional features by using the weights of each feature and the sub-confidence of each feature. If the comprehensive confidence is not lower than the preset value, the helium mass spectrometry leak rate is determined by the leak signal and the initial dynamic threshold.

[0100] The fifth module is used to dynamically update the initial dynamic threshold based on the comprehensive confidence level, and to repeatedly execute the first to fourth modules to continuously determine the helium mass spectrometry leak rate until the detection ends.

[0101] The second module mentioned above is also used for:

[0102] The baseline compensation value is obtained based on the baseline signal and the reconstructed signal obtained in the mid-frequency domain stage of multi-band filtering.

[0103] If the baseline compensation value is not lower than the initial dynamic threshold, then proceed to the third module for processing.

[0104] Example 3: This application provides an electronic device, including: at least one processor, at least one memory, and a data bus;

[0105] In this system, the processor and memory communicate with each other via a data bus; the memory stores program instructions that can be executed by the processor, and the processor calls the program instructions to execute the helium mass spectrometry leak rate determination method that combines confidence quantification and finite state machine control as described in Example 1.

[0106] Example 4: This application provides a non-transitory computer-readable storage medium that stores computer instructions. The computer instructions cause the computer to execute the helium mass spectrometry leak rate determination method of Example 1, which combines confidence quantification and finite state machine control.

[0107] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0108] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0109] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0110] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0111] Those skilled in the art will understand that all or part of the steps in the above facts and methods can be implemented by a program instructing related hardware. The program or the program described therein can be stored in a computer-readable storage medium. When the program is executed, it includes the following steps: at this time, the corresponding method steps are introduced. The storage medium can be ROM / RAM, magnetic disk, optical disk, etc.

[0112] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method of helium mass spectrometric leak rate determination fusing confidence quantification with finite state machine control, characterized in that, The specific steps include the following: S1, perform multi-band filtering on the collected leakage signal, and obtain multi-dimensional features from the leakage signal after multi-band filtering. The multi-dimensional features include stability features, threshold exceedance features, trend features and environmental interference features. S2, if the collected baseline signal without helium background and the environmental interference features respectively meet the corresponding preset conditions, the preset threshold is determined as the initial dynamic threshold. S3, determine the weight of each feature in the multi-dimensional features by using the analytic hierarchy process, and calculate the sub-confidence of each feature in the multi-dimensional features; S4. Calculate the comprehensive confidence of the multi-dimensional features using the weights of each feature and the sub-confidence of each feature. If the comprehensive confidence is not lower than a preset value, determine the helium mass spectrometry leak rate using the leak signal and the initial dynamic threshold. S5, based on the comprehensive confidence level, dynamically update the initial dynamic threshold, and repeat steps S1-S5 to continuously determine the helium mass spectrometry leak rate until the detection ends.

2. The helium mass spectrometry leak rate determination method integrating confidence quantification and finite state machine control according to claim 1, characterized in that, In step S2, the method further includes: Based on the baseline signal and the reconstructed signal obtained in the mid-frequency domain stage of multi-band filtering, the baseline compensation value is obtained. If the baseline compensation value is not lower than the initial dynamic threshold, proceed to step S3.

3. The helium mass spectrometry leak rate determination method based on the fusion of confidence quantification and finite state machine control as described in claim 1, characterized in that, The stability feature is specifically as follows: ； In the formula, Indicates stability characteristics, Indicates the first The signal amplitude at each sampling point The average value of the signal amplitude at all sampling points. This represents the total number of sampling points.

4. The helium mass spectrometry leak rate determination method based on the fusion of confidence quantification and finite state machine control as described in claim 1, characterized in that, The threshold exceedance feature is specifically: ； In the formula, For threshold exceedance features, when hour, ; This indicates the signal amplitude at the current sampling point. This represents the initial dynamic threshold, or the initial dynamic threshold after dynamic updates.

5. The helium mass spectrometry leak rate determination method based on the fusion of confidence quantification and finite state machine control as described in claim 1, characterized in that, The aforementioned trend characteristics are specifically: ;in: ； ； In the formula, It is a trend characteristic. This represents the cumulative value of the first derivative of the signal. The mean of the absolute values ​​of the second derivatives. These represent the weight coefficients of the corresponding items. These are the start time and end time of integration, respectively. Indicates signal Regarding time The first derivative, For the first The absolute value of the second derivative of the signal at each sampling point. This represents the total number of sampling points involved in the calculation.

6. The helium mass spectrometry leak rate determination method based on the fusion of confidence quantification and finite state machine control according to claim 1, characterized in that, The preset condition for the baseline signal is that the signal fluctuation does not exceed 5%; the preset condition for the environmental interference characteristic is that the environmental interference characteristic is less than 0.2, and the environmental interference characteristic specifically refers to: ； In the formula, Characterized by environmental disturbances. Indicates standard ambient temperature. ; This indicates the current ambient temperature.

7. The helium mass spectrometry leak rate determination method based on the fusion of confidence quantification and finite state machine control according to claim 1, characterized in that, The sub-confidence of each feature in the multi-dimensional features is specifically as follows: The sub-confidence of the stability feature : In the formula, This represents the normalized value of the stability characteristic; The sub-confidence of the threshold exceeding the feature : In the formula, This refers to the continuous sampling duration when the baseline compensation value is greater than the initial dynamic threshold or the dynamically updated initial dynamic threshold. Indicates the minimum effective duration. Indicates a threshold exceeding the feature; Sub-confidence of the trend feature :when ,and hour, ; otherwise, In the formula, The cumulative value of the first derivative of the signal. The mean of the absolute values ​​of the second derivatives; Sub-confidence of the environmental interference features : ;in, Characterized by environmental disturbances. , and when hour, .

8. The helium mass spectrometry leak rate determination method based on the fusion of confidence quantification and finite state machine control according to claim 1, characterized in that, The comprehensive confidence level is specifically as follows: ； In the formula, Indicates the overall confidence level. Sub-confidence of stability features For the sub-confidence of the threshold exceeding the feature, Sub-confidence levels for trend features Sub-confidence levels representing characteristics of environmental interference. These represent the weights of the corresponding items.

9. The helium mass spectrometry leak rate determination method based on the fusion of confidence quantification and finite state machine control according to claim 1, characterized in that, The dynamic update of the initial dynamic threshold based on the comprehensive confidence level is specifically as follows: ； In the formula, Indicates the first The initial dynamic threshold for determining the leakage rate of helium mass spectrometry. This represents the overall confidence level of the current number of judgments.

10. The helium mass spectrometry leak rate determination method based on the fusion of confidence quantification and finite state machine control according to claim 1, characterized in that, The dynamic update of the initial dynamic threshold based on the comprehensive confidence level is specifically as follows: when hour, ; when hour, ; when hour, ; In the formula, Indicates the first The initial dynamic threshold for determining the leakage rate of helium mass spectrometry. This represents the overall confidence level of the current number of judgments.

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