Penetrating pipe residual life prediction method, device, equipment and medium

Abstract

The application discloses a permeation tube residual life prediction method, device, equipment and medium, and the method comprises the following steps: collecting and recording online multi-source data in the process of using the permeation tube in real time; a multi-factor quality loss calibration model for calculating a permeation theoretical permeability is established according to factory calibration data of the permeation tube, and a real-time corrected permeability after feedback compensation is obtained according to actual concentration data of each calibration aeration and the calculated theoretical permeability; whenever a new real-time corrected permeability is obtained and the permeation tube has passed the initial stabilization stage, relevant model parameters in a power law attenuation model of the permeation tube are updated in real time online; the current residual service life of the permeation tube is calculated according to the current power law attenuation model after the relevant model parameters are updated, and the current real-time corrected permeability. The application realizes online real-time prediction of the residual life of the permeation tube, and through adaptive updating of the model, the life prediction accuracy is continuously improved over time.

Description

Technical Field

[0001] This application relates to the field of environmental monitoring technology, and in particular to methods, apparatus, equipment and media for predicting the remaining life of permeable pipes. Background Technology

[0002] Before or during use, gas analyzers require calibration with standard gases of known concentrations to ensure the accuracy and reliability of measurement results. Currently, the industry commonly uses high-pressure standard cylinder gas as the calibration gas source. However, permeation tubes are another increasingly widely used dynamic standard gas source generating device. These are sealed tubular chambers made of polymer materials (such as PTFE and FEP), containing liquid or solid standard substances (such as SO2, NO, NO2, and CO). Under constant temperature control, the standard substances permeate through the tube wall at a stable and constant rate, mixing with the carrier gas to generate a standard gas of a known concentration with precise and controllable concentration. Compared to high-pressure standard cylinder gas, permeation tubes have advantages such as small size, no need for high-pressure containers, and long-term continuous and stable operation. It is particularly important to note that as a standard gas source for measurement transmission, the permeation rate of the permeation tube will gradually shift with usage time and the depletion of the internal encapsulating material. Therefore, it is essential to replace the permeation tube promptly according to its service life. If used beyond its expiration date, deviations in the permeation rate will directly lead to inaccurate concentrations of the generated standard gas. This will not only cause systematic deviations in the calibration results of the gas analyzer, but will also further compromise the accuracy and reliability of subsequent actual sample gas test results, ultimately affecting the quality and compliance of atmospheric monitoring data.

[0003] Existing technologies for managing the service life of permeation tubes mainly include the following solutions: Option 1: Fixed-cycle replacement method based on manufacturer's warranty life Currently, mainstream commercial permeation tubing products on the market, such as those from G-Cal and Dynamical, typically have a clearly defined quality assurance lifespan, with most products having a 12-month warranty. For example, NIST's SO2 permeation tubing SRM1627 has an estimated effective lifespan of approximately 9 months under 30°C operating conditions. If not used for an extended period, storing it in a 4°C freezer can extend its lifespan.

[0004] The disadvantage of this scheme is: ① Fixed-cycle replacement cannot reflect the differences in the actual lifespan of the permeation tube under different usage conditions; ② When the temperature is well controlled, the permeation tube can actually be used continuously for several years. Replacing it at a fixed cycle causes unnecessary waste. ③ It is impossible to predict when a significant shift in permeability will occur at the end of the permeation tube's lifespan.

[0005] Option 2: Experience-based judgment method based on visual inspection The presence of visible liquid or solid standard substances inside the permeation tube is used to determine whether the tube has failed. NIST states that as long as visible liquid SO2 remains inside the tube, the calibration of the permeation tube remains valid.

[0006] The disadvantage of this scheme is: ① The equipment needs to be disassembled for visual inspection, and online automatic judgment cannot be achieved; ② For solid-phase packed permeation tubes, inspection is extremely difficult after the packing material is consumed; ③ It cannot provide quantitative predictions of remaining lifespan.

[0007] Option 3: Offline calibration method based on periodic weighing The gravimetric method involves accurately weighing the permeation tube at different points in its service life, calculating its mass loss during the corresponding time period, and then extrapolating the actual permeability. The corresponding standardized operating procedure has been incorporated into the ASTM D4298 standard. Existing research has verified that the relative standard deviation of the actual permeability of the permeation tube determined using this gravimetric method can be controlled within 2%.

[0008] The disadvantage of this scheme is: ① The equipment needs to be shut down and disassembled, and the entire weighing process must be carried out in a clean laboratory environment, which takes a long time. ②It is impossible to achieve online, real-time lifespan prediction; ③ Frequent disassembly and assembly may introduce additional contamination and seal failure risks.

[0009] Option 4: Theoretical lifespan calculation based on temperature as a single factor Existing literature provides a basic formula for calculating the lifespan of permeation tubes: Permeation tube lifespan = Effective mass inside the tube / Permeability (this formula is based on the assumption that the permeability remains constant and does not consider its decay over time). Taking acetaldehyde permeation tubes as an example, given a permeability of 10000 ng / min and a mass inside the tube of 5 g, the theoretical lifespan is approximately 347 days.

[0010] The disadvantage of this scheme is: ① This calculation is based on the assumption that the permeability remains constant, while the actual permeability will decrease over time; ② The coupled effects of multiple factors such as operating temperature, carrier gas flow rate, and ambient humidity on permeability were not considered.

[0011] In summary, current technologies lack a method for online, real-time, and dynamic prediction of the remaining service life of permeation tubes, and even more so, they lack a technical solution that combines service life prediction with automatic equipment calibration mechanisms and dynamic adjustments to calibration cycles. Crucially, existing solutions all employ offline, open-loop management methods, failing to utilize real-time monitoring data during operation to correct the prediction model, resulting in a gradual decrease in prediction accuracy over time.

[0012] The above analysis reveals a long-standing technological gap in the field: the inability to predict the remaining lifespan of permeable tubes online in real time. This gap is not due to a lack of predictive awareness among those skilled in the art, but rather to four interrelated technical obstacles in relevant application scenarios that prevent conventional lifespan prediction methods from being directly applied to permeable tubes: Obstacle 1: The remaining mass cannot be directly measured The remaining mass of the permeation tube is the most direct physical quantity for determining its remaining lifespan. However, in most practical online operation scenarios, the permeation tube is installed inside a sealed, temperature-controlled generator, making it impossible to accurately measure the remaining mass without disassembling the equipment or interrupting the normal operation of the system. Taking CEMS as an example, under normal continuous operation of the entire system, the permeation tube, which serves as the calibration gas source, is sealed inside the temperature-controlled chamber, making it impossible to directly measure its mass without disassembling the equipment or interrupting the normal monitoring process of the CEMS. Existing technologies can only obtain the remaining mass data through indirect calculation: remaining mass of the permeation tube = initial mass - cumulative permeation release mass. However, the calculation of the cumulative permeation release mass depends entirely on the time integration of the permeability—this forms a circular dependency: "using the real-time permeability to calculate the cumulative permeation release mass, then using the cumulative permeation release mass to calculate the remaining mass of the permeation tube, then using the remaining mass of the permeation tube to predict the remaining lifespan, and finally using the remaining lifespan to verify whether the currently measured permeability is within a reasonable and accurate range." The entire calculation chain lacks an independent direct measurement value as a verification benchmark, making it highly susceptible to introducing systematic biases.

[0013] Obstacle 2: Closed-loop positive feedback effect of bias accumulation When the permeability of a permeation tube decreases after long-term use (e.g., the permeability drops from 100% to 85% of the nominal value), if the decreased permeability is still used for mass integration, the following positive feedback effect will occur: Lower permeability measurements → Lower calculated permeation rate per unit time → Underestimation of cumulative permeation release mass → Higher estimated residual mass → Higher predicted remaining service life → Delayed replacement of permeation tubing by maintenance personnel → Continued use of permeation tubing, further decrease in permeability → Continued widening of deviation. Existing offline management methods (such as fixed-cycle replacement, visual inspection, and periodic weighing) do not involve data closure during operation, and therefore cannot detect and suppress the accumulation of the above deviations.

[0014] Obstacle 3: Nonlinear effects of multi-factor coupling The actual permeability of a permeation tube is affected by multiple factors, including temperature, humidity, pressure, and membrane material aging, and these factors are nonlinearly coupled. 1. Temperature has an exponential effect on permeability (approximately 1%-3% / ℃), and different membrane materials (PTFE, FEP) have different temperature coefficients; 2. Humidity has a negligible effect on the permeability of gases such as SO2 and NO, but a significant effect on polar gases such as NO2 (the permeability can increase by more than 15% when the relative humidity increases from 0% to 80%). 3. The collapse of free volume caused by membrane material aging leads to a logarithmic decrease in permeability over time, and this decrease rate is related to temperature and carrier gas composition.

[0015] Existing single-factor theoretical models (such as lifetime calculation formulas that only consider temperature) cannot handle multi-factor coupling, while offline weighting methods, although highly accurate, cannot capture dynamic changes.

[0016] Obstacle 4: Constraints on the continuity and standardization of industry operations Taking stationary pollution source CEMS equipment as an example, the system must operate continuously to meet environmental regulatory requirements and cannot be shut down for calibrating permeation tubes. Furthermore, standards such as HJ75-2017 impose strict limits on calibration errors (relative error not exceeding ±10%). This means that any online prediction method must: 1. Ensure uninterrupted normal monitoring operation of the CEMS; 2. The prediction accuracy must meet the standard requirements (especially at the end of the lifespan, there should be no data exceeding the standard due to prediction inaccuracies). 3. The computational resource usage must be adapted to the limited capabilities of the embedded control system.

[0017] The four obstacles mentioned above are intertwined, making it impossible to simply apply the remaining life prediction methods used for other industrial equipment. Therefore, a systematic solution is needed to address these specific obstacles. Summary of the Invention

[0018] This application provides a method for predicting the remaining life of permeation tubes, which solves the technical problem that existing technologies cannot accurately predict the remaining life of permeation tubes online, in real time, dynamically, and adaptively.

[0019] This application is achieved through the following solution: The method for predicting the remaining life of permeable tubes includes the following steps: Real-time acquisition and recording of online multi-source data during the commissioning of the permeation tube, including constant temperature chamber temperature, carrier gas flow rate, ambient relative humidity, ambient pressure, and the actual standard gas concentration measured by the analyzer during each calibration; A multi-factor mass loss calibration model for calculating the theoretical permeability of the permeation tube is established based on the factory calibration data of the permeation tube. The real-time corrected permeability after feedback compensation is obtained based on the actual concentration data of each calibration ventilation and the calculated theoretical permeability. Whenever a new real-time corrected permeability is obtained and the permeation tube has passed the initial stabilization stage, the relevant model parameters in the power-law decay model of the permeation tube are updated online in real time. The current remaining service life of the permeation tube is calculated based on the updated power-law decay model with relevant model parameters and the current real-time corrected permeability.

[0020] Furthermore, the step of establishing a multi-factor mass loss calibration model for calculating the theoretical permeability of the permeable tube based on the factory calibration data of the permeable tube, and obtaining the real-time corrected permeability after feedback compensation based on the actual concentration data of each calibration ventilation and the calculated theoretical permeability, specifically includes the following steps: Read the factory calibration data of the permeate tube, including the temperature-permeability curve, the permeability correction curve of the permeate tube under different humidity conditions, the membrane material type, and the initial mass. Nominal penetration rate Material temperature coefficient Equilibrium time constant Actual operating temperature Measured absolute temperature Nominal temperature Decay index n The initial value; A multi-factor mass loss calibration model was established, taking into account factors such as temperature, humidity, pressure, and material aging, to calculate the current theoretical permeability of the permeation tube. ; In the formula: The theoretical permeability of the permeation tube at time t (ng / min); Nominal penetration rate (under standard conditions) (In dry air, ng / min) The measured operating temperature (°C) at time t; The nominal temperature (usually 25℃); The temperature coefficient of the material is determined based on the material of the permeation tube. For PTFE, a = 0.025 / ℃; for FEP, a = 0.030 / ℃. Here is the humidity correction factor, which is dimensionless, where: For non-humidity-sensitive gases such as SO2 and NO, =1.0; For humidity-sensitive gases such as NO2, a piecewise linear formula is used: ; in, This represents the percentage of relative humidity. The pressure correction factor is dimensionless and is formulated as follows: ; in, =0.01 is the pressure influence coefficient, which is dimensionless. The measured ambient atmospheric pressure (kPa); The aging correction factor for the membrane material is dimensionless and is formulated as follows: ; in, This represents the cumulative usage time (days). =30 days The aging coefficient is dimensionless; PTFE material. =0.025, FEP material =0.030; Convert theoretical permeability to theoretical standard gas concentration: ; in, Let t be the theoretical standard gas concentration (ppm). The measured absolute temperature (K) of the isothermal chamber in the permeation tube at time t. Let t be the total carrier gas flow rate (L / min) at time t. Let t be the ambient atmospheric pressure (kPa), M be the molar mass of the standard gas in the permeation tube (g / mol), and R be 8.314, which is the ideal gas constant (L·kPa / (mol·K)). The conversion factor is ppm, dimensionless. The unit conversion factor converts the permeability from ng / min to g / min. During each calibration, the system records the actual gas concentration measured by the analyzer. and compared with theoretical standard gas concentration Compare and calculate the concentration deviation factor: ; in, Let be the concentration deviation factor at time t, which is dimensionless. The concentration (ppm) actually measured by the analyzer at time t. like When the theoretical penetration rate is exceeded, feedback compensation is applied to obtain a real-time corrected penetration rate: ; in, The real-time corrected permeability (ng / min) at time t after feedback compensation. β To correct the strength coefficient.

[0021] Furthermore, whenever a new real-time corrected permeability is obtained and the permeation tube has passed the initial stabilization stage, the relevant model parameters in the power-law decay model of the permeation tube are updated online in real time, specifically including the following steps: Whenever a new real-time corrected permeability is obtained and the permeation tube has passed the initial stabilization phase, the decay model is updated using the real-time corrected permeability: ; For the power-law decay model: ; This is a linear regression problem, defined as follows: Observations: ; Time feature vector: ; Parameter vector: ; in, The forgetting factor has a value of 0.95 to 0.99. This represents the optimal estimate of the decay model parameters at time t; For the first i The real-time corrected permeability (ng / min) obtained during the second calibration. This is the decay index.

[0022] Furthermore, whenever a new real-time corrected permeability is obtained and the permeation tube has passed the initial stabilization stage, the update trigger condition for updating the decay model using the real-time corrected permeability is as follows: A batch fitting is automatically performed after every N calibrations are completed. If the prediction error of the remaining service life exceeds a set threshold for M consecutive times, an update is immediately triggered, and the power-law decay model of the permeation tube is updated in real time. <N。

[0023] Furthermore, based on the updated power-law decay model with relevant model parameters and the current real-time corrected permeability, the current remaining service life of the permeation tube is calculated, specifically including the following steps: The cumulative permeate release mass is obtained by integrating the real-time corrected permeability: ; Calculate the remaining mass based on the cumulative permeate release mass and the initial total mass of the permeate tube: ; The remaining useful life is calculated based on the remaining mass and real-time corrected permeability. Down: ; In the formula: for t The estimated remaining useful life (days) at any given time; for t The remaining mass (ng) in the tube calculated at any time. The initial total mass of the permeation tube (ng); The total mass (ng) released up to time t; The failure threshold mass (in ng) is the critical point at which the mass of the packing material inside the tube falls below this value. for t Real-time corrected penetration rate (ng / min) at any given moment; Time unit conversion factor (days / minutes); For safety reasons, the aging of the permeation pipe material and environmental fluctuations are taken into account.

[0024] Furthermore, it also includes the following steps: When the current remaining service life of the permeation tube and the relative deviation between the current permeability and the nominal permeability meet the set conditions, corresponding graded early warning information is generated and sent.

[0025] Furthermore, when the current remaining service life of the permeation tube and the relative deviation between the current permeability and the nominal permeability meet the set conditions, a corresponding early warning message is generated and sent, specifically including the following steps: Real-time determination of whether any triggering condition is met: Condition A: Calculate the relative deviation of permeability at time t: ; in, fort Real-time corrected permeability, in ng / min; (Nominal permeability, unit: ng / min) When An alert is triggered when the value is greater than or equal to the set value. Condition B: If the current remaining useful life A warning will be triggered at a preset time. Condition C: An alert is triggered when the cumulative usage time exceeds the factory warranty life (e.g., 12 months) or the remaining mass calculated through quality loss is lower than the set percentage of the initial total mass. Condition D: Permeability deviation rate after M consecutive calibrations The rate of change exceeds the set value per week; When any triggering condition is met, a corresponding early warning message is generated and sent, and the early warning time, triggering condition, and related data are automatically recorded.

[0026] Furthermore, when generating and sending corresponding early warning information, the early warning information is pre-set with multiple early warning thresholds according to the degree of urgency, thereby achieving tiered early warning. Specifically, when... A "Caution" warning is triggered in advance when the remaining mass is greater than or equal to the set value; a "Replacement Recommended" warning is triggered in advance when the remaining mass is less than or equal to the set percentage of the initial total mass; the current remaining service life... < Trigger an "emergency replacement" warning in advance at the preset time.

[0027] Furthermore, it also includes the following steps: The relative deviation of permeability at time t is calculated. Automatically adjust calibration frequency: When K is ≥, the calibration cycle is adjusted from once a week to H times a week. <H<7; When Z ≥ Z, the calibration cycle is adjusted from once a week to once a day. <Z; After replacing the permeation tube, the automatic re-establishment of the regular calibration cycle is once a week.

[0028] Furthermore, it also includes the following steps: To extend the life of the permeation tube, implement one or more of the following measures based on its current remaining service life: The temperature of the constant temperature chamber of the permeation tube is controlled within the set value ±0.1℃ range; When the equipment is shut down for a long time, it automatically lowers the temperature of the constant temperature chamber of the permeation tube to 4°C in a low-temperature storage mode, minimizing the life loss of the permeation tube when it is not in use. When using, keep the moisture content of the humidity-sensitive carrier gas and diluent gas within the set range.

[0029] This application also provides a device for predicting the remaining life of a permeation tube, including: The real-time data acquisition and recording module is used to acquire and record online multi-source data during the use of the permeation tube, including the temperature of the constant temperature chamber, the carrier gas flow rate, the relative humidity of the environment, the environmental pressure, and the actual standard gas concentration measured by the analyzer during each calibration. The real-time permeability correction module is used to establish a multi-factor mass loss calibration model for calculating the theoretical permeability of the permeable tube based on the factory calibration data of the permeable tube. It obtains the real-time corrected permeability after feedback compensation based on the actual concentration data of each calibration ventilation and the calculated theoretical permeability. The real-time decay model update module is used to update the relevant model parameters in the power-law decay model of the permeation tube online in real time whenever a new real-time corrected permeability is obtained and the permeation tube has passed the initial stabilization stage. The remaining service life calculation module is used to calculate the current remaining service life of the permeation tube based on the updated power-law decay model and the current real-time corrected permeability.

[0030] This application also provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the permeation tube remaining life prediction method.

[0031] This application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the method for predicting the remaining life of the permeation tube.

[0032] This application also provides a computer program product, including a computer program or computer-executable instructions, which, when executed by a processor, implement the method for predicting the remaining life of the permeation tube.

[0033] Compared with the prior art, this application can produce the following beneficial effects: (1) For the first time, online real-time prediction of the remaining service life of the permeation tube was achieved: This application allows for the dynamic acquisition of remaining service life information of the permeation tube without disassembling the equipment or affecting its normal operation. Compared to the offline gravimetric method (which requires shutdown, disassembly, and laboratory weighing), this application upgrades service life assessment from "post-event detection" to "real-time monitoring," ensuring real-time monitoring and reducing monitoring costs. (2) Prediction accuracy has been significantly improved: This application adopts online updating of attenuation model parameters, enabling the prediction model to adaptively adjust to follow the actual attenuation trajectory of the permeation pipe, which is more in line with real working conditions. Compared with the fixed cycle method, it extends the actual service life of the permeation pipe by about 33% and reduces the annual operation and maintenance cost by about 25%. Compared with the single-factor theoretical model, the prediction error is reduced from 153 days to 15 days, and the error rate is reduced by more than 90%.

[0034] (3) Adaptive model updates lead to continuously improving prediction accuracy over time: Because this application adopts an online update method, the attenuation model parameters can be continuously optimized as calibration data accumulates, realizing online adaptive calibration of the prediction model. This allows the prediction accuracy to be continuously optimized and improved over time, with higher accuracy the longer it is used, thus solving the problem that the prediction error of existing single-factor theoretical models continuously increases with the time of use.

[0035] In addition to the purposes, features, and advantages described above, this application has other purposes, features, and advantages. These will be further described in detail below with reference to figures. Attached Figure Description

[0036] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0037] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 This is a flowchart illustrating the method for predicting the remaining life of a permeation tube according to the first preferred embodiment of this application. Figure 2 This is a flowchart illustrating the method for predicting the remaining life of a permeation tube according to the second preferred embodiment of this application. Figure 3 This is a schematic diagram of a multi-threshold graded early warning mechanism for the remaining life of the permeation tube; Figure 4 This is a flowchart illustrating the method for predicting the remaining life of a permeation tube according to the third preferred embodiment of this application. Figure 5 This is a flowchart illustrating the method for predicting the remaining life of a permeation tube according to the fourth preferred embodiment of this application. Figure 6 This is a schematic diagram of the module of the permeation tube remaining life prediction device according to a preferred embodiment of this application; Figure 7 This is a schematic block diagram of an electronic device according to a preferred embodiment of this application; Figure 8 This is a schematic diagram of the internal structure of a computer device according to a preferred embodiment of this application. Detailed Implementation

[0038] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0039] It should be noted that the execution subject of this embodiment can be a computing service system with data processing, network communication and program running functions, such as a tablet computer, personal computer, mobile phone, etc., or a permeable tube remaining life prediction device that can realize the above functions. The following uses the permeable tube remaining life prediction device as the execution subject to describe this embodiment and the following embodiments.

[0040] like Figure 1 As shown, the first preferred embodiment of this application provides a method for predicting the remaining life of a permeable tube, including the following steps: S1. Real-time acquisition and recording of online multi-source data during the use of the permeation tube, including constant temperature chamber temperature, carrier gas flow rate, ambient relative humidity, ambient pressure, and the actual standard gas concentration measured by the analyzer during each calibration. S2. Establish a multi-factor mass loss calibration model for calculating the theoretical permeability of the permeation tube based on the factory calibration data of the permeation tube. Obtain the real-time corrected permeability after feedback compensation based on the actual concentration data of each calibration ventilation and the calculated theoretical permeability. S3. Whenever a new real-time corrected permeability is obtained and the permeation tube has passed the initial stabilization stage, the relevant model parameters in the power-law decay model of the permeation tube are updated online in real time. S4. Calculate the current remaining service life of the permeation tube based on the updated power-law decay model and the current real-time corrected permeability, according to the relevant model parameters.

[0041] In a preferred embodiment of this application, the step of establishing a multi-factor mass loss calibration model for calculating the theoretical permeability of the permeation tube based on the factory calibration data of the permeation tube, and obtaining the real-time corrected permeability after feedback compensation based on the actual concentration data of each calibration ventilation and the calculated theoretical permeability, specifically includes the following steps: S21. Read the factory calibration data of the permeate tube, including the temperature-permeability curve, the permeability correction curve of the permeate tube under different humidity conditions, the membrane material type, and the initial mass. Nominal penetration rate Material temperature coefficient Equilibrium time constant Actual operating temperature Measured absolute temperature Nominal temperature Decay index n The initial value of n is 0 < n < 0.3; S22. The effect of ambient temperature on permeability is exponential. The permeability of the polymer material in the permeation pipe changes by approximately 1%-3% per °C. To ensure the accuracy of the lifespan calculation, a multi-factor mass loss calibration model is established, taking into account factors such as temperature, humidity, pressure, and material aging, to calculate the current theoretical permeability of the permeation pipe. ; This model is based on the principles of permeability physics and material properties, and belongs to the combination of permeability influencing factors known in the field. However, this application parameterizes it and uses it for online calculation, where: The theoretical permeability of the permeation tube at time t (ng / min); Nominal penetration rate (under standard conditions) (In dry air, ng / min) The measured operating temperature (°C) at time t; The nominal temperature (usually 25℃); The temperature coefficient of the material is determined based on the material of the permeation tube. For PTFE, a = 0.025 / ℃; for FEP, a = 0.030 / ℃. Here is the humidity correction factor, which is dimensionless, where: For non-humidity-sensitive gases such as SO2 and NO, =1.0; For humidity-sensitive gases such as NO2, a piecewise linear formula is used: ; in, This represents the percentage of relative humidity. The pressure correction factor is dimensionless and is formulated as follows: ; in, =0.01 is the pressure influence coefficient, which is dimensionless. The measured ambient atmospheric pressure (kPa); The aging correction factor for the membrane material is dimensionless and is formulated as follows: ; in, This represents the cumulative usage time (days). =30 days The aging coefficient is dimensionless; PTFE material. =0.025, FEP material =0.030; S23. Convert theoretical permeability to theoretical standard gas concentration: ; in, Let t be the theoretical standard gas concentration (ppm). The measured absolute temperature (K) of the isothermal chamber in the permeation tube at time t. Let t be the total carrier gas flow rate (L / min) at time t. Let t be the ambient atmospheric pressure (kPa), M be the molar mass of the standard gas in the permeation tube (g / mol), and R be 8.314, which is the ideal gas constant (L·kPa / (mol·K)). The conversion factor is ppm, dimensionless. The unit conversion factor converts the permeability from ng / min to g / min. S24. During each calibration, the system records the actual gas concentration measured by the analyzer. and compared with theoretical standard gas concentration Compare and calculate the concentration deviation factor: ; in, Let be the concentration deviation factor at time t, which is dimensionless. The concentration (ppm) actually measured by the analyzer at time t. S25, if When the value exceeds a preset threshold (e.g., ±3%), it indicates a systematic bias in the theoretical penetration rate model. Therefore, feedback compensation is applied to the theoretical penetration rate to obtain a real-time corrected penetration rate. ; in, The real-time corrected permeability (ng / min) at time t after feedback compensation. β To correct for the intensity coefficient, a value ranging from 0.5 to 0.8, dimensionless, is used to prevent overshoot. The corrected permeability is then calculated. The alternative theoretical penetration rate value will be used for subsequent mass integral and lifetime prediction.

[0042] This embodiment uses a closed-loop logic of "measurement → prediction → comparison → correction" to reverse correct the theoretical permeability using the actual concentration data from each calibration, eliminating the accumulation of biases caused by indirect calculation methods. This can eliminate model biases in real time and ensure the accuracy of subsequent rolling predictions of remaining lifetime.

[0043] In a preferred embodiment of this application, whenever a new real-time corrected permeability is obtained and the permeation tube has passed the initial stabilization stage, the relevant model parameters in the power-law decay model of the permeation tube are updated online in real time, specifically including the following steps: S31. Whenever a new real-time corrected permeability is obtained and the permeation tube has passed the initial stabilization stage, the decay model is updated using the real-time corrected permeability: ; For the power-law decay model: ; This is a linear regression problem, defined as follows: Observations: ; Time feature vector: ; Parameter vector: ; in, The forgetting factor has a value of 0.95 to 0.99. This represents the optimal estimate of the decay model parameters at time t; For the first i The real-time corrected permeability (ng / min) obtained during the second calibration; This is the decay index.

[0044] The attenuation excitation of the permeation tube has three stages, including: Phase 1: Exponential Approximation Model The permeation tube requires a certain equilibration time when first put into use. Studies have shown that the permeability of the permeation tube stabilizes after about 40 days in a gas generator at 50°C, and the permeability verification curve tends to agree with the gravimetric measurement value after about 210 days. This stage uses an exponential approximation model: initial stabilization stage ( ≤ ): ; In the formula, The permeability (ng / min) output by the model describes the long-term decline trend of permeability starting from the steady period. τ (unit: days) is the equilibrium time constant of the permeation pipe material, typically 20–50 days. This represents the current cumulative usage time (days). The first time the permeation tube is put into use (days, set to 0). The stable permeability after equilibrium is (ng / min).

[0045] Phase Two: Power-Law Decay Model During the stable service period, the permeability decreases slowly as the mass of the filler decreases, which can be represented by the following segmented model: (1) Linear decay approximation model (suitable for short-term forecasting): stable service period ( ): ; In the formula, k is the linear decay rate (ng / min / day). Initial balance completion time (configurable). In order to be in Actual penetration rate at any given time (ng / min).

[0046] (2) Power-law decay model (suitable for long-term accurate prediction): ; In the formula, n is the decay index of the permeation pipe material (0 < n < 0.3, this index is not a fixed value, but is obtained by dynamic fitting through online monitoring data). To reach a stable working state at a certain point in time, In order to be in Actual penetration rate at any given time (ng / min).

[0047] This embodiment uses only the power-law decay model for long-term accurate prediction. The linear decay approximation model is only used to illustrate short-term trends or for comparison, and is not used as the main model for lifetime prediction.

[0048] Phase 3: Weighted Recursive Least Squares Online Update (Real-time Update). In this phase, when the permeability changes, the power-law decay model is updated in real time according to step S31, using the corrected permeability. Subsequently, based on the updated power-law decay model and the corrected penetration rate... The corresponding remaining useful life is calculated, thus linking the updated power-law decay model with the remaining useful life. In summary, the power-law decay model is updated in real time whenever the permeability changes, and the remaining useful life of the permeable pipe is calculated based on the updated power-law decay model. This allows the prediction model to adaptively adjust to follow the actual decay trajectory of the permeable pipe, better reflecting real operating conditions, thereby improving the accuracy and precision of the remaining useful life calculation results.

[0049] In a preferred embodiment of this application, whenever a new real-time corrected permeability is obtained and the permeation tube has passed the initial stabilization stage, the update trigger condition for updating the decay model using the real-time corrected permeability is as follows: A batch fitting is automatically performed after every N calibrations are completed, where N can be 10 to 30. If the prediction error of the remaining service life exceeds a set threshold (e.g., 5%) for M consecutive times (e.g., 3 times), an update is immediately triggered, and the power-law decay model of the permeation tube is updated in real time. <N。

[0050] This embodiment sets up a sliding window of length N (taking CEMS as an example, N=10~30, corresponding to approximately 3~8 months of CEMS calibration data). Through this update triggering mechanism, this embodiment ensures that the prediction model can adaptively correlate the actual decay trajectory of the permeation tube, ensuring that the remaining lifetime prediction accuracy continuously improves with usage time. This solves the problem that the prediction error of existing single-factor theoretical models continuously increases with usage time. At the same time, by setting reasonable update triggering conditions, it ensures that the timing and frequency of updates are more scientific and reasonable, avoiding the problems of increased prediction costs caused by excessively frequent updates or large prediction error caused by insufficient update frequency.

[0051] In a preferred embodiment of this application, the current remaining service life of the permeation tube is calculated based on the updated power-law decay model with relevant model parameters and the current real-time corrected permeability. The specific steps include: S41. The cumulative permeation release mass is obtained from the real-time corrected permeability integral: ; S42. Calculate the remaining mass based on the cumulative permeate release mass and the initial total mass of the permeate tube: ; S43. Calculate the remaining useful life based on the remaining mass and real-time corrected permeability. Down: ; In the formula: for t The estimated remaining useful life (days) at any given time; for t The remaining mass (ng) in the tube calculated at any time. The initial total mass of the permeation tube (ng); The cumulative mass (ng) released via permeation up to time t; The failure threshold mass (in ng) is the critical point at which the mass of the packing material inside the tube falls below this value. for t Real-time corrected penetration rate (ng / min) at any given moment; Time unit conversion factor (days / minutes); For safety factors, a value of 0.7 to 0.9 is used to comprehensively consider the aging of the permeation pipe material and environmental fluctuations.

[0052] Based on the physical structure of the permeation tube, its remaining mass and permeability decay exhibit a strong correlation. This embodiment establishes a prediction formula of "remaining mass → permeability → remaining lifetime". When calculating the remaining lifetime, the calculated remaining mass and real-time corrected permeability are introduced. The remaining mass also takes into account the influence of the real-time corrected permeability. Therefore, when calculating the remaining lifetime, this embodiment comprehensively correlates the updated power-law decay model and the real-time corrected permeability, enabling the prediction model to adaptively adjust to follow the actual decay trajectory of the permeation tube, making the prediction results more consistent with the actual operating conditions. At the same time, the decay model parameters can be continuously optimized with the accumulation of calibration data, so the prediction accuracy is continuously optimized and improved over time. The longer it is used, the higher the accuracy becomes, solving the problem that the prediction error of the existing single-factor theoretical model continuously increases with the use time.

[0053] like Figure 2 As shown, in a preferred embodiment of this application, the method for predicting the remaining life of the permeation tube further includes the following steps: S5. When the current remaining service life of the permeation tube and the relative deviation between the current permeability and the nominal permeability meet the set conditions, generate and send corresponding graded early warning information.

[0054] Specifically, when the current remaining service life of the permeation tube and the relative deviation between the current permeability and the nominal permeability meet the set conditions, a corresponding early warning message is generated and sent. The specific steps include: S51. Real-time determination of whether any triggering condition is met: Condition A: Calculate the relative deviation of permeability at time t: ; in, for t Real-time corrected permeability, in ng / min; (Nominal permeability, unit: ng / min), when δ t An alert is triggered when the value is greater than or equal to a set value. Taking CEMS as an example, the permeation tube failure threshold is set to the permeability deviation rate δ. t ≥15%, this threshold takes into account the superposition of the attenuation tolerance of the permeation tube itself (≤5%) and the CEMS calibration tolerance (≤10%), when δ t When the error is ≥15%, the overall calibration error will exceed ±15%, which may lead to the monitoring data exceeding the standard.

[0055] Condition B: If the current remaining useful life An alert will be triggered when the time is less than a preset period (e.g., 30 days). Condition C: An alert is triggered when the cumulative usage time exceeds the factory warranty life (e.g., 12 months) or the remaining mass calculated through quality loss is less than a set percentage (e.g., 20% of the initial total mass). Condition D: Permeability deviation rate after M consecutive calibrations (e.g., 3 times) The rate of change exceeds the set value per week (e.g., the rate of change exceeds 5% per week). S52. When any triggering condition is met, a corresponding warning message is generated and sent through the device display screen, remote operation and maintenance platform, etc., and the warning time, triggering condition and related data are automatically recorded.

[0056] Specifically, such as Figure 3 As shown, when generating and sending corresponding early warning information, the early warning information is pre-set with multiple early warning thresholds according to the degree of urgency, thereby achieving tiered early warning. Specifically, when... A "Caution" warning will be triggered 45 days in advance when the remaining mass is ≥15%; a "Replacement Recommended" warning will be triggered 30 days in advance when the remaining mass is lower than the set percentage of the initial total mass; current remaining service life. When the replacement period is less than 30 days, an "emergency replacement" warning will be triggered 15 days in advance to achieve tiered warnings, which will facilitate on-site maintenance personnel to arrange spare parts procurement and on-site maintenance plans.

[0057] This embodiment avoids data quality risks associated with continued use of permeability exceeding the acceptable range through multi-threshold tiered early warning and dynamic linkage with calibration cycles, while also preventing waste caused by premature replacement. Maintenance personnel can rationally arrange spare parts procurement and on-site maintenance based on the early warning level, reducing unplanned downtime. Furthermore, this embodiment achieves full lifecycle data traceability: it automatically records data throughout the entire process of the permeation tube from commissioning to replacement (temperature history, flow history, permeability deviation for each calibration, model parameter change trajectory, early warning trigger records, etc.), providing a basis for equipment management and quality traceability.

[0058] like Figure 4 As shown, in a preferred embodiment of this application, the method for predicting the remaining life of the permeation tube further includes the following steps: S6. Calculate the relative permeability deviation δ at time t. t Automatically adjust calibration frequency: A、δ t When the calibration rate is ≥8%, the calibration cycle will be adjusted from once a week to H times a week (e.g., once every 3 days). B、δ t When the value is ≥12%, the calibration cycle will be adjusted from once a week to once a day. C. After replacing the permeation tube, the automatic re-establishment of the regular calibration cycle is once a week.

[0059] This embodiment calculates the relative permeability deviation δ at time t. t The calibration frequency is automatically adjusted, no longer following a fixed, conventional calibration cycle, thereby ensuring the validity of end-of-life data.

[0060] like Figure 5 As shown, in a preferred embodiment of this application, the method for predicting the remaining life of the permeation tube further includes the following steps: S7. Based on the current remaining service life, implement one or more of the following measures to extend the service life of the permeation tube: A. High-precision constant temperature control: The temperature of the constant temperature chamber of the permeation tube is controlled within the range of ±0.1℃ of the set value. For example, a semiconductor cooling chip is used to achieve precise temperature control, avoiding permeability drift and thermal stress fatigue of membrane materials caused by temperature fluctuations. B. Storage method: When the equipment is shut down for a long time, the temperature of the constant temperature chamber of the permeation tube is automatically reduced to 4°C in a low-temperature storage mode to minimize the life loss of the permeation tube when it is not in use. C. Moisture protection: During use, keep the moisture content of the humidity-sensitive carrier gas and dilution gas within the set range.

[0061] This embodiment implements corresponding measures based on the accurately predicted remaining service life, such as high-precision constant temperature control, storage methods, and moisture protection, thereby maximizing the service life of the permeation tube and reducing monitoring costs.

[0062] In summary, compared with the prior art, this application has the following beneficial effects: (1) For the first time, online real-time prediction of the remaining life of the permeation tube was achieved. This method allows for the dynamic acquisition of remaining lifespan information for permeation tubes without disassembling the equipment or affecting the normal operation of the CEMS. Compared to the offline gravimetric method (which requires shutdown, disassembly, and laboratory weighing), this application elevates lifespan assessment from "post-event detection" to "real-time monitoring."

[0063] (2) Prediction accuracy is significantly improved Weighted recursive least squares online updating of attenuation model parameters is employed, enabling the prediction model to adaptively follow the actual attenuation trajectory of the permeation tube. Taking a certain type of SO2 permeation tube as an example (initial mass 5000mg, nominal permeability 10000ng / min, initial temperature set at 25℃, flow rate 1L / min, CEMS calibrated weekly), the simulation comparison results are shown in Table 1: Table 1 Compared with the fixed-cycle method, this application extends the actual service life of the permeation pipe by about 33% and reduces the annual operation and maintenance cost by about 25%. Compared with the single-factor theoretical model, the prediction error is reduced from 153 days to 15 days, and the error rate is reduced by more than 90%.

[0064] (3) Proactive early warning and hierarchical management By employing multi-threshold tiered early warning systems and dynamic linkage with calibration cycles, the system avoids data quality risks associated with continued use of data exceeding acceptable penetration rates, while also preventing waste caused by premature replacements. Maintenance personnel can rationally plan spare parts procurement and on-site maintenance based on the early warning level, reducing unplanned downtime.

[0065] (4) Adaptive model update, prediction accuracy improves over time Because of the adoption of recursive least squares online updates with a forgetting factor, the model parameters are continuously optimized as calibration data accumulates. Simulations show that this application enters a convergent state after the 6th calibration (approximately day 42), and thereafter the prediction error stabilizes within ±25 days; after the 20th calibration (approximately day 140), the error shrinks to ±10 days. In contrast, the prediction error of the single-factor theoretical model continues to increase with usage time, reaching -120 days by week 50 (prediction lifetime is too long).

[0066] (5) Full lifecycle data traceability The system automatically records data from the entire process of permeation tubes from commissioning to replacement (temperature history, flow history, permeability deviation for each calibration, model parameter change trajectory, early warning trigger records, etc.), providing a basis for equipment management and quality traceability.

[0067] like Figure 6 As shown, another preferred embodiment of this application also provides a device for predicting the remaining life of a permeation tube, comprising: The real-time data acquisition and recording module is used to acquire and record online multi-source data during the use of the permeation tube, including the temperature of the constant temperature chamber, the carrier gas flow rate, the relative humidity of the environment, the environmental pressure, and the actual standard gas concentration measured by the analyzer during each calibration. The real-time permeability correction module is used to establish a multi-factor mass loss calibration model for calculating the theoretical permeability of the permeable tube based on the factory calibration data of the permeable tube. It obtains the real-time corrected permeability after feedback compensation based on the actual concentration data of each calibration ventilation and the calculated theoretical permeability. The real-time decay model update module is used to update the relevant model parameters in the power-law decay model of the permeation tube online in real time using the weighted recursive least squares method whenever a new real-time corrected permeability is obtained and the permeation tube has passed the initial stabilization stage. The remaining service life calculation module is used to calculate the current remaining service life of the permeation tube based on the updated power-law decay model and the current real-time corrected permeability.

[0068] The permeable tube remaining life prediction device provided in this embodiment adopts the permeable tube remaining life prediction method in the above embodiments, solving the technical problem that the prior art cannot achieve accurate prediction of the remaining life of permeable tubes online, in real time, dynamically, and adaptively. Compared with the prior art, the beneficial effects of the permeable tube remaining life prediction device provided in this embodiment are the same as those of the permeable tube remaining life prediction method provided in the above embodiments, and other technical features in the permeable tube remaining life prediction device are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0069] like Figure 7 As shown, a preferred embodiment of this embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the steps of the permeable tube remaining life prediction method in the above embodiment.

[0070] This embodiment also provides an electronic device that uses the permeable tube remaining life prediction method in the above embodiments to solve the technical problem that the prior art cannot accurately predict the remaining life of permeable tubes online, in real time, dynamically, and adaptively. Compared with the prior art, the beneficial effects of the electronic device provided in this embodiment are the same as those of the permeable tube remaining life prediction method provided in the above embodiments, and other technical features in the electronic device are the same as those disclosed in the method of the above embodiments, which will not be repeated here.

[0071] like Figure 8 As shown, a preferred embodiment of this application also provides a computer device, which may be a terminal or a liveness detection server, and its internal structure diagram may be as follows. Figure 8 As shown. The computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface is used to communicate with other external computer devices via a network connection. When the computer program is executed by the processor, it implements the steps of the aforementioned method for predicting the remaining life of the permeation tube.

[0072] Those skilled in the art will understand that Figure 8 The structure shown is merely a block diagram of a portion of the structure related to the solution of this embodiment, and does not constitute a limitation on the computer device to which the solution of this embodiment is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0073] The computer device provided in this embodiment adopts the permeable tube remaining life prediction method in the above embodiment to solve the technical problem that the prior art cannot accurately predict the remaining life of permeable tubes online, in real time, dynamically and adaptively. Compared with the prior art, the beneficial effects of the computer device provided in this embodiment are the same as the beneficial effects of the permeable tube remaining life prediction method provided in the above embodiment. Moreover, other technical features in the electronic device are the same as the features disclosed in the method of the above embodiment, and will not be described in detail here.

[0074] A preferred embodiment of this application also provides a storage medium including a stored program that, when the program is executed, controls the device containing the storage medium to perform the steps of the permeation tube remaining life prediction method in the above embodiments.

[0075] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0076] If the functions described in this embodiment are implemented as software functional units and sold or used as independent products, they can be stored in one or more computing device-readable storage media. Based on this understanding, the parts of this embodiment that contribute to the prior art or the technical solution can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a computing device (which may be a personal computer, server, mobile computing device, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this embodiment. The aforementioned storage media include: USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, optical disks, and other media capable of storing program code.

[0077] Those skilled in the art will understand that the embodiments of this example can be provided as methods, systems, or computer program products. Therefore, this example can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this example can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code. The solutions in this example can be implemented using various computer languages, such as the object-oriented programming language C++ and the embedded programming language C.

[0078] This embodiment is described with reference to flowchart illustrations and / or block diagrams of the method, apparatus (system), and computer program product according to this embodiment. It should be understood that each block of the flowchart illustrations and / or block diagrams, as well as 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 system that specifies functions in one or more boxes.

[0079] 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 an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0080] 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.

[0081] This embodiment also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the permeable tube remaining life prediction method as described above.

[0082] The computer program product provided in this embodiment solves the technical problem that existing technologies cannot accurately predict the remaining life of permeable tubes online, in real time, dynamically, and adaptively. Compared with the prior art, the beneficial effects of the computer program product provided in this embodiment are the same as those of the permeable tube remaining life prediction method provided in the above embodiments, and will not be repeated here.

[0083] Obviously, those skilled in the art can make various modifications and variations to this embodiment without departing from the spirit and scope of this embodiment. Therefore, if these modifications and variations of this embodiment fall within the scope of the claims of this embodiment and their equivalents, this embodiment is also intended to include these modifications and variations.

Claims

1. A method for predicting the remaining life of a permeable tube, characterized in that, Includes the following steps: Real-time acquisition and recording of online multi-source data during the commissioning of the permeation tube, including constant temperature chamber temperature, carrier gas flow rate, ambient relative humidity, ambient pressure, and the actual standard gas concentration measured by the analyzer during each calibration; A multi-factor mass loss calibration model for calculating the theoretical permeability of the permeation tube is established based on the factory calibration data of the permeation tube. The real-time corrected permeability after feedback compensation is obtained based on the actual concentration data of each calibration ventilation and the calculated theoretical permeability. Whenever a new real-time corrected permeability is obtained and the permeation tube has passed the initial stabilization stage, the relevant model parameters in the power-law decay model of the permeation tube are updated online in real time. The current remaining service life of the permeation tube is calculated based on the updated power-law decay model with relevant model parameters and the current real-time corrected permeability.

2. The method for predicting the remaining life of a permeation tube according to claim 1, characterized in that, The process involves establishing a multi-factor mass loss calibration model for calculating the theoretical permeability of the permeable tube based on its factory calibration data. This model, along with the real-time corrected permeability after feedback compensation, is derived from the actual concentration data of each calibration ventilation and the calculated theoretical permeability. The specific steps include: Read the factory calibration data of the permeate tube, including the temperature-permeability curve, the permeability correction curve of the permeate tube under different humidity conditions, the membrane material type, and the initial mass. Nominal penetration rate Material temperature coefficient Equilibrium time constant Actual operating temperature Measured absolute temperature Nominal temperature Decay index n Initial value; A multi-factor mass loss calibration model was established, taking into account factors such as temperature, humidity, pressure, and material aging, to calculate the current theoretical permeability of the permeation tube. ； In the formula: Let be the theoretical permeability of the permeation tube at time t; Nominal penetration rate; The measured operating temperature at time t; Nominal temperature; This is the material temperature coefficient, and its value is determined based on the different permeation pipe materials. Here is the humidity correction factor, which is dimensionless, where: For non-humidity-sensitive gases, =1.0; For humidity-sensitive gases, a piecewise linear formula is used: ； in, It represents the percentage of relative humidity. The pressure correction factor is dimensionless and is formulated as follows: ; in, =0.01 is the pressure influence coefficient, which is dimensionless. For actual measured ambient atmospheric pressure; The aging correction factor for the membrane material is dimensionless and is expressed by the following formula: ; in, This represents the cumulative usage time (days). =30 days The aging coefficient is dimensionless. Convert theoretical permeability to theoretical standard gas concentration: ; in, Let be the theoretical standard gas concentration at time t. The measured absolute temperature of the isothermal chamber of the permeation tube at time t. Let t be the total carrier gas flow rate. Let be the ambient atmospheric pressure at time t, M be the molar mass of the standard gas in the permeation tube, and R be 8.314, which is the ideal gas constant. The conversion factor is ppm, dimensionless. The unit conversion factor converts the permeability from ng / min to g / min. During each calibration, the system records the actual gas concentration measured by the analyzer. and compared with theoretical standard gas concentration Compare and calculate the concentration deviation factor: ； in, Let be the concentration deviation factor at time t, which is dimensionless. The concentration actually measured by the analyzer at time t; like When the theoretical penetration rate is exceeded, feedback compensation is applied to obtain a real-time corrected penetration rate: ; in, The real-time corrected permeability at time t after feedback compensation. β To correct the strength coefficient.

3. The method for predicting the remaining life of a permeation tube according to claim 2, characterized in that, Whenever a new real-time corrected permeability is obtained and the permeation tube has passed the initial stabilization stage, the relevant model parameters in the power-law decay model of the permeation tube are updated online in real time. The specific steps include: Whenever a new real-time corrected permeability is obtained and the permeation tube has passed the initial stabilization phase, the decay model is updated using the real-time corrected permeability: ; For the power-law decay model: ; This is a linear regression problem, defined as follows: Observations: ; Time feature vector: ; Parameter vector: ; in, The forgetting factor has a value of 0.95 to 0.

99. This represents the optimal estimate of the decay model parameters at time t; For the first i The real-time corrected permeability obtained during the second calibration. This is the decay index.

4. The method for predicting the remaining life of a permeation tube according to claim 3, characterized in that, Whenever a new real-time corrected permeability is obtained and the permeation tube has passed the initial stabilization phase, the update trigger condition for updating the decay model using the real-time corrected permeability is as follows: A batch fitting is automatically performed after every N calibrations are completed. If the prediction error of the remaining service life exceeds a set threshold for M consecutive times, an update is immediately triggered, and the power-law decay model of the permeation tube is updated in real time. <N。 5. The method for predicting the remaining life of a permeation tube according to claim 4, characterized in that, The remaining lifespan of the permeation tube is calculated based on the updated power-law decay model and the current real-time corrected permeability, specifically including the following steps: The cumulative permeate release mass is obtained by integrating the real-time corrected permeability: ; Calculate the remaining mass based on the cumulative permeate release mass and the initial total mass of the permeate tube: ; The remaining useful life is calculated based on the remaining mass and real-time corrected permeability. Down: ; In the formula: for t The remaining useful life predicted at any given time; for t The remaining mass inside the tube calculated at any given time; This represents the initial total mass of the permeation tube; The total mass released up to time t; The failure threshold quality; for t Real-time correction of penetration rate; Time unit conversion factor; For safety reasons, the aging of the permeation pipe material and environmental fluctuations are taken into account.

6. The method for predicting the remaining life of a permeation tube according to claim 1, characterized in that, It also includes the following steps: When the current remaining service life of the permeation tube and the relative deviation between the current permeability and the nominal permeability meet the set conditions, corresponding graded early warning information is generated and sent.

7. The method for predicting the remaining life of a permeation tube according to claim 6, characterized in that, When the current remaining service life of the permeation tube and the relative deviation between the current permeability and the nominal permeability meet the set conditions, a corresponding early warning message is generated and sent. The specific steps include: Real-time determination of whether any trigger condition is met: Condition A: Calculate the relative deviation of permeability at time t: ; in, for t Real-time correction of penetration rate; For nominal penetration rate, when An alert is triggered when the value is greater than or equal to the set value. Condition B: If the current remaining useful life A warning will be triggered at a preset time. Condition C: An early warning is triggered when the cumulative usage time exceeds the factory warranty life or the remaining mass calculated through quality loss is lower than the set percentage of the initial total mass. Condition D: Permeability deviation rate after M consecutive calibrations The rate of change exceeds the set value per week; When any triggering condition is met, a corresponding early warning message is generated and sent, and the early warning time, triggering condition, and related data are automatically recorded.

8. The method for predicting the remaining life of a permeation tube according to claim 7, characterized in that, When generating and sending corresponding early warning information, the early warning information is pre-set with multiple early warning thresholds according to the degree of urgency to achieve tiered early warning. Specifically, when... A "Caution" warning is triggered in advance when the remaining mass is greater than or equal to the set value; a "Replacement Recommended" warning is triggered in advance when the remaining mass is less than the set percentage of the initial total mass; the current remaining service life... < Trigger an "emergency replacement" warning in advance at the preset time.

9. The method for predicting the remaining life of a permeation tube according to claim 7, characterized in that, It also includes the following steps: The relative deviation of permeability at time t is calculated. Automatically adjust calibration frequency: When K is ≥, the calibration cycle is adjusted from once a week to H times a week. <H<7; When Z ≥ Z, the calibration cycle is adjusted from once a week to once a day, K <Z; After replacing the permeation tube, the automatic re-establishment of the regular calibration cycle is once a week.

10. The method for predicting the remaining life of a permeation tube according to claim 1, characterized in that, It also includes the following steps: Based on the current remaining service life, implement one or more of the following measures to extend the service life of the permeation tube: The temperature of the constant temperature chamber of the permeation tube is controlled within the set value ±0.1℃ range; When the equipment is shut down for a long time, it automatically lowers the temperature of the constant temperature chamber of the permeation tube to 4°C in a low-temperature storage mode, minimizing the life loss of the permeation tube when it is not in use. When using, keep the moisture content of the humidity-sensitive carrier gas and diluent gas within the set range.

11. A device for predicting the remaining life of a permeation tube, characterized in that, include: The real-time data acquisition and recording module is used to acquire and record online multi-source data during the use of the permeation tube, including the temperature of the constant temperature chamber, the carrier gas flow rate, the relative humidity of the environment, the environmental pressure, and the actual standard gas concentration measured by the analyzer during each calibration. The real-time permeability correction module is used to establish a multi-factor mass loss calibration model for calculating the theoretical permeability of the permeable tube based on the factory calibration data of the permeable tube. It obtains the real-time corrected permeability after feedback compensation based on the actual concentration data of each calibration ventilation and the calculated theoretical permeability. The real-time decay model update module is used to update the relevant model parameters in the power-law decay model of the permeation tube online in real time whenever a new real-time corrected permeability is obtained and the permeation tube has passed the initial stabilization stage. The remaining service life calculation module is used to calculate the current remaining service life of the permeation tube based on the updated power-law decay model and the current real-time corrected permeability.

12. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for predicting the remaining life of the permeation tube as described in any one of claims 1 to 10.

13. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for predicting the remaining life of the permeation tube as described in any one of claims 1 to 10.

14. A computer program product comprising a computer program or computer-executable instructions, characterized in that, When the computer program or computer-executable instructions are executed by the processor, the remaining life prediction method for permeation tubes as described in any one of claims 1 to 10 is implemented.

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