Clean workshop filter airtightness online monitoring method and system

Abstract

The invention discloses a clean workshop filter airtightness on-line monitoring method and system, and the method comprises the steps: obtaining an initial parameter set, including initial pressure difference, initial flow and initial energy consumption, of a filter in a normal operation state in real time; periodic barometric disturbance is applied to the upstream of the filter, and a dynamic parameter set, including a pressure difference fluctuation value, a flow fluctuation value and an energy consumption fluctuation value, of the filter in the disturbance state is collected; calculating characteristic change rates according to the initial parameter set and the dynamic parameter set, wherein the characteristic change rates comprise a pressure difference change rate, a flow change rate and an energy consumption change rate; and constructing an air tightness index model, and substituting the characteristic change rate to calculate the air tightness index. Compared with a traditional detection method, frequent field operation of professionals is not needed, and the labor cost is reduced; and continuous online monitoring can quickly detect problems, so that the detection efficiency is greatly improved, the influence on workshop production is reduced, and precise monitoring on the air tightness of the filter in the clean workshop is realized.

Description

Technical Field

[0001] This invention relates to the field of airtightness testing technology, and in particular to an online monitoring method and system for the airtightness of cleanroom filters. Background Technology

[0002] In cleanroom operation, the airtightness of filters plays a crucial role in maintaining a clean environment. Current technologies primarily employ aerosol leak detection methods for filter airtightness testing. Aerosol leak detection requires introducing aerosols upstream of the filter and scanning downstream using an optical particle counter or condensation nucleus counter to determine the location and size of leaks by detecting leaking aerosol particles. However, this method has significant drawbacks. Firstly, the operation is complex, requiring specialized personnel for aerosol introduction and equipment operation, resulting in high labor costs. Secondly, the detection efficiency is low, requiring a comprehensive scan of the downstream surface of the filter, which is time-consuming. In large-scale cleanroom applications, the long detection cycle disrupts normal production. Furthermore, existing methods struggle to monitor changes in filter airtightness in real time, failing to promptly detect subtle leaks that gradually appear during operation. Summary of the Invention

[0003] To address at least one of the aforementioned technical problems, the present invention provides a method and system for online monitoring of the airtightness of cleanroom filters.

[0004] In a first aspect, the present invention provides a method for online monitoring of the airtightness of a filter in a cleanroom, the method comprising:

[0005] The filter's initial parameter set under normal operating conditions is acquired in real time, including initial differential pressure, initial flow rate, and initial energy consumption.

[0006] Periodic air pressure disturbances are applied upstream of the filter, and the dynamic parameter set of the filter under the disturbance state is collected, including pressure difference fluctuation value, flow rate fluctuation value and energy consumption fluctuation value;

[0007] The characteristic rates of change, including differential pressure rate of change, flow rate of change, and energy consumption rate of change, are calculated based on the initial parameter set and the dynamic parameter set.

[0008] Construct an airtightness index model and calculate the airtightness index by substituting the characteristic change rate.

[0009] Preferably, the step of constructing the airtightness index model and calculating the airtightness index by substituting the characteristic change rate includes:

[0010] ;

[0011] In the formula, The airtightness index, It is a natural constant. The filter clogging attenuation coefficient, For online monitoring duration; , , These are the rate of change of differential pressure, the rate of change of flow rate, and the rate of change of energy consumption, respectively. , , These are the weighting factors, This is the proportional adjustment factor.

[0012] Preferably, after calculating the airtightness index by substituting the characteristic change rate, the method further includes:

[0013] An array of eddy current sensors is arranged downstream of the filter to collect airflow eddy current characteristic data, including eddy current intensity, eddy current distribution dispersion, and eddy current fluctuation frequency.

[0014] Calculate the dynamic correction factor based on eddy current characteristic data:

[0015] ;

[0016] Where, As a dynamic correction factor, , , These are eddy current intensity, eddy current distribution dispersion, and eddy current oscillation frequency, respectively. This is the normal eddy current reference frequency. , These are the preset thresholds for eddy current intensity and dispersion, respectively. These are the correction factors;

[0017] The airtightness index is corrected based on a dynamic correction factor:

[0018] ;

[0019] Where, These are the airtightness indices before and after the correction, respectively.

[0020] according to The value matches the preset airtightness level and outputs real-time leakage risk assessment results.

[0021] Preferably, the eddy current distribution dispersion is calculated as follows:

[0022] The eddy current intensity values ​​of N regions in the downstream cross section are obtained by using an eddy current sensor array;

[0023] Calculate the standard deviation of the eddy current intensity values ​​for N regions, which is used as the eddy current distribution dispersion.

[0024] Secondly, the present invention also provides an online monitoring system for the airtightness of cleanroom filters, the system comprising:

[0025] The data acquisition unit is used to acquire the initial parameter set of the filter in real time under normal operating conditions, including initial differential pressure, initial flow rate and initial energy consumption;

[0026] The air pressure disturbance unit is used to apply periodic air pressure disturbances to the upstream of the filter and collect the dynamic parameter set of the filter under the disturbance state, including differential pressure fluctuation value, flow rate fluctuation value and energy consumption fluctuation value.

[0027] The rate of change calculation unit is used to calculate characteristic rates of change based on the initial parameter set and the dynamic parameter set, including the rate of change of pressure difference, the rate of change of flow rate, and the rate of change of energy consumption.

[0028] The airtightness analysis unit is used to construct an airtightness index model and calculate the airtightness index by substituting the characteristic rate of change.

[0029] Preferably, the airtightness analysis unit is used for:

[0030] ;

[0031] In the formula, The airtightness index, It is a natural constant. The filter clogging attenuation coefficient is... For online monitoring duration; , , These are the rate of change of differential pressure, the rate of change of flow rate, and the rate of change of energy consumption, respectively. , , These are the weighting factors, This is the proportional adjustment factor.

[0032] Preferably, the system further includes a leakage risk assessment unit, used for:

[0033] An array of eddy current sensors is arranged downstream of the filter to collect airflow eddy current characteristic data, including eddy current intensity, eddy current distribution dispersion, and eddy current fluctuation frequency.

[0034] Calculate the dynamic correction factor based on eddy current characteristic data:

[0035] ;

[0036] In the formula, As a dynamic correction factor, , , These are eddy current intensity, eddy current distribution dispersion, and eddy current oscillation frequency, respectively. This is the normal eddy current reference frequency. , These are the preset thresholds for eddy current intensity and dispersion, respectively. These are the correction factors;

[0037] The airtightness index is corrected based on a dynamic correction factor:

[0038] ;

[0039] In the formula, These are the airtightness indices before and after the correction, respectively.

[0040] according to The value matches the preset airtightness level and outputs real-time leakage risk assessment results.

[0041] Preferably, the eddy current distribution dispersion is calculated as follows:

[0042] The eddy current intensity values ​​of N regions in the downstream cross section are obtained by using an eddy current sensor array;

[0043] Calculate the standard deviation of the eddy current intensity values ​​for N regions, which is used as the eddy current distribution dispersion.

[0044] Thirdly, the present invention also provides an electronic device including a processor and a memory, the memory being used to store computer program code, the computer program code including computer instructions, wherein when the processor executes the computer instructions, the electronic device performs a method as described in the first aspect above and any possible implementation thereof.

[0045] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor of an electronic device, cause the processor to perform a method as described in the first aspect above and any possible implementation thereof.

[0046] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0047] This invention acquires the initial parameter set of the filter under normal operating conditions in real time, including initial differential pressure, initial flow rate, and initial energy consumption, providing basic data for subsequent analysis. Periodic pressure disturbances are applied upstream of the filter, and the dynamic parameter set of the filter under these disturbances is collected, including differential pressure fluctuations, flow rate fluctuations, and energy consumption fluctuations. This dynamic monitoring method can sensitively capture parameter fluctuations caused by changes in filter airtightness. Characteristic change rates, including differential pressure change rate, flow rate change rate, and energy consumption change rate, are calculated based on the initial and dynamic parameter sets. These change rates are used to construct an airtightness index model, and the airtightness index is calculated by substituting the characteristic change rates. Because it is online real-time monitoring, subtle changes in filter airtightness can be detected promptly. Compared to traditional detection methods, it eliminates the need for frequent on-site operations by professional personnel, reducing labor costs. Furthermore, continuous online monitoring can quickly detect problems, greatly improving detection efficiency and reducing the impact on workshop production. This achieves efficient, real-time, and accurate monitoring of filter airtightness in cleanrooms, effectively ensuring the clean environment within the cleanroom.

[0048] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the background art, the accompanying drawings used in the embodiments of the present invention or the background art will be described below.

[0050] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure.

[0051] Figure 1 A schematic flowchart illustrating an online monitoring method for the airtightness of a cleanroom filter, provided in an embodiment of the present invention;

[0052] Figure 2 A schematic flowchart of an online monitoring method for the airtightness of a cleanroom filter, provided as another embodiment of the present invention;

[0053] Figure 3 This is a schematic diagram of the structure of an online monitoring system for the airtightness of a cleanroom filter provided in an embodiment of the present invention;

[0054] Figure 4 This is a schematic diagram of a cleanroom filter airtightness online monitoring system provided in another embodiment of the present invention. Detailed Implementation

[0055] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0056] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0057] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0058] Please see Figure 1 , Figure 1 This is a schematic flowchart illustrating an online monitoring method for the airtightness of a cleanroom filter, provided as an embodiment of the present invention. Figure 1 As shown, the method includes:

[0059] S10. Real-time acquisition of the filter's initial parameter set under normal operating conditions, including initial differential pressure, initial flow rate, and initial energy consumption;

[0060] High-precision differential pressure transmitters (measurement accuracy ≤ ±0.1 Pa) are installed 10-15 cm upstream and downstream of the filter, respectively. These transmitters collect the pressure difference between the upstream and downstream sides of the filter in real time, serving as the initial differential pressure. An electromagnetic flowmeter (measurement accuracy ≤ ±0.5%) is installed in the air supply duct upstream of the filter to monitor the airflow through the filter in real time, serving as the initial flow rate. A power sensor (sampling frequency ≥ 1 kHz) is connected in series in the power supply circuit of the fan driving the airflow to collect the instantaneous power of the fan in real time. Combined with the operating time, the energy consumption per unit time is calculated as the initial energy consumption. All parameters are transmitted to a data acquisition terminal via an industrial bus. The acquisition frequency is set to 1 time / second, and the average value is taken after 30 minutes of continuous acquisition as the final initial parameter set to ensure data stability. Using high-precision sensors (differential pressure transmitter, electromagnetic flowmeter, power sensor) can ensure the accuracy of initial parameter measurements and avoid subsequent analysis deviations due to errors in basic data; fixed acquisition positions (10-15cm upstream and downstream of the filter) can reduce the interference of pipeline turbulence on parameters and ensure data representativeness; continuous acquisition for 30 minutes and averaging can filter out short-term fluctuations and obtain a stable initial benchmark, providing a reliable reference for subsequent comparison of dynamic parameters.

[0061] S20. Apply periodic air pressure disturbances upstream of the filter and collect the dynamic parameter set of the filter under the disturbance state, including pressure difference fluctuation value, flow rate fluctuation value and energy consumption fluctuation value.

[0062] The air pressure disturbance device uses an adjustable frequency pneumatic disturbance valve (response time ≤ 50ms), which is installed in the air supply duct 20-30cm upstream of the filter. It is driven by a PLC controller and applies a sinusoidal air pressure disturbance with a cycle of 10-30 seconds (which can be adjusted according to the filter specifications, such as 20 seconds for HEPA filters) and an amplitude of 5%-15% of the rated air supply pressure (e.g., when the rated pressure is 500Pa, the disturbance range is 475-525Pa).

[0063] Dynamic parameter acquisition and disturbance synchronous triggering: When the disturbance begins, the sampling frequency of the differential pressure transmitter, electromagnetic flowmeter, and power sensor is increased to 10 times / second, and the differential pressure fluctuation value (peak and valley), flow fluctuation value (deviation between instantaneous flow and initial flow), and energy consumption fluctuation value (deviation between instantaneous power and initial power) are recorded in real time within each disturbance cycle. The sampling stops after 5 complete disturbance cycles, forming a dynamic parameter set.

[0064] Periodic air pressure disturbances (sine waves) can simulate airflow fluctuations that may occur in actual operation, and the amplitude is controlled within 5%-15%, which will not affect the normal operation of the cleanroom. The disturbance and acquisition are triggered synchronously, and the sampling frequency is increased (10 times / second), which can accurately capture the instantaneous fluctuations of parameters caused by changes in the airtightness of the filter (such as minor leaks), and avoid missing key dynamic information. Continuous acquisition of 5 cycles can reduce the randomness of a single disturbance and ensure the stability and repeatability of the dynamic parameter set.

[0065] S30. Calculate the characteristic rate of change based on the initial parameter set and the dynamic parameter set, including the rate of change of pressure difference, the rate of change of flow rate and the rate of change of energy consumption.

[0066] The "pressure difference change rate" is calculated using the formula: (Dynamic pressure difference fluctuation peak value - Initial pressure difference) / Initial pressure difference × 100%, where the dynamic pressure difference fluctuation peak value is the maximum fluctuation value within 5 periods to amplify the pressure difference change caused by leakage. The "flow rate change rate" is calculated using the formula: (Dynamic flow rate fluctuation mean value - Initial flow rate) / Initial flow rate × 100%, where the dynamic flow rate fluctuation mean value is the arithmetic mean of flow rate fluctuation values ​​within 5 periods, reflecting the overall flow rate change trend. The "energy consumption change rate" is calculated using the formula: (Dynamic energy consumption fluctuation peak value - Initial energy consumption) / Initial energy consumption × 100%. Since leakage causes changes in fan load, taking the peak value can sensitively capture sudden changes in energy consumption. The calculation results are standardized (the change rate is mapped to the -10%~10% range), removing outliers outside this range (such as extreme data caused by sudden equipment failure), and retaining the effective characteristic change rates.

[0067] S40. Construct an airtightness index model and substitute the characteristic change rate to calculate the airtightness index.

[0068] Preferably, the step of constructing the airtightness index model and calculating the airtightness index by substituting the characteristic change rate includes:

[0069] ;

[0070] In the formula, The airtightness index, It is a natural constant. The filter clogging attenuation coefficient is... For online monitoring duration; , , These are the rate of change of differential pressure, the rate of change of flow rate, and the rate of change of energy consumption, respectively. , , These are the weighting factors, This is the proportional adjustment factor.

[0071] In the above formula, This model simulates the performance degradation of a filter due to long-term clogging. It uses an exponential decay model with the natural constant e as the base, reflecting the performance degradation patterns of physical equipment such as material aging and particle accumulation. Indicates the rate of change of pressure difference The absolute value amplification effect means that when leakage causes abnormal fluctuations in pressure difference, this value increases significantly. Rate of change of flow Linear sensitivity regulation, The contribution weight for controlling traffic changes is leaked, which often leads to abnormally high traffic. To characterize the threshold triggering mechanism, during leakage , This triggered the growth. Among them, The filter clogging attenuation coefficient has a value range of [value range missing]. This value is based on the experimental lifespan of the filter. At that time, the 10-year decay is less than 10%, when At that time, the annual attenuation was greater than 50%. This is the proportional adjustment factor, and its value range is... This value is compatible with filters of different specifications. Therefore, in this embodiment, the numerator attenuation term reflects equipment aging, and the three denominator terms correspond to the core leakage characteristics (pressure difference mutation / flow drift / energy consumption shift), thus enabling accurate calculation of the airtightness index.

[0072] See Figure 2 In one embodiment, after calculating the airtightness index by substituting the characteristic change rate, the method further includes:

[0073] S50. Arrange an array of eddy current sensors downstream of the filter to collect airflow eddy current characteristic data, including eddy current intensity, eddy current distribution dispersion and eddy current fluctuation frequency.

[0074] S60. Calculate the dynamic correction factor based on eddy current characteristic data:

[0075] ;

[0076] Where, As a dynamic correction factor, , , These are eddy current intensity, eddy current distribution dispersion, and eddy current oscillation frequency, respectively. This is the normal eddy current reference frequency. , These are the preset thresholds for eddy current intensity and dispersion, respectively. These are the correction factors;

[0077] S70. Correct the airtightness index according to the dynamic correction factor:

[0078] ;

[0079] In the formula, These are the airtightness indices before and after the correction, respectively.

[0080] S80, according to The value matches the preset airtightness level and outputs real-time leakage risk assessment results.

[0081] In step S50, an array of 8-16 miniature thermal eddy current sensors (measurement accuracy ≤ ±2% FS, response time ≤ 10ms) is uniformly arranged on a horizontal surface 30-50cm downstream of the filter (covering more than 90% of the filter outlet area). The spacing between adjacent sensors is 1 / 5-1 / 4 of the filter's side length (e.g., for a 1m×1m filter, the spacing is set to 20-25cm) to ensure no monitoring blind spots. The eddy current intensity is calculated using the amplitude of airflow velocity fluctuations detected by the sensors, in m / s. Preferably, the eddy current distribution dispersion is calculated as follows: eddy current intensity values ​​for N regions of the downstream cross-section are obtained through the eddy current sensor array; the standard deviation of the eddy current intensity values ​​for the N regions is calculated as the eddy current distribution dispersion. To ensure synchronous acquisition, it is linked to the air pressure disturbance cycle in step S20. Acquisition begins 30 seconds after the disturbance stops (to avoid the disturbance directly affecting eddy current stability), with a sampling frequency of 5 times / second, continuously acquiring data for 3 cycles (10 seconds each), and taking the average value as the eddy current characteristic data.

[0082] For preset threshold , The eddy current intensity is determined based on statistical data from the filter under normal sealing conditions. For example, it is determined by taking the mean eddy current intensity plus twice the standard deviation from 100 normal operation tests. ,like =0.3m / s, the mean of eddy current distribution dispersion + 1.5 times the standard deviation is used as... ,like =0.15; During the initial parameter set acquisition phase, the average fluctuation frequency of the downstream eddy current of the filter is recorded synchronously. These are correction coefficients, obtained through training with experimental data from 50 sets of known leakage states (covering minor, moderate, and severe leaks), for example... =0.8 (eddy current intensity is more sensitive to leakage) =0.5、 =0.3, ensuring the correction factor effectively amplifies the impact of abnormal eddies on airtightness. For the calculation logic of the dynamic correction factor, when both eddy intensity and dispersion are below the threshold, the airflow is considered stable, and no correction is needed. Otherwise, it is calculated using the formula. Quantify the degree of eddy current anomaly.

[0083] Eddy flow characteristic data (intensity, dispersion, frequency) can directly reflect the downstream airflow turbulence caused by filter leakage. For example, strong local eddies will form at the leakage point, and the dispersion of the distribution will increase, thus supplementing the limitations of the previous embodiments that only evaluated macroscopic parameters such as pressure difference and flow rate. Dynamic correction factor By quantifying eddy current anomalies, disturbances caused by factors such as pipeline turbulence and fan fluctuations can be corrected. Deviation, causing the corrected exponent It more closely reflects the actual airtightness condition (experimental data shows that the detection error after correction is reduced to ±3%, which is 60% higher than before correction).

[0084] Furthermore, a preset airtightness level classification is performed: based on the corrected index. Four-level standards are set:

[0085] Security level ( ≥90): No risk of leakage, outputs a green indicator light and a "Excellent airtightness" message;

[0086] Low risk level (80≤ <90): There may be a micro-leak at the micrometer level. A yellow indicator light will be displayed and "It is recommended to recheck within 1 week".

[0087] Medium risk level (60≤ <80): A detectable leak point exists, outputting an orange alarm (audio and visual alert) and "Repair required within 24 hours";

[0088] High risk level ( <60: Severe leakage, output red alarm (high-frequency sound and light + remote push to management terminal) and "stop the machine immediately for maintenance".

[0089] Output format: Real-time display on the workshop monitoring screen. Numerical values, level indicators, and risk warnings are stored synchronously in the database, supporting traceability analysis.

[0090] The high-density arrangement of the eddy current sensor array can capture localized eddy current changes caused by minute leaks. Combined with a graded alarm mechanism, it can issue early warnings in the early stages of leaks, detecting problems earlier than traditional methods and preventing the leak from escalating and contaminating clean areas. Preset thresholds and correction coefficients, calibrated experimentally and dynamically adjusted, can adapt to different types of filters (such as pre-filters and HEPA filters) and workshop conditions (such as different air supply pressures), solving the problem of poor adaptability of single assessment models in diverse scenarios. Simultaneously, the exponential decay correction formula can non-linearly amplify the impact of severe eddy current anomalies, ensuring that high-risk leaks are not underestimated. Four risk levels and clear handling recommendations reduce the difficulty of judgment for operators. Combined with real-time output and historical data storage, it facilitates workshop managers in developing preventative maintenance plans and reducing unplanned downtime.

[0091] In summary, the method provided by this invention acquires the initial parameter set of the filter under normal operating conditions in real time, including initial differential pressure, initial flow rate, and initial energy consumption, providing basic data for subsequent analysis. Periodic pressure disturbances are applied upstream of the filter, and the dynamic parameter set of the filter under the disturbance state is collected, including differential pressure fluctuations, flow rate fluctuations, and energy consumption fluctuations. This dynamic monitoring method can sensitively capture parameter fluctuations caused by changes in filter airtightness. Characteristic change rates, including differential pressure change rate, flow rate change rate, and energy consumption change rate, are calculated based on the initial and dynamic parameter sets. These change rates are used to construct an airtightness index model, and the airtightness index is calculated by substituting the characteristic change rates. Because it is online real-time monitoring, subtle changes in filter airtightness can be detected promptly. Compared with traditional detection methods, it eliminates the need for frequent on-site operations by professional personnel, reducing labor costs. Furthermore, continuous online monitoring can quickly detect problems, greatly improving detection efficiency and reducing the impact on workshop production. This achieves efficient, real-time, and accurate monitoring of filter airtightness in cleanrooms, effectively ensuring the clean environment within the cleanroom.

[0092] See Figure 3 In one embodiment, the present invention also provides an online monitoring system for the airtightness of cleanroom filters, the system comprising:

[0093] The data acquisition unit 100 is used to acquire the initial parameter set of the filter under normal operating conditions in real time, including initial differential pressure, initial flow rate and initial energy consumption;

[0094] The air pressure disturbance unit 200 is used to apply periodic air pressure disturbances to the upstream of the filter and collect the dynamic parameter set of the filter under the disturbance state, including differential pressure fluctuation value, flow rate fluctuation value and energy consumption fluctuation value.

[0095] The rate of change calculation unit 300 is used to calculate characteristic rates of change based on the initial parameter set and the dynamic parameter set, including the rate of change of pressure difference, the rate of change of flow rate and the rate of change of energy consumption.

[0096] The airtightness analysis unit 400 is used to construct an airtightness index model and calculate the airtightness index by substituting the characteristic change rate.

[0097] In one embodiment, the airtightness analysis unit 400 is used for:

[0098] ;

[0099] In the formula, The airtightness index, It is a natural constant. The filter clogging attenuation coefficient is... For online monitoring duration; , , These are the rate of change of differential pressure, the rate of change of flow rate, and the rate of change of energy consumption, respectively. , , These are the weighting factors, This is the proportional adjustment factor.

[0100] See Figure 4 In one embodiment, the system further includes a leakage risk assessment unit 500, used for:

[0101] An array of eddy current sensors is arranged downstream of the filter to collect airflow eddy current characteristic data, including eddy current intensity, eddy current distribution dispersion, and eddy current fluctuation frequency.

[0102] Calculate the dynamic correction factor based on eddy current characteristic data:

[0103] ;

[0104] In the formula, As a dynamic correction factor, , , These are eddy current intensity, eddy current distribution dispersion, and eddy current oscillation frequency, respectively. This is the normal eddy current reference frequency. , These are the preset thresholds for eddy current intensity and dispersion, respectively. These are the correction factors;

[0105] The airtightness index is corrected based on a dynamic correction factor:

[0106] ;

[0107] In the formula, These are the airtightness indices before and after the correction, respectively.

[0108] according to The value matches the preset airtightness level and outputs real-time leakage risk assessment results.

[0109] Preferably, the eddy current distribution dispersion is calculated as follows:

[0110] The eddy current intensity values ​​of N regions in the downstream cross section are obtained by using an eddy current sensor array;

[0111] Calculate the standard deviation of the eddy current intensity values ​​for N regions, which is used as the eddy current distribution dispersion.

[0112] It is understood that the system provided in this embodiment has functions or includes modules that can be used to execute the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0113] The present invention also provides an electronic device including a processor and a memory, the memory being used to store computer program code, the computer program code including computer instructions, wherein when the processor executes the computer instructions, the electronic device performs a method as described in any of the above possible implementations.

[0114] The present invention also provides a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor of an electronic device, cause the processor to perform a method as described in any of the above possible implementations.

[0115] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0116] Those skilled in the art will readily understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. Those skilled in the art will also readily understand that the various embodiments of the present invention have different focuses, and for the sake of convenience and brevity, the same or similar parts may not be repeated in different embodiments. Therefore, parts not described or not described in detail in one embodiment can be referred to in other embodiments.

Claims

1. A method for online monitoring of the airtightness of filters in cleanrooms, characterized in that, The method includes: The filter's initial parameter set under normal operating conditions is acquired in real time, including initial differential pressure, initial flow rate, and initial energy consumption. Periodic air pressure disturbances are applied upstream of the filter, and the dynamic parameter set of the filter under the disturbance state is collected, including pressure difference fluctuation value, flow rate fluctuation value and energy consumption fluctuation value; The characteristic rates of change, including differential pressure rate of change, flow rate of change, and energy consumption rate of change, are calculated based on the initial parameter set and the dynamic parameter set. Construct an airtightness index model and calculate the airtightness index by substituting the characteristic change rate.

2. The method for online monitoring of the airtightness of cleanroom filters according to claim 1, characterized in that, The construction of the airtightness index model, and the calculation of the airtightness index by substituting the characteristic change rate, includes: ； Where, The airtightness index, It is a natural constant. The filter clogging attenuation coefficient, For online monitoring duration; , , These are the rate of change of differential pressure, the rate of change of flow rate, and the rate of change of energy consumption, respectively. , , These are the weighting factors, This is the proportional adjustment factor.

3. The method for online monitoring of the airtightness of cleanroom filters according to claim 1, characterized in that, After calculating the airtightness index by substituting the characteristic change rate, the following is also included: An array of eddy current sensors is arranged downstream of the filter to collect airflow eddy current characteristic data, including eddy current intensity, eddy current distribution dispersion, and eddy current fluctuation frequency. Calculate the dynamic correction factor based on eddy current characteristic data: ； Where, As a dynamic correction factor, , , These are eddy current intensity, eddy current distribution dispersion, and eddy current oscillation frequency, respectively. This is the normal eddy current reference frequency. , These are the preset thresholds for eddy current intensity and dispersion, respectively. These are the correction factors; The airtightness index is corrected based on a dynamic correction factor: ； Where, These are the airtightness indices before and after the correction, respectively. according to The value matches the preset airtightness level and outputs real-time leakage risk assessment results.

4. The method for online monitoring of the airtightness of cleanroom filters according to claim 3, characterized in that, The eddy current distribution dispersion is calculated as follows: The eddy current intensity values ​​of N regions in the downstream cross section are obtained by using an eddy current sensor array; Calculate the standard deviation of the eddy current intensity values ​​for N regions, which is used as the eddy current distribution dispersion.

5. An online monitoring system for the airtightness of a cleanroom filter, characterized in that, The system includes: The data acquisition unit is used to acquire the initial parameter set of the filter in real time under normal operating conditions, including initial differential pressure, initial flow rate and initial energy consumption; The air pressure disturbance unit is used to apply periodic air pressure disturbances to the upstream of the filter and collect the dynamic parameter set of the filter under the disturbance state, including differential pressure fluctuation value, flow rate fluctuation value and energy consumption fluctuation value. The rate of change calculation unit is used to calculate characteristic rates of change based on the initial parameter set and the dynamic parameter set, including the rate of change of pressure difference, the rate of change of flow rate, and the rate of change of energy consumption. The airtightness analysis unit is used to construct an airtightness index model and calculate the airtightness index by substituting the characteristic rate of change.

6. The online monitoring system for the airtightness of cleanroom filters according to claim 5, characterized in that, The airtightness analysis unit is used for: ； In the formula, The airtightness index, It is a natural constant. The filter clogging attenuation coefficient is... For online monitoring duration; , , These are the rate of change of differential pressure, the rate of change of flow rate, and the rate of change of energy consumption, respectively. , , These are the weighting factors, This is the proportional adjustment factor.

7. The online monitoring system for the airtightness of cleanroom filters according to claim 5, characterized in that, The system also includes a leakage risk assessment unit, used for: An array of eddy current sensors is arranged downstream of the filter to collect airflow eddy current characteristic data, including eddy current intensity, eddy current distribution dispersion, and eddy current fluctuation frequency. Calculate the dynamic correction factor based on eddy current characteristic data: ； In the formula, As a dynamic correction factor, , , These are eddy current intensity, eddy current distribution dispersion, and eddy current oscillation frequency, respectively. This is the normal eddy current reference frequency. , These are the preset thresholds for eddy current intensity and dispersion, respectively. These are the correction factors; The airtightness index is corrected based on a dynamic correction factor: ； In the formula, These are the airtightness indices before and after the correction, respectively. according to The value matches the preset airtightness level and outputs real-time leakage risk assessment results.

8. The method for online monitoring of the airtightness of cleanroom filters according to claim 7, characterized in that, The eddy current distribution dispersion is calculated as follows: The eddy current intensity values ​​of N regions in the downstream cross section are obtained by using an eddy current sensor array; Calculate the standard deviation of the eddy current intensity values ​​for N regions, which is used as the eddy current distribution dispersion.

9. An electronic device, characterized in that, include: The electronic device includes a processor and a memory, the memory being used to store computer program code, the computer program code including computer instructions, wherein when the processor executes the computer instructions, the electronic device performs the online monitoring method for the airtightness of cleanroom filters as described in any one of claims 1 to 4.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which includes program instructions that, when executed by a processor of an electronic device, cause the processor to perform the online monitoring method for the airtightness of a cleanroom filter according to any one of claims 1 to 4.

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