Multi-stage vacuum extraction control system

Abstract

The invention relates to the technical field of vacuum extraction control, and discloses a multi-stage vacuum extraction control system which comprises a central processing unit, a multi-stage vacuum pump set control module, a vacuum degree dynamic monitoring module and the like. The multi-stage vacuum pump set control module coordinates all stages of pumps to work according to a preset vacuum degree target value, and the vacuum degree dynamic monitoring module collects pressure data in real time and judges abnormity. The pressure gradient adjusting module adjusts the extraction rate of the adjacent pump to maintain the vacuum gradient stable when the pressure is abnormal, and the abnormity response module controls starting and stopping or rate correction of the pump according to the abnormity level. In addition, the system further has the functions of vacuum pump efficiency evaluation, pressure feedback compensation, leakage detection, redundancy switching, historical data backtracking, collaborative optimization and the like. The system can accurately control the vacuum degree, efficiently deal with abnormities and realize equipment state monitoring and optimization, and is widely applied to the fields with high requirements on the vacuum degree, such as semiconductor manufacturing and vacuum coating.

Description

Technical Field

[0001] The present invention relates to the technical field of vacuum extraction control, and specifically to a multi-stage vacuum extraction control system. Background Art

[0002] In modern industrial production and scientific research experiments, the demand for a vacuum environment is becoming increasingly extensive and stringent. Many processes and researches require precise control of the vacuum degree to ensure product quality, the accuracy of experimental results, and the stable operation of equipment. However, the existing vacuum extraction control systems have many deficiencies and are difficult to meet the actual needs.

[0003] Traditional vacuum extraction systems usually adopt single-stage or simple multi-stage vacuum pump combinations and lack an effective collaborative control mechanism. The working states of each stage of vacuum pumps are often independent of each other and cannot be precisely coordinated according to the real-time needs of the system. For example, in the process of semiconductor chip manufacturing, the lithography link requires an extremely high-precision vacuum environment, and tiny pressure fluctuations may lead to deviations in the chip pattern, affecting the chip performance. However, when the traditional system faces interference factors such as internal temperature changes and gas leakage in the equipment, it cannot quickly adjust the working state of the vacuum pump and is difficult to maintain a stable vacuum degree, thus reducing the product yield rate.

[0004] In terms of vacuum degree monitoring, most of the existing technologies adopt static threshold monitoring methods and cannot adapt to complex and changeable working environments. In practical applications, the vacuum pressure of the system is dynamically affected by various factors, such as environmental temperature and equipment operation time. Static thresholds cannot be adaptively adjusted according to these changes and are prone to misjudgment or missed judgment. In the vacuum coating process, if the vacuum degree abnormality cannot be detected in time due to inaccurate monitoring, it will lead to problems such as uneven coating thickness and reduced film layer quality, increasing production costs and product defect rates.

[0005] When pressure abnormalities or vacuum pump failures occur in the system, the response mechanism of the existing system is also not perfect. On the one hand, there is a lack of fast and effective pressure gradient adjustment means and the vacuum gradient stability of the system cannot be quickly restored when an abnormality occurs, affecting the continuity of the entire production or experimental process. On the other hand, the treatment method for the faulty pump is simple, usually just directly shutting down the machine, without considering the redundancy and fault tolerance of the system, which may lead to a decline in the overall performance of the system or even abnormal operation. In the electron beam welding process, once the vacuum pump fails and cannot be effectively processed in time, the welding process will be forced to interrupt, not only affecting production efficiency but also possibly damaging the welded parts.

[0006] With the development of industrial automation and intelligence, higher requirements are put forward for the intelligence and adaptability of the vacuum extraction control system. However, the existing systems are difficult to achieve real-time evaluation of the working efficiency of vacuum pumps, fault prediction, and optimization control based on historical data. It is impossible to detect potential equipment problems in time and carry out preventive maintenance, resulting in high equipment maintenance costs and shortened service life. It is also impossible to optimize system parameters according to the actual operating conditions and improve energy utilization efficiency. Summary of the Invention

[0007] The purpose of the present invention is to provide a multi-stage vacuum extraction control system to solve the problems raised in the above background technology.

[0008] To achieve the above purpose, the present invention provides the following technical solutions: A multi-stage vacuum extraction control system, the system includes:

[0009] A central processing unit, a multi-stage vacuum pump group control module, a vacuum degree dynamic monitoring module, a pressure gradient adjustment module, and an abnormal response module;

[0010] The multi-stage vacuum pump group control module coordinates the working states of multi-stage vacuum pumps according to a preset vacuum degree target value, and each stage of vacuum pump corresponds to a different vacuum pressure range;

[0011] The vacuum degree dynamic monitoring module real-time collects the pressure data at the outlet ends of each stage of vacuum pumps, and compares the pressure data with the threshold value of the corresponding vacuum pressure range; if the pressure at the outlet end of any stage of vacuum pump deviates from the threshold value of its corresponding range, a pressure abnormal signal is generated and sent to the central processing unit;

[0012] The pressure gradient adjustment module dynamically adjusts the extraction rate of adjacent stages of vacuum pumps according to the pressure abnormal signal to maintain the overall vacuum gradient stability of the system;

[0013] The abnormal response module receives the pressure abnormal signal forwarded by the central processing unit, and triggers corresponding vacuum pump start / stop or rate correction instructions according to the abnormal level.

[0014] Preferably, the specific working process of the vacuum degree dynamic monitoring module includes:

[0015] During the system startup phase, the initial pressure values of each stage of the vacuum pump are collected, and the dynamic threshold range of the vacuum pressure interval for each stage is calculated based on the initial pressure values; during the operation phase, the pressure fluctuation rate, the average pressure, and the instantaneous pressure extreme value at the outlet end of each stage of the vacuum pump are obtained in real time. The pressure fluctuation rate is compared with a preset rate threshold to generate a rate deviation value, the average pressure is compared with the median value of the dynamic threshold range to generate an average deviation value, and the instantaneous pressure extreme value is compared with the boundary value of the dynamic threshold range to generate an extreme deviation value; if the rate deviation value, the average deviation value, or the extreme deviation value exceeds the preset deviation threshold, a pressure anomaly signal for the corresponding stage is generated.

[0016] Preferably, the working logic of the pressure gradient adjustment module includes:

[0017] When a pressure anomaly signal is triggered for a certain stage of the vacuum pump, the collaborative adjustment coefficient of the adjacent stage of the vacuum pump is calculated according to the anomaly level, and the collaborative adjustment coefficient is dynamically generated based on the pressure difference between the current stage and the adjacent stage and the duration of the anomaly; by adjusting the extraction rate of the adjacent stage of the vacuum pump, the pressure of the current stage is returned to within the dynamic threshold range, while maintaining the linear distribution of the overall vacuum gradient of the system.

[0018] Preferably, the central processor is communicatively connected to the vacuum pump performance evaluation module. The vacuum pump performance evaluation module collects the working duration, the start-stop times, and the rate correction frequency of each stage of the vacuum pump, and calculates the performance evaluation value of the vacuum pump based on the following formula:

[0019] E = α·T + β·S + γ·F

[0020] where T is the working duration weight, S is the start-stop times weight, F is the rate correction frequency weight, and α, β, and γ are preset coefficients; if the performance evaluation value is lower than the preset performance threshold, a pump group maintenance signal is generated and sent to the anomaly response module.

[0021] Preferably, the system further includes a multi-stage pressure feedback compensation module, and its working process is as follows:

[0022] After the pressure gradient adjustment module performs rate correction, the real-time pressure data of the adjacent stage of the vacuum pump after correction is collected, and the pressure difference before and after correction is calculated; if the difference exceeds the preset compensation threshold, compensation is performed by reversely adjusting the extraction rate of the current stage of the vacuum pump to ensure the continuity and stability of the pressure gradient.

[0023] Preferably, the central processor is communicatively connected to the vacuum leak identification module. The vacuum leak identification module determines whether there is a leak by analyzing the deviation value between the overall pressure decay rate of the system and the theoretical decay rate; if the deviation value exceeds the preset leak threshold, a leak location signal is generated and an isolation instruction for the corresponding stage of the vacuum pump is triggered.

[0024] Preferably, the specific analysis method of the vacuum leakage identification module includes:

[0025] During the steady-state operation stage of the system, record the reference pressure decay curves of each stage of vacuum pumps; monitor the pressure decay rate in real time and perform dynamic matching with the reference curves; if the matching degree of the real-time decay rate of a certain stage with the reference curve is lower than the preset matching threshold, mark this stage as a suspected leakage stage and start high-frequency pressure sampling to confirm the leakage location.

[0026] Preferably, the system further includes a vacuum pump redundancy switching module, and its working logic is:

[0027] When a certain stage of vacuum pump shuts down due to the instruction of the abnormal response module, collect the remaining load capacity of the adjacent stage of vacuum pump; if the remaining load capacity is higher than the preset redundancy threshold, start the standby vacuum pump and re-allocate the pressure gradient range; if the remaining load capacity is insufficient, realize the degraded operation of the system by dynamically compressing the pressure gradient range.

[0028] Preferably, the central processor is communicatively connected to the historical data backtracking module, and the historical data backtracking module stores the occurrence time and processing records of all pressure abnormal signals, pump group maintenance signals and leakage location signals, and predicts the occurrence probability of future pressure abnormalities through time series analysis, and generates preventive maintenance suggestions.

[0029] Preferably, the system further includes a multi-stage collaborative optimization module, and its optimization process is:

[0030] Based on the prediction results of the historical data backtracking module, dynamically adjust the initial pressure interval threshold and collaborative adjustment coefficient of each stage of vacuum pump, and optimize the pressure gradient distribution through an iterative algorithm, so that the system maintains the target vacuum degree with the minimum energy consumption.

[0031] Compared with the prior art, the beneficial effects of the present invention are:

[0032] In the present invention, the multi-stage vacuum pump group control module coordinates the work of multi-stage vacuum pumps according to the preset vacuum degree target value, and each stage of vacuum pump corresponds to a different vacuum pressure interval, realizing the refined control of the vacuum degree. For example, in semiconductor chip manufacturing, the vacuum degree can be accurately controlled within the required range, reducing chip production defects caused by vacuum degree fluctuations and greatly improving the product yield. At the same time, the vacuum degree dynamic monitoring module collects the pressure data at the outlet end of each stage of vacuum pump in real time, and through complex algorithm analysis, such as comparing the pressure fluctuation rate, mean value and extreme value with the corresponding thresholds, accurately judges the pressure abnormality, providing a reliable basis for accurate control.

[0033] When the pressure of a certain stage of vacuum pump is abnormal, the pressure gradient adjustment module calculates the collaborative adjustment coefficient of the adjacent stage of vacuum pump according to the abnormal level, and dynamically adjusts the extraction rate, not only making the pressure of the current stage return to the normal range, but also maintaining the stability of the overall vacuum gradient of the system. Taking a vacuum coating equipment as an example, even if there are local pressure fluctuations during the coating process, this module can quickly adjust to ensure the uniformity of the coating thickness, improve the film layer quality, avoid coating defects caused by unstable pressure gradient, reduce the scrap rate, and lower the production cost.

[0034] The abnormal response module triggers the start / stop or rate correction instructions of the vacuum pump according to the abnormal level based on the pressure abnormal signal forwarded by the central processing unit, ensuring the stable operation of the system under abnormal conditions. When a certain stage of vacuum pump suddenly fails, it can start the standby pump in time or adjust the working state of other pumps to ensure the continuity of the system. For example, in the electron beam welding process, it can effectively avoid welding interruption caused by vacuum pump failure, improve production efficiency, reduce the scrapping of welded parts, and protect expensive welded part materials.

[0035] The vacuum pump efficiency evaluation module collects the working hours, start / stop times and rate correction frequencies of each stage of vacuum pump, and calculates the efficiency evaluation value through a specific formula. If the evaluation value is lower than the preset threshold, a pump group maintenance signal is generated to realize the real-time monitoring of the health status of the vacuum pump. Discover potential problems in advance, arrange preventive maintenance, avoid sudden equipment failures, extend the service life of the equipment, reduce the equipment maintenance cost, and reduce the production interruption loss caused by equipment failures. After the pressure gradient adjustment module corrects the rate, the multi-stage pressure feedback compensation module collects real-time pressure data for compensation. If the pressure difference exceeds the preset compensation threshold, the extraction rate of the current stage of vacuum pump is adjusted in the reverse direction to ensure the continuity and stability of the pressure gradient. This function is crucial in high-precision vacuum application scenarios, further improving the accuracy and stability of vacuum degree control and ensuring the reliability of the production process. The vacuum leak identification module accurately judges whether there is a leak and locates the leak position by analyzing the deviation between the overall pressure decay rate of the system and the theoretical decay rate, combined with the reference pressure decay curve recorded in the steady-state operation stage. Generate a leak location signal in time and trigger the isolation instruction of the corresponding stage of vacuum pump to prevent the leak from expanding and affecting the vacuum degree of the system. In a large vacuum system, it can quickly detect small leak points, reduce the interference of gas leakage on the process, reduce energy consumption, and improve the safety and reliability of the system operation.

[0036] When a certain - level vacuum pump in the vacuum pump redundancy switching module shuts down, different strategies are adopted according to the remaining load capacity of the adjacent - level vacuum pumps. If the remaining load capacity is sufficient, the standby vacuum pump is started and the pressure gradient range is redistributed; if it is insufficient, the pressure gradient range is dynamically compressed to achieve system degradation operation. This enhances the fault - tolerance ability of the system, ensures that the system can still maintain certain functions when some equipment fails, improves the reliability and availability of the system, and reduces the production downtime caused by equipment failures. The historical data back - tracking module stores the occurrence time and processing records of various signals, predicts the probability of future pressure anomalies through time - series analysis, and generates preventive maintenance suggestions. This provides a scientific basis for equipment maintenance, arranges maintenance plans in advance, and reduces equipment failure rates. The multi - level collaborative optimization module, based on the prediction results of historical data, dynamically adjusts the initial pressure interval thresholds and collaborative adjustment coefficients of each level of vacuum pumps, optimizes the pressure gradient distribution, enables the system to maintain the target vacuum degree with the minimum energy consumption, realizes the intelligent and energy - saving operation of the system, improves energy utilization efficiency, and reduces operating costs. Brief Description of the Drawings

[0037] Figure 1 is the working principle diagram of the multi - level vacuum extraction control system described in the present invention;

[0038] Figure 2 is the working flow chart of the vacuum degree dynamic monitoring module;

[0039] Figure 3 is the working flow chart of the multi - level pressure feedback compensation module;

[0040] Figure 4 is the flow chart of the analysis method of the vacuum leak identification module. Detailed Embodiment

[0041] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0042] Please refer to Figures 1-4 , the present invention provides a technical solution: based on a multi - level vacuum extraction control system, which mainly consists of a central processor, a multi - level vacuum pump group control module, a vacuum degree dynamic monitoring module, a pressure gradient adjustment module, and an abnormal response module.

[0043] During system operation, the multi-stage vacuum pump group control module coordinates the working states of the multi-stage vacuum pumps according to the preset vacuum degree target value. Since each stage of the vacuum pump corresponds to a different vacuum pressure range, this module can accurately allocate tasks. For example, in a specific industrial production scenario, assuming that a vacuum degree target value of -100 kPa needs to be achieved, the system pre-sets that the first-stage vacuum pump is responsible for pumping the pressure from atmospheric pressure to -20 kPa, the second-stage vacuum pump is responsible for pumping the pressure from -20 kPa to -60 kPa, and the third-stage vacuum pump is responsible for pumping the pressure from -60 kPa to -100 kPa. According to this setting, the multi-stage vacuum pump group control module starts and controls each stage of the vacuum pump in an orderly manner to ensure that they work together and operate towards the target vacuum degree.

[0044] The vacuum degree dynamic monitoring module continuously and real-time collects the pressure data at the outlet ends of each stage of the vacuum pumps. In the system startup phase, it first collects the initial pressure values of each stage of the vacuum pumps, and calculates the dynamic threshold range of each stage of the vacuum pressure range based on these initial pressure values. During the operation phase, this module will obtain the pressure fluctuation rate, pressure average value, and instantaneous pressure extreme value at the outlet ends of each stage of the vacuum pumps in real time. Then, it compares the pressure fluctuation rate with the preset rate threshold to generate a rate deviation value, compares the pressure average value with the median value of the dynamic threshold range to generate an average value deviation value, and compares the instantaneous pressure extreme value with the boundary value of the dynamic threshold range to generate an extreme value deviation value. Once the rate deviation value, average value deviation value, or extreme value deviation value exceeds the preset deviation threshold, a corresponding level of pressure anomaly signal will be generated and sent to the central processor. For example, at a certain moment, the pressure fluctuation rate at the outlet end of the first-stage vacuum pump suddenly increases and exceeds the preset rate threshold. The vacuum degree dynamic monitoring module quickly calculates the rate deviation value and finds that it exceeds the preset deviation threshold, so it generates a pressure anomaly signal for the first-stage vacuum pump.

[0045] When the central processor receives the pressure anomaly signal sent by the vacuum degree dynamic monitoring module, it will forward the signal to the anomaly response module and the pressure gradient adjustment module. The pressure gradient adjustment module starts to work according to the pressure anomaly signal. When a certain stage of the vacuum pump triggers a pressure anomaly signal, it will calculate the collaborative adjustment coefficient of the adjacent-stage vacuum pumps according to the anomaly level. This collaborative adjustment coefficient is dynamically generated based on the pressure difference between the current stage and the adjacent stages and the anomaly duration. For example, if the second-stage vacuum pump triggers a pressure anomaly signal, the pressure gradient adjustment module will calculate the pressure differences between the second stage and the first stage and the third stage of the vacuum pumps, and combine the anomaly duration to determine a suitable collaborative adjustment coefficient. Then, by adjusting the pumping rates of the adjacent-stage vacuum pumps, the pressure of the current stage is returned to within the dynamic threshold range, while maintaining the linear distribution of the overall vacuum gradient of the system.

[0046] After receiving the pressure anomaly signal forwarded by the central processing unit, the anomaly response module will trigger corresponding start / stop or rate correction instructions for the vacuum pumps according to the anomaly level. If the anomaly level is relatively low, it may only trigger the rate correction instruction to adjust the extraction rate of the corresponding vacuum pump; if the anomaly level is relatively high, it may directly trigger the start / stop instruction of the vacuum pump to stop the faulty vacuum pump and start the standby vacuum pump to ensure the stable operation of the system.

[0047] The present invention will be further described below in conjunction with Embodiments 1 to 4:

[0048] Embodiment 1:

[0049] This embodiment details the working logic of the pressure gradient adjustment module. When a pressure anomaly occurs in a certain stage of the vacuum pump, this module maintains the stability of the overall vacuum gradient of the system by reasonably adjusting the extraction rates of adjacent stages of vacuum pumps, ensuring that the system can still operate normally under complex working conditions and improving the reliability and stability of the system.

[0050] When a pressure anomaly signal is triggered by a certain stage of the vacuum pump, the pressure gradient adjustment module starts to work. Taking the second-stage vacuum pump triggering the pressure anomaly signal as an example, first calculate the collaborative adjustment coefficients of the adjacent stages of vacuum pumps (i.e., the first-stage and third-stage vacuum pumps) according to the anomaly level. The anomaly level can be comprehensively determined based on the rate deviation value, mean deviation value, and extreme value deviation value generated by the vacuum degree dynamic monitoring module. Assuming that the anomaly level is divided into three levels: low, medium, and high, when in the low anomaly level, the calculation of the collaborative adjustment coefficient mainly focuses on the pressure difference; when in the medium anomaly level, the influence of the pressure difference and the anomaly duration on the collaborative adjustment coefficient is relatively balanced; when in the high anomaly level, the influence of the anomaly duration on the collaborative adjustment coefficient is greater.

[0051] Let the pressure of the current second-stage vacuum pump be P2, the pressure of the first-stage vacuum pump be P1, the pressure of the third-stage vacuum pump be P3, and the anomaly duration be t. When in the medium anomaly level, the formula for calculating the collaborative adjustment coefficient K1 of the first-stage vacuum pump is: where α and β are coefficients preset according to the system characteristics. The formula for calculating the collaborative adjustment coefficient K3 of the third-stage vacuum pump is: where γ and δ are coefficients preset according to the system characteristics.

[0052] After obtaining the collaborative adjustment coefficients, adjust the extraction rates of the adjacent stages of vacuum pumps to make the current-stage pressure return to the dynamic threshold range while maintaining the linear distribution of the overall vacuum gradient of the system. For the first-stage vacuum pump, if its original extraction rate is R1, the adjusted extraction rate R1 ′ is: R1 ′ = R1×(1 + K1). For the third-stage vacuum pump, if its original extraction rate is R3, the adjusted extraction rate R3′ is: R3 ′ = R3 × (1 + K3).

[0053] During the adjustment process, the pressure changes of each stage of the vacuum pump are monitored in real time. If the pressure of the second-stage vacuum pump gradually returns to the dynamic threshold range and the overall vacuum gradient of the system remains linearly distributed, it indicates that the adjustment is effective; if the pressure does not reach the expected effect, the collaborative adjustment coefficient is recalculated according to the actual situation, and the extraction rate is adjusted again until the system returns to stability.

[0054] Example 2:

[0055] This example introduces the working principle and function of the vacuum pump efficiency evaluation module. By collecting data such as the working hours, start-stop times, and rate correction frequencies of each stage of the vacuum pump, the efficiency evaluation value of the vacuum pump is calculated, providing a basis for the system to judge whether the vacuum pump needs maintenance, helping to detect potential problems in advance, reducing the risk of system failures, extending the service life of the equipment, and improving the economy and reliability of the system operation.

[0056] The central processor is communicatively connected to the vacuum pump efficiency evaluation module, and the vacuum pump efficiency evaluation module is responsible for collecting the working hours, start-stop times, and rate correction frequencies of each stage of the vacuum pump. Taking a certain stage of the vacuum pump as an example, let the working hours of this vacuum pump be T, the start-stop times be S, and the rate correction frequencies be F.

[0057] The vacuum pump efficiency evaluation module calculates the efficiency evaluation value E of the vacuum pump based on the following formula: E = α·T + β·S + γ·F, where T is the weight of the working hours, S is the weight of the start-stop times, F is the weight of the rate correction frequencies, and α, β, and γ are preset coefficients. These preset coefficients are determined according to factors such as the type of the vacuum pump, the working environment, and the importance. For example, for a vacuum pump operating in a high-load and high-precision production environment, the impact of the working hours on its efficiency may be more emphasized, and at this time, the value of α is relatively large; while for a vacuum pump with frequent start-stops, the start-stop times have a greater impact on its efficiency, and the value of β is relatively large.

[0058] During the actual operation process, the vacuum pump efficiency evaluation module continuously collects data and calculates the efficiency evaluation value. Assuming that the preset efficiency threshold is Eth, when the calculated efficiency evaluation value E is lower than the preset efficiency threshold Eth, the vacuum pump efficiency evaluation module generates a pump group maintenance signal and sends this signal to the abnormal response module. After receiving the pump group maintenance signal, the abnormal response module can arrange for the maintenance of this vacuum pump according to the actual operation situation of the system, such as arranging maintenance personnel to conduct inspections, replace vulnerable parts, etc., to ensure that the performance of the vacuum pump is always in good condition.

[0059] Example 3:

[0060] This embodiment describes the working process of the multi - level pressure feedback compensation module. After the pressure gradient adjustment module performs rate correction, this module collects real - time pressure data for compensation adjustment to ensure the continuity and stability of the pressure gradient, further improving the accuracy of the system vacuum degree control and meeting the industrial production scenarios with high requirements for vacuum degree.

[0061] The system is set with a multi - level pressure feedback compensation module, which starts to work after the pressure gradient adjustment module performs rate correction. Taking two adjacent levels of vacuum pumps (assumed to be the i - th level and the (i + 1) - th level) as an example, after the pressure gradient adjustment module corrects the extraction rates of these two levels of vacuum pumps, the multi - level pressure feedback compensation module collects the real - time pressure data of the adjacent - level vacuum pumps after correction. Let the pressure of the i - th level vacuum pump before correction be P i-before , and after correction be P i-after ; the pressure of the (i + 1) - th level vacuum pump before correction be P i+1-before , and after correction be P i+1-after .

[0062] Calculate the pressure differences before and after correction. For the i - th level vacuum pump, the pressure difference ΔP i is: ΔP i = |P i-after - P i-before |; for the (i + 1) - th level vacuum pump, the pressure difference ΔP i+1 is: ΔP i+1 = |P i+1-after - P i+1-before |. The preset compensation threshold is ΔP th . If ΔP i >ΔP th or ΔP i+1 >ΔP th , compensation is required.

[0063] When compensation is required, the multi - level pressure feedback compensation module compensates by reversely adjusting the extraction rate of the current - level vacuum pump. For example, if the pressure difference ΔP i of the i - th level vacuum pump exceeds the preset compensation threshold and its pressure increases after correction, it indicates that the previous rate correction has caused too large a pressure change. At this time, the multi - level pressure feedback compensation module reduces the extraction rate of the i - th level vacuum pump; if the pressure decreases after correction, it increases the extraction rate of the i - th level vacuum pump. In this way, the continuity and stability of the pressure gradient are ensured, and the system vacuum degree is more accurately controlled within the target range.

[0064] After adjusting the extraction rate, the multi - level pressure feedback compensation module continues to monitor the pressure change in real - time. If the pressure difference still exceeds the preset compensation threshold, adjustment is made again until the pressure difference meets the requirements.

[0065] Example 4:

[0066] This embodiment mainly elaborates on the specific working methods of the vacuum leakage identification module, the vacuum pump redundant switching module, the historical data backtracking module, and the multi-level collaborative optimization module, as well as their collaborative effects on each other. Through the close cooperation of these modules, the accurate identification and handling of system vacuum leakage, the effective switching of faulty pumps to maintain system operation, the prediction and analysis using historical data, and the optimization of system operation parameters to reduce energy consumption are achieved, comprehensively improving the performance and reliability of the multi-level vacuum extraction control system.

[0067] ① Vacuum leakage identification module: During system operation, the central processor communicates with the vacuum leakage identification module in real time. After the system starts and operates stably for a period of time (such as 30 minutes), the vacuum leakage identification module begins to record the reference pressure decay curves of each stage of the vacuum pump. Taking a system with six-stage vacuum pumps as an example, pumps 1 - 6 are monitored respectively. The pressure data of each stage of the pump is collected every 10 seconds for 2 hours. The data filtering algorithm is used to remove outliers and noise interference, and then the reference pressure decay curves of each stage of the pump are obtained by curve fitting, denoted as C1, C2, C3, C4, C5, and C6 respectively.

[0068] During the normal operation stage of the system, the vacuum leakage identification module collects the pressure data of each stage of the vacuum pump at a frequency of 5 times per second and calculates the real-time pressure decay rate. For example, for pump 4, the real-time pressure decay rate calculated at a certain moment is r4. The algorithm based on cosine similarity is used to measure the similarity between the real-time decay curve and the reference decay curve C4, and the matching degree M4 is obtained. The preset matching threshold is M th = 0.9. When M4 < M th , it is determined that there may be a leakage in the area where pump 4 is located.

[0069] Once pump 4 is determined to be a suspected leakage level, the vacuum leakage identification module immediately increases the pressure acquisition frequency to 50 times per second and expands the monitoring range to the pipeline, valve and other related components connected to pump 4. Through the processing of high-frequency pressure data such as differential analysis and wavelet transform, the leakage location is accurately positioned. Suppose it is found through analysis that there is a leakage at a sealing interface where pump 4 is connected to the pipeline. The vacuum leakage identification module generates a leakage location signal and triggers the isolation instruction for pump 4. The solenoid valve connected to pump 4 is controlled to close, cutting off the connection between pump 4 and the system to prevent the leakage from further deteriorating the system vacuum environment.

[0070] ② Vacuum pump redundancy switching module: The system is equipped with a complete vacuum pump redundancy switching module. When a certain level of vacuum pump shuts down due to an abnormal response to the module instruction, this module responds quickly. For example, if pump 3 receives a shutdown instruction due to a fault, the vacuum pump redundancy switching module immediately collects the remaining load capacities of the adjacent-level vacuum pumps (pump 2 and pump 4). By monitoring parameters such as the motor power and speed of pump 2 and pump 4, and combining their rated performance parameters, the remaining load capacity of pump 2 is calculated as L2, and the remaining load capacity of pump 4 is calculated as L4. The preset redundancy threshold is L th = 70% (the proportion of the remaining load capacity to the total load capacity).

[0071] If L2 + L4 > L th , it indicates that the adjacent-level vacuum pumps have sufficient capacity to bear the additional load. At this time, the vacuum pump redundancy switching module starts the standby vacuum pump, assuming the standby pump is pump 3'. After starting pump 3', according to the performance of each level of vacuum pump, the current vacuum degree distribution of the system, and the requirements of the production process for the vacuum degree, the pressure gradient interval is reallocated. Originally, pump 2 was responsible for pumping the pressure from -30 kPa to -45 kPa, pump 3 was responsible for pumping the pressure from -45 kPa to -60 kPa, and pump 4 was responsible for pumping the pressure from -60 kPa to -75 kPa. After starting pump 3', it is re-planned that pump 2 is responsible for pumping the pressure from -30 kPa to -40 kPa, pump 3' is responsible for pumping the pressure from -40 kPa to -55 kPa, and pump 4 is responsible for pumping the pressure from -55 kPa to -75 kPa.

[0072] If L2 + L4 ≤ L th , that is, the remaining load capacity is insufficient, the vacuum pump redundancy switching module realizes the system's degraded operation by dynamically compressing the pressure gradient range. For example, it is adjusted that pump 2 is responsible for pumping the pressure from -30 kPa to -60 kPa, and pump 4 is responsible for pumping the pressure from -60 kPa to -75 kPa. At the same time, an alarm message is sent to the operator to inform that the system has entered the degraded operation state and remind to repair pump 3 as soon as possible.

[0073] ③ Historical data backtracking module: The central processor maintains data interaction with the historical data backtracking module. The historical data backtracking module is responsible for storing the occurrence time and processing records of all pressure anomaly signals, pump group maintenance signals, and leakage location signals. Each record details the time when the signal was generated (accurate to seconds), the vacuum pump number involved, the anomaly type (such as too high pressure, too low pressure, abnormal fluctuation, etc.), and the processing actions coordinated by the central processor for each module, including the instructions triggered by the anomaly response module (such as starting the standby pump, adjusting the pump speed, etc.), the adjustment strategy and adjustment amplitude of the pressure gradient adjustment module, etc.

[0074] The historical data backtracking module uses a method that combines the seasonal decomposition method (STL) in time series analysis and the autoregressive integrated moving average model (ARIMA) to predict the occurrence probability of future pressure anomalies. First, the historical pressure data is decomposed into a trend term, a seasonal term, and a residual term through STL decomposition to extract the long-term trend and seasonal characteristics in the data. Then, based on the decomposed data, the ARIMA model is used for modeling and prediction. For example, for the pressure data of pump 5, after analysis, the parameters of the ARIMA model are determined to be (p, d, q) = (1, 1, 1), and the model is trained to predict the pressure change in the future for a certain period (such as the next 24 hours).

[0075] According to the prediction results, preventive maintenance suggestions are generated. If the predicted probability of pressure anomaly of pump 5 within the next 12 hours exceeds 40%, it is recommended to arrange technicians to inspect pump 5 within the next 6 hours. The inspection items include the sealing performance of the seals, the wear condition of the pump body, the lubrication condition of the transmission components, etc., to detect and handle potential problems in a timely manner and reduce the possibility of failures.

[0076] ④ Multi-level collaborative optimization module: The system is set with a multi-level collaborative optimization module, which works based on the prediction results of the historical data backtracking module. According to the predicted occurrence probability of future pressure anomalies and the historical data of the system operation, the initial pressure interval threshold and the collaborative adjustment coefficient of each stage of vacuum pumps are dynamically adjusted.

[0077] For example, if the historical data backtracking module predicts a relatively high probability of pressure anomaly of pump 6 in the future, the multi-level collaborative optimization module adjusts the initial pressure interval threshold of pump 6 by analyzing the pressure change situation, energy consumption data of pump 6, and the overall vacuum degree requirement of the system in the historical data. Suppose the original initial pressure interval of pump 6 is [-80 kPa, -100 kPa], and after analysis, it is adjusted to [-82 kPa, -98 kPa]. At the same time, according to the pressure relationship and collaborative working conditions between each stage of vacuum pumps, the collaborative adjustment coefficient related to pump 6 is recalculated.

[0078] The multi - level collaborative optimization module also optimizes the pressure gradient distribution through the Particle Swarm Optimization (PSO) algorithm. Taking a six - stage vacuum pump system as an example, the pressure intervals of each stage of the vacuum pump are used as the position vectors of the particles, and the objective function is set as the system energy consumption. In the initialization stage of the PSO algorithm, a certain number (such as 30) of particles are randomly generated, and an initial velocity is assigned to each particle. During the iteration process, the particles update their velocities and positions according to their own historical optimal positions and the global optimal position. After each iteration, the system energy consumption corresponding to each particle is calculated through the system energy consumption model, and the historical optimal position and the global optimal position are updated. During the iteration process, the operating state of the system is monitored in real time to ensure that the adjusted pressure gradient distribution meets the vacuum degree stability of the system and the requirements of the production process. After multiple iterative optimizations, the optimal pressure gradient distribution is determined and applied to the actual system to achieve the maximum reduction of energy consumption while meeting the production requirements. During the optimization process, factors such as the maintenance cycle and maintenance cost of the vacuum pump are also considered to make the optimization results more in line with the actual operating requirements.

[0079] It should be noted that in this article, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non - exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not expressly listed, or elements inherent to such process, method, article or device.

[0080] Although the embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A multi-stage vacuum extraction control system, characterized in that, It includes a central processing unit, a multi-stage vacuum pump group control module, a vacuum degree dynamic monitoring module, a pressure gradient adjustment module, and an abnormal response module; The multi-stage vacuum pump group control module coordinates the working states of multi-stage vacuum pumps according to a preset vacuum degree target value, and each stage of vacuum pump corresponds to a different vacuum pressure range; The vacuum degree dynamic monitoring module collects the pressure data at the outlet ends of each stage of vacuum pumps in real time, and compares the pressure data with the threshold values of the corresponding vacuum pressure ranges; if the pressure at the outlet end of any stage of vacuum pump deviates from the threshold value of its corresponding range, a pressure abnormal signal is generated and sent to the central processing unit; The pressure gradient adjustment module dynamically adjusts the extraction rates of adjacent stages of vacuum pumps according to the pressure abnormal signal to maintain the stability of the overall vacuum gradient of the system; The abnormal response module receives the pressure abnormal signal forwarded by the central processing unit, and triggers corresponding vacuum pump start / stop or rate correction instructions according to the abnormal level.

2. The multi-stage vacuum extraction control system according to claim 1, characterized in that, The specific working process of the vacuum degree dynamic monitoring module includes: In the system startup stage, the initial pressure values of each stage of vacuum pumps are collected, and the dynamic threshold range of each stage of vacuum pressure range is calculated based on the initial pressure values; in the operation stage, the pressure fluctuation rate, pressure average value, and instantaneous pressure extreme value at the outlet ends of each stage of vacuum pumps are obtained in real time, the rate deviation value is generated by comparing the pressure fluctuation rate with a preset rate threshold, the average value deviation value is generated by comparing the pressure average value with the median value of the dynamic threshold range, and the extreme value deviation value is generated by comparing the instantaneous pressure extreme value with the boundary value of the dynamic threshold range; if the rate deviation value, average value deviation value, or extreme value deviation value exceeds the preset deviation threshold, a pressure abnormal signal of the corresponding level is generated.

3. The multi-stage vacuum extraction control system according to claim 2, wherein, The working logic of the pressure gradient adjustment module includes: When a pressure abnormal signal is triggered by a certain stage of vacuum pump, the collaborative adjustment coefficient of the adjacent stage of vacuum pump is calculated according to the abnormal level, and the collaborative adjustment coefficient is dynamically generated based on the pressure difference between the current stage and the adjacent stage and the abnormal duration; by adjusting the extraction rates of the adjacent stage of vacuum pumps, the pressure of the current stage is returned to within the dynamic threshold range, while maintaining the linear distribution of the overall vacuum gradient of the system.

4. A multi-stage vacuum extraction control system according to claim 3, characterized in that, The central processing unit is communicatively connected to a vacuum pump efficiency evaluation module, and the vacuum pump efficiency evaluation module collects the working duration, start / stop times, and rate correction frequencies of each stage of vacuum pumps, and calculates the efficiency evaluation value of the vacuum pump based on the following formula: E = α·T + β·S + γ·F where T is the working duration weight, S is the start / stop times weight, F is the rate correction frequency weight, and α, β, and γ are preset coefficients; if the efficiency evaluation value is lower than the preset efficiency threshold, a pump group maintenance signal is generated and sent to the abnormal response module.

5. A multi-stage vacuum extraction control system according to claim 4, wherein, The system further includes a multi-stage pressure feedback compensation module, and its working process is: After the rate correction is executed by the pressure gradient adjustment module, the real-time pressure data of the adjacent stage of vacuum pumps after correction is collected, and the pressure difference before and after correction is calculated; If the difference exceeds the preset compensation threshold, compensation is performed by reversely adjusting the extraction rate of the current stage of vacuum pump to ensure the continuity and stability of the pressure gradient.

6. The multi-stage vacuum extraction control system according to claim 5, wherein The central processing unit is communicatively connected to the vacuum leak identification module. The vacuum leak identification module determines whether there is a leak by analyzing the deviation value between the overall pressure decay rate of the system and the theoretical decay rate. If the deviation value exceeds the preset leak threshold, a leak location signal is generated and an isolation instruction for the corresponding stage vacuum pump is triggered.

7. A multi-stage vacuum extraction control system according to claim 6, characterized in that, The specific analysis method of the vacuum leak identification module includes: During the steady-state operation stage of the system, record the reference pressure decay curves of each stage vacuum pump; monitor the pressure decay rate in real time and perform dynamic matching with the reference curves. If the matching degree between the real-time decay rate of a certain stage and the reference curve is lower than the preset matching threshold, mark this stage as a suspected leak stage and start high-frequency pressure sampling to confirm the leak location.

8. A multi-stage vacuum extraction control system according to claim 7, wherein, The system further includes a vacuum pump redundant switching module, and its working logic is: When a certain stage vacuum pump shuts down due to the instruction of the abnormal response module, collect the remaining load capacity of the adjacent stage vacuum pump. If the remaining load capacity is higher than the preset redundancy threshold, start the standby vacuum pump and reallocate the pressure gradient range. If the remaining load capacity is insufficient, the system degrades by dynamically compressing the pressure gradient range.

9. A multi-stage vacuum extraction control system according to claim 8, wherein, The central processing unit is communicatively connected to the historical data backtracking module. The historical data backtracking module stores the occurrence time and processing records of all pressure anomaly signals, pump group maintenance signals, and leak location signals, and predicts the occurrence probability of future pressure anomalies through time series analysis to generate preventive maintenance suggestions.

10. A multi-stage vacuum extraction control system according to claim 9, characterized in that, The system further includes a multi-stage collaborative optimization module, and its optimization process is: Based on the prediction results of the historical data backtracking module, dynamically adjust the initial pressure interval threshold and the collaborative adjustment coefficient of each stage vacuum pump, and optimize the pressure gradient distribution through an iterative algorithm, so that the system maintains the target vacuum degree with the minimum energy consumption.

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