A double cold trap operation monitoring method, double cold trap control system, medium and product

Abstract

A dual cold trap operation monitoring method, a dual cold trap control system, a medium and a product, which involve electrical digital data processing, the method comprising: obtaining temperature and humidity data of a first cold trap, a first condensed water accumulation, temperature and humidity data of a second cold trap and a second condensed water accumulation; determining the first cold trap dehumidification efficiency, the second cold trap dehumidification efficiency, the first temperature gradient, the second temperature gradient and the rate of change of the condensed water accumulation; determining the first operation defect of the first cold trap and / or the second operation defect of the second cold trap according to a preset parameter standard value; based on the first operation defect of the first cold trap and / or the second operation defect of the second cold trap, searching for a dual cold trap treatment scheme with a correlation higher than a preset correlation threshold in a preset operation defect treatment scheme database. Implementing this method can improve the accuracy of dual cold trap operation monitoring.

Description

Technical Field

[0001] This application relates to electronic digital data processing, and in particular to a method for monitoring the operation of a double cold trap, a double cold trap control system, a medium, and a product. Background Art

[0002] In the industrial production process, especially in an environment where humidity needs to be controlled, the moisture content of the gas directly affects the product quality and the service life of the equipment. In order to effectively control the moisture in the gas, cold trap equipment has been widely used. The cold trap equipment effectively removes the moisture in the air through the condensation method to ensure product quality and production efficiency.

[0003] Currently, the control system of the cold trap equipment monitors the temperature and humidity through sensors set at key positions, and adjusts the operating state of the cold trap equipment according to these data to ensure that the cold trap equipment operates in the best state, thereby maintaining high-efficiency dehumidification performance.

[0004] Although the method for monitoring the operation of the cold trap equipment in the related art can improve the dehumidification efficiency to a certain extent, there are still some obvious technical defects. For example, it is difficult to comprehensively capture the humidity distribution and temperature distribution inside the entire cold trap equipment, resulting in the locality and limitation of the monitoring data, making it difficult to make the optimal operation decision and affecting the performance of the entire cold trap equipment. Summary of the Invention

[0005] This application provides a method for monitoring the operation of a double cold trap, a double cold trap control system, a medium, and a product, which is used to improve the accuracy of monitoring the operation of the double cold trap.

[0006] In a first aspect, the present application provides a method for monitoring the operation of a dual cold trap, which is applied to a dual cold trap control system. The method includes: obtaining the temperature and humidity data of the first cold trap, the cumulative amount of the first condensate, the temperature and humidity data of the second cold trap, and the cumulative amount of the second condensate. The temperature and humidity data of the first cold trap includes the first inlet temperature and the first inlet humidity at the inlet of the first cold trap, and the first outlet temperature and the first outlet humidity at the outlet of the first cold trap. The cumulative amount of the first condensate is used to represent the volume of the condensate in the first cold trap. The temperature and humidity data of the second cold trap includes the second inlet temperature and the second inlet humidity at the inlet of the second cold trap, and the second outlet temperature and the second outlet humidity at the outlet of the second cold trap. The cumulative amount of the second condensate is used to represent the volume of the condensate in the second cold trap; determining the dehumidification efficiency of the first cold trap according to the temperature and humidity data of the first cold trap, determining the dehumidification efficiency of the second cold trap according to the temperature and humidity data of the second cold trap, determining the first temperature gradient according to the first inlet temperature and the first outlet temperature, determining the second temperature gradient according to the second inlet temperature and the second outlet temperature, and determining the change rate of the cumulative amount of condensate according to the cumulative amount of the first condensate and the cumulative amount of the second condensate; comparing a preset parameter standard value with the dehumidification efficiency of the first cold trap, the dehumidification efficiency of the second cold trap, the first temperature gradient, the second temperature gradient, and the change rate of the cumulative amount of condensate to determine the first operation defect of the first cold trap and / or the second operation defect of the second cold trap. The operation defect includes abnormal dehumidification efficiency, abnormal temperature gradient, and abnormal condensate; based on the first operation defect of the first cold trap and / or the second operation defect of the second cold trap, searching for a dual cold trap treatment solution with a relevance higher than a preset relevance threshold in a preset operation defect treatment solution database.

[0007] By adopting the above technical solution, the dual cold trap control system comprehensively detects the operation state of the dual cold trap, so as to timely discover operation defects such as abnormal dehumidification efficiency, abnormal temperature gradient, and abnormal condensate, and thus quickly match the corresponding dual cold trap treatment solution through the preset operation defect treatment solution database. This monitoring method based on comprehensive analysis of multiple parameters not only improves the accuracy of fault diagnosis, but also can optimize the operation state of the dual cold trap in real time, effectively avoiding misjudgment that may be caused by the locality and finiteness of monitoring data, ensuring the pertinence and feasibility of the dual cold trap treatment solution, and improving the reliability and stability of the dual cold trap operation.

[0008] In some embodiments in combination with some embodiments of the first aspect, after the step of searching for a dual cold trap treatment solution with a relevance higher than a preset relevance threshold in a preset operation defect treatment solution database based on the first operation defect of the first cold trap and / or the second operation defect of the second cold trap, the method further includes: collecting a first acoustic feature signal and a first vibration feature signal of the first cold trap, and a second acoustic feature signal and a second vibration feature signal of the second cold trap; performing time-frequency domain analysis on the first acoustic feature signal, the first vibration feature signal, the second acoustic feature signal, and the second vibration feature signal to obtain a first abnormal operating condition feature of the first cold trap and a second abnormal operating condition feature of the second cold trap; performing multi-modal feature fusion on the first abnormal operating condition feature and the first operation defect and / or the second abnormal operating condition feature and the second operation defect to obtain a multi-modal feature fusion result; and updating the multi-modal feature fusion result to the dual cold trap treatment solution.

[0009] By adopting the above technical solution, the dual cold trap control system introduces the collection and analysis of acoustic feature signals and vibration feature signals, realizing multi-dimensional monitoring of the dual cold trap. Time-frequency domain analysis can capture subtle abnormal changes during the operation of the dual cold trap, while multi-modal feature fusion organically combines physical features such as acoustics and vibration with operation defects to form a more comprehensive fault feature description. The dual cold trap control system updates the multi-modal feature fusion result to the dual cold trap treatment solution, realizing the continuous optimization and self-improvement of the dual cold trap treatment solution. This method of multi-source information fusion significantly improves the sensitivity and accuracy of abnormal detection, enabling potential problems to be detected at the early stage of the fault for the dual cold trap.

[0010] In some embodiments in combination with some embodiments of the first aspect, after the steps of determining the dehumidification efficiency of the first cold trap according to the first cold trap temperature and humidity data, determining the dehumidification efficiency of the second cold trap according to the second cold trap temperature and humidity data, determining the first temperature gradient according to the first inlet temperature and the first outlet temperature, determining the second temperature gradient according to the second inlet temperature and the second outlet temperature, and determining the change rate of the condensate accumulation amount according to the first condensate accumulation amount and the second condensate accumulation amount, the method further includes: constructing a dual cold trap operating state vector according to the dehumidification efficiency of the first cold trap, the dehumidification efficiency of the second cold trap, the first temperature gradient, the second temperature gradient, and the change rate of the condensate accumulation amount; performing component analysis on the dual cold trap operating state vector to extract the main operating features; and establishing a dual cold trap performance stability evaluation model based on the main operating features to evaluate the stability of the dual cold trap in real time.

[0011] By adopting the above technical solutions, the dual cold trap control system constructs the operating state vectors of the dual cold traps and conducts component analysis, realizing in-depth mining of the main operating characteristics of the dual cold traps. The extraction process of the main operating characteristics can effectively reduce the data dimension, highlight key information, and avoid the interference of secondary factors. The performance stability evaluation model of the dual cold traps established based on the main operating characteristics can evaluate the stability of the dual cold traps in real time and accurately. This data-driven evaluation method not only improves the objectivity and reliability of the dual cold trap state evaluation, but also provides an important basis for predictive maintenance and optimal control, effectively improving the operating efficiency and reliability of the entire dual cold trap system.

[0012] Combined with some embodiments of the first aspect, in some embodiments, after the step of searching for a dual cold trap treatment solution with a relevance higher than a preset relevance threshold in a preset operating defect treatment solution database based on the first operating defect of the first cold trap and / or the second operating defect of the second cold trap, the method further includes: inputting the temperature and humidity data of the first cold trap and the cumulative amount of the first condensate water, and the temperature and humidity data of the second cold trap and the cumulative amount of the second condensate water into a dual cold trap fault prediction model respectively to obtain a cold trap fault prediction result; when the cold trap fault prediction result shows that the fault probability is greater than a preset first probability threshold and less than a preset second probability threshold, triggering a preventive maintenance process for the first cold trap; when the cold trap fault prediction result shows that the fault probability is greater than the preset second probability threshold, triggering a preventive maintenance process for the second cold trap.

[0013] By adopting the above technical solutions, the dual cold trap control system establishes a dual cold trap fault prediction model, realizing hierarchical early warning and preventive maintenance of dual cold trap faults. By setting two different levels of probability thresholds (i.e., the preset first probability threshold and the preset second probability threshold), the dual cold trap control system can take corresponding levels of maintenance measures according to the cold trap fault prediction result. This hierarchical early warning mechanism can not only effectively prevent dual cold trap faults, reduce the risk of sudden shutdowns, but also optimize the allocation of maintenance resources, avoid problems of over-maintenance or under-maintenance, thereby improving the service life and operating efficiency of the dual cold traps and reducing maintenance costs.

[0014] Combined with some embodiments of the first aspect, in some embodiments, the method further includes: calculating the cooperative operation index of the first cold trap and the second cold trap in real time; when the cooperative operation index is lower than a preset cooperative index threshold, adjusting the operating parameters of the dual cold traps to preset optimal operating parameters.

[0015] By adopting the above technical solution, the dual cold trap control system introduces the real-time calculation and monitoring of the collaborative operation index, achieving the collaborative and optimized operation of the dual cold traps. When the collaborative operation index is lower than the preset collaborative index threshold, the dual cold trap control system will automatically adjust the operation parameters of the dual cold traps to the preset optimal operation parameters, ensuring a reasonable load distribution between the two cold traps and the optimal operation efficiency. This adaptive adjustment mechanism based on the collaborative operation index effectively solves problems such as unbalanced load and low efficiency that may occur in the dual cold traps, enabling the dual cold traps to always maintain the best operation state, improving the energy utilization efficiency and ensuring the stability of the dehumidification effect.

[0016] Combined with some embodiments of the first aspect, in some embodiments, the real-time calculation of the collaborative operation index of the first cold trap and the second cold trap specifically includes: obtaining the first operation parameters of the first cold trap and the second operation parameters of the second cold trap, where the operation parameters include coolant flow rate, coolant temperature, coolant pressure, and condensate water discharge rate; calculating the operation state matrix of the dual cold traps according to the first operation parameters and the second operation parameters; performing feature extraction on the operation state matrix to obtain the operation feature vector of the dual cold trap device; calculating the feature similarity between the operation feature vector and the preset optimal operation feature vector; dynamically allocating weights to the first operation parameters and the second operation parameters according to the feature similarity to obtain the first weight coefficient and the second weight coefficient; and determining the collaborative operation index of the first cold trap and the second cold trap based on the first weight coefficient, the second weight coefficient, the dehumidification efficiency of the first cold trap, and the dehumidification efficiency of the second cold trap.

[0017] By adopting the above technical solution, the dual cold trap control system uses the first operation parameters and the second operation parameters to construct a comprehensive collaborative operation evaluation system. This calculation method of the collaborative operation index based on multi-parameter comprehensive evaluation not only considers the overall performance of the dual cold traps but also takes into account the operation efficiency of the first cold trap and the second cold trap, making the collaborative control of the dual cold trap control system more accurate and efficient, and effectively improving the overall performance and stability of the dual cold traps.

[0018] Combined with some embodiments of the first aspect, in some embodiments, after the step of obtaining the first operation parameters of the first cold trap and the second operation parameters of the second cold trap, where the operation parameters include coolant flow rate, coolant temperature, coolant pressure, and condensate water discharge rate, the method further includes: obtaining the first energy consumption data of the first cold trap and the second energy consumption data of the second cold trap; determining the energy consumption efficiency of the dual cold traps based on the first operation parameters and the first energy consumption data, and the second operation parameters and the second energy consumption data; and determining the reason for the energy efficiency degradation of the dual cold traps when the energy consumption efficiency is lower than the preset energy consumption efficiency threshold.

[0019] By adopting the above technical solutions, the dual cold trap control system introduces an energy consumption monitoring and analysis mechanism, achieving energy efficiency optimization of the dual cold trap. This proactive energy efficiency monitoring and diagnostic mechanism can promptly detect problems affecting energy utilization efficiency to determine the reasons for the energy efficiency degradation of the dual cold trap, thereby providing specific basis for equipment optimization and energy-saving transformation. This not only helps reduce operating costs but also enables energy conservation and emission reduction of the dual cold trap, improving the economy and environmental friendliness of the dual cold trap.

[0020] In a second aspect, an embodiment of the present application provides a dual cold trap control system, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the dual cold trap control system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product containing instructions. When the computer program product runs on the dual cold trap control system, it enables the dual cold trap control system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium including instructions. When the instructions run on the dual cold trap control system, it enables the dual cold trap control system to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] It can be understood that the dual cold trap control system provided in the second aspect, the computer program product provided in the third aspect, and the computer storage medium provided in the fourth aspect are all used to execute the method provided in the embodiments of the present application. Therefore, the beneficial effects they can achieve can refer to the beneficial effects in the corresponding method and will not be elaborated here.

[0024] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0025] 1. Due to the adoption of an analysis method based on all-round monitoring of the temperature and humidity data and the condensate accumulation amount of the dual cold trap and multi-modal feature fusion, it is possible to capture the operating state of the dual cold trap in real time and accurately and predict potential faults, effectively solving the problems of one-sidedness and lag in judgment caused by single-parameter monitoring in the related art, and thus realizing intelligent monitoring and preventive maintenance of the dual cold trap.

[0026] 2. Due to the adoption of the double cold trap collaborative operation index calculation and dynamic weight distribution mechanism, the operating state of the double cold trap can be accurately evaluated and the adaptive adjustment of operating parameters can be realized, effectively solving the problems of poor coordination and unbalanced load distribution between the double cold traps in the related technology, and thus realizing the overall optimization of the operating efficiency of the double cold trap.

[0027] 3. Due to the adoption of the method combining energy consumption efficiency monitoring and energy efficiency degradation analysis, the energy consumption status of the double cold trap can be monitored in real time and the efficiency loss can be detected in time, effectively solving the problems of low energy utilization efficiency and extensive energy consumption management in the related technology, and thus realizing the energy conservation and efficiency improvement of the double cold trap. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 is a schematic flow chart of a double cold trap operation monitoring method in an embodiment of the present application;

[0029] Figure 2 is another schematic flow chart of a double cold trap operation monitoring method in an embodiment of the present application;

[0030] Figure 3 is a schematic structural diagram of an entity device of a double cold trap control system in an embodiment of the present application. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification of the present application, the singular forms "a", "an", "the above", "the" and "this" are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations including one or more of the listed items.

[0032] Hereinafter, the terms "first" and "second" are only used for descriptive purposes and cannot be construed as implying or indicating relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0033] The following describes the process of the method provided in this embodiment. Please refer to Figure 1 , which is a schematic flow chart of a double cold trap operation monitoring method in an embodiment of the present application.

[0034] S101. Obtain the temperature and humidity data of the first cold trap, the cumulative amount of the first condensed water, the temperature and humidity data of the second cold trap, and the cumulative amount of the second condensed water. The temperature and humidity data of the first cold trap include the first inlet temperature and the first inlet humidity at the inlet of the first cold trap, and the first outlet temperature and the first outlet humidity at the outlet of the first cold trap. The cumulative amount of the first condensed water is used to represent the volume of the condensed water in the first cold trap. The temperature and humidity data of the second cold trap include the second inlet temperature and the second inlet humidity at the inlet of the second cold trap, and the second outlet temperature and the second outlet humidity at the outlet of the second cold trap. The cumulative amount of the second condensed water is used to represent the volume of the condensed water in the second cold trap;

[0035] Among them, the first cold trap refers to the cold trap device at the front end of the double cold trap device, which is used to initially reduce the moisture content in the gas; the second cold trap refers to the cold trap device at the rear end of the double cold trap device, which is used to further reduce the moisture content in the gas; the temperature and humidity data represent the temperature values and humidity values measured at the inlet and outlet of the cold trap, including the inlet temperature, the inlet humidity, the outlet temperature, and the outlet humidity; the cumulative amount of condensed water refers to the volume of the condensed water collected during the operation of the cold trap, which is used to characterize the dehumidification effect; the temperature is in degrees Celsius, the humidity is expressed as a percentage of relative humidity, and the cumulative amount of condensed water is in milliliters.

[0036] Specifically, the double cold trap control system collects the first inlet temperature and the first inlet humidity through the temperature sensor and the humidity sensor set at the inlet of the first cold trap, and collects the first outlet temperature and the first outlet humidity through the temperature sensor and the humidity sensor set at the outlet of the first cold trap; at the same time, the double cold trap control system monitors the cumulative amount of the first condensed water in real time through the liquid level sensor set at the bottom of the first cold trap. The double cold trap control system collects the second inlet temperature and the second inlet humidity through the temperature sensor and the humidity sensor set at the inlet of the second cold trap, and collects the second outlet temperature and the second outlet humidity through the temperature sensor and the humidity sensor set at the outlet of the second cold trap; at the same time, the double cold trap control system monitors the cumulative amount of the second condensed water in real time through the liquid level sensor set at the bottom of the second cold trap. The sampling period of all sensors is 1 second, and the collected data is transmitted to the double cold trap control system after being filtered.

[0037] S102. Determine the dehumidification efficiency of the first cold trap according to the temperature and humidity data of the first cold trap, determine the dehumidification efficiency of the second cold trap according to the temperature and humidity data of the second cold trap, determine the first temperature gradient according to the first inlet temperature and the first outlet temperature, determine the second temperature gradient according to the second inlet temperature and the second outlet temperature, and determine the change rate of the cumulative amount of condensed water according to the cumulative amount of the first condensed water and the cumulative amount of the second condensed water;

[0038] Among them, the dehumidification efficiency refers to the ability of the cold trap to reduce the moisture content of the gas, expressed as the percentage of the difference between the inlet humidity and the outlet humidity to the inlet humidity; the temperature gradient represents the degree of temperature change inside the cold trap, which is the difference between the inlet temperature and the outlet temperature; the rate of change of the condensate accumulation is the increase in the volume of condensate per unit time, used to characterize the dynamic characteristics of the dehumidification process; the dehumidification efficiency is expressed as a percentage, the temperature gradient is expressed in degrees Celsius per meter, and the rate of change of the condensate accumulation is expressed in milliliters per minute.

[0039] Specifically, the dehumidification efficiency of the first cold trap is determined according to the temperature and humidity data of the first cold trap. The calculation formula is (the first inlet humidity - the first outlet humidity) / the first inlet humidity × 100%. Thus, the dual cold trap control system obtains the dehumidification efficiency of the first cold trap. The dehumidification efficiency of the second cold trap is determined according to the temperature and humidity data of the second cold trap. The calculation formula is (the second inlet humidity - the second outlet humidity) / the second inlet humidity × 100%. Thus, the dual cold trap control system obtains the dehumidification efficiency of the second cold trap. The first temperature gradient is determined according to the first inlet temperature and the first outlet temperature, that is, the difference between the first inlet temperature and the first outlet temperature divided by the length of the cold trap. Thus, the dual cold trap control system obtains the first temperature gradient; the second temperature gradient is determined according to the second inlet temperature and the second outlet temperature, that is, the difference between the second inlet temperature and the second outlet temperature divided by the length of the cold trap. Thus, the dual cold trap control system obtains the first temperature gradient. The rate of change of the condensate accumulation is determined according to the first condensate accumulation and the second condensate accumulation, that is, the dual cold trap control system calculates the volume increment of the condensate in the first cold trap and the volume increment of the condensate in the second cold trap per unit time by continuous sampling to obtain the rate of change of the condensate accumulation. It should be noted that the dual cold trap control system updates the calculation results in real time, and the update period is 10 seconds.

[0040] S103. Compare the preset parameter standard values with the dehumidification efficiency of the first cold trap, the dehumidification efficiency of the second cold trap, the first temperature gradient, the second temperature gradient, and the rate of change of the condensate accumulation to determine the first operation defect of the first cold trap and / or the second operation defect of the second cold trap. The operation defects include abnormal dehumidification efficiency, abnormal temperature gradient, and abnormal condensate.

[0041] Among them, the preset parameter standard value refers to the ideal value range of the first standard cold trap dehumidification efficiency, the second standard cold trap dehumidification efficiency, the first standard temperature gradient, the second standard temperature gradient, and the standard condensate accumulation rate when the dual cold trap device operates normally, and is used as a benchmark for judging the operation state of the dual cold trap; the operation defect represents the abnormal condition that occurs during the operation of the dual cold trap device, including abnormal dehumidification efficiency, abnormal temperature gradient, and abnormal condensate; the first operation defect represents the abnormality detected in the first cold trap, and the second operation defect represents the abnormality detected in the second cold trap.

[0042] A. Abnormal dehumidification efficiency:

[0043] (1) Frost / ice formation on the cold trap surface leads to a decrease in heat transfer efficiency;

[0044] (2) Blockage inside the cold trap affects gas circulation;

[0045] (3) Insufficient refrigeration capacity of the refrigeration system;

[0046] (4) Dirt on the cold trap surface causes a reduction in heat transfer efficiency;

[0047] ...

[0048] B. Abnormal temperature gradient:

[0049] (1) Malfunction of the refrigeration system leads to abnormal temperature control;

[0050] (2) Damage or failure of the cold trap insulation layer;

[0051] (3) Uneven distribution inside the cold trap;

[0052] (4) Malfunction or deviation of the temperature sensor;

[0053] ...

[0054] C. Abnormal condensate water:

[0055] (1) Blockage of condensate water discharge;

[0056] (2) Leakage of the condensate water pipeline;

[0057] (3) Overflow of the condensate water collection tank;

[0058] (4) Malfunction of the condensate water discharge valve;

[0059] (5) Exceedance of the condensate water quality standard.

[0060] ...

[0061] Specifically, first, the dual cold trap control system compares the dehumidification efficiency of the first cold trap with the first standard cold trap dehumidification efficiency (e.g., 90%-95%). When the dehumidification efficiency of the first cold trap is lower than the first standard cold trap dehumidification efficiency, it is determined that the dehumidification efficiency is abnormal. The dual cold trap control system compares the first temperature gradient with the first standard temperature gradient range (e.g., 15-20 °C / m). When the first temperature gradient exceeds the first standard temperature gradient range, it is determined that the temperature gradient is abnormal. The dual cold trap control system compares the change rate of the condensate accumulation amount with the standard condensate accumulation amount change rate (100-150 ml / min). When the change rate of the condensate accumulation amount is abnormal, it is determined that the condensate is abnormal. The same comparison method is used for the second cold trap. The dual cold trap control system determines the type and level of the abnormality according to the set judgment rules (which can be set according to historical data or formulated by experts), and generates corresponding operation defects. The comparison process is executed once per minute to ensure that system abnormalities can be detected in a timely manner.

[0062] S104. Based on the first operation defect of the first cold trap and / or the second operation defect of the second cold trap, search for a dual cold trap treatment solution in the preset operation defect treatment solution database with a relevance higher than the preset relevance threshold.

[0063] Among them, the preset operation defect treatment solution database refers to a data set storing treatment solutions corresponding to various operation defects, and is used to provide guidance for fault handling. Relevance indicates the matching degree between the treatment solution and the current operation defect. The preset relevance threshold refers to the minimum standard for judging whether a treatment solution is applicable, usually set to 0.8. The dual cold trap treatment solution represents the specific solution measures and operation steps for the current operation defect. The treatment solutions in the preset operation defect treatment solution database include information such as defect description, treatment steps, required tools, and expected effects.

[0064] Specifically, first, the dual cold trap control system inputs the detected operation defects (including defect type, severity, occurrence location, etc.) into the defect feature extraction module to generate a standardized defect feature description. Then, in the preset operation defect treatment solution database, the dual cold trap control system calculates the relevance between the current operation defect and each treatment solution in the preset operation defect treatment solution database through a feature matching algorithm. Next, the dual cold trap control system filters out the treatment solutions with a relevance higher than the preset relevance threshold and sorts them from high to low according to the relevance. Finally, the dual cold trap control system pushes the sorted treatment solutions to the operator and records the execution effect of the treatment solutions for subsequent optimization of the preset operation defect treatment solution database. The dual cold trap control system supports real-time update and dynamic optimization of the dual cold trap treatment solution to ensure the practicality and effectiveness of the dual cold trap treatment solution.

[0065] By adopting the above technical solution, the dual cold trap control system comprehensively detects the operating status of the dual cold traps, so as to timely discover operating defects such as abnormal dehumidification efficiency, abnormal temperature gradient, and abnormal condensate water, and then quickly match the corresponding dual cold trap treatment solutions through the preset operating defect treatment solution database. This monitoring method based on comprehensive multi-parameter analysis not only improves the accuracy of fault diagnosis, but also can optimize the operating status of the dual cold traps in real time, effectively avoiding misjudgment that may be caused by the locality and finiteness of monitoring data, ensuring the pertinence and feasibility of the dual cold trap treatment solutions, and improving the reliability and stability of the dual cold trap operation.

[0066] The following further describes the method provided in this embodiment in a more specific process. Please refer to Figure 2 , which is another process schematic diagram of the dual cold trap operation monitoring method in the embodiment of the present application.

[0067] S201. Obtain the temperature and humidity data of the first cold trap, the cumulative amount of the first condensate water, the temperature and humidity data of the second cold trap, and the cumulative amount of the second condensate water. The temperature and humidity data of the first cold trap includes the first inlet temperature and the first inlet humidity at the inlet of the first cold trap, and the first outlet temperature and the first outlet humidity at the outlet of the first cold trap. The cumulative amount of the first condensate water is used to represent the volume of the condensate water in the first cold trap. The temperature and humidity data of the second cold trap includes the second inlet temperature and the second inlet humidity at the inlet of the second cold trap, and the second outlet temperature and the second outlet humidity at the outlet of the second cold trap. The cumulative amount of the second condensate water is used to represent the volume of the condensate water in the second cold trap;

[0068] Specifically, refer to step S101, which will not be elaborated here.

[0069] S202. Determine the dehumidification efficiency of the first cold trap according to the temperature and humidity data of the first cold trap, determine the dehumidification efficiency of the second cold trap according to the temperature and humidity data of the second cold trap, determine the first temperature gradient according to the first inlet temperature and the first outlet temperature, determine the second temperature gradient according to the second inlet temperature and the second outlet temperature, and determine the change rate of the cumulative amount of condensate water according to the cumulative amount of the first condensate water and the cumulative amount of the second condensate water;

[0070] Specifically, refer to step S102, which will not be elaborated here.

[0071] S203. Construct a dual cold trap operation status vector according to the dehumidification efficiency of the first cold trap, the dehumidification efficiency of the second cold trap, the first temperature gradient, the second temperature gradient, and the change rate of the cumulative amount of condensate water;

[0072] Among them, the dual cold trap operation state vector refers to a multi-dimensional data set used to characterize the current operation state of the dual cold trap device, and is represented in the form of an N-dimensional vector for the dehumidification efficiency of the first cold trap, the dehumidification efficiency of the second cold trap, the first temperature gradient, the second temperature gradient, and the change rate of the condensate water accumulation amount of the dual cold trap device; the vector dimension represents the number of operation parameters, which is a 5-dimensional vector in this step; the vector component refers to the standardized value after standardizing each operation parameter, and the value range is 0-1; the standardization process refers to a mathematical process of converting operation parameters with different dimensions into a unified scale, which is used to eliminate the influence of dimensions.

[0073] Specifically, first, the dual cold trap control system standardizes the five operation parameters: the dehumidification efficiency is normalized by dividing by 100%, the temperature gradient is normalized by dividing by the maximum design temperature difference of 20 °C / m, and the change rate of the condensate water accumulation amount is normalized by dividing by the maximum design change rate of the condensate water accumulation amount of 150 ml / min. Then, the dual cold trap control system constructs a 5-dimensional vector in the order of [the dehumidification efficiency of the first cold trap, the dehumidification efficiency of the second cold trap, the first temperature gradient, the second temperature gradient, the change rate of the condensate water accumulation amount]. The dual cold trap control system can update the dual cold trap operation state vector every 10 seconds.

[0074] S204. Perform component analysis on the dual cold trap operation state vector to extract the main operation characteristics;

[0075] Among them, component analysis refers to a mathematical method for dimensionality reduction and feature extraction of multi-dimensional data, which is used to discover the main features and patterns in the data; the main operation characteristics refer to the key parameter combinations that have a significant impact on the operation state of the dual cold trap device.

[0076] Specifically, the dual cold trap control system uses the principal component analysis (PCA) method to process the dual cold trap operation state vector: first, the dual cold trap control system performs centering processing on the dual cold trap operation state vector and calculates the covariance matrix. Then, the dual cold trap control system solves the eigenvalues and eigenvectors of the covariance matrix and sorts them according to the eigenvalue size. Next, the dual cold trap control system calculates the contribution rate of each eigenvector, and the contribution rate refers to the proportion of a certain eigenvector explaining the variance of the original data. When the cumulative contribution rate reaches 85%, the number of main eigenvectors is determined. The cumulative contribution rate represents the sum of the contribution rates of the first N eigenvectors, which is used to determine the number of eigenvectors to be retained. Finally, the dual cold trap control system projects the original dual cold trap operation state vector onto the main feature space to obtain the dimension-reduced feature representation, that is, the main operation characteristics.

[0077] S205. Based on the main operation characteristics, establish a dual cold trap performance stability evaluation model to evaluate the stability of the dual cold trap in real time;

[0078] The steps to build a performance stability evaluation model for a dual cold trap based on deep learning are as follows:

[0079] First, the dual cold trap control system collects the historical operation parameters (including the historical dehumidification efficiency of the first cold trap, the historical dehumidification efficiency of the second cold trap, the historical first temperature gradient, the historical second temperature gradient, and the historical change rate of condensate accumulation) and the historical stability index during the historical operation of the dual cold trap. The dual cold trap control system extracts the historical main operation characteristics according to the historical operation parameters. The specific steps can refer to S204 and will not be elaborated here. The historical stability index corresponding to the historical operation parameters during the historical operation of the dual cold trap can be obtained through expert judgment or machine diagnosis, which is not limited here. The dual cold trap control system stores the collected historical main operation characteristics and the corresponding historical stability index into the dataset D, and the format of each piece of data is (historical main operation characteristics, historical stability index). Among them, the historical main operation characteristics are the input features for model training, and the historical stability index is the output feature for model training.

[0080] Then, the dual cold trap control system constructs a recurrent neural network based on LSTM, including an input layer, 2 LSTM hidden layers, a fully connected layer, and an output layer. The input layer inputs the historical main operation characteristics, the number of hidden layer nodes is set to 64, and the number of fully connected layer nodes is set to 32. The output layer outputs the historical stability index.

[0081] Next, the dual cold trap control system uses the Adam optimizer, sets the learning rate to 0.001, and the training batch size to 32, which can also be set according to the actual situation and is not limited here. 80% of the historical data is divided into the training set, and 20% is divided into the validation set. Train for 100 epochs, and save the model with the highest accuracy on the validation set, which can also be set according to the actual situation and is not limited here. An epoch is the process of the entire training dataset passing through the neural network once. In machine learning and deep learning, an epoch is a unit used to measure the number of times the entire training set is repeatedly learned. Specifically, when the neural network completes a forward calculation and a backward propagation process, that is, all data has been processed by the network once, one epoch is completed. The dual cold trap control system uses binary cross-entropy as the loss function and adopts Early Stopping to prevent overfitting. When the value of the loss function exceeds the preset function threshold, it is determined that the model training is completed, and the performance stability evaluation model for the dual cold trap is obtained. Early Stopping is a technique in deep learning and machine learning to prevent model overfitting. It decides when to stop training by monitoring the performance of the model on the validation set.

[0082] Finally, the dual cold trap control system inputs the input features in the validation set into the dual cold trap performance stability evaluation model, and then obtains the predicted output of the dual cold trap performance stability evaluation model. It compares the predicted output of the dual cold trap performance stability evaluation model with the actual output features in the validation set, and uses some performance metrics such as accuracy, precision, recall, F1-score, mean squared error (MSE), etc. to evaluate the performance of the dual cold trap performance stability evaluation model. According to the performance of the dual cold trap performance stability evaluation model on the validation set, adjust the parameters of the dual cold trap performance stability evaluation model, including adjusting the learning rate, changing the model complexity (such as increasing or decreasing the number of layers or nodes in the neural network), modifying the regularization strength, etc. This process may require multiple iterations, and each time it is adjusted based on the previous learning results to optimize the dual cold trap performance stability evaluation model.

[0083] After establishing the dual cold trap performance stability evaluation model, the dual cold trap control system can evaluate the stability of the dual cold trap device according to the real-time updated main operating characteristics.

[0084] S206. Compare the preset parameter standard values with the first cold trap dehumidification efficiency, the second cold trap dehumidification efficiency, the first temperature gradient, the second temperature gradient, and the change rate of the condensate accumulation amount to determine the first operating defect of the first cold trap and / or the second operating defect of the second cold trap. The operating defect includes abnormal dehumidification efficiency, abnormal temperature gradient, and abnormal condensate.

[0085] Specifically, refer to step S103, which will not be elaborated here.

[0086] S207. Based on the first operating defect of the first cold trap and / or the second operating defect of the second cold trap, search for a dual cold trap treatment plan in the preset operating defect treatment plan database with a relevance higher than the preset relevance threshold.

[0087] Specifically, refer to step S104, which will not be elaborated here.

[0088] S208. Collect the first acoustic feature signal and the first vibration feature signal of the first cold trap, and the second acoustic feature signal and the second vibration feature signal of the second cold trap.

[0089] Among them, the acoustic feature signal refers to the sound wave signal generated by the dual cold trap device during operation, which is used to reflect the operating state and fault characteristics of the dual cold trap device; the vibration feature signal represents the mechanical vibration information of the dual cold trap device during operation, including parameters such as amplitude, frequency, and phase.

[0090] Specifically, the dual cold trap control system collects the first acoustic feature signal and the first vibration feature signal, as well as the second acoustic feature signal and the second vibration feature signal through acoustic sensors and vibration sensors arranged on the first cold trap and the second cold trap. The sampling frequency of the acoustic sensors is set to 44.1 kHz, and the sampling frequency of the vibration sensors is 1 kHz. The collected acoustic feature signals and vibration feature signals are processed through pre-amplification and anti-aliasing filtering. The dual cold trap control system quantifies the acoustic feature signals and vibration feature signals to 16 bits to ensure the acquisition accuracy. The acquired data is stored according to the time stamp, and the data of each sensor is saved separately. The dual cold trap control system completes a full signal acquisition cycle every 100 milliseconds and performs real-time data quality inspection.

[0091] S209. Perform time-frequency domain analysis on the first acoustic feature signal, the first vibration feature signal, the second acoustic feature signal, and the second vibration feature signal to obtain the first abnormal condition feature of the first cold trap and the second abnormal condition feature of the second cold trap;

[0092] Among them, time-frequency domain analysis refers to a method of analyzing signals simultaneously in the time domain and the frequency domain, which is used to extract the dynamic features of signals; the abnormal condition feature represents the feature index when the operating state of the dual cold traps deviates from the normal range; time domain analysis refers to the analysis of the features of signals changing over time; frequency domain analysis refers to the analysis of the frequency components of signals.

[0093] Specifically, first, the dual cold trap control system preprocesses the collected acoustic feature signals and vibration feature signals, including denoising, normalization, and segmentation. Secondly, the dual cold trap control system uses wavelet packet transform to perform multi-scale decomposition on the acoustic feature signals and vibration feature signals to extract time-frequency features. Then, the dual cold trap control system calculates characteristic parameters such as the energy distribution and peak frequency of each frequency band. Then, the dual cold trap control system identifies the abnormal features in the acoustic feature signals and vibration feature signals according to the preset normal condition feature template. Finally, the dual cold trap control system sorts the identified abnormal features according to the time sequence and severity.

[0094] S210. Perform multi-modal feature fusion on the first abnormal condition feature and the first operating defect and / or the second abnormal condition feature and the second operating defect to obtain the multi-modal feature fusion result;

[0095] Specifically, the dual cold trap control system uses a deep learning model to fuse abnormal condition features and operation defects, including the normalization of feature vectors and the adaptive adjustment of weight coefficients. Then, the dual cold trap control system uses the attention mechanism to highlight the influence of important features. Next, the dual cold trap control system realizes the non-linear mapping of features through a multi-layer neural network. Finally, the dual cold trap control system outputs the multi-modal feature fusion result of multi-modal feature fusion. Among them, multi-modal feature fusion refers to the process of comprehensively analyzing feature information from different sources and of different types, which is used to improve the accuracy of fault diagnosis. The dual cold trap control system updates the fusion model parameters every 5 minutes, and the fusion accuracy is not less than 90%.

[0096] S211. Update the multi-modal feature fusion result into the dual cold trap treatment plan;

[0097] Specifically, first, the dual cold trap control system compares the multi-modal feature fusion result with the existing dual cold trap treatment plan to identify the content that needs to be updated. Then, the dual cold trap control system determines the update priority according to the credibility of the multi-modal feature fusion result. Next, the dual cold trap control system modifies the corresponding parameter settings, control strategies and maintenance plans in the dual cold trap treatment plan. The updated dual cold trap treatment plan takes effect after automatic verification, and the dual cold trap control system saves the update record, including the update time, update content and update reason.

[0098] S212. Input the first cold trap temperature and humidity data, the first condensate accumulation, the second cold trap temperature and humidity data, and the second condensate accumulation into the dual cold trap fault prediction model respectively to obtain the cold trap fault prediction result;

[0099] Among them, the dual cold trap fault prediction model is a probability model used to predict the possible faults of the dual cold trap, which is constructed based on machine learning algorithms; the dual cold trap fault prediction result represents the fault type and occurrence probability output by the dual cold trap fault prediction model.

[0100] Specifically, first, the dual cold trap control system performs data preprocessing on the temperature and humidity data and the condensate accumulation, including outlier processing, data normalization and time series alignment. Then, the dual cold trap control system inputs the processed data into the dual cold trap fault prediction model based on the long short-term memory network (LSTM) in a predetermined format. The dual cold trap fault prediction model calculates the possible fault types and occurrence probabilities within the next 24 hours, and at the same time outputs the predicted confidence interval and uncertainty estimate. The dual cold trap control system updates the cold trap fault prediction result every hour, and the prediction accuracy is not less than 85%, and the early warning lead time is not less than 4 hours. The method for constructing the dual cold trap fault prediction model can refer to step S205, which will not be elaborated here.

[0101] S213. When the failure prediction result of the cold trap shows that the failure probability is greater than the preset first probability threshold and less than the preset second probability threshold, trigger the first cold trap preventive maintenance process;

[0102] Among them, the preset first probability threshold refers to the lower limit of the failure probability for triggering mild preventive maintenance, usually set at 60%; the preset second probability threshold refers to the upper limit of the failure probability for which heavy maintenance is required, usually set at 80%; the first cold trap preventive maintenance process refers to the processing procedure for medium-risk failures.

[0103] Specifically, the dual cold trap control system monitors the cold trap failure prediction result in real time. When the failure probability is greater than the preset first probability threshold and less than the preset second probability threshold (for example, between 60% - 80%), it automatically triggers the first preventive maintenance process. The first preventive maintenance process includes mild maintenance items such as a detailed inspection of the equipment status, performance testing of key components, and lubricant replacement. The dual cold trap control system generates a maintenance work order, records the maintenance data in real time during the operation of the operator, and conducts a maintenance effect evaluation after completion.

[0104] S214. When the failure prediction result of the cold trap shows that the failure probability is greater than the preset second probability threshold, trigger the second cold trap preventive maintenance process;

[0105] Among them, the second cold trap preventive maintenance process refers to the processing procedure for high-risk failures and includes more comprehensive maintenance measures.

[0106] Specifically, the dual cold trap control system monitors the cold trap failure prediction result in real time. When the failure probability is greater than the preset second probability threshold (for example, greater than 80%), it immediately activates the second preventive maintenance process. The second preventive maintenance process includes heavy maintenance items such as emergency shutdown of the equipment, replacement of core components, and comprehensive overhaul of the system. At the same time, the dual cold trap control system starts the emergency backup system to ensure production continuity, adopts an expert guidance system during the maintenance process to ensure the maintenance quality, and conducts a comprehensive function test and performance evaluation after the maintenance is completed.

[0107] S215. Obtain the first operating parameters of the first cold trap and the second operating parameters of the second cold trap. The operating parameters include coolant flow rate, coolant temperature, coolant pressure, and condensate discharge rate;

[0108] Among them, the first operating parameter and the second cold trap parameter refer to the key technical indicators describing the operating states of the two cold traps; the coolant flow rate represents the volume of the refrigeration working medium flowing through the cold trap per unit time and has a direct impact on the refrigeration effect; the coolant temperature refers to the real-time temperature value of the refrigeration working medium circulating in the cold trap and directly determines the dehumidification effect; the coolant pressure represents the pressure state of the refrigeration working medium in the double cold trap and affects the phase change process of the refrigerant; the condensate water discharge rate refers to the volume of condensate water discharged from the cold trap per unit time and reflects the dehumidification effect.

[0109] Specifically, the double cold trap control system collects operating parameters in real time through a high-precision sensor network arranged at key positions of the cold trap: the coolant flow rate is measured by a Coriolis flowmeter with an accuracy of ±0.1 L / min and a measurement range of 0 - 100 L / min; the coolant temperature is measured using a PT100 platinum resistance temperature sensor with an accuracy of ±0.1 °C and a temperature measurement range of -50 °C to 50 °C; the coolant pressure is measured by a pressure transmitter with an accuracy of ±0.01 MPa and a range of 0 - 2 MPa; the condensate water discharge rate is measured by an electromagnetic flowmeter with an accuracy of ±0.1 mL / min and a measurement range of 0 - 1000 mL / min. The double cold trap control system adopts a distributed data acquisition architecture with a sampling frequency of 10 times per second. The collected data is denoised through the Kalman filtering algorithm and smoothed through the moving average method.

[0110] S216. Calculate the operation state matrix of the double cold trap according to the first operating parameter and the second operating parameter;

[0111] Specifically, the double cold trap control system organizes the four types of collected parameters into a 4×2 operation state matrix according to a predetermined format. Among them, the operation state matrix refers to a mathematical matrix formed by organizing multiple operating parameters according to a specific structure. The rows of the operation state matrix correspond to different types of operating parameters (coolant flow rate, coolant temperature, coolant pressure, and condensate water discharge rate), and the columns correspond to the two cold traps. The double cold trap control system uses the minimum-maximum normalization method to process the operating parameters, maps all data to the [0, 1] interval, and eliminates the influence of dimensions. The double cold trap control system stores the matrix elements using double-precision floating-point numbers, and the matrix elements represent specific parameter values to ensure calculation accuracy. The double cold trap control system updates the operation state matrix every 5 seconds and simultaneously saves the historical operation state matrix of the most recent 24 hours. The double cold trap control system can also establish a matrix indexing mechanism to support fast retrieval and comparative analysis.

[0112] S217. Extract features from the operation state matrix to obtain the operation feature vector of the double cold trap device;

[0113] Among them, feature extraction refers to extracting key information from the operation state matrix that can characterize the characteristics of the double cold trap; the operation feature vector represents the mathematical expression form of the operation state of the double cold trap.

[0114] Specifically, the method for extracting the operation feature vector can refer to step S204.

[0115] S218. Calculate the feature similarity between the operation feature vector and the preset optimal operation feature vector;

[0116] Among them, the preset optimal operation feature vector refers to the feature performance of the dual cold trap device under the best working conditions; the feature similarity represents the degree of closeness between the current operation feature vector and the optimal operation feature vector; the similarity calculation method refers to the algorithm for measuring vector similarity.

[0117] Specifically, first, the dual cold trap control system loads the preset optimal operation feature vector, and the optimal operation feature vector comes from the historical best operation conditions. Then, the dual cold trap control system calculates the feature similarity between the operation feature vector and the preset optimal operation feature vector. A multi-dimensional similarity calculation method can be adopted: using cosine similarity as the main metric, calculating the cosine value of the included angle of the feature vector, calculating the Euclidean distance as an auxiliary metric at the same time, introducing Mahalanobis distance to consider the correlation between features, using the dynamic time warping (DTW) algorithm to evaluate the similarity of time series features, and adopting a weighted calculation method based on importance for features in different dimensions. Finally, the dual cold trap control system obtains the feature similarity between the operation feature vector and the preset optimal operation feature vector.

[0118] S219. According to the feature similarity, perform dynamic weight allocation on the first operation parameter and the second operation parameter to obtain a first weight coefficient and a second weight coefficient;

[0119] Among them, dynamic weight allocation refers to the process of adjusting the importance of parameters in real time according to the operation status of the dual cold trap; the first weight coefficient and the second weight coefficient represent the relative importance of the two cold trap operation parameters.

[0120] Specifically, the dual cold trap control system adopts a dynamic weight allocation algorithm based on fuzzy control: First, establish a fuzzy rule base of feature similarity and weight, including rules such as "if the similarity is very low, then increase the weight adjustment step size"; Second, use the Mamdani inference method to determine the weight adjustment direction and amplitude. The value range of the weight coefficient is [0, 1], and the sum of the two weights is always equal to 1. The dual cold trap control system performs weight optimization calculation once per minute, and searches for the optimal weight combination through the gradient descent method. The dual cold trap control system can introduce an inertia term to prevent the weight from fluctuating violently, or can set a weight change rate limit, and the single adjustment amplitude does not exceed 10%.

[0121] S220. Based on the first weight coefficient, the second weight coefficient, the dehumidification efficiency of the first cold trap, and the dehumidification efficiency of the second cold trap, determine the collaborative operation index of the first cold trap and the second cold trap;

[0122] Specifically, the dual cold trap control system adopts a multi-level collaborative operation index calculation method. Among them, the collaborative operation index is a comprehensive index for evaluating the collaborative working effect of the dual cold traps. First, the dual cold trap control system performs a weighted operation on the weight coefficient and the dehumidification efficiency of each cold trap. Secondly, the dual cold trap control system introduces a temperature gradient correction factor to consider the influence of the ambient temperature on the dehumidification effect. Then, the dual cold trap control system uses a comprehensive evaluation model based on the entropy weight method to integrate multiple performance indicators, and also considers auxiliary indicators such as energy consumption efficiency and operation stability. Next, the dual cold trap control system uses a neural network model to establish a non-linear mapping relationship. The dual cold trap control system updates the collaborative operation index once per minute, and the index range is [0, 100]. According to the collaborative operation index, the collaborative operation status is divided into five levels: excellent (90 - 100), good (80 - 90), average (70 - 80), poor (60 - 70), and very poor (below 60).

[0123] S221. When the collaborative operation index is lower than the preset collaborative index threshold, adjust the operation parameters of the dual cold traps to the preset optimal operation parameters;

[0124] Among them, the preset collaborative index threshold refers to the pre-set minimum collaborative operation level for triggering parameter adjustment; the preset optimal operation parameters represent the parameter combination when the dual cold traps have the best performance during historical operation, including key indicators such as coolant flow rate, coolant temperature, coolant pressure, and condensate discharge rate.

[0125] Specifically, the dual cold trap control system retrieves the template of the preset optimal operation parameters, and when the collaborative operation index is lower than the preset collaborative index threshold, adjusts the operation parameters of the dual cold traps to the preset optimal operation parameters. After the adjustment is completed, enter a 2-hour observation period. During this period, the dual cold trap control system continuously monitors the change of the collaborative operation index. If the collaborative operation index fails to rise to the preset collaborative index threshold, start the in-depth optimization process.

[0126] S222. Obtain the first energy consumption data of the first cold trap and the second energy consumption data of the second cold trap;

[0127] Among them, the first energy consumption data and the second energy consumption data refer to the energy consumption records of the two cold traps; the energy consumption data includes power consumption, refrigerant consumption, etc.

[0128] Specifically, the dual cold trap control system collects energy consumption data through the smart electricity meters and sensor networks arranged at the preset positions of the first cold trap and the second cold trap to obtain the first energy consumption data and the second energy consumption data.

[0129] S223. Determine the energy consumption efficiency of the dual cold traps based on the first operation parameters and the first energy consumption data, and the second operation parameters and the second energy consumption data;

[0130] Specifically, the dual cold trap control system adopts a multi-dimensional energy consumption efficiency evaluation method: First, calculate the refrigerating capacity of each cold trap, considering the refrigerant enthalpy difference and mass flow rate; Second, calculate the dehumidification amount by combining the temperature and humidity sensor data and the air volume data; Then, establish a correction model considering the influence of environmental temperature and humidity. The dual cold trap control system calculates the energy consumption efficiency every 15 minutes, where the energy consumption efficiency refers to the refrigerating capacity or dehumidification amount generated per unit of energy consumption.

[0131] S224. When the energy consumption efficiency is lower than the preset energy consumption efficiency threshold, determine the reason for the energy efficiency degradation of the dual cold traps.

[0132] Among them, the reason for the energy efficiency degradation refers to the specific factors that cause the energy efficiency of the dual cold trap equipment to decline; the preset energy consumption efficiency threshold represents the acceptable minimum energy efficiency level.

[0133] Specifically, the dual cold trap control system starts a multi-level energy efficiency degradation diagnosis process: First, establish a hierarchical model of the degradation reasons through Fault Tree Analysis (FTA); Then, use a fault diagnosis algorithm based on deep learning to identify abnormal patterns; Next, adopt a fuzzy inference system to evaluate the probabilities of various possible reasons. The dual cold trap control system classifies the degradation reasons into three categories: equipment failure, parameter deviation, and improper control strategy; for equipment failure, focus on checking problems such as compressor efficiency, heat exchanger frosting, and refrigerant leakage; for parameter deviation, analyze the deviation degree of parameters such as temperature, pressure, and flow rate; regarding the control strategy, evaluate the adaptability of the current control algorithm.

[0134] Due to the adoption of an analysis method based on all-round monitoring of the temperature and humidity data of the dual cold traps, the condensate accumulation amount, and multi-modal feature fusion, it is possible to capture the operating state of the dual cold traps in real time and accurately and predict potential faults, effectively solving the problems of one-sidedness and lag in judgment caused by single-parameter monitoring in related technologies, and thus realizing the intelligent monitoring and preventive maintenance of the dual cold traps.

[0135] Due to the adoption of the dual cold trap collaborative operation index calculation and dynamic weight allocation mechanism, it is possible to accurately evaluate the operating state of the dual cold traps and achieve adaptive adjustment of the operating parameters, effectively solving the problems of poor coordination and unbalanced load distribution between the dual cold traps in related technologies, and thus realizing the overall optimization of the operating efficiency of the dual cold traps.

[0136] Due to the adoption of a method combining energy consumption efficiency monitoring and energy efficiency degradation analysis, it is possible to monitor the energy consumption status of the dual cold traps in real time and detect efficiency losses in a timely manner, effectively solving the problems of low energy utilization efficiency and extensive energy consumption management in related technologies, and thus realizing the energy conservation and efficiency improvement of the dual cold traps.

[0137] The following describes the dual cold trap control system in the embodiments of the present invention application from the perspective of hardware processing. Please refer toFigure 3 , which is a schematic structural diagram of an entity device of the dual cold trap control system in the embodiments of the present application.

[0138] It should be noted that Figure 3 the structure of the dual cold trap control system shown is only an example, and should not bring any limitations to the functions and usage scope of the embodiments of the present invention.

[0139] As Figure 3 shown, the dual cold trap control system includes a central processing unit (CPU) 301, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 302 or the program loaded from the storage section 308 into the random access memory (RAM) 303, such as executing the method described in the above embodiments. In the RAM 303, various programs and data required for system operation are also stored. The CPU 301, ROM 302, and RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0140] The following components are connected to the I / O interface 305: an input section 306 including an audio input device, a push button switch, etc.; an output section 307 including a liquid crystal display (LCD), an audio output device, an indicator light, etc.; a storage section 308 including a hard disk, etc.; and a communication section 309 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 309 performs communication processing via a network such as the Internet. A driver 310 is also connected to the I / O interface 305 as needed. A removable medium 311, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the driver 310 as needed, so that the computer program read from it can be installed into the storage section 308 as needed.

[0141] Specifically, according to the embodiments of the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments of the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 309, and / or installed from the removable medium 311. When the computer program is executed by the central processing unit (CPU) 301, various functions defined in the present invention are executed.

[0142] It should be noted that specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present invention, a computer-readable storage medium can be any tangible medium that contains or stores a program, and this program can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0143] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings.

[0144] Specifically, the dual cold trap control system of this embodiment includes a processor and a memory, and a computer program is stored on the memory. When the computer program is executed by the processor, the dual cold trap operation monitoring method provided in the above embodiment is implemented.

[0145] On the other hand, the present invention also provides a computer-readable storage medium, which may be included in the dual cold trap control system described in the above embodiment; or it may exist separately and not be assembled into the dual cold trap control system. The above storage medium carries one or more computer programs. When the above one or more computer programs are executed by a processor of the dual cold trap control system, the dual cold trap control system implements the dual cold trap operation monitoring method provided in the above embodiment.

[0146] As described above, the above embodiments are only used to illustrate the technical solutions of the present application and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the various embodiments of the present application.

[0147] As used in the foregoing embodiments, depending on the context, the term "when" may be construed to mean "if", or "after", or "in response to determining", or "in response to detecting". Similarly, depending on the context, the phrase "upon determining" or "if (the stated condition or event) is detected" may be construed to mean "if determined", or "in response to determining", or "when (the stated condition or event) is detected", or "in response to detecting (the stated condition or event)".

[0148] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the foregoing embodiments can be implemented, and the processes can be completed by relevant hardware instructed by a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the foregoing method embodiments. The foregoing storage medium includes various media that can store program codes, such as ROM or random access memory RAM, magnetic disks, or optical discs.

Claims

1. A double cold trap operation monitoring method, characterized in that: Applied to a dual cold trap control system, the method comprises: Acquire a first cold trap temperature and humidity data, a first condensed water accumulation amount, a second cold trap temperature and humidity data, and a second condensed water accumulation amount, wherein the first cold trap temperature and humidity data include a first inlet temperature and a first inlet humidity at the inlet of the first cold trap and a first outlet temperature and a first outlet humidity at the outlet of the first cold trap, and the first condensed water accumulation amount is used to indicate the volume of condensed water in the first cold trap, the second cold trap temperature and humidity data include a second inlet temperature and a second inlet humidity at the inlet of the second cold trap and a second outlet temperature and a second outlet humidity at the outlet of the second cold trap, and the second condensed water accumulation amount is used to indicate the volume of condensed water in the second cold trap; Determine a first cold trap dehumidification efficiency according to the first cold trap temperature and humidity data, determine a second cold trap dehumidification efficiency according to the second cold trap temperature and humidity data, determine a first temperature gradient according to the first inlet temperature and the first outlet temperature, determine a second temperature gradient according to the second inlet temperature and the second outlet temperature, and determine a condensed water accumulation amount change rate according to the first condensed water accumulation amount and the second condensed water accumulation amount; Comparing the preset parameter standard value with the first cold trap dehumidification efficiency, the second cold trap dehumidification efficiency, the first temperature gradient, the second temperature gradient and the rate of change of the accumulated amount of condensed water, to determine a first operating defect of the first cold trap and / or a second operating defect of the second cold trap, wherein the operating defect includes abnormal dehumidification efficiency, abnormal temperature gradient and abnormal condensed water; Based on the first operation defect of the first cold trap and / or the second operation defect of the second cold trap, searching a preset operation defect processing solution database for a dual cold trap processing solution with a correlation higher than a preset correlation threshold; Calculating the synergistic operation index of the first cold trap and the second cold trap in real time; when the synergistic operation index is lower than a preset synergistic index threshold, adjusting the operation parameters of the double cold traps to preset optimal operation parameters; The real-time calculation of the collaborative operation index of the first cold trap and the second cold trap specifically includes: obtaining a first operating parameter of the first cold trap and a second operating parameter of the second cold trap, the operating parameters including coolant flow, coolant temperature, coolant pressure and condensate discharge rate; calculating an operating state matrix of the double cold trap according to the first operating parameter and the second operating parameter; performing feature extraction on the operating state matrix to obtain an operating feature vector of the double cold trap device; calculating a feature similarity between the operating feature vector and a preset optimal operating feature vector; performing dynamic weight allocation on the first operating parameter and the second operating parameter according to the feature similarity to obtain a first weight coefficient and a second weight coefficient; determining the collaborative operation index of the first cold trap and the second cold trap based on the first weight coefficient, the second weight coefficient, the first cold trap dehumidification efficiency and the second cold trap dehumidification efficiency.

2. The method according to claim 1, characterized in that After the step of searching a preset operation defect processing solution database for a dual cold trap processing solution with a correlation higher than a preset correlation threshold based on the first operation defect of the first cold trap and / or the second operation defect of the second cold trap, the method further comprises: Collecting a first acoustic characteristic signal and a first vibration characteristic signal of the first cold trap, and a second acoustic characteristic signal and a second vibration characteristic signal of the second cold trap; Performing time-frequency domain analysis on the first acoustic characteristic signal, the first vibration characteristic signal, the second acoustic characteristic signal, and the second vibration characteristic signal to obtain a first abnormal operating condition characteristic of the first cold trap and a second abnormal operating condition characteristic of the second cold trap; Performing multimodal feature fusion on the first abnormal operating condition feature and the first operating defect and / or the second abnormal operating condition feature and the second operating defect to obtain a multimodal feature fusion result; The multimodal feature fusion result is updated to the dual cold trap processing scheme.

3. The method according to claim 1, characterized in that After the steps of determining the first cold trap dehumidification efficiency according to the first cold trap temperature and humidity data, determining the second cold trap dehumidification efficiency according to the second cold trap temperature and humidity data, determining the first temperature gradient according to the first inlet temperature and the first outlet temperature, determining the second temperature gradient according to the second inlet temperature and the second outlet temperature, and determining the condensed water accumulation amount change rate according to the first condensed water accumulation amount and the second condensed water accumulation amount, the method further includes: Constructing a double cold trap operation state vector according to the first cold trap dehumidification efficiency, the second cold trap dehumidification efficiency, the first temperature gradient, the second temperature gradient, and the rate of change of the accumulated amount of condensed water; Performing component analysis on the dual cold trap operation state vector to extract main operation characteristics; Based on the main operating characteristics, a double cold trap performance stability evaluation model is established to evaluate the stability of the double cold trap in real time.

4. The method according to claim 1, characterized in that After the step of searching a preset operation defect processing solution database for a dual cold trap processing solution with a correlation higher than a preset correlation threshold based on the first operation defect of the first cold trap and / or the second operation defect of the second cold trap, the method further comprises: Inputting the first cold trap temperature and humidity data and the first condensed water accumulation amount as well as the second cold trap temperature and humidity data and the second condensed water accumulation amount into a double cold trap fault prediction model to obtain a cold trap fault prediction result; When the cold trap fault prediction result shows that the fault probability is greater than a preset first probability threshold and less than a preset second probability threshold, triggering a first cold trap preventive maintenance process; When the cold trap failure prediction result shows that the failure probability is greater than the preset second probability threshold, a second cold trap preventive maintenance process is triggered.

5. The method according to claim 1, characterized in that: After the step of obtaining the first operating parameter of the first cold trap and the second operating parameter of the second cold trap, wherein the operating parameters include coolant flow rate, coolant temperature, coolant pressure, and condensed water discharge rate, the method further includes: Acquire first energy consumption data of the first cold trap and second energy consumption data of the second cold trap; Determining the energy consumption efficiency of the double cold trap based on the first operating parameter and the first energy consumption data, and the second operating parameter and the second energy consumption data; When the energy consumption efficiency is lower than a preset energy consumption efficiency threshold, a cause of energy efficiency degradation of the double cold trap is determined.

6. A double cold trap control system, characterized in that: The dual cold-trap control system comprises: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors call the computer instructions so that the dual cold-trap control system executes the method as described in any one of claims 1 to 5.

7. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on a double cold-trap control system, the double cold-trap control system is caused to execute the method according to any one of claims 1 to 5.

8. A computer program product, characterized in that When the computer program product runs on a double cold-trap control system, the double cold-trap control system is enabled to perform the method according to any one of claims 1 to 5.

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