AI-based adaptive phase change liquid cooling fault control method and system

Abstract

The invention relates to the technical field of adaptive control, in particular to an AI-based adaptive phase change liquid cooling fault control method and system, and the method comprises the following steps: obtaining the temperature, flow velocity and phase change state data of a cooling liquid, monitoring the phase change time from a liquid state to a gel state under different flow velocities and temperature conditions in real time, calculating the phase change delay time increment of adjacent periods, and calculating the phase change delay time increment of the adjacent periods. And obtaining a phase change delay time change trend analysis result. According to the method, the phase change time of the cooling liquid under different flow velocity and temperature conditions is monitored in real time, the heat exchange behavior of the cooling agent can be predicted more accurately, the descending trend of the cooling capacity has quantitative analysis capacity, and on the basis of construction of the heat absorption rate time sequence and change rate analysis, the abnormal fluctuation degree of the heat absorption rate is combined. And the pump speed, flow distribution and flow paths of the coolant are dynamically adjusted, the heat dissipation requirements under different working conditions can be met, and the cooling scheme can be adaptively optimized according to the running trend.

Description

Technical Field

[0001] The present invention relates to the technical field of adaptive control, and particularly to an AI-based adaptive phase change liquid cooling fault control method and system. Background Art

[0002] The technical field of adaptive control includes control methods and strategies for real-time adjustment according to changes in the dynamic characteristics of a system. The core content involves adjusting control parameters through algorithms to maintain the stability of system performance in complex environments or uncertain conditions. Adaptive control is usually applied to multiple fields such as power systems, robot control, autonomous driving, and thermal management systems. Its methods include model reference adaptive control, AI-based adaptive control, and parameter adaptive adjustment. In recent years, with the improvement of computing power and the development of artificial intelligence, adaptive control has gradually combined with technologies such as deep learning and reinforcement learning, expanding its control capabilities in nonlinear systems and complex environments.

[0003] Among them, the AI-based adaptive phase change liquid cooling fault control method refers to using artificial intelligence technology to detect, analyze faults in the phase change liquid cooling system, and adaptively adjust the cooling strategy. This method mainly identifies and models the heat transfer characteristics, phase change process, and key parameter changes that may lead to cooling failure in the phase change liquid cooling system. By using sensors to collect real-time data such as temperature, flow rate, and pressure of the cooling system, and combining machine learning models to predict the phase change state of the cooling medium. According to the prediction results, the operation mode of the liquid cooling system is adaptively adjusted, including adjusting the circulation rate of the coolant, optimizing the working parameters of the heat exchange structure, and dynamically allocating cooling resources to ensure the stable operation of the system under different working conditions.

[0004] In the existing phase change liquid cooling fault control process, there are deficiencies in the dynamic monitoring ability during the phase change process of the coolant. Only relying on the threshold judgment of single parameters such as temperature and flow rate, it is difficult to accurately identify potential hysteresis effects during the phase change process, resulting in limited real-time adjustment of the phase change heat absorption capacity. Due to the lack of tracking of the phase change hysteresis time increment, the existing solutions are difficult to predict the decline trend of the cooling capacity, resulting in passive adjustment only after the cooling capacity has declined, affecting the stability of heat dissipation. In terms of monitoring the heat absorption rate, the existing technologies are mainly based on static models, and it is difficult to capture abnormal fluctuations in the heat absorption capacity of the coolant, resulting in the inability to maintain the optimal heat dissipation efficiency under certain extreme working conditions. The coolant pump speed, flow rate distribution, and flow path adjustment methods are relatively fixed, lacking the adaptive optimization ability for the phase change completion degree of the coolant, reducing the utilization rate of cooling resources under different working conditions. In terms of fault warning, the existing technologies are difficult to comprehensively analyze the cooling capacity decline rate, resulting in insufficient pre-judgment of the cooling system before a fault occurs, increasing maintenance costs and affecting the continuous operation of the equipment. Summary of the Invention

[0005] The object of the present invention is to solve the drawbacks existing in the prior art, and a method for controlling faults of an AI-based adaptive phase change liquid cooling is proposed.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A method for controlling faults of an AI-based adaptive phase change liquid cooling, comprising the following steps:

[0007] S1: Obtain the coolant temperature, flow rate, and phase change state data, monitor in real time the phase change time from liquid state to gel state under different flow rates and temperature conditions, calculate the phase change hysteresis time increment in adjacent cycles, and obtain the analysis result of the phase change hysteresis time change trend;

[0008] S2: Based on the analysis result of the phase change hysteresis time change trend, calculate the phase change hysteresis time growth rate, the heat load change rate, and the flow rate dynamic change rate, determine the cooling capacity decline rate, and obtain the evaluation result of the cooling capacity decline rate;

[0009] S3: Based on the evaluation result of the cooling capacity decline rate, calculate the heat absorption rate of the coolant phase change per unit time and the change rate of the heat absorption rate, judge the deviation degree of the heat absorption rate, and obtain the analysis result of the abnormal fluctuation degree of the heat absorption rate;

[0010] S4: Based on the analysis result of the abnormal fluctuation degree of the heat absorption rate, calculate the phase change completion degree of the coolant at the current flow rate, adjust the coolant pump speed, flow rate distribution, and flow path switching, and obtain the record of the coolant flow rhythm adjustment;

[0011] S5: Based on the record of the coolant flow rhythm adjustment, calculate the heat load balance degree, the change amount of the heat dissipation efficiency, and the reduction amplitude of the local temperature rise before and after the adjustment, analyze and judge the stability of the cooling process, and generate an adaptive cooling control scheme.

[0012] As a further solution of the present invention, the analysis result of the phase change hysteresis time change trend includes the phase change hysteresis time series, the phase change hysteresis time increment data, and the phase change hysteresis time growth trend. The evaluation result of the cooling capacity decline rate includes the phase change hysteresis time growth rate, the heat load change rate, the flow rate dynamic change rate, and the cooling capacity decline rate. The analysis result of the abnormal fluctuation degree of the heat absorption rate includes the heat absorption rate time series, the heat absorption rate change rate, and the analysis result of the heat absorption rate deviation degree. The record of the coolant flow rhythm adjustment includes the coolant pump speed adjustment record, the flow rate distribution adjustment record, and the dynamic flow path switching record. The adaptive cooling control scheme includes the heat load balance degree, the change amount of the heat dissipation efficiency, the reduction amplitude data of the local temperature rise, and the judgment result of the cooling process stability.

[0013] As a further solution of the present invention, the specific steps for obtaining the coolant temperature, flow rate, and phase change state data, monitoring the liquid-to-gel phase change time under different flow rates and temperature conditions, calculating the phase change lag time increment between adjacent cycles, and obtaining the analysis result of the phase change lag time change trend are as follows:

[0014] S111: Obtain the coolant temperature, flow rate, and phase change state data in the phase change liquid cooling system, monitor the liquid-to-gel phase change time under different flow rates and temperature conditions, record the phase change lag time sequence of the coolant under different conditions, and obtain the phase change lag time sequence data;

[0015] S112: Based on the phase change lag time sequence data, use the formula:

[0016]

[0017] Calculate the phase change lag time increment value ΔT φ , and output the phase change lag time increment data, where T φ (t) represents the phase change lag time of the current cycle, T φ (t - 1) represents the phase change lag time of the previous cycle, V i represents the flow rate of the i-th group of coolant, T i represents the coolant temperature corresponding to the i-th group, n represents the total number of flow rate groups, T φ (j) represents the phase change lag time of the j-th group, represents the mean value of all measured phase change lag times, and m represents the total number of data groups;

[0018] S113: Based on the phase change lag time increment data, analyze the change pattern of the phase change lag time, determine the change rate and trend characteristics of the phase change lag time, and obtain the analysis result of the phase change lag time change trend.

[0019] As a further solution of the present invention, based on the analysis result of the phase change lag time change trend, the specific steps for calculating the phase change lag time growth rate, the heat load change rate, the flow rate dynamic change rate, and determining the cooling capacity decline rate to obtain the cooling capacity decline rate evaluation result are as follows:

[0020] S211: Based on the analysis result of the phase change lag time change trend, calculate the phase change lag time growth rate, monitor the phase change lag time data at different time points, calculate the change amount between adjacent time points, and calculate the phase change lag time growth rate based on time interval normalization to obtain the phase change lag time growth rate;

[0021] S212: Obtain the server thermal power consumption data, calculate the change value of the thermal power consumption per unit time, and use the formula:

[0022]

[0023] Calculate the rate of change of heat load R q , where Q i represents the heat power consumption at the i-th time point, and Q i-1 represents the heat power consumption at the previous time point, and Δt i represents the i-th time interval, N represents the total number of time intervals, and Q j represents the heat power consumption data of the j-th group, represents the average value of all measured heat power consumptions, and Q max represents the maximum heat power consumption value, and M represents the total number of data groups;

[0024] S213: Calculate the dynamic change rate of the flow rate in combination with the coolant flow rate data, compare the growth rate of the phase change lag time and the rate of change of the heat load, analyze the decrease in the cooling capacity, and obtain the evaluation result of the cooling capacity decrease rate.

[0025] As a further solution of the present invention, based on the evaluation result of the cooling capacity decrease rate, the specific steps for calculating the heat absorption rate of the coolant phase change per unit time and the rate of change of the heat absorption rate, and judging the deviation degree of the heat absorption rate to obtain the analysis result of the abnormal fluctuation degree of the heat absorption rate are as follows:

[0026] S311: Based on the evaluation result of the cooling capacity decrease rate, obtain the heat absorption amount of the coolant phase change, specific heat capacity, and flow thermal resistance data, calculate the heat absorption rate of the coolant phase change per unit time, and obtain the coolant phase change heat absorption rate data;

[0027] S312: Based on the coolant phase change heat absorption rate data, construct time series data, and use the formula:

[0028]

[0029] Calculate the rate of change of the heat absorption rate R qφ , where q φ,i represents the heat absorption rate of the coolant at the i-th time point, and q φ,i-1 represents the heat absorption rate of the coolant at the previous time point, and Δt i represents the i-th time interval, N represents the total number of time intervals, and q φ,max represents the maximum heat absorption rate value, represents the average value of all measured heat absorption rates;

[0030] S313: Based on the rate of change of the heat absorption rate, judge the degree to which the heat absorption rate deviates from the optimal heat absorption interval, analyze the deviation amplitude and change mode, and obtain the analysis result of the abnormal fluctuation degree of the heat absorption rate.

[0031] As a further solution of the present invention, based on the analysis result of the abnormal fluctuation degree of the heat absorption rate, calculate the completion degree of the coolant phase change at the current flow rate, adjust the coolant pump speed, flow rate distribution and flow path switching, and the specific steps for obtaining the coolant flow rhythm adjustment record are as follows:

[0032] S411: Based on the analysis result of the abnormal fluctuation degree of the heat absorption rate, calculate the completion degree of the coolant phase change at the current flow rate, monitor the phase change heat of the coolant under different flow rate conditions, calculate the phase change heat absorption ratio per unit volume, and obtain the coolant phase change completion degree data;

[0033] S412: Based on the coolant phase change completion degree data, judge whether the current flow rate is too fast or too slow, and use the formula:

[0034]

[0035] Calculate the flow rate deviation degree D v , where v c represents the current coolant flow rate, v opt represents the optimal coolant flow rate, q φ,i represents the coolant heat absorption rate at the i-th time point, represents the average value of all measured heat absorption rates, q φ,max represents the maximum heat absorption rate value, and M represents the total number of data groups;

[0036] S413: Based on the flow rate deviation degree, adjust the coolant pump speed and flow rate distribution, perform dynamic flow path switching, record all adjusted flow parameters, and obtain the coolant flow rhythm adjustment record.

[0037] As a further solution of the present invention, based on the coolant flow rhythm adjustment record, calculate the heat load balance degree, the change amount of heat dissipation efficiency and the reduction amplitude of local temperature rise before and after adjustment, analyze and judge the stability of the cooling process, and the specific steps for generating an adaptive cooling control scheme are as follows:

[0038] S511: Based on the coolant flow rhythm adjustment record, calculate the heat load balance degree during the cooling process before and after adjustment, obtain the heat load distribution data during the cooling process, calculate the heat load difference value of each local area, and perform normalization processing to obtain the heat load balance degree data;

[0039] S512: Based on the heat load balance degree data, calculate the change amount of heat dissipation efficiency, obtain the heat dissipation power data during the cooling process, compare the change of heat dissipation per unit time under different flow rates, calculate the adjustment amplitude of heat dissipation efficiency, and analyze the dynamic change trend of heat dissipation capacity to obtain the change amount of heat dissipation efficiency data;

[0040] S513: Based on the data of the change in heat dissipation efficiency, calculate the reduction amplitude of local temperature rise, analyze the overall operation trend of the cooling process, and judge the stability of the cooling process. Then comprehensively adjust the cooling parameters to obtain an adaptive cooling control scheme.

[0041] An AI-based adaptive phase change liquid cooling fault control system includes:

[0042] The cooling parameter monitoring module obtains the coolant temperature, flow rate, and phase change state data, calls the embedded sensing nodes to monitor the phase change time from liquid state to gel state, records the phase change lag time series, calculates the phase change lag time increment of adjacent cycles, and analyzes the phase change lag time change trend analysis result.

[0043] The phase change lag calculation module calculates the phase change lag time growth rate based on the phase change lag time change trend analysis result, combines the server heat power consumption data to calculate the heat load change rate, and compares the dynamic change rate of the coolant flow rate to obtain the evaluation result of the cooling capacity decline rate.

[0044] The cooling capacity evaluation module calls the heat absorption amount, specific heat capacity, and flow heat resistance data of the coolant phase change based on the evaluation result of the cooling capacity decline rate, calculates the heat absorption rate time series, analyzes the change rate of the heat absorption rate, and obtains the analysis result of the abnormal fluctuation degree of the heat absorption rate.

[0045] The heat absorption rate analysis module calculates the phase change completion degree of the coolant at the current flow rate based on the analysis result of the abnormal fluctuation degree of the heat absorption rate, judges whether the flow rate is abnormal, adjusts the pump speed, flow rate, and path, and obtains the coolant flow rhythm adjustment record.

[0046] The flow rhythm adjustment module calculates the heat load balance degree, heat dissipation efficiency change amount, and local temperature rise reduction amplitude before and after adjustment based on the coolant flow rhythm adjustment record, judges the stability of the cooling process, and generates an adaptive cooling control scheme.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0048] In the present invention, by monitoring the phase change time of the coolant under different flow rates and temperature conditions in real time and calculating the phase change lag time increment, the microscopic changes in the phase change process can be dynamically identified, and the heat exchange behavior of the coolant can be predicted more accurately. By comprehensively calculating the phase change lag time growth rate, the heat load change rate, and the flow rate dynamic change rate, the downward trend of the cooling capacity can be quantitatively analyzed, which helps to adopt proactive adjustment strategies before the cooling performance deteriorates and reduce the failure risk. Based on the construction and change rate analysis of the heat absorption rate time series, the non-linear fluctuations in the cooling process can be identified, so as to ensure that the heat absorption capacity of the coolant is always in the efficient range. According to the abnormal fluctuation degree of the heat absorption rate, the pump speed, flow rate distribution, and flow path of the coolant can be dynamically adjusted to adapt to the heat dissipation requirements under different working conditions. Based on the heat load balance degree, the change amount of heat dissipation efficiency, and the reduction amplitude of local temperature rise before and after the adjustment of the cooling process, a cooling stability analysis system for the dynamic environment is constructed, so that the cooling scheme can be adaptively optimized according to the operation trend, and the stability of the long-term heat dissipation performance is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 is the main process flow chart of the present invention;

[0050] Figure 2 is the process flow chart of step S1 of the present invention;

[0051] Figure 3 is the process flow chart of step S2 of the present invention;

[0052] Figure 4 is the process flow chart of step S3 of the present invention;

[0053] Figure 5 is the process flow chart of step S4 of the present invention;

[0054] Figure 6 is the process flow chart of step S5 of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0055] In order to make the objectives, technical solutions, and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0056] In the description of the present invention, it should be understood that the orientation or positional relationship indicated by terms such as "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. is based on the orientation or positional relationship shown in the drawings. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more, unless otherwise specifically defined.

[0057] Please refer to Figure 1 , an AI-based adaptive phase change liquid cooling fault control method, comprising the following steps:

[0058] S1: Obtain the coolant temperature, flow rate, and phase change state data in the phase change liquid cooling system, monitor the liquid-to-gel phase change time under different flow rates and temperature conditions in real time through an embedded sensing node, record the phase change lag time series, calculate the phase change lag time increment of adjacent cycles, and obtain the analysis result of the phase change lag time change trend;

[0059] S2: Based on the analysis result of the phase change lag time change trend, calculate the phase change lag time growth rate, combine the server heat power consumption data, calculate the heat load change rate, synchronously combine the coolant flow rate data to calculate the flow rate dynamic change rate, compare the phase change lag time growth rate, heat load change rate, and flow rate dynamic change rate, and comprehensively analyze the cooling capacity decline rate to obtain the evaluation result of the cooling capacity decline rate;

[0060] S3: Based on the evaluation result of the cooling capacity decline rate, obtain the coolant phase change heat absorption amount, specific heat capacity, and flow thermal resistance data, calculate the coolant phase change heat absorption rate per unit time, construct the heat absorption rate time series, calculate the heat absorption rate change rate, and judge the degree of deviation of the heat absorption rate from the optimal heat absorption interval to obtain the analysis result of the abnormal fluctuation degree of the heat absorption rate;

[0061] S4: Based on the analysis result of the abnormal fluctuation degree of the heat absorption rate, calculate the phase change completion degree of the coolant at the current flow rate, judge whether the current flow rate is too fast or too slow, adjust the coolant pump speed and flow rate distribution, and perform dynamic flow path switching to obtain the coolant flow rhythm adjustment record;

[0062] S5: Based on the coolant flow rhythm adjustment record, calculate the heat load balance degree, heat dissipation efficiency change amount, and local temperature rise reduction amplitude during the cooling process before and after adjustment, analyze the overall operation trend of the cooling process, and judge the stability of the cooling process to generate an adaptive cooling control scheme.

[0063] The analysis results of the phase change hysteresis time variation trend include the phase change hysteresis time series, the phase change hysteresis time increment data, and the phase change hysteresis time growth trend. The evaluation results of the cooling capacity decline rate include the phase change hysteresis time growth rate, the heat load change rate, the flow rate dynamic change rate, and the cooling capacity decline rate. The analysis results of the abnormal fluctuation degree of the heat absorption rate include the heat absorption rate time series, the heat absorption rate change rate, and the analysis results of the heat absorption rate deviation degree. The coolant flow rhythm adjustment records include the coolant pump speed adjustment records, the flow rate distribution adjustment records, and the dynamic flow path switching records. The adaptive cooling control scheme includes the heat load balance degree, the change amount of the heat dissipation efficiency, the data of the reduction amplitude of the local temperature rise, and the judgment result of the cooling process stability.

[0064] Please refer to Figure 2 , step S1 is as follows:

[0065] S111: Obtain the coolant temperature, flow rate, and phase change state data in the phase change liquid cooling system, monitor the liquid-to-gel phase change time under different flow rates and temperature conditions, record the phase change hysteresis time series of the coolant under different conditions, and obtain the phase change hysteresis time series data;

[0066] Obtain the coolant temperature, flow rate, and phase change state data in the phase change liquid cooling system, use embedded sensing nodes to monitor the phase change behavior of the coolant under different flow rates and temperature conditions in real time, set the monitoring point temperature range to -10°C to 80°C, the flow rate range to 0.1 m / s to 5 m / s, and record the measurement data at a frequency of 10 times per second. In practical applications, the initial temperature of the coolant in a liquid cooling system is set to 25°C and the flow rate is 2 m / s. During the cooling process, when the temperature drops to a certain threshold (such as 10°C), the coolant may enter the supercooled state. When it further drops to 5°C, a gel structure begins to form in some areas. To determine the phase change hysteresis time, record the time series from liquid to gel. Each phase change moment is determined by detecting the sudden change in the coolant viscosity or the change in the optical sensing characteristics by the sensor. During the experiment, the phase change moments under different flow rates and temperature conditions are recorded. For example, under the conditions of a flow rate of 2 m / s and an initial temperature of 25°C, the coolant undergoes a phase change after the temperature drops to 3°C, and the hysteresis time is 15 seconds. All experimental data constitutes the phase change hysteresis time series data.

[0067] S112: Based on the phase change hysteresis time series data, use the formula:

[0068]

[0069] Calculate the phase change hysteresis time increment value ΔT φ , and output the phase change hysteresis time increment data, where T φ (t) represents the phase change hysteresis time of the current cycle, T φ(t - 1) represents the phase change lag time of the previous cycle, V i represents the flow rate of the i-th group of coolant, T i represents the coolant temperature corresponding to the i-th group, n represents the total number of flow rate groups, T φ (j) represents the phase change lag time of the j-th group, represents the mean value of all measured phase change lag times, m represents the total number of data groups;

[0070] Based on the phase change lag time series data, calculate the phase change lag time increment between adjacent cycles. Set the time period interval to 10 seconds, obtain the phase change moment of each cycle, and calculate using the increment calculation formula.

[0071] If the lag time of a certain cycle is 15 seconds and the lag time of the previous cycle is 18 seconds, then its time increment is |15 - 18| = 3 seconds. For the calculation of flow rate and temperature, assume that the average temperatures under five flow rate conditions (0.5, 1.0, 2.0, 3.0, 4.0 m / s) are (8, 6, 4, 5, 7 °C) respectively, then the calculation items:

[0072]

[0073] If the mean value of the phase change lag time is 10 seconds and the sum of the squared differences of the five groups of data is 80, then the standard deviation item is:

[0074]

[0075] Finally, the phase change lag time increment is calculated, and the calculated value is 3 + 12.2 - 8.94 = 6.26 seconds.

[0076] S113: Based on the phase change lag time increment data, analyze the change pattern of the phase change lag time, determine the change rate and trend characteristics of the phase change lag time, and obtain the analysis result of the phase change lag time change trend;

[0077] Based on the change trend of the phase change lag time, analyze the change pattern of the phase change lag time, obtain the change rate and trend characteristics of the phase change lag time, and set the judgment criteria: if the absolute value of the lag time increment is greater than 5 seconds, it is the stage of drastic change; if it is less than 2 seconds, it is the stable stage. The basis for setting this criterion is the time fluctuation range of the phase change process under different flow rates and temperature conditions. According to the lag time data in multiple experimental environments, statistically analyze the phase change lag time increments in different stages, and set the boundary values for extreme changes in the phase change rate to ensure that this setting is applicable to the working conditions with a flow rate ranging from 0.1 m / s to 5 m / s and a temperature range from -10°C to 80°C. Analyze the changes in the phase change lag time at the maximum and minimum flow rates. The experimental data shows that when the flow rate is 0.1 m / s and the temperature is -5°C, the maximum phase change lag time increment can reach 7.2 seconds; while when the flow rate is 5 m / s and the temperature is 80°C, the minimum lag time increment can be as low as 0.8 seconds. Therefore, 5 seconds is set as the drastic change limit, and 2 seconds is set as the stable limit, so that this criterion covers the phase change trends under all flow rate and temperature conditions. In this experiment, the calculated value of the phase change lag time increment is 6.26 seconds, which meets the judgment criteria for the stage of drastic change. Plot the increment values of all cycle lag times into a trend chart and observe the trend curve. If the lag time gradually decreases, it indicates that the phase change accelerates; if the lag time increases, it indicates that the phase change of the coolant is greatly affected by the outside world. Thus, the analysis result of the change trend of the phase change lag time is obtained.

[0078] Please refer to Figure 3 , and step S2 is:

[0079] S211: Based on the analysis result of the phase change lag time change trend, calculate the growth rate of the phase change lag time, monitor the phase change lag time data at different time points, calculate the change amount between adjacent time points, and calculate the growth rate of the phase change lag time based on time interval normalization to obtain the growth rate of the phase change lag time;

[0080] Based on the analysis result of the phase change lag time change trend, calculate the growth rate of the phase change lag time. First, monitor the phase change lag time data at multiple time points and record the phase change time data in different time periods. For example, under different server load conditions, the phase change state change times of the coolant in the pipeline are 12.5 s, 13.8 s, 15.2 s, and 16.5 s respectively, and the time interval is set to 5 s. On this basis, compare the change amounts of the phase change lag time between adjacent time points, that is, calculate the phase change time change amounts in each time period. For example, the phase change lag time changes between adjacent time points are 1.3 s, 1.4 s, and 1.3 s respectively, and use time interval normalization to calculate the growth rate of the phase change lag time. The normalization calculation is carried out by normalizing the ratio of the time change amount to the time interval. For example: Finally, obtain the set of growth rates of the phase change lag time at multiple time points, and perform trend calculation based on this set to finally obtain the growth rate of the phase change lag time.

[0081] S212: Obtain the server's thermal power consumption data, calculate the change value of the thermal power consumption per unit time, and use the formula:

[0082]

[0083] Calculate the thermal load change rate R q , where Q i represents the thermal power consumption at the i-th time point, Q i-1 represents the thermal power consumption at the previous time point, Δt i represents the i-th time interval, N represents the total number of time intervals, Q j represents the thermal power consumption data of the j-th group, represents the average value of all measured thermal power consumptions, Q max represents the maximum thermal power consumption value, M represents the total number of data groups;

[0084] Obtain the server's thermal power consumption data, and calculate the change rate of the thermal power consumption per unit time based on the thermal power consumption at adjacent time points. As shown in Table 2.1, set the thermal power consumption data at multiple time points and calculate its change value.

[0085] Table 2.1 Server Thermal Power Consumption Data Table

[0086]

[0087]

[0088] According to the data in Table 2.1, substitute the data into the calculation to calculate the thermal load change at adjacent moments. For example, |125 - 120| = 5W, |130 - 125| = 5W, |138 - 130| = 8W, the total change is 5 + 5 + 8 = 18W, and the total time interval is 5 + 5 + 5 = 15s. The result of the first calculation:

[0089]

[0090] Calculate the average value The maximum thermal power consumption value Q max = 138W, calculate the second item:

[0091]

[0092] Calculate to get:

[0093]

[0094] Finally, the thermal load change rate is:

[0095] R q = 1.2 + 6.65 = 7.85;

[0096] The calculated heat load change rate can be used for the subsequent calculation of the cooling capacity decline rate.

[0097] S213: Calculate the dynamic change rate of the flow rate by combining the coolant flow rate data, compare the growth rate of the phase change lag time and the heat load change rate, analyze the decline of the cooling capacity, and obtain the evaluation result of the cooling capacity decline rate;

[0098] Synchronously combine the coolant flow rate data to calculate the dynamic change rate of the flow rate, compare the growth rate of the phase change lag time, the heat load change rate, and the dynamic change rate of the flow rate to obtain the cooling capacity decline rate. As shown in Table 2.2, set the coolant flow rates at multiple time points and calculate their change rates.

[0099] Table 2.2 Data table of coolant flow rate changes

[0100]

[0101] Calculate the dynamic change rate of the flow rate, such as:

[0102]

[0103] Substitute the values for calculation:

[0104]

[0105] The calculated dynamic change rate of the flow rate is R v = 0.08. Subsequently, comprehensively compare the growth rate of the phase change lag time, the heat load change rate, and the dynamic change rate of the flow rate, such as R φ = 0.26, R q = 7.85, R v = 0.08, and use the normalized weight to calculate the comprehensive cooling capacity decline rate:

[0106]

[0107] Finally, obtain the evaluation result of the cooling capacity decline rate.

[0108] Please refer to Figure 4 , and the steps of S3 are as follows:

[0109] S311: Based on the evaluation result of the cooling capacity decline rate, obtain the phase change heat absorption amount, specific heat capacity, and flow thermal resistance data of the coolant, calculate the phase change heat absorption rate of the coolant per unit time, and obtain the phase change heat absorption rate data of the coolant;

[0110] Based on the evaluation result of the cooling capacity decline rate, first obtain the phase change heat absorption of the coolant. Usually, the experimental measurement method can be used to obtain the heat absorption data in different temperature ranges. For example, the phase change heat absorptions measured at three typical temperature points of 25°C, 40°C, and 55°C are 220 kJ / kg, 185 kJ / kg, and 160 kJ / kg respectively. Obtain the specific heat capacity parameter. Taking the water-based coolant as an example, its specific heat capacity is about 4.2 kJ / kg·K. For some special coolants, such as the coolant containing nanoparticles, its specific heat capacity may be 3.8 kJ / kg·K. According to the flow state of the coolant in the cooling system, measure its flow heat resistance. The flow heat resistance can be calculated by measuring the pressure difference and flow velocity at both ends of the pipeline. For example, for a certain coolant at a speed of 2 m / s, its flow heat resistance is measured to be 0.15 K·m 2 / W. Subsequently, based on the above parameters, calculate the phase change heat absorption rate of the coolant per unit time, and use the formula Q = lC p ΔT for calculation, where l represents the mass flow rate of the coolant per unit time, C p represents the specific heat capacity, and ΔT represents the temperature. Taking 0.5 kg / s as the measurement value, then at 40°C, the heat absorption rate is calculated as:

[0111] Q = 0.5×4.2×(60 - 40) = 42 kJ / s;

[0112] Apply this calculation to different temperature conditions. For example, at 25°C:

[0113] Q = 0.5×4.2×(60 - 25) = 73.5 kJ / s;

[0114] Finally, obtain the phase change heat absorption rate data of the coolant.

[0115] S312: Based on the phase change heat absorption rate data of the coolant, construct time series data, and use the formula:

[0116]

[0117] Calculate the change rate R of the heat absorption rate qφ , where q φ,i represents the coolant heat absorption rate at the i-th time point, q φ,i-1 represents the coolant heat absorption rate at the previous time point, Δt i represents the i-th time interval, N represents the total number of time intervals, q φ,max represents the maximum heat absorption rate value, represents the average value of all measured heat absorption rates;

[0118] Based on the heat absorption rate of the coolant phase change, time series data is constructed to record the heat absorption rate in different time periods. Data is collected at 10s intervals. For example, the heat absorption rates recorded at 10s, 20s, and 30s are 42kJ / s, 39kJ / s, and 35kJ / s respectively. These are organized into a time series, and the change values of the heat absorption rate at adjacent time points are calculated.

[0119] N = 3 (number of data points), q φ,max = 42kJ / s (highest heat absorption rate),

[0120] Calculate each item:

[0121]

[0122] Finally, the rate of change of the heat absorption rate is obtained.

[0123] Table 3.1 lists the data of the change in the coolant heat absorption rate:

[0124] Table 31 Heat absorption rate time series data

[0125]

[0126]

[0127] As shown in Table 3.1, the heat absorption rate varies greatly in different time periods. Especially, the decline is significant between 20s - 30s. Therefore, it is necessary to further judge the degree to which the heat absorption rate deviates from the optimal heat absorption interval.

[0128] S313: Based on the rate of change of the heat absorption rate, judge the degree to which the heat absorption rate deviates from the optimal heat absorption interval, analyze the deviation amplitude and change pattern, and obtain the analysis result of the abnormal fluctuation degree of the heat absorption rate;

[0129] Based on the rate of change of the heat absorption rate, judge the degree to which the heat absorption rate deviates from the optimal heat absorption interval. First, define the optimal heat absorption interval. The setting basis of this interval is the heat absorption capacity test of the coolant under different temperature, flow rate, and pipe diameter conditions to ensure that the heat absorption rate maintains efficient heat transfer in a stable working state. The specific test process is as follows: Under the conditions of coolant flow rate 2m / s, ambient temperature 25°C, and pipe diameter 0.02m, use a heat flow meter to measure the heat absorption rate of the coolant, and calculate the heat absorption stable interval based on multiple experimental data. Calculate the mean and standard deviation of the heat absorption rate with 100 sets of measurement data. The collected heat absorption rate data interval is 38 - 52kJ / s, and calculate the mean value of the heat absorption rate Calculate the standard deviation σ of the heat absorption rate q = 2.6kJ / s.

[0130] Based on the mean value and the standard deviation, set the optimal interval of the heat absorption rate:

[0131]

[0132] Therefore, the optimal heat absorption rate range is set to 40 - 50 kJ / s. When the heat absorption rate is lower than 40 kJ / s, it indicates a decrease in cooling efficiency. Calculate the deviation of the current heat absorption rate from the optimal range:

[0133] Δq = q opt,min - q φ,min ;

[0134] where:

[0135] q opt,min = 40 kJ / s (minimum value of the optimal range);

[0136] q φ,min = 35 kJ / s (actual minimum heat absorption rate);

[0137] Δq = 40 - 35 = 5 kJ / s;

[0138] It shows that the heat absorption rate has decreased by 5 kJ / s, a 12.5% decrease compared to the optimal range. To set the deviation threshold, it needs to be determined based on the operating stability of the cooling system and combined with the heat exchange performance of the coolant. The specific method is as follows: Set the average heat absorption rate under normal operating conditions of the system as and calculate its relative standard deviation, that is Calculated:

[0139]

[0140] To ensure that the heat absorption rate fluctuation remains within the normal range and avoid the influence of short-term disturbances on the determination of heat absorption capacity, usually 1.5 times the relative standard deviation is used as the reasonable threshold for the heat absorption rate deviation, that is

[0141] δ q = 1.5 × CV = 1.5 × 5.75% = 8.625%;

[0142] Considering the operating safety of the cooling system, the deviation threshold is finally rounded up to 10%. This value indicates that when the heat absorption rate is more than 10% lower than the optimal range, the system enters the low-efficiency heat exchange state. 12.5% is greater than 10%, which further indicates that it has entered the low-efficiency heat exchange state at this time, and finally the analysis result of the abnormal fluctuation degree of the heat absorption rate is obtained.

[0143] Please refer to Figure 5 , the steps of S4 are:

[0144] S411: Based on the analysis result of the abnormal fluctuation degree of the heat absorption rate, calculate the coolant phase change completion degree at the current flow rate, monitor the phase change heat of the coolant under different flow rate conditions, calculate the phase change heat absorption ratio per unit volume, and obtain the coolant phase change completion degree data;

[0145] Based on the analysis result of the abnormal fluctuation degree of the heat absorption rate, obtain the coolant phase change completion degree under the current flow rate condition, calculate the phase change heat absorption ratio of the coolant under different flow rate conditions, establish a time series of the phase change heat, and calculate the proportion of the phase change heat of the coolant per unit volume. In the specific calculation process, it is necessary to collect the flow rate v c , specific heat capacity C p and phase change heat absorption q φ , assuming that the specific heat capacity of a certain test coolant is C p = 4.18 kJ / (kg·K), the flow rate is v c = 2.5 m / s, and the phase change heat absorption is q φ = 225 kJ / kg. Then the mass m of the coolant flowing through per unit time can be calculated through the flow rate and the pipe cross-sectional area. Assuming that the pipe cross-sectional area is A = 0.002 m 2 , then the mass passing through per unit time is:

[0146] m c = ρ·v c ·A·t;

[0147] Let the coolant density be ρ = 1000 kg / m 3 , and the flow-through time be t = 1 s. Then the calculated result is:

[0148] m c = 1000×2.5×0.002×1 = 5 kg;

[0149] Then the phase change heat absorption per unit time is:

[0150] Q φ = m c ·q φ = 5×225 = 1125 kJ;

[0151] This value is used to judge the phase change heat absorption ratio of the current coolant and is used for subsequent cooling efficiency calculation, and then the coolant phase change completion degree data is obtained.

[0152] S412: Based on the coolant phase change completion degree data, judge whether the current flow rate is too fast or too slow, and use the formula:

[0153]

[0154] Calculate the flow rate deviation degree D v , where, v cRepresents the current coolant flow rate, v opt Represents the optimal coolant flow rate, q φ,i Represents the coolant heat absorption rate at the i-th time point Represents the mean value of all measured heat absorption rates, q φ,max Represents the maximum heat absorption rate value, M represents the total number of data sets

[0155] Based on the coolant phase change completion data, determine whether the current flow rate is in the too-fast or too-slow range, calculate the degree of flow rate deviation, and the judgment of the flow rate needs to be based on the optimal coolant flow rate v opt Perform deviation calculation

[0156] v opt Depends on the heat load and the heat transfer capacity of the coolant. In the server cooling system, the common optimal flow rate is v opt = 3.0 m / s. Assume that the current coolant flow rate in a certain environment is v c = 2.5 m / s, calculate its flow rate deviation degree:

[0157]

[0158] If the measured heat absorption rate data is q φ = [220, 225, 230, 240] kJ / kg, calculate its mean value:

[0159]

[0160] Calculate the standard deviation:

[0161]

[0162]

[0163] Substitute into the formula:

[0164]

[0165] This value is used to judge the degree of deviation of the coolant flow rate and assist in adjusting the coolant pump speed and flow distribution to obtain the flow rate deviation degree

[0166] S413: Based on the flow rate deviation degree, adjust the coolant pump speed and flow distribution, perform dynamic flow path switching, record all adjusted flow parameters, and obtain the coolant flow rhythm adjustment record

[0167] Based on the flow rate deviation degree, adjust the coolant pump speed and flow distribution, and perform dynamic flow path switching. During the specific adjustment process, different cooling strategies need to be selected according to different flow rate ranges. If the flow rate deviation degree D v Is less than 0.1, then maintain the current coolant flow rate. If 0.1 ≤ Dv ≤0.3, the pump speed needs to be adjusted by 5%-10%. If D v >0.3, dynamic flow path switching needs to be performed. Among them, the setting basis for the two thresholds of 0.1 and 0.3 lies in the non-linear change law of fluid heat exchange efficiency. When the flow rate deviates within 10% of the optimal range, its impact on the overall heat exchange efficiency is less than 3%. When the deviation exceeds 30%, the heat exchange efficiency drops by more than 15%. Therefore, it is reasonable to use 0.1 and 0.3 as the setting range to reduce the impact of uneven heat exchange in the system. For example, in a certain cooling system, the initial pump speed is set at 1200 rpm. After calculating D v =0.203, the pump speed needs to be adjusted as follows:

[0168] B′ = B × (1 + ΔP);

[0169] Among them, ΔP is the pump speed adjustment ratio, taking 5%. Then:

[0170] B′ = 1200 × (1 + 0.05) = 1260 rpm;

[0171] Synchronously adjust the flow distribution. In a multi-path cooling system, the adjustment of the flow rate needs to be based on the heat load situation of the coolant flow path. Assuming the initial flow distribution is [L1, L2, L3] = [0.3, 0.4, 0.3], due to the large deviation of the flow rate, the flow rate needs to be redistributed so that the path with a larger heat load obtains more flow. For example, after adjustment:

[0172] [L′1, L′2, L′3] = [0.35, 0.45, 0.2];

[0173] Finally, record all the adjusted flow parameters to obtain the coolant flow rhythm adjustment record.

[0174] Table 4.1 Coolant Parameter Value Table

[0175] Parameter Symbol Value Unit Coolant density ρ 1000 <![CDATA[kg / m 3 > Specific heat capacity of coolant <![CDATA[C p > 4.18 kJ / (kg·K) Optimal flow velocity <![CDATA[v opt > 3.0 m / s Current flow velocity <![CDATA[v c > 2.5 m / s Heat absorption during phase change <![CDATA[q φ > 225 kJ / kg Initial pump speed B 1200 rpm Pump speed adjustment ratio ΔP 5％ -

[0176] As shown in Table 4.1, the relevant parameters of the coolant used in the embodiment are listed. Combining the data in the table, further flow rate adjustment calculations can be performed.

[0177] Please refer to Figure 6 , step S5 is:

[0178] S511: Based on the coolant flow rhythm adjustment record, calculate the heat load balance degree during the cooling process before and after adjustment, obtain the heat load distribution data during the cooling process, calculate the heat load difference value of each local area, and perform normalization processing to obtain the heat load balance degree data;

[0179] Based on the records of coolant flow rhythm adjustment, obtain the coolant system parameters before and after adjustment. First, monitor the heat load distribution of the coolant at different flow rates, measure the temperature at each monitoring point, and calculate the heat load data for each region. For example, if the initial temperature of a certain region is 65°C and it drops to 45°C after cooling, and the flow rate is 0.5 L / s, then the heat load of this region can be calculated through heat capacity. At the same time, compare the temperature distributions of each region, calculate the local heat load deviation value, and then obtain the overall heat load balance degree through normalization. Suppose the average heat load of a certain system is 500 W, and the heat load deviation of a certain region is ±50 W, then the calculated balance degree can indicate whether this region is in the heat load optimization state. Combine the monitoring point data to calculate the heat load distribution of the overall system and obtain the heat load balance degree data.

[0180] S512: Based on the heat load balance degree data, calculate the change amount of heat dissipation efficiency, obtain the heat dissipation power data during the cooling process, compare the heat dissipation per unit time changes at different flow rates, calculate the adjustment range of heat dissipation efficiency, and analyze the dynamic change trend of heat dissipation capacity to obtain the change amount data of heat dissipation efficiency;

[0181] Based on the heat load balance degree, obtain the heat dissipation power before and after adjustment, calculate the change in the heat dissipation capacity per unit time of the coolant system, measure the heat dissipation at each monitoring point. If the initial heat dissipation of a certain heat dissipation region is 1200 W and it becomes 1350 W after adjustment, then the change amount of heat dissipation efficiency can be calculated through the heat dissipation power ratio. Further, by comparing the heat dissipation per unit time under different flow rate conditions, for example, when the flow rate is 1.2 m / s, the heat dissipation power increases by 10%, and when it is 0.8 m / s, the heat dissipation power decreases by 5%, calculate the change trend of heat dissipation efficiency, and comprehensively evaluate the adjustment range of the heat dissipation capacity of the coolant system under different working conditions to obtain the change amount data of heat dissipation efficiency.

[0182] Table 5.1 Data Table of Heat Dissipation Efficiency Change

[0183]

[0184] As shown in Table 5.1, the change amounts of heat dissipation efficiency at different monitoring points vary with the adjustment of the coolant flow rhythm.

[0185] S513: Based on the change amount data of heat dissipation efficiency, calculate the reduction amplitude of local temperature rise, analyze the overall operation trend of the cooling process, and judge the stability of the cooling process, and comprehensively adjust the cooling parameters to obtain an adaptive cooling control scheme;

[0186] Based on the change in heat dissipation efficiency, calculate the reduction in local temperature rise, and obtain the temperature changes in each key area before and after adjustment. Assume that in a key area of a certain device, the temperature before adjustment is 85 °C and it drops to 72 °C after adjustment. Then the reduction in local temperature rise can be calculated as 13 °C. Further analyze the temperature change trends in each area to judge the overall stability of the cooling process, and comprehensively adjust the flow rate distribution, coolant flow rate, and circulation strategy to ensure that the system temperature change is within a stable range, and finally obtain an adaptive cooling control scheme.

[0187] Table 5.2 Data table of the reduction in temperature rise

[0188]

[0189] As shown in Table 5.2, the reduction in temperature rise at each monitoring point is different, indicating that the cooling system needs to adjust the flow rate and flow volume according to the heat dissipation requirements of specific areas to optimize temperature stability.

[0190] An AI-based adaptive phase change liquid cooling fault control system includes:

[0191] The cooling parameter monitoring module obtains the coolant temperature, flow rate, and phase change state data, calls the embedded sensing node to monitor the phase change time from liquid state to gel state, records the phase change lag time series, calculates the phase change lag time increment of adjacent cycles, and analyzes the phase change lag time change trend analysis result;

[0192] The phase change lag calculation module calculates the phase change lag time growth rate based on the phase change lag time change trend analysis result, combines the server thermal power consumption data to calculate the heat load change rate, and compares the coolant flow rate dynamic change rate to obtain the cooling capacity decline rate evaluation result;

[0193] The cooling capacity evaluation module calls the coolant phase change heat absorption, specific heat capacity, and flow thermal resistance data based on the cooling capacity decline rate evaluation result, calculates the heat absorption rate time series, analyzes the heat absorption rate change rate, and obtains the heat absorption rate abnormal fluctuation degree analysis result;

[0194] The heat absorption rate analysis module calculates the phase change completion degree of the coolant at the current flow rate based on the heat absorption rate abnormal fluctuation degree analysis result, judges whether the flow rate is abnormal, adjusts the pump speed, flow volume, and path, and obtains the coolant flow rhythm adjustment record;

[0195] The flow rhythm adjustment module calculates the heat load balance degree, heat dissipation efficiency change amount, and local temperature rise reduction amplitude before and after adjustment based on the coolant flow rhythm adjustment record, judges the cooling process stability, and generates an adaptive cooling control scheme.

[0196] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the relevant art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. An AI-based adaptive phase change liquid cooling fault control method, characterized in that, It includes the following steps: S1: Obtain the coolant temperature, flow rate, and phase change state data, monitor the liquid-to-gel phase change time under different flow rates and temperature conditions in real time, calculate the phase change lag time increment between adjacent cycles, and obtain the analysis result of the phase change lag time variation trend; S2: Based on the analysis result of the phase change lag time variation trend, calculate the phase change lag time growth rate, heat load change rate, and flow rate dynamic change rate, determine the cooling capacity decline rate, and obtain the evaluation result of the cooling capacity decline rate; S3: Based on the evaluation result of the cooling capacity decline rate, calculate the phase change heat absorption rate and the change rate of the heat absorption rate per unit time of the coolant, judge the deviation degree of the heat absorption rate, and obtain the analysis result of the abnormal fluctuation degree of the heat absorption rate; S4: Based on the analysis result of the abnormal fluctuation degree of the heat absorption rate, calculate the phase change completion degree of the coolant at the current flow rate, adjust the coolant pump speed, flow rate distribution, and flow path switching, and obtain the coolant flow rhythm adjustment record; S5: Based on the coolant flow rhythm adjustment record, calculate the heat load balance degree, the change amount of heat dissipation efficiency, and the reduction amplitude of local temperature rise before and after adjustment, analyze and judge the stability of the cooling process, and generate an adaptive cooling control scheme.

2. The AI-based adaptive phase change liquid cooling fault control method according to claim 1, wherein The analysis result of the phase change lag time variation trend includes the phase change lag time series, the phase change lag time increment data, and the phase change lag time growth trend. The evaluation result of the cooling capacity decline rate includes the phase change lag time growth rate, the heat load change rate, the flow rate dynamic change rate, and the cooling capacity decline rate. The analysis result of the abnormal fluctuation degree of the heat absorption rate includes the heat absorption rate time series, the heat absorption rate change rate, and the analysis result of the heat absorption rate deviation degree. The coolant flow rhythm adjustment record includes the coolant pump speed adjustment record, the flow rate distribution adjustment record, and the dynamic flow path switching record. The adaptive cooling control scheme includes the heat load balance degree, the change amount of heat dissipation efficiency, the reduction amplitude data of local temperature rise, and the cooling process stability judgment result.

3. The AI-based adaptive phase change liquid cooling fault control method according to claim 1, wherein The specific steps for obtaining the coolant temperature, flow rate, and phase change state data, monitoring the liquid-to-gel phase change time under different flow rates and temperature conditions in real time, calculating the phase change lag time increment between adjacent cycles, and obtaining the analysis result of the phase change lag time variation trend are as follows: S111: Obtain the coolant temperature, flow rate, and phase change state data, monitor the liquid-to-gel phase change time under different flow rates and temperature conditions, record the phase change lag time series of the coolant under different conditions, and obtain the phase change lag time series data; S112: Based on the phase change lag time series data, use the formula: Calculate the increment value ΔT of the phase change lag time φ , and output the phase change lag time increment data, where T φ (t) represents the phase change lag time of the current cycle, and T φ (t - 1) represents the phase change lag time of the previous cycle, and V i represents the flow rate of the i-th group of coolant, and T i represents the coolant temperature corresponding to the i-th group, n represents the total number of flow rate groups, and T φ (j) represents the phase change lag time of the j-th group, represents the mean value of all measured phase change lag times, and m represents the total number of data groups; S113: Based on the phase change lag time increment data, analyze the change pattern of the phase change lag time, determine the change rate and trend characteristics of the phase change lag time, and obtain the analysis result of the phase change lag time variation trend.

4. The AI-based adaptive phase change liquid cooling fault control method according to claim 1, wherein The specific steps for calculating the phase change lag time growth rate, heat load change rate, and flow rate dynamic change rate based on the analysis result of the phase change lag time variation trend, determining the cooling capacity decline rate, and obtaining the evaluation result of the cooling capacity decline rate are as follows: S211: Based on the analysis result of the change trend of the phase change lag time, calculate the growth rate of the phase change lag time, monitor the phase change lag time data at different time points, calculate the change amount between adjacent time points, and calculate the growth rate of the phase change lag time based on time interval normalization to obtain the growth rate of the phase change lag time; S212: Obtain the server thermal power consumption data, calculate the change value of the thermal power consumption per unit time, and use the formula: Calculate the rate of change of the heat load, R q , where Q i represents the heat power consumption at the i-th time point, Q i-1 represents the heat power consumption at the previous time point, Δt i represents the i-th time interval, N represents the total number of time intervals, Q j represents the heat power consumption data of the j-th group, represents the mean value of all measured heat power consumptions, Q max represents the maximum heat power consumption value, M represents the total number of data groups; S213: Combine the coolant flow rate data to calculate the dynamic change rate of the flow rate, compare the growth rate of the phase change lag time and the change rate of the thermal load, analyze the decrease in the cooling capacity, and obtain the evaluation result of the cooling capacity decrease rate.

5. The AI-based adaptive phase change liquid cooling fault control method according to claim 1, wherein The specific steps for calculating the phase change heat absorption rate and the change rate of the heat absorption rate of the coolant per unit time based on the evaluation result of the cooling capacity decrease rate, and judging the deviation degree of the heat absorption rate to obtain the analysis result of the abnormal fluctuation degree of the heat absorption rate are as follows: S311: Based on the evaluation result of the cooling capacity decrease rate, obtain the phase change heat absorption amount, specific heat capacity, and flow thermal resistance data of the coolant, calculate the phase change heat absorption rate of the coolant per unit time, and obtain the phase change heat absorption rate data of the coolant; S312: Based on the phase change heat absorption rate data of the coolant, construct time series data, and use the formula: Calculate the rate of change of the endothermic rate R qφ , where q φ,i represents the coolant endothermic rate at the i-th time point, q φ,i-1 represents the coolant endothermic rate at the previous time point, Δt i represents the i-th time interval, N represents the total number of time intervals, q φ,max represents the maximum endothermic rate value, represents the mean value of all measured endothermic rates; S313: Based on the change rate of the heat absorption rate, judge the degree to which the heat absorption rate deviates from the optimal heat absorption interval, analyze the deviation amplitude and change mode, and obtain the analysis result of the abnormal fluctuation degree of the heat absorption rate.

6. The AI-based adaptive phase change liquid cooling fault control method according to claim 1, wherein The specific steps for calculating the phase change completion degree of the coolant at the current flow rate based on the analysis result of the abnormal fluctuation degree of the heat absorption rate, and adjusting the coolant pump speed, flow rate distribution, and flow path switching to obtain the record of the coolant flow rhythm adjustment are as follows: S411: Based on the analysis result of the abnormal fluctuation degree of the heat absorption rate, calculate the phase change completion degree of the coolant at the current flow rate, monitor the phase change heat of the coolant under different flow rate conditions, and calculate the phase change heat absorption ratio per unit volume to obtain the phase change completion degree data of the coolant; S412: Based on the phase change completion degree data of the coolant, judge whether the current flow rate is too fast or too slow, and use the formula: Calculate the flow rate deviation D v , where v c represents the current coolant flow rate, v opt represents the optimal coolant flow rate, q φ,i represents the coolant heat absorption rate at the i-th time point, represents the mean value of all measured heat absorption rates, q φ,max represents the maximum heat absorption rate value, and M represents the total number of data sets; S413: Based on the flow rate deviation degree, adjust the coolant pump speed and flow rate distribution, perform dynamic flow path switching, and record all adjusted flow parameters to obtain the record of the coolant flow rhythm adjustment.

7. The AI-based adaptive phase change liquid cooling fault control method according to claim 1, wherein The specific steps for calculating the heat load balance degree, the change amount of the heat dissipation efficiency, and the reduction amplitude of the local temperature rise before and after adjustment based on the record of the coolant flow rhythm adjustment, analyzing and judging the stability of the cooling process, and generating an adaptive cooling control scheme are as follows: S511: Based on the record of the coolant flow rhythm adjustment, calculate the heat load balance degree during the cooling process before and after adjustment, obtain the heat load distribution data during the cooling process, calculate the heat load difference value of each local area, and perform normalization processing to obtain the heat load balance degree data; S512: Based on the heat load balance degree data, calculate the change amount of the heat dissipation efficiency, obtain the heat dissipation power data during the cooling process, compare the change of the heat dissipation amount per unit time under different flow rates, calculate the adjustment amplitude of the heat dissipation efficiency, and analyze the dynamic change trend of the heat dissipation capacity to obtain the change amount data of the heat dissipation efficiency; S513: Based on the data of the change in heat dissipation efficiency, calculate the reduction amplitude of local temperature rise, analyze the overall operation trend of the cooling process, and judge the stability of the cooling process. Then comprehensively adjust the cooling parameters to obtain an adaptive cooling control scheme.

8. An AI-based adaptive phase change liquid cooling fault control system, characterized in that, The system is used to execute the method according to any one of claims 1-7, and includes: The cooling parameter monitoring module acquires the coolant temperature, flow rate, and phase change state data, calls the embedded sensing node to monitor the phase change time from liquid state to gel state, records the phase change lag time series, calculates the phase change lag time increment of adjacent cycles, and analyzes to obtain the analysis result of the phase change lag time change trend; The phase change lag calculation module calculates the phase change lag time growth rate based on the analysis result of the phase change lag time change trend, combines the server heat power consumption data to calculate the heat load change rate, and compares the dynamic change rate of the coolant flow rate to obtain the evaluation result of the cooling capacity decline rate; The cooling capacity evaluation module calls the heat absorption amount, specific heat capacity, and flow thermal resistance data of the coolant phase change based on the evaluation result of the cooling capacity decline rate, calculates the heat absorption rate time series, analyzes the heat absorption rate change rate, and obtains the analysis result of the abnormal fluctuation degree of the heat absorption rate; The heat absorption rate analysis module calculates the phase change completion degree of the coolant at the current flow rate based on the analysis result of the abnormal fluctuation degree of the heat absorption rate, judges whether the flow rate is abnormal, adjusts the pump speed, flow rate, and path, and obtains the cooling agent flow rhythm adjustment record; The flow rhythm adjustment module calculates the heat load balance degree, the change amount of heat dissipation efficiency, and the reduction amplitude of local temperature rise before and after adjustment based on the cooling agent flow rhythm adjustment record, judges the stability of the cooling process, and generates an adaptive cooling control scheme.

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