An automated testing method for the electrical performance of a water cooling system

Abstract

This invention relates to the field of data processing technology, specifically to an automated method for detecting the electrical performance of a water-cooling system. The method involves: identifying potential voltage anomaly moments based on voltage differences between any given moment in a voltage time series and adjacent historical time periods; obtaining temperature ranges and voltage values ​​based on temperature and voltage distribution characteristics in a temperature-voltage correlation graph; and determining the reliability of the temperature-voltage correlation based on humidity differences between potential voltage anomaly moments and historical time periods in a humidity time series, and the voltage distribution characteristics within the temperature range containing the potential voltage anomaly moment. By determining the degree of voltage anomaly based on the difference between the voltage values ​​within the temperature range containing the potential voltage anomaly moment and the voltage at the potential anomaly moment, along with the reliability of the temperature-voltage correlation, the method effectively detects the electrical performance of the water-cooling system, improving detection accuracy.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically to an automated testing method for the electrical performance of a water-cooling system. Background Technology

[0002] A water-cooling system is a device that uses chilled water as a medium for cooling. To avoid poor cooling performance due to system malfunctions, it is necessary to monitor the system's operating status to provide timely warnings and adjust equipment conditions. The water-cooling system circulates water through a pump unit; therefore, its cooling effect is closely related to the operating status of the electrical equipment within the system. Thus, monitoring the voltage of the electrical equipment can reflect the normality of both the equipment and the water-cooling system. Existing anomaly detection methods typically use voltage thresholds for judgment, but these methods do not consider the influence of temperature and humidity on voltage within the water-cooling system. This leads to voltage deviations being identified as abnormal under certain temperature and humidity conditions, affecting the accuracy of electrical performance monitoring in the water-cooling system. Summary of the Invention

[0003] To address the aforementioned technical problem of low accuracy in detecting electrical performance based solely on a set voltage threshold, the present invention aims to provide an automated method for detecting the electrical performance of a water-cooling system. The specific technical solution adopted is as follows:

[0004] Acquire the temperature timing, humidity timing, and voltage timing during the operation of the water cooling system;

[0005] The voltage deviation is obtained based on the voltage difference characteristics between any time point and adjacent historical time periods in the voltage time series; suspected voltage anomaly times are obtained based on the voltage deviation at any time point; a temperature-voltage correlation diagram is obtained based on the temperature time series and the voltage time series; and temperature ranges and voltage characterization values ​​are obtained based on the temperature distribution characteristics and voltage distribution characteristics in the temperature-voltage correlation diagram.

[0006] A humidity normality index is obtained based on the humidity difference characteristics between the suspected voltage anomaly time and historical time periods in the humidity time series; a data distribution characteristic value is obtained based on the voltage distribution characteristics of the temperature range where the suspected voltage anomaly time is located; and the temperature-voltage correlation confidence level is obtained based on the humidity normality index and the data distribution characteristic value.

[0007] The degree of voltage anomaly is obtained by comparing the voltage characteristics of the temperature range at which the voltage is suspected to be abnormal with the voltage at the suspected abnormal time, and by the reliability of the temperature-voltage correlation; the electrical performance of the water cooling system is then tested based on the degree of voltage anomaly.

[0008] Further, the step of obtaining the voltage deviation based on the voltage difference characteristics between any time point in the voltage time series and adjacent historical time periods includes:

[0009] Calculate the sum of the absolute values ​​of the voltage difference between the arbitrary time and each historical time in the adjacent historical period to obtain the accumulated voltage difference value; map the accumulated voltage difference value positively to obtain the voltage deviation at the arbitrary time.

[0010] Furthermore, the step of obtaining the suspected voltage anomaly time based on the voltage deviation at any given time includes:

[0011] Any moment when the voltage deviation exceeds a preset deviation threshold is defined as the suspected voltage anomaly moment.

[0012] Further, the step of obtaining the temperature-voltage correlation graph based on the temperature time series and the voltage time series includes:

[0013] The temperature time series is normalized by the maximum and minimum values ​​to obtain temperature mapping values ​​at different times; the voltage time series is normalized by the maximum and minimum values ​​to obtain voltage mapping values ​​at different times; a coordinate graph is constructed with the temperature mapping value on the horizontal axis and the voltage mapping value on the vertical axis, and a temperature-voltage correlation graph is constructed based on the positions of the temperature mapping value and the voltage mapping value at the same time in the coordinate graph.

[0014] Further, the step of obtaining the temperature range and voltage characterization value based on the temperature distribution characteristics and voltage distribution characteristics in the temperature-voltage correlation diagram includes:

[0015] The temperature mapping values ​​of the temperature-voltage correlation graph are divided according to a preset interval to obtain different temperature ranges; the average value of the voltage mapping values ​​corresponding to the temperature ranges is calculated to obtain the voltage characterization value.

[0016] Furthermore, the step of obtaining the humidity normality index based on the humidity difference characteristics between the suspected voltage anomaly time and historical time periods in the humidity time series includes:

[0017] Calculate the average humidity value for the historical period preceding the suspected voltage anomaly to obtain the historical humidity value; calculate the absolute value of the difference between the humidity at the suspected voltage anomaly and the historical humidity value and perform a negative correlation mapping to obtain the humidity normality index at the suspected voltage anomaly.

[0018] Further, the step of obtaining data distribution characteristic values ​​based on the voltage distribution characteristics within the temperature range at the time of the suspected voltage anomaly includes:

[0019] Calculate the ratio of the number of voltage mapping values ​​in the temperature range where the voltage is suspected to be abnormal at the time of the voltage anomaly to the number of voltage mapping values ​​in the temperature-voltage correlation graph to obtain the data proportion feature value; calculate the variance of the voltage mapping values ​​in the temperature range where the voltage is suspected to be abnormal at the time of the voltage anomaly and perform negative correlation mapping to obtain the voltage concentration; calculate the product of the data proportion feature value and the voltage concentration to obtain the data distribution feature value at the time of the suspected voltage anomaly.

[0020] Further, the step of obtaining the temperature-voltage correlation confidence level based on the humidity normality index and the data distribution characteristic value includes:

[0021] Calculate the product of the humidity normality index and the data distribution characteristic value and perform a positive correlation mapping to obtain the reliability of the temperature-voltage correlation at the moment when the voltage is suspected to be abnormal.

[0022] Further, the step of obtaining the degree of voltage anomaly based on the difference between the voltage characterization value in the temperature range where the suspected voltage anomaly occurred and the voltage at the suspected voltage anomaly occurred, and the reliability of the temperature-voltage correlation, includes:

[0023] Calculate the absolute value of the difference between the voltage characterization value in the temperature range where the voltage is suspected to be abnormal at the time of the voltage anomaly and the voltage mapping value corresponding to the time of the suspected voltage anomaly to obtain the voltage difference index; calculate the product of the voltage difference index and the reliability of the temperature-voltage correlation to obtain the degree of voltage anomaly at the time of the suspected voltage anomaly.

[0024] Furthermore, the step of detecting the electrical performance of the water-cooling system based on the degree of voltage anomaly includes:

[0025] When the degree of voltage abnormality at the suspected voltage abnormality moment exceeds a preset abnormality threshold, the electrical performance of the water cooling system becomes abnormal at the suspected voltage abnormality moment.

[0026] The present invention has the following beneficial effects:

[0027] In this invention, obtaining the voltage deviation degree allows for the preliminary determination of potential electrical equipment anomalies based on the operating conditions of the electrical equipment in the water-cooling system, thereby identifying the suspected voltage anomaly moment. Obtaining the temperature-voltage correlation graph reflects the voltage characteristics of the electrical equipment at different temperatures; obtaining temperature ranges distinguishes these characteristics, thus determining the voltage characteristics under different temperature ranges; obtaining voltage characterization values ​​characterizes the overall voltage level within the same temperature range, thereby judging the difference between the voltage at the suspected voltage anomaly moment and the voltage characterization value under the same temperature conditions, reducing the influence of temperature on voltage in the water-cooling system and further improving the accuracy of electrical equipment detection. Obtaining the humidity normality index determines the degree of humidity influence on voltage at the suspected voltage anomaly moment, making voltage detection more accurate; obtaining data distribution characteristic values ​​characterizes the reliability of voltage characterization values ​​within temperature ranges, further improving the accuracy of electrical equipment detection. Obtaining the temperature-voltage correlation reliability characterizes the influence of external factors during voltage anomaly detection, thereby reducing the probability of false detection; obtaining the voltage anomaly degree, based on the analysis of voltage differences under the same temperature conditions, combined with the temperature-voltage correlation reliability, allows for anomaly judgment of electrical equipment, improving the accuracy of electrical equipment detection in the water-cooling system. Attached Figure Description

[0028] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0029] Figure 1 This is a flowchart of an automated testing method for the electrical performance of a water-cooling system, provided as an embodiment of the present invention. Detailed Implementation

[0030] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of an automated testing method for the electrical performance of a water-cooling system proposed according to the present invention. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0031] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0032] The following description, in conjunction with the accompanying drawings, details the specific scheme of the automated testing method for the electrical performance of a water-cooling system provided by the present invention.

[0033] Please see Figure 1 The diagram illustrates a flowchart of an automated testing method for the electrical performance of a water-cooling system according to an embodiment of the present invention. The method includes the following steps:

[0034] Step S1: Obtain the temperature timing, humidity timing, and voltage timing during the operation of the water cooling system.

[0035] In this embodiment of the invention, the implementation scenario is to test the electrical performance of a water-cooling system to improve testing accuracy. First, the voltage timing sequence during the operation of the water-cooling system is acquired. In this embodiment, the voltage timing sequence during the operation of the water pump unit is collected. Since the temperature of the circulating water and the humidity within the water pump unit both cause voltage changes, the circulating water temperature and water pump humidity are collected at the same time intervals. In this embodiment, the data acquisition frequency is once per second, but the implementer can determine the frequency according to the implementation scenario.

[0036] Step S2: Obtain the voltage deviation based on the voltage difference characteristics between any time point and adjacent historical time periods in the voltage time series; obtain the suspected voltage anomaly time based on the voltage deviation at any time point; obtain the temperature-voltage correlation diagram based on the temperature time series and voltage time series; obtain the temperature range and voltage characterization value based on the temperature distribution characteristics and voltage distribution characteristics in the temperature-voltage correlation diagram.

[0037] During normal operation of the water cooling system, the voltage of the water pump unit is relatively stable. Voltage fluctuations may be caused by electrical equipment malfunctions, therefore, it is necessary to preliminarily determine the possible times when electrical equipment malfunctions may occur based on voltage fluctuations. Thus, the voltage deviation is obtained based on the voltage difference characteristics between any given moment and adjacent historical time periods in the voltage time series. Preferably, in this embodiment of the invention, the step of obtaining the voltage deviation includes: calculating the sum of the absolute values ​​of the voltage differences between any given moment and each historical moment in the adjacent historical time periods to obtain a cumulative voltage difference value. In this embodiment of the invention, the adjacent historical time periods are the range of the most recent 20 historical moments before the given moment. If the voltage at any given moment is abnormal, there is a difference from the voltage at historical moments; therefore, the larger the cumulative voltage difference value, the more abnormal the voltage at that given moment is. The cumulative voltage difference value is positively correlated to obtain the voltage deviation at that given moment. The larger the voltage deviation value, the more likely the electrical equipment in the water cooling system is to malfunction at that given moment. Furthermore, the voltage deviation at any given time can be used to obtain the suspected voltage anomaly time. Specifically, any time when the voltage deviation exceeds a preset deviation threshold is taken as the suspected voltage anomaly time. In this embodiment of the invention, the preset deviation threshold is 0.7, which can be determined by the implementer according to the implementation scenario.

[0038] Furthermore, a suspected voltage anomaly indicates a potential electrical equipment malfunction. Since temperature and humidity in the water-cooling system can both potentially cause voltage anomalies, it's necessary to analyze the impact of temperature and humidity on voltage to improve the accuracy of electrical performance testing. Because the temperature of the circulating water in the water-cooling system affects the resistance of electrical equipment—for example, if the power of the cooled target suddenly increases and the cooling system cannot meet its cooling requirements—a sudden temperature change in the circulating water will occur. As the temperature rises, the resistance of the metal conductors inside the electrical equipment increases linearly, thus affecting the equipment's voltage; conversely, as the temperature drops, the resistance of the metal conductors decreases linearly. Therefore, there is a certain correlation between the temperature of the circulating water and the voltage of the electrical equipment. Thus, it is necessary to obtain the correlation between temperature and voltage; therefore, a temperature-voltage correlation diagram is obtained based on temperature and voltage time series data.

[0039] Preferably, in this embodiment of the invention, the step of obtaining the temperature-voltage correlation graph includes: normalizing the temperature time series by maximum and minimum values ​​to obtain temperature mapping values ​​at different times; normalizing the voltage time series by maximum and minimum values ​​to obtain voltage mapping values ​​at different times; it should be noted that maximum and minimum value normalization is prior art, and the specific steps will not be elaborated further. A coordinate graph is constructed with the horizontal axis representing the temperature mapping value and the vertical axis representing the voltage mapping value. The temperature-voltage correlation graph is constructed based on the positions of the temperature mapping value and the voltage mapping value at the same time in the coordinate graph. The temperature-voltage correlation graph characterizes the voltage distribution at different temperatures. Since different temperature values ​​correspond to multiple voltage values, representative voltage values ​​are needed to characterize the voltage characteristics of a certain temperature range for subsequent voltage anomaly analysis. Therefore, temperature ranges and voltage characterization values ​​are obtained based on the temperature distribution characteristics and voltage distribution characteristics in the temperature-voltage correlation graph. Preferably, in this embodiment of the invention, the step of obtaining temperature ranges and voltage characterization values ​​includes: dividing the temperature mapping values ​​of the temperature-voltage correlation graph according to a preset interval to obtain different temperature ranges; in this embodiment of the invention, the preset interval is 0.05, and a temperature range is divided at intervals of 0.05 on the horizontal axis of the temperature mapping values. The implementer can determine this interval according to the implementation scenario. The average value of the voltage mapping values ​​corresponding to the temperature range is calculated to obtain the voltage characterization value; the voltage characterization value reflects the voltage characteristics of the electrical equipment in the corresponding temperature range.

[0040] Step S3: Obtain the humidity normality index based on the humidity difference characteristics between the suspected voltage anomaly moment and the historical time period in the humidity time series; obtain the data distribution characteristic value based on the voltage distribution characteristics of the temperature range where the suspected voltage anomaly moment is located; obtain the temperature-voltage correlation confidence level based on the humidity normality index and the data distribution characteristic value.

[0041] Humidity in electrical equipment can affect voltage. Increased humidity can cause insulating materials such as plastics, rubber, and enameled wires to absorb moisture, leading to decreased insulation performance. High humidity can also cause water vapor to form a thin film on the surface of high-voltage components, reducing insulation resistance and triggering surface discharge, further decreasing voltage stability and potentially causing voltage anomalies. Therefore, the influence of humidity within the equipment needs to be considered when analyzing anomalies. A humidity normality index is obtained based on the difference in humidity between potential voltage anomalies and historical periods in a humidity time series. Preferably, in this embodiment, obtaining the humidity normality index includes: calculating the average humidity of historical periods before the potential voltage anomaly to obtain historical humidity values. Under normal circumstances, electrical equipment in a water-cooled system has good sealing properties, preventing external moisture from seeping into the equipment and causing damage. Therefore, historical humidity values ​​represent the low humidity level inside the equipment under normal conditions. The absolute value of the difference between the humidity at the suspected voltage anomaly and the historical humidity value is calculated and negatively correlated to obtain the humidity normality index at the suspected voltage anomaly. The larger the absolute value of the difference between the humidity at the suspected voltage anomaly and the historical humidity value, the higher the humidity at that suspected voltage anomaly, and the more likely it is to affect the voltage of electrical equipment. The smaller the humidity normality index is. The larger the humidity normality index, the more normal the humidity at that time, and the less likely it is to cause voltage anomalies.

[0042] Furthermore, the more voltage mapping values ​​within a temperature range in the temperature-voltage correlation graph, and the closer the values ​​are, the greater the confidence level and the more representative the voltage characterization value obtained in that temperature range is. In this case, the greater the difference between the voltage and the voltage characterization value within that temperature range, the more indicative of a voltage anomaly. Conversely, the fewer and more dispersed the voltage mapping values ​​within a temperature range, the lower the confidence level and the less representative the voltage characterization value obtained in that temperature range is. Even if the difference between the voltage and the voltage characterization value within that temperature range is large, it cannot accurately characterize the voltage anomaly. Therefore, data distribution characteristic values ​​are obtained based on the voltage distribution characteristics of the temperature range where the suspected voltage anomaly occurs. Preferably, in this embodiment of the invention, the step of obtaining data distribution characteristic values ​​includes: calculating the ratio of the number of voltage mapping values ​​in the temperature range where the suspected voltage anomaly occurs to the number of voltage mapping values ​​in the temperature-voltage correlation graph, to obtain a data proportion characteristic value. The larger the data proportion characteristic value, the more voltage data is available within that temperature range. The variance of the voltage mapping values ​​within the temperature range corresponding to the suspected voltage anomaly is calculated and negatively correlated to obtain the voltage concentration. A smaller variance in the voltage mapping values ​​indicates more concentrated and reliable voltage data within that temperature range, resulting in a higher voltage concentration. The product of the data proportion characteristic value and the voltage concentration is then calculated to obtain the data distribution characteristic value for the suspected voltage anomaly. A larger data distribution characteristic value indicates stronger reliability of the voltage representation values ​​within the temperature range corresponding to the suspected voltage anomaly, and higher persuasiveness in voltage detection.

[0043] After obtaining the influence of humidity and the data distribution characteristics, the reliability of the temperature-voltage correlation can be obtained based on the humidity normality index and the data distribution characteristics. Preferably, in this embodiment of the invention, the step of obtaining the reliability of the temperature-voltage correlation includes: calculating the product of the humidity normality index and the data distribution characteristics and mapping them positively to obtain the reliability of the temperature-voltage correlation at the moment when the voltage is suspected to be abnormal. The higher the reliability of the temperature-voltage correlation, the smaller the influence of humidity at the moment when the voltage is suspected to be abnormal, and the stronger the reliability of the voltage characterization value of the temperature range corresponding to that moment. The lower the reliability of the temperature-voltage correlation, the greater the influence of humidity at the moment when the voltage is suspected to be abnormal, and the weaker the reliability of the voltage characterization value of the temperature range corresponding to that moment, thereby reducing the probability of voltage anomaly detection errors. The formula for obtaining the reliability of the temperature-voltage correlation includes:

[0044]

[0045] In the formula, W represents the reliability of the temperature-voltage correlation at the moment when the voltage is suspected to be abnormal. The expression represents an exponential function with a base of the natural constant, where S represents the humidity normality index, N represents the data proportion characteristic value, and R represents the voltage concentration. This represents the characteristic value of the data distribution.

[0046] Step S4: Obtain the degree of voltage anomaly based on the difference between the voltage characterization value in the temperature range where the suspected voltage anomaly occurred and the voltage at the suspected voltage anomaly, as well as the reliability of the temperature-voltage correlation; and test the electrical performance of the water cooling system based on the degree of voltage anomaly.

[0047] After obtaining the reliability of the temperature-grid correlation at the suspected voltage anomaly moment, the degree of voltage anomaly can be obtained based on the difference between the voltage characterization value of the temperature range where the suspected voltage anomaly moment is located and the voltage at the suspected voltage anomaly moment, and the reliability of the temperature-voltage correlation. Preferably, in this embodiment of the invention, the step of obtaining the degree of voltage anomaly includes: calculating the absolute value of the difference between the voltage characterization value of the temperature range where the suspected voltage anomaly moment is located and the voltage mapping value corresponding to the suspected voltage anomaly moment, to obtain a voltage difference index. The larger the voltage difference index, the greater the difference between the voltage mapping value and the voltage characterization value corresponding to the suspected voltage anomaly moment under the same temperature range, and the more likely the suspected voltage anomaly moment is caused by an electrical equipment malfunction. The product of the voltage difference index and the reliability of the temperature-voltage correlation is calculated to obtain the degree of voltage anomaly at the suspected voltage anomaly moment. Since the voltage difference index only analyzes the voltage difference under the same temperature conditions and does not consider the influence of humidity or the reliability of the voltage characterization value in that temperature range, the voltage difference index is weighted by the reliability of the temperature-voltage correlation. If the voltage difference index is larger and the reliability of the temperature-voltage correlation is also larger, it indicates that the electrical equipment is more likely to be abnormal at the suspected voltage anomaly moment. Conversely, if the reliability of the temperature-voltage correlation is smaller, it indicates that the voltage difference index cannot characterize the electrical equipment abnormality at the suspected voltage anomaly moment, thereby improving the detection accuracy of electrical equipment abnormalities.

[0048] Furthermore, the electrical performance of the water cooling system can be detected based on the degree of voltage anomaly. When the degree of voltage anomaly at the suspected voltage anomaly time exceeds a preset anomaly threshold, the electrical performance of the water cooling system becomes abnormal at the suspected voltage anomaly time. In this embodiment of the invention, the preset anomaly threshold is 0.6. The implementer can determine it according to the implementation scenario. Subsequently, the electrical equipment of the water cooling system can be warned and repaired according to the frequency of anomaly times. This is not limited here.

[0049] In summary, this invention provides an automated method for detecting the electrical performance of a water-cooling system. It identifies potential voltage anomaly moments based on voltage differences between any given moment in a voltage time series and adjacent historical time periods. It obtains temperature ranges and voltage values ​​based on temperature and voltage distribution characteristics in a temperature-voltage correlation graph. Furthermore, it assesses the reliability of the temperature-voltage correlation based on humidity differences between potential voltage anomaly moments and historical time periods in a humidity time series, and the voltage distribution characteristics within the temperature range containing the potential voltage anomaly moment. By determining the degree of voltage anomaly based on the difference between the voltage values ​​within the temperature range containing the potential voltage anomaly moment and the voltage at the potential anomaly moment, along with the reliability of the temperature-voltage correlation, and then detecting the electrical performance of the water-cooling system, the accuracy of the detection is improved.

[0050] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0051] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

Claims

1. An automated testing method for the electrical performance of a water-cooling system, characterized in that, The method includes the following steps: Acquire the temperature timing, humidity timing, and voltage timing during the operation of the water cooling system; The voltage deviation is obtained based on the voltage difference characteristics between any time point and adjacent historical time periods in the voltage time series; suspected voltage anomaly times are obtained based on the voltage deviation at any time point; a temperature-voltage correlation diagram is obtained based on the temperature time series and the voltage time series; and temperature ranges and voltage characterization values ​​are obtained based on the temperature distribution characteristics and voltage distribution characteristics in the temperature-voltage correlation diagram. A humidity normality index is obtained based on the humidity difference characteristics between the suspected voltage anomaly time and historical time periods in the humidity time series; a data distribution characteristic value is obtained based on the voltage distribution characteristics of the temperature range where the suspected voltage anomaly time is located; and the temperature-voltage correlation confidence level is obtained based on the humidity normality index and the data distribution characteristic value. The degree of voltage anomaly is obtained by comparing the voltage characteristics of the temperature range at which the voltage is suspected to be abnormal with the voltage at the suspected abnormal time, and by the reliability of the temperature-voltage correlation; the electrical performance of the water cooling system is then tested based on the degree of voltage anomaly. The step of obtaining the temperature-voltage correlation diagram based on the temperature time series and the voltage time series includes: The temperature time series is normalized by the maximum and minimum values ​​to obtain the temperature mapping values ​​at different times; the voltage time series is normalized by the maximum and minimum values ​​to obtain the voltage mapping values ​​at different times; a coordinate graph is constructed with the temperature mapping value on the horizontal axis and the voltage mapping value on the vertical axis, and a temperature-voltage correlation graph is constructed based on the positions of the temperature mapping value and the voltage mapping value at the same time in the coordinate graph; The step of obtaining the humidity normality index based on the humidity difference characteristics between the suspected voltage anomaly time and historical time periods in the humidity time series includes: Calculate the average humidity of the historical period before the suspected voltage anomaly to obtain the historical humidity value; calculate the absolute value of the difference between the humidity at the suspected voltage anomaly and the historical humidity value and perform a negative correlation mapping to obtain the humidity normality index at the suspected voltage anomaly. The step of obtaining data distribution feature values ​​based on the voltage distribution characteristics within the temperature range at the time of the suspected voltage anomaly includes: Calculate the ratio of the number of voltage mapping values ​​in the temperature range where the voltage is suspected to be abnormal at the time of the voltage anomaly to the number of voltage mapping values ​​in the temperature-voltage correlation graph to obtain the data proportion feature value; calculate the variance of the voltage mapping values ​​in the temperature range where the voltage is suspected to be abnormal at the time of the voltage anomaly and perform negative correlation mapping to obtain the voltage concentration; calculate the product of the data proportion feature value and the voltage concentration to obtain the data distribution feature value at the time of the suspected voltage anomaly. The step of obtaining the temperature-voltage correlation confidence level based on the humidity normality index and the data distribution characteristic value includes: Calculate the product of the humidity normality index and the data distribution characteristic value and perform a positive correlation mapping to obtain the temperature-voltage correlation confidence level at the moment when the voltage is suspected to be abnormal. The step of obtaining the degree of voltage anomaly based on the difference between the voltage characterization value in the temperature range where the suspected voltage anomaly occurred and the voltage at the suspected voltage anomaly occurred, and the reliability of the temperature-voltage correlation, includes: Calculate the absolute value of the difference between the voltage characterization value in the temperature range where the voltage is suspected to be abnormal at the time of the voltage anomaly and the voltage mapping value corresponding to the time of the suspected voltage anomaly to obtain the voltage difference index; calculate the product of the voltage difference index and the reliability of the temperature-voltage correlation to obtain the degree of voltage anomaly at the time of the suspected voltage anomaly.

2. The automated testing method for the electrical performance of a water-cooling system according to claim 1, characterized in that, The step of obtaining the voltage deviation based on the voltage difference characteristics between any time point in the voltage time series and adjacent historical time periods includes: Calculate the sum of the absolute values ​​of the voltage difference between the arbitrary time and each historical time in the adjacent historical period to obtain the accumulated voltage difference value; map the accumulated voltage difference value positively to obtain the voltage deviation at the arbitrary time.

3. The automated testing method for the electrical performance of a water-cooling system according to claim 1, characterized in that, The step of obtaining the suspected voltage anomaly time based on the voltage deviation at any time includes: Any moment when the voltage deviation exceeds a preset deviation threshold is defined as the suspected voltage anomaly moment.

4. The automated testing method for the electrical performance of a water-cooling system according to claim 1, characterized in that, The step of obtaining the temperature range and voltage characterization value based on the temperature distribution characteristics and voltage characteristics in the temperature-voltage correlation diagram includes: The temperature mapping values ​​of the temperature-voltage correlation graph are divided according to a preset interval to obtain different temperature ranges; the average value of the voltage mapping values ​​corresponding to the temperature ranges is calculated to obtain the voltage characterization value.

5. The automated testing method for the electrical performance of a water-cooling system according to claim 1, characterized in that, The step of detecting the electrical performance of the water cooling system based on the degree of voltage anomaly includes: When the degree of voltage abnormality at the suspected voltage abnormality moment exceeds a preset abnormality threshold, the electrical performance of the water cooling system becomes abnormal at the suspected voltage abnormality moment.

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