Liquid cooling pump health monitoring method, device and liquid cooling system

Abstract

The embodiment of the application relates to the technical field of liquid cooling, and discloses a liquid cooling pump health monitoring method and device and a liquid cooling system, the method comprising: acquiring multiple operation data of a target liquid cooling pump, sequentially judging whether each operation data is within a corresponding preset threshold range, and determining the operation data as first operation data when the operation data is outside the preset threshold range; finding a standard operation data group corresponding to the first operation data from a preset database; determining standard threshold ranges corresponding to the other multiple standard operation data; counting the number of operation data outside the standard threshold range to obtain a first number; and generating target liquid cooling pump abnormal information if the first number is greater than or equal to a first preset number. In the above manner, the embodiment of the application can monitor the health condition of the target liquid cooling pump, and further perceive the working abnormality of the target liquid cooling pump in advance, so that a maintenance team can timely handle the abnormal situation of the target liquid cooling pump and reduce economic losses.

Description

Technical Field

[0001] This application relates to the field of liquid cooling technology, specifically to a method, device, and liquid cooling system for monitoring the health of a liquid cooling pump. Background Technology

[0002] With the rapid development of technologies such as 5G, cloud computing, and artificial intelligence, data processing tasks have become increasingly demanding, directly driving a significant increase in data center power density. To effectively manage the heat dissipation of data center equipment, immersion liquid cooling technology has gradually become the preferred solution due to its superior heat dissipation and energy efficiency. Compared to traditional air cooling systems, immersion liquid cooling can remove heat more efficiently while achieving better energy utilization. Therefore, more and more data centers are adopting immersion liquid cooling technology to meet the growing computing demands and energy efficiency goals.

[0003] In immersion liquid cooling systems, the liquid cooling pump plays a crucial role, responsible for circulating the coolant to maintain effective heat exchange. The performance of the liquid cooling pump directly affects the operating temperature of electronic equipment. If the liquid cooling pump fails, the electronic equipment will be unable to dissipate heat effectively, potentially leading to overall system failure or other serious problems in the data center. Therefore, how to monitor and predict the health status of the liquid cooling pump has become an urgent problem to be solved. Summary of the Invention

[0004] In view of the above problems, this application provides a method, device and liquid cooling system for health monitoring of a liquid cooling pump, used to monitor the health of a target liquid cooling pump.

[0005] According to one aspect of the embodiments of this application, a method for health monitoring of a liquid cooling pump is provided. The method includes: acquiring multiple operating data of a target liquid cooling pump; sequentially determining whether each operating data is within its corresponding preset threshold range; if the currently determined operating data is outside its corresponding preset threshold range, then determining the operating data as first operating data; searching a standard operating data group corresponding to the first operating data from a preset database, wherein the standard operating data group includes multiple standard operating data that correspond to each other, the multiple standard operating data including the first operating data, and other multiple standard operating data that correspond one-to-one with the first operating data; determining the standard threshold range corresponding to the other multiple standard operating data; counting the number of operating data outside the corresponding standard threshold range among the multiple operating data to obtain a first number; if the first number is greater than or equal to a first preset number, then generating abnormal information of the target liquid cooling pump.

[0006] In one optional approach, after counting the number of running data outside the corresponding standard threshold range among multiple running data to obtain a first number, the method further includes: if the first number is less than a first preset number, then the multiple running data are determined into a standard running data group and stored in a preset database.

[0007] In one optional approach, after searching for the standard operating data group corresponding to the first operating data in a preset database, the method further includes: if the standard operating data group is not empty, then performing a step of determining the standard threshold range corresponding to other multiple standard operating data; if the standard operating data group is empty, then counting the number of operating data outside the corresponding preset threshold range among the multiple operating data to obtain a second number; if the second number is less than the first preset number, then determining the multiple operating data as a standard operating data group and storing it in the preset database; if the second number is greater than or equal to the first preset number, then generating target liquid cooling pump abnormal information.

[0008] In one alternative approach, the number of running data points outside the corresponding standard threshold range in multiple running data sets is counted to obtain a first quantity. Specifically, this includes: if there are multiple standard running data sets, the number of running data points outside the standard threshold range corresponding to each standard running data set is counted to obtain multiple third quantities; the third quantity with the smallest value among the multiple third quantities is determined as the first quantity.

[0009] In one optional approach, before searching for a standard operating data group corresponding to the first operating data from a preset database, the method further includes: searching for an abnormal operating data group corresponding to the first operating data from a preset abnormal database, wherein the abnormal operating data group includes multiple corresponding abnormal operating data, the multiple abnormal operating data includes the first operating data, and other multiple abnormal operating data that correspond one-to-one with the first operating data; if the abnormal operating data group is not empty, the following steps are performed: determining the abnormal threshold range corresponding to the other multiple abnormal operating data; counting the number of operating data within the corresponding abnormal threshold range among the multiple operating data to obtain a fourth number; if the fourth number is greater than or equal to the first preset number, generating target liquid cooling pump abnormal information; after generating target liquid cooling pump abnormal information, the method further includes: obtaining verification result information obtained after testing the target liquid cooling pump; if the verification result information is normal, determining the multiple operating data as a standard operating data group and storing it in a preset database; if the verification result information is faulty, determining the multiple operating data as an abnormal operating data group and storing it in a preset abnormal database.

[0010] In one alternative approach, the standard operating data includes rotational speed data; before acquiring various operating data of the target liquid-cooled pump, the method further includes: controlling the target liquid-cooled pump to operate at a rotational speed corresponding to preset rotational speed data; searching a preset database for a set of standard operating data corresponding to rotational speed data equal to the preset rotational speed data; and determining preset threshold ranges corresponding to each of the various operating data based on the set of standard operating data.

[0011] In one optional approach, before controlling the target liquid cooling pump to operate at a speed corresponding to preset speed data, the method further includes: determining multiple speed data within a preset speed range based on the preset speed range of the target liquid cooling pump; controlling the target liquid cooling pump to operate at a speed corresponding to one of the speed data; acquiring multiple operating data of the target liquid cooling pump to obtain a standard operating data set corresponding to the speed data; storing the standard operating data set in a preset database; and proceeding to the step of controlling the target liquid cooling pump to operate at a speed corresponding to one of the speed data, until all speed data are traversed.

[0012] In one optional approach, the multiple operating data includes an audio frequency domain data set, which comprises multiple audio frequency domain data. Acquiring the multiple operating data of the target liquid cooling pump specifically includes: acquiring the sound signal of the target liquid cooling pump within a preset time range; converting the sound signal into multiple first audio frequency domain data; determining whether the multiple first audio frequency domain data are within their corresponding preset threshold range; if all the multiple first audio frequency domain data are outside their corresponding preset threshold range, then re-acquiring the sound signal of the target liquid cooling pump within the preset time range and converting it into multiple audio frequency domain data to obtain an audio frequency domain data set; if at least one first sound frequency data is within its corresponding preset threshold range, then determining the multiple first audio frequency domain data as an audio frequency domain data set; sequentially determining whether each operating data is within its corresponding preset threshold range, specifically including: determining whether multiple audio frequency domain data in the audio frequency domain data set are within their corresponding preset threshold range; if all the multiple audio frequency domain data are outside their corresponding preset threshold range, then determining that the sound frequency domain data set in the multiple operating data is outside its corresponding preset threshold range; if at least one audio frequency domain data is within its corresponding preset threshold range, then determining that the multiple operating data are within their corresponding preset threshold range. The audio frequency domain data group in the data is within its corresponding preset threshold range; and / or, multiple operating data include a temperature data group, which includes multiple temperature data; acquiring multiple operating data of the target liquid cooling pump, specifically including: acquiring the first temperature value of the coolant at the outlet of the cabinet in the liquid cooling system where the target liquid cooling pump is located; acquiring the second temperature value of the coolant at the inlet of the cabinet in the liquid cooling system where the target liquid cooling pump is located; determining the difference between the first temperature value and the second temperature value as temperature data; jumping to the step of acquiring the first temperature value of the coolant at the outlet of the cabinet in the liquid cooling system where the target liquid cooling pump is located, until the number of acquired temperature data reaches a second preset number; taking the acquired multiple temperature data as a temperature data group; sequentially determining whether each operating data is within its corresponding preset threshold range, specifically including: determining whether multiple temperature data in the temperature data group are within its corresponding preset threshold range; if multiple temperature data are outside its corresponding preset threshold range, then determining that the temperature data group in the multiple operating data is outside its corresponding preset threshold range; if at least one temperature data is within its corresponding preset threshold range, then determining that the temperature data group in the multiple operating data is within its corresponding preset threshold range.

[0013] According to another aspect of the embodiments of this application, a liquid cooling pump health monitoring device is provided, including: a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface communicate with each other through the communication bus; the memory is used to store executable instructions, which cause the processor to perform the operation of the liquid cooling pump health monitoring method of any of the above embodiments.

[0014] According to another aspect of the embodiments of this application, a liquid cooling system is provided, including: a cabinet, a cooling distribution unit, and a cooling device. The cooling distribution unit includes a target liquid cooling pump, multiple data acquisition units, and the liquid cooling pump health monitoring device described in the above embodiments. The target liquid cooling pump is connected to the inlet and outlet of the cabinet, and is used to drive the coolant to circulate inside and outside the cabinet. The cooling device is used to circulate heat exchange fluid to the cooling distribution unit, so that the coolant and the heat exchange fluid exchange heat within the cooling distribution unit to reduce the temperature of the coolant. The multiple data acquisition units are used to acquire multiple operating data of the target liquid cooling pump. The liquid cooling pump health monitoring device is used to monitor the target liquid cooling pump.

[0015] This application embodiment acquires various operational data of the target liquid cooling pump and determines whether these data are abnormal. This allows for early detection of malfunctions in the target liquid cooling pump, enabling the maintenance team to detect and inspect the pump. This ensures timely replacement of the target liquid cooling pump when it malfunctions, preventing system crashes and economic losses caused by a malfunctioning pump. Furthermore, using multiple operational data points to determine malfunctions effectively avoids misjudgments due to environmental interference, improving the accuracy of anomaly monitoring. The method is simple and convenient, allowing for rapid health monitoring of the target liquid cooling pump and improving detection efficiency.

[0016] The above description is merely an overview of the technical solutions of the embodiments of this application. In order to better understand the technical means of the embodiments of this application and to implement them in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the embodiments of this application more obvious and understandable, specific implementation methods of this application are described below. Attached Figure Description

[0017] The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0018] Figure 1 A structural block diagram of a liquid cooling system according to an embodiment of this application is shown;

[0019] Figure 2 This paper shows a structural block diagram of the primary side of a liquid cooling system provided in an embodiment of this application;

[0020] Figure 3 A flowchart of the liquid cooling pump health monitoring method provided in an embodiment of this application is shown;

[0021] Figure 4A spectrum diagram of the audio frequency domain data provided in an embodiment of this application is shown;

[0022] Figure 5 A partial structural block diagram of the primary side of the liquid cooling system provided in an embodiment of this application is shown;

[0023] Figure 6 A schematic diagram of the structure of the liquid cooling pump health monitoring device provided in an embodiment of this application is shown;

[0024] Figure 7 A schematic diagram of the structure of the liquid cooling pump health monitoring device provided in an embodiment of this application is shown;

[0025] Figure 8 This paper illustrates an interactive schematic diagram of a liquid cooling pump health monitoring system provided in an embodiment of this application.

[0026] Figure 9 This paper shows another structural block diagram of the primary side of the liquid cooling system provided in an embodiment of this application. Detailed Implementation

[0027] Exemplary embodiments of the present application will now be described in more detail with reference to the accompanying drawings. Although exemplary embodiments of the present application are shown in the drawings, it should be understood that the present application may be implemented in various forms and should not be limited to the embodiments set forth herein.

[0028] In a single-phase immersion liquid cooling system, electronic equipment is completely submerged in coolant contained within a specially designed cabinet. The heat generated by the electronic equipment during operation is directly transferred to the coolant. A liquid cooling pump then draws the heat-absorbing coolant out of the cabinet and delivers it to a cooling distribution unit. In this unit, the coolant exchanges heat with a heat exchange fluid provided by a cooling tower, thereby lowering its temperature. The cooled, low-temperature coolant is then recirculated back into the cabinet to continue absorbing heat generated by the electronic equipment. This cycle ensures that the electronic equipment remains within its optimal operating temperature range.

[0029] Liquid cooling pumps play a crucial role in liquid cooling systems, responsible for drawing heat-absorbing coolant from the server rack. If a liquid cooling pump malfunctions, the coolant cannot be removed and cooled in time, causing the internal temperature of the rack to rise continuously. This temperature increase exposes the core components of electronic equipment to an overheated environment, potentially causing damage and affecting the normal operation of the equipment. Furthermore, high temperatures accelerate the aging of electronic components, shortening the lifespan of the equipment. Therefore, monitoring the health of the liquid cooling pump is essential to ensure the continuous and effective operation of the liquid cooling system and to protect electronic equipment from overheating.

[0030] Based on this, the inventors of this application have noted that in existing liquid-cooled data centers, only the overall operating power of the liquid cooling system is typically recorded for ease of estimating the data center's energy consumption. However, the importance of monitoring the health of the liquid cooling pumps themselves is often overlooked. This lack of monitoring means that once a liquid cooling pump fails and causes problems with the liquid cooling system or the entire data center system, the inability to quickly pinpoint the exact location of the fault may require a significant investment of time, manpower, and resources to inspect all potentially faulty parts of the system, making troubleshooting both complex and tedious. Furthermore, to prevent the fault from triggering more serious problems, the entire system operation usually needs to be suspended during the troubleshooting process, which not only leads to economic losses but may also affect the normal service of the data center. Therefore, implementing a liquid cooling pump health monitoring mechanism is crucial for improving the operational efficiency and reliability of liquid-cooled data centers.

[0031] To address the issue of expending significant time, manpower, and resources in troubleshooting liquid-cooled pump failures, the inventors of this application, through in-depth research, discovered that when a liquid-cooled pump is in a healthy state, there is a certain correlation among its various operating data. Typically, a change in one operating data point will cause corresponding changes in other related data, and these trends are usually stable. For example, when the pump's rotational speed increases, the flow rate and power also increase accordingly, while the pump's noise level and temperature may also rise. These changes follow expected physical laws and the design characteristics of the liquid-cooled pump. Conversely, when a liquid-cooled pump malfunctions, the changes in its operating data will no longer follow the normal pattern. A break in the correlation between data points may be observed, or some data points may experience abrupt changes, indicating that certain components of the pump are malfunctioning or their performance is declining. Therefore, monitoring various operating data points of the liquid-cooled pump and analyzing their correlations can accurately determine the current health status of the pump.

[0032] Therefore, based on the above principles, the inventors of this application have designed a liquid cooling pump health monitoring method. This method acquires various operational data during the operation of the liquid cooling pump and determines whether these data are normal. When any operational data is abnormal, a standard database is searched for matching standard operational data. If multiple operational data points do not match the standard operational data, it can be assumed that the correlation between these data points has broken or that some data points have undergone abrupt changes. Therefore, it can be determined that certain components of the liquid cooling pump are malfunctioning or their performance is deteriorating. At this point, relevant early warning information can be issued to technicians, enabling the maintenance team to promptly assess the health status of the liquid cooling pump. Once an abnormality is confirmed, the maintenance team can quickly take action to replace the pump, thereby avoiding the need for significant time, manpower, and resources to troubleshoot the liquid cooling pump failure. This proactive maintenance strategy helps reduce potential downtime, minimize economic losses, and ensure the continuous operation of the data center.

[0033] Please see Figure 1 and Figure 2 , Figure 1 A structural block diagram of the liquid cooling system according to an embodiment of this application is shown. Figure 2 The diagram shows a structural block diagram of the primary side of a liquid cooling system provided in an embodiment of this application. The liquid cooling system 100 mainly consists of a primary side 10 and a secondary side 20.

[0034] The secondary side 20 mainly includes a cooling device 21, used to directly or indirectly dissipate the heat of the coolant 112 to the external environment. The cooling device 21 can be a cooling tower, radiator, or other type of heat dissipation equipment. In some liquid cooling systems 100, the secondary side 20 may also include a cooling cycle to improve the heat exchange efficiency of the secondary side 20 or to adapt to specific environmental conditions. For example, when the cooling device 21 is a cooling tower, the cooling device 21 can provide chilled water to the cooling capacity distribution unit 12. After exchanging heat with the coolant 112 in the cooling capacity distribution unit 12, the chilled water returns to the cooling device 21, dissipating the heat it carries to the external environment to lower its own temperature. The cooled chilled water then flows back to the cooling capacity distribution unit 12, thus completing the cooling cycle of the secondary side 20 and indirectly cooling the coolant 112 in the primary side 10.

[0035] The primary side 10 mainly includes a cabinet 11 and a cooling distribution unit 12. The cabinet 11 has an internal cavity for housing electronic equipment 111 and coolant 112, allowing heat exchange between the electronic equipment 111 and coolant 112 within the cabinet 11. The cooling distribution unit 12 includes a target liquid cooling pump 122, which is primarily responsible for drawing coolant 112 from the cabinet 11 and pushing it into the cooling distribution unit 12 for heat exchange, thus maintaining the circulation of coolant 112 within the liquid cooling system 100 and ensuring continuous heat dissipation for the electronic equipment 111. Specifically, electronic device 111 is immersed in coolant 112. When electronic device 111 generates heat during operation, it exchanges heat with coolant 112, allowing the coolant 112 to absorb the heat and lower its temperature, preventing it from overheating. The high-temperature coolant 112, having absorbed the heat, is then drawn out of cabinet 11 by target liquid cooling pump 122 and flows along... Figure 2 The coolant 112 flows in the direction indicated by arrow A towards the cold energy distribution unit 12 for heat exchange, and the cooled low-temperature coolant 112 then flows along... Figure 2 The coolant flows back to the cabinet 11 in the direction indicated by the middle arrow B, so that the coolant 112 in the cabinet 11 can maintain a low temperature, thereby ensuring that the electronic equipment 111 can continuously dissipate heat.

[0036] In a liquid cooling system, if the liquid cooling pump fails, the coolant cannot be removed and cooled in time, causing the internal temperature of the cabinet to rise continuously. This exposes the core components of the electronic equipment to an overheated environment, affecting the normal operation of the electronic equipment. Therefore, health monitoring of the liquid cooling pump is crucial. According to a first aspect of the embodiments of this application, a method for monitoring the health of a liquid cooling pump is also provided, such as... Figure 3 As shown, Figure 3 A flowchart of a liquid cooling pump health monitoring method provided in this application embodiment is shown. This method is executed by a liquid cooling pump health monitoring device. The device may include a controller, which can be of various types, such as a microcontroller unit (MCU) or a programmable logic controller (PLC). The method includes the following steps:

[0037] Step 110: Obtain various operating data of the target liquid cooling pump.

[0038] During the stable operation of a liquid cooling system, various operating data tend to be stable. However, when the target liquid cooling pump malfunctions, some or all of these operating data will also become abnormal. For example, when the target liquid cooling pump is blocked, its operating current will increase rapidly, the temperature of the target liquid cooling pump itself will rise, and the flow rate of the coolant circulation will decrease. Therefore, during the operation of the target liquid cooling pump, its operating data can be monitored to determine whether the target liquid cooling pump is malfunctioning. Specifically, parameters such as the operating sound, operating current, speed, and flow rate and temperature difference of the coolant circulation can be monitored to assess the health status of the target liquid cooling pump.

[0039] The various operational data can include pump speed, audio frequency data sets, temperature data sets, current data, and coolant flow rate data. The specific methods for acquiring these operational data include: the liquid cooling pump health monitoring equipment receiving signals from various sensors, probes, and other data acquisition units, processing these signals to obtain the relevant operational data; or other modules receiving signals from various sensors, probes, and other data acquisition units, processing the signals to obtain the relevant operational data, and then sending this operational data to the liquid cooling pump health monitoring equipment.

[0040] Step 120: Determine whether each running data is within its corresponding preset threshold range.

[0041] In a liquid cooling system, when the target liquid cooling pump is operating stably and normally, various operating data throughout the system tend to stabilize. For example, when the target liquid cooling pump operates at a fixed speed, the noise it generates, the current flowing through it, the heat dissipated, and the coolant flow rate all tend to stabilize. Therefore, by determining whether these operating data are within their corresponding threshold ranges, if any operating data exceeds its corresponding threshold range, it can be considered that the operating data is abnormal.

[0042] There are several ways to determine the preset threshold range. Taking the current flowing through the target liquid cooling pump as an example, after the target liquid cooling pump is installed, the current signal for a certain period of time can be acquired, and the average value of the current signal for that period can be calculated. The range of 5% above and below the average value can be used as the preset threshold range of the current flowing through the target liquid cooling pump. Alternatively, the preset threshold range of the current can be determined based on the fluctuation range of the current signal within that period of time. Or, multiple current data can be acquired at certain intervals, and the average value of the multiple current data can be calculated. The range of 5% above and below the average value can be used as the preset threshold range of the current flowing through the target liquid cooling pump.

[0043] Step 130: If the currently judged running data is outside its corresponding preset threshold range, then the running data is determined as the first running data.

[0044] Step 140: Search for the standard running data group corresponding to the first running data from the preset database. The standard running data group includes multiple standard running data that correspond to each other. The multiple standard running data includes the first running data and other multiple standard running data that correspond one-to-one with the first running data.

[0045] In the normal operation of the liquid cooling system, when a certain operating data is abnormal, it can be determined that the target liquid cooling pump may be malfunctioning. At this time, in order to avoid misjudgment, it is also necessary to determine whether other operating data are abnormal. The system searches a preset database for standard and operating data sets that match the first operating data. Taking the temperature data of the target liquid-cooled pump as an example, since the coolant exchanges heat with the target liquid-cooled pump as it flows through it, absorbing the heat generated by the pump, and the thermal parameters of this heat exchange process tend to be stable, the temperature difference between the coolant at the inlet and outlet of the target liquid-cooled pump tends to be stable. This temperature difference can be used as the temperature data of the target liquid-cooled pump. Assuming the preset threshold range for temperature data is [-3, 3], if the average value of multiple temperature data sets in the currently acquired operating data sets is 5, it can be preliminarily determined that the temperature of the target liquid-cooled pump is abnormal. At this time, it is necessary to search the preset database for standard operating data sets with a temperature value of 5. These standard operating data sets include not only temperature data with a value of 5, but also pump speed, acoustic frequency data, coolant flow rate data, and current data, as well as other standard operating data corresponding to the temperature data with a value of 5. Of course, the standard operating data sets obtained from the first operating data can be one set or multiple sets.

[0046] Step 150: Determine the standard threshold range corresponding to other standard operating data.

[0047] Because the operating data of the target liquid cooling pump is not a fixed value but fluctuates within a small range, in order to more accurately determine the health status of the target liquid cooling pump and minimize misjudgment, after obtaining the standard operating data set, the corresponding standard threshold range can be determined based on the specific values ​​of other standard operating data. Specifically, the lower threshold can be obtained by subtracting 4% or 5% of the specific value of the standard operating data, and the higher threshold can be obtained by adding 4% or 5% of the specific value. The specific percentage used to determine the standard threshold can be determined according to the actual fluctuation of the data. When the value of an operating data fluctuates greatly, the percentage used to determine its standard threshold range can be slightly larger to reduce the possibility of misjudging normal data as abnormal data; when the value of an operating data fluctuates little, the percentage used to determine its standard threshold range can be slightly smaller to reduce the possibility of misjudging abnormal data as normal data.

[0048] Furthermore, the two percentages used to determine the low and high thresholds of a standard threshold range can be the same, for example, when the range of fluctuation of a data is relatively consistent, the same percentage can be used; or they can be different, for example, when the range of fluctuation of a data is large, if the difference in the upward fluctuation is large and the difference in the downward fluctuation is small, a larger percentage can be used to determine the high threshold and a smaller percentage to determine the low threshold, and if the difference in the upward fluctuation is small and the difference in the downward fluctuation is large, a smaller percentage can be used to determine the high threshold and a larger percentage to determine the low threshold.

[0049] When there are multiple sets of standard operating data, the corresponding standard threshold range can be determined based on the average value of various standard operating data, or the corresponding standard threshold range can be determined based on each standard operating data, thus obtaining multiple sets of standard threshold ranges.

[0050] Step 160: Count the number of running data that are outside the corresponding standard threshold range among various running data to obtain the first number.

[0051] Assuming multiple operating data include pump speed, acoustic frequency domain data set, temperature data set, current data, and coolant flow rate data, when the temperature data set is abnormal, the system can retrieve standard pump speed, standard acoustic frequency domain data, standard current data, and standard coolant flow rate data that match the temperature data set from a preset database. Based on these data, their respective standard threshold ranges can be obtained. If the pump speed and acoustic frequency domain data sets in the multiple operating data sets are both outside their corresponding standard threshold ranges, the first quantity is 2; if the acoustic frequency domain data set, current data, and coolant flow rate data in the multiple operating data sets are all outside their corresponding standard threshold ranges, the first quantity is 3, and so on.

[0052] If the specific values ​​of the coolant flow rate data in multiple operating data sets are within the standard threshold range, it can be determined that the trend of the coolant flow rate data relative to the temperature data set is normal, meaning the correlation between the coolant flow rate data and the temperature data set is normal. Therefore, the coolant flow rate data in multiple operating data sets can be judged to be normal. When the specific values ​​of the coolant flow rate data in multiple operating data sets are outside the standard threshold range, it can be determined that the trend of the coolant flow rate data relative to the temperature data set is abnormal, meaning the correlation between the coolant flow rate data and the temperature data set is broken. This further indicates that at least one of the operating data sets is abnormal. Therefore, by determining whether multiple operating data sets are outside the corresponding standard threshold range, it is possible to determine whether the correlation between the various operating data sets is broken, thereby determining whether the target liquid cooling pump is malfunctioning.

[0053] When there are multiple standard threshold ranges, the number of various operating data outside the standard threshold range of each group can be counted separately to obtain multiple values, and the average of the multiple values ​​can be used as the first quantity.

[0054] Step 170: If the first quantity is greater than or equal to the first preset quantity, then generate abnormal information for the target liquid cooling pump.

[0055] In practical applications, certain data of the target liquid cooling pump may become abnormal due to environmental issues. Therefore, when conducting health monitoring of the target liquid cooling pump, it is not possible to immediately determine that the target liquid cooling pump is abnormal based on the abnormality of one data. For example, when using a microphone to acquire the sound signal of the target liquid cooling pump, the sound frequency domain data group in various operating data may become abnormal due to the sound of the external environment. In this case, if the target liquid cooling pump is directly judged to be abnormal, a misjudgment will occur.

[0056] Therefore, by statistically analyzing the number of operational data points whose correlation with the first operational data point has broken, and if a significant number of operational data points show abnormalities, it can be determined that the correlation between the various operational data points has broken, thus indicating a poor health condition of the target liquid cooling pump. In this case, an anomaly warning should be issued to the maintenance team so that they can promptly inspect the target liquid cooling pump and replace it in a timely manner when an abnormality occurs. For example, if the first operational data point is the temperature difference between the coolant at the inlet and outlet of the target liquid cooling pump, and the other operational data points only include pump speed, audio frequency data set, temperature data set, and coolant flow rate data, then if two or more of the other operational data points are outside their corresponding standard threshold ranges, it can be determined that the correlation between the various operational data points of the target liquid cooling pump has broken, thus indicating a poor health condition of the target liquid cooling pump.

[0057] Of course, if there are many other types of operating data, then even more types of operating data need to be outside their corresponding standard threshold ranges in order to determine that the target liquid cooling pump is abnormal. This will make the health monitoring results more accurate and effectively avoid misjudgment. For example, if there are 6 types of other operating data, then at least 3 types of operating data need to be abnormal; if there are 7 types of other operating data, then at least 3 or 4 types of operating data need to be abnormal, and so on.

[0058] The above embodiments acquire various operational data of the target liquid cooling pump and determine whether these data are abnormal. This allows for early detection of malfunctions in the target liquid cooling pump, enabling the maintenance team to detect and inspect the pump. This ensures timely replacement of the target liquid cooling pump when it malfunctions, preventing system crashes and economic losses caused by a malfunctioning pump. Furthermore, using multiple operational data points to determine malfunctions effectively avoids misjudgments caused by environmental interference, improving the accuracy of anomaly monitoring. The method is simple and convenient, allowing for rapid health monitoring of the target liquid cooling pump and improving detection efficiency.

[0059] As the usage time of a liquid-cooled pump increases, the correlation between various operating data may change. For example, the electrical components inside the liquid-cooled pump may experience wear and tear with increased operating time, which may lead to changes in the heat output of the pump. In other words, under the same pump speed control, a liquid-cooled pump that has been used for a longer period of time will generate more heat than a newly used pump. Therefore, in order to more accurately determine the health status of the liquid-cooled pump under different usage periods, in some embodiments of this application, step 170 is followed by:

[0060] Step 180: If the first quantity is less than the first preset quantity, then the various operating data are determined into a standard operating data group and stored in the preset database.

[0061] When the first operating data is relatively small, it can be determined that the changing trends of other operating data relative to the first operating data are normal. Taking the first operating data as the temperature difference between the coolant at the inlet and outlet of the target liquid cooling pump as an example, when the temperature difference is large, it can be determined that the heat generation of the target liquid cooling pump is also large. At this time, if a standard operating data set matching this temperature difference is found in the preset database, and other operating data all match their respective standard threshold ranges, it can be determined that these operating data are changing according to normal trends. It is possible that the different operating states of the target liquid cooling pump have caused corresponding changes in various operating data of the target liquid cooling pump. That is, due to the actual working conditions, the pump speed of the target liquid cooling pump may be higher, which will lead to a corresponding increase in flow rate and power. At the same time, the noise level and temperature of the pump will also increase accordingly. Therefore, it can be determined that the target liquid cooling pump is in a healthy state.

[0062] At this point, the acquired various operational data can be used as a standard operational data set for subsequent monitoring. This ensures that the data in the preset database is updated in real time, effectively preventing misjudgments caused by using outdated standard operational data, which could lead to persistent anomalies in monitoring the target liquid cooling pump after prolonged use. This guarantees the real-time nature and accuracy of the standard operational data, thereby ensuring the accuracy of target liquid cooling pump health monitoring. Furthermore, when the standard operational data stored in the preset database consists entirely of specific numerical values, multiple values ​​may be used together as operational data when acquiring various operational data to accurately determine the health status of the target liquid cooling pump. In this case, to ensure data consistency in the preset database, the average of these multiple values ​​can be stored as the standard operational data for that specific type of operational data in the preset database.

[0063] Because different liquid cooling pumps may have different structural parameters, the correlation trends between various operating data of different liquid cooling pumps may also be different. Therefore, in order to ensure that the health monitoring of each liquid cooling pump is accurate, a corresponding preset database can be generated for each liquid cooling pump. In this case, for newly installed liquid cooling pumps, the data in its preset database is basically generated by data acquisition. However, the amount of data generated by data acquisition is limited. This can lead to a situation where when one operating data is abnormal, a matching operating data group may not be found in the preset data. To solve this problem, in some embodiments of this application, after step 140, the following is also included:

[0064] Step 141: If the standard operating data group is not empty, then perform the step of determining the standard threshold range corresponding to the other multiple standard operating data.

[0065] Step 142: If the standard running data group is empty, count the number of running data outside the corresponding preset threshold range among the various running data to obtain the second number.

[0066] Step 143: If the second quantity is less than the first preset quantity, then the various operating data are determined into a standard operating data group and stored in the preset database.

[0067] Step 144: If the second quantity is greater than or equal to the first preset quantity, then generate abnormal information for the target liquid cooling pump.

[0068] When the first operating data does not have a matching data in the preset database, it can be determined that the first operating data is abnormal. In this case, it is necessary to determine whether other operating data are abnormal, and then determine whether the target liquid cooling pump is abnormal. The subsequent operation steps between multiple operating data and preset threshold ranges are similar to those between multiple operating data and standard threshold ranges, and will not be repeated here.

[0069] In the above embodiments, when the standard operating data is empty, the number of multiple operating data outside the preset threshold range is counted, that is, the number of abnormal operating data among multiple operating data is counted, and the target liquid cooling pump is judged based on the number. This can ensure that the target liquid cooling pump can be accurately judged even when the standard operating data group is missing. Furthermore, when the second number is less than the first preset number, multiple operating data can be determined as the standard operating data group and stored in the preset database to further improve the data in the preset database and minimize the situation where the standard operating data group is empty.

[0070] In some embodiments of this application, to further ensure the accuracy of health monitoring results, step 160 specifically includes:

[0071] Step 161: If there are multiple standard operating data sets, count the number of operating data sets that are outside the standard threshold range corresponding to each standard operating data set, and obtain multiple third quantities.

[0072] When there is only one set of standard operating data, there is also only one set of standard threshold ranges. That is, each type of operating data corresponds to a standard threshold range. In this case, the number of multiple types of operating data outside the corresponding standard threshold range can be directly counted to obtain the first number.

[0073] When there are multiple sets of standard operating data, if the values ​​of the same standard operating data in different sets differ significantly, directly using the average value of various standard operating data to determine the corresponding standard threshold range may introduce large errors, thus affecting the accuracy of health monitoring. For example, if the flow data values ​​in the obtained standard operating data sets are 100 and 200 respectively, the average value should be 150. The standard threshold range generated based on the average value will be around 150, for example, it could be [142.5, 157.5] or [144, 156]. However, regardless of the threshold range, there is a large error compared to 100 and 200. Therefore, to make the health monitoring results more accurate, the corresponding standard threshold range can be determined based on the standard operating data in each set of standard operating data, resulting in multiple sets of standard threshold ranges. That is, one type of operating data will correspond to multiple standard threshold ranges. In this case, the number of data points outside each set of standard threshold ranges is counted separately to obtain multiple third quantities.

[0074] Step 162: Determine the third quantity with the smallest value among multiple third quantities as the first quantity.

[0075] If the values ​​of multiple third quantities differ significantly, using the average of these third quantities as the first quantity may result in a large error. For example, if there are two sets of standard threshold ranges and four types of operating data besides the first operating data, and the third quantity corresponding to one set of standard threshold ranges is 4 while the third quantity corresponding to another set of standard threshold ranges is 0, then the value of the first quantity obtained by using the average should be 2. In this case, the target liquid cooling pump will be judged to be abnormal.

[0076] However, in reality, a third quantity of 0 indicates the existence of a standard operating data set in the preset database that perfectly matches multiple operating data points. In this case, the target liquid cooling pump's health status should be normal. Therefore, when the values ​​of the third data points differ significantly, using the average value as the first quantity will affect the accuracy of the health monitoring results. Thus, to make the health monitoring results more accurate, the smallest value among the multiple third quantities can be used as the first quantity. In other words, if there exists a set of standard operating data points that matches multiple operating data points, then the values ​​of the multiple operating data points can be considered normal, meaning the target liquid cooling pump's health status is normal.

[0077] The above embodiments, when there are multiple sets of standard operating data, obtain multiple third quantities and take the smallest value among the third quantities as the first quantity. This can effectively avoid the problem of large differences in various standard operating data in multiple sets of standard operating data affecting the health monitoring results, and improve the accuracy of the target liquid cooling pump health monitoring structure.

[0078] In some embodiments of this application, the method further includes the following steps prior to step 140:

[0079] Step 131: Search for the abnormal running data group corresponding to the first running data from the preset abnormal database. The abnormal running data group includes multiple abnormal running data that correspond to each other. The multiple abnormal running data includes the first running data and other multiple abnormal running data that correspond one-to-one with the first running data.

[0080] The preset anomaly database contains various operational data obtained when the liquid cooling pump was in an abnormal state in the past. Specifically, after generating anomaly information for the target liquid cooling pump, if the maintenance team detects that the target liquid cooling pump is indeed in an abnormal state, the various operational data are stored as a group of abnormal operational data in the preset anomaly database. When the abnormal operational data group is empty, it is impossible to determine whether the various operational data are abnormal based on the preset anomaly database; in this case, the preset database must be used for judgment. If the abnormal operational data group is not empty, it is possible to directly determine whether the various operational data are abnormal based on the abnormal operational data group. The specific judgment steps are as follows:

[0081] Step 132: Determine the abnormal threshold range corresponding to other abnormal operating data.

[0082] Step 134: Count the number of running data that are within the corresponding abnormal threshold range among various running data to obtain the fourth quantity.

[0083] The specific process of searching for abnormal operating data groups from the preset abnormal database to obtain the fourth quantity is similar to the specific process of searching for standard operating data groups from the preset database to obtain the first quantity, and will not be repeated here. Furthermore, when there are multiple standard operating data groups, as long as one standard operating data group matches multiple operating data, it can be determined that the multiple operating data are within the range of the preset database, i.e., the multiple operating data are normal data. Similarly, when there are multiple abnormal operating data groups, as long as one abnormal operating data group matches multiple operating data, it can be determined that the multiple operating data are within the range of the preset abnormal database, i.e., the multiple operating data are abnormal data. Therefore, when there are multiple abnormal operating data groups, in order to improve the accuracy of health monitoring, multiple abnormal threshold ranges can be generated accordingly, and the number of multiple operating data falling within the abnormal threshold range can be counted to obtain multiple values. The largest value is then used as the fourth quantity.

[0084] Step 135: If the fourth quantity is greater than or equal to the first preset quantity, then generate the target liquid cooling pump abnormality information.

[0085] If multiple operational data points fall within their corresponding anomaly threshold ranges, then the operational data can be considered to be in an abnormal state. When a large number of operational data points are in an abnormal state, it indicates that the correlation between the multiple operational data points has broken down, thus allowing for a preliminary judgment that the target liquid cooling pump is in an abnormal state. In this case, target liquid cooling pump anomaly information needs to be generated so that the maintenance team can evaluate the target liquid cooling pump. Of course, since when the fourth number is less than the first preset number, it only indicates that the target liquid cooling pump cannot be directly judged to be in an abnormal state through the preset anomaly database, but it does not directly indicate that the target liquid cooling pump is normal, further health checks of the target liquid cooling pump are still required based on the preset database.

[0086] The above embodiments, by setting a preset anomaly database, enable the database to be searched based on the first operating data. If an abnormal operating data group matching multiple operating data can be directly obtained from the preset anomaly database, it can be determined that the target liquid cooling pump is in an abnormal state, effectively improving the efficiency and accuracy of health monitoring.

[0087] To further improve the accuracy of health monitoring, in some embodiments of this application, after steps 170 and 135, the following steps are also included:

[0088] Step 190: Obtain the verification result information after testing the target liquid cooling pump.

[0089] When the liquid cooling pump health monitoring device generates abnormal information about the target liquid cooling pump, it only indicates a potential abnormality. Whether the pump is truly abnormal requires further assessment and testing by the maintenance team. Only then will the health monitoring result be completely accurate. To further improve the accuracy of the health monitoring, accurate health monitoring results can be fed back to the device as verification of the abnormal information. This allows the device to update its preset database and abnormality database based on the verification result, ensuring more accurate results in subsequent monitoring. Verification results can be sent directly from the maintenance team to the device, or via independent devices such as mobile phones or tablets.

[0090] Step 200: If the verification result is normal, the various operating data are identified as a standard operating data group and stored in the preset database.

[0091] If the verification result is normal, it indicates that the liquid cooling pump health monitoring equipment misjudged the current health monitoring result. That is, the liquid cooling pump health monitoring equipment judged normal operating data as abnormal operating data. In this case, multiple operating data can be stored as a standard operating data group in a preset database. This way, if the multiple operating data obtained next time are the same as the current multiple operating data, the matching standard operating data group can be found in the preset database, and no abnormal information of the target liquid cooling pump will be generated, thus avoiding misjudgment of the same multiple operating data.

[0092] When one or more types of operational data have multiple values, the average of these values ​​can be stored in a preset database as the standard operational data. For example, if the differences between the multiple values ​​are small, the average value can be directly used as the standard value for that type of operational data. For instance, when the multiple values ​​are 3, 4, 5, 3, 5, the average value of 4 can be used as the standard value. Alternatively, the value that appears most frequently can be used as the standard operational data stored in the preset database. For instance, if most of the multiple values ​​are the same, and only a few values ​​fluctuate significantly, the value that appears most frequently can be used as the standard value for that type of operational data. For instance, when the multiple values ​​are 0, 5, 5, 5, 5, the value that appears most frequently, 5, can be used as the standard value.

[0093] Step 210: If the verification result is a fault, then the various operating data are identified as an abnormal operating data group and stored in the preset abnormal database.

[0094] If the verification result indicates a fault, it means the health monitoring equipment for the liquid cooling pump has performed accurately. The various operational data acquired in this monitoring can be stored as empirical data in a pre-set anomaly database. When the various operational data acquired in the next health monitoring process are identical to those acquired in this monitoring, the corresponding abnormal operational data group can be retrieved from the pre-set anomaly database. This allows for the direct generation of target liquid cooling pump anomaly information, thereby improving the efficiency of health monitoring. When one or more operational data points have multiple values, the method for determining the abnormal values ​​corresponding to each operational data point is similar to the method for determining the standard values ​​mentioned above, and will not be elaborated further here.

[0095] The above embodiments obtain verification result information to store various operational data obtained during the current health monitoring process as experience data in the corresponding database. This ensures that when the operational data obtained in the next health monitoring process is the same as the current operational data, there will be no misjudgment or the health monitoring process can be completed more quickly, effectively improving the accuracy and efficiency of health monitoring.

[0096] During the operation of the liquid cooling system, the rotation speed of the target liquid cooling pump may be adjusted due to the actual heat generated by the electronic equipment to change the heat exchange capacity of the coolant, thereby ensuring that the temperature inside the cabinet remains stable. Therefore, the rotation speed of the target liquid cooling pump may be different in each health monitoring process. If the same preset threshold range is used in each health monitoring process, it will affect the accuracy of the health monitoring. Therefore, standard operating data may include rotation speed data, and in order to further improve the accuracy of health monitoring, in some embodiments of this application, before step 110, the following steps are also included:

[0097] Step 101: Control the target liquid cooling pump to run at the speed corresponding to the preset speed data.

[0098] The liquid cooling pump health monitoring equipment can control the coolant replacement speed in the cabinet based on the heat generated by the electronic equipment. Specifically, it controls the rotation speed of the target liquid cooling pump based on the heat generated by the electronic equipment, thereby controlling the rate at which the target liquid cooling pump extracts coolant from the cabinet and ultimately controlling the coolant replacement speed. For example, when the electronic equipment generates a large amount of heat, to ensure timely heat dissipation, the coolant replacement speed needs to be accelerated, thus requiring the target liquid cooling pump to operate at a faster speed. Conversely, when the electronic equipment generates a small amount of heat, to avoid resource waste, the coolant replacement speed can be appropriately slowed down, thus requiring the target liquid cooling pump to operate at a slower speed.

[0099] During the operation of the liquid cooling system, the liquid cooling pump health monitoring equipment can directly send control signals to the target liquid cooling pump to make the target liquid cooling pump run at the corresponding speed, or it can send signals to the controller of the target liquid cooling pump to make the controller control the target liquid cooling pump to run at the corresponding speed.

[0100] Step 102: Search the preset database for the standard operating data group that corresponds to the speed data that is equal to the preset speed data.

[0101] Step 103: Determine the preset threshold range for each of the various operating data based on the standard operating data set.

[0102] The process of finding the preset database based on rotational speed data is similar to that of finding the preset database based on the first set of operating data, and will not be repeated here. When there is only one set of standard operating data, the preset threshold range can be directly determined based on this set of standard operating data. The specific determination process is the same as that for determining the standard threshold range, and will not be repeated here. When there are multiple sets of standard operating data, the preset threshold range for each set can be determined based on the average value of each set of standard operating data. The process of determining the preset threshold range based on the average value is the same as that for determining the preset threshold range for a set of standard operating data. Alternatively, the preset threshold range can be determined by the value that appears most frequently in each set of standard operating data. For example, if the temperature data in multiple sets of standard operating data are 0, 5, 5, 5, and 5, the preset threshold range for the temperature data can be determined based on the value 5. The process of determining the preset threshold range based on the value that appears most frequently is the same as that for determining the preset threshold range for a set of standard operating data.

[0103] The above embodiments update the preset threshold range corresponding to various operating data in each health monitoring process in real time by using rotation speed data. This ensures that the preset threshold range used in each health monitoring process is more closely matched with the data range of various operating data during the operation of the target liquid cooling pump, thereby improving the accuracy of the health monitoring process and obtaining the first operating data more accurately. This avoids the problem of inaccurate acquisition of the first operating data due to inaccurate preset threshold range, which would increase unnecessary monitoring steps.

[0104] In some embodiments of this application, before step 101, the following steps are included:

[0105] Step 001: Determine multiple speed data points within the preset speed range of the target liquid cooling pump.

[0106] Step 002: Control the target liquid cooling pump to run at the speed corresponding to one of the speed data.

[0107] In a liquid cooling system, the rotational speed of the target liquid cooling pump is controlled within a certain range, avoiding infinitely large or small speeds. Therefore, corresponding operating data can be obtained based on the target liquid cooling pump's rotational speed to generate a preset database. Specifically, the preset rotational speed range corresponding to the target liquid cooling pump can be divided into n equal parts, resulting in n+1 rotational speed data points. The target liquid cooling pump can then be controlled to operate at the speeds corresponding to these data points. Alternatively, multiple rotational speed data points can be determined based on the actual operating conditions of the target liquid cooling pump. Specifically, since the heat exchange requirements of electronic equipment are relatively stable, the rotational speed of the target liquid cooling pump will mostly be concentrated within a small range. In this case, a smaller sampling interval can be used to generate more rotational speed data within this smaller range, while a larger sampling interval can be used to generate fewer rotational speed data in other ranges. For example, m can be used as the sampling interval within the smaller range, 2m can be used as the sampling interval within the range closer to the smaller range, and 3m can be used as the sampling interval for the remaining ranges, and so on.

[0108] Step 003: Obtain various operating data of the target liquid-cooled pump to obtain a standard operating data set corresponding to the speed data.

[0109] Step 004: Store the standard operating data set in the preset database.

[0110] Step 005: Jump to the step of controlling the target liquid cooling pump to run at the speed corresponding to one of the speed data, until all speed data are traversed.

[0111] The process involves controlling the target liquid cooling pump to operate at different speeds to obtain various operating data corresponding to different speeds, obtaining a standard operating data set corresponding to different speeds, and storing this standard operating data in a preset database to complete data acquisition and generate the preset database.

[0112] The above embodiments control the target liquid cooling pump to operate at different speeds to obtain various operating data corresponding to different speeds as standard operating data sets, thereby generating a preset database. This ensures that the health status of the target liquid cooling pump can be determined based on the data in the preset database during subsequent health monitoring processes. The process of generating the preset database is simple, convenient, and fast, effectively avoiding misjudgments of new target liquid cooling pumps due to the lack of standard operating data.

[0113] The sound information emitted by the liquid cooling pump during normal and stable operation is stable, falling within a relatively stable range in the acoustic frequency domain. However, when the liquid cooling pump is about to malfunction, its sound information will change. Therefore, in some embodiments of this application, various operating data include an acoustic frequency domain data group, which includes multiple acoustic frequency domain data. Step 110 specifically includes:

[0114] Step 111a: Acquire the sound signal of the target liquid cooling pump within a preset time range.

[0115] like Figure 2 As shown, the sound signal of the target liquid cooling pump is acquired by the sound data acquisition unit 1231 and sent to the liquid cooling pump health monitoring device 121. The sound data acquisition unit 1231 can be a sound signal acquisition device such as a sound sensor or microphone. Since the sound frequency emitted by the liquid cooling pump during normal operation is within a certain range, such as... Figure 4 As shown, Figure 4 The illustration shows a spectrum of the sound frequency domain data provided in this application embodiment. After frequency domain processing of the sound signal during normal operation of the liquid cooling pump, its sound frequency will fall between values ​​A and B. That is, sound frequency domain data between values ​​A and B is considered normal data, while data less than value A or greater than value B is considered abnormal data. Furthermore, when using a sound signal acquisition device to acquire sound information, it is easy to acquire ambient sound signals, leading to anomalies in the sound frequency domain data. This can cause misjudgments when using a sound signal at a specific moment to determine whether the sound emitted by the target liquid cooling pump is normal during operation. Therefore, when acquiring sound signals, it is necessary to acquire sound signals within a preset time range. This preset time range can be selected in various ways, such as 10 seconds, 12 seconds, 15 seconds, etc.

[0116] Step 112a: Convert the sound signal into multiple first audio frequency domain data.

[0117] Sound signals are mechanical vibrations that propagate in the form of waves, and mainly include characteristics such as frequency, amplitude, and waveform. The frequency of sound determines its pitch, while the amplitude determines its volume. An amplitude-frequency diagram of sound can visualize the frequency components of a sound signal and their relative intensities. Therefore, analyzing the frequency components of a sound signal can help determine whether the operating sound of a target liquid-cooled pump is abnormal.

[0118] Step 113a: Determine whether multiple first audio domain data are within their corresponding preset threshold range.

[0119] The audio frequency domain data obtained from the audio signal conversion can be continuous, such as... Figure 4 As shown, the frequency of the sound signal is continuous, in which case the number of first audio frequency domain data is infinite; it can also be discrete, meaning the number of first audio frequency domain data is multiple and finite. However, when the target liquid cooling pump malfunctions, its sound signal will be in a continuous abnormal state. Therefore, the first frequency domain data in the abnormal state will all be outside the normal threshold range. In this case, for easier calculation, we can simply determine whether the maximum value of multiple first audio frequency domain data is less than the lower threshold of the preset threshold range, and whether the minimum value is greater than the upper threshold of the preset threshold range, i.e., determine... Figure 4 Is the value D less than the value A, and is the value C greater than the value B?

[0120] Step 114a: If multiple first audio frequency domain data are outside their corresponding preset threshold range, then reacquire the sound signal of the target liquid cooling pump within the preset time range and convert it into multiple audio frequency domain data to obtain an audio frequency domain data group.

[0121] When the target liquid cooling pump is in an abnormal state, the sound frequency it generates will exhibit a continuous abnormality. In contrast, environmental sound anomalies are instantaneous; events such as falling objects or collisions only cause anomalies in the audio frequency domain data for a very short period. Therefore, when multiple audio frequency domain data points show anomalies, to further ensure the accuracy of the audio frequency domain data set, it is necessary to acquire sound signals within a preset time range to determine whether the sound signal of the target liquid cooling pump is abnormal.

[0122] Step 115a: If at least one first sound frequency data is within its corresponding preset threshold range, the multiple first sound frequency domain data are determined as a sound frequency domain data group.

[0123] When at least one first sound frequency data is within a preset threshold range, it indicates that the sound signal within the preset time range is not in a continuous abnormal state. Therefore, multiple first sound frequency domain data can be directly used as a sound frequency domain data group for subsequent monitoring.

[0124] The above embodiment is based on the principle that if the sound signal is abnormal when the target liquid cooling pump is in an abnormal state, it will be in a continuous abnormal state. First, the sound signal within a preset time range is acquired to determine whether there is an abnormality. If the sound signal within that time range is in a continuous abnormal state, then the sound signal within the preset time range is acquired for detection. This can effectively avoid the influence of environmental factors and improve the accuracy of health monitoring.

[0125] In some embodiments of this application, after step 115a, step 210 specifically includes:

[0126] Step 211a: Determine whether multiple audio and sound domain data in the audio and sound domain data group are within their corresponding preset threshold range.

[0127] Step 212a: If multiple audio frequency domain data are outside their corresponding preset threshold range, then determine that the audio frequency domain data group in the multiple operating data is outside its corresponding preset threshold range.

[0128] Step 213a: If at least one audio frequency domain data is within its corresponding preset threshold range, then determine that the audio frequency domain data group in the multiple operating data is within its corresponding preset threshold range.

[0129] In steps 111a to 115a, multiple first audio frequency domain data have been acquired to make a preliminary judgment on the sound signal. If at least one first audio frequency domain data is within a preset threshold range, it indicates that the sound signal is normal. Then, multiple first audio frequency domain data can be directly used as an audio frequency domain data group for detection. At this time, the structure of monitoring whether the sound signal is normal through this audio frequency domain data group will be the same as the result judged in steps 111a to 115a, that is, the sound signal is normal.

[0130] If multiple first audio domain data are abnormal, the sound signal within a preset time range will be reacquired to obtain multiple audio domain data as an audio domain data group. By checking the audio domain data group, if multiple audio domain data are outside the preset range, it is equivalent to the sound signal being continuously abnormal within twice the preset time range, that is, the sound signal is abnormal; if at least one audio domain data is within the preset range, it means that some sound signals in the second acquired sound signal are normal, and it can be determined that the sound abnormality detected in steps 111a to 115a may be affected by ambient sound.

[0131] When the data stored in the preset database and the preset exception database are all specific values, in order to ensure that the data stored in the preset database or the preset exception database remains consistent with the original data in the database, when multiple audio and video domain data in the audio and video domain data group are continuous data, i.e. Figure 4 As shown, if the amplitude-frequency diagram corresponding to the audio frequency domain data set is a continuous curve, then the average value between the corresponding frequency values ​​at the two endpoints of the curve can be stored in the database as the specific value of the audio frequency domain data. That is, the average value between value A and value B, or the average value between value C and value D, can be stored in the database as the specific value of the audio frequency domain data. When multiple audio frequency domain data points in the audio frequency domain data set are relatively discrete, the average value of the multiple audio frequency domain data points can be stored in the database as the specific value of the audio frequency domain data.

[0132] The above embodiment is based on the principle that if the sound signal is abnormal when the target liquid cooling pump is in an abnormal state, it will be in a continuous abnormal state. First, the sound signal within a preset time range is acquired to determine whether there is an abnormality. If the sound signal within that time range is in a continuous abnormal state, then the sound signal within the preset time range is acquired for detection. This can effectively avoid the influence of environmental factors, prevent external interference signals from causing misjudgment, and improve the accuracy of health monitoring.

[0133] like Figure 5 As shown, Figure 5A partial structural block diagram of the primary side of the liquid cooling system provided in this application embodiment is shown. In the liquid cooling system, the coolant 112 not only dissipates heat for the electronic equipment inside the cabinet 11, but also dissipates heat for the target liquid cooling pump 122 when flowing through it. During normal operation of the target liquid cooling pump 122, the thermal parameters of the entire system tend to stabilize. Therefore, the temperature values ​​of the liquid outlet 113 and liquid inlet 114 of the cabinet 11 tend to stabilize, and it can be known that the temperature difference between the liquid outlet 113 and liquid inlet 114 is also basically a stable value without significant deviation. However, due to the long-term operation of the target liquid cooling pump 122, its internal electrical components may wear out. Therefore, when the target liquid cooling pump 122 is close to failure, the heat generation of the target liquid cooling pump 122 will change, specifically, the energy efficiency of the target liquid cooling pump 122 will decrease and the heat generation will increase. Therefore, various operating data include a temperature data group, which includes multiple temperature data. Step 110 specifically includes:

[0134] Step 111b: Obtain the first temperature value of the coolant at the outlet of the cabinet in the liquid cooling system where the target liquid cooling pump is located.

[0135] Step 112b: Obtain the second temperature value of the coolant at the inlet of the cabinet in the liquid cooling system where the target liquid cooling pump is located.

[0136] In the liquid cooling system, to detect the temperature of the coolant 112 inside the cabinet 11, a first temperature probe is installed at the outlet 113 to obtain the temperature of the coolant 112 at the outlet 113 of the cabinet 11, and a second temperature probe is installed at the inlet 114 to obtain the temperature of the coolant 112 at the inlet 114 of the cabinet 11. Therefore, when obtaining the first and second temperature values, the temperature values ​​of the coolant 112 can be obtained directly using the first and second temperature probes, or temperature sensors can be installed at the outlet 113 and inlet 114 of the cabinet 11 respectively to obtain the temperature values.

[0137] Step 113b: Determine the difference between the first temperature value and the second temperature value as the temperature data.

[0138] When the coolant flows through the target liquid cooling pump, it exchanges heat with the pump, resulting in a temperature difference between the coolant temperature at the pump inlet and outlet. This temperature difference is closely related to the pump temperature. Specifically, when the pump temperature is higher, the coolant absorbs more heat from it, and the coolant temperature at the pump outlet is also higher, leading to a larger temperature difference. Therefore, the difference between the first and second temperature values ​​can be used to determine whether the temperature data set is abnormal.

[0139] Step 114b: Jump to the step of obtaining the first temperature value of the coolant at the outlet of the cabinet in the liquid cooling system where the target liquid cooling pump is located, until the number of temperature data obtained reaches the second preset number.

[0140] Step 115b: Combine the acquired temperature data into a single temperature data group.

[0141] Similarly, when the target liquid cooling pump malfunctions, its temperature data will also be continuously abnormal. Therefore, to avoid misjudgment caused by random data, it is necessary to obtain multiple temperature data points to jointly determine whether the target liquid cooling pump is malfunctioning. The number of temperature data points can vary, including 4, 5, 6, etc.

[0142] In some embodiments of this application, after step 115b, step 210 specifically includes:

[0143] Step 211b: Determine whether multiple temperature data in the temperature data group are within their corresponding preset threshold range.

[0144] Step 212b: If multiple temperature data are outside their corresponding preset threshold range, then determine that the temperature data group in the multiple operating data is outside its corresponding preset threshold range.

[0145] Step 213b: If at least one temperature data is within its corresponding preset threshold range, determine that the temperature data group among multiple operating data is within its corresponding preset threshold range.

[0146] The process for determining whether a temperature data set is within its corresponding preset threshold range is similar to the process for determining an audio frequency domain data set, and will not be elaborated here.

[0147] When the data stored in the preset database and the preset exception database are all specific values, in order to ensure that the data stored in the preset database or the preset exception database is consistent with the original data in the database, the average value among multiple temperature data in the temperature data group can be used as the specific value of the temperature data stored in the database. For example, if the multiple temperature data in the temperature data group are 3, 3, 4, 5, and 5, the average value of 4 can be stored as the specific value of the temperature data in the database. Alternatively, the value that appears most frequently in the temperature data group can be stored as the specific value of the temperature data in the database. For example, if the multiple temperature data in the temperature data group are 2, 2, 2, 2, and 5, the value that appears most frequently, 2, can be stored as the specific value of the temperature data in the database.

[0148] The above embodiments achieve the purpose of detecting the temperature of the target liquid cooling pump by collecting the temperature of the coolant entering and exiting the target liquid cooling pump, without the need to install a separate temperature sensor on the target liquid cooling pump body. Furthermore, in addition to using a fluctuation threshold to determine whether the temperature data is abnormal, the temperature data is tested multiple times, and multiple temperature data are used together to determine whether the temperature data is abnormal, effectively improving the accuracy of health monitoring and reducing false alarms.

[0149] In some embodiments of this application, various operating data may also include current data, vibration data, and coolant flow rate data. When the target liquid cooling pump is operating stably and normally, the current tends to be stable without significant fluctuations. However, when the target liquid cooling pump is about to malfunction, the current will change significantly. For example, when the target liquid cooling pump stalls, the current will increase rapidly. Therefore, the health status of the target liquid cooling pump can be predicted by monitoring the current data. Similarly, when the target liquid cooling pump is operating stably and normally, the coolant flow rate data also tends to be stable without significant fluctuations. However, when the target liquid cooling pump is about to malfunction, the coolant flow rate data will change significantly. For example, when the target liquid cooling pump stalls, the coolant flow rate data will decrease rapidly. Therefore, the health status of the target liquid cooling pump can be predicted by monitoring the coolant flow rate data. The data acquisition and health monitoring processes for current data and coolant flow rate data are similar; here, only current data is used as an example for explanation.

[0150] After the target liquid cooling pump is installed, when collecting current data, the current signal is first acquired over a certain period of time. The average value of the current data over this period is then taken as the standard current data and stored in a preset database. During health monitoring, real-time current data of the target liquid cooling pump during operation is acquired. If the real-time current data exceeds the standard threshold range corresponding to the standard current data, it indicates that the current data of the target liquid cooling pump is abnormal.

[0151] In addition, the health status of the target liquid cooling pump is usually calculated on an annual basis. The liquid cooling pump health monitoring equipment does not need to collect various operating data for monitoring at all times. It can be set to collect data according to a preset time interval. Specifically, it can be set to collect data once a day or once a week. This can greatly reduce the computing resource consumption of the liquid cooling pump health monitoring equipment and also reduce the storage cost of collecting relevant data.

[0152] According to a second aspect of the embodiments of this application, a liquid cooling pump health monitoring device is also provided, such as... Figure 6 As shown, Figure 6A schematic diagram of the structure of a liquid cooling pump health monitoring device provided in an embodiment of this application is shown. The liquid cooling pump health monitoring device 600 includes: an acquisition module 601, a judgment module 602, a first determination module 603, a search module 604, a second determination module 605, a statistics module 606, and a generation module 607.

[0153] The acquisition module 601 is used to acquire various operating data of the target liquid cooling pump; the judgment module 602 is used to sequentially judge whether each operating data is within its corresponding preset threshold range; the first determination module 603 is used to determine the operating data as the first operating data when the currently judged operating data is outside its corresponding preset threshold range; the search module 604 is used to search for a standard operating data group corresponding to the first operating data from a preset database, wherein the standard operating data group includes multiple standard operating data that correspond to each other, the multiple standard operating data includes the first operating data, and other multiple standard operating data that correspond one-to-one with the first operating data; the second determination module 605 is used to determine the standard threshold range corresponding to the other multiple standard operating data; the statistics module 606 is used to count the number of operating data outside the corresponding standard threshold range among the multiple operating data to obtain a first number; the generation module 607 is used to generate abnormal information of the target liquid cooling pump when the first number is greater than or equal to the first preset number.

[0154] The above embodiments acquire various operational data of the target liquid cooling pump and determine whether these data are abnormal. This allows for early detection of malfunctions in the target liquid cooling pump, enabling the maintenance team to detect and inspect the pump. This ensures timely replacement of the target liquid cooling pump when it malfunctions, preventing system crashes and economic losses caused by a malfunctioning pump. Furthermore, using multiple operational data points to determine malfunctions effectively avoids misjudgments caused by environmental interference, improving the accuracy of anomaly monitoring. The method is simple and convenient, allowing for rapid health monitoring of the target liquid cooling pump and improving detection efficiency.

[0155] According to a third aspect of the embodiments of this application, a liquid cooling pump health monitoring device is also provided, such as... Figure 7 As shown, Figure 7This diagram illustrates the structure of a liquid-cooled pump health monitoring device according to an embodiment of this application. The specific implementation of this application does not limit the specific implementation of the liquid-cooled pump health monitoring device. The liquid-cooled pump health monitoring device may include: a processor 402, a communication interface 404, a memory 406, and a communication bus 408. The processor 402, communication interface 404, and memory 406 communicate with each other via the communication bus 408. The communication interface 404 is used to communicate with other devices, such as clients or other server network elements. The processor 402 is used to execute program 410, specifically performing the relevant steps described in the embodiments of the liquid-cooled pump health monitoring method.

[0156] Specifically, program 410 may include program code, which includes computer-executable instructions. Processor 402 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of this application. The liquid cooling pump health monitoring device includes one or more processors, which may be processors of the same type, such as one or more CPUs; or processors of different types, such as one or more CPUs and one or more ASICs. Memory 406 is used to store program 410. Memory 406 may include high-speed RAM memory and may also include non-volatile memory, such as at least one disk storage device.

[0157] Specifically, program 410 can be called by processor 402 to cause the liquid cooling pump health monitoring device to perform the following operations: acquire various operating data of the target liquid cooling pump; sequentially determine whether each operating data is within its corresponding preset threshold range; if the currently determined operating data is outside its corresponding preset threshold range, then determine the operating data as the first operating data; search for a standard operating data group corresponding to the first operating data from a preset database, wherein the standard operating data group includes various standard operating data that correspond to each other, the various standard operating data includes the first operating data, and other various standard operating data that correspond one-to-one with the first operating data; determine the standard threshold range corresponding to the other various standard operating data; count the number of operating data outside the corresponding standard threshold range among the various operating data to obtain a first number; if the first number is greater than or equal to the first preset number, then generate abnormal information of the target liquid cooling pump.

[0158] The above embodiments acquire various operational data of the target liquid cooling pump and determine whether these data are abnormal. This allows for early detection of malfunctions in the target liquid cooling pump, enabling the maintenance team to detect and inspect the pump. This ensures timely replacement of the target liquid cooling pump when it malfunctions, preventing system crashes and economic losses caused by a malfunctioning pump. Furthermore, using multiple operational data points to determine malfunctions effectively avoids misjudgments caused by environmental interference, improving the accuracy of anomaly monitoring. The method is simple and convenient, allowing for rapid health monitoring of the target liquid cooling pump and improving detection efficiency.

[0159] According to a third aspect of the embodiments of this application, a liquid cooling pump health monitoring system is also provided, such as... Figure 8 As shown, Figure 8 This illustration shows an interactive schematic diagram of a liquid-cooled pump health monitoring system provided in an embodiment of this application. The system includes a liquid-cooled pump health monitoring device 121, a target liquid-cooled pump 122, and various data acquisition units. These data acquisition units may include a microphone 31, a temperature probe 32, a flow sensor 34, an electricity meter 35, and a current transformer 36, etc. The liquid-cooled pump health monitoring device 121 is electrically connected to both the target liquid-cooled pump 122 and the various data acquisition units, and is used to drive the target liquid-cooled pump 122 to operate at a preset speed, and to acquire various operating data through the target liquid-cooled pump 122 and the various data acquisition units.

[0160] The liquid cooling pump health monitoring system includes a monitoring phase. During this phase, the liquid cooling pump health monitoring device 121 acquires various operating data of the target liquid cooling pump. Specifically, it can acquire pump speed data via data fed back from the liquid cooling pump 122, coolant flow data via the flow sensor 34, current data via the meter 35 and current transformer 36, temperature data via the temperature probe 32, and audio frequency data via the microphone 31. The liquid cooling pump health monitoring device 121 also sequentially determines whether each operating data point is within its corresponding preset threshold range. Furthermore, the liquid cooling pump health monitoring device 121 determines the operating data as the first operating data point when the currently determined operating data point is outside its corresponding preset threshold range. The monitoring device 121 is further configured to search for a standard operating data group corresponding to the first operating data from a preset database. The standard operating data group includes multiple standard operating data that correspond to each other. The multiple standard operating data includes the first operating data and other multiple standard operating data that correspond one-to-one with the first operating data. The liquid cooling pump health monitoring device 121 is further configured to determine the standard threshold range corresponding to the other multiple standard operating data. The liquid cooling pump health monitoring device 121 is further configured to count the number of operating data outside the corresponding standard threshold range among the multiple operating data to obtain a first number. The liquid cooling pump health monitoring device 121 is further configured to generate target liquid cooling pump abnormal information when the first number is greater than or equal to a first preset number.

[0161] The liquid cooling pump health monitoring system includes a data acquisition phase. During this phase, the liquid cooling pump health monitoring device 121 is also used to control the target liquid cooling pump 122 to operate at speeds corresponding to different pump speeds, and to acquire various operating data corresponding to each pump speed. Specifically, the liquid cooling pump health monitoring device 121 can be used to send pump speed 1 to the target liquid cooling pump 122 so that the target liquid cooling pump 122 runs at the speed corresponding to pump speed 1. Then, it can acquire data such as pump speed 1 fed back by the target liquid cooling pump 122, flow rate 1 returned by the flow sensor 34, current 1 returned by the meter 35, temperature difference 1 returned by the temperature probe 32, and audio 1 returned by the microphone 31. The liquid cooling pump health monitoring device 121 can also be used to send pump speed 2 to the target liquid cooling pump 122 so that the target liquid cooling pump 122 runs at the speed corresponding to pump speed 2. Then, it can acquire data such as pump speed 2 fed back by the target liquid cooling pump 122, flow rate 2 returned by the flow sensor 34, current 2 returned by the meter 35, temperature difference 2 returned by the temperature probe 32, and audio 2 returned by the microphone 31. And so on, acquiring various operating data corresponding to pump speeds 1, 2, 3...n respectively, completing data acquisition, and producing a preset database.

[0162] The above embodiments acquire various operational data of the target liquid cooling pump and determine whether these data are abnormal. This allows for early detection of malfunctions in the target liquid cooling pump, enabling the maintenance team to detect and inspect the pump. This ensures timely replacement of the target liquid cooling pump when it malfunctions, preventing system crashes and economic losses caused by a malfunctioning pump. Furthermore, using multiple operational data points to determine malfunctions effectively avoids misjudgments caused by environmental interference, improving the accuracy of anomaly monitoring. The method is simple and convenient, allowing for rapid health monitoring of the target liquid cooling pump and improving detection efficiency.

[0163] According to a fourth aspect of the embodiments of this application, a liquid cooling system is also provided, such as... Figure 1 , Figure 2 and Figure 5 As shown, the liquid cooling system 100 includes a cabinet 11, a cooling capacity distribution unit 12, and a cooling device 21. The cooling capacity distribution unit 12 includes a target liquid cooling pump 122, a multi-data acquisition unit, and a liquid cooling pump health monitoring device 121 as described in the above embodiment. The target liquid cooling pump 122 is connected to the inlet 114 and outlet 113 of the cabinet 11. The target liquid cooling pump 122 is used to drive the coolant 112 to circulate inside and outside the cabinet 11. The cooling device 21 is used to circulate heat exchange fluid to the cooling capacity distribution unit 12, so that the coolant 112 and the heat exchange fluid exchange heat in the cooling capacity distribution unit 12 to reduce the temperature of the coolant 112. The multi-data acquisition unit is used to acquire various operating data of the target liquid cooling pump 122. The liquid cooling pump health monitoring device 121 is used to monitor the target liquid cooling pump 122.

[0164] The system includes various operational data units, such as a sound acquisition unit 1231, a first temperature acquisition unit 1232, a second temperature acquisition unit 1233, a flow rate acquisition unit 1234, a first current acquisition unit 1235, and a second current acquisition unit 1236. The sound acquisition unit 1231 acquires audio frequency data and can be a microphone, sound sensor, etc.; the first temperature acquisition unit 1232 and the second temperature acquisition unit 1233 acquire temperature data and can be a temperature probe, temperature sensor, etc.; the flow rate acquisition unit 1234 acquires coolant flow rate data and can be a flow sensor, etc.; the first current acquisition unit 1235 and the second current acquisition unit 1236 acquire current data, where the first current acquisition unit 1235 can be a current transformer, and the second current acquisition unit 1236 can be a meter, multimeter, etc.

[0165] In addition, such as Figure 9 As shown, Figure 9 This illustration shows another structural block diagram of the primary side of the liquid cooling system provided in this application embodiment. The cooling capacity distribution unit 12 may further include an audio processing module 124. The liquid cooling pump health monitoring device 121 is further used to acquire sound abnormal signals sent by the audio processing module 124. The audio processing module 124 is used to acquire sound signals within a preset time range returned by the sound acquisition unit 1231, and perform frequency domain processing on the sound signals to obtain multiple audio-visual domain data. The audio processing module 124 is also used to determine whether the multiple audio-visual domain data are within their corresponding preset threshold range. The audio processing module 124 is also used to reacquire sound signals within the preset time range returned by the sound acquisition unit 1231 when all multiple audio-visual domain data are outside their corresponding preset threshold range, and perform frequency domain processing on the sound signals to obtain multiple audio-visual domain data, thus obtaining an audio-visual domain data group. The audio processing module 124 is also used to determine whether the audio-visual domain data group is within its corresponding preset threshold range. The audio processing module 124 is also used to generate an abnormal sound signal when the audio domain data group is outside its corresponding preset threshold range, and send the audio domain data group and the abnormal sound signal to the liquid cooling pump health monitoring device 121.

[0166] The above embodiments acquire various operational data of the target liquid cooling pump and determine whether these data are abnormal. This allows for early detection of malfunctions in the target liquid cooling pump, enabling the maintenance team to detect and inspect the pump. This ensures timely replacement of the target liquid cooling pump when it malfunctions, preventing system crashes and economic losses caused by a malfunctioning pump. Furthermore, using multiple operational data points to determine malfunctions effectively avoids misjudgments caused by environmental interference, improving the accuracy of anomaly monitoring. The method is simple and convenient, allowing for rapid health monitoring of the target liquid cooling pump and improving detection efficiency.

[0167] This application provides a computer-readable storage medium storing executable instructions. When these executable instructions are run on a liquid cooling pump health monitoring device, the liquid cooling pump health monitoring device performs the liquid cooling pump health monitoring method in any of the above method embodiments.

[0168] This application provides a computer program that can be called by a processor to cause a liquid cooling pump health monitoring device to execute the liquid cooling pump health monitoring method in any of the above method embodiments.

[0169] This application provides a computer program product, which includes a computer program stored on a computer-readable storage medium. The computer program includes program instructions, which, when executed on a computer, cause the computer to perform the liquid cooling pump health monitoring method in any of the above method embodiments.

[0170] The algorithms or displays provided herein are not inherently related to any particular computer, virtual system, or other device. Various general-purpose systems can also be used in conjunction with the teachings herein. The required structure for constructing such systems is apparent from the above description. Furthermore, the embodiments of this application are not directed to any particular programming language. It should be understood that the content of this application described herein can be implemented using various programming languages, and the above description of specific languages ​​is for the purpose of disclosing the best mode of implementation of this application.

[0171] Numerous specific details are set forth in the specification provided herein. However, it will be understood that embodiments of this application may be practiced without these specific details. In some instances, well-known methods, structures, and techniques have not been shown in detail so as not to obscure the understanding of this specification.

[0172] Similarly, it should be understood that, in order to simplify this application and aid in understanding one or more of the various aspects of the invention, features of the embodiments of this application are sometimes grouped together in a single embodiment, figure, or description thereof in the above description of exemplary embodiments of this application. However, this method of disclosure should not be construed as reflecting an intention that the claimed application requires more features than are expressly recited in each claim.

[0173] Those skilled in the art will understand that modules in the device of the embodiments can be adaptively changed and placed in one or more devices different from that embodiment. Modules, units, or components in the embodiments can be combined into a single module, unit, or component, and can be divided into multiple sub-modules, sub-units, or sub-components. Except where at least some of such features and / or processes or units are mutually exclusive, any combination can be used to combine all features disclosed in this specification (including the accompanying claims, abstract, and drawings) and all processes or units of any method or device so disclosed. Unless expressly stated otherwise, each feature disclosed in this specification (including the accompanying claims, abstract, and drawings) may be replaced by an alternative feature that serves the same, equivalent, or similar purpose.

[0174] It should be noted that the above embodiments are illustrative of this application and not restrictive, and those skilled in the art can devise alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses should not be construed as limiting the claims. The word "comprising" does not exclude the presence of elements or steps not listed in the claims. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. This application can be implemented by means of hardware comprising several different elements and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by the same item of hardware. The use of the words first, second, and third, etc., does not indicate any order. These words can be interpreted as names. The steps in the above embodiments, unless otherwise specified, should not be construed as limiting the order of execution.

Claims

1. A method for monitoring the health of a liquid-cooled pump, characterized in that, The method includes: Acquire various operating data of the target liquid cooling pump; The system sequentially determines whether each piece of operating data is within its corresponding preset threshold range. The preset threshold range is determined by acquiring operating data for a preset time period after the target liquid cooling pump is installed, calculating the average value of the operating data within that time period, and using the range of 5% above and below the average value as the preset threshold range corresponding to the operating data. Alternatively, the preset threshold range corresponding to the operating data can be determined based on the fluctuation range of the operating data within the preset time period. Or, multiple pieces of operating data can be acquired at preset time intervals, and the average value of the multiple pieces of operating data can be calculated. The range of 5% above and below the average value is used as the preset threshold range corresponding to the operating data. If the currently determined running data is outside its corresponding preset threshold range, then the running data is determined as the first running data; The standard running data group corresponding to the first running data is searched in the preset database. The standard running data group includes multiple standard running data that correspond to each other. The multiple standard running data includes the first running data and other multiple standard running data that correspond one-to-one with the first running data. If the standard operating data group is empty, the number of operating data outside the corresponding preset threshold range among the various operating data is counted to obtain a second number; if the second number is less than the first preset number, the various operating data are determined as a standard operating data group and stored in the preset database; if the second number is greater than or equal to the first preset number, target liquid cooling pump abnormal information is generated. If the standard operating data group is not empty, then the standard threshold range corresponding to the other multiple standard operating data is determined, wherein the standard threshold range is the lower threshold of the standard threshold range by subtracting 4% or 5% of the specific value of the standard operating data and adding 4% or 5% of the specific value to the standard threshold range. The number of operational data points outside the corresponding standard threshold range among the various operational data types is counted to obtain a first number; If the first quantity is greater than or equal to the first preset quantity, then abnormal information of the target liquid cooling pump is generated.

2. The method for monitoring the health of a liquid-cooled pump according to claim 1, characterized in that, After calculating the number of operational data points outside the corresponding standard threshold range from the various operational data, and obtaining the first number, the method further includes: If the first quantity is less than the first preset quantity, then the various types of operating data are determined into a standard operating data group and stored in the preset database.

3. The method for monitoring the health of a liquid-cooled pump according to claim 1, characterized in that, The method of counting the number of operational data points outside the corresponding standard threshold range among the various operational data to obtain a first number specifically includes: If there are multiple standard operating data groups, the number of operating data that are outside the standard threshold range corresponding to each standard operating data group is counted to obtain multiple third quantities. The third quantity with the smallest value among the plurality of third quantities is determined as the first quantity.

4. The method for monitoring the health of a liquid-cooled pump according to claim 1, characterized in that, Before searching for the standard running data group corresponding to the first running data in the preset database, the process further includes: Search for an abnormal running data group corresponding to the first running data from a preset abnormal database. The abnormal running data group includes multiple abnormal running data that correspond to each other. The multiple abnormal running data includes the first running data and other multiple abnormal running data that correspond one-to-one with the first running data. If the abnormal operation data group is not empty, then perform the following steps: Determine the abnormal threshold range corresponding to the other various abnormal operating data; The fourth quantity is obtained by counting the number of operational data that fall within the corresponding abnormal threshold range among the various operational data. If the fourth quantity is greater than or equal to the first preset quantity, then abnormal information of the target liquid cooling pump is generated; After generating the abnormal information for the target liquid cooling pump, the following further steps are included: Obtain the verification result information obtained after testing the target liquid cooling pump; If the verification result is normal, the various operating data are identified as a standard operating data group and stored in the preset database; If the verification result information is a fault, the various operating data are identified as an abnormal operating data group and stored in the preset abnormal database.

5. The method for monitoring the health of a liquid-cooled pump according to any one of claims 1-4, characterized in that, The standard operating data includes rotational speed data; Prior to acquiring the various operating data of the target liquid cooling pump, the process further includes: Control the target liquid cooling pump to operate at a speed corresponding to the preset speed data; Search the preset database for the standard operating data group corresponding to the speed data that is equal to the preset speed data; The preset threshold ranges corresponding to each of the various operating data are determined based on the standard operating data set.

6. The method for monitoring the health of a liquid-cooled pump according to claim 5, characterized in that, Before controlling the target liquid cooling pump to operate at a speed corresponding to preset speed data, the method further includes: Based on the preset speed range of the target liquid cooling pump, determine multiple speed data within the preset speed range; Control the target liquid cooling pump to operate at a speed corresponding to one of the speed data; Acquire various operating data of the target liquid-cooled pump to obtain a standard operating data set corresponding to the rotational speed data; Store the standard operating data set into the preset database; Jump to the step of controlling the target liquid cooling pump to operate at a speed corresponding to one of the speed data, until all the speed data are traversed.

7. The method for monitoring the health of a liquid-cooled pump according to claim 1, characterized in that, The various operational data include an audio-visual domain data group, which includes multiple audio-visual domain data. The acquisition of various operating data of the target liquid cooling pump specifically includes: Acquire the acoustic signal of the target liquid cooling pump within a preset time range; The sound signal is converted into multiple first audio frequency domain data; Determine whether the plurality of first audio domain data are within their corresponding preset threshold range; If all of the first audio frequency domain data are outside their corresponding preset threshold range, then the sound signal of the target liquid cooling pump within the preset time range is reacquired and converted into multiple audio frequency domain data to obtain the audio frequency domain data group. If at least one first sound frequency data is within its corresponding preset threshold range, the plurality of first sound frequency domain data are determined as the sound frequency domain data group; The step of sequentially determining whether each piece of running data is within its corresponding preset threshold range specifically includes: Determine whether multiple audio and audio domain data in the audio and audio domain data group are within their corresponding preset threshold range; If all of the multiple audio-visual domain data are outside their corresponding preset threshold range, then it is determined that the audio-visual domain data group among the multiple operating data is outside its corresponding preset threshold range; If at least one audio domain data is within its corresponding preset threshold range, then the audio domain data group among the multiple operating data is determined to be within its corresponding preset threshold range; And / or, The various operational data include a temperature data group, which includes multiple temperature data points. The acquisition of various operating data of the target liquid cooling pump specifically includes: Obtain the first temperature value of the coolant at the outlet of the cabinet in the liquid cooling system where the target liquid cooling pump is located; Obtain the second temperature value of the coolant at the inlet of the cabinet in the liquid cooling system where the target liquid cooling pump is located; The difference between the first temperature value and the second temperature value is determined as the temperature data; Jump to the step of obtaining the first temperature value of the coolant at the outlet of the cabinet in the liquid cooling system where the target liquid cooling pump is located, until the number of temperature data obtained reaches the second preset number; The acquired temperature data are combined into a single temperature data group. The step of sequentially determining whether each piece of running data is within its corresponding preset threshold range specifically includes: Determine whether multiple temperature data in the temperature data group are within their corresponding preset threshold range; If all of the multiple temperature data are outside their corresponding preset threshold range, then it is determined that the temperature data group among the multiple operating data is outside its corresponding preset threshold range; If at least one temperature data point is within its corresponding preset threshold range, then the temperature data group among the multiple operating data points is determined to be within its corresponding preset threshold range.

8. A liquid cooling pump health monitoring device, characterized in that, include: The processor, memory, communication interface, and communication bus are provided, wherein the processor, memory, and communication interface communicate with each other via the communication bus. The memory is used to store executable instructions that cause the processor to perform the operation of the health monitoring method for the liquid cooling pump as described in any one of claims 1-7.

9. A liquid cooling system, characterized in that, include: The cabinet, the cooling distribution unit, and the cooling equipment, wherein the cooling distribution unit includes a target liquid cooling pump, multiple data acquisition units, and the liquid cooling pump health monitoring equipment as described in claim 8; The target liquid cooling pump is connected to the liquid inlet and liquid outlet of the cabinet respectively, and the target liquid cooling pump is used to drive the coolant to circulate inside and outside the cabinet; The cooling device is used to circulate heat exchange fluid to the cooling capacity distribution unit, so that the coolant and the heat exchange fluid exchange heat within the cooling capacity distribution unit to reduce the temperature of the coolant. The multiple data acquisition units are used to acquire multiple operating data of the target liquid cooling pump; The liquid cooling pump health monitoring device is used to monitor the target liquid cooling pump.

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