Server cabinet cooling unit fault early warning system and method

Abstract

The invention discloses a server cabinet cooling unit fault early warning system and method, and relates to the technical field of intelligent early warning, and the method comprises the following steps: setting variables and invariants, calculating a historical temperature change rate, building a first mapping relation, a second mapping relation and a third mapping relation, calculating a temperature margin, and constructing a temperature interval. And judging the first cooling state, and sending a temperature early warning signal. According to the invention, the rated temperature is matched by setting the temperature monitoring points and identifying the type of the cabinet, the accuracy of fault detection is improved, the first screening and the second screening are carried out by using historical data, false alarms and missing alarms are reduced, and the mapping relation is established, so that the early warning system is more flexible and high in adaptability, and the cooling state is judged by constructing the temperature interval. Potential cooling problems are found in advance, early warning signals are sent, historical data and current data are fully utilized, the utilization efficiency of the data is improved, and data support is provided for maintenance and optimization of the cabinet cooling unit.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent early warning, and in particular to a server cabinet cooling unit fault early warning system and method. Background Art

[0002] In recent years, the server cabinet industry has been responding to the high heat density challenges of data centers by adopting high-density design, hot channel closure and intelligent temperature control systems. Server cabinets will pay more attention to intelligence and modularity. Intelligence is reflected in the integration of Internet of Things and artificial intelligence technology to achieve real-time monitoring and early warning of cabinet status.

[0003] At present, in the Chinese invention patent with publication number CN115774650A, an overheating fault identification device, method, apparatus, medium and server cabinet are disclosed. The method obtains the sound signal energy value by extracting features and analyzing the sound signal. When the sound signal energy value is within the preset energy range of the overheating fault, the overheating fault position and temperature value are obtained by combining the installation position of the acoustic wave sensor and the sound signal energy value to identify the overheating fault. However, the related technology does not construct various external parameters based on historical data and the influence of the cabinet's own parameters on the cooling function, so as to accurately warn the temperature, which is easy to cause wrong judgment and waste of time resources. The warning analysis process is not simplified according to the actual measured temperature, which is not conducive to the rapid responsiveness of the warning analysis and has certain limitations. Summary of the invention

[0004] The technical problem solved by the present invention is: the related technology does not construct the influence of various external parameters and the cabinet's own parameters on the cooling function based on historical data, so as to provide accurate temperature warning, which is easy to cause wrong judgment and waste of time resources. The warning analysis process is not simplified according to the actual measured temperature, which is not conducive to the rapid responsiveness of the warning analysis and has certain limitations.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: In a first aspect, a server cabinet cooling unit failure early warning method comprises the following steps:

[0006] Step S100, setting a temperature monitoring point, identifying a cabinet model, and matching a rated temperature according to the cabinet model;

[0007] Step S200, acquiring historical data, performing a first screening on the historical data according to the rated temperature to obtain first data, setting variables and invariants according to the control variable method, performing a second screening on the first data according to the variables and the invariants to obtain second data, and calculating the historical temperature change rate according to the second data;

[0008] Step S300, establishing a first mapping relationship, a second mapping relationship and a third mapping relationship according to the historical temperature change rate;

[0009] Step S400, obtain current data, the current data including current server power, current ambient temperature and current wind pressure, calculate temperature margin according to the current data, construct temperature interval according to the temperature margin, determine the first cooling state according to the temperature interval, and send a temperature warning signal according to the first cooling state.

[0010] As a preferred solution of the server cabinet cooling unit failure early warning method described in the present invention, the method for setting the temperature monitoring point includes:

[0011] Acquire a three-dimensional model of the cabinet, and draw a mapping image of the surface of the server in the three-dimensional model of the cabinet on the surface of the cabinet, which is recorded as a first image, wherein the first image is represented as a mapping image of each surface of the server in the three-dimensional model of the cabinet on each surface of the cabinet;

[0012] Obtaining the intersection of the angle bisector of the first image and the geometric center of the corresponding server, and calculating the distance from the intersection of the angle bisector of the first image to the geometric center of the corresponding server, which is recorded as a first distance;

[0013] A first circle is drawn with the intersection of the angular bisectors of the first image as the center and the first distance as the radius, and the first value is set as the interval distance, where the interval distance is expressed as the length of the interval arc, and a temperature sensor is set at the intersection of the interval distance and the corresponding center of the circle, and the temperature sensor is set on the outer surface of the cabinet.

[0014] As a preferred solution of the server cabinet cooling unit fault warning method described in the present invention, wherein: an industrial camera is used to capture an image of the upper surface of the cabinet, the upper surface image of the cabinet is accompanied by a cabinet QR code, and the cabinet QR code is scanned to identify and obtain the cabinet model;

[0015] Retrieving a cabinet database, inputting the cabinet model into the cabinet database, and matching the rated temperature corresponding to the cabinet model;

[0016] Acquire historical data, and perform a first screening on the historical data according to the rated temperature to obtain first data;

[0017] The first data screening method includes: retrieving an operation database, inputting a cabinet model into the operation database, and matching historical data corresponding to the cabinet model, wherein the historical data includes historical server power, historical ambient temperature, historical wind pressure, and historical cabinet surface average temperature;

[0018] Compare the historical cabinet surface average temperature with the rated temperature, and when the historical cabinet surface average temperature is greater than the rated temperature, delete the corresponding historical data; when the historical cabinet surface average temperature is less than or equal to the rated temperature, set the corresponding historical data as the first data;

[0019] The rated temperature is expressed as the maximum value of the average temperature of the cabinet surface.

[0020] As a preferred solution of the server cabinet cooling unit failure early warning method described in the present invention, the second data screening method includes:

[0021] Calculate the historical temperature change rate according to the second data;

[0022] The historical data including the historical server power, the historical ambient temperature and the historical wind pressure are set as parameters to be set, any parameter to be set is set as a variable, the remaining parameters to be set are set as invariants, the variables and the corresponding invariants are set as reference groups, the reference groups include a first reference group, a second reference group and a third reference group, and reference group rules are obtained, the reference group rules include:

[0023] The first reference group sets the historical server power as a variable, and sets the historical ambient temperature and historical wind pressure as constants;

[0024] The second reference group sets the historical ambient temperature as a variable, and sets the historical server power and historical wind pressure as constants;

[0025] The third reference group sets the historical wind pressure as a variable, and sets the historical server power and the historical ambient temperature as constants;

[0026] Creating a classification model, classifying the first data according to the classification model to obtain second data, wherein the second data includes the second data of the first reference group, the second data of the second reference group, and the second data of the third reference group;

[0027] The classification model includes a first reference group classification model, a second reference group classification model and a third reference group classification model, and the first reference group second data, the second reference group second data, and the third reference group second data are obtained through the first reference group classification model, the second reference group classification model and the third reference group classification model respectively.

[0028] As a preferred solution of the server cabinet cooling unit fault early warning method described in the present invention, the method for constructing the classification model includes:

[0029] Constructing a data table and boundary rules, wherein the boundary rules include variable boundary rules and invariant boundary rules, wherein the variable boundary rules indicate that not all variables are equal, and the invariant boundary rules indicate that all invariants are equal;

[0030] Use SQL statements to set the first column of the data table to the corresponding variable boundary rule, and the second column of the data table to the corresponding invariant boundary rule to build a classification model;

[0031] The query function is started, and the queried data is classified into the corresponding first reference group second data, second reference group second data or third reference group second data.

[0032] As a preferred solution of the server cabinet cooling unit fault early warning method described in the present invention, the expression of the variable boundary rule is:

[0033] l1:x2-x1+x3-x2+...+x m -x m-1 ≠0

[0034] The expression of the invariant boundary rule is:

[0035]

[0036] Among them, l1 and l2 are the variable boundary rule and the corresponding invariant boundary rule, respectively, x m is a variable, y m and k m are the corresponding invariants respectively;

[0037] When x m When it belongs to the first reference group, x m Expressed as historical server power, y m and k m Expressed as historical ambient temperature and historical wind pressure;

[0038] When x m When it belongs to the second reference group, x m Historical ambient temperature, y m and k m Expressed as historical server power and historical wind pressure;

[0039] When x m When it belongs to the third reference group, x m Historical wind pressure, y m and k m Expressed as historical server power and historical ambient temperature.

[0040] As a preferred solution of a server cabinet cooling unit fault early warning method described in the present invention, wherein: the historical cabinet surface average temperature corresponding to the second data is obtained, the historical cabinet surface average temperature corresponding to the second data is expressed as a time series value, and the interval time period of the historical cabinet surface average temperatures adjacent in time sequence is recorded as the first time period, and the calculation method of the historical temperature change rate includes:

[0041] Calculate the difference between the historical average surface temperatures of the cabinets that are adjacent in time sequence, record it as the second difference, calculate the ratio of the second difference to the first time period, record it as the first ratio, and set the first ratio as the historical temperature change rate;

[0042] The first mapping relationship is represented by a mapping relationship between historical server power and historical temperature change rate, the second mapping relationship is represented by a mapping relationship between historical ambient temperature and historical temperature change rate, and the third mapping relationship is represented by a mapping relationship between historical wind pressure and historical temperature change rate;

[0043] The method for constructing the first mapping relationship, the second mapping relationship and the third mapping relationship includes:

[0044] Obtaining a historical temperature change rate corresponding to the first mapping relationship, the second mapping relationship, or the third mapping relationship, obtaining a value of a variable corresponding to the historical temperature change rate, and constructing a mapping relationship between the value of the variable and the historical temperature change rate;

[0045] When the variable corresponding to the first ratio is the historical server power, the corresponding mapping relationship is recorded as the first mapping relationship; when the variable corresponding to the first ratio is the historical ambient temperature, the corresponding mapping relationship is recorded as the second mapping relationship; when the variable corresponding to the first ratio is the historical wind pressure, the corresponding mapping relationship is recorded as the third mapping relationship.

[0046] As a preferred solution of a server cabinet cooling unit fault warning method described in the present invention, wherein: the second time period is set as a monitoring period, current data is obtained, the current data includes current server power, current ambient temperature and current wind pressure, and the current cabinet surface average temperature is calculated. The calculation method of the current cabinet surface average temperature includes:

[0047] Obtain the temperature of each monitoring point at any time point, count the number of monitoring points, calculate the sum of the temperatures at each monitoring point, calculate the ratio of the sum of the temperatures at each monitoring point to the number of monitoring points, record it as a third ratio, and set the third ratio as the current average surface temperature of the cabinet;

[0048] Compare the current average cabinet surface temperature with the rated temperature to obtain a comparison result, wherein the comparison result includes that the current average cabinet surface temperature is greater than the rated temperature, and the current average cabinet surface temperature is less than or equal to the rated temperature, and perform a first operation according to the comparison result, wherein the first operation includes sending an early warning signal and analyzing current data;

[0049] When the average surface temperature of the current cabinet is greater than the rated temperature, a temperature warning signal is sent; otherwise, the current data is analyzed.

[0050] As a preferred solution of the server cabinet cooling unit failure early warning method described in the present invention, the temperature margin calculation method includes:

[0051] When the first operation is to analyze the current data, the current server power, the current ambient temperature and the current wind pressure are obtained, and the current server power, the current ambient temperature and the current wind pressure are respectively input into the first mapping relationship, the second mapping relationship and the third mapping relationship to obtain the corresponding historical temperature change rates, which are respectively recorded as the first current temperature change rate, the second temperature change rate and the third temperature change rate;

[0052] Obtain the current average surface temperature of the cabinet, calculate the product of the current average surface temperature of the cabinet and the first current temperature change rate, the second temperature change rate and the third temperature change rate respectively, record them as the first product, the second product and the third product, set the first product, the second product and the third product as the temperature margin, and select the maximum value of the temperature margin;

[0053] Calculate the sum of the rated temperature and the maximum value of the temperature margin, and record it as the first sum;

[0054] Calculate the difference between the rated temperature and the maximum value of the temperature margin, record it as the first difference, set the first sum as the upper limit of the temperature interval, set the first difference as the lower limit of the temperature interval, and construct the temperature interval;

[0055] Determine a first cooling state based on the current average cabinet surface temperature and the temperature range, wherein the first cooling state includes a normal state and a fault state, and send a temperature warning signal based on the first cooling state;

[0056] When the first cooling state is a fault state, a temperature warning signal is sent, and when the first cooling state is a normal state, no temperature warning signal is sent.

[0057] In a second aspect, a server cabinet cooling unit fault warning system includes a collection module, an analysis module and a warning module;

[0058] The acquisition module is used to set a temperature monitoring point, identify a cabinet model, match a rated temperature according to the cabinet model, obtain historical data, perform a first screening on the historical data according to the rated temperature to obtain first data, set variables and invariants according to a control variable method, and perform a second screening on the first data according to the variables and invariants to obtain second data;

[0059] The analysis module calculates the historical temperature change rate according to the second data, and establishes a first mapping relationship, a second mapping relationship and a third mapping relationship according to the historical temperature change rate;

[0060] The early warning module obtains current data, which includes current server power, current ambient temperature and current wind pressure, calculates a temperature margin according to the current data, constructs a temperature interval according to the temperature margin, determines a first cooling state according to the temperature interval, and sends a temperature early warning signal according to the first cooling state.

[0061] The beneficial effects of the present invention are as follows: by setting temperature monitoring points and identifying cabinet models to match rated temperatures, the actual working state of the cabinet can be more accurately monitored, thereby improving the accuracy of fault detection, and using historical data for first and second screening to ensure that the screened data is representative and reliable. This method effectively excludes irrelevant data, reduces false positives and missed positives, and dynamically adjusts early warning parameters according to the actual operating conditions of the cabinet by calculating the historical temperature change rate and establishing a mapping relationship, making the early warning system more flexible and adaptable. By acquiring current data in real time and calculating the temperature margin, constructing a temperature range to judge the cooling state, potential cooling problems are discovered in advance and early warning signals are sent, thereby reducing the risk of faults. By making full use of historical data and current data, this method improves the efficiency of data utilization and provides data support for the maintenance and optimization of the cabinet cooling unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 A basic flow chart of a server cabinet cooling unit failure warning method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0063] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0064] Example, see Figure 1 , as an embodiment of the present invention, provides a server cabinet cooling unit failure early warning method, comprising the following steps:

[0065] Step S100, setting a temperature monitoring point, identifying a cabinet model, and matching a rated temperature according to the cabinet model;

[0066] Step S200, acquiring historical data, performing a first screening on the historical data according to the rated temperature to obtain first data, setting variables and invariants according to the control variable method, performing a second screening on the first data according to the variables and the invariants to obtain second data, and calculating the historical temperature change rate according to the second data;

[0067] Step S300, establishing a first mapping relationship, a second mapping relationship and a third mapping relationship according to the historical temperature change rate;

[0068] Step S400, obtain current data, the current data including current server power, current ambient temperature and current wind pressure, calculate temperature margin according to the current data, construct temperature interval according to the temperature margin, determine the first cooling state according to the temperature interval, and send a temperature warning signal according to the first cooling state.

[0069] The present invention matches the rated temperature by setting temperature monitoring points and identifying the cabinet model, more accurately monitors the actual working state of the cabinet, thereby improving the accuracy of fault detection, and uses historical data for the first and second screenings to ensure that the screened data is representative and reliable. This method effectively excludes irrelevant data, reduces false positives and missed positives, and dynamically adjusts the early warning parameters according to the actual operation of the cabinet by calculating the historical temperature change rate and establishing a mapping relationship, making the early warning system more flexible and adaptable. By acquiring current data in real time and calculating the temperature margin, constructing a temperature range to judge the cooling state, potential cooling problems are discovered in advance and early warning signals are sent, thereby reducing the risk of faults. By making full use of historical data and current data, this method improves the utilization efficiency of data and provides data support for the maintenance and optimization of the cabinet cooling unit.

[0070] The method for setting the temperature monitoring point includes:

[0071] Acquire a three-dimensional model of the cabinet, and draw a mapping image of the surface of the server in the three-dimensional model of the cabinet on the surface of the cabinet, which is recorded as a first image, wherein the first image is represented as a mapping image of each surface of the server in the three-dimensional model of the cabinet on each surface of the cabinet;

[0072] Obtaining the intersection of the angle bisector of the first image and the geometric center of the corresponding server, and calculating the distance from the intersection of the angle bisector of the first image to the geometric center of the corresponding server, which is recorded as a first distance;

[0073] A first circle is drawn with the intersection of the angular bisectors of the first image as the center and the first distance as the radius, and the first value is set as the interval distance, where the interval distance is expressed as the length of the interval arc, and a temperature sensor is set at the intersection of the interval distance and the corresponding center of the circle, and the temperature sensor is set on the outer surface of the cabinet.

[0074] In the specific implementation, by obtaining the three-dimensional model of the cabinet and drawing the mapping image of the server surface on the cabinet surface, the position of the temperature monitoring point is accurately determined, ensuring that the layout of the temperature sensor is closely related to the heat distribution of the server, and improving the accuracy of temperature monitoring. By calculating the distance from the intersection of the angular bisector of the first image to the geometric center of the corresponding server, and drawing the first circle with this distance as the radius, the layout of the temperature sensor is optimized, ensuring that the sensor can cover the key thermal area, so as to more effectively monitor the temperature change, and setting the temperature sensor at the intersection of the interval distance and the corresponding center of the circle to ensure the continuity and uniformity of temperature monitoring. This method avoids monitoring blind spots and improves the efficiency of temperature monitoring.

[0075] An industrial camera is used to capture an image of the upper surface of the cabinet, the image of the upper surface of the cabinet being accompanied by a cabinet QR code, and the cabinet QR code is scanned to identify and obtain the cabinet model;

[0076] Retrieving a cabinet database, inputting the cabinet model into the cabinet database, and matching the rated temperature corresponding to the cabinet model;

[0077] Acquire historical data, and perform a first screening on the historical data according to the rated temperature to obtain first data;

[0078] The first data screening method includes: retrieving an operation database, inputting a cabinet model into the operation database, and matching historical data corresponding to the cabinet model, wherein the historical data includes historical server power, historical ambient temperature, historical wind pressure, and historical cabinet surface average temperature;

[0079] Compare the historical cabinet surface average temperature with the rated temperature, and when the historical cabinet surface average temperature is greater than the rated temperature, delete the corresponding historical data; when the historical cabinet surface average temperature is less than or equal to the rated temperature, set the corresponding historical data as the first data;

[0080] The rated temperature is expressed as the maximum value of the average temperature of the cabinet surface.

[0081] In the specific implementation, an industrial camera is used to capture the image of the upper surface of the cabinet and scan the QR code to automatically identify the cabinet model. This method reduces the errors and time consumption of manual identification. By retrieving the cabinet database and entering the cabinet model to match the corresponding rated temperature, the accuracy of temperature matching is ensured. This precise matching helps to improve the accuracy of temperature monitoring, obtain historical data and perform a first screening based on the rated temperature, delete historical data that does not meet the conditions, and retain valid first data. This screening method improves the availability and accuracy of data. By comparing the historical average cabinet surface temperature with the rated temperature, temperature anomalies can be detected earlier, thereby enhancing the fault warning performance and helping to take timely measures to prevent the occurrence of faults.

[0082] The second data screening method includes:

[0083] Calculate the historical temperature change rate according to the second data;

[0084] The historical data including the historical server power, the historical ambient temperature and the historical wind pressure are set as parameters to be set, any parameter to be set is set as a variable, the remaining parameters to be set are set as invariants, the variables and the corresponding invariants are set as reference groups, the reference groups include a first reference group, a second reference group and a third reference group, and reference group rules are obtained, the reference group rules include:

[0085] The first reference group sets the historical server power as a variable, and sets the historical ambient temperature and historical wind pressure as constants;

[0086] The second reference group sets the historical ambient temperature as a variable, and sets the historical server power and historical wind pressure as constants;

[0087] The third reference group sets the historical wind pressure as a variable, and sets the historical server power and the historical ambient temperature as constants;

[0088] Creating a classification model, classifying the first data according to the classification model to obtain second data, wherein the second data includes the second data of the first reference group, the second data of the second reference group, and the second data of the third reference group;

[0089] The classification model includes a first reference group classification model, a second reference group classification model and a third reference group classification model, and the first reference group second data, the second reference group second data, and the third reference group second data are obtained through the first reference group classification model, the second reference group classification model and the third reference group classification model respectively.

[0090] In specific implementation, by calculating the historical temperature change rate, the temperature data is analyzed more carefully, the trend and pattern of temperature change are identified, and the server power, ambient temperature and wind pressure in the historical data are set as variables and constants respectively to form different reference groups. This method helps to isolate the impact of a single variable, more accurately evaluate the contribution of each parameter to temperature change, create classification models for different reference groups, and conduct special analysis and classification on the impact of different variables, thereby improving the pertinence and accuracy of the model. The first data is classified by the classification model to obtain the second data. This method makes more effective use of historical data and provides more accurate data support for fault warning. Through effective data screening and fault warning, the reliability of the server cabinet cooling system is improved and system failures caused by temperature problems are reduced.

[0091] The method for constructing the classification model includes:

[0092] Constructing a data table and boundary rules, wherein the boundary rules include variable boundary rules and invariant boundary rules, wherein the variable boundary rules indicate that not all variables are equal, and the invariant boundary rules indicate that all invariants are equal;

[0093] Use SQL statements to set the first column of the data table to the corresponding variable boundary rule, and the second column of the data table to the corresponding invariant boundary rule to build a classification model;

[0094] The query function is started, and the queried data is classified into the corresponding first reference group second data, second reference group second data or third reference group second data.

[0095] The expression of the variable boundary rule is:

[0096] l1:x2-x1+x3-x2+...+x m -x m-1 ≠0

[0097] The expression of the invariant boundary rule is:

[0098]

[0099] Among them, l1 and l2 are the variable boundary rule and the corresponding invariant boundary rule, respectively, x m is a variable, y m and k m are the corresponding invariants respectively;

[0100] When x m When it belongs to the first reference group, x m Expressed as historical server power, y m and k m Expressed as historical ambient temperature and historical wind pressure;

[0101] When x m When it belongs to the second reference group, x m Historical ambient temperature, y m and k m Expressed as historical server power and historical wind pressure;

[0102] When x m When it belongs to the third reference group, x m Historical wind pressure, y m and k m Expressed as historical server power and historical ambient temperature.

[0103] In the specific implementation, by constructing data tables and boundary rules, clearly distinguishing variable boundary rules from invariant boundary rules, it is helpful to accurately identify and classify data during data processing. By using SQL statements, the first column of the data table is set to the corresponding variable boundary rule, and the second column is set to the corresponding invariant boundary rule, so as to efficiently build a classification model. The powerful function of SQL query can quickly process and classify large amounts of data. By starting the query function, the queried data is accurately classified into the corresponding second data of the first reference group, the second data of the second reference group, or the second data of the third reference group. This precise classification is helpful for subsequent data analysis and fault warning.

[0104] The historical average cabinet surface temperature corresponding to the second data is obtained, the historical average cabinet surface temperature corresponding to the second data is expressed as a time series value, and the interval time period of the historical average cabinet surface temperatures adjacent in time sequence is recorded as the first time period. The calculation method of the historical temperature change rate includes:

[0105] Calculate the difference between the historical average surface temperatures of the cabinets that are adjacent in time sequence, record it as the second difference, calculate the ratio of the second difference to the first time period, record it as the first ratio, and set the first ratio as the historical temperature change rate;

[0106] The first mapping relationship is represented by a mapping relationship between historical server power and historical temperature change rate, the second mapping relationship is represented by a mapping relationship between historical ambient temperature and historical temperature change rate, and the third mapping relationship is represented by a mapping relationship between historical wind pressure and historical temperature change rate;

[0107] The method for constructing the first mapping relationship, the second mapping relationship and the third mapping relationship includes:

[0108] Obtaining a historical temperature change rate corresponding to the first mapping relationship, the second mapping relationship, or the third mapping relationship, obtaining a value of a variable corresponding to the historical temperature change rate, and constructing a mapping relationship between the value of the variable and the historical temperature change rate;

[0109] When the variable corresponding to the first ratio is the historical server power, the corresponding mapping relationship is recorded as the first mapping relationship; when the variable corresponding to the first ratio is the historical ambient temperature, the corresponding mapping relationship is recorded as the second mapping relationship; when the variable corresponding to the first ratio is the historical wind pressure, the corresponding mapping relationship is recorded as the third mapping relationship.

[0110] In the specific implementation, the historical temperature change rate is accurately calculated by calculating the difference (second difference) of the average surface temperature of the historical cabinets adjacent in time sequence and the ratio to the first time period (first ratio). This method ensures the accuracy of the temperature change rate, provides a reliable data basis for subsequent analysis, and constructs a mapping relationship between historical server power, historical ambient temperature, historical wind pressure and historical temperature change rate (first mapping relationship, second mapping relationship and third mapping relationship), which helps to analyze the impact of different variables on temperature changes. This method of constructing mapping relationships improves the accuracy of fault warnings, and continuously improves the intelligence level and prediction accuracy of the warning mechanism by clarifying goals, introducing advanced monitoring technologies, establishing data analysis platforms, setting warning thresholds and rules, and other measures.

[0111] The second time period is set as a monitoring period, current data is obtained, and the current data includes current server power, current ambient temperature, and current wind pressure. The current average surface temperature of the cabinet is calculated. The calculation method of the current average surface temperature of the cabinet includes:

[0112] Obtain the temperature of each monitoring point at any time point, count the number of monitoring points, calculate the sum of the temperatures at each monitoring point, calculate the ratio of the sum of the temperatures at each monitoring point to the number of monitoring points, record it as a third ratio, and set the third ratio as the current average surface temperature of the cabinet;

[0113] Compare the current average cabinet surface temperature with the rated temperature to obtain a comparison result, wherein the comparison result includes that the current average cabinet surface temperature is greater than the rated temperature, and the current average cabinet surface temperature is less than or equal to the rated temperature, and perform a first operation according to the comparison result, wherein the first operation includes sending an early warning signal and analyzing current data;

[0114] When the average surface temperature of the current cabinet is greater than the rated temperature, a temperature warning signal is sent; otherwise, the current data is analyzed.

[0115] The calculation method of the temperature margin includes:

[0116] When the first operation is to analyze the current data, the current server power, the current ambient temperature and the current wind pressure are obtained, and the current server power, the current ambient temperature and the current wind pressure are respectively input into the first mapping relationship, the second mapping relationship and the third mapping relationship to obtain the corresponding historical temperature change rates, which are respectively recorded as the first current temperature change rate, the second temperature change rate and the third temperature change rate;

[0117] Obtain the current average surface temperature of the cabinet, calculate the product of the current average surface temperature of the cabinet and the first current temperature change rate, the second temperature change rate and the third temperature change rate respectively, record them as the first product, the second product and the third product, set the first product, the second product and the third product as the temperature margin, and select the maximum value of the temperature margin;

[0118] Calculate the sum of the rated temperature and the maximum value of the temperature margin, and record it as the first sum;

[0119] Calculate the difference between the rated temperature and the maximum value of the temperature margin, record it as the first difference, set the first sum as the upper limit of the temperature interval, set the first difference as the lower limit of the temperature interval, and construct the temperature interval;

[0120] Determine a first cooling state based on the current average cabinet surface temperature and the temperature range, wherein the first cooling state includes a normal state and a fault state, and send a temperature warning signal based on the first cooling state;

[0121] When the first cooling state is a fault state, a temperature warning signal is sent, and when the first cooling state is a normal state, no temperature warning signal is sent.

[0122] In the specific implementation, by setting the monitoring cycle (second time period) and obtaining the current data (current server power, current ambient temperature and current wind pressure) in real time, the real-time monitoring and analysis of the cabinet surface temperature is realized, and by obtaining the temperature of each monitoring point, counting the number of monitoring points, calculating the temperature and value and its ratio to the number of monitoring points (third ratio), the current average cabinet surface temperature is accurately calculated. This method ensures the accuracy of temperature calculation, compares the current average cabinet surface temperature with the rated temperature, and performs the first operation (sending an early warning signal or analyzing the current data) based on the comparison result, timely discovers temperature anomalies and takes measures, which helps prevent the occurrence of faults.

[0123] The present invention matches the rated temperature by setting temperature monitoring points and identifying the cabinet model, more accurately monitors the actual working state of the cabinet, thereby improving the accuracy of fault detection, and uses historical data for the first and second screenings to ensure that the screened data is representative and reliable. This method effectively excludes irrelevant data, reduces false positives and missed positives, and dynamically adjusts the early warning parameters according to the actual operation of the cabinet by calculating the historical temperature change rate and establishing a mapping relationship, making the early warning system more flexible and adaptable. By acquiring current data in real time and calculating the temperature margin, constructing a temperature range to judge the cooling state, potential cooling problems are discovered in advance and early warning signals are sent, thereby reducing the risk of faults. By making full use of historical data and current data, this method improves the utilization efficiency of data and provides data support for the maintenance and optimization of the cabinet cooling unit.

[0124] It should be understood by those skilled in the art that the embodiments of the present invention may be provided as methods, systems or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program codes. Wherein, the storage medium is implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Red-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, disk or optical disk. These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0125] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions of the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A server cabinet cooling unit failure early warning method, characterized in that: The following steps are involved: Step S100, setting a temperature monitoring point, identifying a cabinet model, and matching a rated temperature according to the cabinet model; Step S200, acquiring historical data, performing a first screening on the historical data according to the rated temperature to obtain first data, setting variables and invariants according to the control variable method, performing a second screening on the first data according to the variables and the invariants to obtain second data, and calculating the historical temperature change rate according to the second data; Step S300, establishing a first mapping relationship, a second mapping relationship and a third mapping relationship according to the historical temperature change rate; Step S400, obtain current data, the current data including current server power, current ambient temperature and current wind pressure, calculate temperature margin according to the current data, construct temperature interval according to the temperature margin, determine the first cooling state according to the temperature interval, and send a temperature warning signal according to the first cooling state.

2. A server cabinet cooling unit failure early warning method according to claim 1, characterized in that: The method for setting the temperature monitoring point includes: Acquire a three-dimensional model of the cabinet, and draw a mapping image of the surface of the server in the three-dimensional model of the cabinet on the surface of the cabinet, which is recorded as a first image, wherein the first image is represented as a mapping image of each surface of the server in the three-dimensional model of the cabinet on each surface of the cabinet; Obtaining the intersection of the angle bisector of the first image and the geometric center of the corresponding server, and calculating the distance from the intersection of the angle bisector of the first image to the geometric center of the corresponding server, which is recorded as a first distance; A first circle is drawn with the intersection of the angular bisectors of the first image as the center and the first distance as the radius, and the first value is set as the interval distance, where the interval distance is expressed as the length of the interval arc, and a temperature sensor is set at the intersection of the interval distance and the corresponding center of the circle, and the temperature sensor is set on the outer surface of the cabinet.

3. A server cabinet cooling unit failure early warning method according to claim 1, characterized in that: An industrial camera is used to capture an image of the upper surface of the cabinet, the image of the upper surface of the cabinet being accompanied by a cabinet QR code, and the cabinet QR code is scanned to identify and obtain the cabinet model; Retrieving a cabinet database, inputting the cabinet model into the cabinet database, and matching the rated temperature corresponding to the cabinet model; Acquire historical data, and perform a first screening on the historical data according to the rated temperature to obtain first data; The first data screening method includes: retrieving an operation database, inputting a cabinet model into the operation database, and matching historical data corresponding to the cabinet model, wherein the historical data includes historical server power, historical ambient temperature, historical wind pressure, and historical cabinet surface average temperature; Compare the historical cabinet surface average temperature with the rated temperature, and when the historical cabinet surface average temperature is greater than the rated temperature, delete the corresponding historical data; when the historical cabinet surface average temperature is less than or equal to the rated temperature, set the corresponding historical data as the first data; The rated temperature is expressed as the maximum value of the average temperature of the cabinet surface.

4. A server cabinet cooling unit failure early warning method according to claim 1, characterized in that: The second data screening method includes: Calculate the historical temperature change rate according to the second data; The historical data including the historical server power, the historical ambient temperature and the historical wind pressure are set as parameters to be set, any parameter to be set is set as a variable, the remaining parameters to be set are set as invariants, the variables and the corresponding invariants are set as reference groups, the reference groups include a first reference group, a second reference group and a third reference group, and reference group rules are obtained, the reference group rules include: The first reference group sets the historical server power as a variable, and sets the historical ambient temperature and historical wind pressure as constants; The second reference group sets the historical ambient temperature as a variable, and sets the historical server power and historical wind pressure as constants; The third reference group sets the historical wind pressure as a variable, and sets the historical server power and the historical ambient temperature as constants; Creating a classification model, classifying the first data according to the classification model to obtain second data, wherein the second data includes the second data of the first reference group, the second data of the second reference group, and the second data of the third reference group; The classification model includes a first reference group classification model, a second reference group classification model and a third reference group classification model, and the first reference group second data, the second reference group second data, and the third reference group second data are obtained through the first reference group classification model, the second reference group classification model and the third reference group classification model respectively.

5. A server cabinet cooling unit failure early warning method as claimed in claim 4, characterized in that: The method for constructing the classification model includes: Constructing a data table and boundary rules, wherein the boundary rules include variable boundary rules and invariant boundary rules, wherein the variable boundary rules indicate that not all variables are equal, and the invariant boundary rules indicate that all invariants are equal; Use SQL statements to set the first column of the data table to the corresponding variable boundary rule, and the second column of the data table to the corresponding invariant boundary rule to build a classification model; The query function is started, and the queried data is classified into the corresponding first reference group second data, second reference group second data or third reference group second data.

6. A server cabinet cooling unit failure early warning method as claimed in claim 5, characterized in that: The expression of the variable boundary rule is: l1:x2-x1+x3-x2+...+x m -x m-1 ≠0 The expression of the invariant boundary rule is: Among them, l1 and l2 are the variable boundary rule and the corresponding invariant boundary rule, respectively, x m is a variable, y m and k m are the corresponding invariants respectively; When x m When it belongs to the first reference group, x m Expressed as historical server power, y m and k m Expressed as historical ambient temperature and historical wind pressure; When x m When it belongs to the second reference group, x m Historical ambient temperature, y m and k m Expressed as historical server power and historical wind pressure; When x m When it belongs to the third reference group, x m Historical wind pressure, y m and k m Expressed as historical server power and historical ambient temperature.

7. A server cabinet cooling unit failure early warning method according to claim 1, characterized in that: The historical average cabinet surface temperature corresponding to the second data is obtained, the historical average cabinet surface temperature corresponding to the second data is expressed as a time series value, and the interval time period of the historical average cabinet surface temperatures adjacent in time sequence is recorded as the first time period. The calculation method of the historical temperature change rate includes: Calculate the difference between the historical average surface temperatures of the cabinets that are adjacent in time sequence, record it as the second difference, calculate the ratio of the second difference to the first time period, record it as the first ratio, and set the first ratio as the historical temperature change rate; The first mapping relationship is represented by a mapping relationship between historical server power and historical temperature change rate, the second mapping relationship is represented by a mapping relationship between historical ambient temperature and historical temperature change rate, and the third mapping relationship is represented by a mapping relationship between historical wind pressure and historical temperature change rate; The method for constructing the first mapping relationship, the second mapping relationship and the third mapping relationship includes: Obtaining a historical temperature change rate corresponding to the first mapping relationship, the second mapping relationship, or the third mapping relationship, obtaining a value of a variable corresponding to the historical temperature change rate, and constructing a mapping relationship between the value of the variable and the historical temperature change rate; When the variable corresponding to the first ratio is the historical server power, the corresponding mapping relationship is recorded as the first mapping relationship; when the variable corresponding to the first ratio is the historical ambient temperature, the corresponding mapping relationship is recorded as the second mapping relationship; when the variable corresponding to the first ratio is the historical wind pressure, the corresponding mapping relationship is recorded as the third mapping relationship.

8. A server cabinet cooling unit failure early warning method according to claim 1, characterized in that: The second time period is set as a monitoring period, current data is obtained, and the current data includes current server power, current ambient temperature, and current wind pressure. The current average surface temperature of the cabinet is calculated. The calculation method of the current average surface temperature of the cabinet includes: Obtain the temperature of each monitoring point at any time point, count the number of monitoring points, calculate the sum of the temperatures at each monitoring point, calculate the ratio of the sum of the temperatures at each monitoring point to the number of monitoring points, record it as a third ratio, and set the third ratio as the current average surface temperature of the cabinet; Compare the current average cabinet surface temperature with the rated temperature to obtain a comparison result, wherein the comparison result includes that the current average cabinet surface temperature is greater than the rated temperature, and the current average cabinet surface temperature is less than or equal to the rated temperature, and perform a first operation according to the comparison result, wherein the first operation includes sending an early warning signal and analyzing current data; When the current average cabinet surface temperature is greater than the rated temperature, a temperature warning signal is sent; otherwise, the current data is analyzed.

9. A server cabinet cooling unit failure early warning method according to claim 1, characterized in that: The temperature margin calculation method includes: When the first operation is to analyze the current data, the current server power, the current ambient temperature and the current wind pressure are obtained, and the current server power, the current ambient temperature and the current wind pressure are respectively input into the first mapping relationship, the second mapping relationship and the third mapping relationship to obtain the corresponding historical temperature change rates, which are respectively recorded as the first current temperature change rate, the second temperature change rate and the third temperature change rate; Obtain the current average surface temperature of the cabinet, calculate the product of the current average surface temperature of the cabinet and the first current temperature change rate, the second temperature change rate and the third temperature change rate respectively, record them as the first product, the second product and the third product, set the first product, the second product and the third product as the temperature margin, and select the maximum value of the temperature margin; Calculate the sum of the rated temperature and the maximum value of the temperature margin, and record it as the first sum; Calculate the difference between the rated temperature and the maximum value of the temperature margin, record it as the first difference, set the first sum as the upper limit of the temperature interval, set the first difference as the lower limit of the temperature interval, and construct the temperature interval; Determine a first cooling state based on the current average cabinet surface temperature and the temperature range, wherein the first cooling state includes a normal state and a fault state, and send a temperature warning signal based on the first cooling state; When the first cooling state is a fault state, a temperature warning signal is sent, and when the first cooling state is a normal state, no temperature warning signal is sent.

10. A server cabinet cooling unit fault early warning system, the system is used to execute the server cabinet cooling unit fault early warning method according to claim 1, characterized in that: It includes collection module, analysis module and early warning module; The acquisition module is used to set a temperature monitoring point, identify a cabinet model, match a rated temperature according to the cabinet model, obtain historical data, perform a first screening on the historical data according to the rated temperature to obtain first data, set variables and invariants according to a control variable method, and perform a second screening on the first data according to the variables and invariants to obtain second data; The analysis module calculates the historical temperature change rate according to the second data, and establishes a first mapping relationship, a second mapping relationship and a third mapping relationship according to the historical temperature change rate; The early warning module obtains current data, which includes current server power, current ambient temperature and current wind pressure, calculates a temperature margin according to the current data, constructs a temperature interval according to the temperature margin, determines a first cooling state according to the temperature interval, and sends a temperature early warning signal according to the first cooling state.

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