CN114942952A - Equipment state monitoring system and method for submarine data center - Google Patents

Equipment state monitoring system and method for submarine data center Download PDF

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CN114942952A
CN114942952A CN202210857736.5A CN202210857736A CN114942952A CN 114942952 A CN114942952 A CN 114942952A CN 202210857736 A CN202210857736 A CN 202210857736A CN 114942952 A CN114942952 A CN 114942952A
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赵勃阳
苏洋
蒲定
李耀兵
傅荣荣
苏雨田
程伟锋
魏法军
申万秋
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Shenzhen Hailanyun Data Center Technology Co ltd
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Abstract

The invention discloses an equipment state monitoring system and method for a submarine data center, wherein a prediction analysis module predicts the trend of the change of the internal temperature of a data cabin along with time according to analysis results and external state information of the data cabin, which correspond to a server life analysis module and a shell heat conductivity coefficient analysis module respectively, so as to predict the service life of an internal server in the current data cabin. The invention relates to the technical field of equipment state supervision, which realizes the omnibearing monitoring of a data cabin by considering the inside, the outside and the shell of the data cabin; meanwhile, the biological fouling degree of the seawater on the data cabin and the influence of the biological fouling degree on the heat conductivity coefficients of a water conveying pipeline and an internal heat exchanger and an external heat exchanger on the shell of the data cabin are considered; and then accurately predicting the temperature in the data cabin of the submarine data center at different time by combining historical data, predicting the service life of a server in the data cabin, and realizing effective monitoring and management of the data cabin.

Description

Equipment state monitoring system and method for submarine data center
Technical Field
The invention relates to the technical field of equipment state supervision, in particular to an equipment state supervision system and method for a submarine data center.
Background
The data center is a global cooperative specific equipment network and is in a more important position in the aspects of information transmission, calculation and storage on an internet infrastructure; usually, people will centralize a large number of servers to form a data center, which is convenient for operation, maintenance and management. However, the server can emit a large amount of heat during operation, and in order to ensure normal operation of the server, people usually adopt a heat dissipation device to cool the server, and the method needs to consume more electric energy, is not favorable for saving cost and indirectly causes burden to the environment; therefore, it is thought to construct a subsea data center, which effectively utilizes the advantage of low subsea water temperature, and can improve the heat dissipation efficiency of the data center and reduce power consumption.
The existing equipment state monitoring system for the submarine data center only monitors the sealing performance, the temperature of a data cabin and the working state of an internal server of the submarine data center, does not consider the influence of the seawater environment on the submarine data center, particularly the influence of biofouling in seawater on a submarine data center shell and the heat dissipation efficiency, and further has the great defect.
Disclosure of Invention
The present invention provides a system and a method for monitoring equipment status in a subsea data center, so as to solve the above problems in the background art.
In order to solve the technical problems, the invention provides the following technical scheme: a method of equipment status supervision for a subsea data center, the method comprising the steps of:
s1, acquiring internal state information, shell state information and external state information of a data cabin of the submarine data center every first unit time through a sensor, wherein the first unit time is a preset constant in a database;
s2, calculating the average value of the corresponding service lives of the internal servers in the historical data under different internal state information, acquiring the standard service life corresponding to the internal server in the data cabin, calculating the difference value between the standard service life corresponding to the internal server in the data cabin and the average value of the corresponding service life of the internal servers in the historical data under different internal state information, recording the difference value as the service life deviation value of the internal server in the historical data under different internal state information of the data cabin, analyzing the relationship between the service life deviation value of the internal server in the data cabin and the internal state information, wherein the standard service life of the internal server is the theoretical service life corresponding to the corresponding server prefabricated in the database during production, and the theoretical service life is a fixed value;
s3, analyzing the heat conductivity coefficients and time-varying functions of the heat conductivity coefficients corresponding to the shell water feeding and conveying pipeline and the internal and external heat exchangers of the data cabin at different time according to the shell state information of the data cabin;
s4, predicting the trend of the internal temperature of the data cabin changing along with time according to the analysis result and the external state information of the data cabin respectively corresponding to the S2 and the S3, and further predicting the service life of an internal server in the current data cabin;
s5, monitoring the prediction result of the service life of the internal server in the current data cabin in real time, and acquiring the actual service life of the internal server, wherein the actual service life of the internal server is the time length between the time when the internal server of the data cabin is launched from the data cabin to the current time,
when the prediction result of the service life of the internal server is greater than or equal to a first threshold value, judging that the data cabin is normal,
when the prediction result of the service life of the internal server is smaller than a first threshold value, judging that the data cabin is abnormal, and overhauling or replacing a water conveying pipeline and an internal and external heat exchanger on the shell of the data cabin;
when the prediction result of the service life of the internal server is larger than or equal to the product of the actual service life of the internal server and the second threshold value, the internal server is judged to be normal,
when the prediction result of the service life of the internal server is smaller than the product of the actual service life of the internal server and the second threshold value, the internal server is judged to be abnormal, the internal server in the data cabin is replaced and overhauled,
the first threshold and the second threshold are constants preset in a database.
Further, the data bay internal state information of the subsea data center in S1 includes: the temperature in the data cabin T1 when the data cabin is submerged for time T2 t2 The number of the working servers Nt2 and the heat R generated by each working server in unit time are the same as the heat generated by each working server in working time by default, and the R is obtained by database query;
the shell state information of the data compartment comprises: the state picture of the water supply pipeline of the shell and the internal and external heat exchangers in the seawater polluted by the organisms;
the external state information of the data compartment comprises: the data cabin is launched for time T2, and the temperature of the seawater around the data cabin is T2 t2 。
When the internal state information of the data cabin of the submarine data center is acquired, the internal state information, the external state information and the shell of the data cabin are considered, so that the data cabin is monitored in an all-round mode; the analysis of the shell state information is to consider the biological fouling degree of seawater on the data cabin, and further provide data reference for analyzing the heat conductivity coefficients of a water conveying pipeline and an internal heat exchanger and an external heat exchanger on the shell of the data cabin in the subsequent process; the heat conductivity coefficient directly influences the heat dissipation efficiency of the submarine data center, and further directly threatens the service life of the server in the data cabin.
Further, the method for analyzing the relationship between the life deviation value of the data bay internal server and the internal state information in S2 includes the following steps:
s2.1, acquiring the average value of the corresponding service life of the internal server under the condition that the temperature in the data cabin in the historical data is not changed, and recording the average value of the corresponding service life of the internal server as QT under the condition that the temperature is always T;
s2.2, obtaining a service life deviation value of the internal server under the state that the temperature in the data cabin is always T, recording the service life deviation value as PT, wherein PT = B-QT, and B represents the corresponding standard service life of the internal server of the data cabin;
s2.3, acquiring values corresponding to PT when T is different in the database, and constructing corresponding coordinate points (T, PT);
s2.4, obtaining a relation function between the service life deviation value of the server in the data cabin and the internal state information according to the coordinate points constructed in the S2.3 and a function model prefabricated in a database, and recording the relation function as G (T, PT), wherein the function model prefabricated in the database is a piecewise function and is recorded as G (T, PT)
Figure 699105DEST_PATH_IMAGE001
Wherein, T3 represents the working environment temperature corresponding to the standard life of the server inside the data cabin, a1 is a first coefficient, a2 is a second coefficient, c1 is a third coefficient, c4 is a fourth coefficient, the value of b1 is equal to-a 1 tan h (T3-c 1), the value of b2 is equal to-a 2 tan h (T3-c 2);
in the process of obtaining G (T, PT), performing linear fitting on coordinate points constructed by S2.3 according to a function model prefabricated in a database to obtain a plurality of fitting curves, wherein a first coefficient, a second coefficient, a third coefficient and a fourth coefficient corresponding to different fitting curves are different, the sum of the distance between each fitting curve and each coordinate point constructed by S2.3 is calculated, and the fitting curve with the minimum distance sum is marked as G (T, PT);
s2.5, obtaining the instantaneous deviation rate of the service life of the internal server under the state that the temperature of the data cabin is T, recording the instantaneous deviation rate as VLT,
Figure 423479DEST_PATH_IMAGE002
s2.6, combining the functions obtained from S2.4 and S2.5 to obtain a relation function G1 (T, VLT) between the service life instantaneous deviation rate of the server in the data cabin and the temperature in the data cabin,
Figure 204353DEST_PATH_IMAGE003
，
wherein B is greater than 0 and G (T, PT) is greater than or equal to 0.
In the process of analyzing the relationship between the service life deviation value and the internal state information of the server in the data cabin, the invention acquires the relationship function between the service life deviation value and the internal state information of the server in the data cabin, which is used for further analyzing the relationship between the service life of the server and the temperature of the working environment, so as to be convenient for analyzing the influence of different internal temperatures of the data cabin on the service life of the server in the subsequent process, and simultaneously acquires G (T, PT), and also considers the influence of the external seawater temperature and the number of the servers in the working state on the submarine data center, so that the temperature of the working environment of the server in the data cabin in the submarine data center is not fixed and is dynamically changed, further needs to dynamically analyze the comprehensive influence of the temperature in the data cabin at different times on the service life of the server, and further acquires the relationship function G1 (T, VLT).
Further, the method for analyzing the thermal conductivity and the function of the thermal conductivity changing with time of the water supply pipeline of the shell of the data cabin and the internal and external heat exchangers at different times in S3 includes the following steps:
s3.1, obtaining state pictures of biofouling of a shell water feeding and conveying pipeline and an internal and external heat exchanger in seawater in shell state information of the data cabin;
s3.2, performing data identification on the picture obtained in the S3.1, extracting abnormal area areas in the shell picture of the initial data cabin and the shell picture of the shell in the picture, calculating the ratio of the abnormal area areas of the shell to the total area of the shell, and recording the ratio as W1, wherein the shell picture of the initial data cabin is the shell picture after the data cabin is built and when the data cabin is not launched;
s3.3, performing data identification on the picture acquired in the S3.1, acquiring the maximum area occupied by single fouling organisms in the picture, recording as W2,
the identification of the single fouling organism is obtained by extracting local pictures in the pictures obtained in S3.1 and comparing the local pictures with samples in a database respectively, zooming the sample pictures in the comparison process to ensure that the zoomed pictures are overlapped with the local pictures in the obtained pictures, counting pixel points with the same positions and the same corresponding pixel values in an overlapped area, dividing the total number of the counted pixel points by the total number of the pixel points in the corresponding local pictures of the obtained pictures, marking the obtained quotient as the similarity of the corresponding local pictures and the corresponding samples, and selecting the fouling organism in the sample with the maximum similarity as the identification result of the corresponding fouling organism in the corresponding local pictures;
s3.4, when the data corresponding to the shell in the database prefabricated data are W1 and W2, the heat conductivity coefficients corresponding to the shell water feeding and conveying pipeline and the internal and external heat exchangers are recorded as DR, the data cabin water draining time length x corresponding to DR is obtained, and a coordinate point (x, DR) is constructed;
and S3.5, obtaining corresponding coordinate points (x, DR) when x in the historical data is different values, and fitting to obtain a function DR = FD (x) of the thermal conductivity along with time by combining a linear regression equation formula.
In the process of analyzing the heat conductivity coefficients and the time-varying heat conductivity coefficients corresponding to different time of the shell water feeding conveying pipeline and the inner and outer heat exchangers of the data cabin, the invention considers that not only the temperature difference inside and outside the data cabin but also the heat conductivity coefficients of the shell water feeding conveying pipeline and the inner and outer heat exchangers are considered in the heat transfer process of the data cabin, and the heat conductivity coefficients are influenced by two factors of the heat transfer property of the material and the contact area in the heat transfer process, wherein in seawater, the contact area of the shell water feeding conveying pipeline and the inner and outer heat exchangers in the heat transfer process is defaulted to be unchanged, the heat transfer property of the material is influenced by the degree of biofouling, and the more serious the biofouling, the larger the corresponding influence on the heat transfer property of the material is, and the smaller the corresponding heat conductivity coefficient is.
Further, the method for predicting the trend of the internal temperature of the data cabin changing along with the time in S4 includes the following steps:
s4.1, obtaining the temperature T1 in the data cabin when the launching time of the data cabin is T2 t2 The number of operating servers Nt2 and the heat R generated per unit time by each operating server;
s4.2, obtaining the temperature T2 of the seawater around the data cabin when the launching time of the data cabin is T2 t2 ;
S4.3, obtaining a fitting function of the thermal conductivity coefficient along with time DR = FD (x);
s4.4, obtaining T1 t2 Corresponding instantaneous rate of change VT1 of the internal temperature of the data cabin t2 ，
Figure 392889DEST_PATH_IMAGE004
U is the heat required by the temperature in the data cabin when the temperature rises once, and is a preset constant in the database;
s4.5, predicting a trend function WQ (t) of the internal temperature of the data cabin along with the change of time, wherein the WQ (t) is a piecewise function,
the time when the sensor last acquired the temperature inside the data compartment is denoted T0 and the corresponding temperature is denoted T0 t0 Then the trend function WQ (t) of the internal temperature of the data cabin changing along with the time is corresponding to the function in the time interval when t is larger than t0
Figure 607707DEST_PATH_IMAGE005
Wherein, VT0 t0 Representation T0 t0 A corresponding instantaneous rate of change of the internal temperature of the data compartment;
and when t is less than or equal to t0, the sensor acquires a function of the corresponding straight line of the internal temperature of any two adjacent data cabins, and the function is a trend function of the internal temperature of the data cabins changing along with time in the corresponding time interval.
The number of the servers and R of the working states in the data cabin at different times are obtained, and the purpose is to calculate the heat production speed Nt 2R in the data cabin at different times; the heat conductivity coefficients of the data cabins at different times, the temperatures in the data cabins and the ambient seawater temperatures are obtained for calculating the temperatures of the data cabins at different timesHeat dissipation rate fd (x) (T1) t2 -T2 t2 ）。
Further, the method for predicting the service life of the internal server in the current data bay in S4 includes the following steps:
s4-1, obtaining a relation function G1 (T, VLT) between the instantaneous deviation rate of the service life of the server in the data cabin and the temperature in the data cabin,
Figure 944011DEST_PATH_IMAGE006
，
wherein B > 0 and G (T, PT) ≥ 0
S4-2, acquiring a predicted trend function WQ (t) of the internal temperature of the data cabin changing along with time;
s4-3, obtaining a relation function G1[ WQ (t) and VLWQ (t) between the instantaneous deviation rate of the service life of the server in the data cabin and the time, wherein VLWQ (t) represents the instantaneous deviation rate of the service life of the server in the data cabin corresponding to the time when the water drainage time length of the data cabin is t;
s4-4, obtaining a predicted value ZSM of the service life of the internal server in the current data cabin, wherein the ZSM is a solution of a first equation, and the first equation is
Figure 403942DEST_PATH_IMAGE007
Wherein F3 (ZSM) is the integral of the lifetime deviation, and F3 (ZSM) = (B-ZSM)/B.
In the process of predicting the service life of the internal server in the current data cabin, the invention sets a first equation for solving
Figure 446984DEST_PATH_IMAGE007
The method is used for simultaneously obtaining the predicted value ZSM of the service life of the internal server in the current data cabin and the corresponding service life deviation integral quantity F3 (ZSM) so as to facilitate the management of the data cabin in the subsequent process, and both ZSM and F3 (ZSM) can provide data reference for the management of the data cabin in the subsequent process.
An equipment status supervision system for a subsea data center, the system comprising the following modules:
the system comprises a data cabin information acquisition module, a data cabin information acquisition module and a data center, wherein the data cabin information acquisition module acquires data cabin internal state information, shell state information and external state information of a submarine data center at intervals of first unit time through a sensor, and the first unit time is a preset constant in a database;
the server life analysis module calculates the average value of the corresponding lives of the internal servers under different internal state information of the data cabin in the historical data, acquires the standard life corresponding to the internal server of the data cabin, calculates the difference between the standard life corresponding to the internal server of the data cabin and the average value of the corresponding lives of the internal servers under different internal state information of the data cabin in the historical data, and records the difference as the life deviation value of the internal server under different internal state information of the data cabin in the historical data, and analyzes the relationship between the life deviation value of the internal server of the data cabin and the internal state information, wherein the standard life of the internal server is the corresponding theoretical life of the corresponding server prefabricated in the database during production, and the theoretical life is a fixed value;
the shell heat conductivity coefficient analysis module analyzes the heat conductivity coefficients and time-varying functions of the shell water feeding and conveying pipelines and the inner and outer heat exchangers of the data cabin at different time according to the shell state information of the data cabin;
the prediction analysis module predicts the trend of the internal temperature of the data cabin changing along with time according to the analysis result and the external state information of the data cabin respectively corresponding to the server service life analysis module and the shell heat conductivity coefficient analysis module, and then predicts the service life of the internal server in the current data cabin;
and the equipment management module monitors the prediction result of the service life of the internal server in the current data cabin in real time and acquires the actual service life of the internal server, wherein the actual service life of the internal server is the time length from the time when the internal server of the data cabin is drained to the current time from the data cabin, and the data cabin is managed according to the prediction result of the service life of the internal server of the data cabin and the actual service life of the internal server of the data cabin.
Further, in the data bay information obtaining module, the data bay internal state information of the subsea data center includes: the temperature in the data cabin T1 when the data cabin is submerged for time T2 t2 The number Nt2 of the working servers and the heat R generated by each working server in unit time are defaulted to be the same when each server works, and the heat R is obtained through database query;
the shell state information of the data cabin comprises: the state picture of the water supply pipeline of the shell and the internal and external heat exchangers in the seawater polluted by the organisms;
the external state information of the data compartment comprises: the data cabin is launched for time T2, and the temperature of the seawater around the data cabin is T2 t2 。
Furthermore, in the process of managing the data cabin by the equipment management module according to the prediction result of the service life of the server in the data cabin and the actual service life of the server in the data cabin,
when the prediction result of the service life of the internal server is greater than or equal to a first threshold value, judging that the data cabin is normal,
when the prediction result of the service life of the internal server is smaller than a first threshold value, judging that the data cabin is abnormal, and overhauling or replacing a water conveying pipeline and an internal and external heat exchanger on the shell of the data cabin;
when the prediction result of the service life of the internal server is larger than or equal to the product of the actual service life of the internal server and the second threshold value, the internal server is judged to be normal,
when the prediction result of the service life of the internal server is smaller than the product of the actual service life of the internal server and the second threshold value, the internal server is judged to be abnormal, the internal server in the data cabin is replaced and overhauled,
the first threshold and the second threshold are constants preset in a database.
Compared with the prior art, the invention has the following beneficial effects: the invention considers the interior, the exterior and the shell of the data cabin to realize the omnibearing monitoring of the data cabin; meanwhile, the biological fouling degree of the seawater on the data cabin and the influence of the biological fouling degree on the heat conductivity coefficients of a water conveying pipeline and an internal heat exchanger and an external heat exchanger on the shell of the data cabin are considered; and then accurately estimating the temperature in the data cabin of the submarine data center at different time by combining historical data, predicting the service life of a server in the data cabin, and realizing effective monitoring and management of the data cabin.
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The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic diagram of a system for monitoring status of equipment in a subsea data center according to the present invention;
fig. 2 is a flow chart of an equipment status supervision method for a subsea data center according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-2, the present invention provides a technical solution: a method of equipment status supervision for a subsea data center, the method comprising the steps of:
s1, acquiring internal state information, shell state information and external state information of a data cabin of the submarine data center every first unit time through a sensor, wherein the first unit time is a preset constant in a database;
s2, calculating the average value of the corresponding service lives of the internal servers in the historical data under different internal state information, acquiring the standard service life corresponding to the internal server in the data cabin, calculating the difference value between the standard service life corresponding to the internal server in the data cabin and the average value of the corresponding service life of the internal servers in the historical data under different internal state information, recording the difference value as the service life deviation value of the internal server in the historical data under different internal state information of the data cabin, analyzing the relationship between the service life deviation value of the internal server in the data cabin and the internal state information, wherein the standard service life of the internal server is the theoretical service life corresponding to the corresponding server prefabricated in the database during production, and the theoretical service life is a fixed value;
s3, analyzing the heat conductivity coefficients and time-varying functions of the heat conductivity coefficients corresponding to the shell water feeding and conveying pipeline and the internal and external heat exchangers of the data cabin at different time according to the shell state information of the data cabin;
s4, predicting the trend of the internal temperature of the data cabin changing along with time according to the analysis result and the external state information of the data cabin respectively corresponding to the S2 and the S3, and further predicting the service life of an internal server in the current data cabin;
s5, monitoring the prediction result of the service life of the internal server in the current data cabin in real time, and acquiring the actual service life of the internal server, wherein the actual service life of the internal server is the time length between the time when the internal server of the data cabin is launched from the data cabin to the current time,
when the prediction result of the service life of the internal server is greater than or equal to a first threshold value, judging that the data cabin is normal,
when the prediction result of the service life of the internal server is smaller than a first threshold value, judging that the data cabin is abnormal, and overhauling or replacing a water conveying pipeline and an internal and external heat exchanger on the shell of the data cabin;
when the prediction result of the service life of the internal server is larger than or equal to the product of the actual service life of the internal server and the second threshold value, the internal server is judged to be normal,
when the prediction result of the service life of the internal server is smaller than the product of the actual service life of the internal server and the second threshold value, judging that the internal server is abnormal, replacing and overhauling the internal server of the data cabin,
the first threshold and the second threshold are constants preset in a database.
The data cabin internal state information of the submarine data center in S1 includes: the temperature in the data cabin T1 when the data cabin is submerged for time T2 t2 Clothes for workThe number of the servers Nt2 and the heat R generated by each working server in unit time are the same as the heat generated by each working server in default, and the heat R is obtained by database query;
the shell state information of the data compartment comprises: the state picture of the water supply pipeline of the shell and the internal and external heat exchangers in the seawater polluted by the organisms;
the external state information of the data compartment comprises: the data cabin is launched for time T2, and the temperature of the seawater around the data cabin is T2 t2 。
The method for analyzing the relationship between the life deviation value of the server inside the data compartment and the internal state information in S2 includes the following steps:
s2.1, acquiring the average value of the corresponding service life of the internal server under the condition that the temperature in the data cabin in the historical data is not changed, and recording the average value of the corresponding service life of the internal server as QT under the condition that the temperature is always T;
s2.2, obtaining a service life deviation value of the internal server under the condition that the temperature in the data cabin is always T, and recording the service life deviation value as PT, wherein PT = B-QT, and B represents the corresponding standard service life of the internal server of the data cabin;
s2.3, acquiring values corresponding to PT when T is different in the database, and constructing corresponding coordinate points (T, PT);
s2.4, obtaining a relation function between the service life deviation value of the server in the data cabin and the internal state information according to the coordinate points constructed in the S2.3 and a function model prefabricated in a database, and recording the relation function as G (T, PT), wherein the function model prefabricated in the database is a piecewise function and is recorded as G (T, PT)
Figure 458803DEST_PATH_IMAGE001
Wherein, T3 represents the working environment temperature corresponding to the standard life of the server inside the data cabin, a1 is a first coefficient, a2 is a second coefficient, c1 is a third coefficient, c4 is a fourth coefficient, the value of b1 is equal to-a 1 tan h (T3-c 1), the value of b2 is equal to-a 2 tan h (T3-c 2);
in the process of obtaining G (T, PT), performing linear fitting on coordinate points constructed by S2.3 according to a function model prefabricated in a database to obtain a plurality of fitting curves, wherein a first coefficient, a second coefficient, a third coefficient and a fourth coefficient corresponding to different fitting curves are different, the sum of the distance between each fitting curve and each coordinate point constructed by S2.3 is calculated, and the fitting curve with the minimum distance sum is marked as G (T, PT);
s2.5, obtaining the instantaneous deviation rate of the service life of the internal server under the state that the temperature of the data cabin is T, recording the instantaneous deviation rate as VLT,
Figure 157768DEST_PATH_IMAGE002
s2.6, combining the functions obtained from S2.4 and S2.5 to obtain a relation function G1 (T, VLT) between the service life instantaneous deviation rate of the server in the data cabin and the temperature in the data cabin,
Figure 546024DEST_PATH_IMAGE006
，
wherein B is greater than 0 and G (T, PT) is greater than or equal to 0.
The method for analyzing the heat conductivity coefficients of the shell water feeding and conveying pipeline of the data cabin and the inner and outer heat exchangers at different time and the function of the change of the heat conductivity coefficients along with the time in the S3 comprises the following steps:
s3.1, obtaining state pictures of biofouling of a shell water feeding and conveying pipeline and an internal and external heat exchanger in seawater in shell state information of the data cabin;
s3.2, performing data identification on the picture obtained in the S3.1, extracting abnormal area areas in the shell part in the picture and the shell picture of the initial data cabin, calculating the ratio of the abnormal area areas of the shell to the total area of the shell, and recording the ratio as W1, wherein the shell picture of the initial data cabin is the shell picture after the data cabin is built and when water is not launched;
s3.3, performing data identification on the picture acquired in the S3.1, acquiring the maximum area occupied by single fouling organisms in the picture, recording as W2,
the identification of the single fouling organism is obtained by extracting local pictures in the pictures obtained in S3.1 and comparing the local pictures with samples in a database respectively, zooming the sample pictures in the comparison process to ensure that the zoomed pictures are overlapped with the local pictures in the obtained pictures, counting pixel points with the same positions and the same corresponding pixel values in an overlapped area, dividing the total number of the counted pixel points by the total number of the pixel points in the corresponding local pictures of the obtained pictures, marking the obtained quotient as the similarity of the corresponding local pictures and the corresponding samples, and selecting the fouling organism in the sample with the maximum similarity as the identification result of the corresponding fouling organism in the corresponding local pictures;
s3.4, when the data corresponding to the shell in the database prefabricated data are W1 and W2, the heat conductivity coefficients corresponding to the shell water feeding and conveying pipeline and the internal and external heat exchangers are recorded as DR, the data cabin water draining time length x corresponding to DR is obtained, and a coordinate point (x, DR) is constructed;
and S3.5, obtaining corresponding coordinate points (x, DR) when x in the historical data is different, and fitting to obtain a function DR = FD (x) of the thermal conductivity coefficient along with time by combining a linear regression equation formula.
The method for predicting the trend of the internal temperature of the data cabin along with the change of the time in S4 comprises the following steps:
s4.1, obtaining the temperature T1 in the data cabin when the launching time of the data cabin is T2 t2 The number of the working servers Nt2 and the heat R generated by each working server in unit time;
s4.2, obtaining the temperature T2 of the seawater around the data cabin when the launching time of the data cabin is T2 t2 ;
S4.3, obtaining a fitting function of the thermal conductivity coefficient along with time DR = FD (x);
s4.4, obtaining T1 t2 Corresponding instantaneous rate of change VT1 of internal temperature of data compartment t2 ，
Figure 897370DEST_PATH_IMAGE004
U is the heat required by the temperature in the data cabin when the temperature rises once, and is a preset constant in the database;
s4.5, predicting a trend function WQ (t) of the internal temperature of the data cabin along with the change of time, wherein the WQ (t) is a piecewise function,
the time when the sensor last acquired the temperature inside the data compartment was recorded as T0 and the corresponding temperature was recorded as T0 t0 Then the internal temperature of the data cabinThe degree of the trend function WQ (t) changes along with the time in the time interval corresponding to t > t0, and the corresponding function is
Figure 80089DEST_PATH_IMAGE005
Wherein, VT0 t0 Representation T0 t0 A corresponding instantaneous rate of change of the internal temperature of the data compartment;
and when t is less than or equal to t0, the sensor acquires a function of the corresponding straight line of the internal temperature of any two adjacent data cabins, and the function is a trend function of the internal temperature of the data cabins changing along with time in the corresponding time interval.
In the present embodiment, if the first unit time is 1 minute,
if the time for last time the temperature inside the data compartment was acquired is tz, and the corresponding temperature is 28 degrees celsius,
if the time of the internal temperature of the data cabin obtained for the second time before the current time is tz-1, the corresponding temperature is 27.5 ℃, and the corresponding instantaneous change rate of the internal temperature of the data cabin is 0.01;
then wq (t) =0.01 x (t-tz) +28 when t > tz;
when tz is more than t and more than tz-1, WQ (t) =0.5 t + 28-tz/2.
The method for predicting the service life of the internal server in the current data bay in S4 includes the following steps:
s4-1, obtaining a relation function G1 (T, VLT) between the instantaneous deviation rate of the service life of the server in the data cabin and the temperature in the data cabin,
Figure 531930DEST_PATH_IMAGE006
，
wherein B is greater than 0 and G (T, PT) is greater than or equal to 0
S4-2, acquiring a predicted trend function WQ (t) of the internal temperature of the data cabin changing along with time;
s4-3, obtaining a relation function G1[ WQ (t) and VLWQ (t) between the instantaneous deviation rate of the service life of the server in the data cabin and the time, wherein VLWQ (t) represents the instantaneous deviation rate of the service life of the server in the data cabin corresponding to the time when the water drainage time length of the data cabin is t;
s4-4, obtainingA predicted value of an internal server life in a current data bin, ZSM, a solution of a first equation, the first equation being
Figure 458298DEST_PATH_IMAGE007
Wherein F3 (ZSM) is the integral of the lifetime deviation, and F3 (ZSM) = (B-ZSM)/B.
An equipment status supervision system for a subsea data center, the system comprising the following modules:
the system comprises a data cabin information acquisition module, a data cabin information acquisition module and a data cabin control module, wherein the data cabin information acquisition module acquires data cabin internal state information, shell state information and external state information of a submarine data center at intervals of first unit time through a sensor, and the first unit time is a preset constant in a database;
the server life analysis module is used for calculating the average value of the corresponding life of the internal server in historical data under different internal state information, acquiring the standard life corresponding to the internal server in the data cabin, calculating the difference value between the standard life corresponding to the internal server in the data cabin and the average value of the corresponding life of the internal server in the historical data under different internal state information, recording the difference value as the life deviation value of the internal server under different internal state information of the data cabin in the historical data, and analyzing the relationship between the life deviation value of the internal server in the data cabin and the internal state information, wherein the standard life of the internal server is the theoretical life corresponding to a corresponding server prefabricated in a database during production, and the theoretical life is a fixed value;
the shell heat conductivity coefficient analysis module analyzes the heat conductivity coefficients and time-varying functions of the shell water feeding and conveying pipelines and the inner and outer heat exchangers of the data cabin at different time according to the shell state information of the data cabin;
the prediction analysis module predicts the trend of the internal temperature of the data cabin changing along with time according to the analysis result and the external state information of the data cabin respectively corresponding to the server service life analysis module and the shell heat conductivity coefficient analysis module, and then predicts the service life of the internal server in the current data cabin;
and the equipment management module monitors the prediction result of the service life of the internal server in the current data cabin in real time and acquires the actual service life of the internal server, wherein the actual service life of the internal server is the time length from the time when the internal server of the data cabin is drained to the current time from the data cabin, and the data cabin is managed according to the prediction result of the service life of the internal server of the data cabin and the actual service life of the internal server of the data cabin.
In the data cabin information acquisition module, the data cabin internal state information of the submarine data center includes: the temperature in the data cabin T1 when the data cabin is submerged for time T2 t2 The number of the working servers Nt2 and the heat R generated by each working server in unit time are the same as the heat generated by each working server in working time by default, and the R is obtained by database query;
the shell state information of the data cabin comprises: the state picture of the water supply pipeline of the shell and the internal and external heat exchangers in the seawater polluted by the organisms;
the external state information of the data compartment comprises: the data cabin is launched for time T2, and the temperature of the seawater around the data cabin is T2 t2 。
The equipment management module manages the data cabin according to the prediction result of the service life of the server in the data cabin and the actual service life of the server in the data cabin,
when the prediction result of the service life of the internal server is greater than or equal to a first threshold value, judging that the data cabin is normal,
when the prediction result of the service life of the internal server is smaller than a first threshold value, judging that the data cabin is abnormal, and overhauling or replacing a water conveying pipeline and an internal and external heat exchanger on the shell of the data cabin;
when the prediction result of the service life of the internal server is larger than or equal to the product of the actual service life of the internal server and the second threshold value, the internal server is judged to be normal,
when the prediction result of the service life of the internal server is smaller than the product of the actual service life of the internal server and the second threshold value, the internal server is judged to be abnormal, the internal server in the data cabin is replaced and overhauled,
the first threshold and the second threshold are constants preset in a database.
It should be noted that, in this document, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described above, or equivalents may be substituted for elements thereof. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (9)

1. A method for equipment status supervision for a subsea data center, the method comprising the steps of:
s1, acquiring internal state information, shell state information and external state information of a data cabin of the submarine data center every first unit time through a sensor, wherein the first unit time is a preset constant in a database;
s2, calculating the average value of the corresponding service life of the internal server under different internal state information of the data cabin in the historical data, acquiring the standard service life corresponding to the internal server of the data cabin, calculating the difference between the standard service life corresponding to the internal server of the data cabin and the average value of the corresponding service life of the internal server under different internal state information of the data cabin in the historical data, recording the difference as the service life deviation value of the internal server under different internal state information of the data cabin in the historical data, analyzing the relationship between the service life deviation value of the internal server of the data cabin and the internal state information, wherein the standard service life of the internal server is the corresponding theoretical service life of the corresponding server prefabricated in the database during production, and the theoretical service life is a fixed value;
s3, analyzing the heat conductivity coefficients and time-varying functions of the heat conductivity coefficients corresponding to the shell water feeding and conveying pipeline and the internal and external heat exchangers of the data cabin at different time according to the shell state information of the data cabin;
s4, predicting the trend of the internal temperature of the data cabin changing along with time according to the analysis result and the external state information of the data cabin respectively corresponding to the S2 and the S3, and further predicting the service life of an internal server in the current data cabin;
s5, monitoring the prediction result of the service life of the internal server in the current data cabin in real time, and acquiring the actual service life of the internal server, wherein the actual service life of the internal server is the time length between the time when the internal server of the data cabin is launched from the data cabin to the current time,
when the prediction result of the service life of the internal server is greater than or equal to a first threshold value, judging that the data cabin is normal,
when the prediction result of the service life of the internal server is smaller than a first threshold value, judging that the data cabin is abnormal, and overhauling or replacing a water conveying pipeline and an internal and external heat exchanger on the shell of the data cabin;
when the prediction result of the service life of the internal server is larger than or equal to the product of the actual service life of the internal server and the second threshold value, the internal server is judged to be normal,
when the prediction result of the service life of the internal server is smaller than the product of the actual service life of the internal server and the second threshold value, the internal server is judged to be abnormal, the internal server in the data cabin is replaced and overhauled,
the first threshold and the second threshold are constants preset in a database.
2. According to claimThe equipment state supervision method for the submarine data center is characterized by comprising the following steps: the data cabin internal state information of the submarine data center in S1 includes: the temperature in the data cabin T1 when the data cabin is submerged for time T2 t2 The number of the working servers Nt2 and the heat R generated by each working server in unit time are the same as the heat generated by each working server in working time by default, and the R is obtained by database query;
the shell state information of the data compartment comprises: the state picture of the water supply pipeline of the shell and the internal and external heat exchangers in the seawater polluted by the organisms;
the external state information of the data compartment comprises: the data cabin is launched for time T2, and the temperature of the seawater around the data cabin is T2 t2 。
3. The equipment status supervision method for the subsea data center according to claim 2, characterized by: the method for analyzing the relationship between the life deviation value of the server inside the data compartment and the internal state information in the step S2 includes the following steps:
s2.1, acquiring the average value of the corresponding service life of the internal server under the condition that the temperature in the data cabin in the historical data is not changed, and recording the average value of the corresponding service life of the internal server as QT under the condition that the temperature is always T;
s2.2, obtaining a service life deviation value of the internal server under the condition that the temperature in the data cabin is always T, and recording the service life deviation value as PT, wherein PT = B-QT, and B represents the corresponding standard service life of the internal server of the data cabin;
s2.3, acquiring values corresponding to PT when T is different in the database, and constructing corresponding coordinate points (T, PT);
s2.4, obtaining a relation function between the service life deviation value of the server in the data cabin and the internal state information according to the coordinate points constructed in the S2.3 and a function model prefabricated in the database, marking as G (T, PT), wherein the function model prefabricated in the database is a piecewise function and marking as G (T, PT)
Figure 356726DEST_PATH_IMAGE001
Wherein, T3 represents the working environment temperature corresponding to the standard life of the server inside the data cabin, a1 is a first coefficient, a2 is a second coefficient, c1 is a third coefficient, c4 is a fourth coefficient, the value of b1 is equal to-a 1 tan h (T3-c 1), the value of b2 is equal to-a 2 tan h (T3-c 2);
in the process of obtaining G (T, PT), performing linear fitting on coordinate points constructed by S2.3 according to a function model prefabricated in a database to obtain a plurality of fitting curves, wherein a first coefficient, a second coefficient, a third coefficient and a fourth coefficient corresponding to different fitting curves are different, calculating the sum of the distance between each fitting curve and each coordinate point constructed by S2.3, and marking the fitting curve with the minimum sum of the distances as G (T, PT);
s2.5, obtaining the instantaneous deviation rate of the service life of the internal server under the state that the temperature of the data cabin is T, recording the instantaneous deviation rate as VLT,
Figure 163139DEST_PATH_IMAGE002
s2.6, combining the functions obtained from S2.4 and S2.5 to obtain a relation function G1 (T, VLT) between the service life instantaneous deviation rate of the server in the data cabin and the temperature in the data cabin,
Figure 979786DEST_PATH_IMAGE003
，
wherein B is greater than 0 and G (T, PT) is greater than or equal to 0.
4. The equipment status supervision method for the subsea data center according to claim 3, characterized by: the method for analyzing the heat conductivity coefficients of the shell water feeding and conveying pipeline of the data cabin and the inner and outer heat exchangers at different time and the function of the change of the heat conductivity coefficients along with the time in the S3 comprises the following steps:
s3.1, obtaining state pictures of biofouling of a shell water feeding and conveying pipeline and an internal and external heat exchanger in seawater in shell state information of the data cabin;
s3.2, performing data identification on the picture obtained in the S3.1, extracting abnormal area areas in the shell picture of the initial data cabin and the shell picture of the shell in the picture, calculating the ratio of the abnormal area areas of the shell to the total area of the shell, and recording the ratio as W1, wherein the shell picture of the initial data cabin is the shell picture after the data cabin is built and when the data cabin is not launched;
s3.3, performing data identification on the picture acquired in the S3.1, acquiring the maximum area occupied by a single fouling organism in the picture, marking as W2,
the identification of the single fouling organism is obtained by extracting local pictures in the pictures obtained in S3.1 and comparing the local pictures with samples in a database respectively, zooming the sample pictures in the comparison process to ensure that the zoomed pictures are overlapped with the local pictures in the obtained pictures, counting pixel points with the same positions and the same corresponding pixel values in an overlapped area, dividing the total number of the counted pixel points by the total number of the pixel points in the corresponding local pictures of the obtained pictures, marking the obtained quotient as the similarity of the corresponding local pictures and the corresponding samples, and selecting the fouling organism in the sample with the maximum similarity as the identification result of the corresponding fouling organism in the corresponding local pictures;
s3.4, when the data corresponding to the shell in the database prefabricated data are W1 and W2, the heat conductivity coefficients corresponding to the shell water feeding and conveying pipeline and the internal and external heat exchangers are recorded as DR, the data cabin water draining time length x corresponding to DR is obtained, and a coordinate point (x, DR) is constructed;
and S3.5, obtaining corresponding coordinate points (x, DR) when x in the historical data is different, and fitting to obtain a function DR = FD (x) of the thermal conductivity coefficient along with time by combining a linear regression equation formula.
5. The equipment status supervision method for the subsea data center according to claim 4, characterized by: the method for predicting the trend of the internal temperature of the data cabin changing along with the time in the S4 comprises the following steps:
s4.1, obtaining the temperature T1 in the data cabin when the launching time of the data cabin is T2 t2 The number of operating servers Nt2 and the heat R generated per unit time by each operating server;
s4.2, obtaining the temperature T2 of the seawater around the data cabin when the launching time of the data cabin is T2 t2 ;
S4.3, obtaining a fitting function of the thermal conductivity coefficient along with time DR = FD (x);
s4.4, obtaining T1 t2 Corresponding instantaneous rate of change VT1 of internal temperature of data compartment t2 ，
Figure 293961DEST_PATH_IMAGE004
U is the heat required by the temperature in the data cabin when the temperature rises once, and is a preset constant in the database;
s4.5, predicting a trend function WQ (t) of the internal temperature of the data cabin along with the change of time, wherein the WQ (t) is a piecewise function,
the time when the sensor last acquired the temperature inside the data compartment is denoted T0 and the corresponding temperature is denoted T0 t0 The trend function WQ (t) of the internal temperature of the data cabin changing along with the time is that in the time interval corresponding to t > t0, the corresponding function is
Figure 20609DEST_PATH_IMAGE005
Wherein, VT0 t0 Representation T0 t0 A corresponding instantaneous rate of change of the internal temperature of the data compartment;
and when t is less than or equal to t0, the sensor acquires a function of the corresponding straight line of the internal temperature of any two adjacent data cabins, and the function is a trend function of the internal temperature of the data cabins changing along with time in the corresponding time interval.
6. The equipment status supervision method for the subsea data center according to claim 5, characterized by: the method for predicting the service life of the internal server in the current data bay in S4 includes the following steps:
s4-1, obtaining a relation function G1 (T, VLT) between the instantaneous deviation rate of the service life of the server in the data cabin and the temperature in the data cabin,
Figure 512770DEST_PATH_IMAGE003
wherein B is greater than 0 and G (T, PT) is greater than or equal to 0
S4-2, acquiring a predicted trend function WQ (t) of the internal temperature of the data cabin changing along with time;
s4-3, obtaining a relation function G1[ WQ (t) and VLWQ (t) between the instantaneous deviation rate of the service life of the server in the data cabin and the time, wherein VLWQ (t) represents the instantaneous deviation rate of the service life of the server in the data cabin corresponding to the time when the water drainage time length of the data cabin is t;
s4-4, obtaining a predicted value ZSM of the service life of the internal server in the current data cabin, wherein ZSM is a solution of a first equation, and the first equation is
Figure 567445DEST_PATH_IMAGE006
Wherein, F3 (ZSM) is the integral quantity of lifetime deviation, and F3 (ZSM) = (B-ZSM)/B.
7. An equipment status supervision system for a subsea data center, characterized in that the system comprises the following modules:
the system comprises a data cabin information acquisition module, a data cabin information acquisition module and a data cabin control module, wherein the data cabin information acquisition module acquires data cabin internal state information, shell state information and external state information of a submarine data center at intervals of first unit time through a sensor, and the first unit time is a preset constant in a database;
the server life analysis module calculates the average value of the corresponding lives of the internal servers under different internal state information of the data cabin in the historical data, acquires the standard life corresponding to the internal server of the data cabin, calculates the difference between the standard life corresponding to the internal server of the data cabin and the average value of the corresponding lives of the internal servers under different internal state information of the data cabin in the historical data, and records the difference as the life deviation value of the internal server under different internal state information of the data cabin in the historical data, and analyzes the relationship between the life deviation value of the internal server of the data cabin and the internal state information, wherein the standard life of the internal server is the corresponding theoretical life of the corresponding server prefabricated in the database during production, and the theoretical life is a fixed value;
the shell heat conductivity coefficient analysis module analyzes the heat conductivity coefficients and time-varying functions of the shell water feeding and conveying pipelines and the inner and outer heat exchangers of the data cabin at different time according to the shell state information of the data cabin;
the prediction analysis module predicts the trend of the internal temperature of the data cabin changing along with time according to the analysis result and the external state information of the data cabin respectively corresponding to the server service life analysis module and the shell heat conductivity coefficient analysis module, and then predicts the service life of the internal server in the current data cabin;
and the equipment management module monitors the prediction result of the service life of the internal server in the current data cabin in real time and acquires the actual service life of the internal server, wherein the actual service life of the internal server is the time length from the time when the internal server of the data cabin is drained to the current time, and the data cabin is managed according to the prediction result of the service life of the internal server of the data cabin and the actual service life of the internal server of the data cabin.
8. The equipment status supervision system for a subsea data center according to claim 7, characterized by: in the data cabin information acquisition module, the data cabin internal state information of the submarine data center includes: the temperature in the data cabin T1 when the data cabin is submerged for time T2 t2 The number of the working servers Nt2 and the heat R generated by each working server in unit time are the same as the heat generated by each working server in working time by default, and the R is obtained by database query;
the shell state information of the data compartment comprises: the state picture of the water supply pipeline of the shell and the internal and external heat exchangers in the seawater polluted by the organisms;
the external state information of the data compartment comprises: the data cabin is launched for time T2, and the temperature of the seawater around the data cabin is T2 t2 。
9. The equipment status supervision system for a subsea data center according to claim 7, characterized by: the equipment management module manages the data cabin according to the prediction result of the service life of the server in the data cabin and the actual service life of the server in the data cabin,
when the prediction result of the service life of the internal server is greater than or equal to a first threshold value, judging that the data cabin is normal,
when the prediction result of the service life of the internal server is smaller than a first threshold value, judging that the data cabin is abnormal, and overhauling or replacing a water conveying pipeline and an internal and external heat exchanger on the shell of the data cabin;
when the prediction result of the service life of the internal server is larger than or equal to the product of the actual service life of the internal server and the second threshold value, the internal server is judged to be normal,
when the prediction result of the service life of the internal server is smaller than the product of the actual service life of the internal server and the second threshold value, judging that the internal server is abnormal, replacing and overhauling the internal server of the data cabin,
the first threshold and the second threshold are constants preset in a database.
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