CN117193503A - Heat dissipation host power supply operation monitoring method based on Internet of things - Google Patents
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Abstract
The application belongs to the technical field of data acquisition and intelligent control, and provides a heat dissipation host power supply operation monitoring method based on the Internet of things, which specifically comprises the following steps: the method comprises the steps of arranging a multichannel heat dissipation system based on the Internet of things, wherein the multichannel heat dissipation system comprises temperature sensors, taking the temperature sensors as nodes, acquiring temperature values from all the nodes to form subchannel loads, carrying out load redundancy analysis through the subchannel loads of all the node subchannels, and finally carrying out heat dissipation regulation and control on the multichannel heat dissipation system according to the result of the load redundancy analysis. Through the pertinence detection of each host power supply, under the condition that the condition adaptability of uniform heat dissipation or similar heating characteristics of each machine is not affected, the identification of the split-channel heat dissipation system to the low-demand host power supply when obvious difference occurs between the running loads of each host is improved, the unnecessary high-load running in the system running is reduced, and the stability and the power utilization rate of the heat dissipation system are further ensured.
Description
Technical Field
The application belongs to the technical field of data acquisition and intelligent control, and particularly relates to a heat dissipation host power supply operation monitoring method based on the Internet of things.
Background
The problem of heat dissipation from a power supply of a host computer is one of the common challenges in computer hardware, especially in situations where high performance computers or game computers require a large amount of computing resources, the high load operation or intensive task can make the CPU, GPU and other main computing components work for a long time, and a large amount of heat is easily generated and accumulated in the power supply. In a high-performance computer cluster environment, if each power supply cannot uniformly and efficiently dissipate heat, the application work efficiency of the computer is greatly reduced, and even hardware damage occurs. At present, a host power supply heat dissipation strategy aiming at the high-performance computer cluster environment in the market needs to design and configure a cooling system by utilizing a thermal simulation and emulation method aiming at separate projects, and the heat dissipation strategy usually adopts a split-channel heat dissipation strategy, namely a hot channel and a cold channel are isolated to separate hot air and cold air flow, cold air is injected into a host power supply for heat collection through the cold channel, and the air after heat collection is discharged through the hot channel, so that hot air recycling is reduced, and heat dissipation efficiency is improved. However, since the task amount or load degree of each host in the running process in the high-performance computer cluster environment varies with time, the variation does not occur with a fixed rule or frequency, so that the split-channel heat dissipation strategy often needs to be adjusted. In the prior art, the air quantity in the multichannel heat dissipation system is often controlled through the temperature change of the exhaust air of the hot air port, the pertinence detection and control of each host power supply are lacked, the adaptability of the design to the conditions of uniform heat dissipation or similar heat generation characteristics of each machine is better, but when obvious differences occur among the operation loads of each host, the multichannel heat dissipation strategy always runs under high load, the stability of the heat dissipation system is ensured to a certain extent, huge energy waste is brought, and the electric energy use is low-efficiency.
Disclosure of Invention
The application aims to provide a method and a system for monitoring the operation of a heat dissipation host power supply based on the Internet of things, which are used for solving one or more technical problems in the prior art and at least providing a beneficial selection or creation condition.
To achieve the above object, according to an aspect of the present application, there is provided a heat dissipation host power operation monitoring method based on the internet of things, the method comprising the steps of:
s100, arranging a multichannel heat dissipation system based on the Internet of things, wherein the multichannel heat dissipation system comprises a temperature sensor, and the temperature sensor is used as a node;
s200, obtaining temperature values from all nodes to form sub-channel loads;
s300, carrying out load redundancy analysis through sub-channel loads of all the node sub-channels;
s400, performing heat dissipation regulation and control on the split-channel heat dissipation system according to the result of the load redundancy analysis.
The method for performing load redundancy analysis through the sub-channel load of each node sub-channel in step S300 is as follows: obtaining a peak-average difference value and a peak-average difference value level through channel load, obtaining a first suspicious temperature point through comparison definition, calculating and forming a tracing average ratio based on the channel load between two continuous moments, forming a second suspicious temperature point through comparison definition by utilizing each tracing average ratio at one moment, and finally calculating a charge redundancy value according to a tracing average ratio level and a peak-average difference value level of the third suspicious temperature point according to the first suspicious temperature point and a third suspicious temperature point defined by the second suspicious Wen Dianding.
Further, in step S100, the method for arranging the multichannel heat dissipation system based on the internet of things is as follows: the multichannel heat dissipation system is provided with a plurality of computers or hosts, each computer or host is provided with a power supply, each power supply is internally provided with a temperature sensor, each temperature sensor is taken as a node, and the temperature sensor is a heat sensor or a resistance thermometer; the data collected by each temperature sensor are networked through zigbee technology, wifi technology or Bluetooth technology to form the Internet of things; the sub-channel heat dissipation system also comprises a plurality of sub-channels, wherein the sub-channels are used for conveying cold air or discharging hot air for the power supply, and each sub-channel is in one-to-one correspondence with the power supply.
Further, in step S200, the method for obtaining the temperature value from each node and forming the sub-channel load is as follows: each power supply is provided with a plurality of nodes, each node performs temperature measurement on different components in the power supply and obtains a temperature value, wherein each node under the same power supply is called a sub-channel node of the power supply; and respectively marking the maximum value and the average value of the temperature values corresponding to each sub-channel node in the sub-channel as an uplink value and a uniformity value, and taking a binary group formed by the uplink value and the uniformity value as a sub-channel load of the sub-channel.
Further, in step S300, the method for performing load redundancy analysis through the sub-channel load of each node sub-channel is as follows: setting a time period TP, wherein TP epsilon [0.5,5] hours, and sub-channels in the previous TP time period at the current moment have one-to-one corresponding sub-channel loads at each moment, wherein the time interval for obtaining the sub-channel loads is smaller than the time period TP, and the range of the sub-channel loads is between [0.1,5] seconds; recording the difference between an uplink value and an average value of any sub-channel at one time as a peak-average difference value (HADN), recording the average value of the peak-average difference values at all times as a peak-average difference value level (c.HADN), and recording the moment as a first suspicious temperature point when the HADN is more than c.HADN at any time under the sub-channel;
recording the ratio of the uniformity value at any moment to the uniformity value at the corresponding moment as a trace-back uniformity ratio TGAE, taking the maximum value of all trace-back uniformity ratios in one sub-channel as the trace-back uniformity ratio level MTGAE of the sub-channel, and recording the moment as a second suspicious temperature point if the trace-back uniformity ratio of the sub-channel at the moment is more than 1; for any moment in the sub-channel, if the sub-channel meets the conditions of the first suspicious temperature point and the second suspicious temperature point at the same time, the corresponding moment in the sub-channel is marked as a third suspicious temperature point; calculating the charge redundancy value BRLv by tracing back the average ratio level and the peak-to-average difference value level,
；
wherein avg </SUB > is an average function, j1 is a sequence number of a third suspicious temperature point under the sub-channel, j 1E [1, len.j1], wherein len.j1 is the number of the third suspicious temperature point under the sub-channel, e is a natural constant, and ERR is a standard deviation of each peak-average difference value under the sub-channel.
Because in the screening of the second suspicious temperature point, the situation that the tracing average ratio is larger than 1 or the tracing average ratio is smaller than 1 may occur, so that the second suspicious temperature point is small in difference or does not exist, the accuracy of the result obtained by calculation is insufficient, and in order to enable the calculation and the expression of the charge redundancy value to be more scientific and authoritative and solve the problem, the application also provides a more preferable scheme as follows:
preferably, in step S300, the method for performing load redundancy analysis through sub-channel load of each node sub-channel is: setting a time period to be TP, TP epsilon [0.5,5] hours, carrying out normalization processing on the uniformity value of each sub-channel in the TP period, defining the processed value as a uniformity index, taking the uniformity index of different sub-channels at one time as a column, taking the corresponding uniformity index of one sub-channel at each time in the TP period as a row, constructing a matrix as a redundancy analysis matrix, and adding an empty tuple for each element in the redundancy analysis matrix as an integration factor of the element; defining the uniformity residual error of any integrated factor in the load redundancy analysis matrix as the difference value between the maximum value of all elements in the load redundancy analysis matrix and the element; taking a sequence formed by arranging all the uniformity residual structures from small to large as a residual gradient sequence, and taking the difference value between any element of the residual gradient sequence and the previous element as the range extension HIDG of the integration factor corresponding to the element;
taking the average value of the range-extending amounts of all the integrated factors as the range-extending level, defining that one integrated factor meets the high range-extending requirement when the range-extending amount of the integrated factor is larger than the range-extending level, defining that the integrated factor meeting the high range-extending requirement is the high range-extending factor, and defining that the integrated factor not meeting the high range-extending requirement is the low range-extending factor; taking the ratio of the number of integration factors meeting the high range-extending requirement under one sub-channel to the number of remaining integration factors as a range-extending redundancy ratio RHR of the sub-channel, calculating and obtaining a first charge redundancy sub_BRLv of the sub-channel according to the range-extending quantity and the range-extending redundancy ratio,
sub_BRLv＝exp(RHR×avg<HIDG_Ls>)；
wherein avg </SUB > is an average function, and HIDG_Ls are sets of the range-increasing amounts of each sub-channel;
for any time, the attribute of Cheng Feng difference is added, and the acquisition method is as follows: when each integrated factor at a moment has a high elevation factor, the maximum value in the uplink value of each sub-channel corresponding to the high elevation factor at the moment is marked as a high uplink index, the maximum value in the uplink value corresponding to each low elevation factor at the moment is marked as a low uplink index, and the ratio of the high uplink index to the low uplink index is used as an elevation Cheng Fengcha attribute at the moment; when the high range-increasing factors do not exist in the integrated factors at one moment, the moment uses the Cheng Fengcha attribute of the increment at the previous moment; calculating the charge redundancy value BRLv of the sub-channel according to the first charge redundancy and the definition of the high range factor:
；
wherein i1 is an accumulation variable, and sub_brlv and len are the number of first load redundancy and high range factors of the sub-channel respectively; FMrk (i 1) is a Cheng Fengcha increasing matching function, and the Cheng Fengcha increasing attribute of the corresponding moment of the i 1-th elevation increasing factor is returned through the function; ETP i1 The corresponding uniformity value of the i 1-th elevation factor in the sub-channel is obtained; exp () is a logarithmic function with natural constant e as a base; TGap i1 The difference value of the time scale between the corresponding moment of the ith elevation increment factor and the current moment is the i1 th elevation increment factor.
The element in the load redundancy analysis matrix is a uniformity index in a default state, any element in the load redundancy analysis matrix necessarily has a corresponding moment and a corresponding sub-channel or sub-channel serial number, when any element in the load redundancy analysis matrix calculates or obtains a new variable, the variable is added into the integration factors as the new element, so that each integration factor is in one-to-one correspondence with each element in the load redundancy analysis matrix, and the total amount of the integration factors under one sub-channel is the total amount of the uniformity index obtained in a TP period, namely the number of columns in the load redundancy analysis matrix; if the delta Cheng Feng difference attribute does not exist at the previous moment, the data of the redundancy analysis matrix of the period is removed from the redundancy analysis matrix, i.e. the part of the data at the end of the period is ignored and is not used for calculating the redundancy value.
The beneficial effects are that: the load redundancy value BRLv is obtained according to the time-dependent differential calculation of the temperature of each node, and is transversely analyzed according to each sub-channel, so that the energy consumption rationality of the corresponding sub-channel for the existing temperature control or heat dissipation control can be effectively quantified, the attention of data with high load redundancy is enhanced, the sensitivity of the sub-channel heat dissipation system to the energy consumption redundancy identification is enhanced, the risk sites with the energy consumption redundancy are effectively marked, the risk sites are subjected to the stepwise analysis, the overfitting risk caused by the sensitivity is reduced, and therefore reliable mathematical support is provided for further obtaining the accurate monitoring result of the sub-channel heat dissipation system.
Further, in step S400, according to the result of the load redundancy analysis, the method for performing heat dissipation regulation and control on the split-channel heat dissipation system is as follows: the method comprises the steps of obtaining a first quartile Flg2, a second quartile Flg3 and a third quartile Flg4 in corresponding charge redundancy values of each sub-channel at the current moment, and judging the charge redundancy order FgCls of the sub-channel at the current moment according to the charge redundancy values of the sub-channel:
；
setting a time period as an adjustment analysis period TR, TR epsilon [5,10] minutes, taking the occurrence times of one sub-channel in different charge redundancy orders in the TR period as the charge redundancy order of the sub-channel in the corresponding charge redundancy order, taking the difference between the maximum value and the minimum value in the corresponding charge redundancy order of the different charge redundancy orders as the charge redundancy expression, constructing the charge redundancy expression of each sub-channel into a box graph, identifying an abnormal value, judging that the monitoring result of the current sub-channel heat dissipation system is energy consumption redundancy if the abnormal value exists, reducing the voltage of a fan of the sub-channel heat dissipation system by 5% -10%, otherwise judging that the monitoring result of the current sub-channel heat dissipation system is stable in operation, and sending the monitoring result to a client.
The fan of the multichannel heat radiation system is provided with other control methods, and the method is an auxiliary control method, so that the condition that the voltage of the fan is reduced wirelessly is avoided.
Preferably, all undefined variables in the present application, if not explicitly defined, may be thresholds set manually.
The application also provides a heat dissipation host power supply operation monitoring system based on the Internet of things, which comprises: the method comprises the steps of a heat dissipation host power supply operation monitoring method based on the Internet of things, wherein the heat dissipation host power supply operation monitoring system based on the Internet of things can be operated in computing equipment such as a desktop computer, a notebook computer, a palm computer and a cloud data center, and the operable system can comprise, but is not limited to, a processor, a memory and a server cluster, and the processor executes the computer program to operate in the following units:
the system initial unit is used for arranging a multichannel heat dissipation system based on the Internet of things, wherein the multichannel heat dissipation system comprises a temperature sensor, and the temperature sensor is used as a node;
the load reading unit is used for acquiring temperature values from all the nodes to form sub-channel loads;
the load redundancy analysis unit is used for carrying out load redundancy analysis through the sub-channel load of each node sub-channel;
and the feedback regulation and control unit is used for carrying out heat dissipation regulation and control on the split-channel heat dissipation system according to the result of the load redundancy analysis.
The beneficial effects of the application are as follows: the application provides a heat dissipation host power supply operation monitoring method based on the Internet of things, which strengthens the attention degree of data with high redundancy of load and enhances the sensitivity of a multichannel heat dissipation system to energy redundancy identification by quantifying the energy consumption rationality of a subchannel to the existing temperature control or heat dissipation control, effectively marks out risk sites with energy redundancy, carries out staged analysis on the risk sites, reduces the overfitting risk caused by the sensitivity, provides reliable mathematical support for the monitoring result of a more accurate multichannel heat dissipation system, improves the identification of the multichannel heat dissipation system to the host power supply with low requirement under the condition that the situation adaptability to the uniform heat dissipation or the similar heating characteristics of each machine is not affected by the pertinence detection of each host power supply, reduces the unnecessary high-load operation in the system operation, and further ensures the stability and the power utilization rate of the heat dissipation system.
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The above and other features of the present application will become more apparent from the detailed description of the embodiments thereof given in conjunction with the accompanying drawings, in which like reference characters designate like or similar elements, and it is apparent that the drawings in the following description are merely some examples of the present application, and other drawings may be obtained from these drawings without inventive effort to those of ordinary skill in the art, in which:
FIG. 1 is a flow chart of a method for monitoring the operation of a heat dissipation host power supply based on the Internet of things;
fig. 2 is a diagram showing a structure of a heat dissipation host power operation monitoring system based on the internet of things.
Detailed Description
The conception, specific structure, and technical effects produced by the present application will be clearly and completely described below with reference to the embodiments and the drawings to fully understand the objects, aspects, and effects of the present application. It should be noted that, without conflict, the embodiments of the present application and features of the embodiments may be combined with each other.
Referring to fig. 1, which is a flowchart of a method for monitoring operation of a heat dissipation host power supply based on the internet of things, the method for monitoring operation of a heat dissipation host power supply based on the internet of things according to an embodiment of the present application is described below with reference to fig. 1, and includes the following steps:
s100, arranging a multichannel heat dissipation system based on the Internet of things, wherein the multichannel heat dissipation system comprises a temperature sensor, and the temperature sensor is used as a node;
s200, obtaining temperature values from all nodes to form sub-channel loads;
s300, carrying out load redundancy analysis through sub-channel loads of all the node sub-channels;
s400, performing heat dissipation regulation and control on the split-channel heat dissipation system according to the result of the load redundancy analysis.
Further, in step S100, the method for arranging the multichannel heat dissipation system based on the internet of things is as follows: the multichannel heat dissipation system is provided with a plurality of computers or hosts, each computer or host is provided with a power supply, each power supply is internally provided with a temperature sensor, each temperature sensor is taken as a node, and the temperature sensor is a heat sensor or a resistance thermometer; the data collected by each temperature sensor are networked through zigbee technology, wifi technology or Bluetooth technology to form the Internet of things; the sub-channel heat dissipation system also comprises a plurality of sub-channels, wherein the sub-channels are used for conveying cold air or discharging hot air for the power supply, and each sub-channel is in one-to-one correspondence with the power supply.
Further, in step S200, the method for obtaining the temperature value from each node and forming the sub-channel load is as follows: each power supply is provided with a plurality of nodes, each node performs temperature measurement on different components in the power supply and obtains a temperature value, wherein each node under the same power supply is called a sub-channel node of the power supply; and respectively marking the maximum value and the average value of the temperature values corresponding to each sub-channel node in the sub-channel as an uplink value and a uniformity value, and taking a binary group formed by the uplink value and the uniformity value as a sub-channel load of the sub-channel.
Further, in step S300, the method for performing load redundancy analysis through the sub-channel load of each node sub-channel is as follows: setting a time period TP, TP epsilon [0.5,5] hours, wherein sub-channels in the previous TP time period at the current moment have one-to-one corresponding sub-channel loads at each moment, recording the difference between an uplink value and an average value of any sub-channel at one moment as a peak-average difference value HADN, recording the average value of the peak-average difference values at all moments as a peak-average difference value level c.HADN, and recording the moment as a first suspicious temperature point when any moment under the sub-channel meets the HADN & gtc.HADN;
recording the ratio of the uniformity value at any moment to the uniformity value at the corresponding moment as a trace-back uniformity ratio TGAE, taking the maximum value of all trace-back uniformity ratios in one sub-channel as the trace-back uniformity ratio level MTGAE of the sub-channel, and recording the moment as a second suspicious temperature point if the trace-back uniformity ratio of the sub-channel at the moment is more than 1; for any moment in the sub-channel, if the sub-channel meets the conditions of the first suspicious temperature point and the second suspicious temperature point at the same time, the corresponding moment in the sub-channel is marked as a third suspicious temperature point; calculating the charge redundancy value BRLv by tracing back the average ratio level and the peak-to-average difference value level,
；
wherein avg </SUB > is an average function, j1 is a sequence number of a third suspicious temperature point under the sub-channel, j 1E [1, len.j1], wherein len.j1 is the number of the third suspicious temperature point under the sub-channel, e is a natural constant, and ERR is a standard deviation of each peak-average difference value under the sub-channel.
Preferably, in step S300, the method for performing load redundancy analysis through sub-channel load of each node sub-channel is: setting a time period to be TP, TP epsilon [0.5,5] hours, carrying out normalization processing on the uniformity value of each sub-channel in the TP period, defining the processed value as a uniformity index, taking the uniformity index of different sub-channels at one time as a column, taking the corresponding uniformity index of one sub-channel at each time in the TP period as a row, constructing a matrix as a redundancy analysis matrix, and adding an empty tuple for each element in the redundancy analysis matrix as an integration factor of the element; defining the uniformity residual error of any integrated factor in the load redundancy analysis matrix as the difference value between the maximum value of all elements in the load redundancy analysis matrix and the element; taking a sequence formed by arranging all the uniformity residual structures from small to large as a residual gradient sequence, and taking the difference value between any element of the residual gradient sequence and the previous element as the range extension HIDG of the integration factor corresponding to the element;
taking the average value of the range-extending amounts of all the integrated factors as the range-extending level, defining that one integrated factor meets the high range-extending requirement when the range-extending amount of the integrated factor is larger than the range-extending level, defining that the integrated factor meeting the high range-extending requirement is the high range-extending factor, and defining that the integrated factor not meeting the high range-extending requirement is the low range-extending factor; taking the ratio of the number of integration factors meeting the high range-increasing requirement under one sub-channel to the number of remaining integration factors as a range-increasing redundancy ratio RHR of the sub-channel, and calculating to obtain a first charge redundancy sub_BRLv of the sub-channel according to the range-increasing quantity and the range-increasing redundancy ratio, wherein sub_BRLv=exp (RHR multiplied by avg < HIDG_ls >); wherein avg </SUB > is an average function, and HIDG_Ls are sets of the range-increasing amounts of each sub-channel;
for any time, the attribute of Cheng Feng difference is added, and the acquisition method is as follows: when each integrated factor at a moment has a high elevation factor, the maximum value in the uplink value of each sub-channel corresponding to the high elevation factor at the moment is marked as a high uplink index, the maximum value in the uplink value corresponding to each low elevation factor at the moment is marked as a low uplink index, and the ratio of the high uplink index to the low uplink index is used as an elevation Cheng Fengcha attribute at the moment; when the high range-increasing factors do not exist in the integrated factors at one moment, the moment uses the Cheng Fengcha attribute of the increment at the previous moment; calculating the charge redundancy value BRLv of the sub-channel according to the first charge redundancy and the definition of the high range factor:
；
wherein i1 is an accumulation variable, and sub_brlv and len are the number of first load redundancy and high range factors of the sub-channel respectively; FMrk (i 1) is a Cheng Fengcha increasing matching function, and the Cheng Fengcha increasing attribute of the corresponding moment of the i 1-th elevation increasing factor is returned through the function; ETP i1 The corresponding uniformity value of the i 1-th elevation factor in the sub-channel is obtained; exp () is a logarithmic function with natural constant e as a base; TGap i1 The difference value of the time scale between the corresponding moment of the ith elevation increment factor and the current moment is the i1 th elevation increment factor.
Further, in step S400, according to the result of the load redundancy analysis, the method for performing heat dissipation regulation and control on the split-channel heat dissipation system is as follows: the method comprises the steps of obtaining a first quartile Flg2, a second quartile Flg3 and a third quartile Flg4 in corresponding charge redundancy values of each sub-channel at the current moment, and judging the charge redundancy order FgCls of the sub-channel at the current moment according to the charge redundancy values of the sub-channel:
；
setting a time period as an adjustment analysis period TR, TR epsilon [5,10] minutes, taking the occurrence times of one sub-channel in different charge redundancy orders in the TR period as the charge redundancy order of the sub-channel in the corresponding charge redundancy order, taking the difference between the maximum value and the minimum value in the corresponding charge redundancy order of the different charge redundancy orders as the charge redundancy expression, constructing the charge redundancy expression of each sub-channel into a box graph, identifying an abnormal value, judging that the monitoring result of the current sub-channel heat dissipation system is energy consumption redundancy if the abnormal value exists, reducing the voltage of a fan of the sub-channel heat dissipation system by 5% -10%, otherwise judging that the monitoring result of the current sub-channel heat dissipation system is stable in operation, and sending the monitoring result to a client.
The fan of the multichannel heat radiation system is provided with other control methods, and the method is an auxiliary control method, so that the condition that the voltage of the fan is reduced wirelessly is avoided.
The embodiment of the application provides a heat dissipation host power supply operation monitoring system based on the internet of things, as shown in fig. 2, which is a structural diagram of the heat dissipation host power supply operation monitoring system based on the internet of things, and the embodiment of the application comprises the following components: the system comprises a processor, a memory and a computer program stored in the memory and capable of running on the processor, wherein the steps in the embodiment of the heat dissipation host power supply running monitoring system based on the Internet of things are realized when the processor executes the computer program.
The system comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in units of the following system:
the system initial unit is used for arranging a multichannel heat dissipation system based on the Internet of things, wherein the multichannel heat dissipation system comprises a temperature sensor, and the temperature sensor is used as a node;
the load reading unit is used for acquiring temperature values from all the nodes to form sub-channel loads;
the load redundancy analysis unit is used for carrying out load redundancy analysis through the sub-channel load of each node sub-channel;
and the feedback regulation and control unit is used for carrying out heat dissipation regulation and control on the split-channel heat dissipation system according to the result of the load redundancy analysis.
The heat dissipation host power supply operation monitoring system based on the Internet of things can be operated in computing equipment such as desktop computers, notebook computers, palm computers and cloud servers. The system for monitoring the operation of the heat dissipation host power supply based on the Internet of things can comprise, but is not limited to, a processor and a memory. It will be understood by those skilled in the art that the example is merely an example of a heat dissipation host power operation monitoring system based on the internet of things, and is not limited to a heat dissipation host power operation monitoring system based on the internet of things, and may include more or fewer components than examples, or may combine some components, or different components, for example, the heat dissipation host power operation monitoring system based on the internet of things may further include an input/output device, a network access device, a bus, and so on.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), field programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general processor can be a microprocessor or any conventional processor, and the processor is a control center of the operation system of the operation monitoring system of the heat dissipation host power supply based on the Internet of things, and various interfaces and lines are utilized to connect various parts of the operation system of the operation monitoring system of the heat dissipation host power supply based on the Internet of things.
The memory can be used for storing the computer program and/or the module, and the processor can realize various functions of the heat dissipation host power supply operation monitoring system based on the Internet of things by running or executing the computer program and/or the module stored in the memory and calling the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Although the present application has been described in considerable detail and with particularity with respect to several described embodiments, it is not intended to be limited to any such detail or embodiment or any particular embodiment so as to effectively cover the intended scope of the application. Furthermore, the foregoing description of the application has been presented in its embodiments contemplated by the inventors for the purpose of providing a useful description, and for the purposes of providing a non-essential modification of the application that may not be presently contemplated, may represent an equivalent modification of the application.
Claims (7)
1. The method for monitoring the operation of the heat dissipation host power supply based on the Internet of things is characterized by comprising the following steps of:
s100, arranging a multichannel heat dissipation system based on the Internet of things, wherein the multichannel heat dissipation system comprises a temperature sensor, and the temperature sensor is used as a node;
s200, obtaining temperature values from all nodes to form sub-channel loads;
s300, carrying out load redundancy analysis through sub-channel loads of all the node sub-channels;
s400, performing heat dissipation regulation and control on the split-channel heat dissipation system according to the result of the load redundancy analysis;
the method for performing load redundancy analysis through the sub-channel load of each node sub-channel in step S300 is as follows: obtaining a peak-average difference value and a peak-average difference value level through channel load, obtaining a first suspicious temperature point through comparison of the two variables, calculating and forming a tracing average ratio based on the channel load between two continuous moments, forming a second suspicious temperature point through comparison definition by utilizing each tracing average ratio at one moment, screening a third suspicious temperature point according to the first suspicious temperature point and the second suspicious temperature point, calculating a load redundancy value according to the tracing average ratio level and the peak-average difference value level of the third suspicious temperature point, and taking the load redundancy value as a result of load redundancy analysis.
2. The method for monitoring operation of a heat dissipation host power supply based on the internet of things according to claim 1, wherein in step S100, the method for arranging the multichannel heat dissipation system based on the internet of things is as follows: the multichannel heat dissipation system is provided with a plurality of computers or hosts, each computer or host is provided with a power supply, each power supply is internally provided with a temperature sensor, each temperature sensor is taken as a node, and the temperature sensor is a heat sensor or a resistance thermometer; the data collected by each temperature sensor are transmitted through the Internet of things formed by networking through zigbee technology, wifi technology or Bluetooth technology; the sub-channel heat dissipation system also comprises a plurality of sub-channels, wherein the sub-channels are used for conveying cold air or discharging hot air for the power supply, and each sub-channel is in one-to-one correspondence with the power supply.
3. The method for monitoring the operation of a heat dissipating host power supply based on the internet of things according to claim 1, wherein in step S200, the method for obtaining temperature values from each node and forming a sub-channel load is as follows: each power supply is provided with a plurality of nodes, each node performs temperature measurement on different components in the power supply and obtains a temperature value, wherein each node under the same power supply is called a sub-channel node of the power supply; and respectively marking the maximum value and the average value of the temperature values corresponding to each sub-channel node in the sub-channel as an uplink value and a uniformity value, and taking a binary group formed by the uplink value and the uniformity value as a sub-channel load of the sub-channel.
4. The method for monitoring operation of a heat dissipating host power supply based on the internet of things according to claim 1, wherein in step S300, the method for performing load redundancy analysis by sub-channel loads of each node sub-channel is as follows: setting a time period TP, TP epsilon [0.5,5] hours, recording the difference between an uplink value and an average value of any sub-channel at one time as a peak-average difference value HADN, recording the average value of the peak-average difference values at all times in the previous TP time period at the current time as a peak-average difference value level c.HADN, and recording the time as a first suspicious temperature point when any time under the sub-channel satisfies the HADN & gtc.HADN;
the ratio of the uniformity value at any moment to the uniformity value at the corresponding moment is a trace-back uniformity ratio TGAE, the maximum value of all trace-back uniformity ratios in one sub-channel is used as the trace-back uniformity ratio level MTGAE of the sub-channel, and if the trace-back uniformity ratio of the sub-channel at the moment is more than 1, the moment is recorded as a second suspicious temperature point; for any moment in the sub-channel, if the sub-channel meets the conditions of the first suspicious temperature point and the second suspicious temperature point at the same time, the corresponding moment in the sub-channel is marked as a third suspicious temperature point; calculating the charge redundancy value BRLv by tracing back the average ratio level and the peak-to-average difference value level,
；
wherein j1 is the serial number of the third suspicious temperature point under the sub-channel, len.j1 is the number of the third suspicious temperature point under the sub-channel, and ERR is the standard deviation of each peak-to-average difference value under the sub-channel.
5. The method for monitoring operation of a heat dissipating host power supply based on the internet of things according to claim 1, wherein in step S300, the method for performing load redundancy analysis by sub-channel loads of each node sub-channel is as follows: setting a time period to be TP, TP epsilon [0.5,5] hours, carrying out normalization processing on the uniformity value of each sub-channel in the TP period, defining the processed value as a uniformity index, taking the uniformity index of different sub-channels at one time as a column, taking the corresponding uniformity index of one sub-channel at each time in the TP period as a row, constructing a matrix as a redundancy analysis matrix, and adding an empty tuple for each element in the redundancy analysis matrix as an integration factor of the element; defining the uniformity residual error of any integrated factor in the load redundancy analysis matrix as the difference value between the maximum value of all elements in the load redundancy analysis matrix and the element; taking a sequence formed by arranging all the uniformity residual structures from small to large as a residual gradient sequence, and taking the difference value between any element of the residual gradient sequence and the previous element as the range extension HIDG of the integration factor corresponding to the element;
taking the average value of the range-extending amounts of all the integrated factors as the range-extending level, defining that one integrated factor meets the high range-extending requirement when the range-extending amount of the integrated factor is larger than the range-extending level, defining that the integrated factor meeting the high range-extending requirement is the high range-extending factor, and defining that the integrated factor not meeting the high range-extending requirement is the low range-extending factor; taking the ratio of the number of integration factors meeting the high range-extending requirement under one sub-channel to the number of remaining integration factors as a range-extending redundancy ratio RHR of the sub-channel, and calculating to obtain a first load redundancy of the sub-channel according to the range-extending quantity and the range-extending redundancy ratio;
for any time, the attribute of Cheng Feng difference is added, and the acquisition method is as follows: when each integrated factor at a moment has a high elevation factor, the maximum value in the uplink value of each sub-channel corresponding to the high elevation factor at the moment is marked as a high uplink index, the maximum value in the uplink value corresponding to each low elevation factor at the moment is marked as a low uplink index, and the ratio of the high uplink index to the low uplink index is used as an elevation Cheng Fengcha attribute at the moment; when the high range-increasing factors do not exist in the integrated factors at one moment, the moment uses the Cheng Fengcha attribute of the increment at the previous moment; and obtaining the charge redundancy value BRLv of the sub-channel according to the definition of the first charge redundancy and the high range factor.
6. The method for monitoring the operation of a heat dissipation host power supply based on the internet of things according to claim 1, wherein in step S400, the method for performing heat dissipation regulation and control on a split-channel heat dissipation system according to the result of the load redundancy analysis is as follows: the method comprises the steps of obtaining a first quartile Flg2, a second quartile Flg3 and a third quartile Flg4 in corresponding charge redundancy values of each sub-channel at the current moment, and judging the charge redundancy order FgCls of the sub-channel at the current moment according to the charge redundancy values of the sub-channel:
；
setting a time period as an adjustment analysis period TR, TR epsilon [5,10] minutes, taking the occurrence times of one sub-channel in different charge redundancy orders in the TR period as the charge redundancy order of the sub-channel in the corresponding charge redundancy order, taking the difference between the maximum value and the minimum value in the corresponding charge redundancy order of the different charge redundancy orders as the charge redundancy expression, constructing the charge redundancy expression of each sub-channel into a box graph, identifying an abnormal value, judging that the monitoring result of the current sub-channel heat dissipation system is energy consumption redundancy if the abnormal value exists, otherwise judging that the monitoring result of the current sub-channel heat dissipation system is stable in operation, and sending the monitoring result to a client.
7. The utility model provides a heat dissipation host computer power operation monitoring system based on thing networking which characterized in that, a heat dissipation host computer power operation monitoring system based on thing networking includes: the method comprises the steps of the method for monitoring the operation of the heat dissipation host power supply based on the Internet of things, wherein the system for monitoring the operation of the heat dissipation host power supply based on the Internet of things is operated in computing equipment of a desktop computer, a notebook computer, a palm computer and a cloud data center.
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