CN113849366A - Intelligent integrated management method and system based on multi-source sensing data - Google Patents
Intelligent integrated management method and system based on multi-source sensing data Download PDFInfo
- Publication number
- CN113849366A CN113849366A CN202110962287.6A CN202110962287A CN113849366A CN 113849366 A CN113849366 A CN 113849366A CN 202110962287 A CN202110962287 A CN 202110962287A CN 113849366 A CN113849366 A CN 113849366A
- Authority
- CN
- China
- Prior art keywords
- cooling system
- temperature
- index
- sub
- data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000007726 management method Methods 0.000 title claims abstract description 37
- 238000001816 cooling Methods 0.000 claims abstract description 151
- 238000012545 processing Methods 0.000 claims abstract description 23
- 238000009423 ventilation Methods 0.000 claims description 12
- 238000012544 monitoring process Methods 0.000 claims description 5
- 230000005540 biological transmission Effects 0.000 claims description 4
- 238000005057 refrigeration Methods 0.000 claims description 2
- 238000005265 energy consumption Methods 0.000 abstract description 4
- 230000017525 heat dissipation Effects 0.000 abstract description 3
- 238000000034 method Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 4
- 238000012986 modification Methods 0.000 description 4
- 230000004048 modification Effects 0.000 description 4
- 238000001931 thermography Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000004590 computer program Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000001413 cellular effect Effects 0.000 description 1
- QVFWZNCVPCJQOP-UHFFFAOYSA-N chloralodol Chemical compound CC(O)(C)CC(C)OC(O)C(Cl)(Cl)Cl QVFWZNCVPCJQOP-UHFFFAOYSA-N 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3058—Monitoring arrangements for monitoring environmental properties or parameters of the computing system or of the computing system component, e.g. monitoring of power, currents, temperature, humidity, position, vibrations
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04Q—SELECTING
- H04Q9/00—Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
-
- H—ELECTRICITY
- H05—ELECTRIC TECHNIQUES NOT OTHERWISE PROVIDED FOR
- H05K—PRINTED CIRCUITS; CASINGS OR CONSTRUCTIONAL DETAILS OF ELECTRIC APPARATUS; MANUFACTURE OF ASSEMBLAGES OF ELECTRICAL COMPONENTS
- H05K7/00—Constructional details common to different types of electric apparatus
- H05K7/20—Modifications to facilitate cooling, ventilating, or heating
- H05K7/20709—Modifications to facilitate cooling, ventilating, or heating for server racks or cabinets; for data centers, e.g. 19-inch computer racks
- H05K7/20718—Forced ventilation of a gaseous coolant
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Theoretical Computer Science (AREA)
- General Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Microelectronics & Electronic Packaging (AREA)
- Thermal Sciences (AREA)
- Computing Systems (AREA)
- Quality & Reliability (AREA)
- General Physics & Mathematics (AREA)
- Computer Hardware Design (AREA)
- Signal Processing (AREA)
- Cooling Or The Like Of Electrical Apparatus (AREA)
Abstract
The invention discloses an intelligent comprehensive management method based on multi-source sensing data, which comprises the steps of initializing a multi-source sensor network, obtaining temperature data of each subarea through a temperature sensor, processing the temperature data to obtain the running state of a cooling system, adjusting the cooling system according to the running state and giving an alarm. According to the invention, by introducing the multi-source sensors and arranging the temperature sensors at a plurality of key positions in the server room, the intelligent temperature control of the rack in the server room is realized, the high-efficiency operation of the cooling system is realized to the maximum extent under the condition of ensuring the heat dissipation of the server rack, and the energy consumption of the cooling system is saved.
Description
Technical Field
The invention relates to the technical field of intelligent integrated management, in particular to an intelligent integrated management method and system based on multi-source sensing data.
Background
In the following background discussion, reference is made to certain structures and/or methods. However, the following references should not be construed as an admission that these structures and/or methods constitute prior art. Applicants expressly reserve the right to demonstrate that such structures and/or methods do not qualify as prior art.
With the gradual expansion of the information-oriented social range and the rapid development of technologies such as electronic communication, cloud computing, 5G and digital currency, data centers increasingly become one of important social infrastructures, the energy problem of the computer industry is gradually highlighted, and according to data display of application development guidance (2018) of national data centers published in 5 months of the Ministry of industry and communications, the total size of racks of data centers in China is 166 ten thousand and is increased by 33.4% on a year-by-year basis by 2017, wherein the size of large and ultra-large data centers is increased by 68%.
The power consumption of the data center accounts for 1% of the global power consumption, and accounts for 2.5% of the power consumption in China, and the data center is considered as a type 3 energy-saving and consumption-reducing industry.
Meanwhile, the heat dissipation energy consumption of the data center cannot be ignored, the power consumption of a cooling system of the data center can account for 00% of the total energy consumption of the data center, the cooling system can be efficiently controlled, the energy efficiency of the data center is facilitated, and the method is a target pursued by the technical personnel in the field.
Disclosure of Invention
The invention aims to provide an intelligent integrated management method based on multi-source sensing data, which is used for solving one or more technical problems in the prior art and at least providing a beneficial selection or creation condition.
In order to achieve the technical purpose, the technical scheme of the invention is as follows:
the intelligent integrated management method based on the multi-source sensing data comprises the following steps:
step 1, initializing a multi-source sensor network;
step 2, obtaining temperature data of each sub-area through a temperature sensor;
step 3, processing the temperature data to obtain the running state of the cooling system;
and 4, adjusting the cooling system and giving an alarm according to the running state.
Further, in step 1, the substep of initializing the multisource sensor network is:
the area needing to be monitored in the server room comprises a plurality of subregions, or the server room is divided into 8 to 20 subregions on average, the point needing to be monitored in each subregion is an air inlet of each rack, an air outlet of each rack, an air supply end and an air return end of a cooling system in the subregion, data acquired by the sensor network comprises the temperature of the air supply end of the cooling system, the temperature of the air return end of the cooling system, the air supply speed, the temperature of the air inlet of the rack and the temperature of the air outlet of the rack, an environment temperature sensor is further arranged in the subregion, all the sensors are connected with the sensor network, and the sensor network realizes data transmission by utilizing a Multi-hop LQI routing protocol.
Further, the connection may be a wired connection, or may be a wireless transmission, including one or more of the following technologies: NB-IoT, LTE-M, Weightless, HaLow, LoRa, Sigfox, RPMA, Neul, BLE.
Preferably, the sensor network may be constructed based on an infrared thermal imaging technology, and the temperature of the location to be monitored is obtained by intercepting the monitoring point in the infrared thermal imaging, and the cooling system may be a refrigeration device such as an air conditioner or a refrigerator.
Further, in step 2, the sub-step of obtaining the temperature data of each sub-region by the temperature sensor is as follows:
the temperature sensor obtains the temperature of the monitoring point of each sub-area in each sub-area and sends the temperature to the sensor network.
Further, in step 3, the sub-step of processing the temperature data to obtain the operating state of the cooling system is as follows:
step 3.1, calculate the second index I1 for each subregiondAnd the first index I1u：
I1u＝{1-[Σ(Rin-R↑)/((THRS↑-R↑)×Rn)]}×100％，
In the formula, I1uIs a first index, Rin is the air inlet of the frameThe temperature of the air inlet of the rack is higher than the temperature of the air inlet of the rack, R ℃ × (Rin-R ℃) × (Rn-R ℃) is a first parameter of the air inlet temperature of the rack, THRS ℃ is an upper limit of a warning value of the air inlet temperature of the rack, and Rn is the number of the racks, (Rin-R ℃) is a difference value between the temperature of the air inlet of the rack in the sub-region and a first threshold value of the air inlet temperature of the rack, and Σ (Rin-R ℃) is obtained by summing all the difference values (Rin-R ℃);
I1d＝{1-[Σ(R↓-Rin)/((R↓-THRS↓)×Rn)]}×100％，
in the formula, I1dThe index is a second index, Rin is the temperature of the rack air inlet, R ↓ is a second parameter of the rack air inlet temperature, THRS ↓ is the lower limit of the rack air inlet temperature warning value, Rn is the number of the racks, (R ↓ -Rin) is the difference value between the second parameter of the rack air inlet temperature and the temperature of the rack air inlet in the sub-region, and Σ (R ↓ -Rin) is the sum of all the difference values (R ↓ -Rin);
in one embodiment, the value of a first parameter R ↓ of the rack air inlet temperature is 26 ℃, the value of an upper limit THRS ↓ of the rack air inlet temperature warning value is 35 ℃, the value of a second parameter R ↓ of the rack air inlet temperature is 20 ℃, the value of a lower limit THRS ↓ of the rack air inlet temperature warning value is 16 ℃,
step 3.2, calculating the efficiency index ACE of the cooling system:
ACE＝[(ACh-ACs)/(R'out-R'in)]-1，
in the formula, ACE is the efficiency index of the cooling system, ACh is the return air temperature of the cooling system, ACs is the supply air temperature of the cooling system, R 'out is the average value of the exhaust air temperatures of all the racks in the current management area of the cooling system, and R' in is the average value of the inlet air temperatures of all the racks in the current management area of the cooling system;
and 3.3, judging whether the cooling system works in an ideal state.
Further, in step 3.3, the sub-step of determining whether the cooling system is operating in an ideal state is:
step 3.3.1, judging whether the efficiency index ACE of the cooling system is in an ideal range, wherein the ideal range is [ -0.1, 0.1], and if the efficiency index ACE of the cooling system is larger than the upper limit of the ideal range, judging:
counting that the second index I1d is greater than the first index I1u and the number of the sub-areas with the difference greater than 20% exceeds a threshold value delta in all the sub-areas, so that the current ventilation capacity of the cooling system is marked to be insufficient;
counting that the second index I1d is greater than the first index I1u and the number of the sub-areas with the difference greater than 20% does not exceed the threshold value delta in all the sub-areas, so that the current cooling capacity of the cooling system is marked to be insufficient;
if the cooling system efficiency index ACE is less than the lower limit of the desired range, it is judged:
in all the subareas, counting that the second index I1d is smaller than the first index I1u and the number of the subareas with the difference larger than 20% exceeds a threshold value delta, and marking the surplus of the current ventilation capacity of the cooling system;
and in all the subareas, counting that the second index I1d is smaller than the first index I1u and the number of the subareas with the difference larger than 20% does not exceed the threshold value delta, and marking the surplus of the current cooling capacity of the cooling system.
In one embodiment, the threshold δ is 3.
Further, in step 4, the sub-steps of adjusting the cooling system and giving an alarm according to the operation state are as follows:
step 4.1, if the value of the cooling system efficiency index ACE of the cooling system is not in the ideal range, adjusting according to the judgment result of the step 3.3.1, and if the ventilation capacity of the cooling system is not enough, increasing the wind power of the cooling system (namely increasing the fan rotating speed of the cooling system by 10 percentage points); if the cooling capacity of the cooling system is not enough, the refrigerating capacity of the cooling system is increased (namely the refrigerating capacity of the cooling system is increased by 10 percentage points); if the ventilation capacity of the cooling system is surplus, reducing the wind power of the cooling system (reducing the fan rotating speed of the cooling system by 10 percentage points); if the cooling capacity of the cooling system is surplus, reducing the refrigerating capacity of the cooling system (namely reducing the refrigerating capacity of the cooling system by 10 percentage points);
step 4.2, executing the steps 3.1 to 3.3 and 4.1 according to a set time interval, and if the cooling system efficiency index ACE still does not reach the ideal range after the wind power or the refrigerating capacity reaches the limit after the set times of repeated execution, giving an alarm;
in one embodiment, the cooling capacity of the cooling system is insufficient, and after a set number of times of 8, each time at an interval of 30min, the cooling system efficiency index ACE is 1.2, and the ideal range is not reached, and an alarm is issued that the cooling system cannot reach the ideal state.
Intelligent integrated management system based on multisource sensing data, the system includes:
a sensor network: the data processing module is used for collecting data of the temperature sensor, transmitting the data to the data processing module and managing the temperature sensor;
a data processing module: for processing data of sensor network, calculating second index I1 in real timedAnd a first index I1uAnd a cooling system efficiency index, outputting the result to a cooling system control module;
a cooling system control module: for receiving the results of the data processing module and controlling the wind power and cooling capacity of the cooling system.
Compared with the prior art, the invention has the following beneficial technical effects:
through introducing multisource sensor, temperature sensor is arranged to a plurality of key positions in server computer lab, has realized the intelligent temperature control of frame in the server computer lab, under the heat dissipation condition of having guaranteed the server frame, the maximize has realized cooling system's high efficiency function, has practiced thrift cooling system's energy consumption.
Drawings
The foregoing and other features of the present invention will become more apparent to those skilled in the art from the following detailed description of the embodiments taken in conjunction with the accompanying drawings, in which like reference characters designate the same or similar elements, and in which it is apparent that the drawings described below are merely exemplary of the invention and that other drawings may be derived therefrom without the inventive faculty, to those skilled in the art, and in which:
FIG. 1 is a flow chart of an intelligent integrated management method based on multi-source sensing data provided by the invention;
fig. 2 is a block diagram illustrating a structure of an intelligent integrated management system based on multi-source sensing data according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more clear, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. The specific embodiments described herein are merely illustrative of the invention and are not intended to be limiting.
It is also to be understood that the following examples are illustrative of the present invention and are not to be construed as limiting the scope of the invention, and that certain insubstantial modifications and adaptations of the invention by those skilled in the art in light of the foregoing description are intended to be included within the scope of the invention. The specific process parameters and the like of the following examples are also only one example within a suitable range, i.e., those skilled in the art can select the appropriate range through the description herein, and are not limited to the specific values exemplified below.
The intelligent comprehensive management method based on multi-source sensing data provided by the invention is exemplarily described below.
Fig. 1 is a flow chart of an intelligent integrated management method based on multi-source sensing data, and the following describes, with reference to fig. 1, an intelligent integrated management method based on multi-source sensing data according to an embodiment of the present invention, the method including the following steps:
step 1, initializing a multi-source sensor network;
step 2, obtaining temperature data of each sub-area through a temperature sensor;
step 3, processing the temperature data to obtain the running state of the cooling system;
and 4, adjusting the cooling system and giving an alarm according to the running state.
Further, in step 1, the substep of initializing the multisource sensor network is:
the area needing to be monitored in the server room comprises a plurality of subregions, or the server room is divided into 8 to 20 subregions on average, the point needing to be monitored in each subregion is an air inlet of each rack, an air outlet of each rack, an air supply end and an air return end of a cooling system in the subregion, data acquired by the sensor network comprises the temperature of the air supply end of the cooling system, the temperature of the air return end of the cooling system, the air supply speed, the temperature of the air inlet of the rack and the temperature of the air outlet of the rack, an environment temperature sensor is further arranged in the subregion, all the sensors are connected with the sensor network, and the sensor network realizes data transmission by utilizing a Multi-hop LQI routing protocol.
Preferably, the sensor network may be constructed based on an infrared thermal imaging technology, and the temperature of the point to be monitored is obtained by intercepting the monitoring point in the infrared thermal imaging.
Further, in step 2, the sub-step of obtaining the temperature data of each sub-region by the temperature sensor is as follows:
the temperature sensor obtains the temperature of the monitoring point of each sub-area in each sub-area and sends the temperature to the sensor network.
Further, in step 3, the sub-step of processing the temperature data to obtain the operating state of the cooling system is as follows:
step 3.1, calculate the second index I1 for each subregiondAnd the first index I1u：
I1u＝{1-[Σ(Rin-R↑)/((THRS↑-R↑)×Rn)]}×100％，
In the formula, I1uThe frame air inlet temperature alarm value is a first index, Rin is the temperature of a frame air inlet, R ≠ is a first parameter of the frame air inlet temperature, THRS ≠ is an upper limit of the frame air inlet temperature alarm value, Rn is the number of frames, (Rin-R ℃) is the difference value between the temperature of the frame air inlet in a sub-region and a first threshold value of the frame air inlet temperature, and Σ (Rin-R ℃) is used for summing all the difference values (Rin-R ℃);
I1d＝{1-[Σ(R↓-Rin)/((R↓-THRS↓)×Rn)]}×100％，
in the formula, I1dFor a second index, Rin is the temperature of the rack air inlet, R ↓ is a second parameter of the rack air inlet temperature, THRS ↓ is a lower limit of the rack air inlet temperature warning value, Rn is the number of the racks, and (R ↓ -Rin) is the second parameter of the rack air inlet temperature and the temperature of the rack air inlet in the subregionThe difference value of degrees, Σ (R ↓ -Rin), is to sum all the difference values (R ↓ -Rin);
in one embodiment, the value of a first parameter R ↓ of the rack air inlet temperature is 26 ℃, the value of an upper limit THRS ↓ of the rack air inlet temperature warning value is 35 ℃, the value of a second parameter R ↓ of the rack air inlet temperature is 20 ℃, the value of a lower limit THRS ↓ of the rack air inlet temperature warning value is 16 ℃,
step 3.2, calculating the efficiency index ACE of the cooling system:
ACE＝[(ACh-ACs)/(R'out-R'in)]-1，
in the formula, ACE is the efficiency index of the cooling system, ACh is the return air temperature of the cooling system, ACs is the supply air temperature of the cooling system, R 'out is the average value of the exhaust air temperatures of all the racks in the current management area of the cooling system, and R' in is the average value of the inlet air temperatures of all the racks in the current management area of the cooling system;
and 3.3, judging whether the cooling system works in an ideal state.
Further, in step 3.3, the sub-step of determining whether the cooling system is operating in an ideal state is:
step 3.3.1, judging whether the efficiency index ACE of the cooling system is in an ideal range, wherein the ideal range is [ -0.1, 0.1], and if the efficiency index ACE of the cooling system is larger than the upper limit of the ideal range, judging:
counting that the second index I1d is greater than the first index I1u and the number of the sub-areas with the difference greater than 20% exceeds a threshold value delta in all the sub-areas, so that the current ventilation capacity of the cooling system is marked to be insufficient;
counting that the second index I1d is greater than the first index I1u and the number of the sub-areas with the difference greater than 20% does not exceed the threshold value delta in all the sub-areas, so that the current cooling capacity of the cooling system is marked to be insufficient;
if the cooling system efficiency index ACE is less than the lower limit of the desired range, it is judged:
in all the subareas, counting that the second index I1d is smaller than the first index I1u and the number of the subareas with the difference larger than 20% exceeds a threshold value delta, and marking the surplus of the current ventilation capacity of the cooling system;
and in all the subareas, counting that the second index I1d is smaller than the first index I1u and the number of the subareas with the difference larger than 20% does not exceed the threshold value delta, and marking the surplus of the current cooling capacity of the cooling system.
In one embodiment, the threshold δ is 3.
Further, in step 4, the sub-steps of adjusting the cooling system and giving an alarm according to the operation state are as follows:
step 4.1, if the value of the cooling system efficiency index ACE of the cooling system is not in the ideal range, adjusting according to the judgment result of the step 3.3.1, and if the ventilation capacity of the cooling system is not enough, increasing the wind power of the cooling system (namely increasing the fan rotating speed of the cooling system by 10 percentage points); if the cooling capacity of the cooling system is not enough, the refrigerating capacity of the cooling system is increased (namely the refrigerating capacity of the cooling system is increased by 10 percentage points); if the ventilation capacity of the cooling system is surplus, reducing the wind power of the cooling system (reducing the fan rotating speed of the cooling system by 10 percentage points); if the cooling capacity of the cooling system is surplus, reducing the refrigerating capacity of the cooling system (namely reducing the refrigerating capacity of the cooling system by 10 percentage points);
step 4.2, executing the steps 3.1 to 3.3 and 4.1 according to a set time interval, and if the cooling system efficiency index ACE still does not reach the ideal range after the wind power or the refrigerating capacity reaches the limit after the set times of repeated execution, giving an alarm;
in one embodiment, the cooling capacity of the cooling system is insufficient, and after a set number of times of 8, each time at an interval of 30min, the cooling system efficiency index ACE is 1.2, and the ideal range is not reached, and an alarm is issued that the cooling system cannot reach the ideal state.
Intelligent integrated management system based on multisource sensing data, the system includes:
referring to fig. 2, fig. 2 is a schematic block diagram of a structure of an intelligent integrated management system based on multi-source sensing data according to an embodiment of the present invention;
a sensor network: the data processing module is used for collecting data of the temperature sensor, transmitting the data to the data processing module and managing the temperature sensor;
a data processing module: for processing data of sensor network, calculating second index I1 in real timedAnd a first index I1uAnd a cooling system efficiency index, outputting the result to a cooling system control module;
a cooling system control module: for receiving the results of the data processing module and controlling the wind power and cooling capacity of the cooling system.
The intelligent integrated management system based on the multi-source sensing data can be operated in computing equipment such as desktop computers, notebooks, palm computers and cloud servers. The intelligent integrated management system based on multi-source sensing data can be operated by a system comprising, but not limited to, a processor and a memory. Those skilled in the art will appreciate that the examples are merely examples of the intelligent integrated management system based on multi-source sensing data, and do not constitute a limitation of the intelligent integrated management system based on multi-source sensing data, and may include more or less components than the intelligent integrated management system based on multi-source sensing data, or combine some components, or different components, for example, the intelligent integrated management system based on multi-source sensing data may further include input and output devices, network access devices, buses, and the like.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. The general processor can be a microprocessor or the processor can be any conventional processor and the like, the processor is a control center of the intelligent integrated management system operation system based on the multi-source sensing data, and various interfaces and lines are utilized to connect all parts of the whole intelligent integrated management system operable system based on the multi-source sensing data.
The memory can be used for storing the computer program and/or the module, and the processor realizes various functions of the intelligent integrated management system based on the multi-source sensing data 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 required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Although the present invention has been described in considerable detail and with reference to certain illustrated embodiments, it is not intended to be limited to any such details or embodiments or any particular embodiment, so as to effectively encompass the intended scope of the invention. Furthermore, the foregoing describes the invention in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial modifications of the invention, not presently foreseen, may nonetheless represent equivalent modifications thereto.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.
Claims (7)
1. The intelligent integrated management method based on the multi-source sensing data is characterized by comprising the following steps:
step 1, initializing a multi-source sensor network;
step 2, obtaining temperature data of each sub-area through a temperature sensor;
step 3, processing the temperature data to obtain the running state of the cooling system;
and 4, adjusting the cooling system and giving an alarm according to the running state.
2. The intelligent integrated management method based on multi-source sensing data according to claim 1, wherein in step 1, the sub-step of initializing the multi-source sensor network is as follows:
the area needing to be monitored in the server room comprises a plurality of sub-areas, the point needing to be monitored in each sub-area is an air inlet of each rack in the sub-area, each rack air outlet, an air supply end and an air return end of a cooling system in each sub-area, data acquired by the sensor network comprises the temperature of the air supply end of the cooling system, the temperature of the air return end of the cooling system, the air supply speed, the temperature of the rack air inlet and the temperature of the rack air outlet, an environment temperature sensor is further arranged in each sub-area, all the sensors are connected with the sensor network, and the sensor network realizes data transmission by utilizing a Multi-hop LQI routing protocol.
3. The intelligent integrated management method based on multi-source sensing data according to claim 1, wherein in the step 2, the sub-step of obtaining the temperature data of each sub-area through the temperature sensor is as follows:
the temperature sensor obtains the temperature of the monitoring point of each sub-area in each sub-area and sends the temperature to the sensor network.
4. The intelligent integrated management method based on multi-source sensing data according to claim 1, wherein in the step 3, the sub-step of processing the temperature data to obtain the operation state of the cooling system comprises:
step 3.1, calculate the second index I1 for each subregiondAnd the first index I1u：
I1u＝{1-[Σ(Rin-R↑)/((THRS↑-R↑)×Rn)]}×100％，
In the formula, I1uThe frame air inlet temperature alarm value is a first index, Rin is the temperature of a frame air inlet, R ≠ is a first parameter of the frame air inlet temperature, THRS ≧ R ≧ is the upper limit of the frame air inlet temperature alarm value, (Rin-R ≧ R) is the difference between the temperature of the frame air inlet in the subregion and a first threshold of the frame air inlet temperature, Rn is the number of frames, and Σ (Rin-R ≧ is used for summing all the differences;
I1d＝{1-[Σ(R↓-Rin)/((R↓-THRS↓)×Rn)]}×100％，
in the formula, I1dThe index is a second index, Rin is the temperature of the rack air inlet, R ↓ is a second parameter of the rack air inlet temperature, THRS ↓ is the lower limit of the rack air inlet temperature warning value, Rn is the number of the racks, (R ↓ -Rin) is the difference value between the second parameter of the rack air inlet temperature and the temperature of the rack air inlet in the sub-region, and Σ (R ↓ -Rin) is to sum all the difference values;
step 3.2, calculating the efficiency index ACE of the cooling system:
ACE＝[(ACh-ACs)/(R'out-R'in)]-1，
in the formula, ACE is the efficiency index of the cooling system, ACh is the return air temperature of the cooling system, ACs is the air supply temperature of the cooling system, R 'out is the average value of the air exhaust temperatures of all the racks in the current management area of the cooling system, and R' in is the average value of the air exhaust temperatures of all the racks in the current management area of the cooling system;
and 3.3, judging whether the cooling system works in an ideal state.
5. The intelligent integrated management method based on multi-source sensing data of claim 4, wherein in step 3.3, the sub-step of judging whether the cooling system works in an ideal state is as follows:
step 3.3.1, judging whether the efficiency index ACE of the cooling system is in an ideal range, wherein the ideal range is [ -0.1, 0.1], and if the efficiency index ACE of the cooling system is larger than the upper limit of the ideal range, judging:
counting that the second index I1d is greater than the first index I1u and the number of the sub-areas with the difference greater than 20% exceeds a threshold value delta in all the sub-areas, so that the current ventilation capacity of the cooling system is marked to be insufficient;
counting that the second index I1d is greater than the first index I1u and the number of the sub-areas with the difference greater than 20% does not exceed the threshold value delta in all the sub-areas, so that the current cooling capacity of the cooling system is marked to be insufficient;
if the cooling system efficiency index ACE is less than the lower limit of the desired range, it is judged:
in all the subareas, counting that the second index I1d is smaller than the first index I1u and the number of the subareas with the difference larger than 20% exceeds a threshold value delta, and marking the surplus of the current ventilation capacity of the cooling system;
and in all the subareas, counting that the second index I1d is smaller than the first index I1u and the number of the subareas with the difference larger than 20% does not exceed the threshold value delta, and marking the surplus of the current cooling capacity of the cooling system.
6. The intelligent integrated management method based on multi-source sensing data of claim 1, wherein in the step 4, the sub-steps of adjusting the cooling system according to the running state and giving an alarm are as follows:
step 4.1, if the value of the cooling system efficiency index ACE of the cooling system is not in the ideal range, adjusting according to the judgment result of the step 3.3.1, and if the ventilation capacity of the cooling system is not enough, increasing the wind power of the cooling system; if the cooling capacity of the cooling system is not enough, the refrigerating capacity of the cooling system is increased; if the ventilation capacity of the cooling system is surplus, reducing the wind power of the cooling system; if the cooling capacity of the cooling system is surplus, reducing the refrigerating capacity of the cooling system;
step 4.2, the steps 3.1 to 3.3 and 4.1 are carried out at set time intervals, and if after a set number of repetitions, an alarm is given if the cooling system efficiency index ACE has not yet reached the desired range after the wind or refrigeration capacity has reached its limit.
7. Intelligent integrated management system based on multisource sensing data, its characterized in that, the system includes:
a sensor network: the data processing module is used for collecting data of the temperature sensor, transmitting the data to the data processing module and managing the temperature sensor;
a data processing module: for processing data of sensor network, calculating second index I1 in real timedAnd a first index I1uAnd a cooling system efficiency index, outputting the result to a cooling system control module;
a cooling system control module: for receiving the results of the data processing module and controlling the wind power and cooling capacity of the cooling system.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110962287.6A CN113849366A (en) | 2021-08-20 | 2021-08-20 | Intelligent integrated management method and system based on multi-source sensing data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110962287.6A CN113849366A (en) | 2021-08-20 | 2021-08-20 | Intelligent integrated management method and system based on multi-source sensing data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113849366A true CN113849366A (en) | 2021-12-28 |
Family
ID=78975775
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110962287.6A Pending CN113849366A (en) | 2021-08-20 | 2021-08-20 | Intelligent integrated management method and system based on multi-source sensing data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113849366A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116261315A (en) * | 2023-05-12 | 2023-06-13 | 合肥创科电子工程科技有限责任公司 | Cabinet temperature regulation control system |
| CN117724933A (en) * | 2023-12-20 | 2024-03-19 | 江苏海鋆自动化技术有限公司 | Data center communication thermal management detection method and system |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103673200A (en) * | 2012-09-06 | 2014-03-26 | 中国建筑科学研究院 | Energy consumption control system and method for data center |
| CN108668513A (en) * | 2018-07-10 | 2018-10-16 | 广东宏达通信有限公司 | A kind of data center apparatus cooling system |
| CN111669953A (en) * | 2020-07-10 | 2020-09-15 | 深圳安腾创新科技有限公司 | Energy-saving container formula data center |
-
2021
- 2021-08-20 CN CN202110962287.6A patent/CN113849366A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103673200A (en) * | 2012-09-06 | 2014-03-26 | 中国建筑科学研究院 | Energy consumption control system and method for data center |
| CN108668513A (en) * | 2018-07-10 | 2018-10-16 | 广东宏达通信有限公司 | A kind of data center apparatus cooling system |
| CN111669953A (en) * | 2020-07-10 | 2020-09-15 | 深圳安腾创新科技有限公司 | Energy-saving container formula data center |
Non-Patent Citations (2)
| Title |
|---|
| 傅烈虎: "数据中心热环境实测与仿真计算", 《暖通空调》 * |
| 王宁: "数据中心机房热环境的数值模拟和节能优化研究", 《中国优秀博硕士学位论文全文数据库（硕士） 信息科技辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116261315A (en) * | 2023-05-12 | 2023-06-13 | 合肥创科电子工程科技有限责任公司 | Cabinet temperature regulation control system |
| CN116261315B (en) * | 2023-05-12 | 2023-07-11 | 合肥创科电子工程科技有限责任公司 | Cabinet temperature regulation control system |
| CN117724933A (en) * | 2023-12-20 | 2024-03-19 | 江苏海鋆自动化技术有限公司 | Data center communication thermal management detection method and system |
| CN117724933B (en) * | 2023-12-20 | 2024-06-11 | 江苏海鋆自动化技术有限公司 | Data center communication thermal management detection method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113849366A (en) | Intelligent integrated management method and system based on multi-source sensing data | |
| US9158311B2 (en) | Integrating a data center thermal control system and individual fan controllers for controlling a thermal environment in a data center room | |
| US10390463B2 (en) | Backflow prevention for computing devices | |
| CN113853096B (en) | Internet of things linkage-based temperature regulation and control method and system for IDC machine room | |
| US20170219241A1 (en) | Data Center Infrastructure Management (DCIM) system comprising predictive analytics | |
| CN117193503B (en) | Heat dissipation host power supply operation monitoring method based on Internet of things | |
| US10078610B2 (en) | System and method for optimized thermal control for management controller offline | |
| CN112347154A (en) | Slag crust shedding index calculation method, terminal equipment and storage medium | |
| CN111511177A (en) | Thermal management method, system, terminal and medium | |
| CN113375476B (en) | Power control method and device for cooling tower fan group and computer equipment | |
| CN108205370A (en) | A kind of chip radiation method and device | |
| CN114282727A (en) | Computer room temperature prediction method and system and electronic equipment | |
| CN109489190A (en) | Method and system for regulating and controlling cold quantity of machine room | |
| CN107300278B (en) | System and method for minimizing compressor usage in an HVAC system | |
| CN111048851B (en) | Battery temperature management system control method, device, equipment and storage medium | |
| CN117193500A (en) | LSTM-based server dynamic management method, system, terminal and storage medium | |
| CN112378043A (en) | Cooling water system control method, equipment, device and storage medium | |
| CN112752491B (en) | Intelligent linkage control method for data center air conditioner and terminal equipment | |
| CN116360566A (en) | Fan speed regulating method, device, computer equipment, storage medium and program product | |
| CN112268355B (en) | Air conditioner target temperature adjusting method and terminal equipment | |
| JP2023043544A (en) | Temperature control device, temperature control system, temperature control method and program | |
| CN113780973B (en) | Intelligent management method and system for improving park safety | |
| CN113294902B (en) | Energy-saving control method and device for air conditioner and air conditioner control terminal | |
| CN116828799B (en) | Air quantity control method and device for air conditioner of data center machine room and electronic equipment | |
| CN114828579B (en) | Energy-saving control method of container data center and related equipment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20211228 |