CN109269027B - Automatic optimization air conditioner refrigeration control method, system and device - Google Patents
Automatic optimization air conditioner refrigeration control method, system and device Download PDFInfo
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- 238000004378 air conditioning Methods 0.000 claims description 4
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/54—Control or safety arrangements characterised by user interfaces or communication using one central controller connected to several sub-controllers
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/72—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure
- F24F11/74—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity
- F24F11/77—Control systems characterised by their outputs; Constructional details thereof for controlling the supply of treated air, e.g. its pressure for controlling air flow rate or air velocity by controlling the speed of ventilators
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
- F24F11/86—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling compressors within refrigeration or heat pump circuits
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/20—Humidity
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- 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
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- Y02B—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. HOUSING, HOUSE APPLIANCES OR RELATED END-USER APPLICATIONS
- Y02B30/00—Energy efficient heating, ventilation or air conditioning [HVAC]
- Y02B30/70—Efficient control or regulation technologies, e.g. for control of refrigerant flow, motor or heating
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Abstract
The invention discloses an automatic optimization-seeking air conditioner refrigeration control method, system and device, and the technical scheme is characterized in that operation working points are arranged in a machine room corresponding to air conditioners, an environment monitoring device is installed at each operation working point, working condition parameter data of a parameter X of each operation working point is collected in real time through the environment monitoring device, an average parameter X in a machine room channel is calculated, variation deviation RX and RX = (X-Xin)/X every 10 minutes is detected and calculated, the starting and stopping of the air conditioners and the output size are changed, the operation rule is analyzed through continuous 24h monitoring and recording of the variation deviation RX, extraction of an optimal operation curve L is formed, the output of a refrigeration system is predicted, the actual output is compared with an actual output curve to adjust, and the refrigeration system is continuously and circularly reciprocated to operate at the optimal point all the time, and the purposes of reliable data acquisition, hot spot elimination, improvement of machine room operation reliability and great reduction of refrigeration air conditioner energy consumption are achieved.
Description
Technical Field
The invention relates to the technical field of air conditioner refrigeration control, in particular to an automatic optimization air conditioner refrigeration control method, system and device.
Background
With the development of electronic commerce and enterprise informatization, the number and scale of the built IDC (Internet Data Center, internet Data Center for short) machine rooms are continuously enlarged. The machine room is used as a hub for exchanging and storing information of each unit, and scientific management is particularly important.
The existing air-conditioning refrigeration scheme of the machine room mainly comprises the following two types: in the first scheme, each air conditioner in a machine room sets a working point and operates independently; the second scheme is group control, networking operation, and unified issuing of working points or operation according to independent working points. The main principle of the first scheme is as follows: when the air conditioner outlet/return air temperature is higher than a set value t + t1, the air conditioner is started; and when the outlet/return air temperature is lower than the set value t-t1, the air conditioner stops. The main principle of the second scheme is as follows: connecting all air conditioners by using a network cable, setting a host, and starting a group control function; the working points can be issued uniformly or not by the host, and the air conditioner operates according to the set working points.
Both of the above solutions have the following disadvantages: 1. generally, the collected air inlet and outlet working conditions of the air conditioner are different from the actual temperature condition in the machine room; 2. each air conditioner works independently, so that hot spots of a machine room are easy to form; 3. in the operation process, the set value is basically unchanged, the improvement cannot be carried out according to the actual working condition, and the energy consumption is larger.
Disclosure of Invention
The invention aims to solve the technical problems that only the air inlet and outlet working conditions of the air conditioner are collected, so that the data source is inaccurate, the air conditioner is easy to form a machine room hot spot when operating alone, and meanwhile, the set value cannot be improved according to the actual working conditions in the operation process, so that the energy consumption is larger.
In order to solve the technical problems, the invention adopts the following basic principles: the method comprises the steps of setting operation working points corresponding to air conditioners in a machine room, installing an environment monitoring device at each operation working point, collecting working condition parameter data of a parameter X of each operation working point in real time through the environment monitoring device, calculating an average parameter X in a channel of the machine room, calculating variation deviation RX of every 10 minutes by detecting, wherein RX = (X-Xin)/X, changing the sizes of start and stop and output of the air conditioners, analyzing an operation rule through continuous 24h monitoring and recording of the variation deviation RX, extracting an optimal operation curve L, predicting the output of a refrigerating system, comparing with an actual output curve, adjusting the actual output, continuously circulating and reciprocating, and enabling the refrigerating system to operate at the optimal point all the time.
The invention aims to provide an automatic optimization air conditioner refrigeration control method, which achieves the purposes of reliable data acquisition, hot spot elimination, machine room operation reliability improvement and great reduction of energy consumption of a refrigeration air conditioner.
The technical purpose of the invention is realized by the following technical scheme: an automatic optimization air conditioner refrigeration control method comprises the following steps:
step S1: collecting working condition parameter data of a parameter X of each operating working point in a machine room channel through an environment monitoring device, and calculating an average value Xin of the parameters X of all the operating working points;
step S2: after the operation is carried out for delta t minutes, working condition parameter data of the parameter X of each operation working point in a machine room channel are collected through an environment monitoring device, and variation deviation RX, RX = (X-Xin)/X of each operation working point is calculated;
and step S3: judging according to the value obtained by RX calculation and issuing a related instruction to control the start-stop and output sizes of the compressor and the fan, specifically comprising the following steps: if RX is less than or equal to 0, the central intelligent controller receives the instruction and issues a corresponding operating point to control the compressor and the fan of the corresponding air conditioner not to output in a standby state; if RX is greater than 0, the central intelligent controller receives the instruction and issues a corresponding operating point to control a compressor and a fan of a corresponding air conditioner to change output;
and step S4: circularly executing the steps S2 and S3, continuously operating for 24h, and accumulating variation deviation RX data of every 10 minutes in all operating working points 24 h;
step S5: calculating a simulated variation deviation curve Rl according to variation deviation RX data of every 10 minutes in all the operating working points 24 h;
step S6: analyzing an operation rule according to the variation deviation curve Rl, and forming and outputting an optimal Xin operation curve L of 24h in the future;
step S7: according to the optimal Xin operation curve L, the central intelligent controller issues each operation working point, and controls a compressor and a fan of the air conditioner corresponding to each operation working point to execute change output;
step S8: after the operation is carried out for delta t minutes, working condition parameter data of the parameter X of each operation working point in a machine room channel are collected through an environment monitoring device, the average value Xin of the parameter X of all the operation working points is calculated, and the average value Xin is compared with an optimal Xin operation curve L;
step S9: according to the comparison result, the central intelligent controller issues each operation working point and controls the sizes of the compressor and the fan start-stop and the output of the corresponding air conditioner, specifically: detecting that the obtained Xin is less than or equal to the optimal Xin within the corresponding time, receiving an instruction by the central intelligent controller, issuing a corresponding operation working point, and controlling the compressor and the fan of the corresponding air conditioner not to output in a standby state; detecting the calculated Xin which is the optimal Xin within the corresponding time, receiving the instruction by the central intelligent controller, issuing a corresponding operating working point, controlling the compressor and the fan of the corresponding air conditioner to change the output, and increasing the output until the detected and calculated Xin is less than or equal to the optimal Xin within the corresponding time;
step S10: and (5) circulating the step S8 and the step S9, running for 24h, and returning to the step 1 to continuously circulate and reciprocate.
As a further optimization, the parameters X are temperature T and humidity Φ.
As a further optimization, the step S3: if the RT and the R phi are less than or equal to 0, the main control board receives the instruction, issues a corresponding operating working point, and controls the corresponding compressor and the corresponding fan not to output and be in a standby state; if the RT and/or R phi is greater than 0, the main control board receives the instruction and issues a corresponding operating point, the corresponding compressor and the corresponding fan are controlled to change output, and the output is increased until the RT and the R phi are less than or equal to 0.
As a further optimization, Δ t =10, 20, 30 or 60.
The second purpose of the invention is to provide an air conditioner refrigeration control system capable of automatically optimizing, so as to achieve the purposes of reliably acquiring data, eliminating hot spots, improving the operation reliability of a machine room and greatly reducing the energy consumption of a refrigeration air conditioner.
The technical purpose of the invention is realized by the following technical scheme: an automatic air conditioner refrigeration control system who seeks optimization, includes collection module, storage module, processing module and control module, wherein:
the acquisition module is used for acquiring working condition parameter data of the parameter X of each operating working point in the machine room channel and sending the data to the storage module;
the storage module is used for storing the data acquired by the acquisition module and variation deviation RX data of each delta t minute in all the operating working points 24 h;
the processing module is used for calculating an average value Xin of the parameters X of all the operating working points, judging that RX is less than or equal to 0 or RX is greater than 0, calculating the variation deviation RX and RX = (X-Xin)/X of each operating working point, judging according to the value obtained by RX calculation, issuing a relevant instruction to the control module, calculating a simulated variation deviation curve Rl according to the variation deviation RX data of each delta t minute in 24h of all the operating working points, analyzing an operating rule according to the variation deviation curve Rl, forming and outputting a future 24h optimal Xin operating curve L, calculating and comparing the average value Xin of the parameters X of all the operating working points with the optimal Xin operating curve L;
and the control module is used for issuing instructions to each operating working point and controlling the start and stop of the compressor and the fan of the corresponding air conditioner and the output size.
As a further optimization, the parameters X are temperature T and humidity Φ.
As a further optimization, Δ t =10, 20, 30 or 60.
The third purpose of the invention is to provide an air conditioner refrigeration control device capable of automatically optimizing, which achieves the purposes of reliably acquiring data, eliminating hot spots, improving the operation reliability of a machine room and greatly reducing the energy consumption of a refrigeration air conditioner.
The technical purpose of the invention is realized by the following technical scheme: an air conditioner refrigeration control device capable of automatically optimizing comprises an environment monitoring device and a central intelligent controller, wherein each operating working point is provided with the environment monitoring device, each environment monitoring device is connected with the central intelligent controller, and the central intelligent controller is connected with the environment monitoring devices and controls the starting and stopping of a compressor and a fan of an air conditioner and the output of the fan of each operating working point; the environment monitoring device collects working condition parameter data of a parameter X of an operating working point and sends the collected data to the central intelligent controller;
the central intelligent controller calculates the average value Xin of the parameters X of all the operating working points, the variation deviation RX of each operating working point, data collected by the storage environment monitoring device and variation deviation RX data of each delta t minute in all the operating working points within 24 h; the method comprises the steps that RX is less than or equal to 0 or RX is greater than 0, if RX is less than or equal to 0, the central intelligent controller issues corresponding operating working points, corresponding compressors and fans are controlled not to output in a standby state, and if RX is greater than 0, the central intelligent controller issues corresponding operating working points, and corresponding compressors and fans are controlled to change output; calculating a variation deviation curve Rl according to variation deviation RX data of each delta t minute in all operating working points 24h, analyzing an operating rule according to the variation deviation curve Rl, and forming and outputting an optimal Xin operating curve L of 24h in the future; the average value Xin of the parameters X of all the operating working points is calculated according to the data of the parameters X adopted by the environment monitoring device, the average value Xin is compared with the optimal Xin operating curve L, each operating working point is issued according to the comparison result, and the corresponding compressor and the corresponding fan are controlled to start and stop and output.
As a further optimization, the environment monitoring device includes a temperature sensor and a humidity sensor, and correspondingly, the parameter X includes a temperature T and a humidity phi.
As a further optimization, Δ t =10, 20, 30 or 60.
The invention has the following beneficial effects: according to the air conditioner refrigeration control method, system and device, the temperature field in the channel is monitored and controlled through the environment monitoring device, the output operation curve is predicted by calculating and accumulating the temperature and humidity variation deviation, so that the refrigeration system is output to perform continuous optimizing operation, the refrigeration requirement in a machine room can be better met, and the whole refrigeration system can be more energy-saving.
Drawings
FIG. 1 is a logic block diagram of an automatic optimization air conditioner refrigeration control method disclosed by an embodiment of the invention;
FIG. 2 is a schematic structural diagram of an automatically optimized air conditioning refrigeration control system according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an automatic optimization air conditioner refrigeration control device according to an embodiment of the present invention.
In the figure: 1. an acquisition module; 11. a temperature sensing unit; 12. a humidity sensing unit; 2. a storage module; 3. a processing module; 4. a control module; 100. an environmental monitoring device; 101. a temperature sensor; 102. a humidity sensor; 200. a central intelligent controller.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The embodiment of the invention discloses an automatic optimization-seeking air conditioner refrigeration control method, which comprises the following steps as shown in figure 1:
step S1: collecting working condition parameter data of a parameter X of each operating working point in a channel through an environment monitoring device, and calculating an average value Xin of the parameters X of all the operating working points; a plurality of air conditioners are uniformly arranged in the machine room, one or more operation working points are arranged in a channel of the machine room corresponding to the vicinity of the output end of each air conditioner, and each operation working point is provided with an environment monitoring device which is used for collecting related working parameters X of the operation working points in real time;
s2, after the operation is carried out for delta t minutes, acquiring working condition parameter data of a parameter X of each operation working point in a machine room channel through an environment monitoring device, and calculating variation deviation RX, RX = (X-Xin)/X of each operation working point;
and step S3: judging according to the value obtained by RX calculation and issuing a related instruction to control the start-stop and output sizes of the compressor and the fan, specifically comprising the following steps: if RX is less than or equal to 0, the central intelligent controller receives the instruction, issues a corresponding operating point and controls a compressor and a fan of a corresponding air conditioner not to output in a standby state; if RX is greater than 0, the central intelligent controller receives the instruction and issues a corresponding operating point, the corresponding compressor and fan of the air conditioner are controlled to change output, and the output is increased until RX is less than or equal to 0;
and step S4: circularly executing the steps S2 and S3, continuously operating for 24h, and accumulating variation deviation RX data of each delta t minute in all operating working points 24 h;
step S5: calculating a simulated variation deviation curve Rl according to variation deviation RX data of each delta t minute in all operating working points 24h, firstly calculating an average value RXin of variation deviations RX of all operating working points in each delta t minute, wherein RXin = (RX 1+ RX2+ … RXn)/Xn is the number of the operating working points, and simulating and generating the variation deviation curve Rl according to the RXin of each delta t minute in 24h, for example, simulating and generating the variation deviation curve Rl by using MATLAB software;
step S6: analyzing an operation rule according to a variation deviation curve Rl, forming and outputting an optimal Xin operation curve L of 24h in the future, calculating the optimal Xin in each delta t minute by adopting a formula y = f (Xin, RX, delta t) = (Xin + RX)/(delta t), and forming the optimal Xin operation curve L of 24h in the future according to the optimal Xin in each delta t minute in 24 h;
step S7: according to the optimal Xin operation curve L, the central intelligent controller issues each operation working point, and the compressor and the fan of the air conditioner corresponding to each operation working point execute change output;
step S8: after the operation is carried out for delta t minutes, working condition parameter data of the parameter X of each operation working point in a machine room channel are collected through an environment monitoring device, the average value Xin of the parameter X of all the operation working points is calculated, and the average value Xin is compared with an optimal Xin operation curve L;
step S9: according to the comparison result, the central intelligent controller issues each operation working point and controls the start and stop of the compressor and the fan of the corresponding air conditioner and the output size, specifically: detecting that the calculated Xin is less than or equal to the optimal Xin within the corresponding time, receiving an instruction by the central intelligent controller, issuing a corresponding operating working point, and controlling a compressor and a fan of the corresponding air conditioner not to output in a standby state; detecting the calculated Xin > and the optimal Xin within the corresponding time, receiving the instruction by the central intelligent controller, issuing a corresponding operating point, controlling the compressor and the fan of the corresponding air conditioner to change output, and increasing the output until the Xin obtained by detection and calculation is less than or equal to the optimal Xin within the corresponding time;
step S10: and (5) circulating the step S8 and the step S9, operating for 24h, and returning to the step 1 to continuously circulate.
In the present embodiment, Δ t =10, 20, 30 or 60, facilitating data acquisition and calculation of relevant parameters at intervals of 10, 20, 30 or 60 minutes.
In this embodiment, the environment monitoring device includes temperature sensor and humidity transducer, and is corresponding, and parameter X is temperature T and humidity phi, through the double data acquisition of temperature T and humidity phi and control better satisfies the interior refrigeration demand of computer lab. Further preferably, step S3: if the RT and the R phi are less than or equal to 0, the main control board receives the instruction, issues a corresponding operating working point, and controls the corresponding compressor and the corresponding fan not to output and be in a standby state; if the RT and/or R phi is greater than 0, the main control board receives the instruction and issues a corresponding operating point, the corresponding compressor and the corresponding fan are controlled to change output, and the output is increased until the RT and the R phi are less than or equal to 0.
According to the air conditioner refrigeration control method, the temperature field in the channel is monitored and controlled through the environment monitoring device, the output operation curve is predicted by calculating and accumulating the temperature and humidity variation deviation, so that the refrigeration system is continuously optimized to operate, the refrigeration requirement in a machine room can be better met, and the whole refrigeration system can be more energy-saving.
The embodiment of the invention also discloses an automatic optimization-seeking air conditioner refrigeration control system, which comprises an acquisition module 1, a storage module 2, a processing module 3 and a control module 4, wherein the acquisition module 1 comprises a memory module, the storage module 2 comprises a memory module, the processing module 3 comprises a memory module, and the control module 4 comprises a control module, wherein the memory module comprises a memory module, a control module, a memory module, a control module and a control module, the memory module and the control module are respectively connected with the memory module, and the control module comprises:
the acquisition module 1 is used for acquiring working condition parameter data of the parameter X of each operating working point in the machine room channel and sending the data to the storage module 2;
the storage module 2 is used for storing the data acquired by the acquisition module 1 and variation deviation RX data of each delta t minute in all the operating working points 24 h;
the processing module 3 is configured to calculate an average value Xin of the parameters X of all the operating working points, determine that RX is less than or equal to 0 or RX is greater than 0, calculate a variation deviation RX of each operating working point, RX = (X-Xin)/X, determine according to a value obtained by RX calculation, issue a related instruction to the control module 4, calculate a simulated variation deviation curve Rl according to variation deviation RX data per Δ t minute in all the operating working points 24h, analyze an operating rule according to the variation deviation curve Rl, form and output an optimal Xin operating curve L for the future 24h, and calculate and compare the average value Xin of the parameters X of all the operating working points with the optimal Xin operating curve L;
and the control module 4 is used for issuing instructions to each operation working point and controlling the start and stop of the compressor and the fan of the corresponding air conditioner and the output size.
The air conditioner refrigeration control system has the following specific operation flow:
step 1: working condition parameter data of the parameter X of each operating working point in a machine room channel are acquired through an acquisition module 1, the acquired data are sent to a storage module 2, and a processing module 3 calculates the average value Xin of the parameter X of all the operating working points;
and 2, step: after the air-conditioning refrigeration system operates for delta t minutes, working condition parameter data of a parameter X of each operating working point in a machine room channel are collected through the collection module 1 and sent to the storage module 2, the processing module 3 calculates variation deviation RX and RX = (X-Xin)/X of each operating working point and sends the calculated data to the storage module 2;
and 3, step 3: the processing module 3 judges according to the value obtained by RX calculation, and the control module 4 issues related instructions to control the start and stop of the compressor and the fan of the air conditioner and the output size, specifically: if RX is less than or equal to 0, the control module 4 receives the instruction and issues a corresponding operating point to control the compressor and the fan of the corresponding air conditioner not to output in a standby state; if RX is greater than 0, the control module 4 receives the instruction, issues a corresponding operating point, controls a compressor and a fan of a corresponding air conditioner to change output, and increases the output until RX is less than or equal to 0;
and 4, step 4: circularly executing the steps 2 and 3, continuously operating for 24h, and accumulating variation deviation RX data of each delta t minute in all the operating working points 24h by the storage module 2;
and 5: according to the variation deviation RX data of each delta t minute in all the operating working points 24h, the processing module 3 calculates a variation deviation curve Rl, firstly calculates an average value RXin of the variation deviations RX of all the operating working points in each delta t minute, RXin = (RX 1+ RX2+ … RXn)/Xn, wherein n is the number of the operating working points, and generates the variation deviation curve Rl according to the simulation of the RXin of each delta t minute in 24h, for example, generates the variation deviation curve Rl by using MATLAB software;
step 6: the processing module 3 analyzes an operation rule according to the variation deviation curve Rl to form and output a future 24h optimal Xin operation curve L, calculates the optimal Xin in each delta t minute by adopting a formula y = f (Xin, RX, delta t) = (Xin + RX)/. DELTA.t, and forms the future 24h optimal Xin operation curve L according to the optimal Xin in each delta t minute in 24 h;
and 7: according to the optimal Xin operation curve L, the control module 4 issues each operation working point, and controls the compressor and the fan of the air conditioner corresponding to each operation working point to execute change output;
and step 8: after the operation is carried out for delta t minutes, working condition parameter data of the parameter X of each operation working point in a machine room channel are collected through the collection module 1, the processing module 3 calculates the average value Xin of the parameter X of all the operation working points, and the average value Xin is compared with the optimal Xin operation curve L;
and step 9: according to the comparison result, the control module 4 issues each operation working point, and controls the start and stop of the compressor and the fan of the corresponding air conditioner and the output size, specifically: detecting that the calculated Xin is less than or equal to the optimal Xin within the corresponding time, receiving an instruction by the control module 4, issuing a corresponding operating working point, and controlling a compressor and a fan of the corresponding air conditioner not to output and be in a standby state; detecting the calculated Xin which is the optimal Xin within the corresponding time, receiving the instruction by the control module 4, issuing the corresponding operating working point, controlling the corresponding compressor and fan of the air conditioner to change the output, and increasing the output until the calculated Xin is less than or equal to the optimal Xin within the corresponding time;
step 10: and (5) circulating the step 8 and the step 9, running for 24h, and returning to the step 1 to continuously circulate.
In the present embodiment, Δ t =10, 20, 30 or 60, at intervals of 10, 20, 30 or 60 minutes to facilitate data acquisition and calculation of relevant parameters.
In this embodiment, the acquisition module 1 includes a temperature sensing unit 11 and a humidity sensing unit 12, and correspondingly, the parameter X is a temperature T and a humidity phi, and the refrigeration requirement in the machine room is better satisfied through dual data acquisition and control of the temperature T and the humidity phi.
The embodiment of the invention also discloses an automatic optimization-seeking air conditioner refrigeration control device, which comprises an environment monitoring device 100 and a central intelligent controller 200, wherein each operating working point is provided with the environment monitoring device 100, each environment monitoring device 100 is connected with the central intelligent controller 200, and the central intelligent controller 200 is connected with the central intelligent controller and controls the starting, stopping and output of the compressor and the fan of the air conditioner at each operating working point; the environment monitoring device 100 collects working condition parameter data of a parameter X of an operating working point and sends the collected data to the central intelligent controller 200;
the central intelligent controller 200 calculates an average value Xin of the parameters X of all the operating points, a variation deviation RX of each operating point, and data acquired by the storage environment monitoring device 100 and variation deviation RX data per Δ t minute in all the operating points 24 h; judging whether RX is less than or equal to 0 or more than 0, if RX is less than or equal to 0, issuing corresponding operation working points by the central intelligent controller 200, controlling the corresponding compressors and fans not to output in a standby state, and if RX is more than 0, issuing the corresponding operation working points by the central intelligent controller 200, and controlling the corresponding compressors and fans to change output; calculating a variation deviation curve Rl according to variation deviation RX data of each delta t minute in all operating working points 24h, analyzing an operating rule according to the variation deviation curve Rl, and forming and outputting an optimal Xin operating curve L of 24h in the future; the average value Xin of the parameters X of all the operating working points is calculated according to the data of the parameters X adopted by the environment monitoring device 100, compared with the optimal Xin operating curve L, and according to the comparison result, each operating working point is issued to control the start and stop and output of the corresponding compressor and fan.
The specific operation flow of the air conditioner refrigeration control device is as follows:
step 1: collecting working condition parameter data of the parameter X of each operation concern point in the channel through the environment monitoring device 100, and calculating an average value Xin of the parameter X of all the operation points by the central intelligent controller 200; a plurality of air conditioners are uniformly arranged in the machine room, one or more operation working points are arranged in a channel of the machine room corresponding to the vicinity of the output end of each air conditioner, an environment monitoring device 100 is arranged at each operation working point, and the environment monitoring device 100 is used for collecting related working parameters X of the operation working points in real time;
and 3, step 3: the central intelligent controller 200 judges and issues related instructions to control the start and stop of the compressor and the fan and the output size according to the value obtained by RX calculation, specifically: if RX is less than or equal to 0, the central intelligent controller 200 receives the instruction and issues a corresponding operating point to control the compressor and the fan of the corresponding air conditioner not to output in a standby state; if RX is greater than 0, the central intelligent controller 200 receives the instruction and issues a corresponding operating point, controls a compressor and a fan of a corresponding air conditioner to change output, and increases the output until RX is less than or equal to 0;
and 4, step 4: circularly executing the steps 2 and 3, continuously operating for 24h, and accumulating variation deviation RX data of each delta t minute in all the operating working points 24h by the central intelligent controller 200;
and 5: the central intelligent controller 200 calculates a variation deviation curve Rl according to variation deviation RX data of every Δ t minute in all the operating working points 24h, calculates an average value RXin of the variation deviations RX of all the operating working points in every Δ t minute, RXin = (RX 1+ RX2+ … RXn)/Xn, n is the number of the operating working points, and generates the variation deviation curve Rl according to the RXin simulation of every Δ t minute in 24h, for example, generates the variation deviation curve Rl by using MATLAB software;
step 6: the central intelligent controller 200 analyzes the operation rule according to the variation deviation curve Rl, forms and outputs an optimal Xin operation curve L of 24h in the future, calculates the optimal Xin in each Δ t minute by adopting a formula y = f (Xin, RX, Δ t) = (Xin + RX)/. DELTA.t, and forms the optimal Xin operation curve L of 24h in the future according to the optimal Xin in each Δ t minute in 24 h;
and 7: the central intelligent controller 200 issues each operation working point according to the optimal Xin operation curve L, and controls the compressor and the fan of the air conditioner corresponding to each operation working point to execute change output;
and 8: after the operation is carried out for delta t minutes, working condition parameter data of the parameter X of each operation working point in the machine room channel is collected through the environment monitoring device 100, the central intelligent controller 200 calculates the average value Xin of the parameter X of all the operation working points and compares the average value Xin with the optimal Xin operation curve L;
and step 9: according to the comparison result, the central intelligent controller 200 issues each operation working point, and controls the sizes of the compressor and the fan start-stop and the output of the corresponding air conditioner, specifically: detecting that the calculated Xin is less than or equal to the optimal Xin within the corresponding time, receiving an instruction by the central intelligent controller 200, issuing a corresponding operation working point, and controlling the compressor and the fan of the corresponding air conditioner not to output in a standby state; detecting the calculated Xin, wherein the calculated Xin corresponds to the optimal Xin within the corresponding time, receiving an instruction by the central intelligent controller 200, issuing a corresponding operating working point, controlling a corresponding compressor and a corresponding fan of the air conditioner to change output, and increasing the output until the calculated Xin is detected to be less than or equal to the optimal Xin within the corresponding time;
step 10: and (5) circulating the step 8 and the step 9, running for 24 hours, and returning to the step 1 to continuously circulate.
In the present embodiment, Δ t =10, 20, 30 or 60, facilitating data acquisition and calculation of relevant parameters at intervals of 10, 20, 30 or 60 minutes.
In this embodiment, the environment monitoring apparatus 100 includes a temperature sensor 101 and a humidity sensor 102, and correspondingly, the parameter X is a temperature T and a humidity Φ, and the refrigeration requirement in the machine room is better satisfied through dual data acquisition and control of the temperature T and the humidity Φ.
The present embodiment is only for explaining the present invention, and it is not limited to the present invention, and those skilled in the art can make modifications of the present embodiment without inventive contribution as needed after reading the present specification, but all of them are protected by patent law within the scope of the claims of the present invention.
Claims (10)
1. An automatic optimization-seeking air conditioner refrigeration control method is characterized in that: the method comprises the following steps:
step S1: collecting working condition parameter data of a parameter X of each operating working point in a machine room channel through an environment monitoring device, and calculating an average value Xin of the parameters X of all the operating working points;
step S2: after the operation is carried out for delta t minutes, acquiring working condition parameter data of a parameter X of each operation working point in a machine room channel through an environment monitoring device, and calculating variation deviation RX, RX = (X-Xin)/X of each operation working point;
and step S3: judging according to the value obtained by RX calculation and issuing related instructions to control the start-stop and output sizes of the compressor and the fan, specifically comprising the following steps: if RX is less than or equal to 0, the central intelligent controller receives the instruction and issues a corresponding operating point to control the compressor and the fan of the corresponding air conditioner not to output in a standby state; if RX is greater than 0, the central intelligent controller receives the instruction and issues a corresponding operating point to control a compressor and a fan of a corresponding air conditioner to change output;
and step S4: circularly executing the steps S2 and S3, continuously operating for 24h, and accumulating variation deviation RX data of every 10 minutes in all operating working points 24 h;
step S5: calculating a simulated variation deviation curve Rl according to variation deviation RX data of every 10 minutes in all the operation working points within 24 h;
step S6: analyzing an operation rule according to the variation deviation curve Rl, and forming and outputting an optimal Xin operation curve L of 24h in the future;
step S7: according to the optimal Xin operation curve L, the central intelligent controller issues each operation working point, and controls a compressor and a fan of the air conditioner corresponding to each operation working point to execute change output;
step S8: after the operation is carried out for delta t minutes, working condition parameter data of the parameter X of each operation working point in a machine room channel are collected through an environment monitoring device, the average value Xin of the parameter X of all the operation working points is calculated, and the average value Xin is compared with an optimal Xin operation curve L;
step S9: according to the comparison result, the central intelligent controller issues each operation working point and controls the sizes of the compressor and the fan start-stop and the output of the corresponding air conditioner, specifically: detecting that the obtained Xin is less than or equal to the optimal Xin within the corresponding time, receiving an instruction by the central intelligent controller, issuing a corresponding operation working point, and controlling the compressor and the fan of the corresponding air conditioner not to output in a standby state; detecting the calculated Xin which is the optimal Xin within the corresponding time, receiving the instruction by the central intelligent controller, issuing a corresponding operating working point, controlling the compressor and the fan of the corresponding air conditioner to change the output, and increasing the output until the detected and calculated Xin is less than or equal to the optimal Xin within the corresponding time;
step S10: and (5) circulating the step S8 and the step S9, running for 24h, and returning to the step 1 to continuously circulate and reciprocate.
2. The automatic optimizing air conditioner refrigeration control method according to claim 1, characterized in that: the parameter X is temperature T and humidity phi.
3. The automatic optimizing air conditioner refrigeration control method according to claim 2, characterized in that: the step S3: if the RT and the R phi are less than or equal to 0, the main control board receives the instruction and issues a corresponding operating point to control the corresponding compressor and the corresponding fan not to output and be in a standby state; if RT and/or R phi is greater than 0, the main control board receives the instruction, issues the corresponding operating working point, controls the corresponding compressor and fan to change output, and increases the output until RT and R phi are less than or equal to 0.
4. An automatic optimizing air conditioner refrigeration control method according to any one of claims 1-3, characterized in that: said Δ t =10, 20, 30 or 60.
5. An automatic air conditioner refrigeration control system who seeks optimization which characterized in that: including collection module, storage module, processing module and control module, wherein:
the acquisition module is used for acquiring working condition parameter data of the parameter X of each operating working point in the machine room channel and sending the data to the storage module;
the storage module is used for storing the data acquired by the acquisition module and variation deviation RX data of each delta t minute in all the operating working points 24 h;
the processing module is used for calculating the average value Xin of the parameters X of all the operating working points, judging that RX is less than or equal to 0 or RX is greater than 0, calculating the variation deviation RX of each operating working point, wherein RX = (X-Xin)/X, judging according to the value obtained by RX calculation, issuing a relevant instruction to the control module, calculating a simulation variation deviation curve Rl according to the variation deviation RX data of each delta t minute in all the operating working points 24h, analyzing the operation rule according to the variation deviation curve Rl, forming and outputting an optimal Xin operation curve L of the future 24h, calculating and comparing the average value Xin of the parameters X of all the operating working points with the optimal Xin operation curve L;
and the control module is used for issuing instructions to each operating working point and controlling the start and stop of the compressor and the fan of the corresponding air conditioner and the output size.
6. An automatically optimized air conditioning refrigeration control system as set forth in claim 5, wherein: the parameter X is temperature T and humidity phi.
7. An automatically optimized refrigeration control system for air conditioners according to claim 5, wherein: said Δ t =10, 20, 30 or 60.
8. An automatic air conditioner refrigeration controlling means who seeks optimization which characterized in that: the system comprises an environment monitoring device and a central intelligent controller, wherein each operating working point is provided with the environment monitoring device, each environment monitoring device is connected with the central intelligent controller, and the central intelligent controller is connected with and controls the starting and stopping of a compressor and a fan of an air conditioner and the output of the compressor and the fan of each operating working point; the environment monitoring device collects working condition parameter data of a parameter X of an operating working point and sends the collected data to the central intelligent controller;
the central intelligent controller calculates the average value Xin of the parameters X of all the operating working points, the variation deviation RX of each operating working point, data collected by the storage environment monitoring device and variation deviation RX data of each delta t minute in all the operating working points within 24 h; judging whether RX is less than or equal to 0 or more than 0, if RX is less than or equal to 0, issuing a corresponding operation working point by the central intelligent controller, controlling the corresponding compressor and fan not to output in a standby state, and if RX is more than 0, issuing the corresponding operation working point by the central intelligent controller, and controlling the corresponding compressor and fan to change output; calculating a variation deviation curve Rl according to variation deviation RX data of each delta t minute in all operating working points 24h, analyzing an operating rule according to the variation deviation curve Rl, and forming and outputting an optimal Xin operating curve L of 24h in the future; the average value Xin of the parameters X of all the operating working points is calculated according to the data of the parameters X adopted by the environment monitoring device, the average value Xin is compared with the optimal Xin operating curve L, each operating working point is issued according to the comparison result, and the corresponding compressor and the corresponding fan are controlled to start and stop and output.
9. An automatic optimizing air conditioner refrigeration control device according to claim 8, characterized in that: the environment monitoring device comprises a temperature sensor and a humidity sensor, and correspondingly, the parameter X comprises a temperature T and a humidity phi.
10. An automatically optimized refrigeration control device for air conditioner according to claim 8, characterized in that: said Δ t =10, 20, 30 or 60.
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| CN112945314B (en) * | 2021-05-13 | 2021-08-06 | 武汉慧联无限科技有限公司 | Environmental temperature and humidity monitoring method and device, terminal equipment and storage medium |
| CN114017378A (en) * | 2021-11-04 | 2022-02-08 | 中广核全椒风力发电有限公司 | Curve deviation comparative analysis method based on fan position characteristic formation and application |
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