CN112032972A - Internet of things central air conditioner self-optimizing control system and method based on cloud computing - Google Patents
Internet of things central air conditioner self-optimizing control system and method based on cloud computing Download PDFInfo
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- CN112032972A CN112032972A CN202011128254.3A CN202011128254A CN112032972A CN 112032972 A CN112032972 A CN 112032972A CN 202011128254 A CN202011128254 A CN 202011128254A CN 112032972 A CN112032972 A CN 112032972A
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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
- F24F11/50—Control or safety arrangements characterised by user interfaces or communication
- F24F11/56—Remote control
- F24F11/58—Remote control using Internet communication
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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
- F24F2110/12—Temperature of the outside air
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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
- F24F2110/22—Humidity of the outside air
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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
- F24F2110/30—Velocity
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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
- F24F2110/40—Pressure, e.g. wind pressure
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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
- F24F2130/00—Control inputs relating to environmental factors not covered by group F24F2110/00
- F24F2130/20—Sunlight
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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
- F24F2140/00—Control inputs relating to system states
- F24F2140/20—Heat-exchange fluid temperature
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- Fuzzy Systems (AREA)
- Mathematical Physics (AREA)
- Air Conditioning Control Device (AREA)
Abstract
The invention discloses a cloud computing-based Internet of things central air conditioner self-optimization control system and method, which comprises a data statistics module, a real-time data acquisition module and a dynamic optimization module, wherein the data statistics module is used for carrying out statistics on the data; the data statistics module is used for counting the working data and the historical environmental data of the air conditioning equipment and generating optimal allocation algorithms under different working conditions according to the working data and the historical environmental data of the equipment; the real-time data acquisition module is used for acquiring current data from each data monitoring device of the air conditioning equipment in real time; the dynamic optimizing module is used for matching an optimal allocation algorithm according to the current data to obtain an optimized execution parameter; the optimal allocation algorithm under different working conditions is generated through the equipment working data and the historical environment data, the optimal execution parameters are obtained by matching the optimal allocation algorithm according to the current data of the air conditioning equipment, the purposes of avoiding the invalid operation of the design margin redundancy under the non-design extreme working conditions, eliminating human factors, automatically adjusting the parameters and realizing the purposes of remote control, intelligence, high efficiency and adaptability to various working conditions are achieved.
Description
Technical Field
The invention relates to the technical field of electromechanical control of the Internet of things, in particular to a self-optimizing control system and a self-optimizing control method for a central air conditioner of the Internet of things based on cloud computing.
Background
The prior building energy-saving automatic control basically adopts the steps that data of bottom equipment is transmitted to a programmable logic controller, then working condition types are distinguished, and a bottom equipment energy-saving strategy suitable for the working condition conditions is edited according to the working condition type conditions, so that the starting, stopping or output quantity of the bottom equipment is controlled, and the purpose of saving energy is achieved. For the control of the platform of the Internet of things, the equipment signals or the execution results are directly transmitted to the cloud control platform, the cloud control platform only plays a role in controlling the switching value of data, the analysis of the acquired data is limited in such a mode, the remote control intellectualization of the Internet of things cannot be realized, and some professional designers regard automatic control as a universal key capable of solving all problems, so that the pursuit of the rationality of the parameter design of the heating ventilation air conditioning system is abandoned.
Disclosure of Invention
In order to solve the technical problems, the invention provides a cloud computing-based Internet of things central air conditioner self-optimization control system and method, so as to achieve the purposes of avoiding invalid operation of design margin redundancy under non-design extreme working conditions, eliminating human factors, automatically adjusting parameters by the system, and realizing remote control, intelligence, high efficiency and adaptability to various working conditions.
In order to achieve the purpose, the technical scheme of the invention is as follows: the self-optimizing control system of the Internet of things central air conditioner based on cloud computing comprises a data statistics module, a real-time data acquisition module and a dynamic optimizing module;
the data statistics module is used for counting the working data and the historical environmental data of the air conditioning equipment and generating optimal allocation algorithms under different working conditions according to the working data and the historical environmental data of the air conditioning equipment;
the real-time data acquisition module is used for acquiring current data from each data monitoring device on the air conditioning equipment in real time;
and the dynamic optimizing module is used for matching an optimal allocation algorithm according to the current data to obtain an optimized execution parameter.
Compared with the prior art, the optimal allocation algorithm under different working conditions is generated through the equipment working data and the historical environment data, and then the optimal allocation algorithm is matched according to the current data of the air conditioning equipment to obtain the optimized execution parameters, so that the autonomous optimization is realized, and the purposes of flexibility and energy conservation are achieved.
Further, the air conditioner operation data includes: the flow rate, flow, current, voltage, wind pressure, wind speed and the like of the supplied and returned water.
Further, the historical environmental data includes: air temperature, air humidity, cleanliness, physicochemical effects and the like.
Further, each data monitoring device on the air conditioning equipment comprises: energy meters, flow meters, differential pressure sensors, temperature and humidity sensor voltmeters, air quality sensors, timers and the like.
Further, the current data includes: environmental condition data and load data, the environmental condition data comprising: outdoor temperature, outdoor humidity, solar irradiance, wind speed, and the like; the load data includes: indoor temperature, indoor humidity, and barometric pressure data, etc.
The self-optimizing control method of the Internet of things central air conditioner based on cloud computing comprises the following steps:
counting working data and historical environment data of air conditioning equipment, and generating optimal allocation algorithms under different working conditions according to the working data and the historical environment data of the air conditioning equipment;
collecting current data from each data monitoring device on the air conditioning equipment in real time;
and matching the optimal allocation algorithm according to the current data to obtain the optimized execution parameters and transmitting the optimized execution parameters to the air conditioning equipment.
The invention has the following advantages:
(1) according to the invention, the optimal allocation algorithm under different working conditions is generated through the equipment working data and the historical environment data, and the optimal execution parameter is obtained by matching the optimal allocation algorithm according to the current data of the air conditioning equipment, so that the aims of avoiding the invalid operation of the design margin redundancy under the non-design extreme working conditions, automatically adjusting the parameters by the system, and realizing the purposes of remote control, intelligence, high efficiency and adaptability to various working conditions are fulfilled.
(2) The invention realizes the independent optimization of the central air-conditioning system, and achieves the purposes of flexibility and energy conservation.
(3) The invention eliminates human factors, automatically adjusts parameters and realizes intelligent management and control of the Internet of things.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below.
Fig. 1 is a schematic structural diagram of a cloud computing-based internet-of-things central air conditioner self-optimization control system disclosed by an embodiment of the invention;
fig. 2 is a flow chart of a cloud computing-based internet of things central air conditioner self-optimization control method disclosed by the embodiment of the invention;
the corresponding part names indicated by the numbers and letters in the drawings:
1. a data statistics module; 2. a real-time data acquisition module; 3. and a dynamic optimizing module.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.
The invention provides an Internet of things central air conditioner self-optimizing control system and method based on cloud computing.
The present invention will be described in further detail with reference to examples and specific embodiments.
As shown in fig. 1, the cloud computing based internet of things central air conditioning self-optimization control system comprises a data statistics module 1, a real-time data acquisition module 2 and a dynamic optimization module 3;
the data statistics module 1 is used for counting the working data and the historical environmental data of the air conditioning equipment and generating optimal allocation algorithms under different working conditions according to the working data and the historical environmental data of the equipment;
the real-time data acquisition module 2 is used for acquiring current data from each data monitoring device on the air conditioning equipment in real time;
and the dynamic optimizing module 3 is used for matching an optimal allocation algorithm according to the current data to obtain an optimized execution parameter.
Compared with the prior art, the optimal allocation algorithm under different working conditions is generated through the equipment working data and the historical environment data, and then the optimal allocation algorithm is matched according to the current data of the air conditioning equipment to obtain the optimized execution parameters, so that the autonomous optimization is realized, and the purposes of flexibility and energy conservation are achieved.
Taking a water chilling unit as an example, when the water chilling unit outlet temperature is adjusted, IF i is (RH%) × 10(0.0275 × t-1) + t e (a, B) and Ty e (m, n), when the conditions are met, the output is a set value a, the self-optimization setting of one parameter can be completed, and by analogy, other parameter groups are set
Wherein the air conditioning equipment operating data includes: the flow rate, flow, current, voltage, wind pressure, wind speed and the like of the supplied and returned water.
Wherein the historical environmental data comprises: air temperature, air humidity, cleanliness, physicochemical effects and the like.
Wherein each data monitoring device on the air conditioning equipment comprises: energy meters, flow meters, differential pressure sensors, temperature and humidity sensor voltmeters, air quality sensors, timers and the like.
Wherein the current data comprises: environmental condition data and load data, the environmental condition data comprising: outdoor temperature, outdoor humidity, solar irradiance, wind speed, and the like; the load data includes: indoor temperature, indoor humidity, and barometric pressure data, etc.
As shown in fig. 2, a cloud computing-based internet of things central air conditioner self-optimization control method includes:
s1: counting working data and historical environment data of air conditioning equipment, and generating optimal allocation algorithms under different working conditions according to the working data and the historical environment data of the air conditioning equipment;
s2: collecting current data from each data monitoring device on the air conditioning equipment in real time;
s3: and matching the optimal allocation algorithm according to the current data to obtain the optimized execution parameters and transmitting the optimized execution parameters to the air conditioning equipment.
The overall implementation process of the system is as follows:
performing bottom networking according to customer requirements and field conditions, and uploading digital quantity or analog quantity information acquired by each controlled device to a system;
establishing a sequential control and a sequential control process which are interlocked with each other based on a safe sequential logic relation according to each unit of controlled equipment in a determined system, for example, in the system, firstly starting a freezing water pump, then starting a cooling water pump after delaying for 60 seconds without faults, then starting a water chilling unit after delaying for 60 seconds without faults, and starting a cooling tower after delaying for 60 seconds without faults; on the aspect of an automatic control strategy, a model parameter system established based on the relation between the specification and the local climate condition, such as the outdoor design temperature and humidity of certain area in summer and the human body feeling temperature and humidity (such as 26 ℃ and 60 percent Rt) is established as the basis of the control strategy;
on the basis, when environmental condition data (external disturbance) and load data (internal disturbance) change, an optimized execution parameter system (automatic optimization) is dynamically set according to original data obtained by monitoring an energy meter, a flow meter, a differential pressure sensor, a temperature and humidity sensor, an air quality sensor, a timer and the like in real time, the effectiveness of the parameter system is verified between the system and a model preset by a platform continuously and automatically, whether original working condition parameters, user side working condition parameters, equipment health degree parameters and the like need to be updated or not is determined by automatic program decision after verification, and once a control system determines to be the more optimized executable parameter system, an up-term parameter group is expired and is updated to be the current-term parameter system.
Under the running state of the self-optimizing system, the energy saving rate of the whole system under the same working condition and the 24-hour continuous running state can be calculated to reach 20%.
The system is more intelligent and has a learning function, and an energy-saving strategy under a single energy-saving condition or state condition is eliminated, so that the system is always kept in the most possible optimized running state under the nonlinear working condition.
Through the mode, the cloud computing-based Internet of things central air conditioner self-optimization control system and method provided by the invention generate optimal allocation algorithms under different working conditions through equipment working data and historical environment data, and then match the optimal allocation algorithms according to the current data of air conditioning equipment to obtain optimized execution parameters, so that the aims of avoiding invalid operation of design margin redundancy under non-design extreme working conditions, eliminating human factors, automatically adjusting parameters by the system and realizing remote control, intelligence, high efficiency and adaptation to various working conditions are fulfilled.
The foregoing is only a preferred embodiment of the cloud computing based internet of things central air conditioner self-optimization control system and method disclosed in the present invention, and it should be noted that, for those skilled in the art, variations and modifications can be made without departing from the inventive concept of the present invention, and these variations and modifications are within the scope of the present invention.
Claims (6)
1. A self-optimizing control system of an Internet of things central air conditioner based on cloud computing is characterized by comprising a data statistics module, a real-time data acquisition module and a dynamic optimizing module;
the data statistics module is used for counting the working data and the historical environmental data of the air conditioning equipment and generating optimal allocation algorithms under different working conditions according to the working data and the historical environmental data of the air conditioning equipment;
the real-time data acquisition module is used for acquiring current data from each data monitoring device on the air conditioning equipment in real time;
and the dynamic optimizing module is used for matching an optimal allocation algorithm according to the current data to obtain an optimized execution parameter.
2. The cloud computing based internet of things central air conditioning self-optimizing control system according to claim 1, wherein the air conditioning equipment working data comprises: the flow rate, flow, current, voltage, wind pressure and wind speed of the supplied and returned water.
3. The cloud-computing-based internet of things central air conditioner self-optimizing control system of claim 1, wherein the historical environmental data comprises: air temperature, air humidity, cleanliness and physicochemical effects.
4. The cloud computing based internet of things central air conditioning self-optimizing control system according to claim 1, wherein each data monitoring device on the air conditioning device comprises: the energy meter, the flowmeter, differential pressure sensor, humiture sensor voltmeter, air quality sensor, time-recorder.
5. The cloud computing based internet of things central air conditioning self-optimizing control system of claim 4, wherein the current data comprises: environmental condition data and load data, the environmental condition data comprising: outdoor temperature, outdoor humidity, solar irradiance and wind speed; the load data includes: indoor temperature, indoor humidity, and barometric pressure data.
6. A self-optimizing control method of an Internet of things central air conditioner based on cloud computing is characterized by comprising the following steps:
counting working data and historical environment data of air conditioning equipment, and generating optimal allocation algorithms under different working conditions according to the working data and the historical environment data of the air conditioning equipment;
collecting current data from each data monitoring device on the air conditioning equipment in real time;
and matching the optimal allocation algorithm according to the current data to obtain the optimized execution parameters and transmitting the optimized execution parameters to the air conditioning equipment.
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112856636A (en) * | 2021-01-29 | 2021-05-28 | 江西锋铄新能源科技有限公司 | Computing power type central air conditioner |
| CN113938473A (en) * | 2021-10-12 | 2022-01-14 | 平安银行股份有限公司 | Automatic Mock method, device, equipment and storage medium based on flow |
| WO2023103660A1 (en) * | 2021-12-08 | 2023-06-15 | 上海中联重科桩工机械有限公司 | Parameter optimization method and system for construction device, electronic device, and storage medium |
| CN116379588A (en) * | 2023-04-08 | 2023-07-04 | 广州施杰节能科技有限公司 | Cold water main machine load distribution optimizing and adjusting method and system thereof |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102721156A (en) * | 2012-06-30 | 2012-10-10 | 李钢 | Central air-conditioning self-optimization intelligent fuzzy control device and control method thereof |
| EP2873930A1 (en) * | 2012-07-09 | 2015-05-20 | Panasonic Intellectual Property Management Co., Ltd. | Air conditioning management device and air conditioning management system |
| CN107860102A (en) * | 2017-10-18 | 2018-03-30 | 深圳市中电电力技术股份有限公司 | A kind of method and device for controlling central air-conditioning |
| CN110440396A (en) * | 2019-07-11 | 2019-11-12 | 雄安达实智慧科技有限公司 | The central air-conditioning global optimization energy-saving control method and system of cloud side end collaboration |
| CN111561772A (en) * | 2020-07-15 | 2020-08-21 | 上海有孚智数云创数字科技有限公司 | Cloud computing data center precision air conditioner energy-saving control method based on data analysis |
-
2020
- 2020-10-21 CN CN202011128254.3A patent/CN112032972A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102721156A (en) * | 2012-06-30 | 2012-10-10 | 李钢 | Central air-conditioning self-optimization intelligent fuzzy control device and control method thereof |
| EP2873930A1 (en) * | 2012-07-09 | 2015-05-20 | Panasonic Intellectual Property Management Co., Ltd. | Air conditioning management device and air conditioning management system |
| CN107860102A (en) * | 2017-10-18 | 2018-03-30 | 深圳市中电电力技术股份有限公司 | A kind of method and device for controlling central air-conditioning |
| CN110440396A (en) * | 2019-07-11 | 2019-11-12 | 雄安达实智慧科技有限公司 | The central air-conditioning global optimization energy-saving control method and system of cloud side end collaboration |
| CN111561772A (en) * | 2020-07-15 | 2020-08-21 | 上海有孚智数云创数字科技有限公司 | Cloud computing data center precision air conditioner energy-saving control method based on data analysis |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112856636A (en) * | 2021-01-29 | 2021-05-28 | 江西锋铄新能源科技有限公司 | Computing power type central air conditioner |
| CN113938473A (en) * | 2021-10-12 | 2022-01-14 | 平安银行股份有限公司 | Automatic Mock method, device, equipment and storage medium based on flow |
| WO2023103660A1 (en) * | 2021-12-08 | 2023-06-15 | 上海中联重科桩工机械有限公司 | Parameter optimization method and system for construction device, electronic device, and storage medium |
| CN116379588A (en) * | 2023-04-08 | 2023-07-04 | 广州施杰节能科技有限公司 | Cold water main machine load distribution optimizing and adjusting method and system thereof |
| CN116379588B (en) * | 2023-04-08 | 2023-09-12 | 广州施杰节能科技有限公司 | Cold water main machine load distribution optimizing and adjusting method and system thereof |
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