Central air conditioner energy consumption scoring and control method based on Internet of Things

Abstract

The invention discloses a control method for energy consumption scoring of a central air conditioner based on the Internet of Things, and the method obtains an energy consumption score through calculation and analysis, thereby selecting whether to intelligently regulate and control the parameters and temperature of the central air conditioner or not, and carrying out the accurate safety score display and warning prompt for an Internet of Things central air conditioner system. The intelligent management and control module automatically adjusts operation parameters in real time according to the energy consumption score, and the lowest energy consumption and the fastest time are selected to reach the required temperature while the optimal sensible temperature of the user is fully considered; the method is used for comprehensively monitoring and analyzing the energy consumption of the central air conditioner, so that the purpose of reducing the energy consumption is achieved on the basis of proper temperature.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption control of central air conditioners, and in particular to a control method based on energy consumption scoring of central air conditioners of the Internet of Things. Background Art

[0002] With the continuous development and progress of science and technology, intelligent buildings have gradually become a new development trend, and central air-conditioning systems are no exception. However, traditional central air-conditioning systems consume relatively more electricity, and the number of control points often accounts for a large proportion of the total number of automated control points in the entire building. Therefore, we need to master a large amount of statistical data and carry out a comprehensive upgrade of the central air-conditioning system in terms of energy saving.

[0003] At present, there are many ways to realize the automation of central air-conditioning systems. While central air-conditioning systems bring comfortable environments to enterprises and business groups, they also have problems such as high costs and high energy consumption. In the existing technology, central air-conditioning often sets common temperatures or manually adjusts temperatures and unit parameters, while ignoring the relationship between the human body's optimal temperature and the changing relationship between indoor and outdoor temperatures, and thus cannot effectively provide comfortable indoor temperatures for the surrounding people at the lowest energy consumption.

[0004] In order to solve the deficiencies in the prior art, the purpose of the present invention is to provide a comprehensive and systematic method for energy saving and consumption analysis of central air conditioners based on the Internet of Things, which is used for comprehensive monitoring and analysis of the energy consumption of central air conditioners, so as to achieve the purpose of reducing energy consumption on the basis of suitable temperature. Summary of the invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of the present invention is to provide a control method for central air-conditioning energy consumption scoring based on the Internet of Things. The ultimate goal is to enable the central air-conditioning system to operate and manage safely with lower energy consumption while meeting the optimal temperature for the human body.

[0006] The purpose of the present invention is to provide a method for determining the energy consumption score of a central air conditioner based on the Internet of Things for evaluating the current score level of the central air conditioner, thereby selecting whether to intelligently adjust the parameters and temperature of the central air conditioner.

[0007] The specific methods include: The data acquisition module collects, extracts and records data, collects the indoor temperature of a target area through an indoor temperature sensor, and collects the outdoor temperature of a target area through an outdoor temperature sensor; Calculate the optimal comfort temperature for users at a certain moment: ; C t1 is the outdoor temperature at a certain moment; C t2 Set the indoor temperature for a certain moment; K is the correction coefficient related to the number of people per unit area; X1 is the actual number of people per unit area; X2 is the standard number of people per unit area; Y is the actual area; R is the population density assessment index per unit area; The data acquisition module collects and extracts recorded data to obtain the water supply temperature and return water temperature of the IoT central air conditioner; obtains the inlet / outlet water temperature of the condenser and evaporator of the chiller at this moment, obtains the chilled water flow of the condenser, obtains the fixed bearing pressure value and fouling coefficient of the water channel standard; calculates the unit time efficiency coefficient formula based on the supply data, and determines the energy-saving scoring standard of the central air conditioner based on the calculation results; Refrigeration efficiency coefficient per unit time: μ= ; Or the heating efficiency coefficient per unit time: μ= ; in: C t适 The optimal comfort temperature for the user at a certain moment as mentioned above; C 蒸1 is the water inlet temperature of the chiller evaporator; C 冷2 is the outlet water temperature of the chiller condenser; S 水 is the chilled water flow rate of the middle condenser; C 供 Supply water temperature for central air conditioning; C 回 is the return water temperature of the central air conditioner; n is the fixed bearing pressure value of the chiller water circuit standard; Q 冷 is the cooling capacity of the central air-conditioning system per unit time; W 冷 is the power consumed by the central air conditioner per unit time; P is the fouling coefficient of the chiller; Q 热 is the nominal heating capacity per unit time; W 热 is the power consumed by the central air conditioner per unit time; By calculating the time efficiency coefficient and the statistics and analysis of the time taken to reach the user's optimal comfortable temperature, a complete set of scoring criteria is obtained; the specific scoring criteria are shown in the chart: .

[0008] According to the above scoring criteria, the traceability result of the central air-conditioning system includes one of the following: strong security, weak security, strong anomaly, weak anomaly, and sampling anomaly.

[0009] After completing the calculation and analysis of the central air-conditioning energy consumption score, the IoT central air-conditioning system will immediately reflect the score to the backend management system, and provide users with different reminders and alarm measures based on different scores.

[0010] The intelligent management and control module divides different management and control modes according to different scores, including environmental protection mode, performance mode, default mode, holiday mode, headwind mode, maintenance mode, terminal control mode and other modes. Each mode corresponds to different configuration parameters, thereby managing the IoT central air-conditioning system more intuitively and accurately.

[0011] If the score is strong security, the IoT central air conditioner will run steadily according to the current parameters; If the score is in the weak security and weak abnormality range, the IoT central air-conditioning system will automatically push abnormality and weak security warnings, and automatically adjust the operating parameters in real time to reach the strong security range. At the same time, it will prompt the user that the parameters required for strong security have been adjusted.

[0012] If the score is in a strong abnormality, a strong abnormality warning will be pushed, and the current parameters of the strong abnormality will be stopped immediately and switched to the user's common mode.

[0013] The main configuration parameters of its intelligent management and control module are: outdoor ambient temperature, host return water temperature, fan coil mode, wind speed, set temperature, polling time interval and maximum polling number. Beneficial Effects

[0014] The beneficial effects of the present invention are as follows: Compared with the prior art, the present invention has the following technical innovations: 1. Obtain energy consumption scores through calculation and analysis, and provide accurate safety scores and warning prompts for IoT central air-conditioning systems; 2. The intelligent management and control module automatically adjusts the operating parameters in real time according to the energy consumption score, fully considering the user's optimal body temperature while selecting the lowest energy consumption and fastest time to reach the required temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The optimal comfort temperature for the user at multiple moments in Example 1; Figure 2 This is the energy consumption score comparison table; Figure 3 Implementation steps for scoring energy consumption; Figure 4 Alarm steps for IoT central air conditioner. DETAILED DESCRIPTION

[0016] As shown in the drawings, the present invention provides specific embodiments, and those skilled in the art can explain the present invention in detail and meticulously through the specific embodiments.

[0017] Example 1 The implementation method is: The optimal comfort temperature for users at a certain moment is calculated according to the formula: 1. The implementation steps for the user's optimal comfort temperature at a certain moment are: (1) Get the outdoor temperature C at a certain moment t1 ; (2) Get the indoor set temperature C at a certain moment t2 ; (3) Obtain the actual number of people X1 per unit area; (4) Obtain the standard number of people per unit area X2 (5) Obtain the actual area Y; ; The optimal comfort temperature calculated according to the formula is as follows: When the outdoor temperature Ct1 is 32℃, the indoor set temperature Ct2 is 27℃, the actual number of people X1 per unit area is 3, the standard number of people X2 per unit area is 5, the actual area Y is 15㎡, and the optimal comfort temperature is 24.4℃.

[0018] When the outdoor temperature Ct1 is 15℃, the indoor set temperature Ct2 is 23℃, the actual number of people X1 per unit area is 1, the standard number of people X2 per unit area is 5, the actual area Y is 3㎡, and the optimal comfort temperature is 21.8℃.

[0019] When the outdoor temperature Ct1 is 22℃, the indoor set temperature Ct2 is 30℃, the actual number of people X1 per unit area is 7, the standard number of people X2 per unit area is 5, the actual area Y is 32㎡, and the optimal comfort temperature is 23.5℃.

[0020] 2. The implementation steps of energy consumption scoring are as follows Figure 3 As shown: (1) Obtain several sets of supply water temperature and return water temperature when the central air conditioner to be rated completes the current cycle operation; (2) Obtain the inlet / outlet water temperatures of the condensers and evaporators of several groups of chillers when the central air conditioner to be rated has completed the current cycle operation; (3) Obtain the chilled water flow of several groups of condensers of the central air conditioner to be rated after completing the current cycle operation; (4) Obtain the fixed pressure values ​​and fouling coefficients of several sets of water channel standards when the central air conditioner to be rated completes the current cycle of operation; (5) Obtain the cooling capacity of the central air conditioning system per unit time or the nominal heating capacity per unit time of the central air conditioning to be rated after the central air conditioning has completed the current cycle operation; (6) Obtain the power consumed by the central air conditioner to be rated in several units of time after the central air conditioner has completed the current cycle operation; (7) Using the formula: Refrigeration efficiency coefficient per unit time μ= ; or Heating efficiency coefficient per unit time μ= ; Based on the processing of the collected data by the back-end preset neural network model of the IoT central air conditioning system, the final energy consumption score is obtained as follows: Figure 2 shown.

[0021] The analysis and processing unit will use the cloud server to comprehensively organize and compare the scoring results, corrective measures, alarm information and user preference data transmitted by the data sorting unit to obtain the final score and measures. The IoT central air-conditioning system will immediately reflect the score in the backend management system and make different reminders and alarm measures for users according to different scores.

[0022] 3. IoT central air conditioning alarm steps Figure 4 As shown: (1) Use the Flink streaming engine to obtain real-time data of the target central air conditioner; (2) Determine the type of rule engine that processes IoT central air conditioning business data, and determine the target format of the expression corresponding to the rule engine based on the type of rule engine; (3) Convert the rules corresponding to the IoT central air conditioning business data into expressions in the target format; (4) Send the expression in the target format to the rule engine so that the rule engine processes the IoT central air conditioning business data through the expression in the target format.

[0023] The present invention relates to the technical field of central air-conditioning energy consumption. The present invention may include a matching energy consumption analysis system, a background management system, a mobile phone terminal, a thermostat, and a computer terminal. The energy consumption scoring method is intuitive in the management system and has high operating efficiency. Customers can intuitively understand the entire energy consumption analysis of the central system through the mobile phone terminal and the background management system. The method of the present invention obtains the water supply temperature and return water temperature at the end of the fan coil unit of the central air-conditioning system of the Internet of Things, and calculates them so that the central air-conditioning system can more accurately reflect the energy consumption, which greatly improves the stability and practicality of the system, and has a qualitative leap in the remote monitoring and energy consumption analysis of central air-conditioning in the building automation system and its energy-saving technology.

Claims

1. A control method for energy consumption scoring of central air conditioners based on the Internet of Things, characterized in that: Collect the indoor temperature and outdoor temperature of a target area through temperature sensors; Calculate the optimal comfort temperature for users at a certain moment: ； C t1 is the outdoor temperature at a certain moment; C t2 Set the indoor temperature for a certain moment; K is the correction coefficient related to the number of people per unit area; X1 is the actual number of people per unit area; X2 is the standard number of people per unit area; Y is the actual area; R is the population density assessment index per unit area; Get the supply water temperature and return water temperature of the central air conditioner; get the inlet water temperature and outlet water temperature of the condenser and evaporator of the chiller at this moment; get the chilled water flow of the condenser; get the fixed pressure value and fouling coefficient of the water channel standard, and calculate the refrigeration efficiency coefficient per unit time: μ= ; Or the heating efficiency coefficient per unit time: μ= ; in: C t适 The optimal comfort temperature for the user at a certain moment as mentioned above; C 蒸1 is the water inlet temperature of the chiller evaporator; C 冷2 is the outlet water temperature of the chiller condenser; S 水 is the chilled water flow rate of the middle condenser; C 供 Supply water temperature for central air conditioning; C 回 is the return water temperature of the central air conditioner; n is the fixed bearing pressure value of the chiller water circuit standard; Q 冷 is the cooling capacity of the central air-conditioning system per unit time; W 冷 is the power consumed by the central air conditioner per unit time; P is the fouling coefficient of the chiller; Q 热 is the nominal heating capacity per unit time; W 热 is the power consumed by the central air conditioner per unit time; The scoring standard is obtained by calculating the time efficiency coefficient and the statistics and analysis of the time taken to reach the user's optimal comfort temperature; After completing the calculation and analysis of the central air-conditioning energy consumption score, the IoT central air-conditioning system will immediately reflect the score to the backend management system, and provide users with different reminders and alarm measures based on different scores.

2. A control method based on energy consumption scoring of central air conditioner in Internet of Things as claimed in claim 1, characterized in that: The scoring criteria are: When the efficiency coefficient μ<0.3 and the time spent by the user at the optimal comfortable temperature T ≤ 1min, it is rated as strong safety; When the efficiency coefficient μ is between 0.3 and 2.0, and the time T required for the user to reach the optimal comfortable temperature is ≤ 2 min, it is assessed as weak safety; When the efficiency coefficient μ is between 2.0 and 8.0, and the time spent by the user at the optimal comfortable temperature T ≤ 1min, it is assessed as weak safety; When the efficiency coefficient μ is between 8.0 and 15, and the time spent by the user at the optimal comfortable temperature T ≤ 1min, it is evaluated as a strong abnormality; When the efficiency coefficient μ is between 2.0 and 8.0, and the time T taken for the user to reach the optimal comfortable temperature is between 1 and 2 minutes, it is evaluated as a weak abnormality; When the efficiency coefficient μ is between 8.0 and 15, and the time T taken for the user to reach the optimal comfortable temperature is between 1 and 2 minutes, it is evaluated as a strong abnormality; When the efficiency coefficient μ<0.3 and the time T taken for the user to reach the optimal comfortable temperature is between 2 and 5 minutes, it is evaluated as a weak abnormality; When the efficiency coefficient μ is between 0.3 and 2.0, and the time T taken for the user to reach the optimal comfortable temperature is between 2 and 5 minutes, it is evaluated as a weak abnormality; When the efficiency coefficient μ is between 2.0 and 8.0, and the time T taken for the user to reach the optimal comfortable temperature is between 2 and 5 minutes, it is evaluated as a weak abnormality; When the efficiency coefficient μ is between 8.0 and 15, and the time T taken for the user to reach the optimal comfortable temperature is between 2 and 5 minutes, it is evaluated as a strong abnormality; When the efficiency coefficient μ<0.3 and the time spent by the user at the optimal comfortable temperature T>5min, it is assessed as weak safety; When the efficiency coefficient μ is between 0.3 and 2.0, and the time T taken for the user to reach the optimal comfortable temperature is greater than 5 minutes, it is evaluated as a weak abnormality; When the efficiency coefficient μ is between 2.0 and 8.0, and the time T taken for the user to reach the optimal comfortable temperature is greater than 5 minutes, it is evaluated as a weak abnormality; When the efficiency coefficient μ is between 8.0 and 15, and the time T spent at the user's optimal comfort temperature is greater than 5 minutes, it is evaluated as a weak abnormality; when the efficiency coefficient μ is greater than 15, it is evaluated as abnormal sampling.

3. A control method based on energy consumption scoring of central air conditioner in Internet of Things as claimed in claim 2, characterized in that: According to the scoring criteria, the traceability results of IoT central air conditioners are classified as follows: strong security, weak security, strong anomalies, weak anomalies, and sampling anomalies.

4. A control method based on energy consumption scoring of central air conditioner in Internet of Things as claimed in claim 2, characterized in that: The IoT central air conditioning control module is divided into different control modes according to different scores, including environmental protection mode, performance mode, default mode, holiday mode, headwind mode, maintenance mode, terminal control mode and other modes. Each mode corresponds to different configuration parameters.

5. A control method based on energy consumption scoring of central air conditioner in Internet of Things as claimed in claim 4, characterized in that: If the score is strong security, the IoT central air conditioner will operate steadily according to the current parameters.

6. A control method based on energy consumption scoring of central air conditioner in Internet of Things as claimed in claim 4, characterized in that: If the score is in the weak security and weak abnormality range, the IoT central air-conditioning system will automatically push abnormality and weak security warnings, and automatically adjust the operating parameters in real time to reach the strong security range. At the same time, it will prompt the user that the parameters required for strong security have been adjusted.

7. A control method based on energy consumption scoring of central air conditioner in Internet of Things as claimed in claim 4, characterized in that: If the score is in a strong abnormality, a strong abnormality warning will be pushed, and the current parameters of the strong abnormality will be stopped immediately and switched to the user's common mode.

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