Two-network balance adjusting system driven by intelligent Internet of Things

Abstract

The invention discloses a two-network balance adjusting system driven by an intelligent internet of things, relates to the technical field of heating systems, and aims to solve the technical problem that a traditional heating system cannot accurately adjust the heating amount due to data missing and simple algorithm, and the two-network balance adjusting system comprises a data acquisition unit used for acquiring related data of the heating system, comprising indoor and outdoor temperature, heating system pressure temperature and heat meter data; the data center unit is used for storing and processing data of the heating system, coordinating resource distribution and a data processing flow through a cloud computing platform, and generating a control instruction by utilizing data and algorithms in a two-network balance database and a model library so as to realize accurate adjustment and optimization of the heating system; the user terminal unit comprises a smart phone or a computer and is used for realizing interaction between a user and the system; and the automatic control unit controls the heating system equipment to operate according to an instruction of the data center. The system has the advantages that accurate distribution of heating heat is achieved, and the comfort level of a user is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of heating systems, and more specifically, to a two-network balancing and regulating system driven by an intelligent Internet of Things. Background Art

[0002] Energy conservation and emission reduction in heating system operation and heating failure have always been common but difficult to solve problems in northern my country. This affects not only the quality of life of the people, but also the energy conservation and emission reduction indicators of enterprises and economic development. Especially in recent years, with the rapid development of my country's economy and technology and the continuous improvement of people's living standards, people's requirements for heating are not only to meet the temperature standards in winter, but also to provide safe, energy-saving, intelligent and humanized high-quality services.

[0003] In the field of centralized heating, traditional heating systems usually use relatively simple control methods. For example, some heating systems only adjust the operation of heating equipment based on fixed time intervals or approximate regional temperature feedback. The heating of some old residential areas relies on manual experience to adjust the speed of water pumps and heating power. There is a lack of accurate temperature and pressure data monitoring, and it is impossible to make fine adjustments based on the actual needs of each user or each building; This traditional method has obvious shortcomings: due to the inability to accurately obtain key data such as indoor and outdoor temperature, pressure and flow of the heating system, it is impossible to accurately adjust the heating system, resulting in uneven heat distribution. In the same heating area, some users' indoor temperatures are too high, causing energy waste; while some users' indoor temperatures do not meet the standards, affecting the comfort of residents' lives, and it is also difficult to meet the requirements of energy conservation and emission reduction.

[0004] In view of this, we propose a two-network balancing regulation system driven by intelligent Internet of Things. Summary of the invention

[0005] The purpose of the present invention is to provide a two-network balancing and adjustment system driven by an intelligent Internet of Things to solve the technical problem that the traditional heating system cannot accurately adjust the heating amount due to data missing and simple algorithms.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions: a two-network balancing and regulating system driven by an intelligent Internet of Things, comprising: A data collection unit is used to collect heating system related data, including indoor and outdoor temperatures, heating system pressure and temperature, and heat meter data; The data center unit is used to store and process heating system data, coordinate resource allocation and data processing processes through the cloud computing platform, and use the data and algorithms in the two-network balance database and model library to generate control instructions to achieve precise regulation and optimization of the heating system; User terminal units, including smartphones or computers, enable user interaction with the system; The automatic control unit controls the operation of the heating system equipment according to the instructions of the data center.

[0007] Preferably, the data acquisition unit comprises: Indoor temperature data acquisition module, used to collect indoor temperature data and provide the system with outdoor basic temperature information; Outdoor temperature data acquisition module, used to collect outdoor temperature data and provide the system with indoor basic temperature information; Heating system pressure and temperature data acquisition module, used to collect pressure and temperature data of the heating system; The heat meter data acquisition module is used to collect heat meter data.

[0008] Preferably, the data center unit includes a cloud computing platform, which is composed of cloud computing hardware and cloud computing software. The cloud computing hardware provides the physical basis for data storage and computing, and the cloud computing software is responsible for coordinating data processing procedures and resource allocation.

[0009] Preferably, the data center unit further includes a two-network balance database and a two-network balance model library, and the two-network balance database includes: The basic parameter database covers indoor temperature data, outdoor temperature data, heating system pressure and heating system return water temperature data. It works with the heat supply database to provide necessary data for accurate calculation of heat supply and is the basis for reasonable supply of heating heat. The heat supply database, combined with the data information provided by the basic parameter database, is used to store and process the heat supply data and plays a core role in the energy regulation of the heating system; The heat load database, together with the heat energy supply database, provides basic data support for heat meter data. By storing and analyzing heat load data, it participates in heating regulation decisions and achieves energy-saving optimization.

[0010] Preferably, the two-network balance model library includes: Heat load calculation model, calculates heat load, provides a basic basis for heating regulation, and ensures that the heating system can provide heat according to actual demand; The two-network balance model realizes the effective regulation of heating quantity and heating load through algorithmic relationship, maintains the stable and economic operation of the heating system, and ensures the balance of heating among different users; Water pump scheduling algorithm, which controls the operation of water pumps according to indoor and outdoor temperatures; The heater scheduling algorithm controls the operation of the heater based on the relationship between the heat load and the heat meter value.

[0011] Preferably, the automatic control unit comprises: The inverter receives instructions from the data center to drive the water pump, adjusts the pump speed according to system requirements, ensures the stability of the heating water flow, and ensures the normal water circulation of the heating system; PID controller accurately adjusts the power of the heating device to maintain a stable heating temperature so that the heating temperature meets user needs and system settings; The valve controls the opening of the pipeline according to the instructions, optimizes the distribution of heating water flow, and ensures the balanced heat supply to each part of the heating system.

[0012] Preferably, the heat load calculation model calculates the intermediate variables according to the building heat storage characteristics and the indoor and outdoor temperature difference. , ,in, Indicates the heat storage coefficient index per unit area of ​​building mass. represents the total floor area of ​​the building, Indicates the indoor temperature setting value. Indicates the actual operating value of the indoor temperature. Indicates the outdoor temperature, is the empirical coefficient; Calculate heat load , ,in, Indicates the amount of heat energy supplied per unit time, Indicates the minimum value of supply and return water temperature.

[0013] Preferably, the two-network balance model first trains an initial adjustment coefficient through big data analysis and machine learning algorithm based on the system historical operation data and basic parameter data collected in real time. , for each set of heat loads in the historical data and heat meter values , calculate the intermediate variable , ,in, and They are all empirical coefficients, and their values ​​depend on the characteristics of the heating system and the statistical analysis results of historical operating data; Calculate the initial adjustment factor , ,in, and All are weight coefficients; Using the adjustment factor Calculate the heat supply per unit time , ,in, represents the efficiency coefficient, Indicates the value of the heat meter.

[0014] Preferably, the pump scheduling algorithm calculates , ,in, A comprehensive indicator used to measure the difference between indoor and outdoor temperatures. Indicates the minimum value of the difference between the indoor temperature setting value and the actual collected value. Indicates the temperature difference upward adjustment coefficient, is the empirical coefficient; according to and Dispatching water pumps in relation to when , then the water pump is adjusted upward by ; when , then the water pump is adjusted downward by ; when , then the water pump is maintained, where, .

[0015] Preferably, the heater scheduling algorithm comprehensively considers the difference between the heat load and the heat meter value and the energy conversion characteristics of the heater to calculate the intermediate variable , ,in, and is the empirical coefficient; Determine the amount of heat energy supplied per unit time , ,in, Represents the efficiency coefficient.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention can obtain detailed data such as indoor and outdoor temperature, heating system pressure and flow in real time by designing a precise data collection and analysis structure, and use intelligent algorithms for processing, effectively solving the problem that traditional heating systems cannot accurately adjust the heating amount due to missing data and simple algorithms, and realizes the precise distribution of heating heat, improves user comfort, and avoids local overheating or overcooling.

[0017] 2. The present invention uses the two-network balancing and adjustment system structure constructed with the help of intelligent Internet of Things technology to realize the automated and intelligent control of the heating system, and dynamically adjusts the operating status of the water pump and the heater according to the actual needs of different users and regions. Compared with traditional manual or rough control methods, it greatly improves the operating efficiency of the heating system, reduces energy waste, and achieves the effect of energy conservation and emission reduction. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION

[0019] To facilitate those skilled in the art to understand the technical solution of the present invention, the technical solution of the present invention is further described in conjunction with the accompanying drawings.

[0020] Embodiment 1, as Figure 1 As shown, the present invention provides a two-network balancing and regulating system driven by an intelligent Internet of Things, comprising: Data acquisition unit, including: Indoor temperature data acquisition module, used to collect indoor temperature data and provide the system with outdoor basic temperature information; Outdoor temperature data acquisition module, used to collect outdoor temperature data and provide the system with indoor basic temperature information; Heating system pressure and temperature data acquisition module, used to collect pressure and temperature data of the heating system; Heat meter data collection module, used for collecting heat meter data; Data center unit, including: The cloud computing platform consists of cloud computing hardware and cloud computing software. Cloud computing hardware provides the physical basis for data storage and computing, while cloud computing software is responsible for coordinating data processing flows and resource allocation to ensure efficient operation of the data center and provide support for data processing and instruction generation for the entire system. Second network balance database, including: The basic parameter database covers indoor temperature data, outdoor temperature data, heating system pressure and heating system return water temperature data. It works with the heat supply database to provide necessary data for accurate calculation of heat supply and is the basis for reasonable supply of heating heat. The heat supply database, combined with the data information provided by the basic parameter database, is used to store and process the heat supply data and plays a core role in the energy regulation of the heating system; The heat load database, in collaboration with the heat energy supply database, provides basic data support for heat meter data. By storing and analyzing heat load data, it participates in heating regulation decisions and achieves energy-saving optimization. Second network balance model library, including: Heat load calculation model, calculates heat load, provides a basic basis for heating regulation, and ensures that the heating system can provide heat according to actual demand; The two-network balance model realizes the effective regulation of heating quantity and heating load through algorithmic relationship, maintains the stable and economic operation of the heating system, and ensures the balance of heating among different users; Water pump scheduling algorithm, which controls the operation of water pumps according to indoor and outdoor temperatures; Heater scheduling algorithm, which controls the operation of heaters based on the relationship between heat load and heat meter values; User terminal units, including smartphones or computers, enable interaction between users and the system. Users can query the heating load adjustment status and heating load values ​​in real time through mobile phone APP or computer client, and submit basic data information such as indoor temperature setting value and heat supply demand to the system, enhancing the user-friendliness and interactivity of the system and enabling users to remotely monitor and participate in the heating system; Automatic control unit, comprising: The inverter receives instructions from the data center to drive the water pump, adjusts the pump speed according to system requirements, ensures the stability of the heating water flow, and ensures the normal water circulation of the heating system; PID controller accurately adjusts the power of the heating device to maintain a stable heating temperature so that the heating temperature meets user needs and system settings; The valve controls the opening of the pipeline according to the instructions, optimizes the distribution of heating water flow, and ensures the balance of heat supply to each part of the heating system. When the data center cannot match the corresponding operating condition model during the analysis and processing process, the command control signal sent to the automatic control unit is a power-off signal, which is fed back to the user terminal to ensure the safe and stable operation of the system.

[0021] In an embodiment of the present invention, the heat load calculation model calculates the intermediate variable according to the heat storage characteristics of the building and the difference between indoor and outdoor temperatures. , ,in, Indicates the heat storage coefficient index per unit area of ​​building mass. represents the total floor area of ​​the building, Indicates the indoor temperature setting value. Indicates the actual operating value of the indoor temperature. Indicates the outdoor temperature, It is an empirical coefficient, and its value is determined by analyzing and fitting a large amount of experimental data of different building types and heating environments; Calculate heat load , ,in, Indicates the amount of heat energy supplied per unit time, Indicates the minimum value of the supply and return water temperature, calculated by heat load , providing a basic basis for heating regulation and ensuring that the heating system can provide heat according to actual needs.

[0022] In the embodiment of the present invention, the two-network balance model first trains an initial adjustment coefficient through big data analysis and machine learning algorithm based on the system historical operation data and the basic parameter data collected in real time. , for each set of heat loads in the historical data and heat meter values , calculate the intermediate variable , ,in, and They are all empirical coefficients, and their values ​​depend on the characteristics of the heating system and the statistical analysis results of historical operating data; Calculate the initial adjustment factor , ,in, and All are weight coefficients; Using the adjustment factor Calculate the heat supply per unit time , ,in, represents the efficiency coefficient, It represents the value of the heat meter. Through this two-step relationship, effective regulation of heating quantity and heating load can be achieved, stable and economical operation of the heating system can be maintained, and the balance of heating among different users can be guaranteed.

[0023] In an embodiment of the present invention, the pump scheduling algorithm calculates , ,in, A comprehensive indicator used to measure the difference between indoor and outdoor temperatures. Indicates the minimum value of the difference between the indoor temperature setting value and the actual collected value. Indicates the temperature difference upward adjustment coefficient, It is an empirical coefficient, which is determined according to the hydraulic characteristics of the heating system and actual operating experience; according to and Dispatching water pumps in relation to when , then the water pump is adjusted upward by ; when , then the water pump is adjusted downward by ; when , then the water pump is maintained, where, , based on this algorithm, the water pump operation is reasonably regulated to ensure that the heating water flow meets the system requirements.

[0024] In an embodiment of the present invention, the heater scheduling algorithm comprehensively considers the difference between the heat load and the heat meter value and the energy conversion characteristics of the heater to calculate the intermediate variable , ,in, and It is an empirical coefficient determined through research and experiments on heater performance and heating heat transfer process; Determine the amount of heat energy supplied per unit time , ,in, Represents the efficiency coefficient.

[0025] Embodiment 2, as Figure 1 As shown, it is known that: Heat storage coefficient index per unit area of ​​building mass ; Total floor area of ​​the building ; Indoor temperature setting value ; Actual operating value of indoor temperature ; Outdoor temperature ; Minimum value of supply and return water temperature ; Heat supply per unit time ; Temperature difference upward adjustment coefficient ; Empirical coefficient , , , , , , , , , ; First calculate the intermediate variables in the heat load calculation model : , Calculate heat load : ， Calculating intermediate variables in the two-network balance model : Suppose 5 sets of data are extracted from the system historical data: , ; For the first set of data: ， For the second set of data: ， For the third set of data: ， For the fourth set of data: ， For the fifth set of data: ， ， calculate : , calculate : because , according to the water pump scheduling algorithm, the water pump is adjusted upward.

[0026] Example 3: Figure 1 As shown, it is known that: Heat storage coefficient index per unit area of ​​building mass ; Total floor area of ​​the building ; Indoor temperature setting value ; Actual operating value of indoor temperature ; Outdoor temperature ; Minimum value of supply and return water temperature ; Heat supply per unit time ; Temperature difference upward adjustment coefficient ; Empirical coefficient , , , , , , , , , ; First calculate the intermediate variables in the heat load calculation model : , Calculate heat load : ， Calculating intermediate variables in the two-network balance model : Assume that 4 sets of data are extracted from the system historical data: , ; For the first set of data: ， For the second set of data: ， For the third set of data: ， For the fourth set of data: ， calculate : , calculate : because , according to the water pump scheduling algorithm, the water pump is ordered to be lowered.

[0027] The embodiments of the present invention disclose preferred embodiments, but are not limited thereto. A person skilled in the art can easily understand the spirit of the present invention based on the above embodiments and make different extensions and changes. However, as long as they do not deviate from the spirit of the present invention, they are all within the protection scope of the present invention.

Claims

1. A two-network balancing and regulating system driven by an intelligent Internet of Things, characterized in that: include: A data collection unit is used to collect heating system related data, including indoor and outdoor temperatures, heating system pressure and temperature, and heat meter data; The data center unit is used to store and process heating system data, coordinate resource allocation and data processing processes through the cloud computing platform, and use the data and algorithms in the two-network balance database and model library to generate control instructions to achieve precise regulation and optimization of the heating system; User terminal units, including smartphones or computers, enable user interaction with the system; The automatic control unit controls the operation of the heating system equipment according to the instructions of the data center.

2. According to the second network balancing and regulating system driven by the intelligent Internet of Things according to claim 1, it is characterized in that: The data acquisition unit comprises: Indoor temperature data acquisition module, used to collect indoor temperature data and provide the system with outdoor basic temperature information; Outdoor temperature data acquisition module, used to collect outdoor temperature data and provide the system with indoor basic temperature information; Heating system pressure and temperature data acquisition module, used to collect pressure and temperature data of the heating system; The heat meter data acquisition module is used to collect heat meter data.

3. The two-network balancing and regulating system driven by the intelligent Internet of Things according to claim 2 is characterized in that: The data center unit includes a cloud computing platform, which is composed of cloud computing hardware and cloud computing software. The cloud computing hardware provides the physical basis for data storage and computing, and the cloud computing software is responsible for coordinating data processing procedures and resource allocation.

4. The two-network balancing and regulating system driven by the intelligent Internet of Things according to claim 3 is characterized in that: The data center unit further includes a two-network balance database and a two-network balance model library, wherein the two-network balance database includes: The basic parameter database covers indoor temperature data, outdoor temperature data, heating system pressure and heating system return water temperature data. It works with the heat supply database to provide necessary data for accurate calculation of heat supply and is the basis for reasonable supply of heating heat. The heat supply database, combined with the data information provided by the basic parameter database, is used to store and process the heat supply data and plays a core role in the energy regulation of the heating system; The heat load database, together with the heat energy supply database, provides basic data support for heat meter data. By storing and analyzing heat load data, it participates in heating regulation decisions and achieves energy-saving optimization.

5. The two-network balancing and regulating system driven by the intelligent Internet of Things according to claim 4 is characterized in that: The two-network balance model library includes: Heat load calculation model, calculates heat load, provides a basic basis for heating regulation, and ensures that the heating system can provide heat according to actual demand; The two-network balance model realizes the effective regulation of heating quantity and heating load through algorithmic relationship, maintains the stable and economic operation of the heating system, and ensures the balance of heating among different users; Water pump scheduling algorithm, which controls the operation of water pumps according to indoor and outdoor temperatures; The heater scheduling algorithm controls the operation of the heater based on the relationship between the heat load and the heat meter value.

6. The two-network balancing and regulating system driven by the intelligent Internet of Things according to claim 5 is characterized in that: The automatic control unit comprises: The inverter receives instructions from the data center to drive the water pump, adjusts the pump speed according to system requirements, ensures the stability of the heating water flow, and ensures the normal water circulation of the heating system; PID controller accurately adjusts the power of the heating device to maintain a stable heating temperature so that the heating temperature meets user needs and system settings; The valve controls the opening of the pipeline according to the instructions, optimizes the distribution of heating water flow, and ensures the balanced heat supply to each part of the heating system.

7. The two-network balancing and regulating system driven by the intelligent Internet of Things according to claim 6 is characterized in that: The heat load calculation model calculates intermediate variables based on the building's thermal storage characteristics and the difference between indoor and outdoor temperatures. , ,in, Indicates the heat storage coefficient index per unit area of ​​building mass. represents the total floor area of ​​the building, Indicates the indoor temperature setting value. Indicates the actual operating value of the indoor temperature. Indicates the outdoor temperature, is the empirical coefficient; Calculate heat load , ,in, Indicates the amount of heat energy supplied per unit time, Indicates the minimum value of supply and return water temperature.

8. The two-network balancing and regulating system driven by the intelligent Internet of Things according to claim 7 is characterized in that: The two-network balance model first trains an initial adjustment coefficient based on the system's historical operation data and real-time collected basic parameter data through big data analysis and machine learning algorithms. , for each set of heat loads in the historical data and heat meter values , calculate the intermediate variable , ,in, and They are all empirical coefficients, and their values ​​depend on the characteristics of the heating system and the statistical analysis results of historical operating data; Calculate the initial adjustment factor , ,in, and All are weight coefficients; Using the adjustment factor Calculate the heat supply per unit time , ,in, represents the efficiency coefficient, Indicates the value of the heat meter.

9. The two-network balancing and regulating system driven by the intelligent Internet of Things according to claim 8 is characterized in that: The pump scheduling algorithm calculates , ,in, A comprehensive indicator used to measure the difference between indoor and outdoor temperatures. Indicates the minimum value of the difference between the indoor temperature setting value and the actual collected value. Indicates the temperature difference upward adjustment coefficient, is the empirical coefficient; according to and Dispatching water pumps in relation to when , then the water pump is adjusted upward by ; when , then the water pump is adjusted downward by ; when , then the water pump is maintained, where, .

10. The two-network balancing and regulating system driven by the intelligent Internet of Things according to claim 9, characterized in that: The heater scheduling algorithm comprehensively considers the difference between the heat load and the heat meter value and the energy conversion characteristics of the heater to calculate the intermediate variable , ,in, and is the empirical coefficient; Determine the amount of heat energy supplied per unit time , ,in, Represents the efficiency coefficient.

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