Intelligent Internet of Things-driven Two-network Balance Regulation System

Abstract

The present invention discloses a two-network balance adjustment system driven by an intelligent Internet of Things, which relates to the technical field of heating systems and aims to solve the technical problem that the traditional heating system cannot accurately adjust the heating amount due to data loss and simple algorithms. It includes: a data acquisition unit for collecting data related to the heating system, including indoor and outdoor temperatures, heating system pressure and temperature, and heat meter data; a data center unit for storing and processing heating system data, coordinating resource allocation and data processing processes through a cloud computing platform, and using the data and algorithms in the two-network balance database and model library to generate control instructions to achieve precise adjustment and optimization of the heating system; a user terminal unit, including a smart phone or a computer, to realize the interaction between the user and the system; and an automatic control unit for controlling the operation of the heating system equipment according to the instructions of the data center. The present invention has the advantages of realizing the accurate distribution of heating heat and improving the user comfort level.

Description

Technical Field

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

[0002] Regarding the problems of energy conservation, emission reduction and unqualified heat supply in the operation of heating systems, they have always been common but difficult to solve problems after heating in the northern part of China. Especially in recent years, with the rapid development of China'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 standard in winter, but also to require high-quality services that are safe, energy-saving, intelligent and user-friendly.

[0003] In the field of central heating, traditional heating systems usually adopt 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 in some old communities relies on manual experience to judge and adjust the pump speed and heating power, lacking accurate monitoring of temperature and pressure data, and unable to perform refined adjustment according to the actual needs of each user or each building;

[0004] This traditional method has obvious deficiencies: since key data such as indoor and outdoor temperatures, the pressure and flow of the heating system cannot be accurately obtained, the accurate adjustment of the heating system cannot be realized, resulting in uneven heat distribution. In the same heating area, the indoor temperature of some users is too high, causing energy waste; while the indoor temperature of some users does not meet the standard, affecting the living comfort of residents, and at the same time it is difficult to meet the requirements of energy conservation and emission reduction.

[0005] In view of this, we propose a two-network balance adjustment system driven by intelligent Internet of Things. Summary of the Invention

[0006] The purpose of the present invention is to provide a two-network balance adjustment system driven by intelligent Internet of Things to solve the technical problem that the traditional heating system cannot accurately adjust the heating amount due to data loss and simple algorithms.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: A two-network balance adjustment system driven by intelligent Internet of Things, comprising:

[0008] A data acquisition unit for collecting data related to the heating system, including indoor and outdoor temperatures, heating system pressure and temperature, and heat meter data;

[0009] A data center unit for storing and processing heating system data, coordinating resource allocation and data processing processes through a cloud computing platform, and generating control instructions by using the data and algorithms in the two-network balance database and model library to achieve accurate adjustment and optimization of the heating system;

[0010] A user terminal unit, including a smart phone or a computer, which realizes the interaction between the user and the system;

[0011] An automatic control unit, which controls the operation of the heating system equipment according to the instructions of the data center;

[0012] Preferably, the data acquisition unit includes:

[0013] An indoor temperature data acquisition module, which is used to acquire indoor temperature data and provide basic indoor temperature information for the system;

[0014] An outdoor temperature data acquisition module, which is used to acquire outdoor temperature data and provide basic outdoor temperature information for the system;

[0015] A heating system pressure and temperature data acquisition module, which is used to acquire the pressure and temperature data of the heating system;

[0016] A heat meter data acquisition module, which is used to acquire heat meter data.

[0017] Preferably, the data center unit includes a cloud computing platform, which consists of cloud computing hardware and cloud computing software. The cloud computing hardware provides the physical basis for data storage and operation, and the cloud computing software is responsible for coordinating the data processing process and resource allocation.

[0018] Preferably, the data center unit further includes a secondary network balance database and a secondary network balance model library. The secondary network balance database includes:

[0019] A basic parameter database, which covers indoor temperature data, outdoor temperature data, the pressure of the heating system, and the return water temperature data of the heating system. It cooperates with the heat energy supply amount database to provide necessary data for accurately calculating the heat energy supply amount, and is the basis for the reasonable supply of heating heat;

[0020] A heat energy supply amount database, which combines the data information provided by the basic parameter database and is used to store and process the heat energy supply amount data, playing a core role in the energy regulation of the heating system;

[0021] A heat load database, which cooperates with the heat energy supply amount database to provide basic data support for the heat meter data. By storing and analyzing the heat load data, it participates in the heating regulation decision-making and realizes energy-saving optimization.

[0022] Preferably, the secondary network balance model library includes:

[0023] A heat load calculation model, which calculates the heat load and provides a basic basis for heating regulation to ensure that the heating system can provide heat according to actual needs;

[0024] The two-network balance model realizes the effective regulation of the heating quantity and heating load through algorithmic relationships, maintains the stable and economic operation of the heating system, and ensures the balance of heating among different users;

[0025] The water pump scheduling algorithm controls the operation of the water pump according to the indoor and outdoor temperatures;

[0026] The heater scheduling algorithm controls the operation of the heater according to the relationship between the heat load and the value of the heat meter.

[0027] Preferably, the automatic control unit includes:

[0028] The frequency converter receives instructions from the data center to drive the water pump, adjusts the water pump speed according to system requirements, ensures the stability of the heating water flow, and ensures the normal water circulation of the heating system;

[0029] The PID controller accurately adjusts the power of the heating device, maintains the stability of the heating temperature, and makes the heating temperature meet the user's needs and system settings;

[0030] The valve controls the pipe opening according to instructions, optimizes the distribution of heating water flow, and ensures the balance of heat supply to each part of the heating system.

[0031] Preferably, the heat load calculation model calculates intermediate variables based on the building's heat storage characteristics and the temperature difference between indoors and outdoors , , where represents the heat storage coefficient index per unit area of the building mass, represents the total building area of the building, represents the indoor temperature set value, represents the actual operating value of the indoor temperature, represents the outdoor temperature, is an empirical coefficient;

[0032] Calculate the heat load , , where represents the heat energy supply per unit time, represents the minimum value of the supply and return water temperatures.

[0033] Preferably, the two-network balance model first trains an initial adjustment coefficient through big data analysis and machine learning algorithms based on the system's historical operation data and real-time collected basic parameter data , for each group of heat loads and the heat meter values in the historical data, calculate the intermediate variable , , where and They are all empirical coefficients, and their values depend on the characteristics of the heating system and the statistical analysis results of historical operation data;

[0034] Calculate the initial adjustment coefficient , , where, and are both weight coefficients;

[0035] Use the adjustment coefficient to calculate the heat energy supply per unit time , , where, represents the efficiency coefficient, represents the value of the heat meter, represents the heat load.

[0036] Preferably, the water pump scheduling algorithm calculates , , where, is a comprehensive index for measuring the indoor-outdoor temperature difference, represents the minimum value in the difference between the indoor temperature set value and the actually collected value, represents the temperature difference upward adjustment coefficient, is an empirical coefficient;

[0037] According to and relationship to schedule the water pump;

[0038] When , then the water pump is adjusted upward, and the upward adjustment amount is ;

[0039] When , then the water pump is adjusted downward, and the downward adjustment amount is ;

[0040] When , then the water pump is maintained, where, .

[0041] Preferably, the heater scheduling algorithm comprehensively considers the difference relationship between the heat load and the heat meter value and the energy conversion characteristics of the heater to calculate the intermediate variable , , where, and are empirical coefficients;

[0042] Determine the heat energy supply per unit time , , where, represents the efficiency coefficient.

[0043] Compared with the prior art, the beneficial effects of the present invention are:

[0044] 1. The present invention can obtain detailed data such as indoor and outdoor temperatures, heating system pressure and flow rate in real time by designing a precise data acquisition and analysis structure, and use intelligent algorithms for processing, effectively solving the problem that the traditional heating system cannot accurately adjust the heating amount due to data loss and simple algorithms, achieving precise distribution of heating heat, improving the comfort of users, and avoiding local overheating or overcooling phenomena.

[0045] 2. The two-network balance adjustment system structure constructed by the present invention with the help of intelligent Internet of Things technology realizes automatic and intelligent control of the heating system, dynamically adjusts the operating states of water pumps and heaters according to the actual needs of different users and regions. Compared with the 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

[0046] Figure 1 It is a schematic diagram of the system framework of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0047] To facilitate those skilled in the art to understand the technical solution of the present invention, the technical solution of the present invention will be further described below in conjunction with the accompanying drawings of the specification.

[0048] Example 1, as Figure 1 shown, the present invention provides a two-network balance adjustment system driven by intelligent Internet of Things, including:

[0049] A data acquisition unit, including:

[0050] An indoor temperature data acquisition module, used to acquire indoor temperature data and provide basic indoor temperature information for the system;

[0051] An outdoor temperature data acquisition module, used to acquire outdoor temperature data and provide basic outdoor temperature information for the system;

[0052] A heating system pressure and temperature data acquisition module, used to acquire the pressure and temperature data of the heating system;

[0053] A heat meter data acquisition module, used to acquire heat meter data;

[0054] A data center unit, including:

[0055] A cloud computing platform, composed of cloud computing hardware and cloud computing software. The cloud computing hardware provides the physical basis for data storage and operation, and the cloud computing software is responsible for coordinating the data processing process and resource allocation to ensure the efficient operation of the data center and provide support for the data processing and instruction generation of the entire system;

[0056] A two-network balance database, including:

[0057] The basic parameter database, covering indoor temperature data, outdoor temperature data, heating system pressure, and heating system return water temperature data, collaborates with the heat energy supply database to provide necessary data for accurately calculating the heat energy supply, and is the basis for the reasonable supply of heating heat;

[0058] The heat energy supply database, combining the data information provided by the basic parameter database, is used to store and process heat energy supply data, and plays a core role in the energy regulation of the heating system;

[0059] The heat load database collaborates with the heat energy supply database to provide basic data support for the heat meter data. By storing and analyzing heat load data, it participates in the heating regulation decision-making to achieve energy-saving optimization;

[0060] The secondary network balance model library includes:

[0061] The heat load calculation model calculates the heat load, provides a basic basis for heating regulation, and ensures that the heating system can provide heat according to actual needs;

[0062] The secondary network balance model effectively regulates the heating quantity and heating load through algorithmic relationships, maintains the stable and economic operation of the heating system, and ensures the balance of heating among different users;

[0063] The water pump scheduling algorithm controls the operation of the water pump according to indoor and outdoor temperatures;

[0064] The heater scheduling algorithm controls the operation of the heater according to the relationship between the heat load and the heat meter value;

[0065] The user terminal unit includes a smart phone or a computer to realize the interaction between the user and the system. The user can query the heating load regulation situation and heating load value in real time through the mobile APP or computer client. At the same time, the user can submit basic data information such as indoor temperature set value and heat energy supply demand to the system, enhancing the user-friendliness and interactivity of the system, and realizing the remote monitoring and participation of the user in the heating system;

[0066] The automatic control unit includes:

[0067] The frequency converter receives instructions from the data center to drive the water pump, adjusts the water pump speed according to system requirements, ensures the stable heating water flow, and ensures the normal water circulation of the heating system;

[0068] The PID controller accurately adjusts the power of the heating device to maintain the stable heating temperature, making the heating temperature meet user requirements and system settings;

[0069] The valve controls the pipe opening according to instructions, optimizes the distribution of heating water flow, ensures the balance of heat supply in each part of the heating system, and when the data center cannot match and form a corresponding working condition model during the analysis and processing process, the instruction 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.

[0070] In an embodiment of the present invention, the heat load calculation model calculates an intermediate variable according to the building heat storage characteristics and the indoor and outdoor temperature differences , , where represents the heat storage coefficient index per unit area of building mass, represents the total building area of the building, represents the indoor temperature set value, represents the actual operating value of the indoor temperature, represents the outdoor temperature, is an empirical coefficient, and its value is determined by analyzing and fitting a large amount of experimental data for different building types and heating environments;

[0071] Calculate the heat load , , where represents the heat energy supply per unit time, represents the minimum value of the supply and return water temperatures. By calculating the heat load , it provides a basic basis for heating adjustment to ensure that the heating system can provide heat according to actual needs.

[0072] In an embodiment of the present invention, the secondary network balance model first trains an initial adjustment coefficient based on the system historical operation data and the basic parameter data collected in real time through big data analysis and machine learning algorithms , for each group of heat loads and heat meter values in the historical data, calculate the intermediate variable , , where and are both empirical coefficients, and their values depend on the characteristics of the heating system and the statistical analysis results of the historical operation data;

[0073] Calculate the initial adjustment coefficient , , where and are both weight coefficients;

[0074] Use the adjustment coefficient to calculate the heat energy supply per unit time , , where represents the efficiency coefficient, represents the value of the heat meter. Through this two-step relational expression, the effective regulation of the heating quantity and heating load is realized, maintaining the stable and economic operation of the heating system and ensuring the balance of heating among different users.

[0075] In the embodiment of the present invention, the water pump scheduling algorithm calculates , , where is a comprehensive index for measuring the indoor-outdoor temperature difference, represents the minimum value in the difference between the indoor temperature set value and the actually collected value, represents the temperature difference upward adjustment coefficient, is an empirical coefficient, determined according to the hydraulic characteristics and actual operation experience of the heating system;

[0076] According to and to schedule the water pump;

[0077] When , the water pump is commanded to increase, and the increase amount is ;

[0078] When , the water pump is commanded to decrease, and the decrease amount is ;

[0079] When , the water pump is commanded to maintain, where . According to this algorithm, the operation of the water pump is reasonably regulated to ensure that the heating water flow meets the system requirements.

[0080] In the embodiment of the present invention, the heater scheduling algorithm comprehensively considers the difference relationship between the heat load and the value of the heat meter and the energy conversion characteristics of the heater to calculate the intermediate variable , , where and are empirical coefficients, determined through research and experiments on the heater performance and the heating heat transfer process;

[0081] Determine the heat energy supply per unit time , , where represents the efficiency coefficient.

[0082] Example 2, as Figure 1 shown, it is known that:

[0083] The heat storage coefficient index per unit area of the building quality ;

[0084] The total building area of the building ;

[0085] Indoor temperature set value ;

[0086] Actual operating value of indoor temperature ;

[0087] Outdoor temperature ;

[0088] Minimum value of supply and return water temperatures ;

[0089] Thermal energy supply per unit time ;

[0090] Temperature difference adjustment coefficient ;

[0091] Empirical coefficient , , , , , , , , , ;

[0092] First, calculate the intermediate variables in the heat load calculation model : ,

[0093] Calculate the heat load : ,

[0094] Calculate the intermediate variables in the secondary network balance model :

[0095] Suppose 5 groups of data are extracted from the system historical data: , ;

[0096] For the first group of data: ,

[0097] For the second group of data: ,

[0098] For the third group of data: ,

[0099] For the fourth group of data: ,

[0100] For the fifth group of data:

[0101] ,

[0102] ,

[0103] Calculate : ,

[0104] Calculate :

[0105] Since , according to the water pump scheduling algorithm, the water pump is lowered.

[0106] Example 3: As Figure 1 shown, it is known that:

[0107] Thermal storage coefficient index per unit area of building quality ;

[0108] Total building area of the building ;

[0109] Indoor temperature set value ;

[0110] Actual operating value of indoor temperature ;

[0111] Outdoor temperature ;

[0112] Minimum value of supply and return water temperature ;

[0113] Thermal energy supply per unit time ;

[0114] Temperature difference increase coefficient ;

[0115] Empirical coefficient , , , , , , , , , ;

[0116] First, calculate the intermediate variable in the heat load calculation model : ,

[0117] Calculate the heat load : ,

[0118] Calculate the intermediate variable in the secondary network balance model :

[0119] Suppose 4 groups of data are extracted from the system historical data: , ;

[0120] For the first group of data: ,

[0121] For the second group of data: ,

[0122] For the third group of data: ,

[0123] For the fourth group of data: ,

[0124] Calculate : ,

[0125] Calculate :

[0126] Since , according to the water pump scheduling algorithm, the water pump is commanded to increase.

[0127] The embodiments disclosed in the present invention are preferred embodiments, but not limited thereto. Those of ordinary skill in the art can easily understand the spirit of the present invention based on the above embodiments and make different extensions and changes. As long as they do not depart from the spirit of the present invention, they are 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; Automatic control unit, which controls the operation of heating system equipment according to the instructions of the data center; The data center unit further includes a two-network balance database and a two-network balance model library, wherein the two-network balance model library includes: Heat load calculation model, calculate heat load , providing a basic basis for heating regulation and ensuring 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; 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. Indicates heat load.

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 indoor basic temperature information; Outdoor temperature data acquisition module, used to collect outdoor temperature data and provide the system with outdoor 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 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 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.

6. The two-network balancing and regulating system driven by the intelligent Internet of Things according to claim 5 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.

7. The two-network balancing and regulating system driven by the intelligent Internet of Things according to claim 6 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, .

8. The two-network balancing and regulating system driven by the intelligent Internet of Things according to claim 7 is 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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