Intelligent Secondary Network Balancing System and Its Network Tuning Method

Abstract

The present invention provides an intelligent secondary network balancing system and its network adjustment method, which includes a typical user room temperature collector, an intelligent heating balance valve, a unit control cabinet, a relay control cabinet, a heat exchange station control cabinet, a secondary network variable frequency circulating pump, and an upper platform. The typical user room temperature collector is installed in the user room of a building. The typical user room temperature collector communicates with the upper platform through the Internet of Things, and LORA communication is used between the typical user room temperature collector and the unit control cabinet. The intelligent heating balance valve is installed at the heat inlet of each household or the heat inlet of each unit or the heat inlet of each building. The intelligent heating balance valve is equipped with a supply water temperature sensor, a return water temperature sensor, a supply water pressure sensor, an inlet pressure sensor, and an outlet pressure sensor. Bus, LORA, and Bluetooth communication are used between the intelligent heating balance valve and the unit control cabinet, and Internet of Things communication is used between the intelligent heating balance valve and the upper platform.

Description

Technical Field

[0001] The present invention belongs to the field of heating balance, and particularly relates to an intelligent secondary network balance system and a network adjustment method thereof. Background Art

[0002] At present, with the continuous application of informatization and digital technologies in the field of urban central heating, the control of the central heating system has become more and more refined. Most heat exchange stations have achieved remote centralized monitoring, and the automation degree of the primary network operation of the heating system is relatively high. However, the automation degree of the secondary network regulation is very low, and the balance problem basically needs to be adjusted manually. At present, only a very small part of the secondary network can achieve remote control relying on the Internet of Things technology, but there are problems such as single balance strategy, long balance period, low applicability, low automation degree, and high risk of communication failure.

[0003] The existing technology "Automatic balance adjustment method for secondary side heating based on room temperature and its intelligent energy consumption monitoring system" is based on the hydraulic balance of the heat network, and then correlates the room temperature with the opening degree of the electric valve at the unit heat inlet, and controls the opening degree through the real-time room temperature. However, in the actual heating system, the heat inertia of heat users is very strong. It takes more than 12 hours for the heating parameters to change to the room temperature change. Moreover, the room temperature is easily affected by weather and human activities. Therefore, the room temperature change cannot directly reflect the amount of heat supply in a short time. Even if the building with good insulation stops heating for one day, the room temperature drop is less than 1°C. Compared with the direct parameters such as the supply water temperature, return water temperature, supply water pressure, return water pressure, and heat value at the heat inlet, the opening degree of the regulating valve at the heat inlet and the adjustment of the heat source equipment can achieve rapid response of the parameters, while the room temperature is an indirect parameter and a trend parameter. Therefore, it is unreasonable to use the average room temperature of heat users collected by the monitoring system in each cycle as the opening degree command of the control valve at the heat inlet, which is very easy to overshoot, break the original hydraulic balance state, and affect the stability of the heating system.

[0004] For some systems with large room temperature deviation in the building, that is, systems with large room temperature dispersion, although the average room temperature of the controlled loop is basically the same as the average room temperature of the entire secondary network, the heating quality does not meet the standard. For systems such as vertical single-pipe up-supply and down-return, the average room temperature of the building or unit meets the standard, but the room temperature of the bottom heat users is too low, causing complaints. Simply increasing the opening degree of the regulating valve at the building heat inlet and increasing the flow rate can improve the room temperature of low-temperature users, but it will also make the room temperature of heat users with originally high room temperature even higher, resulting in too high heat supply in the whole building and causing energy waste.

[0005] Therefore, there is an urgent need for an intelligent secondary network balance system and a network adjustment method thereof. Summary of the Invention

[0006] The present invention provides an intelligent secondary network balancing system and a network adjustment method thereof, which solve the problem in the prior art that the flow coefficients for balancing the flow of the secondary network are mostly empirical estimated values, lacking a verification method, which is not conducive to determining the minimum flow rate and pressure difference required by the secondary network, resulting in waste of the power consumption of the circulating water pump.

[0007] The technical solution of the present invention is implemented as follows: The intelligent secondary network balancing system includes a typical user room temperature collector, an intelligent heating balance valve, a unit control cabinet, a relay control cabinet, a heat exchange station control cabinet, a secondary network variable-frequency circulating water pump, and a host platform. The typical user room temperature collector is installed in the user rooms of a building. The typical user room temperature collector communicates with the host platform through the Internet of Things, and the typical user room temperature collector and the unit control cabinet communicate with each other using LORA. Among them, the intelligent heating balance valve is installed at the heat inlet of each household or the heat inlet of each unit or the heat inlet of each building. The intelligent heating balance valve is provided with a supply water temperature sensor, a return water temperature sensor, a supply water pressure sensor, an inlet pressure sensor, and an outlet pressure sensor. The intelligent heating balance valve and the unit control cabinet communicate with each other using a bus, LORA, and Bluetooth. The intelligent heating balance valve and the host platform communicate with each other through the Internet of Things. The unit control cabinet and the relay control cabinet communicate with each other using LORA. The unit control cabinet and the host platform communicate with each other through the Internet of Things. The relay control cabinet and the heat exchange station control cabinet communicate with each other using LORA. The secondary network variable-frequency circulating water pump and the heat exchange station control cabinet communicate with each other using wired communication. The heat exchange station control cabinet and the host platform communicate with each other using a wired network and the Internet of Things. The host platform collects the parameters of all typical user room temperature collectors and intelligent heating balance valves. The position information of each intelligent heating balance valve in the host platform is associated with the heating area of the controlled loop and the position information of all typical user room temperature collectors in the controlled loop. Among them, the typical user room temperature is generally collected in individual representative users and does not cover all users. Therefore, it is selected according to the design requirements.

[0008] Most of the secondary network circulating water pumps in traditional heat exchange stations operate with constant pressure difference and variable frequency. The supply and return water pressure difference at the outlet of the secondary network of the heat exchange station is set according to experience and is not linked to the actual balance situation of the secondary network balance, and cannot accurately reflect the pressure difference required by the secondary network. Moreover, the flow coefficients for balancing the flow of the secondary network in the traditional method are mostly empirical estimated values, lacking a verification method, which is not conducive to determining the minimum flow rate and pressure difference required by the secondary network, resulting in waste of the power consumption of the circulating water pump.

[0009] As a preferred embodiment, the water supply temperature sensor of the intelligent heating balance valve is installed on the water supply pipe of the heat user to collect the water supply temperature of the heat user. The return water temperature sensor is installed on the sleeve of the valve body of the intelligent heating balance valve to collect the return water temperature. The water supply pressure sensor is installed on the water supply pipe of the heat user to collect the water supply temperature of the heat user. The inlet pressure sensor is installed at the inlet of the intelligent heating balance valve to collect the inlet pressure of the intelligent heating balance valve. The outlet pressure sensor is installed at the outlet of the intelligent heating balance valve to collect the outlet pressure of the intelligent heating balance valve.

[0010] As a preferred embodiment, the intelligent heating balance valve stores the impedance values corresponding to each opening. When the intelligent balance valve is at a preset opening, the intelligent heating balance valve calculates the real-time flow value passing through the intelligent heating balance valve based on the pressure difference between the inlet pressure sensor and the outlet pressure sensor and the impedance value of the current opening, and calculates the instantaneous heat power and the cumulative heat value based on the flow value, the temperature value collected by the water supply temperature sensor, and the temperature value collected by the return water temperature sensor.

[0011] As a preferred embodiment, the intelligent heating balance valve has an anti-interference mode by automatically adjusting the opening. After the anti-interference mode is turned on, when there are disturbances in the flow rate, inlet pressure, and outlet pressure in the heat supply network, the intelligent heating balance valve automatically adjusts the opening to keep the flow rate in the previous adjustment cycle constant.

[0012] As a preferred embodiment, the upper platform automatically screens the users in the most unfavorable loop. When all the intelligent heating balance valves in the intelligent secondary network balance system are in the fully open state, the upper platform calculates the target parameter St that the intelligent heating balance valve needs to control at this time. The deviation value ΔS between the actual parameter Sa and the target parameter St is ΔS = St - Sa. The intelligent heating balance valve with the largest deviation value ΔS is marked by the upper platform as the intelligent heating balance valve in the most unfavorable loop.

[0013] As a preferred embodiment, during the network adjustment process, the intelligent heating balance valve marked as the most unfavorable loop remains fully open all the time, and the anti-interference mode is turned off. During the balance adjustment process of the intelligent secondary network balance system, if the opening degree of another intelligent heating balance valve reaches full open, and its deviation value ΔS is the largest among the deviation values ΔS of all intelligent heating balance valves in the secondary network circulation system, the upper platform will re-mark this intelligent heating balance valve as the intelligent heating balance valve of the most unfavorable loop. At the same time, the anti-interference mode of this intelligent heating balance valve is turned off, the mark of the previous intelligent heating balance valve of the most unfavorable loop is cancelled, and the anti-interference mode is turned on. When the actual parameter Sa of the intelligent balance valve of the most unfavorable loop starts to be greater than the target parameter St, that is, when the intelligent balance valve of the most unfavorable loop has a tendency to close, the upper platform will reduce the frequency of the secondary network circulation pump to make the actual parameter Sa of the intelligent heating balance valve of the most unfavorable loop consistent with the target parameter St. During the process of using the network adjustment method, the intelligent heating balance valve of the most unfavorable loop remains fully open all the time.

[0014] As a preferred embodiment, when the upper platform interacts with the intelligent heating balance valve, the upper platform collects data such as the opening degree value, the supply water pressure value, the return water pressure value, the differential pressure value between the supply and return water, the flow value, and the heat value collected by the intelligent heating balance valve.

[0015] The network adjustment method for the intelligent secondary network balance system, the method includes the following steps:

[0016] First, perform the initial adjustment. The upper platform automatically selects the direct parameter method or the indirect parameter method for adjustment according to the collected data and the manually input information. After being confirmed by the operator of the upper platform, the initial adjustment starts. When the actual parameters Sa of all intelligent heating balance valves in the specified heat network circulation system are the same as the target parameters St, the first-step initial adjustment is completed;

[0017] Subsequently, enter the second-step fine adjustment stage. The upper platform counts the daily average room temperature value Tnpi of all room temperature collectors corresponding to the controlled loop of each intelligent heating balance valve i, compares this value with the daily average room temperature value Tnp of all typical user room temperature collectors in the entire circulation system, and calculates the deviation value between Tnpi and Tnp. The upper platform issues a fine adjustment control command to the intelligent heating balance valve whose deviation value exceeds the set value, adds a coefficient k to the previous target parameter St of each intelligent heating balance valve, that is, the upper platform calculates the kSt value of each intelligent heating balance valve, and issues commands to the intelligent heating balance valve whose deviation value exceeds the set value at regular intervals. When all the deviation values between Tnpi and Tnp are less than the set value, the second-step fine adjustment is completed. At this time, the upper platform issues a command to maintain the current opening degree to all intelligent heating balance valves in the specified heat network circulation system, and cancels the anti-interference mode;

[0018] Perform the adjustment of the third step to optimize the room temperature dispersion of the controlled loop. The upper platform calculates the room temperature dispersion value of the corresponding controlled loop through the daily average temperature of the room temperature collector of each intelligent heating balance valve controlled loop. The absolute value of the difference between the daily average room temperature value Tni of each typical user room temperature collector in the controlled loop and the daily average room temperature value Tnpi of all room temperature collectors is averaged to obtain the room temperature dispersion Dmi corresponding to the i-th intelligent heating balance valve. When Dmi is greater than the set value, the upper platform increases the frequency of the secondary network variable-frequency circulating water pump. When Dmi is less than the set value, the upper platform decreases the frequency of the secondary network variable-frequency circulating water pump. Adjust once a day until the room temperature dispersion Dmi of all controlled loops corresponding to intelligent heating balance valves reaches the set value, and the entire debugging process ends.

[0019] As a preferred implementation manner, in the direct parameter method in the initial adjustment step, the upper platform directly sets the target value and issues it to all intelligent heating balance valves; in the indirect parameter method, the upper platform counts the actual values of all intelligent heating balance valves in the same circulating system, calculates the average value to obtain the target value, and then issues it to all intelligent heating balance valves by the platform.

[0020] As a preferred implementation manner, when the upper platform automatically selects the direct parameter method or the indirect parameter method for adjustment, two automatic acquisition judgment conditions and manual input judgment conditions are used for method selection.

[0021] As a preferred implementation manner, when the three-step adjustment reaches the balanced operation state through the network adjustment method, all the heat inlet water supply pressure, return water pressure, water supply temperature, return water temperature, and calculated flow rate are in a stable state. If the heat inlet parameters change beyond the set value, the upper platform automatically determines the operating problems and issues a prompt and alarm on the upper platform.

[0022] After adopting the above technical solutions, the beneficial effects of the present invention are as follows: It can adopt a variety of balancing methods to adapt to more heating systems with different operating modes; the balancing adjustment work is divided into three levels, and the depth of balance implementation can be selected according to the actual situation of the system, that is, it can choose to only complete the first-step initial adjustment, or all three steps can be completed, and all heating systems can use this system and method, and there is no infeasibility in certain situations; there are various communication methods to ensure the online rate of the equipment; it can automatically identify the most unfavorable loop and dynamically judge. During the dynamic change process of the secondary network, the operating parameters of the most unfavorable loop are associated with the circulating water pump to prevent over-supply and minimize the power consumption of the secondary network circulating pump.

[0023] When in the automatic balance state, the intelligent heating balance valve can automatically eliminate the interference generated by the regulation of other valves, with high stability and improved automatic balance efficiency. It can solve the controllability of the indoor temperature dispersion inside the controlled point after reaching the average room temperature balance, obtain the optimal operation parameters of the secondary network circulating pump. That is, by setting the indoor temperature dispersion, the corresponding minimum circulating flow can be obtained, and the circulating power consumption can be associated with the indoor temperature dispersion. The coefficient of this association can be measured and recorded to guide the operators to adjust the settings. It can automatically identify possible operation problems based on the collected data and guide the personnel to conduct troubleshooting. Brief Description of the Drawings

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0025] Figure 1 It is a flowchart of the network adjustment method of the present invention;

[0026] Figure 2 It is a flowchart of the determination condition method in the embodiment of the present invention. Detailed Embodiments

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only some of the embodiments of the present invention, rather than all of them. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0028] Embodiment:

[0029] As Figure 1As shown in the figure, the intelligent secondary network balance system includes: a typical user room temperature collector, an intelligent heating balance valve, a unit control cabinet, a relay control cabinet, a heat exchange station control cabinet, a secondary network variable-frequency circulating pump, and an upper platform. The typical user room temperature collector is installed in the rooms of six types of heat users, namely, the top-side household, the top-middle household, the middle-side household, the middle-middle household, the bottom-middle household on each floor of each building. The typical user room temperature collector communicates with the upper platform through the Internet of Things, and communicates with the unit control cabinet through LORA. The intelligent heating balance valve is installed at the heat inlet of each household, or the heat inlet of each unit, or the heat inlet of each building. The intelligent heating balance valve is equipped with a supply water temperature sensor, a return water temperature sensor, a supply water pressure sensor, an inlet pressure sensor, and an outlet pressure sensor. The intelligent heating balance valve communicates with the unit control cabinet through a bus, LORA, and Bluetooth, and communicates with the upper platform through the Internet of Things. The unit control cabinet communicates with the relay control cabinet through LORA, and communicates with the upper platform through the Internet of Things. The relay control cabinet communicates with the heat exchange station control cabinet through LORA. The secondary network variable-frequency circulating pump communicates with the heat exchange station control cabinet through wired communication. The heat exchange station control cabinet communicates with the upper platform through a wired network and the Internet of Things. The upper platform collects the parameters of all typical user room temperature collectors and intelligent heating balance valves. The position information of each intelligent heating balance valve, the heating area of the controlled loop, and the position information of all typical user room temperature collectors in the controlled loop are associated in the upper platform. In the same secondary network circulation system, the upper platform automatically screens the users in the most unfavorable loop, uses the network adjustment method, automatically calculates through the upper platform and the heat exchange station control cabinet, and issues control instructions to the intelligent heating balance valve to achieve heat network balance and room temperature equilibrium. Control instructions are issued to the secondary network variable-frequency circulating pump to adjust the room temperature dispersion of the users inside the controlled loop.

[0030] Typical user room temperature collector: Installed in the rooms of six types of heat users, namely, the top-side household, the top-middle household, the middle-side household, the middle-middle household, and the bottom-middle household on each floor of each building, for collecting the indoor temperature information of users.

[0031] Intelligent heating balance valve: Installed at the heat inlet of each household or the heat inlet of each building, for adjusting the room temperature of the users inside the controlled loop. Unit control cabinet: Used to control the intelligent heating balance valve and the unit control cabinet, and receive and process the signals of the intelligent heating balance valve and the unit control cabinet. Relay control cabinet: Used to connect the relay control cabinet and the heat exchange station, and receive and process the signals of the heat exchange station. Variable-frequency circulating pump: Used to adjust the room temperature dispersion of the users inside the controlled loop. Upper platform: Used to receive and process the parameter information of all typical user room temperature collectors and intelligent heating balance valves, and transmit through methods such as a bus, LORA, and Bluetooth.

[0032] Associate the parameter information of all typical user room temperature collectors, and use the network adjustment method. Through automatic calculation by the upper platform and the heat exchange station control cabinet, send control instructions to the intelligent heat supply balance valve to achieve heat network balance and room temperature equilibrium. Send control instructions to the secondary network variable frequency circulating water pump to adjust the user room temperature dispersion inside the controlled loop. The present invention adopts Internet of Things technology and big data analysis technology to realize an intelligent secondary network balance system for central heating, which can automatically adjust the user room temperature inside the controlled loop according to the heat consumption demand of users, so as to achieve the purpose of energy conservation.

[0033] The water supply temperature sensor of the intelligent heat supply balance valve is installed on the water supply pipe of the heat user to collect the water supply temperature of the heat user. The return water temperature sensor is installed on the sleeve of the valve body of the intelligent heat supply balance valve to collect the return water temperature. The water supply pressure sensor is installed on the water supply pipe of the heat user to collect the water supply temperature of the heat user. The inlet pressure sensor is installed at the inlet of the intelligent heat supply balance valve to collect the inlet pressure of the intelligent heat supply balance valve. The outlet pressure sensor is installed at the outlet of the intelligent heat supply balance valve to collect the outlet pressure of the intelligent heat supply balance valve. The intelligent heat supply balance valve can store the impedance value corresponding to each opening. When the intelligent balance valve is at a certain opening, the intelligent heat supply balance valve calculates the real-time flow value through the intelligent heat supply balance valve based on the pressure difference value between the inlet pressure sensor and the outlet pressure sensor and the impedance value of the current opening. Through the flow value, the temperature value collected by the water supply temperature sensor and the temperature value collected by the return water temperature sensor, the instantaneous heat power and the cumulative heat value are calculated. The intelligent heat supply balance valve automatically adjusts the opening, and the set and constant parameters include: water supply pressure value, return water pressure value, water supply and return water pressure difference value, flow value, heat value. The commands that the upper platform can send to the intelligent heat supply balance valve include: opening value, water supply pressure value, return water pressure value, water supply and return water pressure difference value, flow value, heat value. The intelligent heat supply balance valve has a flow anti-interference mode. After the flow anti-interference mode is turned on, when the intelligent heat supply balance valves in the same heat network circulation system execute the commands of the water supply pressure value, return water pressure value, water supply and return water pressure difference value, flow value, and heat value sent by the upper platform, during the intermittent period when the intelligent heat supply balance valve automatically adjusts the opening, if the network operation parameters fluctuate, resulting in a change in the flow of the intelligent heat supply balance valve, the intelligent heat supply balance valve will automatically adjust the opening, combine with the real-time flow value, ensure the flow of the previous adjustment cycle is constant, and then perform the actions of the next adjustment cycle.

[0034] The core of the intelligent heating balance valve is an intelligent instrument. It can store the impedance values corresponding to each opening degree. When the intelligent balance valve is at a certain opening degree, it calculates the real-time flow value through the intelligent heating balance valve by calculating the pressure difference between the inlet pressure sensor and the outlet pressure sensor and the impedance value of the current opening degree. The instantaneous heat power and the cumulative heat value are calculated through the flow value, the temperature value collected by the supply water temperature sensor, and the temperature value collected by the return water temperature sensor. The intelligent heating balance valve also has a flow anti-interference mode. When the intelligent heating balance valve in the same heat network circulation system executes the flow regulation command issued by the upper platform, the intelligent heating balance valve will automatically adjust the opening degree, combined with the real-time flow value, to ensure the constancy of the flow in the previous regulation cycle, and then perform the actions of the next regulation cycle.

[0035] The upper platform automatically screens the users of the most unfavorable loop, which means that before using the network adjustment method for a certain secondary network circulation system, all the intelligent heating balance valves in this secondary network circulation system are in the fully open state. The upper platform calculates the target parameter St that the intelligent heating balance valve needs to control at this time. The deviation value ΔS between the actual parameter Sa and the target parameter St is ΔS = St - Sa. The intelligent heating balance valve with the largest deviation value ΔS is marked by the upper platform as the intelligent heating balance valve of the most unfavorable loop. During the network adjustment process, the intelligent heating balance valve of the most unfavorable loop always remains in the fully open state and closes the anti-interference mode. During the balance adjustment process of the intelligent secondary network balance system, if another intelligent heating balance valve reaches the fully open state and its deviation value ΔS is the largest among the deviation values ΔS of all the intelligent heating balance valves in this secondary network circulation system, the upper platform will re-mark this intelligent heating balance valve as the intelligent heating balance valve of the most unfavorable loop, and at the same time close the anti-interference mode of this intelligent heating balance valve, cancel the mark of the previous intelligent heating balance valve of the most unfavorable loop, and at the same time turn on the anti-interference mode. When the actual parameter Sa of the intelligent balance valve of the most unfavorable loop starts to be greater than the target parameter St, that is, when the intelligent balance valve of the most unfavorable loop has a tendency to close, the upper platform will reduce the frequency of the secondary network circulation pump to make the actual parameter Sa of the intelligent balance valve of the most unfavorable loop consistent with the target parameter St. During the process of using the network adjustment method, the intelligent heating balance valve of the most unfavorable loop always remains in the fully open state.

[0036] The network adjustment method for the intelligent secondary network balance system, the method includes the following steps:

[0037] The network adjustment method is divided into three steps. The first step is the initial adjustment, the second step is the fine adjustment combined with the room temperature, and the third step is the adjustment to optimize the room temperature dispersion of the controlled loop. The initial adjustment method includes:

[0038] Direct parameter method: flow coefficient consistent adjustment method, supply-return water pressure difference consistent adjustment method. In the direct parameter method, the platform operator directly sets the target value, which is sent from the upper platform to all intelligent heat supply balance valves; Indirect parameter method: daily heat coefficient consistent adjustment method, return water temperature consistent adjustment method, supply-return water average temperature consistent adjustment method. The indirect parameter method needs to statistically calculate the actual values of all intelligent heat supply balance valves in the same circulation system through the platform, calculate the average value to obtain the target value, and then send it from the platform to all intelligent heat supply balance valves. The upper platform automatically recommends one or several of the above initial adjustment methods based on the collected data and manually input information. After being confirmed by the upper platform operator, the first step of initial adjustment is implemented. The upper platform operator can also independently select the adjustment method. When the actual parameters Sa of all intelligent heat supply balance valves in the specified heat network circulation system are the same as the target parameters St, the first step of initial adjustment is completed;

[0039] Enter the second step of fine adjustment stage. The upper platform statistically calculates the daily average room temperature value Tnpi of all room temperature collectors in the controlled loop corresponding to each intelligent heat supply balance valve i, compares this value with the daily average room temperature value Tnp of all typical user room temperature collectors in the entire circulation system, and calculates the deviation value between Tnpi and Tnp. The upper platform sends a fine adjustment control instruction to the intelligent heat supply balance valve whose deviation value exceeds the set value, and adds a coefficient k to the target parameter St of each intelligent heat supply balance valve before. That is, the platform calculates the kSt value of each intelligent heat supply balance valve and sends a command to the intelligent heat supply balance valve whose deviation value exceeds the set value once a day. When all the deviation values between Tnpi and Tnp are less than the set value, the second step of fine adjustment is completed;

[0040] At this time, the platform sends a command to maintain the current opening to all intelligent heat supply balance valves in the specified heat network circulation system, cancels the anti-interference mode, and proceeds to the third step of adjusting the room temperature dispersion of the controlled loop. First, the platform calculates the room temperature dispersion value corresponding to the controlled loop through the daily average temperature of the room temperature collectors in the controlled loop of each intelligent heat supply balance valve. The calculation method of the room temperature dispersion value is: for the daily average room temperature value Tni of each typical user room temperature collector in the controlled loop of the i-th intelligent heat supply balance valve, the absolute value of the difference from the daily average room temperature value Tnpi of all room temperature collectors is averaged to obtain the room temperature dispersion Dmi corresponding to the i-th intelligent heat supply balance valve. When Dmi is greater than the set value, the upper platform increases the frequency of the secondary network variable-frequency circulating water pump. When Dmi is less than the set value, the upper platform reduces the frequency of the secondary network variable-frequency circulating water pump. It is adjusted once a day until the room temperature dispersion Dmi of all controlled loops corresponding to intelligent heat supply balance valves reaches the set value, and the entire debugging process ends.

[0041] In the direct parameter method in the initial adjustment step, the upper platform directly sets the target value and sends it to all intelligent heat supply balance valves; in the indirect parameter method, the upper platform counts the actual values of all intelligent heat supply balance valves in the same circulating system, calculates the average value to obtain the target value, and then the platform sends it to all intelligent heat supply balance valves.

[0042] As Figure 2 shown, for automatically recommending one or several of the above initial adjustment methods, two automatic acquisition judgment conditions and one manual input judgment condition are used for method selection. Judgment condition 1 is: under the stable state of the secondary network water supply temperature, the difference between the maximum value and the minimum value of the water supply temperature collected by all intelligent heat supply balance valves, and whether this difference is greater than the set value. Judgment condition 2 is: the amplitude value of the daily change of the secondary network water supply temperature, that is, the difference between the maximum value and the minimum value of the water supply temperature collected by the intelligent heat supply balance valve at different times of each day. If this difference is greater than the set value. Judgment condition 3 is: whether the heat user is in the operation mode of independently regulating the heat supply; through the above three judgment conditions, the upper platform automatically recommends one or several initial adjustment methods for the upper platform operator to select.

[0043] Figure 2 In it, the method symbols used after judgment are: Method 1: Flow coefficient consistent adjustment method, Method 2: Supply and return water pressure difference consistent adjustment method, Method 3: Daily heat coefficient consistent method, Method 4: Return water temperature consistent adjustment method, Method 5: Supply and return water average temperature consistent adjustment method

[0044] When the upper platform automatically selects the direct parameter method or the indirect parameter method for adjustment, two automatic acquisition judgment conditions and a manual input judgment condition are used for method selection.

[0045] After reaching the balanced operation state through three-step adjustment by the network adjustment method, all the water supply pressure, return water pressure, water supply temperature, return water temperature, and calculated flow rate of the heat supply inlets are in a stable state. If the parameters of a certain heat supply inlet change beyond the set value, the upper platform automatically determines the possible operation problems and issues prompts and alarms on the upper platform. The corresponding relationships between parameter changes and possible operation problems are as follows: a. When the water supply temperature drops and the water supply pressure remains unchanged, the possible operation problem may be that the thermal insulation quilt upstream of the water supply pipe is damaged; b. When the water supply temperature drops and the water supply pressure drops, the possible operation problem may be that there is a leak in the pipeline upstream of the water supply pipe; c. When the return water temperature rises, the outlet pressure drops, and the difference between the water supply pressure and the inlet pressure increases, the possible operation problem may be that there is a leak in the pipeline downstream of the return water pipe; d. When the water supply temperature remains unchanged, the return water temperature drops, and the difference between the water supply pressure and the inlet pressure increases, the possible operation problem may be that the pipeline of the heat user is blocked; e. When the water supply temperature remains unchanged, the return water temperature drops, the water supply pressure drops, and the difference between the water supply pressure and the inlet pressure decreases, there is a blockage upstream of the water supply pipe; f. When the water supply temperature remains unchanged, the return water temperature drops, the outlet pressure rises, and the difference between the water supply pressure and the inlet pressure decreases, the possible operation problem may be that there is a blockage downstream of the return water pipe; g. When the water supply temperature remains unchanged, the return water temperature drops, and the difference between the inlet pressure and the outlet pressure increases, the possible operation problem may be that the inside of the intelligent heat supply balance valve is blocked; h. When the water supply temperature remains unchanged, the return water temperature rises, the water supply pressure drops, and the return water pressure rises, the possible operation problem may be that the user has privately installed a circulating pump; I. When the water supply temperature remains unchanged, the return water temperature drops, the water supply pressure drops, and the return water pressure drops, the possible operation problem may be that a heat user has privately drained water. The operator conducts manual operation to troubleshoot the problem based on the prompt or alarm.

[0046] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. Intelligent secondary network balancing system, characterized in that, It includes a typical user room temperature collector, an intelligent heat supply balance valve, a unit control cabinet, a relay control cabinet, a heat exchange station control cabinet, a secondary network variable-frequency circulating water pump, and an upper platform. The typical user room temperature collector is installed in the user room of a building. The typical user room temperature collector communicates with the upper platform through the Internet of Things. LORA communication is used between the typical user room temperature collector and the unit control cabinet. The intelligent heat supply balance valve is installed at the heat inlet of each household, or the heat inlet of each unit, or the heat inlet of each building. The intelligent heat supply balance valve is equipped with a supply water temperature sensor, a return water temperature sensor, a supply water pressure sensor, an inlet pressure sensor, and an outlet pressure sensor. Bus, LORA, and Bluetooth communications are used between the intelligent heat supply balance valve and the unit control cabinet. Internet of Things communication is used between the intelligent heat supply balance valve and the upper platform. LORA communication is used between the unit control cabinet and the relay control cabinet. Internet of Things communication is used between the unit control cabinet and the upper platform. LORA communication is used between the relay control cabinet and the heat exchange station control cabinet. Wired communication is used between the secondary network variable-frequency circulating water pump and the heat exchange station control cabinet. Wired network and Internet of Things communication are used between the heat exchange station control cabinet and the upper platform. The upper platform collects the parameters of all typical user room temperature collectors and intelligent heat supply balance valves. The position information of each intelligent heat supply balance valve in the upper platform is associated with the heating area of the controlled loop and the position information of all typical user room temperature collectors in the controlled loop. The upper platform automatically screens the users in the most unfavorable loop. When all the intelligent heat supply balance valves in the intelligent secondary network balance system are in the fully open state, the upper platform calculates the target parameter St that the intelligent heat supply balance valve needs to control at this time. The deviation value ΔS between the actual parameter Sa and the target parameter St is ΔS = St - Sa. The intelligent heat supply balance valve with the largest deviation value ΔS is marked by the upper platform as the intelligent heat supply balance valve in the most unfavorable loop.

2. The intelligent secondary network balancing system according to claim 1, characterized in that: The supply water temperature sensor of the intelligent heat supply balance valve is installed on the water supply pipe of the heat user to collect the supply water temperature of the heat user. The return water temperature sensor is installed on the sleeve of the valve body of the intelligent heat supply balance valve to collect the return water temperature. The supply water pressure sensor is installed on the water supply pipe of the heat user to collect the supply water temperature of the heat user. The inlet pressure sensor is installed at the inlet of the intelligent heat supply balance valve to collect the inlet pressure of the intelligent heat supply balance valve. The outlet pressure sensor is installed at the outlet of the intelligent heat supply balance valve to collect the outlet pressure of the intelligent heat supply balance valve.

3. The intelligent secondary network balancing system according to claim 2, characterized in that: The intelligent heat supply balance valve stores the impedance value corresponding to each opening. When the intelligent balance valve is at the preset opening, the intelligent heat supply balance valve calculates the real-time flow value passing through the intelligent heat supply balance valve through the pressure difference between the inlet pressure sensor and the outlet pressure sensor and the impedance value of the current opening. The instantaneous heat power and the cumulative heat value are calculated through the flow value, the temperature value collected by the supply water temperature sensor, and the temperature value collected by the return water temperature sensor.

4. The intelligent secondary network balancing system according to claim 3, wherein: The intelligent heating balance valve has an anti-interference mode by automatically adjusting the opening. After the anti-interference mode is turned on, when there are disturbances in the flow rate, inlet pressure, and outlet pressure of the heat network, the intelligent heating balance valve automatically adjusts the opening to keep the flow rate in the previous adjustment cycle constant.

5. The intelligent secondary network balancing system according to claim 1, wherein: During the network adjustment process, the intelligent heating balance valve marked as the most unfavorable loop remains fully open and the anti-interference mode is turned off. During the balance adjustment process of the intelligent secondary network balance system, if the opening of another intelligent heating balance valve reaches full open and its deviation value ΔS is the largest among the deviation values ΔS of all intelligent heating balance valves in the secondary network circulation system, the upper platform will re-mark this intelligent heating balance valve as the most unfavorable loop intelligent heating balance valve, and at the same time turn off the anti-interference mode of this intelligent heating balance valve, cancel the mark of the previous most unfavorable loop intelligent heating balance valve, and turn on the anti-interference mode. When the actual parameter Sa of the most unfavorable loop intelligent balance valve starts to be greater than the target parameter St, that is, when the most unfavorable loop intelligent balance valve has a tendency to close, the upper platform will reduce the frequency of the secondary network circulation pump to make the actual parameter Sa of the most unfavorable loop intelligent balance valve consistent with the target parameter St. During the process of using the network adjustment method, the most unfavorable loop intelligent heating balance valve is always in the fully open state.

6. The intelligent secondary network balancing system according to claim 1, wherein: When the upper platform interacts with the intelligent heating balance valve, the upper platform collects data such as the opening value, supply water pressure value, return water pressure value, supply and return water pressure difference value, flow rate value, and heat value collected by the intelligent heating balance valve.

7. Method for adjusting network of intelligent secondary network balancing system, characterized in that, The method includes the following steps: First, perform the initial adjustment. The upper platform automatically selects the direct parameter method or the indirect parameter method for adjustment based on the collected data and the manually input information. After being confirmed by the operator of the upper platform, the initial adjustment is started. When the actual parameters Sa of all intelligent heating balance valves in the specified heat network circulation system are the same as the target parameters St, the first-step initial adjustment is completed; Subsequently, enter the second-step fine adjustment stage. The upper platform calculates the daily average room temperature value Tnpi of all room temperature collectors corresponding to the controlled loop of each intelligent heating balance valve i, compares this value with the daily average room temperature value Tnp of all typical user room temperature collectors in the entire circulation system, and calculates the deviation value between Tnpi and Tnp. The upper platform sends a fine adjustment control command to the intelligent heating balance valve whose deviation value exceeds the set value, adds a coefficient k to the previous target parameter St of each intelligent heating balance valve, that is, the upper platform calculates the kSt value of each intelligent heating balance valve, and regularly sends commands to the intelligent heating balance valve whose deviation value exceeds the set value. When all the deviation values between Tnpi and Tnp are less than the set value, the second-step fine adjustment is completed. At this time, the upper platform sends a command to keep the current opening to all intelligent heating balance valves in the specified heat network circulation system and cancels the anti-interference mode; Perform the third-step optimization of the regulation of the room temperature dispersion of the controlled loop. The upper platform calculates the room temperature dispersion value of the corresponding controlled loop through the daily average temperature of the room temperature collectors of each intelligent heating balance valve controlled loop. The average value of the absolute value of the difference between the daily average room temperature value Tni of each typical user room temperature collector of the controlled loop and the daily average room temperature value Tnpi of all room temperature collectors is taken to obtain the room temperature dispersion Dmi corresponding to the i-th intelligent heating balance valve. When Dmi is greater than the set value, the upper platform increases the frequency of the secondary network variable-frequency circulating water pump. When Dmi is less than the set value, the upper platform decreases the frequency of the secondary network variable-frequency circulating water pump. It is adjusted once a day until the room temperature dispersion Dmi of all controlled loops corresponding to the intelligent heating balance valves reaches the set value, and the entire commissioning process ends.

8. The network adjustment method of the intelligent secondary network balance system according to claim 7, characterized in that, In the direct parameter method in the initial adjustment step, the upper platform directly sets the target value and sends it to all intelligent heating balance valves. In the indirect parameter method, the upper platform counts the actual values of all intelligent heating balance valves in the same circulating system, calculates the average value to obtain the target value, and then sends it to all intelligent heating balance valves by the platform.

9. The method for adjusting the network of the intelligent secondary network balance system according to claim 7, characterized in that, When the upper platform automatically selects the direct parameter method or the indirect parameter method for adjustment, two automatic acquisition judgment conditions and manual input judgment conditions are used for method selection.

10. The method for adjusting the network of the intelligent secondary network balance system according to claim 7, characterized in that, When the three-step adjustment reaches the balanced operation state through the network adjustment method, all the supply water pressure, return water pressure, supply water temperature, return water temperature, and calculated flow rate of the thermal inlet are in a stable state. If the thermal inlet parameters change beyond the set value, the upper platform automatically determines the occurrence of operation problems and issues a prompt and alarm on the upper platform.

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