Smart energy heat supply pipe network flow adjusting method and system

Abstract

The invention discloses a flow regulation method and system for a smart energy heat supply pipe network, and particularly relates to the field of heat supply regulation, and the method comprises the steps: predicting the heat demand of a heat user through obtaining the historical behavior data of the heat user; whether the flow of the heat supply pipe network needs to be adjusted or not is judged according to the heat demand predicted value; the heat supply time period is divided into a heat conveying stage and a heat exchange distribution stage, fluid noise in the heat supply pipe network in the heat conveying stage is analyzed, and the heat loss condition of the heat supply pipe network is evaluated; analyzing the heat flow density of the heat exchanger in the heat exchange distribution stage, and judging the heat flow density distribution condition of the heat exchanger; analyzing the thermal inertia of the heat building in the heat supply area, and evaluating the thermal performance of the heat building; based on the heat loss of the heat supply pipe network, the heat flow density distribution condition of the heat exchanger and the heat performance of the heat utilization building, the heat supply pipe network flow adjusting model is established, the heat supply pipe network flow is accurately adjusted, the energy utilization efficiency of a heat supply system can be improved, and heat energy waste is reduced.

Description

Technical Field

[0001] The present invention relates to the field of heat supply regulation, and more specifically, to a method and system for regulating flow in a smart energy heating network. Background Art

[0002] Traditional heating network flow regulation usually relies on manual operation or simple mechanical control, and the heating system mainly adjusts the flow according to the overall temperature and pressure changes of the heating network. This regulation method has defects in practical applications and lacks accurate analysis of key factors in the heating network. Traditional heating network flow regulation does not take into account the energy loss in the heat transmission process, which leads to easy waste of heat energy and low heat exchange efficiency during operation of the heating system, and cannot guarantee the temperature requirements of heat users. Traditional heating network regulation methods cannot dynamically respond to the actual heat needs of heat users, resulting in insufficient flexibility in the regulation of the heating system and difficulty in adapting to the diverse needs of different heat-using buildings in the heating area. It not only reduces the overall operating efficiency of the heating system, but also increases energy consumption and operating costs, limiting the intelligent development of the heating system. Summary of the invention

[0003] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a flow regulation method for a smart energy heating network to solve the problems raised in the above-mentioned background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions: The flow regulation method of the smart energy heating network includes the following steps: Step S1: Analyze the historical behavior data of heat users in the heating area to obtain the predicted heat demand of heat users; Step S2: judging whether it is necessary to adjust the flow of the heating network according to the predicted heat demand of the heat user; Step S3: When it is determined that the flow rate of the heating network needs to be adjusted, the heating time period is divided into a heat transport stage and a heat exchange distribution stage; the fluid noise in the heating network in the heat transport stage is analyzed to evaluate the heat loss of the heating network; the heat flux density of the heat exchanger in the heating network in the heat exchange distribution stage is analyzed to determine the heat flux density distribution of the heat exchanger; Step S4: Analyze the thermal inertia of heat-using buildings in the heating area and evaluate the thermal performance of the heat-using buildings; Step S5: A heating network flow regulation model is established according to the heat loss of the heating network, the heat flux density distribution of the heat exchanger and the thermal performance of the heat-using building. The heating network flow regulation model is used to regulate the flow of the heating network.

[0005] In a preferred embodiment, in step S1: the behavioral characteristics of heat users, environmental characteristics and historical heat usage data of heat-using buildings are input as features into a random forest model to predict the heat demand of heat users in the heating area. The heat demand of heat users in the heating area is the heat demand prediction value output by the random forest model.

[0006] In a preferred embodiment, in step S2: a heat demand threshold is preset, and the heat demand prediction value is compared with the heat demand threshold: When the heat demand forecast value is greater than or equal to the heat demand threshold, it means that the demand of heat users has reached the standard that requires flow regulation, and the heating system will adjust the flow of the heating network; When the predicted heat demand value is less than the heat demand threshold, it means that the demand of heat users has not reached the standard that requires flow regulation, and the heating system will not adjust the flow of the heating pipeline network.

[0007] In a preferred embodiment, in step S3: extract features related to heat loss, including the frequency of the noise signal, the intensity of the noise signal and the amplitude of the noise signal, and calculate the heat loss value of the heating network in the heat transport stage: ; in, It indicates the heat loss value of the heating network in the heat transport stage and is used to evaluate the heat loss of the heating network. Indicates The frequency of the noise signal at each sampling point; Indicates The strength of the noise signal at each sampling point; Indicates The amplitude of the noise signal at each sampling point; Indicates the number of sampling points in the pipe network; The heat flux density of the heat exchanger in the heating network during the heat exchange distribution stage is analyzed to determine the heat flux density distribution of the heat exchanger, specifically: Evaluate the heat flux distribution of the heat exchanger based on the heat flux density: ; in, is the heat flux distribution unevenness coefficient, which indicates the degree of uneven distribution of heat flux inside the heat exchanger; It is Heat flux density at each monitoring point; is the average heat flux density of all monitoring points; Represents the number of monitoring points used to measure heat flux at key locations of the heat exchanger.

[0008] In a preferred embodiment, in step S4: the thermal inertia coefficient of the thermal building is calculated using the formula: ; in, is the thermal inertia coefficient of the heat-using building; is the total heat capacity of the heat-using building; is the total thermal resistance of the building envelope; After calculating the thermal inertia coefficient of the building, the thermal performance evaluation coefficient of the thermal building is further analyzed, specifically: ; in, It is the thermal performance evaluation coefficient of thermal buildings; It is the heat energy actually consumed by the heat-using building; is the thermal inertia coefficient of the heat-using building; is the total area of ​​buildings using heat; It is the temperature difference between the outside and inside of the thermal building; It is the ideal indoor and outdoor temperature difference set when the heating system is designed or planned.

[0009] In a preferred embodiment, in step S5: a heat supply network flow regulation model is established according to the normalized heat loss value, the heat flux density distribution unevenness coefficient and the thermal performance evaluation coefficient: ; in, It is the flow adjustment coefficient, which is used to adjust the flow of the heat pipe network; Indicates the heat loss value of the heating network during the heat transport stage; is the coefficient of heat flux distribution inhomogeneity; It is the thermal performance evaluation coefficient of thermal buildings; , , They are the heat loss value, heat flux density distribution unevenness coefficient and the weight of thermal performance evaluation coefficient; Preset a flow regulation coefficient threshold, and compare the flow regulation coefficient with the flow regulation coefficient threshold; When the flow rate regulation coefficient is greater than or equal to the flow rate regulation coefficient threshold, it is necessary to increase the heat supply by increasing the flow rate of the heating network; When the flow regulation coefficient is less than the flow regulation coefficient threshold, it indicates that the heating system is in an ideal operating state, and you can choose to maintain the existing flow or reduce the flow appropriately.

[0010] Technical effects and advantages of the method of the present invention: 1. By analyzing the historical behavior data of heat users, the heat demand of heat users is predicted, so that the heat demand of heat users can better match the actual demand and reduce energy waste; according to the predicted value of heat demand, it is determined whether the flow of the heating network needs to be adjusted to further improve energy utilization efficiency; the heating time period is divided into the heat transmission stage and the heat exchange distribution stage, the fluid noise and heat flux density are analyzed, and the heat loss of the heating network and the heat flux density distribution of the heat exchanger are accurately evaluated to provide assistance for the subsequent flow regulation of the heating network.

[0011] 2. By analyzing the thermal inertia of heat-using buildings and evaluating their thermal performance, it is ensured that heat regulation not only meets the heat demand of heat-using buildings, but also maintains the temperature stability of heat-using buildings under different environmental conditions; by establishing a flow regulation model for the heating network, dynamic regulation and optimization of the flow is achieved, which improves the energy-saving effect of the heating system, improves the heating experience of heat users, and extends the service life of heating equipment, with significant technical advantages and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 This is a schematic diagram of the flow rate regulation method of the smart energy heating network of the present invention; Figure 2 This is a schematic diagram of the structure of the flow regulation system of the smart energy heating network of the present invention; DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] Reference Figure 1 .

[0015] The embodiment provides a method for regulating flow rate of a smart energy heating network according to the present invention, which comprises the following steps: Step S1: Analyze the historical behavior data of heat users in the heating area to obtain the predicted heat demand of heat users; Step S2: judging whether it is necessary to adjust the flow of the heating network according to the predicted heat demand of the heat user; Step S3: When it is determined that the flow rate of the heating network needs to be adjusted, the heating time period is divided into a heat transport stage and a heat exchange distribution stage; the fluid noise in the heating network in the heat transport stage is analyzed to evaluate the heat loss of the heating network; the heat flux density of the heat exchanger in the heating network in the heat exchange distribution stage is analyzed to determine the heat flux density distribution of the heat exchanger; Step S4: Analyze the thermal inertia of heat-using buildings in the heating area and evaluate the thermal performance of the heat-using buildings; Step S5: A heating network flow regulation model is established according to the heat loss of the heating network, the heat flux density distribution of the heat exchanger and the thermal performance of the heat-using building. The heating network flow regulation model is used to regulate the flow of the heating network.

[0016] In step S1: the historical behavior data of heat users in the heating area are analyzed to obtain the predicted heat demand of heat users, specifically: Heat users within the heating area usually refer to individuals and buildings that consume heat energy in the heating system; collect historical behavior data of heat users within the heating area, including life habit data of heat users, such as work and rest time, difference in heat usage between weekdays and weekends, change in heat usage pattern on holidays, etc.; personal life habit data of users can be obtained through smart home systems or heating system terminal devices; collect historical heat usage data of heat-using buildings within the heating area, including heat usage records of heat-using buildings in the past period of time; historical heat usage data can be obtained through the monitoring system of the heating pipeline network; collect environmental data that affects heat demand, such as indoor temperature of heat-using buildings, outdoor temperature, humidity, weather conditions, etc.; environmental data can be obtained through environmental sensors installed in users' homes or regional meteorological stations.

[0017] The behavioral characteristics of heat users are extracted based on their living habit data to find out the relationship between their behavioral patterns and heat demand. Environmental characteristics are extracted through environmental data to help predict fluctuations in heating demand. The historical heat usage data of heat-using buildings is used as features to capture the heat usage patterns of heat-using buildings. For example, the heat usage data of past days and weeks can be used to identify the heat usage of heat-using buildings.

[0018] In the smart heating system, the random forest model is an integrated learning method that is suitable for processing multi-dimensional feature data. By modeling the historical behavior data of heat users, environmental data, and historical heat usage data of heat-using buildings with the random forest model, it can be used to predict the heat demand of heat users in the heating area.

[0019] The behavioral characteristics of heat users, environmental characteristics and historical heat usage data of heat-using buildings are input as features into the random forest model to predict the heat demand of heat users in the heating area. The heat demand of heat users in the heating area is the heat demand prediction value output by the random forest model.

[0020] In step S2: by predicting the heat demand of heat users, the heating system can identify the peaks and troughs of heating in advance, help accurately adjust the flow of the heating network, and avoid overheating or insufficient heating; regulating the flow of the heating network based on the predicted heat demand can also improve the response speed of the heating system, reduce heating delays, enhance the overall stability and reliability of the heating system, and achieve smarter and more efficient management.

[0021] Determine whether the heat supply network flow needs to be adjusted based on the predicted heat demand of heat users, specifically: The heat demand threshold is preset. The heat demand threshold is set based on the historical heat demand data of the heating area, the volatility of heat demand, and the responsiveness of the heating system. The heat demand forecast value is compared with the heat demand threshold: When the predicted heat demand value is greater than or equal to the heat demand threshold, it means that the demand of heat users has reached the standard that requires flow regulation, and the heating system will adjust the flow of the heating network. This situation usually occurs when the heat demand of heat users increases, such as in cold weather or when the number of family members of heat users increases, the heat demand of heat users will increase. If the heating system does not adjust the flow in time, it may lead to insufficient heating, and the indoor temperature cannot reach the comfortable standard, affecting the quality of life. The heating system will adjust the flow of the heating network according to the judgment results, including increasing the flow of hot water, raising the temperature of hot water, and adjusting the valve opening in the heating network to ensure that heat energy can be quickly delivered to the heat user end. When the predicted heat demand value is less than the heat demand threshold, it means that the demand of heat users has not reached the standard that requires flow regulation. The heating system will not adjust the flow of the heating network, and will maintain the existing flow state of the heating network. Maintaining the existing flow state of the heating network will help avoid unnecessary energy waste. Frequent flow regulation will not only increase the operating load of the heating system, but may also lead to excessive energy consumption and accelerated wear of heating equipment. By maintaining the existing flow state, the heating system can achieve higher energy utilization efficiency and reduce heating costs. At the same time, the heating system will continue to monitor the heat demand of heat users in real time to ensure timely response and flow regulation when the heat demand increases.

[0022] In step S3: In the heat transport stage, fluid noise can be used to measure the stability of fluid flow in the pipeline. By analyzing the fluid noise to evaluate the heat loss, unstable factors in the transport process, such as turbulence or pipeline damage, can be discovered in time, which helps to optimize the transport efficiency. In the heat exchange distribution stage, heat energy is distributed from the heat exchange station to all heat users. By analyzing the heat flux density of the heat exchanger and evaluating the distribution of the heat flux, it is possible to identify the inefficiency or uneven heat energy problems that may exist in the heat exchange process and make targeted adjustments.

[0023] The heat transport stage refers to the process of transporting heat energy from the heating end to each heat exchange station through the pipeline network; the heat distribution stage refers to the process of transporting heat energy from the heat exchange station to the heat user end through the pipeline network; the division of heating time periods can be dynamically adjusted according to actual conditions; for example, under different weather conditions or changes in heat user demand, the heating system can readjust the time ratio of the heat transport and heat distribution stages to achieve optimal energy utilization efficiency.

[0024] Analyze the fluid noise in the heating network during the heat transport stage and evaluate the heat loss of the heating network, specifically: In the heat transport stage, heat energy is transmitted through the flow of fluid (hot water or steam) in the heating network. Fluid noise is the sound generated by friction, pressure fluctuations and other factors when the fluid flows in the heating network. By analyzing fluid noise, the heating system can identify possible heat loss problems in the heat transport process. Noise sensors are installed at key locations in the heating network. Noise data from different sampling points in the network are collected in real time through the noise sensors. The frequency, amplitude and other parameters of the noise are recorded. The noise signal is analyzed using a data processing algorithm to extract features related to heat loss, including the frequency, intensity and amplitude of the noise signal. These features are analyzed to calculate the heat loss value of the heating network in the heat transport stage: ; in, It indicates the heat loss value of the heating network in the heat transport stage and is used to evaluate the heat loss of the heating network. Indicates The frequency of the noise signal at each sampling point; Indicates The strength of the noise signal at each sampling point; Indicates The amplitude of the noise signal at each sampling point; Indicates the number of sampling points in the pipe network.

[0025] The larger the heat loss value, the greater the heat loss of the heating network, which means that in the heating network during the heat transport stage, more heat energy is lost during the transmission process and cannot be effectively transported to the heat exchange station; the larger the heat loss value, the more unstable the fluid flow state in the heating network may be, and there may be problems such as increased pipeline friction and decreased pipeline insulation performance; the increase in heat loss will lead to insufficient heat energy actually reaching the heat exchange station, which cannot meet the temperature requirements and affect the heating effect; the increase in heat loss will lead to a decrease in the energy utilization efficiency of the heating system, and may also accelerate the aging of pipelines and equipment, and increase the frequency of maintenance and replacement.

[0026] The heat flux density of the heat exchanger in the heating network during the heat exchange distribution stage is analyzed to determine the heat flux density distribution of the heat exchanger, specifically: During the heat exchange distribution stage, ensuring uniform distribution of heat flux density inside the heat exchanger is the key to evaluating the performance of the heat exchanger and the uniformity of heat energy distribution. Heat flux density refers to the amount of heat energy flowing through a unit area and is measured by a heat flux sensor. Uniform heat flux density distribution can ensure that all heat users can obtain sufficient heat, while uneven distribution may lead to insufficient or overheating of certain areas, affecting the operating efficiency of the entire heating system.

[0027] Heat flux sensors are installed at key locations of the heat exchanger (inlet, outlet, and tube wall) to measure the heat flux density in real time. The heat flux density data of each part of the heat exchanger is continuously collected through the heat flux sensor network; the heat flux distribution of the heat exchanger is evaluated based on the heat flux density: ; in, is the heat flux distribution unevenness coefficient, which indicates the degree of uneven distribution of heat flux inside the heat exchanger; It is Heat flux density at each monitoring point; is the average heat flux density of all monitoring points; Represents the number of monitoring points used to measure heat flux at key locations of the heat exchanger.

[0028] The larger the heat flux distribution unevenness coefficient, the more uneven the heat flux density distribution inside the heat exchanger; uneven heat flux density distribution may lead to reduced heat transfer efficiency in the heat exchanger, and some areas inside the heat exchanger may be overheated or underheated; uneven heat flux density distribution will cause the fluid in some areas inside the heat exchanger to not be sufficiently heated or cooled, resulting in local temperatures that are too high or too low, reducing the overall efficiency of the heat exchanger; when the heat flux distribution unevenness coefficient is large, the heat exchanger needs to be optimized and adjusted in time to improve the heat flux density distribution.

[0029] In step S4: the thermal inertia of a heat-using building refers to the speed at which the heat-using building absorbs and releases heat; if the thermal inertia of the heat-using building is high, the heating system can advance or postpone heating by adjusting the flow of the heating pipe network, thereby achieving more efficient use of energy; by real-time monitoring and analysis of the thermal inertia of the heat-using building, the heating system can make differentiated adjustments to the flow of the heating pipe network in large-scale heat-using building complexes.

[0030] Analyze the thermal inertia of heat-using buildings in the heating area and evaluate the thermal performance of heat-using buildings, specifically: Collect basic information of heat-using buildings in the heating area, including building area, thermal conductivity of walls, windows and roofs; collect building area through building design drawings or property management system; collect thermal conductivity of walls, windows and roofs from technical specifications of building materials or actual test data; the thermal performance of heat-using buildings is also affected by the external environment, and indoor and outdoor temperature data are obtained through temperature sensors; the actual heat energy consumed by heat-using buildings is obtained through the monitoring system of the heating system; the thermal inertia coefficient of heat-using buildings reflects the resistance of heat-using buildings to temperature changes, and the calculation formula is: ; in, is the thermal inertia coefficient of the heat-using building; is the total heat capacity of the heat-using building; It is the total thermal resistance of the building envelope, which is the sum of the reciprocals of the thermal conductivities of walls, windows and roof, and reflects the thermal insulation performance of the building.

[0031] After calculating the thermal inertia coefficient of the building, the thermal performance evaluation coefficient of the thermal building is further analyzed, specifically: ; in, It is the thermal performance evaluation coefficient of thermal buildings; It is the heat energy actually consumed by the heat-using building; is the thermal inertia coefficient of the heat-using building; is the total area of ​​buildings using heat; It is the temperature difference between the outside and inside of the thermal building; It is the ideal indoor and outdoor temperature difference set when the heating system is designed or planned, and is used to correct the impact of the actual temperature difference on the thermal performance evaluation of heat-using buildings.

[0032] The larger the thermal performance evaluation coefficient, the higher the thermal performance of the heat-using building during the heating process; the larger the thermal performance evaluation coefficient, the stronger the thermal inertia of the building, which can more effectively resist external temperature changes and maintain the stability of indoor temperature; the larger the thermal performance evaluation coefficient, the better the thermal insulation effect of the heat-using building, and the heat energy can be maintained longer in the heat-using building, which helps to reduce heating costs, reduce the load and wear of heating equipment, and extend the service life of the equipment.

[0033] In step S5: a heating network flow regulation model is established according to the heat loss of the heating network, the heat flux density distribution of the heat exchanger and the thermal performance of the heat-using building. The heating network flow regulation model is used to regulate the flow of the heating network, specifically: The heat loss value corresponding to the heat loss of the heating network, the heat flux distribution unevenness coefficient corresponding to the heat flux distribution of the heat exchanger, and the thermal performance evaluation coefficient corresponding to the thermal performance of the heat-using building are normalized, and the flow regulation model of the heating network is established based on the normalized heat loss value, heat flux distribution unevenness coefficient and thermal performance evaluation coefficient: ; in, It is the flow adjustment coefficient, which is used to adjust the flow of the heat pipe network; Indicates the heat loss value of the heating network during the heat transport stage; is the coefficient of heat flux distribution inhomogeneity; It is the thermal performance evaluation coefficient of thermal buildings; , , They are the heat loss value, the heat flux density distribution unevenness coefficient and the weights of the thermal performance evaluation coefficient, which are used to adjust the impact on the flow regulation coefficient of the heating network.

[0034] A flow regulation coefficient threshold is preset, and the flow regulation coefficient threshold is set according to the design standard of the heating system and the historical operation data of the heating system, and the flow regulation coefficient is compared with the flow regulation coefficient threshold; When the flow rate regulation coefficient is greater than or equal to the flow rate regulation coefficient threshold, it indicates that the heat loss in the heating system is large, the heat flux density is unevenly distributed, and the thermal performance of the heat-using building is insufficient. It is necessary to increase the heat supply by increasing the flow rate of the heating pipe network to ensure that the heating needs of heat users are met. Increasing the heating flow rate can be achieved by adjusting the opening of the heating valve and increasing the power of the hot water pump. When the flow regulation coefficient is less than the flow regulation coefficient threshold, it means that the heating system is in an ideal operating state, with small heat loss, the heat exchanger working well and the thermal performance of the heat-using building being high. You can choose to maintain the existing flow or reduce the flow appropriately. Reducing the heating flow can be achieved by reducing the opening of the heating valve, reducing the power of the hot water pump, etc., thereby reducing unnecessary energy consumption.

[0035] The smart energy heating network flow regulation system disclosed in the present invention comprises: a heat demand prediction module, a flow regulation judgment module, a heat loss evaluation module, a heat flux density distribution judgment module, a thermal performance evaluation module, and a flow regulation module; Heat demand prediction module: Analyze the historical behavior data of heat users in the heating area to obtain the predicted heat demand of heat users; Flow regulation judgment module: determines whether the flow of the heating network needs to be regulated based on the predicted heat demand of heat users; Heat loss assessment module: Analyze the fluid noise in the heating network during the heat transport stage and assess the heat loss of the heating network; Heat flux density distribution judgment module: analyzes the heat flux density of the heat exchanger in the heating network during the heat exchange distribution stage to determine the heat flux density distribution of the heat exchanger; Thermal performance evaluation module: Analyze the thermal inertia of heat-using buildings in the heating area and evaluate the thermal performance of heat-using buildings; Flow regulation module: A flow regulation model for the heating network is established based on the heat loss of the heating network, the heat flux density distribution of the heat exchanger, and the thermal performance of the heat-using building. The flow regulation model for the heating network is used to regulate the flow of the heating network.

[0036] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented by software, the above embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or may be transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium may be a solid-state hard disk.

[0037] Those of ordinary skill in the art will appreciate that the modules and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0038] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and modules described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0039] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the modules is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0040] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, and may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0041] In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0042] If the functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, and other media that can store program codes.

[0043] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0044] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for regulating flow in a smart energy heating network, characterized in that: The following steps are involved: Step S1: Analyze the historical behavior data of heat users in the heating area to obtain the predicted heat demand of heat users; Step S2: judging whether it is necessary to adjust the flow of the heating network according to the predicted heat demand of the heat user; Step S3: When it is determined that the flow rate of the heating network needs to be adjusted, the heating time period is divided into a heat transport stage and a heat exchange distribution stage; the fluid noise in the heating network in the heat transport stage is analyzed to evaluate the heat loss of the heating network; the heat flux density of the heat exchanger in the heating network in the heat exchange distribution stage is analyzed to determine the heat flux density distribution of the heat exchanger; Step S4: Analyze the thermal inertia of heat-using buildings in the heating area and evaluate the thermal performance of the heat-using buildings; Step S5: A heating network flow regulation model is established according to the heat loss of the heating network, the heat flux density distribution of the heat exchanger and the thermal performance of the heat-using building. The heating network flow regulation model is used to regulate the flow of the heating network.

2. A method for regulating flow rate of a smart energy heating network according to claim 1, characterized in that: In step S1: The behavioral characteristics of heat users, environmental characteristics and historical heat usage data of heat-using buildings are input as features into the random forest model to predict the heat demand of heat users in the heating area. The heat demand of heat users in the heating area is the heat demand prediction value output by the random forest model.

3. A method for regulating flow rate of a smart energy heating network according to claim 2, characterized in that: In step S2: Preset the heat demand threshold and compare the heat demand forecast value with the heat demand threshold: When the heat demand forecast value is greater than or equal to the heat demand threshold, it means that the demand of heat users has reached the standard that requires flow regulation, and the heating system will adjust the flow of the heating network; When the predicted heat demand value is less than the heat demand threshold, it means that the demand of heat users has not reached the standard that requires flow regulation, and the heating system will not adjust the flow of the heating pipeline network.

4. A method for regulating flow rate of a smart energy heating network according to claim 3, characterized in that: In step S3: Extract features related to heat loss, including the frequency, intensity and amplitude of the noise signal, and calculate the heat loss value of the heating network in the heat transport stage: ; in, It indicates the heat loss value of the heating network in the heat transport stage and is used to evaluate the heat loss of the heating network. Indicates The frequency of the noise signal at each sampling point; Indicates The strength of the noise signal at each sampling point; Indicates The amplitude of the noise signal at each sampling point; Indicates the number of sampling points in the pipe network; The heat flux density of the heat exchanger in the heating network during the heat exchange distribution stage is analyzed to determine the heat flux density distribution of the heat exchanger, specifically: Evaluate the heat flux distribution of the heat exchanger based on the heat flux density: ; in, is the heat flux distribution unevenness coefficient, which indicates the degree of uneven distribution of heat flux inside the heat exchanger; It is Heat flux density at each monitoring point; is the average heat flux density of all monitoring points; Represents the number of monitoring points used to measure heat flux at key locations of the heat exchanger.

5. A method for regulating flow rate of a smart energy heating network according to claim 4, characterized in that: In step S4: The thermal inertia coefficient of thermal buildings is calculated using the formula: ; in, is the thermal inertia coefficient of the heat-using building; is the total heat capacity of the heat-using building; is the total thermal resistance of the building envelope; After calculating the thermal inertia coefficient of the building, the thermal performance evaluation coefficient of the thermal building is further analyzed, specifically: ; in, It is the thermal performance evaluation coefficient of thermal buildings; It is the heat energy actually consumed by the heat-using building; is the thermal inertia coefficient of the heat-using building; is the total area of ​​buildings using heat; It is the temperature difference between the outside and inside of the thermal building; It is the ideal indoor and outdoor temperature difference set when the heating system is designed or planned.

6. A method for regulating flow rate of a smart energy heating network according to claim 5, characterized in that: In step S5: The flow regulation model of the heating network is established based on the normalized heat loss value, heat flux density distribution unevenness coefficient and thermal performance evaluation coefficient: ; in, It is the flow adjustment coefficient, which is used to adjust the flow of the heat pipe network; Indicates the heat loss value of the heating network during the heat transport stage; is the coefficient of heat flux distribution inhomogeneity; It is the thermal performance evaluation coefficient of thermal buildings; , , They are the heat loss value, heat flux density distribution unevenness coefficient and the weight of thermal performance evaluation coefficient; Preset a flow regulation coefficient threshold, and compare the flow regulation coefficient with the flow regulation coefficient threshold; When the flow rate regulation coefficient is greater than or equal to the flow rate regulation coefficient threshold, it is necessary to increase the heat supply by increasing the flow rate of the heating network; When the flow regulation coefficient is less than the flow regulation coefficient threshold, it indicates that the heating system is in an ideal operating state, and you can choose to maintain the existing flow or reduce the flow appropriately.

7. A smart energy heating network flow regulation system, applied to a smart energy heating network flow regulation method according to any one of claims 1 to 6, characterized in that: include: Heat demand prediction module: Analyze the historical behavior data of heat users in the heating area to obtain the predicted heat demand of heat users; Flow regulation judgment module: determines whether the flow of the heating network needs to be regulated based on the predicted heat demand of heat users; Heat loss assessment module: Analyze the fluid noise in the heating network during the heat transport stage and assess the heat loss of the heating network; Heat flux density distribution judgment module: analyzes the heat flux density of the heat exchanger in the heating network during the heat exchange distribution stage to determine the heat flux density distribution of the heat exchanger; Thermal performance evaluation module: Analyze the thermal inertia of heat-using buildings in the heating area and evaluate the thermal performance of heat-using buildings; Flow regulation module: A flow regulation model for the heating network is established based on the heat loss of the heating network, the heat flux density distribution of the heat exchanger, and the thermal performance of the heat-using building. The flow regulation model for the heating network is used to regulate the flow of the heating network.

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