Heat supply management method and system based on Internet of Things

Abstract

The invention discloses a heat supply management method and system based on the Internet of Things. The method comprises the steps that historical heat supply output heat data of a heat supply system and historical indoor temperature change data of a user are obtained and fitted, and a fitting model is obtained; acquiring a current indoor temperature and an indoor target temperature, and determining an indoor temperature target difference based on the current indoor temperature and the indoor target temperature; determining initial output heat of the heat supply system based on the indoor temperature target difference and a fitting model; based on the initial output heat, determining a pre-estimated time duration for the indoor temperature to reach the indoor target temperature, and determining outdoor temperature prediction data within the pre-estimated time duration; analyzing the outdoor temperature prediction data to determine a correction coefficient of output heat; and correcting the initial output heat of the heat supply system according to the correction coefficient, and performing heat supply management by the heat supply system. Real-time data can be collected based on the Internet of Things, the indoor and outdoor temperature change condition is determined, the output heat of the system is accurately adjusted according to the indoor and outdoor temperature change condition, and therefore the heat supply efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of heat supply management, and in particular to a heat supply management method and system based on the Internet of Things. Background Art

[0002] With the continuous development and popularization of Internet of Things (IoT) technology, its application in various fields is becoming more and more extensive. In the field of heating management, IoT technology has brought revolutionary changes and innovations to the heating system of buildings. The widespread application of sensors, intelligent controllers and interconnected devices enables the heating system to achieve remote monitoring, intelligent regulation and data analysis, thereby achieving more efficient energy utilization, smarter temperature control management and more sustainable operation mode.

[0003] Traditional heating management methods do not use the method of collecting data through the Internet of Things and applying it to heating management to optimize the management of heating. Instead, they adopt manual intervention and static control strategies. It is difficult to make accurate dynamic adjustments to changes in the external environment, resulting in low heating efficiency, which in turn causes the heating output effect of the heating system to fail to meet actual needs, greatly increasing energy waste and operating costs. Summary of the invention

[0004] In order to solve the above technical problems, the present invention provides a heating management method and system based on the Internet of Things, comprising: Obtain historical heating output data of the heating system and historical temperature change data of the user's room; Fitting the historical heating output heat data and the historical temperature change data to obtain the output heat-temperature change fitting model; Obtaining the current indoor temperature and indoor target temperature of the user's room, and determining the indoor temperature target difference based on the indoor temperature and the indoor target temperature; Determine the initial output heat of the heating system based on the indoor temperature target difference and the output heat-temperature change fitting model; Determining an estimated time length for the indoor temperature to reach the indoor target temperature based on the initial output heat, and determining outdoor temperature prediction data within the estimated time length; Analyze the outdoor temperature forecast data and determine the correction factor of the heat output of the heating system based on the analysis results; The initial heat output of the heating system is corrected according to the correction coefficient to obtain the final heat output, and the heating system provides heat according to the final heat output.

[0005] Furthermore, the historical heating output heat data and the historical temperature change data are fitted to obtain the output heat-temperature change fitting model, including: Preprocess the historical heating output data and historical temperature change data, including removing abnormal values ​​and duplicate values ​​and filling missing values; A fitting data set is constructed based on the preprocessed historical heating output heat data and historical temperature change data, and the fitting data set is divided into a training set and a test set according to a certain ratio; Use the linear regression model to perform fitting training on the training set of the fitting data set to obtain an initial fitting model; The test set of the fitting data set is input into the initial prediction model, and the initial fitting model is tested and verified until the output data of the initial fitting model meets the preset conditions, thereby obtaining the output heat-temperature change fitting model.

[0006] Further, the obtaining of the current indoor temperature and the indoor target temperature of the user's room, and determining the indoor temperature target difference based on the indoor temperature and the indoor target temperature, includes: The indoor temperature and the indoor target temperature of the current user's room are obtained, and the difference between the indoor target temperature and the indoor temperature is calculated to obtain the indoor temperature target difference.

[0007] Furthermore, the method of determining the initial output heat of the heating system based on the indoor temperature target difference and the output heat-temperature change fitting model includes: The indoor temperature target difference is input into the output heat-temperature change fitting model, and the corresponding data is output according to the output heat-temperature change fitting model to obtain the initial output heat of the heating system.

[0008] Furthermore, the step of determining the estimated time length for the indoor temperature to reach the indoor target temperature based on the initial output heat, and determining the outdoor temperature prediction data within the estimated time length, includes: Obtaining initial heat output, indoor temperature, and indoor target temperature, and taking the initial heat output and indoor temperature as starting conditions, and taking the indoor target temperature as a target condition; Input the starting conditions and target conditions into the preset heating simulation model to perform multiple simulations, and calculate the length of time required to complete each simulation; Calculate the average time required to complete each simulation and use the average as the estimated time; Obtain the user's outdoor weather data, and input the weather data as input data into a preset outdoor temperature prediction model; The outdoor temperature prediction model outputs an outdoor temperature prediction data segment, and outdoor temperature prediction data corresponding to the estimated time length is intercepted from the beginning of the outdoor temperature prediction data segment.

[0009] Furthermore, the outdoor temperature prediction data is analyzed, and a correction coefficient of the heat output of the heating system is determined according to the analysis result, including: Obtain outdoor temperature data, and plot the outdoor temperature data into a time series curve graph to obtain an outdoor temperature change curve graph; Determine the starting point, peak point, valley point and end point in the outdoor temperature change curve diagram, and divide the outdoor temperature change curve diagram into a plurality of curve segments according to the starting point, peak point, valley point and end point; Obtaining the average temperature and the average slope value of each curve segment, and determining the number of curve segments; Calculating a temperature variation coefficient according to an average temperature value and an average slope value of each curve segment and the number of curve segments, and determining a correction coefficient of heat output of the heating system according to the temperature variation coefficient; The temperature variation coefficient is calculated as: T=(a*P+b*L) / n, Wherein, T is the temperature variation coefficient, a is the first conversion coefficient, P is the temperature average value, b is the second conversion coefficient, L is the average slope value, and n is the number of curve segments.

[0010] Furthermore, the method of determining the corresponding correction coefficient based on the change amount of each key characteristic parameter in each time period includes: A correction coefficient-temperature variation coefficient interval correspondence relationship is preset, and the correction coefficient-temperature variation coefficient interval correspondence relationship is associated with a corresponding correction coefficient for each variation interval; The temperature variation coefficient is obtained, and based on the mapping relationship between the temperature variation coefficient interval to which the temperature variation coefficient belongs and the correction coefficient-temperature variation coefficient interval correspondence relationship, the correction coefficient corresponding to the temperature variation coefficient interval is selected as the corresponding correction coefficient.

[0011] The present invention also provides a heat supply management system based on the Internet of Things, comprising: An acquisition module is used to acquire historical heating output heat data of the heating system and historical temperature change data of the user's room; A fitting module is used to fit the historical heating output heat data and the historical temperature change data to obtain an output heat-temperature change fitting model; A calculation module, used to obtain the indoor temperature and the indoor target temperature of the current user's room, and determine the indoor temperature target difference based on the indoor temperature and the indoor target temperature; An output module, used for determining an initial output heat of a heating system based on an indoor temperature target difference and an output heat-temperature change fitting model; A determination module, used to determine the estimated time length for the indoor temperature to reach the indoor target temperature based on the initial output heat, and determine the outdoor temperature prediction data within the estimated time length; An analysis module is used to analyze the outdoor temperature prediction data and determine the correction coefficient of the heat output of the heating system according to the analysis results; The management module is used to correct the initial output heat of the heating system according to the correction coefficient to obtain the final output heat, and the heating system provides heat according to the final output heat.

[0012] Compared with the prior art, the heat supply management method and system based on the Internet of Things in the embodiment of the present invention have the following beneficial effects: By analyzing the outdoor temperature forecast data and the real-time indoor temperature, the present invention can accurately adjust the heat output of the heating system to cope with climate change, thereby improving the stability and comfort of the system; Through the analysis and modeling of historical data, the system can achieve energy efficiency improvement and adjust the output heat of the heating system according to the indoor temperature requirements and outdoor climate conditions to improve energy utilization efficiency and reduce energy consumption costs; Through the prediction model and real-time data monitoring, the system of the present invention can achieve real-time response and adjust the output heat of the heating system according to the real-time changes in indoor temperature and outdoor temperature to ensure the comfort of the indoor temperature. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 1 is a schematic diagram of the process structure of a heating management method based on the Internet of Things in an embodiment of the present invention; Figure 2 It is a schematic diagram of the composition of a heat supply management system based on the Internet of Things in an embodiment of the present invention. DETAILED DESCRIPTION

[0014] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0015] In the description of the present application, it should be understood that the terms "center", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the platform or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0016] The terms "second" and "second" are used for descriptive purposes only and should not be understood as indicating or implying a relative degree of importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined with "second" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, unless otherwise specified, "multiple" means two or more.

[0017] In the description of this application, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technical personnel in this field, the specific meanings of the above terms in this application can be understood according to specific circumstances.

[0018] like Figure 1 As shown, in an embodiment of the present application, a heating management method based on the Internet of Things is provided, including: S100: obtaining historical heating output heat data of the heating system and historical temperature change data of the user's room; S200: fitting the historical heating output heat data and the historical temperature change data to obtain an output heat-temperature change fitting model; S300: obtaining the indoor temperature and the indoor target temperature of the current user's room, and determining the indoor temperature target difference based on the indoor temperature and the indoor target temperature; S400: determining the initial output heat of the heating system based on the indoor temperature target difference and the output heat-temperature change fitting model; S500: determining the estimated time length for the indoor temperature to reach the indoor target temperature based on the initial output heat, and determining the outdoor temperature prediction data within the estimated time length; S600: analyzing the outdoor temperature prediction data, and determining the correction coefficient of the heat output of the heating system according to the analysis result; S700: correcting the initial output heat of the heating system according to the correction coefficient to obtain the final output heat, and the heating system performs heating according to the final output heat.

[0019] Furthermore, the present invention can accurately adjust the output heat of the heating system to cope with climate change by analyzing outdoor temperature prediction data and real-time indoor temperature, thereby improving the stability and comfort of the system; the present invention can achieve energy efficiency improvement through analysis and modeling of historical data, and adjust the output heat of the heating system according to indoor temperature requirements and outdoor climate conditions to improve energy utilization efficiency and reduce energy consumption costs; the present invention can achieve real-time response through predictive models and real-time data monitoring, and adjust the output heat of the heating system according to real-time indoor and outdoor temperature changes to ensure the comfort of indoor temperature.

[0020] In an embodiment of the present application, a heating management method based on the Internet of Things is provided, wherein the historical heating output heat data and the historical temperature change data are fitted to obtain an output heat-temperature change fitting model, including: preprocessing the historical heating output heat data and the historical temperature change data, the preprocessing including removing outliers and duplicate values ​​and filling missing values; constructing a fitting data set according to the preprocessed historical heating output heat data and the historical temperature change data, and dividing the fitting data set into a training set and a test set according to a certain ratio; using a linear regression model to perform fitting training on the training set of the fitting data set to obtain an initial fitting model; inputting the test set of the fitting data set into the initial prediction model, testing and verifying the initial fitting model, until the output data of the initial fitting model meets the preset conditions, and obtaining the output heat-temperature change fitting model.

[0021] Specifically, the historical heating output heat data and historical temperature change data need to be preprocessed, which includes removing outliers and duplicate values ​​to ensure the accuracy and consistency of the data. At the same time, missing values ​​need to be filled, because there are often some missing data points in the actual data, which need to be filled by appropriate methods to ensure the integrity and availability of the data; based on the preprocessed data, a fitting data set of heating output heat data and temperature change data is constructed, including heating output heat and corresponding temperature changes; the fitting data set is divided into a training set and a test set according to a certain ratio, the training set is used to train the model, and the test set is used to verify the performance of the model; the training set is fitted and trained using a linear regression model to obtain an initial fitting model, which can be used to establish a linear relationship between heating output heat and temperature changes; the test set of the fitting data set is input into the initial model, and the model is tested and verified. By comparing the predicted output of the model with the actual data, the fitting effect of the model can be evaluated until the output data of the model meets the preset conditions. This step can improve the accuracy and reliability of the data by removing outliers and duplicate values ​​and filling missing values, providing a reliable data basis for subsequent modeling and analysis; by using a linear regression model for training and verification, a relationship model between the heat output and temperature changes can be established, thereby achieving an understanding and prediction of the behavior of the heating system; the model is applied to the test set data, and the model's predicted output and actual data are compared to evaluate the accuracy and reliability of the model, providing a reference for subsequent predictions and adjustments. In general, this step can establish a fitting model between the heat output and temperature changes, providing a basis for subsequent intelligent control and optimization, thereby achieving intelligent regulation and optimization of the heating system.

[0022] In an embodiment of the present application, a heating management method based on the Internet of Things is provided, wherein the indoor temperature and the indoor target temperature of the current user's room are obtained, and the indoor temperature target difference is determined based on the indoor temperature and the indoor target temperature, including: obtaining the indoor temperature and the indoor target temperature of the current user's room, and calculating the difference between the indoor target temperature and the indoor temperature to obtain the indoor temperature target difference.

[0023] In an embodiment of the present application, a heating management method based on the Internet of Things is provided, which determines the initial output heat of the heating system based on the indoor temperature target difference and the output heat-temperature change fitting model, including: inputting the indoor temperature target difference into the output heat-temperature change fitting model, and performing corresponding data output according to the output heat-temperature change fitting model to obtain the initial output heat of the heating system.

[0024] In an embodiment of the present application, a heating management method based on the Internet of Things is provided, which determines the estimated time length for the indoor temperature to reach the indoor target temperature based on the initial output heat, and determines the outdoor temperature prediction data within the estimated time length, including: obtaining the initial output heat and the indoor temperature and the indoor target temperature, and taking the initial output heat and the indoor temperature as the starting conditions, and taking the indoor target temperature as the target condition; inputting the starting conditions and the target conditions into a preset heating simulation model for multiple simulations, and counting the time length required for each simulation to complete; calculating the average time length required for each simulation to complete, and taking the average time length as the estimated time length; obtaining the user's outdoor weather data, and inputting the weather data as input data into a preset outdoor temperature prediction model; the outdoor temperature prediction model outputs a segment of outdoor temperature prediction data, and intercepting the outdoor temperature prediction data corresponding to the estimated time length from the starting part of the outdoor temperature prediction data segment.

[0025] Specifically, the initial output heat, indoor temperature and indoor target temperature are obtained, and this information will be used as the starting conditions and target conditions of the heating simulation model; the starting conditions and target conditions are input into the preset heating simulation model for multiple simulations, and the time required for each simulation to be completed is counted; the time required for multiple simulations to be completed is counted, and the average value is calculated, and the average value is used as the estimated time length to determine the operating time of the heating system under different conditions; the user's outdoor weather data is obtained, and this data includes information such as temperature, humidity, wind speed, etc. This data will be input into the outdoor temperature prediction model as input data; the weather data is input into the preset outdoor temperature prediction model, and the model is used for output to obtain a segment of outdoor temperature prediction data, which can predict the outdoor temperature changes in the future period of time; the outdoor temperature prediction data corresponding to the estimated time length is intercepted from the starting part of the outdoor temperature prediction data segment, and the outdoor temperature changes in the future time period are determined, providing a reference for the adjustment and optimization of the heating system. This step can evaluate the operation time and stability of the heating system under different conditions through multiple simulations, providing a reference for the optimization and adjustment of the system; calculating the estimated time length can help predict the operation time of the heating system under different conditions, providing users with a more accurate estimate of the heating time; through the outdoor temperature prediction model, the outdoor temperature changes in the future period can be obtained, providing a reference for the adjustment and optimization of the heating system, and helping the system to better cope with future climate change. In general, this step can realize the estimation of the operation time of the heating system and the prediction of outdoor temperature changes, providing a basis for the intelligent regulation and optimization of the system, thereby improving the efficiency of the system and user experience.

[0026] In an embodiment of the present application, a heating management method based on the Internet of Things is provided, wherein the outdoor temperature prediction data is analyzed, and a correction coefficient of the heat output of the heating system is determined according to the analysis result, including: obtaining outdoor temperature data, and plotting the outdoor temperature data into a time series curve graph to obtain an outdoor temperature change curve graph; determining the starting point, peak point, valley point and end point in the outdoor temperature change curve graph, and dividing the outdoor temperature change curve graph into a plurality of curve segments according to the starting point, peak point, valley point and end point; obtaining the temperature average value and the average slope value of each curve segment, and determining the number of curve segments; calculating the temperature change coefficient according to the temperature average value and the average slope value of each curve segment and the number of curve segments, and determining the correction coefficient of the heat output of the heating system according to the temperature change coefficient; the calculation formula of the temperature change coefficient is: T=(a*P+b*L) / n, Wherein, T is the temperature variation coefficient, a is the first conversion coefficient, P is the temperature average value, b is the second conversion coefficient, L is the average slope value, and n is the number of curve segments.

[0027] Specifically, outdoor temperature data is obtained and plotted into a time series graph to show the changing trend of outdoor temperature over time; the starting point, peak point, valley point and end point are determined in the outdoor temperature change graph, and these characteristic points can help divide the temperature change graph into several curve segments; the outdoor temperature change graph is divided into several curve segments according to the characteristic points, so as to subsequently analyze the temperature change trend of each curve segment; the temperature average value and average slope value of each curve segment are obtained, and these values ​​can reflect the overall trend and rate of change of temperature change; the number of curve segments is determined according to the characteristic points of the curve segments, and the overall temperature change is determined; the temperature change coefficient is calculated according to the temperature average value and average slope value of each curve segment and the number of curve segments, and this coefficient can be used to determine the correction coefficient of the heat output of the heating system. This step can understand the overall trend of the temperature, including the starting point, peak point, valley point and end point, as well as the overall trend of change, by analyzing the outdoor temperature change curve; by calculating the temperature change coefficient, the correction coefficient of the heat output of the heating system can be determined, so that the system can be more intelligently adjusted according to the actual temperature change, improving the energy efficiency of the system and user comfort; based on the calculation of the temperature change coefficient, the heating system can realize data-driven regulation and dynamically adjust the output heat according to the actual temperature change trend, so as to better meet the user's heating needs. In general, this step can realize the analysis of the trend of outdoor temperature changes, and determine the correction coefficient of the heat output of the heating system based on the temperature change coefficient, which provides a basis for the intelligent regulation and optimization of the system, thereby improving the energy efficiency of the system and user experience.

[0028] In an embodiment of the present application, a heating management method based on the Internet of Things is provided, wherein a corresponding correction coefficient is determined based on the amount of change of each key characteristic parameter in each time period, including: presetting a correction coefficient-temperature change coefficient interval correspondence relationship, wherein each change amount interval is associated with a corresponding correction coefficient; obtaining the temperature change coefficient, and based on a mapping relationship between the temperature change coefficient interval to which the temperature change coefficient belongs within the correction coefficient-temperature change coefficient interval correspondence relationship, selecting a correction coefficient corresponding to the temperature change coefficient interval as the corresponding correction coefficient.

[0029] Specifically, for each variation interval of each key characteristic parameter, a correction coefficient-temperature variation coefficient interval correspondence is pre-set, and this correspondence can be a pre-defined table for determining the correction coefficient corresponding to each temperature variation coefficient; according to the previously calculated temperature variation coefficient, the current temperature variation coefficient of the system is obtained, and this coefficient can reflect the trend and rate of the current temperature change; based on the temperature variation coefficient interval to which the temperature variation coefficient belongs, the correction coefficient corresponding to the temperature variation coefficient interval is selected as the corresponding correction coefficient through the pre-set correction coefficient-temperature variation coefficient interval correspondence. This step can dynamically select the corresponding correction coefficient according to the current temperature variation coefficient through the pre-set interval correspondence, so that the output heat of the heating system can better adapt to the actual temperature change; based on the mapping relationship of the temperature variation coefficient, the system can realize intelligent control, select the optimal correction coefficient according to the actual temperature change trend, thereby improving the energy efficiency of the system and the comfort of the user; through the pre-setting of the interval correspondence, the system can realize the fine adjustment of the correction coefficient, making the control of the system more accurate and flexible. In general, this step can realize the dynamic adjustment of the output heat correction coefficient of the heating system, and select the most suitable correction coefficient according to the actual temperature change, thereby improving the intelligent control and energy efficiency of the system.

[0030] like Figure 2 As shown, in an embodiment of the present application, a heating management system based on the Internet of Things is provided, including: an acquisition module, used to acquire historical heating output heat data of the heating system and historical temperature change data of the user's room; a fitting module, used to fit the historical heating output heat data and the historical temperature change data to obtain an output heat-temperature change fitting model; a calculation module, used to acquire the indoor temperature and the indoor target temperature of the current user's room, and determine the indoor temperature target difference based on the indoor temperature and the indoor target temperature; an output module, used to determine the initial output heat of the heating system based on the indoor temperature target difference and the output heat-temperature change fitting model; a determination module, used to determine the estimated time length for the indoor temperature to reach the indoor target temperature based on the initial output heat, and determine the outdoor temperature prediction data within the estimated time length; an analysis module, used to analyze the outdoor temperature prediction data, and determine the correction coefficient of the output heat of the heating system according to the analysis result; a management module, used to correct the initial output heat of the heating system according to the correction coefficient to obtain the final output heat, and the heating system performs heating according to the final output heat.

[0031] In summary, the embodiment of the present invention provides a heating management method and system based on the Internet of Things, which includes: obtaining and fitting the historical heating output heat data of the heating system and the historical temperature change data of the user's room to obtain a fitting model; obtaining the current indoor temperature and the indoor target temperature, and determining the indoor temperature target difference based thereon; determining the initial output heat of the heating system based on the indoor temperature target difference and the fitting model; determining the estimated time length for the indoor temperature to reach the indoor target temperature based on the initial output heat, and determining the outdoor temperature prediction data within the estimated time length; analyzing the outdoor temperature prediction data to determine the correction coefficient of the output heat; correcting the initial output heat of the heating system according to the correction coefficient, and the heating system uses this to perform heating management. The present invention can collect real-time data based on the Internet of Things, determine the indoor and outdoor temperature changes, and accurately adjust the system's output heat according to it, thereby improving the heating efficiency.

[0032] Finally, it should be noted that: Obviously, a person skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalent technology, the present invention is also intended to include these modifications and variations.

[0033] The above is only an example of implementation of the present invention, but it cannot be used to limit the scope of the present invention. Any structural changes made according to the present invention, as long as they do not lose the essence of the present invention, should be regarded as falling within the scope of protection of the present invention and being restricted. Technical personnel in the relevant technical field can clearly understand that for the convenience and simplicity of description, the specific working process and related instructions of the platform described above can refer to the corresponding process in the aforementioned platform embodiment, and will not be repeated here.

[0034] The term "comprises" or any other similar term is intended to cover a non-exclusive inclusion such that a process, platform, article, or apparatus / platform that includes a list of elements includes not only those elements but also other elements not expressly listed or inherent to such process, platform, article, or apparatus / platform.

[0035] So far, the technical solutions of the present invention have been described in conjunction with the further embodiments shown in the accompanying drawings. However, it is easy for a person skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, a person skilled in the art can make equivalent changes or substitutions to closely related technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0036] The above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention.

Claims

1. A heating management method based on the Internet of Things, characterized in that: include: Obtain historical heating output data of the heating system and historical temperature change data of the user's room; Fitting the historical heating output heat data and the historical temperature change data to obtain the output heat-temperature change fitting model; Obtaining the current indoor temperature and indoor target temperature of the user's room, and determining the indoor temperature target difference based on the indoor temperature and the indoor target temperature; Determine the initial output heat of the heating system based on the indoor temperature target difference and the output heat-temperature change fitting model; Determining an estimated time length for the indoor temperature to reach the indoor target temperature based on the initial output heat, and determining outdoor temperature prediction data within the estimated time length; Analyze the outdoor temperature forecast data and determine the correction factor of the heat output of the heating system based on the analysis results; The initial heat output of the heating system is corrected according to the correction coefficient to obtain the final heat output, and the heating system provides heat according to the final heat output.

2. The method for heat supply management based on the Internet of Things according to claim 1, characterized in that: The fitting of the historical heating output heat data and the historical temperature change data to obtain the output heat-temperature change fitting model includes: Preprocess the historical heating output data and historical temperature change data, including removing abnormal values ​​and duplicate values ​​and filling missing values; A fitting data set is constructed based on the preprocessed historical heating output heat data and historical temperature change data, and the fitting data set is divided into a training set and a test set according to a certain ratio; Use the linear regression model to perform fitting training on the training set of the fitting data set to obtain an initial fitting model; The test set of the fitting data set is input into the initial prediction model, and the initial fitting model is tested and verified until the output data of the initial fitting model meets the preset conditions, thereby obtaining the output heat-temperature change fitting model.

3. The method for heat supply management based on the Internet of Things according to claim 2, characterized in that: The obtaining of the current indoor temperature and the indoor target temperature of the user's room, and determining the indoor temperature target difference based on the indoor temperature and the indoor target temperature, includes: The indoor temperature and the indoor target temperature of the current user's room are obtained, and the difference between the indoor target temperature and the indoor temperature is calculated to obtain the indoor temperature target difference.

4. The method for heat supply management based on the Internet of Things according to claim 3, characterized in that: The method of determining the initial output heat of the heating system based on the indoor temperature target difference and the output heat-temperature change fitting model includes: The indoor temperature target difference is input into the output heat-temperature change fitting model, and the corresponding data is output according to the output heat-temperature change fitting model to obtain the initial output heat of the heating system.

5. A heating management method based on the Internet of Things according to claim 4, characterized in that: The step of determining the estimated time length for the indoor temperature to reach the indoor target temperature based on the initial output heat, and determining the outdoor temperature prediction data within the estimated time length, includes: Obtaining initial heat output, indoor temperature, and indoor target temperature, and taking the initial heat output and indoor temperature as starting conditions, and taking the indoor target temperature as a target condition; Input the starting conditions and target conditions into the preset heating simulation model to perform multiple simulations, and calculate the length of time required to complete each simulation; Calculate the average time required to complete each simulation and use the average as the estimated time; Obtain the user's outdoor weather data, and input the weather data as input data into a preset outdoor temperature prediction model; The outdoor temperature prediction model outputs an outdoor temperature prediction data segment, and outdoor temperature prediction data corresponding to the estimated time length is intercepted from the beginning of the outdoor temperature prediction data segment.

6. A heating management method based on the Internet of Things according to claim 5, characterized in that: The outdoor temperature prediction data is analyzed, and the correction coefficient of the heat output of the heating system is determined according to the analysis result, including: Obtain outdoor temperature data, and plot the outdoor temperature data into a time series curve graph to obtain an outdoor temperature change curve graph; Determine the starting point, peak point, valley point and end point in the outdoor temperature change curve diagram, and divide the outdoor temperature change curve diagram into a plurality of curve segments according to the starting point, peak point, valley point and end point; Obtaining the average temperature and the average slope value of each curve segment, and determining the number of curve segments; Calculating a temperature variation coefficient according to an average temperature value and an average slope value of each curve segment and the number of curve segments, and determining a correction coefficient of heat output of the heating system according to the temperature variation coefficient; The temperature variation coefficient is calculated as: T=(a*P+b*L) / n, Wherein, T is the temperature variation coefficient, a is the first conversion coefficient, P is the temperature average value, b is the second conversion coefficient, L is the average slope value, and n is the number of curve segments.

7. A heating management method based on the Internet of Things according to claim 6, characterized in that: The method of determining the corresponding correction coefficient based on the change amount of each key characteristic parameter in each time period includes: A correction coefficient-temperature variation coefficient interval correspondence relationship is preset, and the correction coefficient-temperature variation coefficient interval correspondence relationship is associated with a corresponding correction coefficient for each variation interval; The temperature variation coefficient is obtained, and based on the mapping relationship between the temperature variation coefficient interval to which the temperature variation coefficient belongs and the correction coefficient-temperature variation coefficient interval correspondence relationship, the correction coefficient corresponding to the temperature variation coefficient interval is selected as the corresponding correction coefficient.

8. A heating management system based on the Internet of Things, characterized in that: include: An acquisition module is used to acquire historical heating output heat data of the heating system and historical temperature change data of the user's room; A fitting module is used to fit the historical heating output heat data and the historical temperature change data to obtain an output heat-temperature change fitting model; A calculation module, used to obtain the indoor temperature and the indoor target temperature of the current user's room, and determine the indoor temperature target difference based on the indoor temperature and the indoor target temperature; An output module, used for determining an initial output heat of a heating system based on an indoor temperature target difference and an output heat-temperature change fitting model; A determination module, used to determine the estimated time length for the indoor temperature to reach the indoor target temperature based on the initial output heat, and determine the outdoor temperature prediction data within the estimated time length; An analysis module is used to analyze the outdoor temperature prediction data and determine the correction coefficient of the heat output of the heating system according to the analysis results; The management module is used to correct the initial output heat of the heating system according to the correction coefficient to obtain the final output heat, and the heating system provides heat according to the final output heat.

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