A heating management method and system based on the Internet of Things

Abstract

The application discloses a kind of based on Internet of Things's heating management method and system, it includes: obtaining and fitting the historical heating output heat data of heating system and the historical temperature variation data of user indoor obtains fitting model;Current indoor temperature and indoor target temperature are obtained, and determine indoor temperature target difference based on it;Determine the initial output heat of heating system based on indoor temperature target difference and fitting model;Determine the estimated time length that indoor temperature reaches indoor target temperature based on initial output heat, and determine the outdoor temperature prediction data in the estimated time length;Outdoor temperature prediction data is analyzed to determine the correction coefficient of output heat;According to correction coefficient, the initial output heat of heating system is corrected, and heating management is carried out by heating system with it.The present application can collect real-time data based on Internet of Things, determine the temperature variation of indoor and outdoor, and accurately adjust the output heat of system according to it, so as to improve heating efficiency.

Description

Technical Field

[0001] This invention relates to the field of heating management technology, and in particular to a heating management method and system based on the Internet of Things. Background Technology

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

[0003] Traditional heating management methods do not utilize data collected through the Internet of Things (IoT) for optimized heating management. Instead, they rely on manual intervention and static control strategies, which make it difficult to make accurate dynamic adjustments to changes in the external environment. This results in low heating efficiency, causing the heating system to fail to meet actual needs and significantly increasing energy waste and operating costs. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a heating management method and system based on the Internet of Things (IoT), comprising:

[0005] Acquire historical heat output data of the heating system and historical temperature change data of users' indoor spaces;

[0006] By fitting historical heating output data and historical temperature change data, a heating output-temperature change fitting model is obtained.

[0007] Obtain the current indoor temperature and target indoor temperature of the user's room, and determine the target difference between the indoor temperature and the target indoor temperature;

[0008] The initial output heat of the heating system is determined based on the indoor temperature target difference and the output heat-temperature change fitting model.

[0009] Based on the initial heat output, determine the estimated time length for the indoor temperature to reach the target indoor temperature, and determine the outdoor temperature prediction data within this estimated time length;

[0010] Analyze outdoor temperature forecast data and determine the correction coefficient for the output heat of the heating system based on the analysis results;

[0011] The initial output heat of the heating system is corrected according to the correction factor to obtain the final output heat, and the heating system provides heat according to the final output heat.

[0012] Furthermore, the process of fitting historical heat output data and historical temperature change data to obtain a heat output-temperature change fitting model includes:

[0013] The historical heating output data and historical temperature change data are preprocessed, including removing outliers and duplicates and filling in missing values.

[0014] A fitting dataset is constructed based on the preprocessed historical heating output heat data and historical temperature change data, and the fitting dataset is divided into a training set and a test set according to a certain ratio.

[0015] The initial fitting model is obtained by using a linear regression model to fit the training set of the dataset.

[0016] The test set of the fitted dataset is input into the initial prediction model to test and verify the initial fitted model until the output data of the initial fitted model meets the preset conditions, thus obtaining the output heat-temperature change fitted model.

[0017] Furthermore, the step of obtaining the current indoor temperature and the target indoor temperature, and determining the target indoor temperature difference based on the indoor temperature and the target indoor temperature, includes:

[0018] Obtain the current indoor temperature and target indoor temperature of the user's room, and calculate the difference between the target indoor temperature and the indoor temperature to obtain the target indoor temperature difference.

[0019] Furthermore, the determination of the initial output heat of the heating system based on the indoor temperature target difference and the output heat-temperature change fitting model includes:

[0020] The target difference in indoor temperature 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.

[0021] Furthermore, the step of determining the estimated time length for the indoor temperature to reach the target indoor temperature based on the initial output heat, and determining the outdoor temperature prediction data within this estimated time length, includes:

[0022] Obtain the initial output heat, indoor temperature, and indoor target temperature, and use the initial output heat and indoor temperature as the starting conditions and the indoor target temperature as the target conditions;

[0023] Input the initial and target conditions into the preset heating simulation model and perform multiple simulations, and count the time required to complete each simulation.

[0024] Calculate the average time required to complete each simulation, and use this average as the estimated time.

[0025] Acquire outdoor weather data from users and input the weather data into a preset outdoor temperature prediction model;

[0026] The outdoor temperature prediction model outputs a segment of outdoor temperature prediction data, and outdoor temperature prediction data corresponding to the estimated time length is extracted from the beginning of the outdoor temperature prediction data segment.

[0027] Furthermore, the analysis of outdoor temperature prediction data and the determination of correction coefficients for the output heat of the heating system based on the analysis results include:

[0028] Acquire outdoor temperature data and plot the outdoor temperature data into a time series curve to obtain the outdoor temperature change curve.

[0029] Determine the starting point, peak point, trough point, and end point in the outdoor temperature change curve, and divide the outdoor temperature change curve into several curve segments based on the starting point, peak point, trough point, and end point.

[0030] Obtain the average temperature and average slope for each curve segment, and determine the number of curve segments;

[0031] The temperature variation coefficient is calculated based on the average temperature and average slope of each curve segment and the number of curve segments, and the correction coefficient for the output heat of the heating system is determined based on the temperature variation coefficient.

[0032] The formula for calculating the coefficient of temperature change is:

[0033] T = (a*P + b*L) / n,

[0034] Where T is the temperature change coefficient, a is the first conversion coefficient, P is the average temperature, b is the second conversion coefficient, L is the average slope value, and n is the number of curve segments.

[0035] Furthermore, determining the corresponding correction coefficient based on the change in each key feature parameter within each time period includes:

[0036] A pre-defined correspondence between the correction coefficient and the temperature change coefficient interval is established, and for each interval of change, a corresponding correction coefficient is associated with it.

[0037] Obtain the temperature change coefficient, and based on the mapping relationship between the temperature change coefficient interval to which the temperature change coefficient belongs and the corresponding relationship between the correction coefficient and the temperature change coefficient interval, select the correction coefficient corresponding to the temperature change coefficient interval as the corresponding correction coefficient.

[0038] The present invention also provides a heating management system based on the Internet of Things, comprising:

[0039] The acquisition module is used to acquire historical heating output data of the heating system and historical temperature change data of users' indoor spaces;

[0040] The fitting module is used to fit historical heating output data and historical temperature change data to obtain a fitting model of output heat-temperature change.

[0041] The calculation module is used to obtain the current indoor temperature and the target indoor temperature of the user's room, and to determine the target difference between the indoor temperature and the target indoor temperature.

[0042] The output module is 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;

[0043] The determination module is used to determine the estimated time length for the indoor temperature to reach the indoor target temperature based on the initial output heat, and to determine the outdoor temperature prediction data within the estimated time length.

[0044] The analysis module is used to analyze outdoor temperature forecast data and determine the correction coefficient for the output heat of the heating system based on the analysis results.

[0045] 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.

[0046] Compared with existing technologies, the heating management method and system based on the Internet of Things (IoT) of this invention have the following advantages:

[0047] This invention analyzes outdoor temperature forecast data and real-time indoor temperature to accurately adjust the output heat of the heating system in order to cope with climate change, thereby improving the stability and comfort of the system.

[0048] This invention, through the analysis and modeling of historical data, enables the system to improve energy efficiency. It adjusts the output heat of the heating system according to indoor temperature requirements and outdoor climate conditions, thereby improving energy utilization efficiency and reducing energy consumption costs.

[0049] This invention enables the system to respond in real time through predictive models and real-time data monitoring. It adjusts the output heat of the heating system according to real-time changes in indoor and outdoor temperatures to ensure indoor temperature comfort. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the process structure of the IoT-based heating management method in an embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of the composition of the IoT-based heating management system in an embodiment of the present invention. Detailed Implementation

[0052] The specific embodiments of this application will be described in further detail below with reference to the accompanying drawings and examples. The following examples are for illustrative purposes only and are not intended to limit the scope of the invention.

[0053] In the description of this application, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this application and simplifying the description, and do not indicate or imply that the platform or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application.

[0054] The terms “second” and “second” are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with “second” or “second” may explicitly or implicitly include one or more of that feature. In the description of this application, unless otherwise stated, “multiple” means two or more.

[0055] In the description of this application, it should be noted that, unless otherwise expressly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection between two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0056] like Figure 1As shown in the embodiments of this application, a heating management method based on the Internet of Things is provided, including: S100: acquiring historical heating output data of the heating system and historical temperature change data of the user's indoor space; S200: fitting the historical heating output data and historical temperature change data to obtain an output heat-temperature change fitting model; S300: acquiring the current indoor temperature and target indoor temperature of the user's indoor space, and determining the target difference in indoor temperature based on the indoor temperature and the target indoor temperature; S400: determining the initial output heat of the heating system based on the target difference in indoor temperature and the output heat-temperature change fitting model; S500: determining the estimated time length for the indoor temperature to reach the target indoor 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 for the output heat of the heating system based on the analysis results; 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 provides heating based on the final output heat.

[0057] Furthermore, by analyzing outdoor temperature forecast data and real-time indoor temperature, this invention can accurately adjust the output heat of the heating system to cope with climate change, thereby improving system stability and comfort. Through the analysis and modeling of historical data, this invention enables energy efficiency improvements by adjusting the output heat of the heating system according to indoor temperature requirements and outdoor climate conditions, thereby improving energy utilization efficiency and reducing energy costs. Through predictive models and real-time data monitoring, this invention enables real-time response by adjusting the output heat of the heating system based on real-time changes in indoor and outdoor temperatures to ensure indoor temperature comfort.

[0058] In an embodiment of this application, a heating management method based on the Internet of Things is provided. The method involves fitting historical heating output heat data and historical temperature change data to obtain an output heat-temperature change fitting model. This includes: preprocessing the historical heating output heat data and historical temperature change data, including removing outliers and duplicates and filling in missing values; constructing a fitting dataset based on the preprocessed historical heating output heat data and historical temperature change data, and dividing the fitting dataset into a training set and a test set according to a certain ratio; using a linear regression model to train the training set of the fitting dataset to obtain an initial fitting model; inputting the test set of the fitting dataset into the initial prediction model to test and verify the initial fitting model until the output data of the initial fitting model meets preset conditions, thus obtaining the output heat-temperature change fitting model.

[0059] Specifically, historical heating output data and historical temperature change data need to be preprocessed. This includes removing outliers and duplicates to ensure data accuracy and consistency. Missing values ​​also need to be filled in, as real-world data often contains missing data points, which need to be filled using appropriate methods to ensure data integrity and usability. Based on the preprocessed data, a fitted dataset of heating output data and temperature change data is constructed, including heating output and corresponding temperature changes. The fitted dataset is divided into training and testing sets according to a certain ratio. The training set is used to train the model, while the testing set is used to verify the model's performance. A linear regression model is used to fit and train the training set, obtaining an initial fitted model. The linear regression model can be used to establish a linear relationship between heating output and temperature changes. The testing set of the fitted dataset is input into the initial model to test and verify the model. By comparing the model's predicted output with the actual data, the model's fitting effect can be evaluated until the model's output data meets the preset conditions. This step, by removing outliers and duplicates and filling in missing values, improves the accuracy and reliability of the data, providing a reliable data foundation for subsequent modeling and analysis. Using a linear regression model for training and validation establishes a model of the relationship between heat output and temperature changes, enabling the understanding and prediction of heating system behavior. Applying the model to the test set data and comparing the model's predicted output with actual data evaluates the model's accuracy and reliability, providing a reference for subsequent predictions and adjustments. In summary, this step establishes a fitting model between heat output and temperature changes, laying the foundation for subsequent intelligent control and optimization, thereby achieving intelligent regulation and optimization of the heating system.

[0060] In an embodiment of this application, a heating management method based on the Internet of Things is provided. The step of obtaining the current indoor temperature and the target indoor temperature of a user's room, and determining the target indoor temperature difference based on the indoor temperature and the target indoor temperature, includes: obtaining the current indoor temperature and the target indoor temperature of a user's room, and calculating the difference between the target indoor temperature and the indoor temperature to obtain the target indoor temperature difference.

[0061] In an embodiment of this application, a heating management method based on the Internet of Things is provided. The method for 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: inputting the indoor temperature target difference into the output heat-temperature change fitting model, and outputting corresponding data according to the output heat-temperature change fitting model to obtain the initial output heat of the heating system.

[0062] In an embodiment of this application, a heating management method based on the Internet of Things is provided. The method involves determining the estimated time length for the indoor temperature to reach the target indoor temperature based on the initial output heat, and determining the outdoor temperature prediction data within the estimated time length. This includes: acquiring the initial output heat, indoor temperature, and target indoor temperature, using the initial output heat and indoor temperature as starting conditions and the target indoor temperature as a target condition; inputting the starting conditions and target conditions into a preset heating simulation model for multiple simulations, and calculating the time required to complete each simulation; calculating the average time required to complete each simulation and using this average as the estimated time length; acquiring outdoor weather data for the user and inputting this weather data into a preset outdoor temperature prediction model; and outputting an outdoor temperature prediction data segment from the outdoor temperature prediction model, extracting outdoor temperature prediction data corresponding to the estimated time length starting from the beginning of the outdoor temperature prediction data segment.

[0063] Specifically, the initial output heat, indoor temperature, and target indoor temperature are acquired, serving as the starting and target conditions for the heating simulation model. These conditions are then input into the preset heating simulation model for multiple simulations, with the time required to complete each simulation being recorded. The average time of each simulation is calculated and used as the estimated time to determine the operating time of the heating system under different conditions. Outdoor weather data, including temperature, humidity, and wind speed, is acquired and input into the outdoor temperature prediction model. This weather data is then input into the preset outdoor temperature prediction model, which outputs a segment of outdoor temperature prediction data to predict future outdoor temperature changes. Starting from the beginning of this segment, outdoor temperature prediction data corresponding to the estimated time is extracted to determine future outdoor temperature changes, providing a reference for adjusting and optimizing the heating system. This step, through multiple simulations, assesses the operating time and stability of the heating system under different conditions, providing a reference for system optimization and adjustment. Calculating the estimated time helps predict the operating time of the heating system under various conditions, offering users more accurate heating time forecasts. Furthermore, using an outdoor temperature prediction model, it obtains information on future outdoor temperature changes, providing a reference for system adjustment and optimization, and helping the system better cope with future climate change. In summary, this step enables the prediction of heating system operating time and outdoor temperature changes, laying the foundation for intelligent system control and optimization, thereby improving system efficiency and user experience.

[0064] In an embodiment of this application, a heating management method based on the Internet of Things is provided. The method involves analyzing outdoor temperature prediction data and determining a correction coefficient for the output heat of the heating system based on the analysis results. This includes: acquiring outdoor temperature data and plotting it as a time series curve to obtain an outdoor temperature change curve; determining the starting point, peak point, trough point, and ending point in the outdoor temperature change curve, and dividing the curve into several curve segments based on these points; acquiring the average temperature and average slope of each curve segment and determining the number of curve segments; calculating a temperature change coefficient based on the average temperature and average slope of each curve segment and the number of curve segments; and determining a correction coefficient for the output heat of the heating system based on the temperature change coefficient. The formula for calculating the temperature change coefficient is:

[0065] T = (a*P + b*L) / n,

[0066] Where T is the temperature change coefficient, a is the first conversion coefficient, P is the average temperature, b is the second conversion coefficient, L is the average slope value, and n is the number of curve segments.

[0067] Specifically, outdoor temperature data is acquired and plotted as a time series curve to show the trend of outdoor temperature changes over time. The starting point, peak point, trough point, and ending point are identified in the outdoor temperature change curve; these characteristic points help divide the curve into several segments. Based on these characteristic points, the outdoor temperature change curve is divided into several segments for subsequent analysis of the temperature change trend of each segment. The average temperature and average slope of each segment are obtained; these values ​​reflect the overall trend and rate of temperature change. The number of segments is determined based on the characteristic points, thus determining the overall temperature change situation. Based on the average temperature and average slope of each segment and the number of segments, a temperature change coefficient is calculated; this coefficient can be used to determine the correction factor for the output heat of the heating system. This step analyzes the outdoor temperature change curve to understand the overall temperature trend, including the starting point, peak point, trough point, and ending point, as well as the overall changing trend. By calculating the temperature change coefficient, a correction factor for the heating system's output heat can be determined, enabling the system to adjust more intelligently according to actual temperature changes, improving system energy efficiency and user comfort. Based on the calculation of the temperature change coefficient, the heating system can achieve data-driven regulation, dynamically adjusting the output heat according to the actual temperature change trend, thereby better meeting users' heating needs. In summary, this step enables the analysis of outdoor temperature change trends and the determination of the correction factor for the heating system's output heat based on the temperature change coefficient, providing a foundation for intelligent system regulation and optimization, thereby improving system energy efficiency and user experience.

[0068] In an embodiment of this application, a heating management method based on the Internet of Things is provided. The step of determining the corresponding correction coefficient based on the change of each key characteristic parameter in each time period includes: pre-setting a correspondence between correction coefficient and temperature change coefficient intervals, wherein the correspondence between correction coefficient and temperature change coefficient intervals is associated with a corresponding correction coefficient for each interval of change; obtaining the temperature change coefficient, and selecting the correction coefficient corresponding to the temperature change coefficient interval as the corresponding correction coefficient based on the mapping relationship between the temperature change coefficient interval to which the temperature change coefficient belongs and the correspondence between correction coefficient and temperature change coefficient intervals.

[0069] Specifically, for each range of change in each key characteristic parameter, a pre-defined correspondence between correction coefficients and temperature change coefficient ranges is established. This correspondence can be a predefined table used to determine the correction coefficient corresponding to each temperature change coefficient. Based on the previously calculated temperature change coefficients, the current temperature change coefficient of the system is obtained, reflecting the current trend and rate of temperature change. Based on the temperature change coefficient range to which the temperature change coefficient belongs, the correction coefficient corresponding to the pre-defined correction coefficient-temperature change coefficient range is selected as the appropriate correction coefficient. This step, through the pre-defined range correspondence, dynamically selects the corresponding correction coefficient based on the current temperature change coefficient, allowing the heating system's output heat to better adapt to actual temperature changes. Based on the mapping relationship of temperature change coefficients, the system can achieve intelligent control, selecting the optimal correction coefficient according to the actual temperature change trend, thereby improving system energy efficiency and user comfort. Through the pre-defined range correspondence, the system can achieve fine-tuning of the correction coefficient, making system control more precise and flexible. In summary, this step enables dynamic adjustment of the heating system's output heat correction coefficient, selecting the most suitable correction coefficient based on actual temperature changes, thereby improving the system's intelligent control and energy efficiency.

[0070] like Figure 2As shown in the embodiments of this application, an IoT-based heating management system is provided, comprising: an acquisition module for acquiring historical heating output data of the heating system and historical temperature change data of users' rooms; a fitting module for fitting the historical heating output data and historical temperature change data to obtain an output heat-temperature change fitting model; a calculation module for acquiring the current indoor temperature and indoor target temperature of users' rooms, and determining the indoor temperature target difference based on the indoor temperature and indoor target temperature; an output module for determining 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 for 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; an analysis module for analyzing the outdoor temperature prediction data, and determining the correction coefficient for the output heat of the heating system based on the analysis results; and a management module for correcting the initial output heat of the heating system according to the correction coefficient to obtain the final output heat, which is then used by the heating system to provide heating.

[0071] In summary, this invention provides a heating management method and system based on the Internet of Things (IoT), comprising: acquiring and fitting historical heating output data of the heating system and historical temperature change data of users' indoor spaces to obtain a fitting model; acquiring the current indoor temperature and the target indoor temperature, and determining the target indoor temperature difference based on them; determining the initial heating output of the heating system based on the target indoor temperature difference and the fitting model; determining the estimated time length for the indoor temperature to reach the target indoor temperature based on the initial heating output, and determining the predicted outdoor temperature data within the estimated time length; analyzing the predicted outdoor temperature data to determine a correction coefficient for the heating output; correcting the initial heating output of the heating system according to the correction coefficient, and using this correction for heating system management. This invention can collect real-time data based on the IoT, determine indoor and outdoor temperature changes, and accurately adjust the system's heating output accordingly, thereby improving heating efficiency.

[0072] Finally, it should be noted that those skilled in the art can obviously make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims and their equivalents, this invention also intends to include these modifications and variations.

[0073] The above description is merely one embodiment of the present invention, and should not be construed as limiting the scope of the invention. Any structural changes made based on the present invention, as long as they do not depart from the essence of the invention, should be considered as falling within the protection scope of the present invention and subject to its restrictions. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process and related descriptions of the platform described above can be referred to the corresponding processes in the foregoing platform embodiments, and will not be repeated here.

[0074] The term "comprising" or any other similar term is intended to cover non-exclusive inclusion, such that a process, platform, article, or device / platform that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to those processes, platforms, articles, or devices / platforms.

[0075] The technical solutions of the present invention have been described in conjunction with the accompanying drawings and further embodiments. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to closely related technical features, and the technical solutions resulting from such changes or substitutions will all fall within the scope of protection of the present invention.

[0076] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention.

Claims

1. A heating management method based on the Internet of Things, characterized in that, include: Acquire historical heat output data of the heating system and historical temperature change data of users' indoor spaces; By fitting historical heating output data and historical temperature change data, a heating output-temperature change fitting model is obtained. Obtain the current indoor temperature and target indoor temperature of the user's room, and determine the target difference between the indoor temperature and the target indoor temperature; The initial output heat of the heating system is determined based on the indoor temperature target difference and the output heat-temperature change fitting model. Based on the initial heat output, determine the estimated time length for the indoor temperature to reach the target indoor temperature, and determine the outdoor temperature prediction data within this estimated time length; Analyze outdoor temperature forecast data and determine the correction coefficient for the output heat of the heating system based on the analysis results; The initial output heat of the heating system is corrected according to the correction factor to obtain the final output heat, and the heating system provides heat according to the final output heat. The analysis of outdoor temperature prediction data and the determination of correction coefficients for the output heat of the heating system based on the analysis results include: Acquire outdoor temperature data and plot the outdoor temperature data into a time series curve to obtain the outdoor temperature change curve. Determine the starting point, peak point, trough point, and end point in the outdoor temperature change curve, and divide the outdoor temperature change curve into several curve segments based on the starting point, peak point, trough point, and end point. Obtain the average temperature and average slope for each curve segment, and determine the number of curve segments; The temperature variation coefficient is calculated based on the average temperature and average slope of each curve segment and the number of curve segments, and the correction coefficient for the output heat of the heating system is determined based on the temperature variation coefficient. The formula for calculating the coefficient of temperature change is: T = (a*P + b*L) / n, Where T is the temperature change coefficient, a is the first conversion coefficient, P is the average temperature, b is the second conversion coefficient, L is the average slope value, and n is the number of curve segments; The correction coefficient for determining the output heat of the heating system based on the temperature change coefficient includes: A pre-defined correspondence between the correction coefficient and the temperature change coefficient interval is established, and for each interval of change, a corresponding correction coefficient is associated with it. Obtain the temperature change coefficient, and based on the mapping relationship between the temperature change coefficient interval to which the temperature change coefficient belongs and the corresponding relationship between the correction coefficient and the temperature change coefficient interval, select the correction coefficient corresponding to the temperature change coefficient interval as the corresponding correction coefficient.

2. The heating management method based on the Internet of Things according to claim 1, characterized in that, The process of fitting historical heating output data and historical temperature change data to obtain an output heat-temperature change fitting model includes: The historical heating output data and historical temperature change data are preprocessed, including removing outliers and duplicates and filling in missing values. A fitting dataset is constructed based on the preprocessed historical heating output heat data and historical temperature change data, and the fitting dataset is divided into a training set and a test set according to a certain ratio. The initial fitting model is obtained by using a linear regression model to fit the training set of the dataset. The test set of the fitted dataset is input into the initial prediction model to test and verify the initial fitted model until the output data of the initial fitted model meets the preset conditions, thus obtaining the output heat-temperature change fitted model.

3. The heating management method based on the Internet of Things according to claim 2, characterized in that, The step of obtaining the current indoor temperature and the target indoor temperature, and determining the target indoor temperature difference based on the indoor temperature and the target indoor temperature, includes: Obtain the current indoor temperature and target indoor temperature of the user's room, and calculate the difference between the target indoor temperature and the indoor temperature to obtain the target indoor temperature difference.

4. The heating management method based on the Internet of Things according to claim 3, characterized in that, The determination of 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 target difference in indoor temperature 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 process of determining the estimated time length for the indoor temperature to reach the target indoor temperature based on the initial output heat, and determining the outdoor temperature prediction data within this estimated time length, includes: Obtain the initial output heat, indoor temperature, and indoor target temperature, and use the initial output heat and indoor temperature as the starting conditions and the indoor target temperature as the target conditions; Input the initial and target conditions into the preset heating simulation model and perform multiple simulations, and count the time required to complete each simulation. Calculate the average time required to complete each simulation, and use this average as the estimated time. Acquire outdoor weather data from users and input the weather data into a preset outdoor temperature prediction model; The outdoor temperature prediction model outputs a segment of outdoor temperature prediction data, and outdoor temperature prediction data corresponding to the estimated time length is extracted from the beginning of the outdoor temperature prediction data segment.

6. A heating management system based on the Internet of Things (IoT), applied to the heating management method based on the IoT as described in any one of claims 1-5, characterized in that, include: The acquisition module is used to acquire historical heating output data of the heating system and historical temperature change data of users' indoor spaces; The fitting module is used to fit historical heating output data and historical temperature change data to obtain a fitting model of output heat-temperature change. The calculation module is used to obtain the current indoor temperature and the target indoor temperature of the user's room, and to determine the target difference between the indoor temperature and the target indoor temperature. The output module is 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; The determination module is used to determine the estimated time length for the indoor temperature to reach the indoor target temperature based on the initial output heat, and to determine the outdoor temperature prediction data within the estimated time length. The analysis module is used to analyze outdoor temperature forecast data and determine the correction coefficient for the output heat of the heating system based on 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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