Intelligent heat supply regulation and control method and system based on user behaviors and outdoor temperature

Abstract

The invention provides an intelligent heat supply regulation and control method and system based on user behaviors and outdoor temperature, and relates to the technical field of intelligent heat supply. And the indoor heat state data, the outdoor temperature data and the valve opening degree data are input into a temperature regulation and control model, a heat supply regulation and control instruction of a future time period is obtained, in the future time period, heat supply parameters of the corresponding building are adjusted based on the building regulation and control instruction, and the valve opening degree of the corresponding user is adjusted based on the user regulation and control instruction. Accurate thermal load prediction and regulation are achieved, the hysteresis of temperature regulation is reduced, and individual requirements are met.

Description

Technical Field

[0001] This invention relates to the field of intelligent heating technology, and more specifically, to an intelligent heating control method and system based on user behavior and outdoor temperature. Background Technology

[0002] In existing technologies, heating control systems based on climate compensators monitor outdoor temperature in real time and determine the ideal water supply temperature based on a preset curve between outdoor temperature and water supply temperature. Then, by controlling the opening of an electric regulating valve or the frequency of a variable frequency water pump, the actual water supply temperature is adjusted to the set value, thereby indirectly affecting the indoor temperature of all users. This heating control system relies entirely on outdoor temperature and cannot perceive users' daily behavior patterns, resulting in the inability to meet personalized needs and causing energy waste. Summary of the Invention

[0003] In view of this, the purpose of the present invention is to provide an intelligent heating control method and system based on user behavior and outdoor temperature, so as to achieve accurate heat load prediction and control, reduce the lag of temperature control and meet personalized needs.

[0004] Firstly, this application provides an intelligent heating control method based on user behavior and outdoor temperature, including: Acquire current indoor thermal status data, outdoor temperature data, and valve opening data for each user; the indoor thermal status data includes indoor temperature data and user-set temperature data. Indoor thermal state data, outdoor temperature data, and valve opening data are input into the temperature control model to obtain heating control instructions for future periods. The temperature control model is configured to: predict the predicted temperature setpoint curve for each user in the future period based on indoor thermal state data and valve opening data; determine the required total heat load for each building in the future period based on outdoor temperature data and the predicted temperature setpoint curves for all users; generate building control instructions based on the required total heat load; and generate user control instructions based on each user's indoor thermal state data and corresponding predicted temperature setpoint curves. In the future, the heating parameters of the corresponding building will be adjusted based on building control commands, and the valve opening of the corresponding user will be adjusted based on user control commands.

[0005] Optionally, obtain each user's current indoor thermal status data, outdoor temperature data, and valve opening data, including: The indoor temperature collected by the temperature sensor set in the user's room is used as the user's current indoor temperature data; The temperature setpoint received by the intelligent control terminal set in the user's room is used as the current set temperature data; Obtain the ambient temperature collected by the temperature sensor located outdoors from the user's location as the current outdoor temperature data; The valve opening degree fed back by the smart valve set on the user's indoor heating circuit is used as the current valve opening degree data.

[0006] Optionally, the temperature regulation model includes a temperature prediction sub-model and a hierarchical regulation sub-model; Indoor thermal state data, outdoor temperature data, and valve opening data are input into the temperature control model to obtain heating control instructions for future periods, including: Indoor thermal state data and valve opening data are input into the temperature prediction sub-model to obtain the predicted temperature setting curve for each user in the future period. Outdoor temperature data and the predicted temperature set curves for all users are input into the hierarchical control sub-model so that the hierarchical control sub-model can determine the required total heat load for each building in the future period, generate building control instructions based on the required total heat load, and generate user control instructions based on the indoor thermal state data and corresponding predicted temperature set curves for each user.

[0007] Optionally, the intelligent heating control method based on user behavior and outdoor temperature provided in this application further includes: Obtain a training sample dataset; the training sample dataset includes multiple training sample datasets; each training sample dataset includes historical indoor thermal state data, historical valve opening data, and the corresponding actual required temperature; Based on the training sample dataset, iterative training is performed on the initial temperature prediction sub-model until the termination condition for iterative training is met. Then, based on the parameters of the initial temperature prediction sub-model updated during the last iteration, a temperature prediction sub-model is obtained. The iterative training operation includes: Select target training sample data from the training sample data set; Input the historical indoor thermal state data and historical valve opening data from the target training sample data into the initial temperature prediction sub-model so that the initial temperature prediction sub-model can obtain the predicted temperature based on the historical indoor thermal state data and historical valve opening data. Based on the prediction error between the predicted required temperature and the actual required temperature in the target training sample data, the parameters of the initial temperature prediction sub-model are updated.

[0008] Optionally, historical indoor thermal status data includes user-defined behavior patterns, which include periodic patterns based on date type and time and / or user comfort temperature preferences under the defined patterns.

[0009] Optionally, user control commands are generated based on each user's indoor thermal state data and the corresponding predicted temperature setpoint curve, including: Based on the deviation between each user's current indoor temperature data and the target temperature, the valve opening adjustment amount corresponding to each user is obtained; where the target temperature is the user's current set temperature data or the temperature value at the corresponding moment in the predicted temperature set curve for each user.

[0010] Optionally, the intelligent heating control method based on user behavior and outdoor temperature provided in this application further includes: After adjusting the valve opening of the corresponding user based on the user's control command, the updated indoor thermal status data of each user is obtained; Based on the updated indoor thermal state data, the parameters of the temperature control model were optimized.

[0011] Secondly, this application provides an intelligent heating control system based on user behavior and outdoor temperature, comprising: The data acquisition module is used to acquire the current indoor thermal status data, outdoor temperature data, and valve opening data of each user; the indoor thermal status data includes indoor temperature data and user-set temperature data. The data processing module is used to input indoor thermal state data, outdoor temperature data, and valve opening data into the temperature control model to obtain heating control instructions for future periods. The temperature control model is configured to: predict the predicted temperature setpoint curve for each user in the future period based on indoor thermal state data and valve opening data; determine the required total heat load for each building in the future period based on outdoor temperature data and the predicted temperature setpoint curves for all users; generate building control instructions based on the required total heat load; and generate user control instructions based on the indoor thermal state data and corresponding predicted temperature setpoint curves for each user. The temperature control module is used to adjust the heating parameters of the corresponding building based on building control commands and to adjust the valve opening of the corresponding user based on user control commands in the future.

[0012] Thirdly, this application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the above-mentioned intelligent heating control method based on user behavior and outdoor temperature.

[0013] Fourthly, this application provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the aforementioned intelligent heating control method based on user behavior and outdoor temperature.

[0014] This invention provides an intelligent heating control method and system based on user behavior and outdoor temperature. By acquiring the current indoor thermal state data, outdoor temperature data, and valve opening data of each user, the system inputs these data into a temperature control model to obtain heating control instructions for future periods. In the future periods, the system adjusts the heating parameters of the corresponding building based on the building control instructions and adjusts the valve opening of the corresponding user based on the user control instructions, thereby achieving accurate heat load prediction and control, reducing the lag in temperature control, and meeting personalized needs.

[0015] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation on the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 The diagram shows a flowchart of an intelligent heating control method based on user behavior and outdoor temperature provided by an embodiment of the present invention. Figure 2 A schematic flowchart illustrating the training process of a temperature prediction sub-model provided in an embodiment of the present invention is shown. Figure 3 A schematic diagram of the structure of an intelligent heating control system based on user behavior and outdoor temperature provided in an embodiment of the present invention is shown. Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present invention is shown. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0019] This application provides an intelligent heating control method based on user behavior and outdoor temperature. (See attached document.) Figure 1 As shown in the figure, the flow of the intelligent heating control method based on user behavior and outdoor temperature provided in this application embodiment is as follows: Step 110: Obtain the current indoor thermal status data, outdoor temperature data, and valve opening data for each user; the indoor thermal status data includes indoor temperature data and user-set temperature data.

[0020] In this embodiment of the application, the current indoor temperature data, set temperature data, outdoor temperature data, and valve opening data of each user can be obtained in the following ways: The system acquires the indoor temperature collected by a temperature sensor located inside the user's room as the user's current indoor temperature data; acquires the temperature setpoint received by the intelligent control terminal located inside the user's room as the current set temperature data; acquires the ambient temperature collected by a temperature sensor located outside the user's room as the current outdoor temperature data; and acquires the valve opening degree fed back by the intelligent valve on the heating circuit located inside the user's room as the current valve opening degree data.

[0021] In practice, temperature sensors deployed indoors collect the temperature of the area in real time as the user's current indoor temperature data; intelligent control terminals deployed indoors receive the user's temperature setting commands as the current set temperature data; temperature sensors deployed outdoors collect the ambient temperature in real time as the current outdoor temperature data; and intelligent valves deployed on the user's indoor heating circuit read or receive valve opening signals from the intelligent valves in real time as the current valve opening data.

[0022] Step 120: Input indoor thermal state data, outdoor temperature data, and valve opening data into the temperature control model to obtain heating control instructions for future periods. The temperature control model is configured to: predict the predicted temperature setpoint curve for each user for future periods based on indoor thermal state data and valve opening data; determine the required total heat load for each building for future periods based on outdoor temperature data and the predicted temperature setpoint curves for all users; generate building control instructions based on the required total heat load; and generate user control instructions based on the indoor thermal state data and corresponding predicted temperature setpoint curves for each user.

[0023] In this embodiment, the temperature control model includes a temperature prediction sub-model and a tiered control sub-model. Indoor thermal state data and valve opening data are input into the temperature prediction sub-model to obtain the predicted temperature setting curves for each user's future time period. Outdoor temperature data and the predicted temperature setting curves for all users are input into the tiered control sub-model, enabling the tiered control sub-model to determine the required total heat load for each building's future time period, generate building control instructions based on the required total heat load, and generate user control instructions based on each user's indoor thermal state data and corresponding predicted temperature setting curves.

[0024] In this embodiment of the application, user control instructions are generated based on the indoor thermal state data of each user and the corresponding predicted temperature setting curve, including: obtaining the valve opening adjustment amount corresponding to each user based on the deviation between the current indoor temperature data of each user and the target temperature; wherein, the target temperature is the current user-set temperature data of each user or the temperature value at the corresponding moment in the predicted temperature setting curve of each user.

[0025] In a specific embodiment, the indoor temperature data and user-set temperature data of each user are processed by a temperature prediction sub-model to obtain the predicted temperature setting curve for each user in the future (e.g., within the next 24 hours). The user-set temperature data can be the set temperature data within a periodic pattern based on date type and time and / or the user's comfortable temperature preference in the setting pattern, such as a daytime temperature of 22°C and a nighttime temperature of 18°C. The predicted temperature setting curve for the future period is the mapping relationship between each future time point and its corresponding required temperature. For example, it is a temperature setting curve that predicts user A will set the temperature to 18°C ​​at 8:00 AM and 22°C at 5:00 PM tomorrow. The tiered control sub-model processes outdoor temperature data and the predicted temperature setpoint curves for all users using the following formula to obtain the total required heat load for each building in the future time period:

[0026] In the formula, The required total heat load, For outdoor temperature data, The average predicted temperature for all users is denoted as A, where A is a building structural parameter. The average predicted temperature for all users is determined based on the predicted temperature setting curve for all users.

[0027] In this embodiment, after obtaining the valve opening adjustment amount corresponding to each user based on the deviation between the current indoor temperature data and the target temperature, the valve opening is dynamically adjusted according to the real-time indoor temperature data through control algorithms such as PID (proportional-integral-derivative) to solve the problem of uneven heating and cooling for different users and ensure that the indoor temperature data reaches the user's set temperature data.

[0028] Step 130: In the future time period, adjust the heating parameters of the corresponding building based on the building control command, and adjust the valve opening of the corresponding user based on the user control command.

[0029] In this embodiment, the building control command is sent to the building-level control device installed at the building's heat inlet. The building-level control device adjusts the heating parameters of the entire building according to the parameters in the building control command, such as adjusting the primary side water supply temperature or adjusting the total circulation flow rate. This allows the building to match the total heat load predicted for future periods from the heat source side in advance. Through the feedforward adjustment of the building, changes in external temperature and overall demand can be addressed from the source, alleviating the problem of temperature response lag caused by system thermal inertia and improving the timeliness and economy of macro heat supply. Meanwhile, user control commands are transmitted via the communication network to the smart valve actuators in each user's home. Each actuator, based on the target valve opening calculated for that user in the control command, drives the valve mechanism to precisely adjust the flow of heat medium into the user's heat dissipation terminal. The adjustment process for each user is a continuous feedback control process. By comparing the actual indoor temperature collected by the user's indoor temperature sensor in real time with the set temperature in the corresponding time period in the predicted temperature setting curve, the control algorithm continuously fine-tunes the valve opening to eliminate deviations. Through precise terminal control, it is possible to compensate for uneven heat distribution between households caused by factors such as hydraulic imbalance in the pipe network, building orientation, and floor location, ensuring that the indoor temperature of each user can independently and stably meet their individual needs. Thus, on the basis of overall building energy conservation, it achieves refined on-demand heating and comfort guarantee.

[0030] The intelligent heating control method based on user behavior and outdoor temperature provided in this application improves the accuracy and humanization of control by predicting user behavior to drive actual demand for heating. By using feedforward prediction for each building, the heating system is adjusted in advance before changes in weather and user behavior, effectively overcoming the problem of large inertia in the heating system, making the indoor temperature more stable and significantly improving the user experience. By adjusting the heating level for each user, the heating level is automatically reduced during periods when the user is not at home, and the total load is optimized at the heating system level, ensuring the comfort of users at home and achieving the dual goals of energy saving and comfort.

[0031] This application provides a training method for a temperature prediction sub-model. (See attached document.) Figure 2 As shown in the embodiments of this application, the training method for the temperature prediction sub-model is as follows: Step 210: Obtain the training sample data set; wherein, the training sample data set includes multiple training sample data; each training sample data includes historical indoor thermal state data, historical valve opening data, and the corresponding actual required temperature.

[0032] In this embodiment, the historical indoor thermal status data includes user-defined behavior patterns, which include periodic patterns based on date type and time and / or the user's comfort temperature preferences within the defined patterns. For example, a user's "away from home" mode from 9:00 AM to 6:00 PM on weekdays and "sleep" mode at night constitutes a periodic pattern; the user's comfort temperature preferences within the defined patterns include a daytime temperature of 22°C and a nighttime temperature of 18°C.

[0033] Step 220: Select target training sample data from the training sample data set.

[0034] Step 230: Input the historical indoor thermal state data and historical valve opening data from the target training sample data into the initial temperature prediction sub-model so that the initial temperature prediction sub-model can obtain the required temperature based on the historical indoor thermal state data and historical valve opening data.

[0035] Step 240: Based on the prediction error between the predicted required temperature and the actual required temperature in the target training sample data, update the parameters of the initial temperature prediction sub-model.

[0036] Step 250: Determine whether the iterative training termination condition is met; if yes, proceed to step 260; if no, return to step 220; wherein, the iterative training termination condition is that the number of iterations is not less than the number threshold, or the prediction error is not higher than the error threshold.

[0037] Step 260: Based on the parameters of the initial temperature prediction sub-model updated during the last iteration training operation, obtain the temperature prediction sub-model.

[0038] To improve the prediction accuracy of the temperature control model, this embodiment introduces a self-learning mechanism that can automatically adjust the parameters of the temperature prediction sub-model as user habits change. Specifically, this embodiment obtains updated indoor thermal state data for each user after adjusting the valve opening based on the user's control command; and optimizes the parameters of the temperature control model based on the updated indoor thermal state data as historical indoor thermal state data. This allows the temperature control model to automatically adjust as user habits and building characteristics change, thus maintaining the efficient and stable operation of the temperature control model.

[0039] This application provides an intelligent heating control system based on user behavior and outdoor temperature. (See attached document.) Figure 3 As shown, the intelligent heating control system based on user behavior and outdoor temperature provided in this application includes: The data acquisition module 310 is used to acquire the current indoor thermal status data, outdoor temperature data, and valve opening data of each user; the indoor thermal status data includes indoor temperature data and user-set temperature data. The data processing module 320 is used to input indoor thermal state data, outdoor temperature data, and valve opening data into the temperature control model to obtain heating control instructions for future periods. The temperature control model is configured to: predict the predicted temperature setpoint curve for each user in the future period based on indoor thermal state data and valve opening data; determine the required total heat load for each building in the future period based on outdoor temperature data and the predicted temperature setpoint curves for all users; generate building control instructions based on the required total heat load; and generate user control instructions based on the indoor thermal state data and corresponding predicted temperature setpoint curves for each user. The temperature control module 330 is used to adjust the heating parameters of the corresponding building based on building control commands and to adjust the valve opening of the corresponding user based on user control commands in the future.

[0040] It should be noted that the principle of the intelligent heating control system based on user behavior and outdoor temperature provided in this application embodiment to solve the technical problem is similar to the intelligent heating control method based on user behavior and outdoor temperature provided in this application embodiment. Therefore, the implementation of the intelligent heating control system based on user behavior and outdoor temperature provided in this application embodiment can refer to the implementation of the intelligent heating control method based on user behavior and outdoor temperature provided in this application embodiment, and the repeated parts will not be described again.

[0041] After introducing the intelligent heating control method and device based on user behavior and outdoor temperature provided in the embodiments of this application, the electronic equipment provided in the embodiments of this application will be briefly introduced next.

[0042] See Figure 4 As shown, the electronic device 500 provided in this application embodiment includes at least a processor 501, a memory 502, and a computer program stored in the memory 502 and executable on the processor 501. When the processor 501 executes the computer program, it implements the intelligent heating control method based on user behavior and outdoor temperature provided in this application embodiment.

[0043] The electronic device 500 provided in this application embodiment may further include a bus 503 connecting different components (including processor 501 and memory 502). The bus 503 represents one or more types of bus structures, including memory bus, peripheral bus, local area bus, etc.

[0044] Memory 502 may include a readable storage medium in the form of volatile memory, such as random access memory (RAM) 5021 and / or cache memory 5022, and may further include read-only memory (ROM) 5023. Memory 502 may also include a program tool 5025 having a set (at least one) of program modules 5024, including but not limited to an operating subsystem, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0045] Processor 501 can be a single processing element or a collective term for multiple processing elements. For example, processor 501 can be a central processing unit (CPU), or one or more integrated circuits configured to implement the intelligent heating control method based on user behavior and outdoor temperature provided in the embodiments of this application. Specifically, processor 501 can be a general-purpose processor, including but not limited to CPUs, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0046] Electronic device 500 can communicate with one or more external devices 504 (e.g., keyboard, remote control, etc.), and also with one or more devices that enable a user to interact with electronic device 500 (e.g., mobile phone, computer, etc.), and / or with devices that enable electronic device 500 to communicate with one or more other electronic devices 500 (e.g., router, modem, etc.). This communication can be performed through input / output (I / O) interface 505. Furthermore, electronic device 500 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) through network adapter 506. Figure 4 As shown, network adapter 506 communicates with other modules of electronic device 500 via bus 503. It should be understood that, although... Figure 4As not shown, other hardware and / or software modules may be used in conjunction with the electronic device 500, including but not limited to microcode, device drivers, redundant processors, external disk drive arrays, Redundant Arrays of Independent Disks (RAID) subsystems, tape drives, and data backup storage subsystems.

[0047] It should be noted that, Figure 4 The electronic device 500 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0048] The following describes the computer-readable storage medium provided in the embodiments of this application. The computer-readable storage medium provided in the embodiments of this application stores computer instructions, which, when executed by a processor, implement the intelligent heating control method based on user behavior and outdoor temperature provided in the embodiments of this application. Specifically, the computer instructions can be built into or installed in the processor, so that the processor can implement the intelligent heating control method based on user behavior and outdoor temperature provided in the embodiments of this application by executing the built-in or installed computer instructions.

[0049] In addition, the intelligent heating control method based on user behavior and outdoor temperature provided in this application embodiment can also be implemented as a computer program product. The computer program product includes program code, which implements the intelligent heating control method based on user behavior and outdoor temperature provided in this application embodiment when running on a processor.

[0050] The computer program product provided in this application embodiment may employ one or more computer-readable storage media, which may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination thereof. Specifically, more specific examples (a non-exhaustive list) of computer-readable storage media include electrical connections with one or more wires, portable disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0051] The computer program product provided in this application embodiment can be a CD-ROM and include program code, and can also run on electronic devices such as computers. However, the computer program product provided in this application embodiment is not limited thereto. In this application embodiment, the computer-readable storage medium can be any tangible medium that contains or stores program code, which can be used by or in conjunction with an instruction execution system, device, or apparatus.

[0052] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0053] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0054] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0055] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A smart heating control method based on user behavior and outdoor temperature, characterized in that, include: Acquire current indoor thermal status data, outdoor temperature data, and valve opening data for each user; The indoor thermal status data includes indoor temperature data and user-set temperature data; The indoor thermal state data, the outdoor temperature data, and the valve opening data are input into the temperature control model to obtain heating control instructions for future periods. The temperature control model is configured to: predict the predicted temperature setting curve for each user for future periods based on the indoor thermal state data and the valve opening data; determine the required total heat load for each building for the future periods based on the outdoor temperature data and the predicted temperature setting curves for all users; generate building control instructions based on the required total heat load; and generate user control instructions based on each user's indoor thermal state data and the corresponding predicted temperature setting curve. During the future period, the heating parameters of the corresponding building will be adjusted based on the building control command, and the valve opening of the corresponding user will be adjusted based on the user control command.

2. The intelligent heating control method based on user behavior and outdoor temperature according to claim 1, characterized in that, Obtain current indoor thermal status data, outdoor temperature data, and valve opening data for each user, including: The indoor temperature collected by the temperature sensor set in the user's room is used as the user's current indoor temperature data; The temperature setpoint received by the intelligent control terminal set in the user's room is used as the current set temperature data; Obtain the ambient temperature collected by the temperature sensor located outdoors from the user's location as the current outdoor temperature data; The valve opening degree fed back by the smart valve set on the user's indoor heating circuit is used as the current valve opening degree data.

3. The intelligent heating control method based on user behavior and outdoor temperature according to claim 1, characterized in that, The temperature control model includes a temperature prediction sub-model and a hierarchical control sub-model. The indoor thermal state data, the outdoor temperature data, and the valve opening data are input into the temperature control model to obtain heating control instructions for future time periods, including: The indoor thermal state data and the valve opening data are input into the temperature prediction sub-model to obtain the predicted temperature setting curve for each user in the future time period. The outdoor temperature data and the predicted temperature setting curves of all users are input into the hierarchical control sub-model so that the hierarchical control sub-model can determine the required total heat load of each building for the future period, generate building control instructions based on the required total heat load, and generate user control instructions based on the indoor thermal state data of each user and the corresponding predicted temperature setting curve.

4. The intelligent heating control method based on user behavior and outdoor temperature according to claim 3, characterized in that, Also includes: Obtain a training sample data set; wherein, the training sample data set includes multiple training sample data; each training sample data includes historical indoor thermal state data, historical valve opening data, and the corresponding actual required temperature; Based on the training sample dataset, an iterative training operation is performed on the initial temperature prediction sub-model until the iterative training termination condition is met. Then, based on the parameters of the initial temperature prediction sub-model updated during the last execution of the iterative training operation, the temperature prediction sub-model is obtained. The iterative training operation includes: Select target training sample data from the training sample data set; The historical indoor thermal state data and historical valve opening data in the target training sample data are input into the initial temperature prediction sub-model so that the initial temperature prediction sub-model can obtain the predicted temperature based on the historical indoor thermal state data and historical valve opening data. Based on the prediction error between the predicted required temperature and the actual required temperature in the target training sample data, the parameters of the initial temperature prediction sub-model are updated.

5. The intelligent heating control method based on user behavior and outdoor temperature according to claim 4, characterized in that, The historical indoor thermal status data includes user-defined behavior patterns, which include periodic patterns based on date type and time and / or user comfort temperature preferences within the defined patterns.

6. The intelligent heating control method based on user behavior and outdoor temperature according to claim 1, characterized in that, User control commands are generated based on each user's indoor thermal status data and the corresponding predicted temperature setpoint curve, including: Based on the deviation between the current indoor temperature data of each user and the target temperature, the valve opening adjustment amount corresponding to each user is obtained; wherein, the target temperature is the current user-set temperature data of each user or the temperature value at the corresponding moment in the predicted temperature setting curve corresponding to each user.

7. The intelligent heating control method based on user behavior and outdoor temperature according to any one of claims 1 to 6, characterized in that, Also includes: After adjusting the valve opening of the corresponding user based on the user control command, the updated indoor thermal status data of each user is obtained; Based on the updated indoor thermal state data, the parameters of the temperature control model are optimized.

8. A smart heating control system based on user behavior and outdoor temperature, characterized in that, include: The data acquisition module is used to acquire the current indoor thermal status data, outdoor temperature data, and valve opening data of each user; The indoor thermal status data includes indoor temperature data and user-set temperature data; The data processing module is used to input the indoor thermal state data, the outdoor temperature data, and the valve opening data into the temperature control model to obtain heating control instructions for future periods. The temperature control model is configured to: predict the predicted temperature setpoint curve for each user for future periods based on the indoor thermal state data and the valve opening data; determine the required total heat load for each building for the future periods based on the outdoor temperature data and the predicted temperature setpoint curves for all users; generate building control instructions based on the required total heat load; and generate user control instructions based on the indoor thermal state data and corresponding predicted temperature setpoint curves for each user. The temperature control module is used to adjust the heating parameters of the corresponding building based on the building control command during the future time period, and to adjust the valve opening of the corresponding user based on the user control command.

9. An electronic device, characterized in that, It includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the intelligent heating control method based on user behavior and outdoor temperature as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the intelligent heating control method based on user behavior and outdoor temperature as described in any one of claims 1 to 7.

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