Heat supply scheduling method and system based on user side room temperature big data and related device

Abstract

The invention provides a heat supply scheduling method and system based on user side room temperature big data and a related device. The heat supply scheduling method comprises the following steps that 1, user side indoor real-time temperature information, heat supply pipe network side real-time heat supply information and environment real-time data are collected; 2, on the basis of a pre-constructed reinforcement learning scheduling model, a personalized heat supply scheduling strategy is generated in combination with user side indoor real-time temperature information, heat supply pipe network side real-time heat supply information and environment real-time data; step 3, carrying out on-demand heat supply according to a personalized heat supply scheduling strategy; according to the method, the problem of uneven cold and heat of the user side is solved, energy waste caused by invalid heat supply is reduced to the maximum extent, and an efficient, energy-saving and comfortable personalized heat supply solution is formed.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control technology for urban centralized heating, specifically relating to a heating scheduling method, system, and related devices based on big data of room temperature on the user side. Background Technology

[0002] Existing centralized heating systems mostly adopt a "regional unified control" model, using outdoor temperature as the core basis for formulating heating strategies, ignoring individual differences in users' indoor insulation conditions, heating habits, and daily routines. This leads to two major problems: First, uneven heating among users, with some users experiencing excessively high indoor temperatures, resulting in energy waste, while others experience excessively low indoor temperatures, affecting their living comfort; second, a lack of personalized adaptation in control, with traditional scheduling methods relying on fixed algorithms or manual experience, failing to dynamically respond to the real-time heating needs of different users, resulting in low energy efficiency.

[0003] With the widespread adoption of IoT technology, collecting room temperature data on the user side has become feasible. However, existing smart heating solutions mostly focus on regional load forecasting and peak shaving, failing to delve into individual user heating patterns and lacking precise scheduling technology based on personalized needs. Therefore, developing personalized "on-demand heating" scheduling methods has become a key direction for solving the mismatch between heating supply and demand, improving user comfort, and enhancing energy efficiency. Summary of the Invention

[0004] The purpose of this invention is to provide a heating scheduling method, system and related device based on user-side room temperature big data, which solves the above-mentioned shortcomings in the prior art.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows: In a first aspect, the present invention provides a heating scheduling method based on user-side room temperature big data, comprising the following steps: Step 1: Collect real-time indoor temperature information on the user side, real-time heating information on the heating network side, and real-time environmental data; Step 2: Based on the pre-built reinforcement learning scheduling model, combined with real-time indoor temperature information on the user side, real-time heating information on the heating network side, and real-time environmental data, a personalized heating scheduling strategy is generated. Step 3: Provide heating on demand based on the personalized heating scheduling strategy.

[0006] Preferably, the real-time indoor temperature information on the user side includes hourly indoor temperature, thermostatic valve operation records, and window opening duration; the real-time heating information includes supply water temperature, return water temperature, flow rate, and outdoor meteorological parameters; and the real-time environmental parameters include temperature, humidity, and wind speed.

[0007] Preferably, the pre-built reinforcement learning scheduling model includes a state space, an action space, and a reward function, wherein: The state space includes the deviation between the actual indoor temperature and the set temperature on the user side, outdoor environmental parameters, pipeline operation parameters, energy consumption data, and heating cost data. The operational space includes the heat source output power adjustment range, the flow distribution ratio of the pipeline branch, and the opening level of the intelligent temperature control valve. The reward function includes rewards for achieving room temperature targets, rewards for energy saving, and penalties for excessive consumption.

[0008] Preferably, the objective function of the reinforcement learning scheduling model is: User room temperature compliance rate ≥95%, heating energy consumption per unit area reduced by ≥15%, and total heating cost reduced by ≥10%.

[0009] Preferably, the personalized heating scheduling strategy includes: Heating parameters are dynamically adjusted based on the room temperature deviation and heating habits of individual users. Users within the same heating area are clustered by load, and heat source allocation and pipeline flow scheduling are optimized by group.

[0010] Secondly, the present invention provides a heating scheduling system based on user-side room temperature big data, comprising: The data acquisition unit is used to collect real-time indoor temperature information on the user side, real-time heating information on the heating network side, and real-time environmental data. The scheduling strategy generation unit is used to generate personalized heating scheduling strategies based on a pre-built reinforcement learning scheduling model, combined with real-time indoor temperature information on the user side, real-time heating information on the heating network side, and real-time environmental data. The heating dispatching unit is used to provide heating on demand based on personalized heating dispatching strategies.

[0011] Thirdly, the present invention provides an electronic device including a processor and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the electronic device performs the method described thereon.

[0012] Fourthly, the present invention provides a computing device cluster, comprising at least one computing device, each computing device including a processor and a memory; The processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device to cause the cluster of computing devices to perform the method according to the method.

[0013] Fifthly, the present invention provides a computer program product, the computer program product including computer-executable instructions, which, when executed, implement the method described.

[0014] In a sixth aspect, the present invention provides a computer-readable storage medium storing computer-executable instructions that, when executed by a processor, implement the method described herein.

[0015] Compared with the prior art, the beneficial effects of the present invention are: This invention provides a heating scheduling method based on user-side room temperature big data. By integrating user-side room temperature big data with reinforcement learning algorithms, it achieves a paradigm shift in the heating system from "extensive and uniform" to "precise and personalized," creating comprehensive benefits significantly superior to traditional methods. Specifically, in terms of user comfort, this method can dynamically sense and respond to each user's real-time room temperature deviation and personalized heating habits. For example, it automatically identifies the different needs of working people and elderly users and generates customized strategies, thereby significantly increasing the room temperature compliance rate from 78% in the traditional model to over 97%, fundamentally solving the systemic problem of uneven heating and cooling, and greatly improving the comfort of users' living and working environments. In terms of energy and economic efficiency, this method, through the "multi-objective comprehensive reward" mechanism of the reinforcement learning model, guides the system to continuously optimize heat source output, pipeline distribution, and terminal control under the core premise of ensuring comfort. This achieves the dual goals of reducing heating energy consumption per unit area by 15%-20% and reducing total heating costs by 10%-15%, effectively reducing energy waste caused by overheating or supply-demand mismatch.

[0016] Meanwhile, the regional coordinated control mechanism in the formulated heating strategy further optimizes the regional energy distribution efficiency by intelligently clustering user loads, achieving "one policy per household," avoiding local imbalances, and forming an optimal solution that balances individual comfort and overall energy efficiency, providing a reliable technical path for the construction and operation of smart heating systems. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the process involved in an embodiment of the present invention. Detailed Implementation

[0018] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0019] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0020] It should also be understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0021] As used in this application specification and the appended claims, the term "if" may be interpreted, depending on the context, as "when," "once," "in response to determination," or "in response to detection." Similarly, the phrase "if determined" or "if detected [the described condition or event]" may be interpreted, depending on the context, as meaning "once determined," "in response to determination," "once detected [the described condition or event]," or "in response to detection [the described condition or event]."

[0022] Furthermore, in the description of this application and the appended claims, the terms "first," "second," "third," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0023] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0024] Example 1 This embodiment provides a heating scheduling method based on user-side room temperature big data, which includes the following steps: Step 1: Collect real-time indoor temperature information on the user side. The real-time indoor temperature information on the user side includes hourly indoor temperature, thermostatic valve operation records, and window opening duration. Collect real-time heating information from the heating network side, including supply water temperature, return water temperature, flow rate, and outdoor meteorological parameters; The system uses real-time environmental data, including temperature, humidity, and wind speed. Step 2: Based on the pre-built reinforcement learning scheduling model, combined with real-time indoor temperature information on the user side, real-time heating information on the heating network side, and real-time environmental data, a personalized heating scheduling strategy is generated. Step 3: Provide heating on demand based on the personalized heating scheduling strategy.

[0025] Example 2 Based on Example 1, this example provides a heating scheduling method based on user-side room temperature big data. The pre-constructed reinforcement learning scheduling model includes a state space, an action space, and a reward function, wherein: The state space includes the deviation between the actual indoor temperature and the set temperature on the user side, outdoor environmental parameters, pipeline operation parameters, energy consumption data, and heating cost data.

[0026] The operating space includes the range of heat source output power adjustment, the flow distribution ratio of pipeline branches, and the opening level of intelligent temperature control valves.

[0027] The reward function includes room temperature compliance reward, energy saving reward, and excessive consumption penalty; Model meteorological optimization is achieved by precisely adjusting heating parameters based on the state space, action space, and reward function.

[0028] Example 3 Based on Example 1, this example provides a heating scheduling method based on user-side room temperature big data, wherein the personalized heating scheduling strategy includes: For individual users: dynamically adjust heating parameters based on their room temperature deviation and heating habits; For the same heating area: users within the same heating area are clustered by load, and heat source allocation and pipeline flow scheduling are optimized by group.

[0029] Example 4 Based on Example 1, this example provides a heating scheduling method based on user-side room temperature big data. The method utilizes a heating execution unit to provide heating on demand according to a personalized heating scheduling strategy. The heating execution unit includes a heat source control device, a pipeline flow control valve, and an intelligent temperature control valve.

[0030] Example 5 Based on Example 2, this example provides a heating scheduling method based on user-side room temperature big data. The objective function of the reinforcement learning scheduling model is: User room temperature compliance rate ≥95%, heating energy consumption per unit area reduced by ≥15%, and total heating cost reduced by ≥10%.

[0031] This is intended to achieve both energy-saving and economic benefits while ensuring user comfort.

[0032] Example 6 This embodiment provides a heating scheduling method based on user-side room temperature big data, which includes the following steps: Data collection deployment: In a residential community, one high-precision temperature sensor (measurement accuracy ±0.2℃) and one smart temperature control valve (adjustment response time 25 seconds) are installed in each of 1,000 households. Three outdoor meteorological monitoring stations are set up in the community to simultaneously collect hourly indoor temperature, temperature control valve operation records, outdoor temperature, humidity, wind speed and pipeline operation data. The data collection frequency is once every 5 minutes and the data is transmitted to the edge computing node via 5G network.

[0033] Model Training: Based on the historical data of the cell over the past 90 days, the state space, action space, and reward function of the reinforcement learning scheduling model are initialized, where: The status space includes the deviation between the actual indoor temperature and the set temperature on the user side, outdoor environmental parameters, pipeline operation parameters, energy consumption data, and heating cost data; The operating range includes the set heat source output power adjustment range of 50%-100%, the intelligent temperature control valve opening degree of 1%, and the flow distribution ratio of the pipeline branch.

[0034] A multi-objective optimization function was constructed using room temperature compliance rewards, energy-saving rewards, and excessive consumption penalties. Define the constraints for the multi-objective optimization function, including: User room temperature compliance rate ≥ 95%; Energy consumption per unit area for heating is reduced by ≥15%; Total heating costs reduced by ≥10%.

[0035] Scheduling strategy generation: The constructed reinforcement learning scheduling model is continuously trained iteratively to uncover the heating patterns of different users. For example, for working users, a scheduling strategy is generated that "the room temperature is maintained at 18℃ from 8:00 to 18:00 on weekdays, and rises to 22℃ from 18:00 to 23:00"; for elderly users, a stable heating strategy is generated that "the room temperature is maintained at 21-22℃ throughout the day"; and for users in the same heating area, load clustering is performed, and scheduling strategies for optimizing heat source allocation and pipeline flow scheduling are optimized by group.

[0036] Execution and Optimization: The heating execution unit dynamically adjusts according to the scheduling instructions. After one month of operation, the rate of users meeting the room temperature standard increased from 78% in the traditional mode to 97%, the heating energy consumption per unit area decreased by 18%, and the total heating cost decreased by 12%. Every 7 days, the model parameters are fine-tuned based on the new data to adapt to changes in the heating habits of some users (such as adjustments to heating demand during holidays).

[0037] Example 7 This embodiment provides a heating scheduling method based on user-side room temperature big data, which includes the following steps: This example focuses on a commercial building heating scenario (an office building), whose heating characteristics are "high demand from 9:00 to 18:00 on weekdays and low demand at other times", and the insulation conditions of offices on different floors and with different orientations vary greatly.

[0038] Data collection: Temperature sensors and smart temperature control valves were deployed in 80 offices across 20 floors of the office building to collect additional personnel density data for each office (through the access control system). The data collection frequency was once every 3 minutes.

[0039] Regional coordinated regulation: The reinforcement learning scheduling model clusters 80 offices into 3 groups based on orientation and personnel density. Offices facing south with good lighting and high personnel density belong to group A, offices facing north belong to group B, and temporary spaces such as meeting rooms belong to group C.

[0040] Personalized strategies: For Group A, a strategy of "room temperature 23℃ from 9:00 to 18:00 on weekdays, 17℃ at other times" was generated; for Group B, a strategy of "room temperature 24℃ from 9:00 to 18:00 on weekdays, 18℃ at other times" was generated; and for Group C, a strategy of "start and stop as needed, preheat to 22℃ 1 hour before use" was generated.

[0041] Implementation results: After two months of operation, the office building's room temperature compliance rate reached 96%, the heating energy consumption per unit area decreased by 20%, and the heating cost was reduced by 15% compared to the traditional unified control model. It effectively solved the problems of uneven heating in different areas and ineffective heating in empty rooms.

[0042] Example 8 This embodiment provides a heating dispatching system based on user-side room temperature big data, including: The data acquisition unit is used to collect real-time indoor temperature information on the user side, real-time heating information on the heating network side, and real-time environmental data. The scheduling strategy generation unit is used to generate personalized heating scheduling strategies based on a pre-built reinforcement learning scheduling model, combined with real-time indoor temperature information on the user side, real-time heating information on the heating network side, and real-time environmental data. The heating dispatching unit is used to provide heating on demand based on personalized heating dispatching strategies.

[0043] Example 9 This embodiment also provides a computing device. The computing device includes a bus, a processor, a memory, and a communication interface. The processor, memory, and communication interface communicate with each other via the bus. The computing device can be a server or a terminal device. It should be understood that this application does not limit the number of processors and memory in the computing device.

[0044] A bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. Buses can be categorized as address buses, data buses, control buses, etc. For ease of representation, a bus can include a path for transmitting information between various components of a computing device (e.g., memory, processor, communication interfaces).

[0045] The processor may include any one or more of the following: central processing unit (CPU), graphics processing unit (GPU), tensor processing unit (TPU), application specific integrated circuit (ASIC), field-programmable gate array (FPGA), microprocessor (MP), or digital signal processor (DSP).

[0046] Memory can include volatile memory, such as random access memory (RAM). Processors can also include non-volatile memory. volatile memory, such as read-only memory (ROM). ROM (memory only), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0047] The memory stores executable program code, which the processor executes to implement the functions of the aforementioned units, thereby achieving, for example, the method described in Embodiment 1. That is, the memory may store instructions for the methods and functions relating to the computing device in any of the above embodiments.

[0048] The communication interface uses transceiver modules such as, but not limited to, network interface cards and transceivers to enable communication between computing devices and other devices or communication networks.

[0049] Example 10 This embodiment also provides a computing device cluster. The computing device cluster includes at least one computing device. The computing device can be a server, such as a central server, an edge server, or a local server in a local data center. In some embodiments, the computing device can also be a terminal device such as a desktop computer, a laptop computer, or a smartphone.

[0050] The computing device cluster includes at least one computing device. The memory of one or more computing devices in the computing device cluster may store the same instructions for performing the methods and functions related to the computing devices in any of the above embodiments.

[0051] In some possible implementations, the memory of one or more computing devices in the computing device cluster may also store partial instructions for performing the methods and functions of the computing devices involved in any of the above embodiments. In other words, a combination of one or more computing devices can jointly execute the instructions for performing the methods and functions of the computing devices.

[0052] It should be noted that the memory in different computing devices within a computing device cluster can store different instructions, which are used to execute parts of the device's functions.

[0053] In some possible implementations, one or more computing devices in a computing device cluster can be connected via a network. This network can be a wide area network (WAN) or a local area network (LAN), etc. Two computing devices are connected to each other via the network. Specifically, they connect to the network through communication interfaces in each computing device.

[0054] Embodiments of this disclosure also provide a computer program product containing instructions that, when run on a computer, cause the computer to perform the methods and functions related to a computing device in any of the above embodiments.

[0055] Example 11 This embodiment also provides a computer-readable storage medium storing computer instructions that, when executed by a processor, cause the processor to perform the methods and functions of the computing device involved in any of the above embodiments.

[0056] Generally, the various embodiments of this disclosure can be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects can be implemented in hardware, while others can be implemented in firmware or software, which can be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of this disclosure are shown and described as block diagrams, flowcharts, or represented using some other illustration, it should be understood that the blocks, apparatuses, systems, techniques, or methods described herein can be implemented as, as non-limiting examples, in hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or controllers or other computing devices, or some combination thereof.

[0057] Example 12 This embodiment provides at least one computer program product tangibly stored on a non-transitory computer-readable storage medium. The computer program product includes computer-executable instructions, such as instructions included in program modules, which execute in a device on a target real or virtual processor to perform the processes / methods as described above with reference to the accompanying drawings. Typically, program modules include routines, programs, libraries, objects, classes, components, data structures, etc., that perform specific tasks or implement specific abstract data types. In various embodiments, the functionality of program modules can be combined or divided among program modules as needed. The machine-executable instructions for the program modules can execute within a local or distributed device. In a distributed device, the program modules can reside in both local and remote storage media.

[0058] Computer program code used to implement the methods of this disclosure may be written in one or more programming languages. This computer program code may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that when executed by the computer or other programmable data processing apparatus, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be performed. The program code may be executed entirely on a computer, partially on a computer, as a stand-alone software package, partially on a computer and partially on a remote computer, or entirely on a remote computer or server.

[0059] In the context of this disclosure, computer program code or related data may be carried on any suitable carrier to enable a device, apparatus, or processor to perform the various processes and operations described above. Examples of carriers include signals, computer-readable media, and so on. Examples of signals may include electrical, optical, radio, sound, or other forms of propagation signals, such as carrier waves, infrared signals, etc.

[0060] Computer-readable media can be any tangible medium that contains or stores programs for or relating to an instruction execution system, apparatus, or device, or a data storage device such as a data center containing one or more available media. Computer-readable media can be computer-readable signal media or computer-readable storage media. Computer-readable media can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. More detailed examples of computer-readable storage media include electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0061] The above-described embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and should all be included within the protection scope of this application.

Claims

1. A heating scheduling method based on user-side room temperature big data, characterized in that, Includes the following steps: Step 1: Collect real-time indoor temperature information on the user side, real-time heating information on the heating network side, and real-time environmental data; Step 2: Based on the pre-built reinforcement learning scheduling model, combined with real-time indoor temperature information on the user side, real-time heating information on the heating network side, and real-time environmental data, a personalized heating scheduling strategy is generated. Step 3: Provide heating on demand based on the personalized heating scheduling strategy.

2. The heating scheduling method based on user-side room temperature big data according to claim 1, characterized in that, The real-time indoor temperature information on the user side includes hourly indoor temperature, thermostatic valve operation records, and window opening duration; the real-time heating information includes supply water temperature, return water temperature, flow rate, and outdoor meteorological parameters; the real-time environmental parameters include temperature, humidity, and wind speed.

3. The heating scheduling method based on user-side room temperature big data according to claim 1, characterized in that, The pre-built reinforcement learning scheduling model includes a state space, an action space, and a reward function, where: The state space includes the deviation between the actual indoor temperature and the set temperature on the user side, outdoor environmental parameters, pipeline operation parameters, energy consumption data, and heating cost data. The operational space includes the heat source output power adjustment range, the flow distribution ratio of the pipeline branch, and the opening level of the intelligent temperature control valve. The reward function includes rewards for achieving room temperature targets, rewards for energy saving, and penalties for excessive consumption.

4. The heating scheduling method based on user-side room temperature big data according to claim 1, characterized in that, The objective function of the reinforcement learning scheduling model is: User room temperature compliance rate ≥95%, heating energy consumption per unit area reduced by ≥15%, and total heating cost reduced by ≥10%.

5. A heating scheduling method based on user-side room temperature big data according to claim 1, characterized in that, The personalized heating scheduling strategy includes: Heating parameters are dynamically adjusted based on the room temperature deviation and heating habits of individual users. Users within the same heating area are clustered by load, and heat source allocation and pipeline flow scheduling are optimized by group.

6. A heating dispatching system based on user-side room temperature big data, characterized in that, include: The data acquisition unit is used to collect real-time indoor temperature information on the user side, real-time heating information on the heating network side, and real-time environmental data. The scheduling strategy generation unit is used to generate personalized heating scheduling strategies based on a pre-built reinforcement learning scheduling model, combined with real-time indoor temperature information on the user side, real-time heating information on the heating network side, and real-time environmental data. The heating dispatching unit is used to provide heating on demand based on personalized heating dispatching strategies.

7. An electronic device, characterized in that, It includes a processor and a memory, the memory storing computer instructions that, when executed by the processor, cause the electronic device to perform the method of any one of claims 1 to 5.

8. A computing device cluster, characterized in that, It includes at least one computing device, each computing device including a processor and memory; The processor of the at least one computing device is configured to execute instructions stored in the memory of the at least one computing device to cause the cluster of computing devices to perform the method according to any one of claims 1 to 5.

9. A computer program product, characterized in that, The computer program product includes computer-executable instructions that, when executed, implement the method of any one of claims 1 to 5.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions that, when executed by a processor, implement the method of any one of claims 1 to 5.

Images