Intelligent self-adaptive heat supply regulation and control system and method

Abstract

The invention provides an intelligent self-adaptive heat supply regulation and control system and method, and the method comprises the following steps: taking obtained user outdoor environment data, user side heat load demands and user behavior data as the input of a pre-constructed prediction model, and predicting to obtain a heat load curve in future time; the obtained user temperature preference difference and the thermal load curve in the future time are used as input of a pre-constructed multi-objective optimization algorithm, and an optimal regulation and control strategy of the pipe network is obtained; the pipe network is regulated and controlled based on the optimal regulation and control strategy; according to the method, collaborative optimization is realized in three dimensions of energy conservation, user experience and system robustness, and an effective technical path is provided for low-carbon transformation in the heat supply industry.

Description

Technical Field

[0001] The present invention belongs to the intersection of heating engineering and intelligent control technology, and specifically relates to an intelligent adaptive heating control system and method. Background Art

[0002] In the process of global energy structure transformation towards low carbon, heating system is the main carrier of building energy consumption (accounting for 40%-60% of total building energy consumption), and its efficiency and intelligent upgrade have become key links. Traditional heating technology uses fossil energy as the main heat source, facing the problems of low energy utilization rate (average thermal efficiency is only 68-72%), high carbon emission intensity (carbon emission per unit area of ​​heating is 15-20kgCO2 / m 2 a) and poor user comfort. Especially in the context of frequent extreme weather events (for example, the frequency of extreme cold waves has increased by 30% globally in the past five years), the traditional system's extensive control model of fixed time and temperature can no longer adapt to dynamic changes in thermal demand. The 15%-20% energy waste during off-peak hours and room temperature fluctuations of more than ±3°C in extreme weather conditions are particularly prominent.

[0003] From the perspective of technological evolution, existing smart heating solutions suffer from three major technical gaps: First, data perception is limited in dimension, covering only temperature parameters. It lacks the simultaneous collection of multiple data sources, such as humidity (which affects human thermal comfort by up to 20%) and user activity (dynamic heat demand fluctuates by 10-15%). This results in a deviation of more than 25% between the heat load model and actual demand. Second, predictive control accuracy is insufficient. Traditional ARIMA models have a temperature prediction error of more than 4°C for sudden weather events, and the delay from data collection to device response is greater than 15 minutes, creating a vicious cycle of "control lag and supply and demand mismatch." Third, control execution lacks coordination. The control logic of the heat source, pipeline network, and user side operates independently, resulting in a room temperature difference of up to 5-8°C between the end and the head end. The hydraulic imbalance problem of "hot near and cold far" leads to a user complaint rate of over 20%.

[0004] The current heating industry is facing the need to transform from "meeting basic heating needs" to "providing precise and comfortable services". Traditional active temperature control technologies (such as electric heating compensation and mechanical valve group adjustment) have inherent defects such as high energy consumption (auxiliary heating energy consumption accounts for 10-15%), high system complexity (equipment maintenance costs increase by 8-10% annually) and lack of personalized response capabilities. Summary of the Invention

[0005] The purpose of the present invention is to provide an intelligent adaptive heating control system and method, which solves the defects of existing heating systems that are difficult to achieve coordinated control of multi-scale thermal dynamic processes, resulting in low energy efficiency and poor control accuracy.

[0006] In order to achieve the above object, the technical solution adopted in the present invention is: The present invention provides an intelligent adaptive heating control method, comprising the following steps: The acquired user outdoor environment data, user-side heat load demand, and user behavior data are used as inputs to the pre-built prediction model to predict the heat load curve in the future. The obtained user temperature preference differences and the future heat load curve are used as inputs to the pre-built multi-objective optimization algorithm to obtain the optimal control strategy for the pipe network. The pipeline network is regulated based on the optimal control strategy.

[0007] Preferably, the pre-built prediction model is constructed in the following manner: Build a Transformer model based on the distributed TensorFlow architecture; The constructed Transformer model is trained using the acquired historical heating data, historical environmental data, and user behavior data to obtain a pre-built prediction model.

[0008] Preferably, the pre-built multi-objective optimization algorithm is constructed as follows: A multi-objective optimization algorithm is constructed with energy efficiency, comfort and stability as the goals; The constraints of the objective function are that the indoor temperature meets the preset temperature range and the pressure on the main board meets the preset pressure range.

[0009] Preferably, the pre-built multi-objective optimization algorithm is solved using a particle swarm optimization algorithm to obtain the optimal control strategy of the pipeline network.

[0010] Preferably, the user's temperature preference difference is obtained by: Define the state space S = {time, temperature, humidity, personnel presence, activity intensity}, and the action space A = {heating up, cooling down, maintaining, dormancy}; Build a user-personalized thermal comfort model; The user personalization model is used to obtain the user's temperature preference differences.

[0011] In a second aspect, the present invention provides an intelligent adaptive heating control system, comprising: The heat load prediction unit is used to use the acquired user outdoor environment data, user-side heat load demand, and user behavior data as inputs to a pre-built prediction model to predict the heat load curve in the future; A control strategy formulation unit, which uses the acquired user temperature preference differences and the future heat load curve as inputs to a pre-built multi-objective optimization algorithm to obtain the optimal control strategy for the pipe network; The control unit is used to control the pipeline network based on the optimal control strategy.

[0012] In a third aspect, the present invention provides an electronic device comprising a processor and a memory, wherein the memory stores computer instructions, and when the computer instructions are executed by the processor, the electronic device executes the described method.

[0013] In a fourth aspect, the present invention provides a computing device cluster, comprising at least one computing device, each computing device comprising 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, so that the computing device cluster performs the method.

[0014] In a fifth aspect, the present invention provides a computer program product, which includes computer-executable instructions, and the computer-executable instructions implement the method when executed.

[0015] In a sixth aspect, the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions implement the described method when executed by a processor.

[0016] Compared with the prior art, the present invention has the following beneficial effects: This invention provides an intelligent, adaptive heating control method that achieves dynamic optimization and personalized services for heating systems. It uses a predictive model to proactively calculate heat load curves. By integrating outdoor environmental parameters, user heating behavior, and real-time demand data, it significantly improves load forecasting accuracy, laying a data foundation for subsequent control. This predictive capability enables the system to anticipate heat demand fluctuations 24-48 hours in advance, effectively alleviating the energy waste caused by delayed response times in traditional systems. The introduction of a multi-objective optimization algorithm overcomes the limitations of single-objective temperature control. Based on the heat load curve and incorporating user-specific temperature preferences, this algorithm constructs a multi-dimensional evaluation system encompassing energy efficiency, comfort compliance, and network balance. This method improves the energy efficiency of the heating system while ensuring user comfort, while also reducing inefficient heat loss through differentiated temperature control strategies. Finally, a closed-loop control mechanism forms a technical closed loop for sustainable optimization. By directly feeding back the optimal strategy to the network execution end, the system implements a real-time iteration of "prediction-optimization-execution-feedback." Its self-learning capabilities exponentially improve control accuracy over time.

[0017] Overall, this technical solution achieves coordinated optimization in the three dimensions of energy conservation, user experience and system robustness, providing an effective technical path for the low-carbon transformation of the heating industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a flowchart of an embodiment of the present invention. DETAILED DESCRIPTION

[0019] In the following description, specific details such as specific system structures and techniques are provided for purposes of illustration rather than limitation to facilitate a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to avoid obscuring the description of the present application with unnecessary detail.

[0020] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or collections thereof.

[0021] It will also be understood that the term "and / or" used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0022] As used in this specification and the appended claims, the term "if" can be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]," depending on the context.

[0023] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0024] References to "one embodiment" or "some embodiments" in this specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present application. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in other embodiments" appearing in various places in 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 "including," "comprising," "having," and variations thereof all mean "including but not limited to," unless otherwise specifically emphasized.

[0025] Example 1 This embodiment provides an intelligent adaptive heating control method, including the following steps: The acquired user outdoor environment data, user-side heat load demand, and user behavior data are used as inputs to the pre-built prediction model to predict the heat load curve in the future. The obtained user temperature preference differences and the future heat load curve are used as inputs to the pre-built multi-objective optimization algorithm to obtain the optimal control strategy for the pipe network. The pipeline network is regulated based on the optimal control strategy.

[0026] Example 2 Based on Example 1, this embodiment provides an intelligent adaptive heating control method, a pre-built prediction model, and a specific construction method is: Build a Transformer model based on the distributed TensorFlow architecture; The constructed Transformer model is trained using the acquired historical heating data, historical environmental data, and user behavior data to obtain a pre-built prediction model.

[0027] Example 3 Based on Example 1, this embodiment provides an intelligent adaptive heating control method, a pre-built multi-objective optimization algorithm, and a specific construction method is: A multi-objective optimization algorithm is constructed with energy efficiency, comfort and stability as the goals; The constraints of the objective function are that the indoor temperature meets the preset temperature range and the pressure on the main board meets the preset pressure range.

[0028] Example 4 On the basis of Example 1, this embodiment provides an intelligent adaptive heating control method, which uses a particle swarm algorithm to solve the pre-built multi-objective optimization algorithm to obtain the optimal control strategy of the pipe network.

[0029] Example 5 Based on Example 1, this embodiment provides an intelligent adaptive heating control method. The specific method for obtaining the temperature preference difference of users is as follows: Define the state space S = {time, temperature, humidity, personnel presence, activity intensity}, and the action space A = {heating up, cooling down, maintaining, dormancy}; Build a user-personalized thermal comfort model; The user personalization model is used to obtain the user's temperature preference differences.

[0030] Example 6 This embodiment provides an intelligent adaptive heating control method, comprising the following steps: Step 1: Deploy a multi-dimensional data collection network Environmental layer monitoring unit: Deployed on the user side, it includes a miniature weather station (model WS-MINI-5D) with integrated temperature sensors (accuracy ±0.1°C), humidity sensors (accuracy ±3% RH), anemometers, and pyranometers. It is arranged in a cellular configuration with a radius of 500 meters to collect outdoor environmental data in real time.

[0031] Pipeline network layer sensing unit: includes pressure-flow composite sensors (accuracy ±0.5%) installed on main pipes above DN100, and wireless temperature sensors (sampling frequency ≤10 seconds) installed at branch pipe nodes, achieving data transmission delay of less than 200ms through the LoRaWAN network.

[0032] User-level interaction unit: This includes the intelligent temperature control terminal (model HT-CT100), which integrates millimeter-wave radar and NLP voice modules to monitor occupant presence, activity intensity, and temperature preference in real time to build a personalized thermal comfort model for the user.

[0033] Step 2: Intelligent Decision-Making Center Build a Transformer model based on the distributed TensorFlow architecture; The constructed Transformer model is trained using the acquired historical heating data, historical environmental data, and user behavior data to obtain a pre-built prediction model. The acquired user outdoor environment data, user-side heat load demand, and user behavior data are used as inputs to the pre-built prediction model to predict the heat load curve in the future. Define the state space S = {time, temperature, humidity, personnel presence, activity intensity}, and the action space A = {heating up, cooling down, maintaining, dormancy}; Build a user-personalized thermal comfort model; Obtaining the user's temperature preference differences using the user personalization model; A multi-objective optimization algorithm is constructed with energy efficiency, comfort and stability as the goals; The constraints of the objective function include the indoor temperature meeting 18-24°C and the pressure on the main board meeting 0.3-0.6MPa; The obtained user temperature preference differences and the future heat load curve are used as inputs to the pre-built multi-objective optimization algorithm to obtain the optimal control strategy for the pipe network. The pipeline network is regulated based on the optimal control strategy.

[0034] Through the above-mentioned technical solution, the present invention has achieved a thermal efficiency improvement of 92.3% in a 500,000 square meter demonstration community, a room temperature control accuracy of ±0.4°C, a 21.7% reduction in energy consumption, and an 83% decrease in user complaint rate, effectively solving the problems of energy waste, control lag, and insufficient comfort in traditional heating systems.

[0035] Example 7 This embodiment provides an intelligent adaptive heating control system, including: The heat load prediction unit is used to use the acquired user outdoor environment data, user-side heat load demand, and user behavior data as inputs to a pre-built prediction model to predict the heat load curve in the future; A control strategy formulation unit, which uses the acquired user temperature preference differences and the future heat load curve as inputs to a pre-built multi-objective optimization algorithm to obtain the optimal control strategy for the pipe network; The control unit is used to control the pipeline network based on the optimal control strategy.

[0036] Example 8 This embodiment also provides a computing device. The computing device includes a bus, a processor, a memory, and a communication interface. The processor, the memory, and the 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 memories in the computing device.

[0037] A bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus. Buses can be categorized as address buses, data buses, control buses, and so on. For ease of presentation, a bus can include the pathways that transmit information between various components of a computing device (e.g., memory, processor, and communication interfaces).

[0038] The processor may include any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a tensor processing unit (TPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a microprocessor (MP), or a digital signal processor (DSP).

[0039] The memory may include volatile memory, such as random access memory (RAM). The processor may also include non-volatile memory, such as read-only memory (ROM), flash memory, hard disk drive (HDD), or solid state drive (SSD).

[0040] The memory stores executable program code, and the processor executes the executable program code to implement the functions of the aforementioned units, thereby implementing, for example, the method described in Example 1. That is, the memory may store instructions for the methods and functions of the computing device described in any of the above embodiments.

[0041] The communication interface uses a transceiver module such as, but not limited to, a network interface card or a transceiver to implement communication between the computing device and other devices or a communication network.

[0042] Example 9 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.

[0043] 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 executing the method and functions related to the computing device in any of the above embodiments.

[0044] In some possible implementations, the memory of one or more computing devices in the computing device cluster may also store partial instructions for executing the methods and functions related to the computing devices in any of the above embodiments. In other words, the combination of one or more computing devices can jointly execute instructions for executing the methods and functions of the computing devices.

[0045] It should be noted that the memories in different computing devices in the computing device cluster may store different instructions, each for executing part of the functions of the apparatus.

[0046] In some possible implementations, one or more computing devices in a computing device cluster may be connected via a network. The network may be a wide area network (WAN) or a local area network (LAN). Two computing devices are connected via the network. Specifically, the connection to the network is achieved via a communication interface in each computing device.

[0047] An embodiment of the present disclosure further provides a computer program product comprising instructions, which, when executed on a computer, enables the computer to execute the method and functions involving a computing device in any of the above embodiments.

[0048] Example 10 This embodiment further provides a computer-readable storage medium having computer instructions stored thereon. When a processor executes the instructions, the processor executes the methods and functions related to the computing device in any of the above embodiments.

[0049] In general, various embodiments of the present disclosure may be implemented in hardware or dedicated circuitry, software, logic, or any combination thereof. Some aspects may be implemented in hardware, while other aspects may be implemented in firmware or software, which may be executed by a controller, microprocessor, or other computing device. Although various aspects of the embodiments of the present disclosure are shown and described as block diagrams, flow charts, or using some other pictorial representation, it should be understood that the blocks, devices, systems, techniques, or methods described herein may be implemented as, by way of non-limiting example, hardware, software, firmware, dedicated circuitry or logic, general-purpose hardware or a controller or other computing device, or some combination thereof.

[0050] Example 11 The present 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 are executed in a device on a real or virtual processor of a target to perform the process / method described above with reference to the accompanying drawings. Generally, 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 between program modules as needed. The machine-executable instructions for the program modules can be executed in local or distributed devices. In distributed devices, program modules can be located in local and remote storage media.

[0051] The computer program code for implementing the disclosed method can be written in one or more programming languages. These computer program codes can be provided to the processor of a general-purpose computer, a special-purpose computer or other programmable data processing device so that the program code, when executed by the computer or other programmable data processing device, causes the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on a computer, partially on a computer, as an independent software package, partially on a computer and partially on a remote computer or entirely on a remote computer or server.

[0052] In the context of the present disclosure, computer program code or related data may be carried by 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 the like. Examples of signals may include electrical, optical, radio, acoustic, or other forms of propagated signals, such as carrier waves, infrared signals, and the like.

[0053] A computer-readable medium may be any tangible medium containing or storing a program 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. A computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium. A computer-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination thereof. More detailed examples of computer-readable storage media include an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical storage device, a magnetic storage device, or any suitable combination thereof.

[0054] The above-described embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present application, and should all be included in the scope of protection of the present application.

Claims

1. An intelligent adaptive heating control method, characterized in that: The following steps are involved: The acquired user outdoor environment data, user-side heat load demand, and user behavior data are used as inputs to the pre-built prediction model to predict the heat load curve in the future. The obtained user temperature preference differences and the future heat load curve are used as inputs to the pre-built multi-objective optimization algorithm to obtain the optimal control strategy for the pipe network. The pipeline network is regulated based on the optimal control strategy.

2. The intelligent adaptive heating control method according to claim 1, characterized in that: The pre-built prediction model is constructed as follows: Build a Transformer model based on the distributed TensorFlow architecture; The constructed Transformer model is trained using the acquired historical heating data, historical environmental data, and user behavior data to obtain a pre-built prediction model.

3. The intelligent adaptive heating control method according to claim 1, characterized in that: Pre-built multi-objective optimization algorithm, the specific construction method is: A multi-objective optimization algorithm is constructed with energy efficiency, comfort and stability as the goals; The constraints of the objective function are that the indoor temperature meets the preset temperature range and the pressure on the main board meets the preset pressure range.

4. The intelligent adaptive heating control method according to claim 1, characterized in that: The particle swarm optimization algorithm is used to solve the pre-built multi-objective optimization algorithm to obtain the optimal control strategy of the pipeline network.

5. The intelligent adaptive heating control method according to claim 1, characterized in that: The temperature preference differences of users are obtained by: Define the state space S = {time, temperature, humidity, personnel presence, activity intensity}, and the action space A = {heating up, cooling down, maintaining, dormancy}; Build a user-personalized thermal comfort model; The user personalization model is used to obtain the user's temperature preference differences.

6. An intelligent adaptive heating control system, characterized in that: include: The heat load prediction unit is used to use the acquired user outdoor environment data, user-side heat load demand, and user behavior data as inputs to a pre-built prediction model to predict the heat load curve in the future; A control strategy formulation unit, which uses the acquired user temperature preference differences and the future heat load curve as inputs to a pre-built multi-objective optimization algorithm to obtain the optimal control strategy for the pipe network; The control unit is used to control the pipeline network based on the optimal control strategy.

7. An electronic device, characterized in that: The electronic device comprises a processor and a memory, wherein computer instructions are stored in the memory. When the computer instructions are executed by the processor, the electronic device executes the method according to any one of claims 1 to 5.

8. A computing device cluster, characterized in that: 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 a memory of the at least one computing device, so that the computing device cluster performs the method according to any one of claims 1 to 5.

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

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

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