A heat supply system data processing control optimization method based on edge computing and related device

Abstract

The application provides a heat supply system data processing control optimization method based on edge computing and a related device, which comprises the following steps: collecting heat supply operation parameters of multiple pipe network nodes of a heat supply system; performing classified and summarized processing based on the collected heat supply operation parameters to establish a correlation relationship between the heat supply operation parameters; taking the real-time monitored heat supply operation parameters and the correlation relationship as inputs of a dynamic adjustment decision model to generate an optimization control instruction; and generating a local control instruction according to the optimization control instruction to control the operation of a heat supply device at the node. The application deploys an edge computing unit at the pipe network nodes to complete data collection, correlation analysis and local control instruction generation nearby, effectively reduces data transmission delay, reduces the load of cloud computing, and simultaneously realizes adaptive adjustment of a control strategy according to a dynamic adjustment decision model, thereby significantly improving the response speed, operation stability and energy utilization efficiency of the heat supply system.

Description

Technical Field

[0001] This invention belongs to the field of heating, specifically relating to a data processing control optimization method and related devices for heating systems based on edge computing. Background Technology

[0002] In traditional heating systems, data processing and control largely rely on a centralized cloud architecture. Various heating data must be aggregated to a cloud server for analysis and decision-making before instructions are distributed to individual execution units. This model has revealed numerous drawbacks in actual operation. Firstly, heating systems have numerous nodes, including heat sources, pipe networks, heat exchange stations, and user terminals, generating massive amounts of data with high real-time requirements. Long-distance data transmission can cause delays, leading to sluggish control command responses and making it difficult to adjust promptly according to changes in heating demand. Secondly, the centralized processing architecture concentrates network traffic load; if the cloud server fails or the network becomes congested, the entire heating system's operation may be affected, making stability difficult to guarantee.

[0003] Existing heating data processing technologies attempt to classify and summarize data, but most only reach the preliminary data organization level, failing to establish effective connections between highly similar data nodes and thus unable to deeply explore the value behind the data. Their ability to analyze and predict the operational patterns of heating systems is insufficient. Furthermore, control strategies often rely on experience-based static adjustment methods, making it difficult to dynamically adjust control strategies based on real-time monitored heating parameters, resulting in low heating efficiency and significant energy loss. While some technologies have introduced intelligent methods, there is still a gap in the integrated application of edge computing technology, failing to fully utilize the advantages of edge computing in areas such as local data processing and low-latency response. Summary of the Invention

[0004] The purpose of this invention is to provide a data processing and control optimization method and related device for heating systems based on edge computing. This invention solves the problems of data response delay, poor system stability, insufficient data value mining, and static control strategies in existing heating systems that adopt a centralized cloud architecture. These problems result in low heating efficiency, serious energy loss, and a lack of deep integration and application of edge computing technology in the heating field.

[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 data processing and control optimization method for a heating system based on edge computing, comprising the following steps: Collect heating operation parameters from multiple pipeline nodes of the heating system; Based on the collected heating operation parameters, the parameters are classified and summarized to establish the correlation between them. The real-time monitored heating operation parameters and their correlations are used as inputs to the dynamic adjustment decision model to generate optimized control commands. Based on the optimized control instructions, local control instructions are generated to control the operation of the heating equipment at the local node.

[0006] Preferably, the collected heating operation parameters are categorized and summarized to establish the correlation between them. Specifically, the method is as follows: The collected heating operation parameters are classified and summarized to obtain a structured dataset; Calculate the similarity between data points in the structured dataset; Based on the obtained similarity, the correlation between the obtained heating operation parameters is established.

[0007] Preferably, the collected heating operation parameters are classified and summarized to obtain a structured dataset. Specifically, the method is as follows: Preliminary rule-based classification is performed based on the source, type, and characteristics of the data to obtain classification results; The classification results are corrected using a machine learning model to obtain a structured dataset.

[0008] Preferably, the similarity between data in the obtained structured dataset is calculated using the following method: The obtained structured dataset is converted into standardized vectors; The similarity between the standardized vectors is calculated.

[0009] Preferably, the method for constructing the dynamic adjustment decision model is as follows: A basic regulation model was established based on the physical mechanism of the heating system; The basic regulation model is trained using historical heating operation parameters to obtain a dynamic regulation decision model.

[0010] Preferably, the heating operation parameters include temperature, pressure, and flow rate data.

[0011] Secondly, the present invention provides a data processing, control, and optimization system for a heating system based on edge computing, comprising: The parameter acquisition unit is used to collect heating operation parameters from multiple network nodes of the heating system. The correlation establishment unit is used to classify and summarize the collected heating operation parameters and establish the correlation between the heating operation parameters. The control command generation unit is used to take the real-time monitored heating operation parameters and the correlation as input to the dynamic adjustment decision model to generate optimized control commands; The equipment operation control unit is used to generate local control commands based on the optimized control commands to control the operation of the heating equipment at the node.

[0012] 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.

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

[0014] Fifthly, 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 data processing and control optimization method for heating systems based on edge computing. By using edge computing units to collect data from nearby pipeline nodes and generate control commands locally, it avoids the communication delays caused by long-distance data transmission and centralized decision-making in traditional cloud architectures. This significantly shortens the response cycle from parameter perception to equipment action, improving the heating system's adaptability to dynamic conditions such as heat load fluctuations. Simultaneously, edge nodes independently complete data acquisition, relationship establishment, and control command generation, reducing reliance on cloud servers. Even if cloud communication is interrupted, each node can maintain normal operation based on its local model, effectively enhancing the overall system stability and anti-interference capability. Furthermore, this invention explicitly establishes relationships between heating operation parameters after classifying and summarizing the collected parameters, using this as a key input to the decision-making model—a step that transcends traditional methods. Beyond traditional data processing, this approach reveals the inherent coupling patterns between parameters at different nodes, enabling control decisions to consider global collaborative changes and providing richer information support for precise regulation. Inputting real-time monitoring parameters and their correlations into the dynamic adjustment decision model means that control commands are continuously updated according to the actual operating status of the system. This allows the control strategy to adaptively track the time-varying characteristics of the heating system, avoiding overheating or underheating, and reducing ineffective energy consumption while ensuring user comfort. Finally, edge nodes complete data processing and local control command generation locally, only needing to upload essential core data to the cloud. This significantly reduces the need for long-distance transmission of massive amounts of raw heating data, alleviates network bandwidth pressure and central server computing load, and makes overall system resource allocation more rational and efficient. It is particularly suitable for large-scale heating networks with numerous nodes and high data density. Attached Figure Description

[0016] Figure 1This is a flowchart of a method involved in an embodiment of the present invention. Detailed Implementation

[0017] 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.

[0018] 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.

[0019] 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.

[0020] 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]."

[0021] 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.

[0022] 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.

[0023] Example 1 This embodiment provides a data processing and control optimization method for a heating system based on edge computing. Edge computing units are deployed at various nodes of the heating system's pipeline network, including heat sources, heat exchange stations, and key pipeline sections. These edge computing units possess data acquisition, analysis, and decision-making capabilities, enabling them to process heating data from their local node and surrounding area locally. Each edge computing unit is equipped with a high-performance processor and storage device, allowing it to collect various heating parameters such as temperature, pressure, and flow rate in real time, and perform preliminary data analysis and processing.

[0024] Specifically, it includes the following steps: Step 1: Collect heating operation parameters from multiple network nodes of the heating system; Step 2: Based on the collected heating operation parameters, classify and summarize them to establish the correlation between the heating operation parameters; In this embodiment, preliminary rule classification is first performed based on the source, type, and characteristics of the data to obtain the classification results; Then, machine learning models are used to optimize and correct the classification results to obtain a structured dataset; this ensures the accuracy and efficiency of data classification and improves data accessibility and management efficiency.

[0025] The heating parameter data of each node in the structured dataset is transformed into standardized vectors. The similarity value between the vectors is calculated using a similarity algorithm. When the similarity value exceeds a set threshold, the data is considered to have high similarity, and an association link is established to facilitate subsequent analysis and data mining of the relationship between highly similar nodes, providing deeper information for the analysis of the operating rules of the heating system.

[0026] Step 3: Use the real-time monitored heating operation parameters and the aforementioned correlation as input to the dynamic adjustment decision model to generate optimized control commands.

[0027] In this embodiment, a basic regulation model is established based on the physical mechanism of the heating system; The basic regulation model is trained using historical heating operation parameters to obtain a dynamic regulation decision model.

[0028] Step 4: Generate local control commands based on the optimized control commands to control the operation of the heating equipment at the node.

[0029] By predicting the changing trends of heating demand and combining them with the current operating status of the system, the system automatically adjusts control parameters, generates optimal control commands, and achieves dynamic regulation of the heating system.

[0030] In this embodiment, the local control command is to automatically adjust the water supply temperature, water supply pressure and / or valve opening of the heat exchange station.

[0031] Example 2 This embodiment provides a data processing, control, and optimization system for a heating system based on edge computing, comprising: The parameter acquisition unit is used to collect heating operation parameters from multiple network nodes of the heating system. The correlation establishment unit is used to classify and summarize the collected heating operation parameters and establish the correlation between the heating operation parameters. The control command generation unit is used to take the real-time monitored heating operation parameters and the correlation as input to the dynamic adjustment decision model to generate optimized control commands; The equipment operation control unit is used to generate local control commands based on the optimized control commands to control the operation of the heating equipment at the node.

[0032] Example 3 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.

[0033] 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).

[0034] 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).

[0035] 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).

[0036] 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.

[0037] 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.

[0038] Example 4 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.

[0039] 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.

[0040] Example 5 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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 data processing and control optimization method for a heating system based on edge computing, characterized in that, Includes the following steps: Collect heating operation parameters from multiple pipeline nodes of the heating system; Based on the collected heating operation parameters, the parameters are classified and summarized to establish the correlation between them. The real-time monitored heating operation parameters and their correlations are used as inputs to the dynamic adjustment decision model to generate optimized control commands. Based on the optimized control instructions, local control instructions are generated to control the operation of the heating equipment at the local node.

2. The edge computing-based data processing and control optimization method for a heating system according to claim 1, characterized in that, Based on the collected heating operation parameters, the relationships between these parameters are categorized and summarized. The specific method is as follows: The collected heating operation parameters are classified and summarized to obtain a structured dataset; Calculate the similarity between data points in the structured dataset; Based on the obtained similarity, the correlation between the obtained heating operation parameters is established.

3. The edge computing-based data processing and control optimization method for a heating system according to claim 2, characterized in that, The collected heating operation parameters are categorized and summarized to obtain a structured dataset. The specific method is as follows: Preliminary rule-based classification is performed based on the source, type, and characteristics of the data to obtain classification results; The classification results are corrected using a machine learning model to obtain a structured dataset.

4. The edge computing-based data processing and control optimization method for a heating system according to claim 2, characterized in that, The method for calculating the similarity between data points in the structured dataset is as follows: The obtained structured dataset is converted into standardized vectors; The similarity between the standardized vectors is calculated.

5. The edge computing-based data processing and control optimization method for a heating system according to claim 1, characterized in that, The method for constructing a dynamic adjustment decision model is as follows: A basic regulation model was established based on the physical mechanism of the heating system; The basic regulation model is trained using historical heating operation parameters to obtain a dynamic regulation decision model.

6. The data processing and control optimization method for a heating system based on edge computing according to claim 1, characterized in that, The heating operation parameters include temperature, pressure, and flow rate data.

7. A data processing, control, and optimization system for a heating system based on edge computing, characterized in that, include: The parameter acquisition unit is used to collect heating operation parameters from multiple network nodes of the heating system. The correlation establishment unit is used to classify and summarize the collected heating operation parameters and establish the correlation between the heating operation parameters. The control command generation unit is used to take the real-time monitored heating operation parameters and the correlation as input to the dynamic adjustment decision model to generate optimized control commands; The equipment operation control unit is used to generate local control commands based on the optimized control commands to control the operation of the heating equipment at the node.

8. An electronic device, characterized in that, The device 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 6.

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 6.

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 6.

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