Intelligent energy-saving control method for temperature regulation equipment

Abstract

This invention discloses an intelligent energy-saving control method for temperature regulating equipment, relating to the field of intelligent control technology. The method includes: acquiring the temperature regulating pipelines connected to the temperature regulating equipment; constructing a regulating pipeline network based on the pipeline connection relationships; monitoring temperature regulation through the regulating pipeline network to acquire a temperature monitoring dataset; performing pipeline transport loss analysis on the temperature monitoring dataset to acquire a pipeline loss prediction model; planning the pipeline network based on the pipeline loss prediction model and preset pipeline loss indicators to output N regulating pipelines; setting N temperature regulators for each of the N regulating pipelines, and controlling the temperature of each of the N regulating pipelines based on the N temperature regulators. This method solves the technical problem of large energy loss during pipeline transport in existing temperature regulating equipment. By segmenting and regulating the pipeline temperature, it reduces energy loss during pipeline transport, achieving the technical effect of energy-saving control.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and more specifically to an intelligent energy-saving control method for temperature regulation equipment. Background Technology

[0002] In the current fields of energy management and industrial control, the importance of temperature control equipment is self-evident. They are widely used in various industrial processes, from chemical production and pharmaceutical manufacturing to food processing and energy conversion, all of which rely on the precise control provided by temperature control equipment. However, with the escalating global energy crisis and increased environmental awareness, the energy efficiency and energy consumption problems faced by traditional temperature control equipment during operation are becoming increasingly prominent. Improving the energy efficiency and reducing energy consumption of temperature control equipment has become an urgent problem to be solved. Traditional temperature control equipment often suffers from low energy efficiency and increased unnecessary energy consumption due to factors such as unreasonable pipeline layout and significant energy losses during transmission. Summary of the Invention

[0003] This application provides an intelligent energy-saving control method for temperature regulation equipment, which solves the technical problem of large energy loss in the pipeline transportation process of temperature regulation equipment in the prior art.

[0004] In view of the above problems, embodiments of this application provide an intelligent energy-saving control method for temperature regulation equipment.

[0005] This application provides an intelligent energy-saving control method for temperature regulation equipment, the method comprising:

[0006] The process involves: acquiring the temperature regulation pipelines connected to the temperature regulation equipment; constructing a regulation pipeline network based on the pipeline connection relationships; monitoring temperature regulation by connecting the regulation pipeline network to obtain a temperature monitoring dataset; introducing a pipeline loss function to analyze pipeline transport loss in the temperature monitoring dataset to obtain a pipeline loss prediction model; planning the pipeline network based on the pipeline loss prediction model and a preset pipeline loss index to output N regulation pipelines, wherein the pipeline loss index corresponding to each regulation pipeline is less than the preset pipeline loss index; setting N temperature regulators for each of the N regulation pipelines, and controlling the temperature of each of the N regulation pipelines based on the N temperature regulators, wherein the N temperature regulators are integrated and controlled by the temperature regulation equipment.

[0007] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0008] First, the temperature regulation pipelines connected to the temperature regulation equipment are obtained, and a regulation pipeline network is constructed based on the pipeline connection relationships. Temperature regulation is monitored through the connection of the regulation pipeline network to obtain a temperature monitoring dataset. Next, a pipeline loss function is introduced to analyze the pipeline transport loss of the temperature monitoring dataset, obtaining a pipeline loss prediction model. Then, based on the pipeline loss prediction model and preset pipeline loss indicators, pipeline planning is performed on the regulation pipeline network, outputting N regulation pipelines, where the pipeline loss indicator for each regulation pipeline is less than the preset pipeline loss indicator. Finally, N temperature controllers are set up for each of the N regulation pipelines, and the temperature of each of the N regulation pipelines is controlled by the N temperature controllers, which are integrated and controlled by the temperature regulation equipment. This solves the technical problem of large energy loss in the pipeline transport process of temperature regulation equipment in the prior art. By segmenting the pipeline temperature, the energy loss in pipeline transport is reduced, achieving the technical effect of energy-saving control. Attached Figure Description

[0009] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0010] Figure 1 This is a schematic flowchart of an intelligent energy-saving control method for temperature regulation equipment provided in an embodiment of this application;

[0011] Figure 2 This is a schematic flowchart illustrating the pipeline transport loss analysis in the intelligent energy-saving control method for temperature regulation equipment provided in this application embodiment. Detailed Implementation

[0012] This application provides an intelligent energy-saving control method for temperature control equipment, which solves the technical problem of large energy loss in the pipeline transportation process of temperature control equipment in the prior art.

[0013] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0014] It should be noted that the terms "comprising" and "having" are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or server that includes a series of steps or units is not necessarily limited to those steps or units that are explicitly listed, but may include other steps or modules that are not explicitly listed or that are inherent to these processes, methods, products, or devices. Example 1

[0015] like Figure 1 As shown in the embodiment of this application, an intelligent energy-saving control method for temperature regulation equipment is provided, wherein the method includes:

[0016] Obtain the temperature regulating pipeline connected to the temperature regulating device.

[0017] Temperature control piping is an indispensable part of temperature control, responsible for delivering heat or cold from the temperature control equipment to the area requiring regulation. Imaging technologies, such as infrared thermal imagers, are used to scan the temperature control piping connected to the temperature control equipment, thereby obtaining information about the piping.

[0018] Based on the pipe connection relationship of the temperature regulating pipes, a regulating pipe network is constructed.

[0019] After obtaining information about the temperature regulation piping connected to the temperature control equipment, a piping network is constructed in modeling software based on the piping connections, including the location, attributes, and parameters of each pipe segment and node. The piping network is a model reflecting the connections of the temperature regulation piping, including key nodes such as the start and end points, branch points, and junction points of the pipes, as well as the pipe segments connecting these nodes. Furthermore, the piping network is not only a static model but can also be dynamically updated and adjusted according to actual needs. For example, when changes occur in the piping or new pipes are added, these changes can be reflected by modifying the network model. By constructing the piping network, the layout and flow path of the temperature regulation piping can be displayed, providing a foundation for subsequent analysis, optimization, and control.

[0020] Connect the regulating pipeline network to monitor temperature regulation and obtain a temperature monitoring dataset.

[0021] Temperature sensors are deployed at multiple locations within the regulating pipeline network to measure temperature data in the pipelines in real time. Temperature monitoring is performed by connecting these sensors to the regulating pipeline network, resulting in a temperature monitoring dataset. This dataset contains temperature information from various locations within the regulating pipeline network, providing a data foundation for pipeline planning and temperature control.

[0022] A pipeline loss function is introduced to analyze pipeline transport loss in the temperature monitoring dataset, and a pipeline loss prediction model is obtained.

[0023] To optimize the energy efficiency of temperature control equipment and reduce energy loss during pipeline transportation, a pipeline loss function needs to be introduced to analyze pipeline transportation losses in temperature monitoring datasets. This allows for the construction of a pipeline loss prediction model to predict and assess energy losses under different pipeline configurations and operating conditions. Based on the pipeline loss function, the prediction model can predict the corresponding energy loss value according to a given pipeline configuration and operating state. The pipeline loss function is a function that comprehensively considers the influence of factors such as pipeline material, diameter, length, fluid properties, flow rate, and temperature on energy loss, based on principles of fluid mechanics, thermodynamics, and heat transfer. By using these factors as input variables and combining them with temperature data from the temperature monitoring dataset, the energy loss value of the pipeline can be calculated.

[0024] Furthermore, such as Figure 2 As shown, a pipeline loss function is introduced to analyze pipeline transport losses in the temperature monitoring dataset. The method includes:

[0025] The pipeline loss function is introduced to perform heat loss analysis on the temperature monitoring dataset to obtain heat conduction loss data, convection loss data, and environmental impact loss data. Based on the heat conduction loss data, the convection loss data, and the environmental impact loss data, a mapping relationship between pipeline length and pipeline loss monitoring indicators is established. According to the mapping relationship, a pipeline loss prediction model is generated. The pipeline loss prediction model is then connected to the pipeline planning module.

[0026] Heat loss analysis is performed on temperature monitoring datasets based on pipeline loss functions to obtain data on heat conduction loss, convection loss, and environmental impact loss. Specifically, by analyzing the temperature gradient and the thermal conductivity of the pipeline material in the temperature monitoring dataset, energy loss due to heat conduction can be calculated. Convection loss is caused by convective heat transfer between the fluid inside the pipeline and the external environment; by analyzing fluid velocity, ambient temperature, and the convective heat transfer coefficient of the pipeline surface, convection loss can be estimated. Environmental factors, such as wind direction, wind speed, and solar radiation, may also cause energy loss to the pipeline; environmental impact loss data is obtained by combining temperature monitoring data with environmental loss coefficients. Based on the heat conduction loss, convection loss, and environmental impact loss data, the relationship between these data and parameters such as pipeline length, diameter, material, and fluid properties is analyzed. Using statistical methods or machine learning algorithms, such as regression analysis and neural networks, a mapping relationship between pipeline length and pipeline loss monitoring indicators (such as total heat loss rate and heat loss per unit length) is established. This mapping relationship reflects the heat loss characteristics under different pipeline configurations. Based on the established mapping relationship, a pipeline loss prediction model is generated. The pipeline loss prediction model can predict the corresponding heat loss value based on given pipeline parameters (such as length, diameter, material, etc.) and operating conditions (such as fluid flow rate, temperature, etc.). The pipeline planning module is used to divide the pipeline. By connecting the pipeline loss prediction model to the pipeline planning module, the pipeline loss prediction model can be used to evaluate the heat loss under different division schemes during the pipeline planning process, thereby selecting the pipeline configuration scheme with the minimum energy loss and the highest efficiency.

[0027] Furthermore, the method further includes incorporating the pipeline loss function to perform heat loss analysis on the temperature monitoring dataset, and also includes:

[0028] Obtain pipeline material information, pipeline layer structure, and pipeline geometry data; perform heat loss impact analysis based on the pipeline material information, pipeline layer structure, and pipeline geometry data to obtain loss impact indicators; and perform feedback optimization of the pipeline loss prediction model according to the loss impact indicators.

[0029] Preferably, information on pipeline materials, pipeline layer structure, and pipeline geometry is acquired. Pipeline material information includes physical properties such as material type, thermal conductivity, and coefficient of thermal expansion. Different materials have different thermal properties, which directly affect the heat loss of the pipeline. Pipeline layer structure refers to the multi-layered structure of the pipeline, such as insulation layers and heat preservation layers. The design of the pipeline layer structure has a significant impact on heat conduction and heat loss; for example, multi-layered insulation structures can more effectively reduce heat loss. Pipeline geometry data includes geometric parameters such as pipeline length, diameter, and tortuosity. These geometric parameters determine the flow state and heat exchange of the fluid in the pipeline. Combining the pipeline material information, layer structure, and geometry data, and using basic thermal principles such as heat conduction, convection, and radiation, a heat loss impact analysis of the pipeline system is performed. This analysis examines the influence of different parameters on heat loss, such as the effect of material thermal conductivity on heat conduction loss and the effect of pipeline diameter on fluid velocity and convection loss. Through heat loss impact analysis, a series of loss impact indicators are obtained, such as heat conduction loss coefficient, convection loss coefficient, and environmental impact factors. These indicators can quantify the degree of influence of different factors on heat loss. The obtained loss impact indicators are used as feedback inputs into the pipeline loss prediction model. By adjusting the model parameters, the model can more accurately reflect the heat loss of the pipeline system.

[0030] Based on the pipeline loss prediction model and the preset pipeline loss index, pipeline planning is performed on the regulating pipeline network, and N regulating pipelines are output, wherein the pipeline loss index corresponding to each regulating pipeline is less than the preset pipeline loss index.

[0031] When planning a regulating pipeline network, combining a pipeline loss prediction model with preset pipeline loss indicators can ensure that the planned regulating pipelines meet both functional requirements and energy efficiency requirements. Specifically, based on actual conditions and energy efficiency requirements, preset pipeline loss indicators are set. These indicators serve as standards for evaluating the suitability of the regulating pipeline plan. Using the pipeline loss prediction model, loss prediction is performed on each pipeline configuration in the regulating pipeline network. The predicted pipeline loss indicators are compared with the preset indicators, and pipeline configurations that meet the preset indicators (i.e., those with pipeline losses less than the preset indicators) are selected. From these pipeline configurations that meet the preset indicators, N pipelines are chosen as regulating pipelines.

[0032] Furthermore, before planning the pipeline network based on the pipeline loss prediction model and preset pipeline loss indicators, the following steps are included:

[0033] The regulating pipeline network is analyzed to determine whether it includes multiple pipeline transmission lines. If it does, the pipeline loss prediction model is invoked to predict the multiple pipeline transmission lines, and multiple pipeline loss prediction indices are output. The pipeline transmission lines with prediction indices greater than the preset pipeline loss index are identified. Pipeline planning is performed on the identified pipeline transmission lines according to the pipeline planning module.

[0034] Before planning the pipeline network, it is necessary to analyze the structure of the pipeline network, especially to determine whether there are multiple pipeline transmission lines. If so, the pipeline loss prediction model should be used to predict the losses of each line, and the corresponding planning should be carried out based on the prediction results. Specifically, the regulating pipeline network is analyzed holistically to determine if it contains multiple independent or parallel pipeline transmission lines. These lines may have different lengths, materials, diameters, and fluid properties, resulting in varying heat loss characteristics. If the regulating pipeline network contains multiple transmission lines, an established pipeline loss prediction model is sequentially invoked to predict the loss for each line. Detailed parameters for each line are input, such as material information, pipeline layer structure, geometric data, and fluid properties, to obtain accurate pipeline loss prediction indices. These predicted pipeline loss indices are compared with preset pipeline loss indices to identify transmission lines whose predicted losses exceed the preset indices. These lines are marked as requiring optimization. The pipeline planning module is used to plan these marked transmission lines in detail. Based on feedback from the pipeline loss prediction model, the layout, material selection, pipeline diameter, or insulation measures of these lines are adjusted to reduce heat loss and meet the preset indices. Through these steps, it is ensured that the impact of heat loss is fully considered during the pipeline planning process of the regulating pipeline network, and effective optimization is achieved through the pipeline loss prediction model.

[0035] Furthermore, the pipeline planning module performs pipeline planning for the identified pipeline transmission lines, including:

[0036] The pipeline planning module calls the pipeline loss prediction model to analyze the marked pipeline transmission line, locates the pipeline nodes that reach the preset pipeline loss index, and so on until the marked pipeline transmission line planning is completed, outputting N regulating pipelines corresponding to the marked pipeline transmission line; wherein, locating the pipeline nodes that reach the preset pipeline loss index includes obtaining multiple pipeline nodes from multiple predictions, and calculating the average value of the multiple pipeline nodes as the located pipeline nodes.

[0037] Preferably, for each identified pipeline transmission line, the pipeline planning module calls the pipeline loss prediction model for analysis. Based on input pipeline material information, pipeline layer structure, geometric data, and fluid characteristics, the pipeline loss prediction model predicts the pipeline's heat loss. The pipeline loss prediction model is then used to segment the identified pipeline transmission line for prediction. The entire line can be divided into multiple segments or nodes, and each segment or node is individually predicted to determine if it meets preset pipeline loss indicators. This process is repeated until a pipeline node configuration that meets the preset indicators is found. During this process, multiple predicted pipeline node configurations may be obtained. Statistical analysis is performed on these multiple predicted configurations, using the mean, median, or other statistical measures to obtain a more stable and reliable pipeline node configuration. After completing the planning of the identified pipeline transmission line, N corresponding regulating pipelines are output. These regulating pipelines are designed based on the prediction results of the pipeline loss prediction model and the planning suggestions of the pipeline planning module, aiming to reduce pipeline heat loss and improve system energy efficiency. By following the steps above, we can ensure that the impact of heat loss is fully considered in the planning of the pipeline network, and achieve accurate pipeline planning and optimization through the synergistic effect of the pipeline loss prediction model and the pipeline planning module.

[0038] N temperature regulators are set up for each of the N regulating pipelines, and the temperature of each of the N regulating pipelines is controlled by the N temperature regulators. The N temperature regulators are integrated and controlled by the temperature regulating equipment.

[0039] After obtaining N regulating pipelines, to ensure that the temperature of each pipeline can be effectively controlled to meet the requirements, N temperature controllers can be installed for each of these N regulating pipelines, and integrated and controlled by a single temperature control device. Specifically, based on the characteristics of each regulating pipeline (such as pipeline length, diameter, fluid type, etc.) and the required temperature control accuracy, a suitable temperature controller is selected; the N temperature controllers are then deployed on their respective regulating pipelines. These temperature controllers should be able to accurately measure the temperature in the pipeline and adjust the output of heating or cooling equipment as needed to maintain a constant temperature, and the N temperature controllers are integrated and controlled by the temperature control device.

[0040] Furthermore, the method of controlling the temperature of the N regulating pipelines according to the N temperature regulators includes:

[0041] The loss index of the previous regulating pipeline is sent to the temperature regulator of the next regulating pipeline for feedback, and the optimized temperature regulation parameters of the next regulating pipeline are obtained; the temperature of the next regulating pipeline is controlled according to the optimized temperature regulation parameters.

[0042] To achieve more precise and dynamic pipeline temperature control, a feedback mechanism is used to adjust the temperature regulation parameters of the next regulating pipeline based on the loss indicators of the previous regulating pipeline. Specifically, the actual pipeline loss indicators of the previous regulating pipeline, including heat loss, pressure loss, or other relevant parameters, are monitored and recorded. This data is then sent to the temperature controller of the next regulating pipeline. Upon receiving the feedback data, the temperature controller of the next regulating pipeline performs a series of processing and analysis steps, including comparing the current temperature regulation parameters with the expected effect, assessing whether adjustment is needed, and determining the direction and magnitude of the adjustment. Based on the feedback data, the temperature controller calculates optimized temperature regulation parameters, including the output power of heating / cooling equipment, valve opening, and flow rate, aiming to reduce pipeline losses and maintain a stable temperature. The optimized temperature regulation parameters are then sent to the actuators (such as heaters, coolers, and valves) to achieve temperature control of the next regulating pipeline. The actuators then perform corresponding operations based on the received temperature regulation parameters, such as adjusting the output power of heating / cooling equipment or changing the valve opening, thereby achieving temperature control of the next regulating pipeline. This feedback mechanism allows for dynamic adjustment of temperature control parameters based on actual pipeline losses, thereby improving system efficiency and stability.

[0043] Furthermore, the methods include:

[0044] Obtain the distribution location information of the N temperature regulators;

[0045] Based on the distribution location information, a distance clustering analysis is performed on the N temperature regulators to identify the identified temperature regulators, wherein the distance clustering index between the identified temperature regulators is greater than a preset threshold.

[0046] The marked temperature regulator is reset to a single temperature regulator, and the reset temperature regulator replaces the marked temperature regulator for temperature regulation.

[0047] To optimize the efficiency and response speed of a temperature control system, distance clustering analysis can be performed on the locations of temperature controllers to identify those that are too close together and consider merging or resetting them. Specifically, the distribution location information of N temperature controllers is obtained; based on this information, distance clustering analysis is performed on the N temperature controllers by calculating the distance between each controller and other temperature controllers to determine which controllers are too close to each other and can be clustered; in the distance clustering analysis, a preset threshold is set to represent the maximum acceptable distance between temperature controllers. If the distance between two or more temperature controllers is less than this threshold, they are considered flagged temperature controllers. Since the distance clustering index of these temperature controllers exceeds the preset threshold, it means they may be too close and can be merged or reset; the flagged temperature controllers are then reset to a single temperature controller, which replaces the flagged temperature controllers for temperature regulation. In this way, distance clustering analysis can be performed based on the distribution location information of temperature controllers to identify those that are too close together, allowing for appropriate measures to be taken to merge or reset them, thereby improving the overall performance and efficiency of the system.

[0048] Furthermore, resetting the first temperature regulator according to the identified temperature regulator includes:

[0049] When the marked temperature regulator is not at a pipe intersection, the reset command is not activated; when the marked temperature regulator is at a pipe intersection, the reset command is activated, and the marked temperature regulator is reset to a temperature regulator according to the reset command.

[0050] Preferably, based on the previously analyzed location information of the identified temperature regulators, it is determined whether these temperature regulators are located at pipeline intersections. Pipeline intersections are typically where multiple pipelines converge, and are crucial for temperature control and flow distribution. If the identified temperature regulators are not located at pipeline intersections, it means that these temperature regulators may only regulate the temperature of a single pipeline segment, and their location is not a critical node in the system. In this case, the reset command is not activated, and the existing temperature regulator configuration remains unchanged. If the identified temperature regulators are located at pipeline intersections, this indicates that these temperature regulators not only affect the temperature of a single pipeline but may also have a significant impact on the flow distribution and temperature balance of multiple pipelines. In this case, the reset command is activated, preparing to reset the temperature regulators. According to the reset command, the identified temperature regulators located at pipeline intersections are reset to a single temperature regulator. This is achieved by turning off or removing redundant temperature regulators and configuring the remaining temperature regulator to cover the regulation range of the original multiple temperature regulators, ensuring that the new temperature regulator can effectively control the temperature near the intersection.

[0051] In summary, the embodiments of this application have at least the following technical effects:

[0052] First, the temperature regulation pipelines connected to the temperature regulation equipment are obtained, and a regulation pipeline network is constructed based on the pipeline connection relationships. Temperature regulation is monitored through the connection of the regulation pipeline network to obtain a temperature monitoring dataset. Next, a pipeline loss function is introduced to analyze the pipeline transport loss of the temperature monitoring dataset, obtaining a pipeline loss prediction model. Then, based on the pipeline loss prediction model and preset pipeline loss indicators, pipeline planning is performed on the regulation pipeline network, outputting N regulation pipelines, where the pipeline loss indicator for each regulation pipeline is less than the preset pipeline loss indicator. Finally, N temperature controllers are set up for each of the N regulation pipelines, and the temperature of each of the N regulation pipelines is controlled by the N temperature controllers, which are integrated and controlled by the temperature regulation equipment. This solves the technical problem of large energy loss in the pipeline transport process of temperature regulation equipment in the prior art. By segmenting the pipeline temperature, the energy loss in pipeline transport is reduced, achieving the technical effect of energy-saving control.

[0053] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0054] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0055] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. An intelligent energy-saving control method for temperature regulation equipment, characterized in that, The method includes: Obtain the temperature regulating pipeline connected to the temperature regulating device; Based on the pipe connection relationship of the temperature regulating pipes, construct a regulating pipe network; Connect the regulating pipeline network to monitor temperature regulation and obtain a temperature monitoring dataset. A pipeline loss function is introduced to analyze pipeline transport loss in the temperature monitoring dataset, and a pipeline loss prediction model is obtained. Based on the pipeline loss prediction model and the preset pipeline loss index, pipeline planning is performed on the regulating pipeline network to output N regulating pipelines, wherein the pipeline loss index corresponding to each regulating pipeline is less than the preset pipeline loss index. N temperature regulators are set up for each of the N regulating pipelines, and the temperature of each of the N regulating pipelines is controlled by the N temperature regulators. The N temperature regulators are integrated and controlled by the temperature regulating equipment. The method involves introducing a pipeline loss function to analyze pipeline transport losses in the temperature monitoring dataset, including: The pipeline loss function is introduced to perform heat loss analysis on the temperature monitoring dataset to obtain heat conduction loss data, convection loss data, and environmental impact loss data. Using the heat conduction loss data, the convection loss data, and the environmental impact loss data, a mapping relationship between pipeline length and pipeline loss monitoring indicators is established, and a pipeline loss prediction model is generated based on the mapping relationship. The pipeline loss prediction model is then connected to the pipeline planning module. Obtain the distribution location information of the N temperature regulators; Based on the distribution location information, a distance clustering analysis is performed on the N temperature regulators to identify the identified temperature regulators, wherein the distance clustering index between the identified temperature regulators is greater than a preset threshold. The marked temperature regulator is reset to a single temperature regulator, and the reset temperature regulator replaces the marked temperature regulator for temperature regulation. The method further includes introducing the pipeline loss function to perform heat loss analysis on the temperature monitoring dataset, and the method also includes: Obtain information on pipeline materials, pipeline layer structure, and pipeline geometry; Based on the pipeline material information, the pipeline layer structure, and the pipeline geometric data, a heat loss impact analysis is performed to obtain loss impact indicators; The pipeline loss prediction model is optimized based on the loss impact index. Resetting the first temperature regulator according to the identified temperature regulator includes: The reset command is not activated when the temperature regulator is not at a pipe intersection. When the marked temperature regulator is at a pipe intersection, a reset command is activated, and the marked temperature regulator is reset to a temperature regulator according to the reset command; The method for controlling the temperature of the N regulating pipelines using the N temperature regulators includes: The loss index of the previous regulating pipeline is sent to the temperature regulator of the next regulating pipeline for feedback, and the optimized temperature regulation parameters of the next regulating pipeline are obtained. The temperature of the next regulating pipeline is controlled based on the optimized temperature regulation parameters obtained from the feedback.

2. The intelligent energy-saving control method for temperature regulation equipment as described in claim 1, characterized in that, Before planning the regulating pipeline network based on the pipeline loss prediction model and preset pipeline loss indicators, the following steps are included: The regulating pipeline network is analyzed to determine whether it includes multiple pipeline transmission lines; If the regulating pipeline network includes multiple pipeline transmission lines, the pipeline loss prediction model is invoked to predict the multiple pipeline transmission lines and output multiple pipeline loss prediction indicators. Identify the pipeline transmission lines whose predicted pipeline loss indicators are greater than the preset pipeline loss indicator from among the multiple pipeline loss prediction indicators; The pipeline planning module performs pipeline planning for the identified pipeline transmission lines.

3. The intelligent energy-saving control method for temperature regulation equipment as described in claim 2, characterized in that, The pipeline planning module performs pipeline planning for the identified pipeline transmission lines, including: The pipeline planning module calls the pipeline loss prediction model to analyze the marked pipeline transmission line, locate the pipeline node that reaches the preset pipeline loss index, and so on until the marked pipeline transmission line planning is completed, and outputs N regulating pipelines corresponding to the marked pipeline transmission line. The process of locating pipeline nodes that meet the preset pipeline loss index includes obtaining multiple pipeline nodes from multiple predictions and calculating the average value of the multiple pipeline nodes as the located pipeline node.

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