Pipeline heat loss prediction method and equipment based on online monitoring and medium

Abstract

The embodiment of the invention discloses a pipeline heat loss prediction method and device based on online monitoring and a medium. The method comprises the following steps: monitoring thermal parameters of the heat supply pipeline in real time through monitoring equipment deployed on the heat supply pipeline; according to the failure days, the failure range and the failure probability of the heat supply pipeline heat preservation layer, a theoretical formula for calculating heat loss through the heat supply parameters is constructed; the theoretical formula is used as a control equation, and a finite element analysis method is used for conducting simulation analysis on the heat transfer process of the heat pipeline; correcting the theoretical formula by using monitoring data, theoretical data and simulation data to obtain a heat loss prediction model of the heat pipeline; and utilizing the prediction model to predict the heat loss under any heat supply parameter setting. According to the embodiment, the accuracy of heat loss prediction can be improved.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of heating technology, and in particular to a pipeline heat loss prediction method, device and medium based on online monitoring. Background Art

[0002] Heat loss in heating pipes refers to the heat lost in the process of heat transfer from the inside of the heating pipe to the outside due to heat dissipation in the pipe. Heat loss will lead to reduced heating efficiency, serious energy waste, and cause economic and environmental problems.

[0003] At present, the calculation of heat loss of heating pipelines can only be carried out through theoretical formulas in relevant specifications such as CJJ / T 34-2022 "Urban Heating Pipeline Design Standard", GB 50264-2013 "Industrial Equipment and Pipeline Insulation Engineering Design Code", etc., to estimate the overall heat loss of long-distance heating pipelines.

[0004] Since the theoretical formula in the specification is calculated according to ideal conditions and is not combined with the actual situation on site, the heat loss calculated is not accurate. In addition, there is currently a lack of effective means to verify the heat loss calculated by the theoretical formula. Therefore, it is impossible to determine whether the calculated value of the theoretical formula in the specification is consistent with the actual situation of the pipeline. Summary of the invention

[0005] The embodiments of the present invention provide a pipeline heat loss prediction method, device and medium based on online monitoring to solve the above technical problems.

[0006] In a first aspect, an embodiment of the present invention provides a pipeline heat loss prediction method based on online monitoring, comprising:

[0007] By deploying monitoring equipment on the heating pipeline, the thermal parameters of the heating pipeline can be monitored in real time;

[0008] According to the failure days, failure range and failure probability of the insulation layer of the heating pipeline, a theoretical formula for calculating heat loss using heating parameters is constructed;

[0009] Taking the theoretical formula as the control equation, the finite element analysis method is used to simulate and analyze the heat transfer process of the heat pipe;

[0010] Using monitoring data, theoretical data and simulation data, the theoretical formula is modified to obtain a heat loss prediction model for the heat pipe;

[0011] The prediction model is used to predict heat loss under any heating parameter settings.

[0012] In a second aspect, an embodiment of the present invention provides an electronic device, the electronic device comprising:

[0013] one or more processors;

[0014] a memory for storing one or more programs,

[0015] When the one or more programs are executed by the one or more processors, the one or more processors implement the pipeline heat loss prediction method based on online monitoring described in any embodiment.

[0016] In a third aspect, an embodiment of the present invention further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the pipeline heat loss prediction method based on online monitoring as described in any embodiment.

[0017] Embodiments of the present invention In summary, this embodiment provides a pipeline heat loss prediction method based on online monitoring, which can predict the heat dissipation loss of the entire long-distance heating pipeline, so as to achieve the purpose of changing the flow rate of the heating medium, the temperature of the medium and other parameters, and accurately estimating the heat dissipation loss value of the entire long-distance heating pipeline. Specifically, this embodiment installs corresponding monitoring equipment on the on-site heating equipment and establishes a corresponding monitoring platform, so that users can monitor the operation of the on-site heating equipment in real time; it also uses a finite element simulation method to simulate heat loss, and by comparing and verifying the monitoring data, simulation data and theoretical data, it can reduce the errors caused by various reasons as much as possible, so that users can more realistically and easily obtain the pipeline heat loss value. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0019] Figure 1 is a flow chart of a pipeline heat loss prediction method based on online monitoring provided by an embodiment of the present invention;

[0020] Figure 2 is a schematic diagram of a monitoring platform provided by an embodiment of the present invention;

[0021] Figure 3 is the fitting result of the relationship between the thermal conductivity of the polyurethane foam and the number of soaking days provided by the embodiment of the present invention;

[0022] Figure 4 It is a simulation schematic diagram of a pipeline provided by an embodiment of the present invention;

[0023] Figure 5A schematic structural diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of the present invention.

[0025] In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicating the orientation or positional relationship, are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, the terms "first", "second", and "third" are used for descriptive purposes only, and cannot be understood as indicating or implying relative importance.

[0026] In the description of the present invention, it is also necessary to explain that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0027] Figure 1 1 is a flow chart of a pipeline heat loss prediction method based on online monitoring provided by an embodiment of the present invention. The method is applicable to the case where the pipeline heat loss is predicted and evaluated before adjusting the heating parameter settings, and is executed by an electronic device. Figure 1 As shown, the method specifically includes:

[0028] S110. Monitor the thermal parameters of the heating pipeline in real time by deploying monitoring equipment on the heating pipeline.

[0029] In this embodiment, corresponding monitoring equipment, such as thermometers, heat flux meters, etc., are installed for the heating pipeline to be predicted, to monitor the temperature, heat flux density and other parameters of the pipeline surface under different heating parameter settings in real time. In the case where the heating pipeline is a steel pipe, these parameters on the pipeline surface are approximately equal to the heating parameters such as the temperature and heat flux density of the heating medium, so the temperature and heat flux density on the pipeline surface can be regarded as the measured values ​​of the heating parameters. Under normal circumstances, these measured values ​​are different from the theoretical values ​​of the heating parameters set by the heating staff (that is, the user in this embodiment).

[0030] Optional, such as Figure 2 As shown, a corresponding monitoring platform can be established to display the measured data in real time, so that users can monitor the operation of the on-site heating equipment in real time.

[0031] S120. Construct a theoretical formula for calculating heat loss using heating parameters based on the failure days, failure range and failure probability of the insulation layer of the heating pipeline.

[0032] The basic theoretical formula for heat loss calculation comes from the existing basic specifications. This embodiment takes into account the impact of the failure of the pipeline insulation layer on the overall heat loss based on the actual situation of the pipeline, and supplements the theoretical formula in the relevant specifications as follows based on the failure days, failure range and failure probability of the insulation layer:

[0033]

[0034] Q1=πD1q1 (2)

[0035]

[0036] Q2=πD1q2 (4)

[0037]

[0038] λ=a×e b×d +c (7)

[0039] Where:

[0040] ΔQ-total heat loss of long-distance heating pipeline (W);

[0041] Q1; Q2-heat loss per unit length (W / m);

[0042] q1; q2-heat flux density (W / m 2 );

[0043] β-additional coefficient of heat dissipation loss;

[0044] L-total length of pipeline (m);

[0045] N-Number of insulation layer joints (positions);

[0046] -Probability of failure of insulation layer at joint (0 to 1);

[0047] W- insulation layer joint width (m);

[0048] D1-outer diameter of insulation layer (m);

[0049] D0-outer diameter of pipe or equipment (m);

[0050] T0-external surface temperature of pipe or equipment (℃), or temperature of heating medium;

[0051] T a - Ambient temperature (°C);

[0052] α s -External surface heat transfer coefficient [W / (m 2 ℃)];

[0053] V- is wind speed (m / s);

[0054] λ- thermal conductivity of the insulation layer [W / (m 2 ·℃)], namely thermal conductivity;

[0055] d- the number of days of water seepage in the insulation layer;

[0056] a, b, c - fitting parameters related to insulation material;

[0057] x,y,z-empirical coefficients or derived coefficients.

[0058] In particular, this embodiment constructs a relationship model between the thermal conductivity λ of the insulation layer and the number of days of water seepage (i.e., soaking days), as shown in formula (7), where a<0, b<0. Taking the insulation layer as polyurethane foam as an example, the fitting relationship between its thermal conductivity and soaking days is: λ=-0.01701e -0.2989d +0.04954, as Figure 3 shown.

[0059] x, y, z are empirical coefficients or derived coefficients obtained according to the on-site conditions and / or relevant specifications. Taking the long-distance heat supply pipeline from a power plant in Inner Mongolia to Datong as an example, x = 0.06506, y = 11.6, z = 7. The relevant derivation process is prior art and will not be repeated in this embodiment.

[0060] S130, using the theoretical formula as a control equation and utilizing a finite element analysis method to perform simulation analysis on the heat transfer process of the heat pipe.

[0061] In this step, the supplemented theoretical formula is applied to the finite element analysis, and the heat transfer process of the heat pipe under different heating parameter settings is simulated through the finite element analysis. Due to the finite element mesh division in the simulation, the calculation results are different from those calculated directly by the theoretical formula.

[0062] In a specific implementation, first, according to the diameter and wall thickness of the heat pipe, a heat pipe heating model is established in the finite element simulation software, and the temperature and flow rate of the heating medium in the heating parameter setting are input as boundary conditions. Then, the heat pipe heating model is meshed; and the theoretical formula (including the basic theoretical formula in the specification and the supplemented theoretical formula) is applied to each grid to obtain the model equation of each grid. Finally, the model equation of each grid, as well as the heat balance equation and flow balance equation between grids are used as control equations to simulate and analyze the pipeline heat transfer process.

[0063] Taking ANSYS simulation software as an example, the simulation results of temperature distribution in the pipeline are as follows: Figure 4 In addition, the above simulation can also be realized by using Abaqus, NASTRAN, etc.

[0064] S140, using the monitoring data, theoretical data and simulation data, the theoretical formula is corrected to obtain a heat loss prediction model for the heat pipe.

[0065] This step combines field monitoring data, theoretical calculation data, and finite element simulation data to comprehensively modify the theoretical formula and obtain a final heat loss prediction model suitable for the current pipeline.

[0066] In a specific implementation, under different heating parameter settings, the real-time monitored thermal parameters of the heating pipeline are substituted into the theoretical formula to obtain a heat loss corresponding to each heating parameter setting; different heating parameter settings (i.e., theoretical values) are substituted into the theoretical formula to obtain another heat loss corresponding to each heating parameter setting; at the same time, different heating parameter settings are substituted into simulation calculations to obtain yet another heat loss corresponding to each heating parameter setting. For ease of distinction and description, in this embodiment, the one heat loss is referred to as the first heat loss, the other heat loss is referred to as the second heat loss, and the yet another heat loss is referred to as the third heat loss. According to each of the first heat loss, the second heat loss, and the third heat loss, the coefficient of the theoretical formula is corrected, and the corrected theoretical formula is the heat loss prediction model applicable to the current pipeline.

[0067] Furthermore, the correction process may include the following steps:

[0068] Step 1: A sample is formed by the first thermal parameter and the second thermal parameter under the same heating parameter setting, and a coefficient correction is performed on the theoretical formula according to each sample so that the two thermal parameters in each sample achieve the least square error.

[0069] Step 2: another sample is formed by the first thermal parameter and the second thermal parameter under the same heating parameter setting, and the coefficient of the theoretical formula is corrected again according to each other sample so that the two heat losses in each sample achieve the least squares error.

[0070] In the above two steps, it is assumed that the actual measured data has higher accuracy. The actual measured data is used as the benchmark data to correct the theoretical calculation data and the simulation calculation data respectively to improve the accuracy of the theoretical calculation and the simulation calculation.

[0071] Step 3: Determine a final set of coefficients based on the twice corrected coefficients, substitute them into the above model, and obtain the final prediction model.

[0072] Optionally, the coefficients after the two corrections can be averaged to obtain the final coefficients. The advantage of this method is that it is simple and easy to implement and has a small amount of calculation.

[0073] Optionally, the second heat loss and the third heat loss under different heating parameter settings can be recalculated according to the parameters after the two corrections, and the second heat loss and the third heat loss recalculated under the same heating parameter settings constitute another new sample. Then, according to each new sample, the theoretical formula is coefficient corrected for the third time so that the two heat losses in each new sample achieve the least squares error. The advantage of this method is that it can eliminate more errors in the field measured data. For the convenience of distinction and description, in this embodiment, one sample is called the first sample, the other sample is called the second sample, and the new sample is called the third sample.

[0074] S150: Utilize the prediction model to predict heat loss under any heating parameter setting.

[0075] The final prediction model can be integrated into the monitoring platform, allowing users to clearly understand the specific heat loss when changing the temperature, flow rate and other parameters of the heating medium, thereby forming a set of pipeline heat loss online monitoring and online evaluation system.

[0076] Optionally, when the user wants to change the current heating parameter settings, the user may first use the prediction model to predict the heat loss of the pipeline under the new heating parameter settings, and evaluate whether the new heating parameter settings are reasonable.

[0077] Optionally, the model can also be used to predict at what temperature and flow rate of the heating medium, when the proportion of overall heat loss to total heat is minimal, under the premise of meeting heating requirements, so as to achieve the ultimate goal of energy conservation, emission reduction and cost savings.

[0078] In summary, this embodiment provides a pipeline heat loss prediction method based on online monitoring, which can predict the overall heat dissipation loss of the long-distance heat supply pipeline, so as to reasonably set the flow rate of the heating medium, the medium temperature and other parameters, so that the overall fan heat loss accounts for the minimum ratio of the overall heat supply. Specifically:

[0079] 1. In existing specification calculations, the possibility of failure at the joints of the insulation layer and its influence on the overall heat loss are usually ignored. This embodiment supplements the basic theoretical formula in the specification based on factors such as the failure days, failure range, and failure probability of the insulation layer, making the theoretical calculation more accurate.

[0080] 2. The heat loss calculated by theoretical formula in the existing specifications lacks effective means to verify it, so it is impossible to determine whether the calculated value of the theoretical formula in the specifications conforms to the actual situation of this project. This embodiment installs corresponding monitoring equipment on the on-site heating equipment and establishes a corresponding monitoring platform, so that users can monitor the operation of the on-site heating equipment in real time.

[0081] 3. In addition to theoretical calculation and on-site monitoring, this embodiment also uses a finite element simulation method to simulate heat loss. By comparing and verifying the monitoring data, simulation data and theoretical data, the errors caused by various reasons are reduced as much as possible, allowing users to obtain pipeline heat loss values ​​more realistically and easily.

[0082] Figure 5 A schematic diagram of the structure of an electronic device provided by an embodiment of the present invention is shown in FIG. Figure 5 As shown, the device includes a processor 60, a memory 61, an input device 62 and an output device 63; the number of processors 60 in the device can be one or more. Figure 5 A processor 60 is taken as an example; the processor 60, the memory 61, the input device 62 and the output device 63 in the device can be connected by a bus or other means. Figure 5 The example of connecting through bus is taken in the following.

[0083] The memory 61 is a computer-readable storage medium that can be used to store software programs, computer executable programs and modules, such as program instructions / modules corresponding to the pipeline heat loss prediction method based on online monitoring in the embodiment of the present invention. The processor 60 executes various functional applications and data processing of the device by running the software programs, instructions and modules stored in the memory 61, that is, realizing the above-mentioned pipeline heat loss prediction method based on online monitoring.

[0084] The memory 61 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required for a function; the data storage area may store data created according to the use of the terminal, etc. In addition, the memory 61 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other non-volatile solid-state storage device. In some instances, the memory 61 may further include a memory remotely arranged relative to the processor 60, and these remote memories may be connected to the device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0085] The input device 62 may be used to receive input digital or character information and generate key signal input related to user settings and function control of the device. The output device 63 may include a display device such as a display screen.

[0086] An embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the pipeline heat loss prediction method based on online monitoring of any embodiment is implemented.

[0087] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) 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 fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a device or a device or used in combination with it.

[0088] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0089] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0090] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0091] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention 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 replace some or all of the technical features therein by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A pipeline heat loss prediction method based on online monitoring, characterized in that: include: By deploying monitoring equipment on the heating pipeline, the thermal parameters of the heating pipeline can be monitored in real time; According to the failure days, failure range and failure probability of the insulation layer of the heating pipeline, a theoretical formula for calculating heat loss using heating parameters is constructed; Taking the theoretical formula as the control equation, the finite element analysis method is used to simulate and analyze the heat transfer process of the heat pipe; Using monitoring data, theoretical data and simulation data, the theoretical formula is modified to obtain a heat loss prediction model for the heat pipe; The prediction model is used to predict heat loss under any heating parameter settings.

2. The method according to claim 1, characterized in that The monitoring device deployed on the heating pipeline monitors the thermal parameters of the heating pipeline in real time, including: The temperature and heat flux density of the outer surface of the heat pipe are monitored in real time using the thermometer and heat flux density meter deployed on the outer surface of the heat pipe.

3. The method according to claim 1, characterized in that According to the failure days, failure range and failure probability of the insulation layer of the heating pipeline, a theoretical formula for calculating heat loss using heating parameters is constructed, including: According to the failure days, failure range and failure probability of the insulation layer of the heating pipeline, the theoretical formula based on the relevant specifications is supplemented as follows: Q1=πD1q1 (2) Q2=πD1q2 (4) λ=a×e b×d +c (7) Where: ΔQ - total heat loss of long-distance heating pipeline (W); Q1; Q2 - heat loss per unit length (W / m); q1; q2 - heat flux (W / m 2 ); β - additional coefficient of heat loss; L – total length of pipeline (m); N - number of insulation layer joints (places); ——Probability of failure of insulation layer at joint (range 0 to 1); W - insulation layer joint width (m); D1 - outer diameter of insulation layer (m); D0 - outer diameter of pipe or equipment (m); T0 - the external surface temperature of the pipe or equipment, or the temperature of the heating medium (℃); T a - Ambient temperature (℃); α s ―External surface heat transfer coefficient [W / (m 2 ℃)]; V - wind speed (m / s); λ―Thermal conductivity of the insulation layer [W / (m 2 ℃)]; d - the number of days of water seepage in the insulation layer; a, b, c - fitting parameters related to the insulation material; x,y,z—empirical coefficients or derived coefficients.

4. The method according to claim 1, characterized in that: The method of using the theoretical formula as the control equation and utilizing the finite element analysis method to simulate and analyze the heat transfer process of the heat pipe includes: According to the diameter and wall thickness of the heat pipe, a heat supply model of the heat pipe is established in the finite element simulation software; Meshing the heat pipe heating model; Applying the theoretical formula to each grid to obtain a model equation for each grid; The model equations of each grid and the heat balance equations between grids are used as control equations to simulate and analyze the pipeline heat transfer process.

5. The method according to claim 1, characterized in that The method of using monitoring data, theoretical data and simulation data to correct the theoretical formula includes: Under different heating parameter settings, the real-time monitored heating pipe thermal parameters are substituted into the theoretical formula to obtain the first heat loss; Substituting different heating parameter settings into the theoretical formula, the second heat loss is obtained; Substitute different heating parameter settings into the simulation calculation to obtain the third heat loss; The coefficient of the theoretical formula is corrected according to the first heat loss, the second heat loss and the third heat loss.

6. The method according to claim 5, characterized in that The coefficient correction of the theoretical formula according to the first heat loss, the second heat loss and the third heat loss includes: The first heat loss and the second heat loss under the same heating parameter setting constitute a first sample, and the theoretical formula is corrected once according to each first sample so that the two heat losses in each first sample achieve the least square error; The first heat loss and the third heat loss under the same heating parameter setting constitute the second sample, and the coefficient of the theoretical formula is corrected again according to each second sample, so that the two heat losses in each second sample achieve the least square error; The average of the two corrected coefficients is taken as the final coefficient.

7. The method according to claim 1, characterized in that The said modifying the coefficient of the theoretical formula according to the first heat loss, the second heat loss and the third heat loss, Includes, including: The first heat loss and the second heat loss under the same heating parameter setting constitute a first sample, and the theoretical formula is corrected once according to each first sample so that the two heat losses in each first sample achieve the least square error; The first heat loss and the third heat loss under the same heating parameter setting constitute the second sample, and the coefficient of the theoretical formula is corrected again according to each second sample, so that the two heat losses in each second sample achieve the least square error; According to the twice corrected coefficients, the second heat loss and the third heat loss under different heating parameter settings are recalculated respectively, and the second heat loss and the third heat loss recalculated under the same heating parameter setting constitute a third sample; The theoretical formula is subjected to a third coefficient correction according to each third sample, so that the two heat losses in each third sample achieve a minimum square error.

8. The method according to claim 1, characterized in that The method of using the prediction model to predict heat loss under any heating parameter setting includes: In response to a user's operation of changing the heating parameters, the prediction model is used to predict the heat loss under the new heating parameters, so as to provide a decision basis for the user whether to change the heating parameters; and / or The prediction model is used to predict the optimal heating parameters under the premise of meeting the heating requirements.

9. An electronic device, characterized in that: include: one or more processors; a memory for storing one or more programs, When the one or more programs are executed by the one or more processors, the one or more processors implement the pipeline heat loss prediction method based on online monitoring as described in any one of claims 1-8.

10. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the program is executed by a processor, the pipeline heat loss prediction method based on online monitoring as described in any one of claims 1-8 is implemented.

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