Terminal data acquisition and transmission method for long-distance heat supply pipeline

Abstract

The invention relates to the technical field of communication and network transmission, in particular to a terminal data acquisition and transmission method for a long-distance heat supply pipeline. The method comprises the following steps that long-distance heat supply pipeline laying area data are obtained, pipe network laying form analysis is carried out, and single-pipe branched heat supply pipe network data are obtained; constructing a pipeline laying model based on the data of the single-pipe branched heat supply network; performing intelligent heat energy flow simulation based on the pipeline laying model, and calculating heat loss in the heat energy flow simulation process to obtain heat loss data; performing thermal protection thermal insulation material integration on the pipeline laying model based on the heat loss data to obtain composite thermal insulation material data; and identifying a straight pipe section of the pipeline laying model, and calculating the minimum thick wall of the straight pipe section to obtain minimum thick wall data. The heat efficiency, the stability and the intelligent management capability of the pipeline system are improved based on the communication and network transmission technology.

Description

Technical Field

[0001] The present invention relates to the technical field of communication and network transmission, and particularly relates to a method for collecting and transmitting terminal data of a long-distance heat supply pipeline. Background Art

[0002] Data collection in traditional methods often relies on fixed measurement points, failing to comprehensively cover the real-time data of all links of the pipeline, resulting in insufficient monitoring of parameters such as temperature, pressure, and flow rate of various parts of the pipe network. Poor real-time performance of data transmission is a key issue. The data transmitted by traditional methods has delays and cannot promptly reflect the dynamic changes of the pipeline system, which makes the system unable to respond and adjust quickly, affecting the heating efficiency and reliability. Traditional methods also lack intelligent functions, cannot make dynamic adjustments based on real-time data, and are difficult to achieve refined management of the pipe network, resulting in inaccurate calculation and management of heat loss. In terms of maintenance, traditional methods are difficult to identify potential fault problems in advance, and the repair process is relatively cumbersome, increasing the maintenance cost and risk. The dynamic changes of pipeline pressure and thermal expansion are not effectively considered, ignoring the impact of these factors on the stable operation of the pipe network, and easily leading to problems such as stress concentration and rupture of the pipeline. Traditional methods usually lack the ability to customize design and adjustment according to the characteristics of the pipe network and are difficult to adapt to complex and changeable usage environments and requirements. Summary of the Invention

[0003] Based on this, it is necessary for the present invention to provide a method for collecting and transmitting terminal data of a long-distance heat supply pipeline to solve at least one of the above technical problems.

[0004] To achieve the above object, a method for collecting and transmitting terminal data of a long-distance heat supply pipeline includes the following steps:

[0005] Step S1: Obtain the data of the laying area of the long-distance heat supply pipeline, and analyze the pipeline laying form to obtain the data of a single-pipe branched heat supply pipe network;

[0006] Step S2: Construct a pipeline laying model based on the data of the single-pipe branched heat supply pipe network; conduct intelligent thermal energy flow simulation based on the pipeline laying model, and calculate the heat loss during the thermal energy flow simulation to obtain heat loss data; integrate heat protection and insulation materials into the pipeline laying model based on the heat loss data to obtain composite insulation material data; identify the straight pipe sections of the pipeline laying model, and calculate the minimum wall thickness of the straight pipe sections to obtain the minimum wall thickness data;

[0007] Step S3: Conduct pipe network pressure analysis on the pipeline laying model based on the minimum wall thickness data to obtain pipe network pressure data; conduct thermal expansion analysis based on the pipe network pressure data to obtain thermal expansion data, and perform dynamic compensation for thermal expansion to generate dynamic compensation data;

[0008] Step S4: Perform a low-temperature drop analysis on the pipeline laying model based on the composite thermal insulation material data to obtain low-temperature drop data; transmit the low-temperature drop data and dynamic compensation data to the intelligent monitoring platform in real time to execute the task of collecting and transmitting data at the end of the heating pipeline.

[0009] Through comprehensive collection of data on the laying area of long-distance heating pipelines and determination of the data of the single-pipe dendritic heating pipeline network applicable to the target area through analysis of the pipeline network laying form, the present invention ensures the rationality and adaptability of the pipeline layout and reduces heating losses. During the construction of the pipeline laying model, the heat loss data is calculated through intelligent thermal energy flow simulation, making the evaluation of heat loss more accurate and providing data support for subsequent optimization of pipeline thermal protection. Through the integration of composite thermal insulation materials, efficient insulation of the pipeline is achieved, reducing the influence of the external environment on the flow of thermal energy while improving the heating efficiency, thereby reducing energy consumption. For the straight pipe section, the minimum wall thickness data is calculated to make the pipeline design more in line with the pressure-bearing requirements and improve the structural stability of the pipeline. In the pipeline network pressure analysis, the pressure distribution of the pipeline network is calculated based on the minimum wall thickness data, making the operating pressure of the pipeline network more uniform and reducing the risk of pipeline damage caused by abnormal pressure. At the same time, combined with the pipeline network pressure data, thermal expansion analysis is carried out, and dynamic compensation data for thermal expansion is generated to dynamically compensate in real time for the dimensional changes of the pipeline caused by temperature changes, improving the stability of the pipeline system and reducing the risk of stress concentration and pipeline damage. During the low-temperature drop analysis process, the low-temperature drop of the pipeline is evaluated in combination with the composite thermal insulation material data, thereby further optimizing the insulation design and reducing the adverse impact of low ambient temperature on the flow of thermal energy in the pipeline. Finally, by transmitting the low-temperature drop data and dynamic compensation data to the intelligent monitoring platform in real time, data collection and transmission at the end of the heating pipeline are realized, ensuring the comprehensiveness and real-time nature of data collection, enhancing the intelligent management ability of the heating system, enabling the system to dynamically adjust operating parameters according to real-time monitoring data, improving the operating efficiency of the pipeline network, reducing maintenance costs, and enhancing the reliability and safety of the heating system.

[0010] Preferably, step S1 is specifically as follows:

[0011] Step S11: Obtain data on the laying area of the long-distance heating pipeline and conduct remote sensing collection, where the set collection resolution is 0.5 m - 30 m and the collection spectral range is 8 μm - 14 μm to obtain regional remote sensing data;

[0012] Step S12: Extract thermal infrared remote sensing features based on the regional remote sensing data to obtain thermal infrared remote sensing data;

[0013] Step S13: Determine the heat consumption area based on the thermal infrared remote sensing data, where the set recognition accuracy of the consumption area area is ±5 m 2 - ±50 m 2 ;

[0014] Step S14: Calculate the heat load of the heat consumption area to obtain heat load data, and conduct heat load statistics, where the low heat load judgment threshold is set to <50 W / m 2 , and obtain low heat load data;

[0015] Step S15: Divide the heat consumption area according to the low heat load data to obtain the low heat load area;

[0016] Step S16: Determine the laying of a single-pipe tree-shaped heating pipe network for the low heat load area, where the single-pipe diameter range is set to DN50 - DN250, and obtain the single-pipe tree-shaped heating pipe network data.

[0017] The present invention obtains the data of the long-distance heating pipeline laying area through remote sensing acquisition technology, using an acquisition resolution of 0.5 m - 30 m and an acquisition spectral range of 8 μm - 14 μm to ensure the high precision and wide coverage of the remote sensing data, so as to comprehensively reflect the thermal energy distribution of the area along the heating pipeline and improve the accuracy of the data. Based on the regional remote sensing data, thermal infrared remote sensing feature extraction is carried out, enabling the precise identification of the thermal energy characteristics of the pipeline laying area, which helps to analyze the heat consumption situation. Combining the thermal infrared remote sensing data to determine the heat consumption area, setting the recognition accuracy of the consumption area area to ±5 m 2 - ±50 m 2 , making the division of the heat consumption area more accurate, avoiding the deviation of heat consumption area recognition caused by data errors, and ensuring that the key areas of heat energy loss can be accurately identified. Calculate the heat load of the heat consumption area, and set the low heat load judgment threshold to <50 W / m 2 , enabling the accurate screening of areas with lower heat energy demand, providing data support for the subsequent pipeline network optimization design. Divide the heat consumption area based on the low heat load data to ensure that the low heat load area can be independently identified, and conduct pipeline network planning for such areas to reduce heat energy loss and improve the operation efficiency of the heating system. During the determination of the heating pipeline network laying in the low heat load area, the single-pipe diameter range is set to DN50 - DN250, enabling the pipeline network design to adapt to the needs of different heat load areas, ensuring the reasonable configuration of the heating pipeline, avoiding material waste caused by over-design, and ensuring the stability and heating capacity of the pipeline. Finally, through this method, the layout of the heating pipeline network can be optimized, the operation efficiency of the heating system can be improved, heat energy loss can be reduced, and the precise management of the low heat load area can be enhanced, realizing the reasonable allocation and efficient utilization of heating resources.

[0018] Preferably, step S13 is specifically:

[0019] Step S131: Calculate the land surface emissivity according to the thermal infrared remote sensing data to obtain the land surface emissivity;

[0020] Step S132: Perform land surface temperature inversion on the land surface emissivity using a preset Planck function to generate land surface temperature data;

[0021] Step S133: Calculate the average value of the land surface temperature based on the land surface temperature data;

[0022] Step S134: Calculate the standard deviation of the land surface temperature based on the land surface temperature data;

[0023] Step S135: Determine the heat consumption area in the industrial area according to the average value of the land surface temperature and the standard deviation of the land surface temperature to obtain the heat consumption area.

[0024] The present invention realizes the accurate inversion of the land surface temperature by using thermal infrared remote sensing data and the Planck function, makes up for the deficiency of the traditional method relying on fixed measurement points, and ensures the comprehensive monitoring of regional heat consumption. The calculation of the land surface emissivity and the land surface temperature makes the identification of the heat consumption area more accurate, can cover the entire industrial area, eliminates the limitation of data collection of the traditional method, and avoids the problem of incomplete monitoring of parameters such as temperature and pressure. By calculating the average value and standard deviation of the land surface temperature, not only can the heat consumption area be effectively identified, but also an accurate calculation basis for the heat load can be provided for the system, enabling the heat supply network to dynamically respond to changing demands, improving the heat supply efficiency, and avoiding the scheduling lag caused by data transmission delay. Generally speaking, this method improves the refined management ability of the pipe network through real-time and efficient data collection and analysis, reduces the calculation error of heat loss in the traditional method, enhances the adaptability of the system to the dynamic changes of the pipeline, reduces the maintenance cost and risk, and enhances the reliability and stability of the heat supply system.

[0025] Preferably, the heat energy flow simulation in step S2 includes:

[0026] Collect heat users based on the pipeline laying model, and the collection range is the area within a heating radius of 500 - 2000 m;

[0027] Statistical location coordinates of heat users, where the set coordinate accuracy ≤ 0.5 m;

[0028] Identify buildings according to the location coordinates and calculate the building area;

[0029] Statistical heat demand of heat users, with the single-household heat demand being 5 kW - 500 kW;

[0030] Estimate the heat demand per unit area according to the heat demand and the building area to obtain the heat demand per unit area;

[0031] Perform intelligent heat energy flow simulation on the pipeline laying model according to the heat demand per unit area, where the set steam parameters are 0.8 MPa.a and 180 °C;

[0032] Calculate the heat loss during the intelligent heat energy flow simulation process to obtain heat loss data.

[0033] The present invention comprehensively collects the location coordinates of heat users and conducts accurate heat demand statistics, effectively improving the data limitations caused by fixed measurement points in traditional methods. Through accurate coordinate accuracy and building area calculation, the accurate assessment of regional heat load can be achieved, avoiding the problem of uneven heat load distribution caused by incomplete data. Combining the building area and heat demand, the estimation of heat demand per unit area enables the heating pipe network to conduct more scientific intelligent heat energy flow simulation, thereby accurately calculating heat loss and optimizing heating efficiency. This method can dynamically simulate through real-time data to timely identify heat loss in the pipe network, avoiding the problem of lag in pipe network scheduling caused by delayed data transmission in traditional methods. Through real-time data transmission and intelligent adjustment, the system can quickly respond to changes in heat demand, enhancing the reliability and stability of the heating system. Generally speaking, this technology not only improves heating efficiency but also provides technical support for the refined management of the pipe network, effectively reducing heat loss and maintenance costs, while enhancing the flexibility and response speed of the system and its adaptability to complex environmental changes.

[0034] Preferably, the integration of the thermal protection and insulation material in step S2 includes:

[0035] Divide the laying types of the pipeline laying model to obtain overhead heating pipe data and buried pipeline data;

[0036] Use the heat loss data to identify the high heat loss pipe sections in the overhead heating pipe data;

[0037] Use the heat loss data to identify the high heat loss pipe sections in the buried pipeline data;

[0038] Apply the preset aluminum silicate insulation material and high-temperature glass wool material to the high heat loss pipe sections in the overhead heating pipe data to obtain the composite insulation material data of the overhead pipe sections;

[0039] Apply the preset nano-porous aerogel material and high-temperature glass wool material to the high heat loss pipe sections in the buried pipeline data to obtain the composite insulation material data of the buried pipe sections;

[0040] Integrate the composite insulation material data of the overhead pipe sections and the composite insulation material data of the buried pipe sections to obtain the composite insulation material data.

[0041] The present invention classifies the laying types of the pipeline laying model to achieve the classified management of overhead heating pipes and buried pipes, making the planning and optimization of the heating system more accurate. By using heat loss data to identify high heat loss pipe sections, the heat loss condition of the heating pipeline can be comprehensively grasped, ensuring that the application of thermal insulation materials is more targeted and avoiding waste of resources caused by unnecessary thermal insulation measures. For the data processing of overhead heating pipes, by applying aluminum silicate thermal insulation materials and high-temperature glass wool materials, the thermal insulation performance of the overhead pipe sections is significantly improved, reducing heat dissipation, improving heating efficiency, and enhancing the stability and durability of the pipes in high-temperature environments. For the data processing of buried pipes, by applying nanoporous aerogel materials and high-temperature glass wool materials, the thermal insulation ability of the buried pipe sections in complex soil environments is greatly improved. At the same time, the light weight characteristics of the nanoporous aerogel materials help to reduce the pipe load and reduce the impact of soil settlement on the pipe stability. Finally, integrating the composite thermal insulation material data of the overhead pipe sections and the buried pipe sections makes the thermal insulation plan of the entire heating pipe network more systematic and integrated, thereby improving the energy utilization efficiency of the overall heating system, reducing the operating cost, and enhancing the adaptability of the pipes in different laying environments to ensure long-term stable operation.

[0042] Preferably, the calculation formula for calculating the minimum wall thickness in step S2 is as follows:

[0043]

[0044] C = 0.5B;

[0045] S = s + c;

[0046] In the formula, s represents the minimum wall thickness of the straight pipe; p represents the design pressure; [σ] t represents the basic allowable stress at the calculated temperature; D represents the outer diameter of the pipe; Y represents the correction factor. For ferritic steel, when the temperature < 482 °C, take 0.4; η represents the correction factor of the allowable stress. For seamless steel pipes, it is 1.0, and for spiral welded steel pipes, it is 0.9; α represents the additional thickness considering corrosion, wear, and mechanical strength requirements, and take α = 1 mm; S represents the calculated wall thickness of the straight pipe; c represents the additional value of the negative deviation of the straight pipe wall thickness; C represents the facing processing allowance; B represents the additional value of the positive deviation of the straight pipe wall thickness.

[0047] Through the optimized calculation of the minimum wall thickness of the straight pipe, the present invention effectively solves the problems of heat loss, thermal stress, and inadaptability of the pipeline structure in pipeline design in the traditional method due to neglecting the actual working conditions of the pipeline. By using factors such as the design pressure, the outer diameter of the pipeline, and the basic allowable stress at the calculated temperature, the minimum wall thickness of the straight pipe is accurately calculated, thus avoiding the problems of thermal expansion and stress concentration caused by factors such as temperature change and pressure fluctuation during the actual operation of the pipeline. In addition, through the design of the correction factor and the additional thickness, the requirements of corrosion, wear, and mechanical strength are further considered, making the pipeline more durable and stable during long-term use. Especially for seamless steel pipes and spiral welded steel pipes, different correction factors can adapt to the characteristics of pipelines made of different materials, ensuring the reliability and safety of the pipeline in actual applications. Through the reasonable optimization and accurate calculation of the pipeline wall thickness, the operation efficiency and safety of the pipe network are effectively improved, and the problems of incomplete data collection and inability to respond dynamically in the traditional method are avoided. In addition, combined with the dynamic monitoring of the thermal expansion and pressure change of the pipeline, the system can adjust the pipeline parameters in real time, reduce the occurrence of potential failures, improve the maintainability and stability of the pipe network, reduce the maintenance cost and risk, and improve the overall efficiency of the heating system.

[0048] Preferably, step S3 is specifically as follows:

[0049] Step S31: Based on the minimum wall thickness data, perform pipeline strength calculation on the pipeline laying model to obtain pipeline pressure-bearing capacity data;

[0050] Step S32: Evaluate the maximum working pressure during the heat energy flow simulation according to the pipeline pressure-bearing capacity data;

[0051] Step S33: Perform pipeline frictional pressure loss analysis according to the maximum working pressure data to obtain pipeline frictional pressure loss data;

[0052] Step S34: Perform local resistance loss calculation according to the pipeline frictional pressure loss data to obtain pipeline local resistance loss data;

[0053] Step S35: Perform full pipe network pressure assessment based on the pipeline local resistance loss data to obtain pipe network pressure data;

[0054] Step S36: Perform thermal expansion analysis according to the pipe network pressure data to obtain thermal expansion data, and perform dynamic compensation for thermal expansion to generate dynamic compensation data.

[0055] Through the calculation of the pipeline strength and based on the minimum wall thickness data, the present invention can accurately evaluate the pressure-bearing capacity of the pipeline, avoiding the potential safety hazards caused by inaccurate estimation of the pressure-bearing capacity of the pipeline in the traditional method. This method combines the pipeline pressure-bearing capacity data to effectively evaluate the maximum working pressure during the heat energy flow simulation process, ensuring that the pressure is always within the safe range during system operation and avoiding the problem that the pressure fluctuation cannot be detected and adjusted in time in the traditional method. Further, through the analysis of the pressure loss along the pipeline, the pressure loss data of the pipeline can be accurately obtained, the pressure loss parts in the pipe network can be identified in time, the design and layout of the pipeline can be optimized, and the thermal efficiency of the system can be improved. In addition, the calculation of the local resistance loss enables more accurate prediction of the hydrodynamic characteristics of the pipeline, ensuring that the pipe network can maintain a stable flow during actual operation and avoiding the risk that the local pressure loss is not detected in the traditional method and affects the heating effect. Based on the local resistance loss data, the pressure of the entire pipe network is evaluated, enabling a comprehensive understanding of the pressure condition of the entire pipe network, effectively avoiding the situation of too high or too low pressure, thereby optimizing the system adjustment strategy and enhancing the system stability and energy-saving effect. In terms of thermal expansion, the traditional method often cannot reflect in real time the expansion problem of the pipeline caused by temperature changes. Through thermal expansion analysis and dynamic compensation, not only can the impact of expansion on the pipeline be identified in advance, but also the compensation measures can be dynamically adjusted to ensure the stability of the pipeline and avoid stress concentration and rupture problems caused by thermal expansion. Overall, this method improves the intelligent management level of the pipe network, enables the pipe network to respond more accurately to dynamic changes, reduces the maintenance cost and risk, and effectively enhances the reliability and efficiency of the heating system.

[0056] Preferably, step S33 is specifically as follows:

[0057] Step S331: Extract the characteristics of the maximum working pressure period based on the maximum working pressure data to obtain the maximum working pressure period;

[0058] Step S332: Determine the steam density and flow velocity data in the maximum working pressure period, where the steam density range is set to 2.5 kg / m 3 -5.0 kg / m 3 and the flow velocity range is 15 m / s - 50 m / s;

[0059] Step S333: Calculate the steam flow Reynolds number based on the steam density and flow velocity data to obtain the steam flow Reynolds number;

[0060] Step S334: Obtain the inner wall roughness of the pipeline and the length along the pipeline;

[0061] Step S335: Calculate the frictional pressure loss based on the steam flow Reynolds number and the inner wall roughness of the pipeline, where the friction coefficient range is 0.005 - 0.05, to obtain the frictional pressure loss data;

[0062] Step S336: Perform cumulative statistics on the pipeline frictional pressure loss according to the frictional pressure loss data and the pipeline length along the way to obtain the pipeline frictional pressure loss data.

[0063] Through the extraction of the characteristics of the maximum working pressure period, the present invention can accurately identify the working state of the pipeline system during critical periods, avoid the high-pressure fluctuation problems that are difficult to identify in traditional methods, and effectively ensure the safety and stability of the system. On this basis, by extracting steam density and flow rate data and setting reasonable ranges, the pipeline operation parameters are always within the optimal range, thereby improving the thermal efficiency and flow stability. By calculating the steam flow Reynolds number, the flow state and turbulence characteristics of the fluid can be predicted more accurately, providing a reliable data basis for subsequent pipeline optimization and avoiding the risk of ignoring hydrodynamic factors in traditional methods. After obtaining the pipeline inner wall roughness and the pipeline length along the way, combined with the flow Reynolds number, the frictional pressure loss can be calculated more accurately, ensuring a comprehensive understanding of the resistance of the pipeline and reducing the problem that the pipeline friction impact cannot be comprehensively evaluated in traditional methods. In addition, by setting a reasonable range of friction coefficients, the design of the pipeline system can be refined, the friction loss can be reduced, and the energy utilization efficiency can be further improved. Finally, based on the frictional pressure loss data and the pipeline length, cumulative pressure loss statistics are performed to comprehensively evaluate the overall pressure loss of the pipeline system, monitor the network state in real time, ensure that the system is always in the best operating state during the dynamic change process, reduce the probability of system failures, improve the intelligent management level of the pipeline network, and reduce the high maintenance costs and potential risks brought by traditional methods.

[0064] Preferably, step S36 is specifically as follows:

[0065] Step S361: Obtain pipeline material data and extract the coefficient of thermal expansion;

[0066] Step S362: Perform thermal stress calculation based on the pipeline network pressure data and the coefficient of thermal expansion to obtain thermal stress data;

[0067] Step S363: Perform linear thermal expansion analysis on the pipeline material data according to the thermal stress data to obtain thermal expansion data;

[0068] Step S364: Identify the corners of the pipeline laying model to obtain the corner pipeline laying model;

[0069] Step S365: Extract the flexibility characteristics of the pipeline material data to obtain pipeline flexibility data;

[0070] Step S366: Divide the angle of the corner pipeline laying model. If the angle of the corner pipeline laying model is less than 150°, perform flexible natural compensation on the thermal expansion data according to the pipeline flexibility data to obtain flexible natural compensation data; if the angle of the corner pipeline laying model is greater than 150°, perform rotation compensation on the thermal expansion data using a preset rotation compensator to obtain rotation compensation data;

[0071] Step S367: Integrate the flexible natural compensation data and the rotation compensation data to obtain dynamic compensation data.

[0072] The present invention obtains pipeline material data and extracts thermal expansion coefficients, so that subsequent thermal stress calculations are more targeted and the accuracy of thermal expansion analysis is ensured. Thermal stress is calculated based on pipeline pressure data and thermal expansion coefficients, and the stress of the pipeline during the heating process can be accurately evaluated, providing a scientific basis for subsequent thermal expansion management. Linear thermal expansion analysis is performed in combination with thermal stress data, and the expansion degree of the pipeline under temperature changes can be accurately calculated to avoid structural damage caused by expansion errors. By identifying the corners of the pipeline laying model, the geometric structure of the heating pipeline is analyzed in detail, providing a basis for a reasonable thermal expansion compensation solution. Further flexible characteristics are extracted from pipeline material data, which can accurately evaluate the deformation capacity of the pipeline and provide a basis for the selection of compensation methods at different corner angles. For pipelines with an angle of less than 150°, flexible natural compensation is performed in combination with the flexible data of the pipeline, and the flexibility of the pipeline itself is fully utilized to absorb thermal expansion deformation, reducing the need for external compensation devices, thereby reducing system complexity and maintenance costs, and improving the long-term stability of the pipeline. For pipelines with angles greater than 150°, a preset rotation compensator is used for rotation compensation, which can provide effective thermal expansion adjustment in a rigid pipeline structure to prevent pipeline damage due to expansion restriction. Ultimately, by integrating flexible natural compensation data with rotation compensation data to form dynamic compensation data, the thermal expansion compensation management of pipelines is made more systematic and refined, improving the adaptability of pipelines in different environments and operating conditions.

[0073] Preferably, step S4 is specifically:

[0074] Step S41: extracting thermal conductivity features and thermal insulation material thickness based on the composite thermal insulation material data to obtain thermal conductivity and thermal insulation material thickness;

[0075] Step S42: Calculate the thermal resistance per unit area according to the thermal conductivity and the thickness of the thermal insulation material, so as to obtain the thermal resistance data of the thermal insulation layer;

[0076] Step S43: collecting the internal and external temperatures of the pipeline laying model, and performing temperature difference calculation to obtain the internal and external temperature difference of the pipeline;

[0077] Step S44: Perform heat flux density analysis based on the thermal resistance data of the insulation layer and the temperature difference between the inside and outside of the pipeline to obtain the heat flux density;

[0078] Step S45: Perform temperature attenuation analysis on the pipeline laying model according to the heat flux density to obtain the temperature attenuation;

[0079] Step S46: Perform low temperature drop judgment according to the temperature attenuation to obtain the low temperature drop data;

[0080] Step S47: Transmit the low temperature drop data and the dynamic compensation data to the intelligent monitoring platform in real time to execute the data acquisition and transmission task of the heat supply pipeline terminal.

[0081] In the present invention, by extracting the thermal conductivity characteristics and thickness of the composite insulation material data, the thermal performance of the insulation layer can be accurately quantified, providing a basis for subsequent heat loss calculation. Calculating the thermal resistance per unit area based on the thermal conductivity and the thickness of the insulation material can accurately reflect the heat insulation effect of the insulation layer, making the evaluation of the pipeline insulation performance more scientific and reasonable. Collecting the temperatures inside and outside the pipeline and calculating the temperature difference helps to monitor the heat loss of the pipeline in real time and provides accurate data for subsequent heat flux analysis. Combining the thermal resistance data of the insulation layer and the pipeline temperature difference for heat flux density analysis can clarify the intensity of heat transfer and avoid the imbalance of heat supply management caused by inaccurate heat flux calculation. Further performing temperature attenuation analysis based on the heat flux density can effectively predict the temperature loss along the pipeline, providing a basis for optimizing the heat supply strategy. Through low temperature drop judgment, the low temperature fault area occurring during the heat supply process can be quickly identified, enabling the system to give early warnings and take corresponding compensation measures to prevent local temperature anomalies from affecting the overall heat supply stability. Finally, transmitting the low temperature drop data and the dynamic compensation data to the intelligent monitoring platform in real time realizes the real-time acquisition and transmission of the heat supply pipeline terminal data, ensuring that the system can respond to temperature changes in a timely manner, optimize the heat supply dispatching, and improve the overall heat supply efficiency and stability. Description of the Drawings

[0082] Other features, objects, and advantages of the present invention will become more apparent by reading the detailed description of the non-limiting embodiments with reference to the following drawings:

[0083] Figure 1 It is a schematic flow chart of the steps of the method for collecting and transmitting terminal data of the long-distance heat supply pipeline of the present invention;

[0084] Figure 2 It is a detailed schematic flow chart of step S13 in the present invention;

[0085] Figure 3 It is a detailed schematic flow chart of step S3 in the present invention;

[0086] The realization, functional features, and advantages of the present invention will be further described in conjunction with embodiments with reference to the accompanying drawings. Detailed implementation manners

[0087] The technical method of the present invention for a patent will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0088] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0089] It should be understood that although the terms "first", "second", etc. may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0090] To achieve the above object, please refer to Figures 1 to 3 , the present invention provides a method for collecting and transmitting terminal data of a long-distance heating pipeline, and the method includes the following steps:

[0091] Step S1: Obtain the data of the laying area of the long-distance heating pipeline, and analyze the pipeline network laying form to obtain the data of the single-pipe branched heating pipeline network;

[0092] In this embodiment, to obtain the data of the long-distance heat supply pipeline laying area, it is necessary to use a Geographic Information System (GIS) to collect data such as the terrain, landform, soil type, groundwater level, geological structure, and building distribution along the pipeline. High-resolution remote sensing images, unmanned aerial vehicle (UAV) aerial survey data, geographical survey data, and existing underground pipeline databases are used to collect spatial information of the pipeline laying area. After data collection, spatial analysis technology is used to calculate the terrain slope, surface elevation, and geological stability of the pipeline laying path, and the temperature change range of the heat supply area is calculated based on historical meteorological data to obtain the pipeline network laying environment parameters. Subsequently, an analysis of the pipeline network laying form is carried out. The main pipe and branch pipes of the heat supply pipeline are identified using the pipeline network topology analysis method. Combining the urban heat supply planning data and the user heat load demand, the optimal path of the heat supply main pipe is determined based on fluid mechanics calculations. The pipeline length, the number of elbows, and the number of branch pipes are calculated, and the structural data of the single-pipe tree-shaped heat supply pipeline network are determined according to the user distribution. Finally, the data of the single-pipe tree-shaped heat supply pipeline network, including pipeline network nodes, pipe segment lengths, branch pipe connection methods, and spatial coordinates of the heat supply area, are generated and stored in the pipeline network database.

[0093] Step S2: Construct a pipeline laying model based on the data of the single-pipe tree-shaped heat supply pipeline network; conduct an intelligent heat energy flow simulation based on the pipeline laying model, and calculate the heat loss during the heat energy flow simulation to obtain heat loss data; integrate heat protection and insulation materials into the pipeline laying model based on the heat loss data to obtain composite insulation material data; identify the straight pipe sections of the pipeline laying model, and calculate the minimum wall thickness of the straight pipe sections to obtain the minimum wall thickness data;

[0094] In this embodiment, based on the data of a single-pipe tree-shaped heating pipe network, a pipe laying model is constructed using a computer-aided design (CAD) tool, and three-dimensional pipe network structure data is established according to the pipe diameter, wall thickness, elbow angle, and branch connection method. Based on the constructed pipe laying model, the computational fluid dynamics (CFD) method is used for intelligent heat energy flow simulation. The heating medium parameters are set, including the supply water temperature (set at 80°C - 120°C), the flow rate (calculated according to the pipe diameter and the user's heat load, generally in the range of 1.5 - 2.5 m / s), and the finite volume method (FVM) is used for solution to calculate the heat energy flow characteristics inside the pipe. According to the simulation calculation results, the heat loss along the pipe is calculated using the energy balance equation, the heat loss of different pipe diameters and different materials is analyzed using heat transfer methods, the heat loss data is obtained, and stored in the database. Based on the calculated heat loss data, suitable thermal insulation materials are selected, and a multi-layer thermal insulation structure is integrated according to the standard that the thermal conductivity (W / m·K) is less than 0.04 and the heat-resistant temperature is greater than 150°C, including a high-density polyurethane layer, a reflective aluminum foil layer, etc. The thickness of the composite thermal insulation material is calculated, the composite thermal insulation material data is generated, and stored in the database. Then, the straight pipe sections of the pipe laying model are identified, the minimum wall thickness of the straight pipe sections is calculated based on the internal pressure distribution of the pipe, the critical wall thickness value is calculated using the mechanical strength analysis method to ensure that the pipe wall does not undergo plastic deformation under the maximum working pressure (generally 1.0 - 2.5 MPa), and finally the minimum wall thickness data is generated and stored.

[0095] Step S3: Based on the minimum wall thickness data, perform pipe network pressure analysis on the pipe laying model to obtain pipe network pressure data; perform thermal expansion analysis according to the pipe network pressure data to obtain thermal expansion data, and perform dynamic compensation for thermal expansion to generate dynamic compensation data;

[0096] In this embodiment, based on the minimum wall thickness data, perform pipe network pressure analysis on the pipe laying model, use the finite element analysis (FEA) method for stress calculation, establish a pipe wall stress distribution model based on the internal fluid pressure of the pipe (set in the range of 0.6 - 2.5 MPa) and the external environmental pressure (depending on the burial depth, generally 0.1 - 0.4 MPa), calculate the internal and external pressure distribution borne by the pipe wall, and obtain the pipe network pressure data. Subsequently, perform thermal expansion analysis according to the pipe network pressure data, use the thermal expansion calculation formula, and calculate the expansion amount of the pipe at different operating temperatures (80°C - 120°C) through the linear expansion coefficient of the material (generally 10×10 -6 -15×10 -6 / °C). According to the calculated thermal expansion amount, perform dynamic compensation analysis based on the compensator parameters (such as the compensation amount of the bellows compensator is in the range of 50 - 150 mm), determine the installation position and quantity of the compensator, optimize the compensator spacing, generate dynamic compensation data, and store it in the database.

[0097] Step S4: Conduct a low-temperature drop analysis on the pipeline laying model based on the composite thermal insulation material data to obtain low-temperature drop data; transmit the low-temperature drop data and dynamic compensation data to the intelligent monitoring platform in real time to perform the data acquisition and transmission task for the heat supply pipeline terminal.

[0098] In this embodiment, based on the composite thermal insulation material data, a low-temperature drop analysis is conducted on the pipeline laying model. The steady-state heat transfer calculation method is used to calculate the temperature drop amplitude on the pipeline surface under the external environmental temperature (in the range of -40°C to 10°C), and combined with factors such as wind speed (2 - 10 m / s) and air humidity (30% - 90%), the cooling effect of the thermal insulation layer is analyzed to obtain low-temperature drop data. Then, the low-temperature drop data and dynamic compensation data are transmitted to the intelligent monitoring platform. Using Internet of Things (IoT) technology, the data of the pipeline terminal is collected in real time through wireless sensor nodes, including parameters such as supply water temperature, return water temperature, pipeline pressure, heat loss, and compensator displacement. The data collected by the sensor is transmitted to the data center through LoRa, NB-IoT, or optical fiber communication methods, and stored in the monitoring database after data preprocessing. The intelligent monitoring platform performs real-time calculations based on the collected data and provides functions such as heat supply pipeline status monitoring, operation parameter optimization, and abnormal warning to ensure the stable operation of the heat supply system.

[0099] Preferably, step S1 is specifically as follows:

[0100] Step S11: Obtain the data of the long-distance heat supply pipeline laying area and conduct remote sensing acquisition, where the set acquisition resolution is 0.5 m - 30 m and the acquisition spectral range is 8 μm - 14 μm to obtain regional remote sensing data;

[0101] In this embodiment, when obtaining the data of the long-distance heat supply pipeline laying area and conducting remote sensing acquisition, first select the geographical scope of the area, and conduct data acquisition through a high-resolution remote sensing satellite or a remote sensing device carried by a drone. The set acquisition resolution range is 0.5 meters to 30 meters to ensure that the topographic and thermal distribution information of the area can be captured in detail. The remote sensing device selects a thermal infrared sensor with appropriate spectral characteristics, and the acquisition spectral range is 8 μm - 14 μm to ensure that the heat changes involved in the pipeline operation can be covered. During the process of remote sensing data acquisition, ensure the continuity and integrity of the data, avoid the generation of blind spots, and at the same time, accurately adjust the angle of the acquisition device to ensure the accuracy and efficiency of the data.

[0102] Step S12: Extract the thermal infrared remote sensing features according to the regional remote sensing data to obtain thermal infrared remote sensing data;

[0103] In this embodiment, when extracting thermal infrared remote sensing features based on regional remote sensing data, a remote sensing image processing software (such as ENVI) is used to perform image analysis on the collected thermal infrared data. First, noise and background information are removed through a preprocessing step to obtain a clear thermal infrared image. Then, specific image processing algorithms are used to extract the thermal infrared features in the image, mainly focusing on the heat distribution and temperature difference changes. By setting a suitable algorithm model, such as the normalized difference thermal imaging algorithm, the temperature change regions in the thermal infrared image are obtained. The finally extracted thermal infrared data needs to be further verified to ensure its effectiveness and accuracy, and to provide a stable data basis for subsequent steps.

[0104] Step S13: Determine the heat consumption area based on the thermal infrared remote sensing data, where the recognition accuracy of the area of the consumption area is set to ±5m 2 -±50m 2 ;

[0105] In this embodiment, when determining the heat consumption area based on the thermal infrared remote sensing data, first, regions with lower temperatures or heat consumption characteristics are identified through the thermal infrared image, and the region growing algorithm is used to classify and calibrate the heat source regions. For the area recognition of the heat consumption area, the error tolerance range is set to ±5m 2 to ±50m 2 , and precise calculations are carried out within this error range. By analyzing the temperatures of different regions, an automatic segmentation algorithm is used to divide the image into regions, and the heat consumption regions within the regions are separately identified, and their specific positions and heat consumption intensities are marked. The main tools used in this process include image processing software and region analysis algorithms to ensure that the identified heat consumption regions are consistent with the actual situation and meet the required error range.

[0106] Step S14: Calculate the heat load of the heat consumption area to obtain heat load data and conduct heat load statistics, where the low heat load judgment threshold is set to <50W / m 2 , and low heat load data is obtained;

[0107] In this embodiment, when calculating the heat load of the heat consumption area, in combination with the identified heat consumption area, a professional thermodynamics software (such as ANSYS Fluent) is used to simulate and calculate the heat load within the area. First, by setting a heat load calculation model, basic parameters such as the temperature, wind speed, pipe material, and heat flow within the area are input. Then, based on the set low heat load judgment threshold (<50W / m 2 ), the heat load distribution is analyzed, and the regions with heat load lower than this threshold are marked to obtain low heat load data. During the calculation process, it is required to adjust the heat load distribution according to the actual heating demand and the heat flow of the pipeline to ensure that the marking of the low heat load area can reflect the actual heating requirements.

[0108] Step S15: Divide the heat consumption area according to the low heat load data to obtain the low heat load area;

[0109] In this embodiment, when dividing the heat consumption area according to the low heat load data, the heat load data and area analysis software (such as ArcGIS) are used to further divide the low heat load area. According to the heat load distribution map, the clustering algorithm is adopted to finely divide the low heat load area to ensure that the heat load in each area is not lower than the set standard. Through this division method, different heat load areas can be accurately separated, avoiding the confusion between the low heat load area and the normal load area. The key parameters involved in this process include heat load data, area recognition accuracy, and standard division threshold, ensuring that the division accuracy reaches ±50m 2 range.

[0110] Step S16: Determine the laying of a single-pipe tree-shaped heating pipe network in the low heat load area, where the single-pipe diameter range is set to DN50 - DN250, and obtain the single-pipe tree-shaped heating pipe network data.

[0111] In this embodiment, when determining the laying of a single-pipe tree-shaped heating pipe network in the low heat load area, first, a detailed analysis of the low heat load area is carried out, and a single-pipe heating system is selected for pipe network design. According to the heat load data and the area of the area, the single-pipe diameter range is set from DN50 to DN250, and a suitable pipe diameter is selected to meet the heat load transmission requirements in the area. Then, the design of the heating pipe network is simulated through pipe network layout software (such as Pipesim) to ensure that the laying of the pipe network in the low heat load area can efficiently transmit heat and achieve energy-saving effects. During the entire design process, the parameters involved include the pipe diameter of the pipe network, laying depth, and heat load transmission requirements, ensuring that the design scheme achieves the best effect in practical applications.

[0112] Preferably, step S13 is specifically:

[0113] Step S131: Calculate the land surface emissivity according to the thermal infrared remote sensing data to obtain the land surface emissivity;

[0114] In this embodiment, when calculating the land surface emissivity based on thermal infrared remote sensing data, it is first necessary to extract the thermal infrared remote sensing data of the area to ensure that the data resolution is between 0.5 m and 30 m to guarantee the calculation accuracy. A standard thermal infrared radiation model is selected, and based on the relationship between the radiation intensity of each pixel in the remote sensing data and the land surface temperature, the land surface emissivity of the area is calculated. Specifically, using the known emissivity values of substances, by comparing with the collected radiation intensity, the radiometric calibration method is adopted to determine the emissivity of each pixel point. For different land surface substances, the calculation parameters of the emissivity are adjusted according to their radiation characteristics to ensure that the thermal radiation reflected by the land surface is accurately captured. During this calculation process, the radiation intensity error tolerance range needs to be set at ±5% to ensure the reliability of the calculation.

[0115] Step S132: Use the preset Planck function to perform land surface temperature inversion on the land surface emissivity to generate land surface temperature data;

[0116] In this embodiment, when using the preset Planck function to perform land surface temperature inversion on the land surface emissivity, first, according to the relationship between Planck's law and the land surface emissivity, appropriate inversion parameters are set, including the spectral range of the emissivity and the corresponding temperature range. Using the remote sensing data and the preset Planck function, the land surface temperature inversion process is carried out. The Planck function calculates the corresponding land surface temperature based on different radiation intensity values to ensure that the calculation accuracy of the temperature during the inversion process is not lower than ±0.5 °C. In actual operation, it is necessary to perform spectral data conversion on the collected emissivity through an algorithm to ensure that the thermal infrared radiation intensity is converted into an accurate land surface temperature value. During this process, the standard setting range for temperature inversion is -50 °C to +70 °C to cover all land surface temperature changes.

[0117] Step S133: Calculate the average land surface temperature based on the land surface temperature data;

[0118] In this embodiment, when calculating the average land surface temperature based on the land surface temperature data, first, all the land surface temperature data of the area is extracted, and data cleaning and outlier removal are performed to ensure the effectiveness of the data. Then, by performing weighted averaging on the effective land surface temperature data, the average land surface temperature within the area is calculated. The calculation method of weighted averaging sets weights according to the temperature distribution characteristics of the area. For example, different weights can be set according to the land surface substance type or the heat load intensity to ensure the representativeness of the average value. During the calculation process of the average land surface temperature, it is required that the calculation result error does not exceed ±1 °C to ensure the stability and accuracy of the average value.

[0119] Step S134: Calculate the standard deviation of the land surface temperature based on the land surface temperature data;

[0120] In this embodiment, when calculating the standard deviation of the surface temperature based on the surface temperature data, first, the surface temperature data in the area is normalized. After removing outliers, the standard deviation of all valid surface temperature data is calculated. During the calculation of the standard deviation, it is necessary to ensure that the temperature fluctuations between different surface features in the area can be accurately reflected. By calculating the deviation between each data point and the mean value, the standard deviation of the surface temperature in the area is obtained, ensuring that the error during the calculation process does not exceed ±0.5°C. This standard deviation reflects the degree of dispersion of the temperature distribution in the area and provides a basis for the subsequent division of the heat consumption area.

[0121] Step S135: Determine the heat consumption area of the industrial area according to the mean surface temperature and the standard deviation of the surface temperature, and obtain the heat consumption area.

[0122] In this embodiment, when determining the heat consumption area of the industrial area according to the mean surface temperature and the standard deviation of the surface temperature, first, according to the obtained mean surface temperature and standard deviation, set the division criteria for the heat consumption area. For example, set the temperature difference range of the heat consumption area to be ±2°C, and use the mean surface temperature and the standard deviation to identify the area with large temperature fluctuations as the heat consumption area. In actual operation, by setting the area with large temperature fluctuations as the heat consumption area, it is ensured that the heat consumption characteristics of this area can be accurately identified and calibrated. When dividing the area, it is necessary to ensure that the area error of each heat consumption area does not exceed ±5m 2 , avoiding being too refined or too rough in division, so as to achieve the precise positioning of the heat consumption area.

[0123] Preferably, the heat energy flow simulation in step S2 includes:

[0124] Collect heat users based on the pipeline laying model, where the collection range is the area within a heating radius of 500 - 2000m;

[0125] In this embodiment, when collecting heat users based on the pipeline laying model, first determine the heating radius range, which is the area within a range of 500m to 2000m. The demarcation of this area is carried out through high-precision remote sensing technology combined with the Geographic Information System (GIS). Ensure that each heat user in the collected area can be covered. The boundary of this area is adjusted according to the design requirements of the heating pipe network and regional needs to maintain temperature equilibrium within the radius range. The heat users in the area are obtained through positioning data and classified. The specific location of each heat user is recorded using the Global Positioning System (GPS) to ensure an accuracy of ±0.5m. During the data collection process, the accuracy of the positioning data is jointly determined by the signal strength of the device and environmental factors, and this accuracy requirement must be ensured.

[0126] Statistical location coordinates of heat users, where the set coordinate accuracy ≤ 0.5m;

[0127] In this embodiment, when counting the location coordinates of heat users, based on the collected positioning information, the coordinate points of each heat user are recorded one by one to ensure that the positioning accuracy reaches ≤0.5 m. The location data is collected with high precision by GPS devices and fed back to the data storage system through real-time data transmission to ensure seamless connection. The recording and storage method of each coordinate adopts a high-precision positioning algorithm, and the coordinate accuracy is strictly controlled according to different types of heat users (such as residential, commercial, etc.). This data is uploaded to the centralized management system in a timely manner through the transmission network to ensure the real-time update and high precision of the location coordinates.

[0128] Identify buildings based on the location coordinates and calculate the building area;

[0129] In this embodiment, when identifying buildings based on the location coordinates and calculating the building area, first, satellite remote sensing images are used in combination with a geographic information system (GIS) to identify the buildings in the coordinate area where each heat user is located. The contour of the buildings in this area is analyzed through image recognition technology to confirm the scope and shape of the buildings. For the identified buildings, through spatial analysis methods, GIS tools are used to calculate the total area of the buildings. The building area calculation method in this process adopts a boundary tracking algorithm, which combines terrain data and building shape features to estimate the area with high precision, and the error is controlled within ±1 m 2 or less.

[0130] Count the heat demand of heat users, and the heat demand per household is 5 kW - 500 kW;

[0131] In this embodiment, when counting the heat demand of heat users, first, based on the identified building area, combined with the functional type and usage intensity of the buildings, the heat demand of each building is estimated according to the known heat load standard. According to the heat user category, the heat demand range per household is set from 5 kW to 500 kW. In actual operation, residential buildings are calculated according to the heat demand quota per square meter, while commercial buildings are adjusted accordingly based on factors such as their actual usable area and number of floors. The heat demand of each heat user is summarized during the statistics to ensure that the error range is within ±10%. This process ensures that the heat demand of each heat user has a high degree of accuracy and provides basic data for subsequent heat energy flow simulation.

[0132] Estimate the heating demand per unit area based on the heat demand and the building area to obtain the heating demand per unit area;

[0133] In this embodiment, when estimating the heating demand per unit area based on the heating demand and the building area, the heat demand value per square meter of the building is first determined. By performing a ratio operation on the heat demand data and the total building area, the heating demand per unit area is obtained. This estimation method is adjusted according to different types of buildings (such as high-rise, low-rise, etc.). Specifically, residential buildings use the standard heat demand calculation formula, while commercial buildings additionally consider external environmental factors (such as sunshine duration, seasonal changes, etc.). The calculation result of the heating demand per unit area will be corrected within the error control range to ensure that its error range does not exceed ±5W / m 2 .

[0134] Perform intelligent thermal energy flow simulation on the pipeline laying model according to the heating demand per unit area, where the steam parameters are set to 0.8MPa.a and 180°C;

[0135] In this embodiment, when performing intelligent thermal energy flow simulation on the pipeline laying model according to the heating demand per unit area, the steam parameters are first set to 0.8MPa and 180°C, and these parameters are input into the thermal energy flow simulation software. During this process, the steam pressure and temperature involved in the pipeline laying model are used to simulate and calculate the distribution of the heat flow. During the simulation process, multiple factors such as the flow characteristics of the pipeline, the heat conduction efficiency, and the heat loss need to be included in the calculation scope to ensure that the simulation model accurately reflects the actual situation. The steam parameters need to ensure that the needs of the heat users are met, while avoiding excessive pressure leading to system instability.

[0136] Calculate the heat loss during the intelligent thermal energy flow simulation process to obtain heat loss data.

[0137] In this embodiment, when calculating the heat loss during the intelligent thermal energy flow simulation process, using the aforementioned simulation results, comprehensively considering factors such as pipeline material, steam pressure, temperature, and environmental factors, the heat loss is calculated. First, based on the heat flow situation inside the pipeline, analyze the temperature drop amplitude of each part of the pipeline to ensure the accuracy of the loss data. By setting appropriate heat loss standards (such as the pipeline internal temperature difference control standard is ≤3°C), dynamic heat loss monitoring of the pipeline is carried out, and finally heat loss data is obtained. During this process, real-time monitoring devices and sensors are used to collect the temperature data inside and outside the pipeline to support the heat loss analysis and ensure the timeliness and accuracy of the heat loss data.

[0138] Preferably, the heat protection and insulation material integration in step S2 includes:

[0139] Divide the laying types of the pipeline laying model to obtain overhead heating pipe data and buried pipeline data;

[0140] In this embodiment, when classifying the laying types of the pipeline laying model, first, according to the actual layout of the pipelines, two types are defined: overhead heating pipelines and buried pipelines. Overhead heating pipelines are suspended in the air through supports and are usually set between buildings or above the road surface, while buried pipelines are buried underground. For accurate classification, a Geographic Information System (GIS) is used in combination with the pipeline layout drawings to label the pipelines, and they are classified according to the installation location and usage scenarios of the pipelines. The layout of each pipeline type is confirmed using high-precision surveying instruments to ensure accurate collection of data for different pipeline types, and the lengths, burial depths, and respective maintenance requirements of overhead and buried pipelines are recorded separately.

[0141] Use the heat loss data to identify the high heat loss pipe sections in the overhead heating pipe data;

[0142] In this embodiment, when using the heat loss data to identify the high heat loss pipe sections in the overhead heating pipe data, first obtain the temperature data of the overhead pipelines, and use heat sensors to collect the temperature changes on the pipeline surface in real time. In the pipeline distribution area, multiple temperature monitoring points are set to ensure real-time collection of the temperature data on the pipeline surface. By comparing the set temperature standard (for example, when the temperature deviation on the pipeline surface exceeds 5°C, it is considered a high heat loss), data analysis algorithms are used to identify the pipe sections with larger heat losses. After the temperature changes of each pipe section are collected by the monitoring equipment, differential analysis is performed to obtain the pipe sections with high heat losses. For the processing of temperature data, ensure that the error range during all heat loss calculations is controlled within ±2°C.

[0143] Use the heat loss data to identify the high heat loss pipe sections in the buried pipeline data;

[0144] In this embodiment, when using the heat loss data to identify the high heat loss pipe sections in the buried pipeline data, the same method as that for overhead pipelines is adopted, and the identification is based on the temperature data of the buried pipelines. Since the buried pipelines are buried underground, the temperature acquisition process requires monitoring by burying underground temperature sensors. The temperature sensors are placed at different depths outside the pipelines and upload the temperature data in real time through underground transmission. By comparing the set heat loss standard, if the pipeline temperature drops by more than a preset threshold (for example, the temperature drops by more than 4°C), then it is determined that this pipe section is a high heat loss pipe section. The transmission of temperature data is carried out through wired or wireless sensor networks to ensure that the temperature changes of the buried pipelines can be timely fed back to the data analysis platform.

[0145] Apply the preset aluminosilicate thermal insulation material and high-temperature glass wool material to the high heat loss pipe sections in the overhead heating pipe data to obtain the composite insulation material data for the overhead pipe sections;

[0146] In this embodiment, when applying the preset aluminosilicate thermal insulation material and high-temperature glass wool material to the high heat loss pipe sections in the overhead heating pipe data, first, according to the identified high heat loss pipe sections, obtain the specific dimensions of each pipe section (such as pipe outer diameter and length). For these high heat loss pipe sections, select the aluminosilicate thermal insulation material and high-temperature glass wool material for composite thermal insulation treatment. The aluminosilicate thermal insulation material is applied to the outer layer of the pipe, with high heat resistance performance to ensure that the heat on the pipe surface does not leak; while the high-temperature glass wool is used as the inner layer material to reduce the heat loss on the pipe surface. The insulation thickness of each pipe section should be determined based on the specific temperature monitoring data and the working pressure of the pipe to ensure that the insulation effect meets the design requirements. Use professional tools to cut and install the insulation material to ensure the material's airtightness and stable installation.

[0147] Apply the preset nanoporous aerogel material and high-temperature glass wool material to the high heat loss pipe sections in the buried pipe data to obtain the buried pipe section composite insulation material data;

[0148] In this embodiment, when applying the preset nanoporous aerogel material and high-temperature glass wool material to the high heat loss pipe sections in the buried pipe data, for the high heat loss pipe sections of the buried pipe, select the nanoporous aerogel material and high-temperature glass wool material for composite thermal insulation. Due to its excellent thermal insulation performance, the nanoporous aerogel material is used as the first layer of thermal insulation material for the buried pipe, and the outer layer is wrapped with high-temperature glass wool to enhance the thermal insulation effect. The aerogel material effectively reduces heat conduction through its fine pore structure, and the glass wool enhances its heat resistance and stability. The application of all materials strictly calculates the required material thickness according to the pipe outer diameter and burial depth, and uses automated equipment for accurate cutting and installation of the materials to ensure that the thermal insulation effect meets the predetermined requirements.

[0149] Integrate the overhead pipe section composite insulation material data and the buried pipe section composite insulation material data to obtain the composite insulation material data.

[0150] In this embodiment, when integrating the overhead pipe section composite insulation material data and the buried pipe section composite insulation material data to obtain the composite insulation material data, first summarize all the heat loss data and insulation material data of the overhead pipes and buried pipes. On this basis, combine factors such as the pipe material, pipe layout method, and external environmental conditions to analyze the use effect of the composite insulation material. Through data fusion technology, merge the data of the aluminosilicate insulation layer and glass wool layer of the overhead pipe section, and the insulation data of the nanoporous aerogel and glass wool layer of the buried pipe section. Classify the insulation data of each pipe section, and combine the overall layout of the pipes to quantify the insulation effects of different pipe types and high heat loss pipe sections. Through this data integration process, generate the final composite insulation material data for subsequent pipe maintenance and performance evaluation.

[0151] Preferably, the calculation formula for calculating the minimum wall thickness in step S2 is as follows:

[0152]

[0153] C = 0.5B;

[0154] S = s + c;

[0155] In the formula, s represents the minimum wall thickness of the straight pipe; p represents the design pressure; [σ] t represents the basic allowable stress at the calculated temperature; D represents the outer diameter of the pipe; Y represents the correction factor. For ferritic steel, when the temperature < 482 °C, take 0.4; η represents the correction factor of the allowable stress. For seamless steel pipes, it is 1.0, and for spiral welded steel pipes, it is 0.9; α represents the additional thickness considering corrosion, wear and mechanical strength requirements, take α = 1 mm; S represents the calculated wall thickness of the straight pipe; c represents the additional value of the negative deviation of the straight pipe wall thickness; C represents the facing processing allowance; B represents the additional value of the positive deviation of the straight pipe wall thickness.

[0156] In this embodiment, the present invention analyzes and integrates the minimum wall thickness calculation formula. The main purpose of this formula is to calculate the minimum wall thickness of the straight pipe section, considering the combined effects of multiple factors, including the minimum wall thickness of the straight pipe, correction factor, correction factor, correction factor of the allowable stress, additional thickness considering corrosion, wear and mechanical strength requirements, calculated wall thickness of the straight pipe, additional value of the negative deviation of the straight pipe wall thickness, facing processing allowance, and additional value of the positive deviation of the straight pipe wall thickness. The function of the formula is to ensure that the pipe can withstand the influence of various external factors such as design pressure, temperature, corrosion, wear and manufacturing errors in the working environment by accurately calculating the required wall thickness of the pipe, thereby ensuring the structural safety and long-term stability of the pipe. This formula comprehensively considers multiple influencing factors, including design pressure, basic allowable stress of the material, temperature change, manufacturing tolerance, and corrosion and wear suffered by the pipe. Design pressure p and basic allowable stress [σ] at the calculated temperature tDetermines the bearing capacity of the pipeline under high pressure and high temperature conditions, ensuring that the pipeline can operate safely without rupture or excessive expansion. The correction factors Y and η are adjusted according to different material properties, especially the influence of temperature on material strength, to ensure the reliability of the pipeline under various working conditions. The additional thickness α is used to compensate for corrosion, wear, and mechanical strength requirements encountered during the use of the pipeline, ensuring that the pipeline still maintains sufficient strength and safety even after long-term use. The negative deviation c and positive deviation B in the formula take into account the inevitable dimensional errors and tolerances during the manufacturing process, ensuring that even if there are certain errors in the production process of the pipeline, it can still meet the use requirements and avoid potential safety hazards caused by manufacturing errors. The butt welding processing allowance C provides an additional safety margin for the pipeline connection part, considering the stress concentration problem that occurs at the pipeline connection, and further improving the stability of the overall structure. Through the application of this formula, the minimum wall thickness of the straight pipe can be scientifically determined, which can not only effectively improve the working efficiency of the pipeline, but also extend its service life, prevent ruptures, leaks, or other structural failures caused by improper wall thickness design, thus providing sufficient guarantee for the long-term use of the pipeline.

[0157] Preferably, step S3 is specifically as follows:

[0158] Step S31: Based on the minimum wall thickness data, perform pipeline strength calculation on the pipeline laying model to obtain pipeline bearing capacity data;

[0159] In this embodiment, when performing pipeline strength calculation, first, according to the pipeline material and the minimum wall thickness data of the pipeline (such as the wall thickness of carbon steel pipeline is 8 mm, the wall thickness of stainless steel pipe is 6 mm, etc.), combined with the outer diameter and internal pressure conditions of the pipeline, use mechanical analysis methods to calculate the bearing capacity of the pipeline. This step requires extracting data such as the material, wall thickness, outer diameter, inner diameter, and working pressure of the pipeline from the pipeline design drawings. In addition, the environmental temperature and corrosion factor of the pipeline also need to be considered to accurately reflect the strength performance of the pipeline under different working conditions. According to the pressure difference inside and outside the pipeline and the design pressure, calculate the maximum pressure value that can be borne, and ensure that the pipeline will not rupture or deform under different conditions. This data serves as an important basis for whether the pipeline meets the design requirements.

[0160] Step S32: Evaluate the maximum working pressure during the heat energy flow simulation according to the pipeline bearing capacity data;

[0161] In this embodiment, based on the pipeline pressure-bearing capacity data obtained in step S31, combined with the flow rate and temperature conditions of the pipeline, a heat energy flow simulation is carried out to evaluate the maximum working pressure in the pipeline system. First, collect the heat medium flow rate and temperature data flowing in the pipeline, which can be obtained through devices such as flow meters and temperature sensors. Then, by establishing a thermodynamic model, simulate the heat energy transmission process in the pipeline system. During this process, factors such as the length of the pipeline, the number of elbows, and valves must be considered because these factors will affect the pressure. According to the simulation results, obtain the maximum working pressure that occurs during the actual heat supply process of the pipeline. All simulation processes need to use simulation software that complies with industry standards, such as ANSYS Fluent, CFX, etc. for calculation, and set the pressure upper limit threshold to ensure that it does not exceed the pressure-bearing capacity of the pipeline.

[0162] Step S33: Analyze the pipeline frictional pressure loss based on the maximum working pressure data to obtain the pipeline frictional pressure loss data;

[0163] In this embodiment, after obtaining the maximum working pressure, the pipeline frictional pressure loss is analyzed next. First, starting from the maximum working pressure, calculate the pressure loss by measuring the pressure data at different positions of the pipeline. During this process, the principles of fluid mechanics need to be used for analysis, considering factors such as the inner wall roughness of the pipeline, the pipeline length, and the fluid flow velocity. These parameters are obtained through real-time monitoring by devices such as flow meters and pressure sensors. When calculating, a pressure measurement point is set at a certain distance (such as every 100 meters) along the pipeline to obtain the pressure data of each measurement point, and calculate the frictional pressure loss along the pipeline according to the flow velocity, pipe diameter, and pipeline surface smoothness. This step requires ensuring that the data acquisition device has high precision and the measurement error does not exceed ±1%.

[0164] Step S34: Calculate the local resistance loss based on the pipeline frictional pressure loss data to obtain the pipeline local resistance loss data;

[0165] In this embodiment, based on the pipeline frictional pressure loss data, further calculate the local resistance loss. The local resistance loss usually occurs at parts such as elbows, valves, and joints of the pipeline. Therefore, pressure sensors are installed at these positions to monitor the pressure changes at these parts in real time. By the relationship between the pressure difference and the flow rate, combined with specific parameters such as the elbow radius, joint type, and valve opening and closing state of the pipeline, calculate the resistance loss of each local part. This process requires detailed modeling of the geometric shape of each local component and calculation of its resistance loss in combination with fluid mechanics formulas. During the data acquisition and calculation process, the design standards of the pipeline and the actual operating conditions need to be considered to ensure that all calculation results are accurate and in line with the actual situation.

[0166] Step S35: Based on the local resistance loss data of the pipeline, conduct a full-pipeline network pressure assessment to obtain the pipeline network pressure data;

[0167] In this embodiment, after obtaining the local resistance loss data of the pipeline, the full-pipeline network pressure assessment is then carried out. By integrating the local resistance loss data of each part and considering factors such as the flow rate and working conditions of the full-pipeline network, the pressure distribution of the entire pipeline network is calculated. At this time, a pipeline network hydraulic model needs to be used. The local resistance loss data is input into the model to simulate the pressure changes in each part of the pipeline network. The pressure of each pipeline segment can be accumulated through the length, flow velocity of the pipeline and the local resistance loss data, and finally the pressure data of the full-pipeline network is obtained. During this process, it is necessary to ensure that the flow rate data for calculation comes from actual flow rate monitoring devices, and the pressure calculation does not exceed the pressure-bearing capacity of the pipeline.

[0168] Step S36: Based on the pipeline network pressure data, conduct thermal expansion analysis to obtain thermal expansion data, and perform dynamic thermal expansion compensation to generate dynamic compensation data.

[0169] In this embodiment, based on the pipeline network pressure data, the thermal expansion analysis of the pipeline is carried out. First, collect the temperature data of different pipeline segments in the pipeline network. Considering the thermal expansion coefficient of the pipeline material, calculate the thermal expansion amount generated by the pipeline due to temperature changes. This data is obtained in real time by installing temperature sensors and combining with the fluid flow situation. According to the pipeline material and working environment, calculate the thermal expansion value of the pipeline. The dynamic thermal expansion compensation is based on the above analysis results. By adjusting the fixed supports of the pipeline or setting expansion joints, etc., dynamically compensate for the pipeline displacement caused by thermal expansion to prevent pipeline deformation or rupture. During the compensation process, it is necessary to ensure that the compensation device can adapt to the thermal expansion amount of the pipeline and ensure the stability of the system operation. Finally, through data recording and analysis, generate thermal expansion compensation data for the subsequent maintenance and adjustment of the pipeline.

[0170] Preferably, step S33 is specifically:

[0171] Step S331: Based on the maximum working pressure data, extract the characteristics of the maximum working pressure period to obtain the maximum working pressure period;

[0172] In this embodiment, real-time pressure data is obtained through a pressure sensor installed on the pipeline. According to the maximum working pressure data evaluated in step S32, the pressure change in the pipeline system is monitored to determine the time period when the maximum working pressure is reached. Specifically, the pressure data is processed in a time series, and the time periods when the pressure value reaches or approaches the maximum working pressure are screened out. To accurately extract the maximum working pressure time period, a pressure threshold needs to be set, usually set to a pressure value within the range of 90%-100% of the maximum working pressure. Based on this standard, the corresponding time period is extracted. During this time period, the system will record the pressure change in real time and automatically mark this time period as the maximum working pressure time period, providing a data basis for subsequent analysis.

[0173] Step S332: Determine the steam density and flow velocity data during the maximum working pressure time period, where the steam density range is set to 2.5 kg / m 3 -5.0 kg / m 3 and the flow velocity range is 15 m / s - 50 m / s;

[0174] In this embodiment, during the maximum working pressure time period, temperature and pressure sensors are used to obtain the temperature and pressure data of the steam in the pipeline. Based on these data, the steam density is calculated by referring to relevant steam thermodynamics tables or using the steam state equation. The density data will be verified within the set range of 2.5 kg / m 3 -5.0 kg / m 3 Meanwhile, the steam flow velocity in the pipeline is measured by a flowmeter or a flow velocity sensor. According to the standard flow velocity range of 15 m / s - 50 m / s, the accuracy of the data acquisition device is ensured to avoid errors when the flow velocity is lower or higher than this range. During the entire data acquisition process, parameters such as temperature, pressure, and flow velocity need to be collected according to industry standards to ensure data reliability. All measured steam density and flow velocity data will be recorded in the system for subsequent calculations.

[0175] Step S333: Calculate the steam flow Reynolds number based on the steam density and flow velocity data to obtain the steam flow Reynolds number;

[0176] In this embodiment, based on the steam density and flow velocity data obtained in the previous step, combined with the inner diameter of the pipeline, the Reynolds number of steam flow is calculated through fluid mechanics. First, determine the inner diameter data of the pipeline, which can be obtained from the pipeline design drawings or actual measurements. Then, use these steam density and flow velocity data to calculate the Reynolds number according to the relevant calculation methods of fluid mechanics. When calculating, the viscosity data of the steam need to be considered, and these data are obtained by referring to the physical property table of the steam. The calculation result of the Reynolds number can determine whether the flow belongs to laminar flow, turbulent flow or transitional flow, and this data plays a key role in the subsequent calculation of frictional pressure loss. The calculation of the Reynolds number requires a high degree of accuracy, and it is necessary to ensure that the measurement errors of all parameters are within the allowable range, usually the error does not exceed ±2%.

[0177] Step S334: Obtain the inner wall roughness of the pipeline and the pipeline length along the path;

[0178] In this embodiment, in order to accurately calculate the frictional pressure loss, it is necessary to obtain the inner wall roughness of the pipeline and the pipeline length along the path. The inner wall roughness data are obtained through the standards provided by the pipeline manufacturer or on-site measurements. The common range of inner wall roughness values is from 0.01 mm to 1.0 mm, and the specific value depends on the pipeline material and service life. The pipeline length along the path is extracted from the pipeline design drawings, and the length is usually in meters. For long-distance heating pipelines, the length along the path reaches several kilometers. Therefore, when collecting data, it is necessary to ensure that the length data of each section of the pipeline is accurate and error-free to ensure effective pressure loss calculation.

[0179] Step S335: Calculate the frictional pressure loss according to the Reynolds number of steam flow and the inner wall roughness of the pipeline, where the friction coefficient ranges from 0.005 to 0.05, and obtain the frictional pressure loss data;

[0180] In this embodiment, according to the Reynolds number of steam flow calculated in step S333 and the inner wall roughness data of the pipeline obtained in step S334, the frictional pressure loss is calculated using the principles of fluid mechanics. In this process, first, it is necessary to determine the friction coefficient, and the range of this coefficient is usually set between 0.005 and 0.05, and the specific value is selected according to the changes in the Reynolds number and the inner wall roughness of the pipeline. By calculating the frictional force of the fluid in the pipeline, the data of the frictional pressure loss are obtained. The frictional pressure loss is an important factor affecting the fluid flow efficiency of the pipeline. Therefore, when calculating, it is necessary to consider the service life of the pipeline and the frictional changes in actual operation. During the calculation process, it is necessary to ensure the accuracy of the input data and ensure that all sensor measurement errors are within the standard allowable range.

[0181] Step S336: Conduct a cumulative statistical analysis of the pipeline frictional pressure loss along the path according to the frictional pressure loss data and the pipeline length along the path, and obtain the pipeline frictional pressure loss data along the path.

[0182] In this embodiment, based on the frictional pressure loss data calculated in step S335 and combined with the data of the pipeline's along - length, the cumulative statistics of the pipeline's along - length pressure loss are carried out. In this step, the pressure loss of each section of the pipeline will be accumulated according to its length and the frictional pressure loss value. This process requires segmentation according to the specific length of each section of the pipeline to ensure that the pressure loss of each section is calculated separately and finally merged into the pressure loss data of the entire pipeline system. During this statistical process, it is necessary to ensure that the calculation of the pressure loss covers all influencing factors, such as the local resistance losses of pipe elbows, valves, etc. Through precise calculation, the along - length pressure loss of the entire pipeline system is obtained, providing a basis for subsequent pipeline regulation and operation optimization.

[0183] Preferably, step S36 is specifically as follows:

[0184] Step S361: Obtain the pipeline material data and extract the coefficient of thermal expansion;

[0185] In this embodiment, the material data of the heating pipeline is obtained by using the pipeline design database or the technical parameters provided by the pipeline manufacturer. The material data includes specific information such as the material of the pipeline (such as carbon steel, stainless steel, ductile iron, polyurethane - insulated pipe, etc.), wall thickness, pipe diameter, service life, etc. For the extraction of the coefficient of thermal expansion, it is queried based on the physical property data of the pipeline material. Usually, refer to standards such as GB / T 699 - 2015 "High - quality Carbon Structural Steel" or GB / T 17395 - 2008 "Dimensions, Shape, Weight and Tolerances for Seamless Steel Tubes" to obtain the linear coefficient of thermal expansion of different materials within different temperature ranges. For example, the linear coefficient of thermal expansion of carbon steel pipelines is usually between 11×10 -6 / °C and 14×10 -6 / °C, while the coefficient of thermal expansion of stainless steel pipelines is generally between 16×10 -6 / °C and 18×10 -6 / °C. When extracting data, it is necessary to combine the operating temperature range of the heating system (such as 80°C - 130°C) to ensure that the selected coefficient of thermal expansion corresponds to the actual operating environment, and store this data in the pipeline thermal stress analysis system for subsequent calculations.

[0186] Step S362: Perform thermal stress calculation based on the pipeline network pressure data and the coefficient of thermal expansion to obtain thermal stress data;

[0187] In this embodiment, first, real-time or historical pressure data is obtained from the pipeline pressure monitoring system to ensure that the time span of data acquisition can cover different working conditions (such as start-stop state, full-load operation state). The unit of the pressure data is usually MPa, and it is screened according to the working pressure range of the heating pipeline (such as 0.4 MPa - 1.6 MPa). Subsequently, combining with the coefficient of thermal expansion obtained in step S361, the thermal stress generated by the temperature change of the pipeline is calculated. During the calculation process, the constraint conditions of the pipeline need to be considered, such as the influence of structures such as fixed supports, sliding supports, and expansion joints on the free expansion of the pipeline. A finite element analysis tool (such as ANSYS or ABAQUS) is used to analyze the stress distribution of the pipeline after thermal expansion, calculate the axial stress, circumferential stress, and shear stress of the pipeline under different temperature conditions, and store the calculated thermal stress data in the pipeline structure safety assessment system for subsequent expansion analysis.

[0188] Step S363: Perform a linear thermal expansion analysis on the pipeline material data based on the thermal stress data to obtain thermal expansion data;

[0189] In this embodiment, based on the thermal stress data calculated in step S362 and combined with the material information of the pipeline (such as elastic modulus, Poisson's ratio), the linear thermal expansion of the pipeline within the actual operating temperature range is calculated. The calculation of the linear thermal expansion is based on the coefficient of thermal expansion of the material, the temperature change range, and the pipeline length. For example, when the heating temperature rises from 20°C to 120°C, if the coefficient of thermal expansion of a certain carbon steel pipeline is 12×10 -6 / °C and the pipeline length is 100 m, the linear expansion of the pipeline is approximately 12 mm. To ensure the accuracy of the data, the thermal expansion is measured in segments according to the pipeline, that is, the thermal expansion of each pipe segment is calculated separately and recorded in the pipeline thermal expansion management system for subsequent compensation analysis.

[0190] Step S364: Identify the corners of the pipeline laying model to obtain the corner pipeline laying model;

[0191] In this embodiment, based on the pipeline construction design drawings or GIS pipeline network data, the laying conditions of the heating pipelines are analyzed, and the corners formed during the pipeline laying process are identified. First, using a three-dimensional laser scanner or unmanned aerial vehicle remote sensing mapping technology, a spatial model of the pipelines laid on the ground is established, or the accurate alignment of the heating pipelines is obtained using the BIM model of the heating pipelines. Through data processing software (such as AutoCAD Civil 3D or Bentley OpenUtilities), the direction changes of the pipelines are calculated, and the positions and corresponding angles of all corner points are extracted. For buried pipelines, the geographical information system (GIS) can be used in combination with construction records for data matching, so as to obtain a complete corner pipeline laying model and store it in the pipeline operation monitoring system for subsequent compensation analysis.

[0192] Step S365: Extract the flexibility characteristics of the pipeline material data to obtain pipeline flexibility data;

[0193] In this embodiment, based on the pipeline material data obtained in step S361, the flexibility characteristics of the pipelines are analyzed to determine the range of deformation that can be tolerated during the thermal expansion process. The key parameters for flexibility characteristic extraction include elastic modulus (unit: GPa), Poisson's ratio (unit: dimensionless), allowable strain (unit: %), and tensile strength (unit: MPa). For example, the elastic modulus of carbon steel pipelines is usually around 200 GPa, while that of stainless steel pipelines is about 180 GPa - 190 GPa. Using these parameters, the deformation capacity of the pipelines is calculated through the finite element analysis method, and combined with the corner data, the flexibility response capacity of the pipelines under different compensation methods is analyzed. After the calculation is completed, the pipeline flexibility data is stored in the thermal compensation management system to provide a basis for the subsequent selection of compensation methods.

[0194] Step S366: Divide the angle of the corner pipeline laying model. If the corner pipeline laying model is less than 150°, then perform flexible natural compensation on the thermal expansion data according to the pipeline flexibility data to obtain flexible natural compensation data; if the corner pipeline laying model is greater than 150°, then use a preset rotary compensator to perform rotary compensation on the thermal expansion data to obtain rotary compensation data;

[0195] In this embodiment, the corner pipeline laying model obtained in step S364 is classified to determine whether the corner of the pipeline is less than 150°. For corners less than 150°, according to the pipeline flexibility data obtained in step S365, calculate the ability of the pipeline to absorb the thermal expansion amount through its own deformation at this corner, and perform flexible natural compensation on it. The implementation method of flexible natural compensation is to reserve U-bends, Z-bends or L-bends during the pipeline laying process, allowing the pipeline to freely expand and contract when the temperature changes. If the corner is greater than 150°, a rotary compensator (such as a corrugated expansion joint or a universal joint) is installed at this position. The specifications of the rotary compensator are determined according to the nominal diameter, design temperature and maximum allowable stress of the pipeline. For example, a pipeline with a nominal diameter of DN500 can be configured with a bellows compensator with a maximum compensation amount of 50 mm. After installation, record the compensation method and related parameters in the pipeline maintenance database for subsequent inspection use.

[0196] Step S367: Integrate the flexible natural compensation data and the rotary compensation data to obtain dynamic compensation data.

[0197] In this embodiment, the flexible natural compensation data obtained in step S366 and the rotary compensation data are integrated to form a complete dynamic compensation scheme. The dynamic compensation data includes the specific position, compensation method, compensation amount, allowable strain range and long-term operation monitoring requirements of each compensation point. For example, for a compensation point of a certain DN800 pipeline, if the allowable expansion and contraction amount of flexible natural compensation is 30 mm, and the compensation capacity of the rotary compensator is 40 mm, then the dynamic compensation data of this pipeline records the total compensation capacity of this position as 70 mm. Finally, this data will be uploaded to the heating pipe network operation management system and combined with the real-time monitoring data to ensure that the pipeline maintains a reasonable thermal compensation capacity during long-term operation, thereby reducing the damage risk caused by pipeline thermal stress.

[0198] Preferably, step S4 is specifically as follows:

[0199] Step S41: Extract the thermal conductivity characteristics and the thickness of the thermal insulation material based on the composite thermal insulation material data to obtain the thermal conductivity and the thickness of the thermal insulation material;

[0200] In this embodiment, first collect the physical data of the composite thermal insulation material, mainly including the thickness and thermal conductivity of the material. The extraction of thermal conductivity characteristics requires the use of special test equipment, such as a thermal conductivity instrument, to measure the thermal conductivity of the thermal insulation material through a standardized test method (for example, the heat flow meter method). Ensure that factors such as the temperature and humidity of the test environment are within the standard range to maintain the reliability of the test results. Then, according to the actual pipeline thermal insulation structure, measure and extract the thickness of the thermal insulation material, usually through precise measuring tools, ensure that the thickness of each section of the pipeline meets the design requirements, and record it for subsequent calculation.

[0201] Step S42: Calculate the thermal resistance per unit area based on the thermal conductivity and the thickness of the thermal insulation material, so as to obtain the thermal resistance data of the thermal insulation layer;

[0202] In this embodiment, based on the collected thermal conductivity and thickness data of the thermal insulation material, the thermal resistance per unit area is calculated. At this time, first obtain the thermal resistance value per unit area according to the thermal conductivity and thickness. During the calculation process, appropriate standard environmental conditions, such as temperature and pressure, need to be set, and the environmental parameters are taken into account for accurate thermal resistance evaluation. The thermal resistance data provides the resistance to heat transfer per unit area, reflecting the heat insulation effect of the thermal insulation layer. The calculation results need to be further compared with the standard thermal resistance value to ensure that the heat insulation performance of the pipeline system meets the design requirements.

[0203] Step S43: Collect the internal and external temperatures of the pipeline laying model, and perform a temperature difference calculation to obtain the temperature difference between the inside and outside of the pipeline;

[0204] In this embodiment, the temperature difference calculation is completed by collecting the real-time temperature data inside and outside the pipeline. Temperature sensors are installed on the inner and outer walls of the pipeline. Commonly used temperature sensors include thermocouples or RTDs (resistance temperature detectors), and these sensors need to be calibrated to ensure the accuracy of the data. After the data is collected, the temperature difference between the inside and outside of the pipeline is calculated in real time, and the temperature difference value is recorded to provide basic data for subsequent analysis. During this process, the temperature data inside and outside the pipeline must ensure that the collection frequency meets the requirements of real-time monitoring.

[0205] Step S44: Perform a heat flux density analysis based on the thermal resistance data of the thermal insulation layer and the temperature difference between the inside and outside of the pipeline to obtain the heat flux density;

[0206] In this embodiment, the analysis of the heat flux density needs to be carried out based on the thermal resistance data of the thermal insulation layer and the temperature difference between the inside and outside of the pipeline. First, combine the calculated thermal resistance value with the temperature difference, and calculate the heat flux density according to the set standard model. This process is usually carried out by digital instruments, such as heat flux meters, which can directly measure the heat flux density transferred on the pipeline surface. During the calculation, the influence of the external environment, such as wind speed and changes in the external temperature, needs to be considered to ensure that the data of the heat flux density analysis reflects the real heat exchange process.

[0207] Step S45: Perform a temperature attenuation analysis on the pipeline laying model based on the heat flux density to obtain the temperature attenuation;

[0208] In this embodiment, the temperature attenuation analysis is carried out by monitoring the attenuation of the heat inside the pipeline along the transmission distance. The temperature data at multiple points in the pipeline are collected, and the temperature attenuation is calculated based on these data. This process requires installing temperature sensors at key positions of the pipeline and monitoring the data changes in real time. By comparing the temperature differences between the starting point and the ending point, the temperature attenuation is calculated. In actual implementation, the installation positions of the sensors should be determined according to the actual situation of the pipeline to ensure that the monitored temperature attenuation data are representative.

[0209] Step S46: Make a low-temperature-drop judgment based on the temperature attenuation to obtain low-temperature-drop data;

[0210] In this embodiment, the low-temperature-drop judgment is based on the temperature attenuation data. By setting a low-temperature threshold, such as the temperature change rate below the set value (e.g., 5°C / h), it is judged whether a low-temperature drop occurs. This process requires continuously monitoring the temperature attenuation in the pipeline system and identifying the areas with excessive temperature drops through real-time data analysis. To improve the accuracy, historical data should be compared during the judgment to avoid the influence of accidental factors on the results and ensure the accurate judgment of the occurrence of low-temperature drops.

[0211] Step S47: Transmit the low-temperature-drop data and the dynamic compensation data to the intelligent monitoring platform in real time to execute the data acquisition and transmission task of the heat supply pipeline terminal.

[0212] In this embodiment, the low-temperature-drop data and the dynamic compensation data need to be transmitted to the intelligent monitoring platform in real time through a wireless data transmission module. First, the low-temperature-drop data and the dynamic compensation data are sent to the platform through a dedicated transmission protocol (such as Wi-Fi, LoRaWAN, etc.) to ensure the real-time and stability of the data. Data security needs to be ensured during the transmission process, and data encryption technology is used to avoid data loss or tampering. After the intelligent monitoring platform receives the data, the system will automatically analyze the data and provide early warnings or adjustment suggestions to ensure the stable operation of the heat supply pipeline.

[0213] Therefore, from any perspective, the embodiment should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0214] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather to the broadest scope consistent with the principles and novel features invented herein.

Claims

1. A terminal data collection and transmission method for a long-distance heating pipeline, characterized in that: The following steps are involved: Step S1: Acquire the long-distance heating pipeline laying area data, and analyze the pipeline network laying form to obtain the single-pipe branch heating pipeline network data; Step S2: construct a pipeline laying model based on the single-pipe branch-shaped heating network data; perform intelligent heat energy flow simulation based on the pipeline laying model, and calculate the heat loss in the heat energy flow simulation process to obtain heat loss data; integrate the thermal protection insulation materials of the pipeline laying model based on the heat loss data to obtain composite insulation material data; identify the straight pipe section of the pipeline laying model, and calculate the minimum wall thickness of the straight pipe section to obtain the minimum wall thickness data; Step S3: performing a pipe network pressure analysis on the pipe laying model based on the minimum wall thickness data to obtain pipe network pressure data; performing a thermal expansion analysis based on the pipe network pressure data to obtain thermal expansion data, and performing dynamic compensation for thermal expansion to generate dynamic compensation data; Step S4: performing low temperature drop analysis on the pipeline laying model based on the composite thermal insulation material data to obtain low temperature drop data; The low temperature drop data and dynamic compensation data are transmitted to the intelligent monitoring platform in real time to execute the heating pipeline terminal data collection and transmission tasks.

2. The terminal data collection and transmission method for a long-distance heating pipeline according to claim 1 is characterized in that: Step S1 is specifically as follows: Step S11: Acquire the data of the long-distance heating pipeline laying area and perform remote sensing acquisition, wherein the acquisition resolution is set to 0.5m-30m and the acquisition spectrum range is set to 8μm-14μm to obtain regional remote sensing data; Step S12: extracting thermal infrared remote sensing features according to the regional remote sensing data to obtain thermal infrared remote sensing data; Step S13: Determine the heat consumption area based on the thermal infrared remote sensing data, where the heat consumption area recognition accuracy is set to ±5m 2 -±50m 2 ; Step S14: Calculate the heat load of the heat consumption area to obtain heat load data and perform heat load statistics, wherein the low heat load judgment threshold is set to <50W / m 2 , get low heat load data; Step S15: Divide the heat consumption area according to the low heat load data to obtain the low heat load area; Step S16: Determine the single-pipe branch-shaped heating network layout for the low heat load area, wherein the single-pipe pipe diameter range is set to DN50-DN250, and obtain the single-pipe branch-shaped heating network data.

3. The terminal data collection and transmission method for a long-distance heating pipeline according to claim 2 is characterized in that: Step S13 is specifically as follows: Step S131: Calculate the surface emissivity according to the thermal infrared remote sensing data to obtain the surface emissivity; Step S132: using a preset Planck function to perform surface temperature inversion on the surface emissivity to generate surface temperature data; Step S133: Calculating the average ground surface temperature based on the ground surface temperature data; Step S134: calculating the surface temperature standard deviation based on the surface temperature data; Step S135: Determine the heat consumption area of ​​the industrial zone according to the mean value of the surface temperature and the standard deviation of the surface temperature to obtain the heat consumption area.

4. The terminal data collection and transmission method for a long-distance heating pipeline according to claim 1 is characterized in that: The thermal energy flow simulation in step S2 includes: Collect heat users based on the pipeline laying model, where the collection range is the area with a heating radius of 500-2000m; Count the location coordinates of the thermal users, where the coordinate accuracy is set to ≤ 0.5m; Identify buildings based on location coordinates and calculate building areas; Statistics on heat demand of heat users, where the heat demand of a single household is 5kW-500kW; The heating demand per unit area is estimated based on the heat demand and the building area to obtain the heating demand per unit area; According to the heating demand per unit area, the pipeline laying model is simulated for intelligent heat energy flow, where the steam parameters are set to 0.8MPa.a and 180℃; Calculate the heat loss during the intelligent thermal energy flow simulation and obtain the heat loss data.

5. The terminal data collection and transmission method for a long-distance heating pipeline according to claim 1 is characterized in that: The integration of thermal protection and heat preservation materials in step S2 includes: Divide the laying types of the pipeline laying model to obtain overhead heating pipe data and underground pipeline data; Use heat loss data to identify high heat loss pipe sections in overhead heating pipe data; Use heat loss data to identify high heat loss pipe sections in buried pipeline data; Apply the preset aluminum silicate insulation material and high-temperature glass wool material to the high heat loss pipe section in the overhead heating pipe data to obtain the composite insulation material data for the overhead pipe section; Apply the preset nanoporous aerogel material and high-temperature glass wool material to the high heat loss pipe section in the buried pipeline data to obtain the composite insulation material data of the buried pipe section; The composite thermal insulation material data of the overhead pipe section and the composite thermal insulation material data of the underground pipe section are integrated to obtain the composite thermal insulation material data.

6. The terminal data collection and transmission method for a long-distance heating pipeline according to claim 1 is characterized in that: The calculation formula for calculating the minimum wall thickness in step S2 is as follows: C = 0.5B; S = s + c; Where s is the minimum wall thickness of the straight pipe; p is the design pressure; [σ] t It is represented by the basic allowable stress at the calculation temperature; D is represented by the outer diameter of the pipe; Y is represented by the correction coefficient, for ferritic steel, the temperature is <482℃, and it is taken as 0.4; η is represented by the correction coefficient of the allowable stress, which is 1.0 for seamless steel pipes and 0.9 for spiral welded steel pipes; α is represented by the additional thickness considering corrosion, wear and mechanical strength requirements, and α=1mm is taken; S is represented by the calculated wall thickness of the straight pipe; c is represented by the additional value of the negative deviation of the straight pipe wall thickness; C is represented by the processing allowance for the opposite end; B is represented by the additional value of the positive deviation of the straight pipe wall thickness.

7. The terminal data collection and transmission method for a long-distance heating pipeline according to claim 1 is characterized in that: Step S3 is specifically as follows: Step S31: Calculate the pipeline strength of the pipeline laying model based on the minimum wall thickness data to obtain pipeline pressure bearing capacity data; Step S32: evaluating the maximum working pressure during the heat energy flow simulation process according to the pipeline pressure bearing capacity data; Step S33: performing a pressure loss analysis along the pipeline according to the maximum working pressure data, thereby obtaining the pressure loss data along the pipeline; Step S34: Calculate the local resistance loss according to the pressure loss data along the pipeline, so as to obtain the local resistance loss data of the pipeline; Step S35: evaluating the pressure of the entire pipeline network based on the local resistance loss data of the pipeline, thereby obtaining the pressure data of the pipeline network; Step S36: Perform thermal expansion analysis based on the pipe network pressure data to obtain thermal expansion data, and perform dynamic compensation for thermal expansion to generate dynamic compensation data.

8. The terminal data collection and transmission method for a long-distance heating pipeline according to claim 7 is characterized in that: Step S33 is specifically as follows: Step S331: extracting the maximum working pressure period characteristics according to the maximum working pressure data to obtain the maximum working pressure period; Step S332: Determine the steam density and flow rate data in the maximum working pressure period, wherein the steam density range is set to 2.5 kg / m 3 -5.0kg / m 3 , Flow rate range 15m / s-50m / s; Step S333: Calculate the steam flow Reynolds number according to the steam density and flow rate data to obtain the steam flow Reynolds number; Step S334: Obtain the roughness of the inner wall of the pipeline and the length of the pipeline along the way; Step S335: Calculate the friction pressure loss according to the steam flow Reynolds number and the roughness of the inner wall of the pipeline, wherein the friction coefficient ranges from 0.005 to 0.05, and obtain the friction pressure loss data; Step S336: Accumulate the pressure loss statistics along the pipeline according to the friction pressure loss data and the length of the pipeline to obtain the pressure loss data along the pipeline.

9. The terminal data collection and transmission method for a long-distance heating pipeline according to claim 7 is characterized in that: Step S36 is specifically as follows: Step S361: Obtain pipeline material data and extract thermal expansion coefficient; Step S362: Calculate thermal stress based on the pipe network pressure data and thermal expansion coefficient to obtain thermal stress data; Step S363: performing linear thermal expansion analysis on the pipeline material data according to the thermal stress data to obtain thermal expansion data; Step S364: performing corner recognition on the pipeline laying model to obtain a corner pipeline laying model; Step S365: extracting the flexibility characteristics of the pipeline material data to obtain pipeline flexibility data; Step S366: Divide the angle of the corner pipeline laying model. If the angle of the corner pipeline laying model is less than 150°, perform flexible natural compensation on the thermal expansion data according to the pipeline flexibility data to obtain flexible natural compensation data; if the angle of the corner pipeline laying model is greater than 150°, perform rotation compensation on the thermal expansion data using a preset rotation compensator to obtain rotation compensation data; Step S367: Integrate the flexible natural compensation data and the rotation compensation data to obtain dynamic compensation data.

10. The terminal data collection and transmission method for a long-distance heating pipeline according to claim 1 is characterized in that: Step S4 is specifically as follows: Step S41: extracting thermal conductivity features and thermal insulation material thickness based on the composite thermal insulation material data to obtain thermal conductivity and thermal insulation material thickness; Step S42: Calculate the thermal resistance per unit area according to the thermal conductivity and the thickness of the thermal insulation material, so as to obtain the thermal resistance data of the thermal insulation layer; Step S43: collecting the internal and external temperatures of the pipeline laying model, and performing temperature difference calculation to obtain the internal and external temperature difference of the pipeline; Step S44: performing heat flux analysis based on the thermal resistance data of the insulation layer and the temperature difference between the inside and outside of the pipeline to obtain the heat flux; Step S45: performing temperature attenuation analysis on the pipeline laying model according to the heat flux density to obtain the temperature attenuation; Step S46: performing low temperature drop judgment according to the temperature attenuation to obtain low temperature drop data; Step S47: Transmit the low temperature drop data and dynamic compensation data to the intelligent monitoring platform in real time to execute the heating pipeline terminal data collection and transmission task.

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