A Health Status Monitoring System and Method for Heating Station Equipment

Abstract

This invention relates to the field of equipment condition monitoring technology, and in particular to a health status monitoring system and method for heating station equipment. The method includes the following steps: acquiring structural data of the heating station equipment; performing topological analysis on the heating station based on the structural data to generate topological data of the heating station equipment; using the topological data to perform virtual simulation modeling on the structural data of the heating station equipment to generate a three-dimensional model of the heating station equipment; extracting the heat exchanger region from the three-dimensional model of the heating station equipment and performing heat exchange pipeline path analysis on the heat exchanger region to generate heat exchange flow data; and acquiring surface acoustic wave sensor data based on the heat exchanger region to obtain surface acoustic wave group velocity change data. This invention comprehensively improves the accuracy of monitoring and risk warning capabilities for the health status of heating station equipment through structural topology modeling, refined heat exchange region monitoring, dynamic damage accumulation analysis, and closed-loop operation optimization.

Description

Technical Field

[0001] This invention relates to the field of equipment condition monitoring technology, and in particular to a health status monitoring system and method for heating station equipment. Background Technology

[0002] In the early days, the maintenance of heating station equipment relied mainly on regular manual inspections and experience-based judgment, which had shortcomings such as monitoring blind spots and response delays. With the development of sensor technology, real-time acquisition of parameters such as temperature, pressure, and vibration became possible, gradually enriching the data foundation for equipment operation status. In the information age, while traditional threshold-based monitoring methods can achieve basic alarms, they struggle to accurately reflect the overall health status of the equipment, easily leading to false alarms and missed alarms. With the rise of big data and machine learning technologies, the health monitoring of heating station equipment has entered an intelligent stage. By constructing multi-dimensional data models of equipment operation and employing algorithms such as condition assessment, fault diagnosis, and predictive maintenance, a comprehensive assessment of the equipment's health status and early warning can be achieved. However, current technologies often struggle to perform fine-grained monitoring of critical components such as heat exchangers, lack detailed analysis of heat exchange pipeline paths, and rely primarily on experience or static indicators to assess thermal stress and damage, failing to reflect unsteady dynamic processes. This results in low accuracy in monitoring the health status and risk warning capabilities of heating station equipment. Summary of the Invention

[0003] Therefore, it is necessary to provide a health status monitoring system and method for heating station equipment to solve at least one of the above-mentioned technical problems.

[0004] To achieve the above objectives, a method for monitoring the health status of heating station equipment is provided, the method comprising the following steps:

[0005] Step S1: Obtain the structural data of the heating station equipment; perform topological analysis on the heating station based on the structural data of the heating station equipment to generate the topological data of the heating station equipment; use the topological data of the heating station equipment to perform virtual simulation modeling on the structural data of the heating station equipment to generate a three-dimensional model of the heating station equipment.

[0006] Step S2: Extract the heat exchanger area from the 3D model of the heating station equipment, and perform heat exchange pipeline path analysis on the heat exchanger area to generate heat exchange flow data; collect surface acoustic wave sensor data based on the heat exchanger area to obtain heat exchange surface wave group velocity change data.

[0007] Step S3: Calculate the transient thermal stress based on the wave group velocity change data of the heat exchange surface; perform unsteady-state damage accumulation analysis on the transient thermal stress using heat exchange flow data to generate cumulative micro-damage data of the heat exchanger; optimize the heat exchange pipeline path using the cumulative micro-damage data of the heat exchanger to generate optimized heat exchange pipeline data.

[0008] Step S4: Obtain the operating data of the heating station; extract the heat exchange start-up and shutdown data from the operating data of the heating station, and optimize the start-up and shutdown pipeline control based on the heat exchange start-up and shutdown data to obtain the start-up and shutdown path that minimizes cumulative damage; adjust the operating strategy based on the start-up and shutdown path that minimizes cumulative damage, and feed the adjustment results back to the control system to realize closed-loop monitoring of the health status of the heat exchanger of the heating station and early warning of damage risk.

[0009] This invention generates a highly realistic 3D model of the thermal power station equipment through topology analysis and virtual simulation modeling, providing a digital foundation for subsequent heat exchange pipeline analysis and sensor data mapping, effectively improving modeling efficiency and accuracy. By using surface acoustic wave (SAW) sensors to collect surface wave group velocity change data of the heat exchanger and combining this with heat transfer characteristics for transient thermal stress and unsteady-state damage accumulation analysis, it can identify micro-structural damage and achieve early warning of potential failures caused by thermal stress. Based on the accumulated micro-damage data, the heat exchange pipeline path is dynamically adjusted to generate optimal heat exchange optimization pipeline data, improving heat exchange efficiency while extending equipment life, exhibiting high adaptability and autonomous optimization capabilities. Through in-depth mining of start-up and shutdown data and pipeline control optimization, the risk of fatigue damage to equipment during frequent start-ups and shutdowns is effectively reduced, ensuring system operational stability and extending the service life of key components. By integrating simulation models, sensor data, operational data, and control feedback, a closed-loop monitoring system for the health status of the heat exchanger is constructed, realizing a shift from "response maintenance" to "predictive maintenance," improving equipment reliability and the level of intelligent management. This method integrates structural modeling, sensing, data fusion, path optimization, and control feedback, possessing strong versatility and scalability. It provides technical support for the construction of smart energy systems and promotes the transformation and upgrading of heating station equipment from traditional management to intelligent operation and maintenance. Therefore, this invention comprehensively improves the accuracy of monitoring and risk warning capabilities for the health status of heating station equipment through structural topology modeling, refined heat exchange area monitoring, dynamic damage accumulation analysis, and closed-loop operation optimization.

[0010] Preferably, step S1 includes the following steps:

[0011] Step S11: Obtain the structural data of the heating station equipment;

[0012] Step S12: Identify the structural components of the heating station equipment structure data to extract equipment unit data; construct an equipment connection diagram based on the equipment unit data to generate an initial structure diagram of the heating station;

[0013] Step S13: Perform graph structure analysis on the initial structure diagram of the heating station, extract node connection relationships, and generate heating station equipment topology data; use the heating station equipment topology data to perform spatial association modeling on the equipment unit data, and generate structure mapping data;

[0014] Step S14: Perform virtual simulation modeling based on the structural mapping data to generate an initial three-dimensional structural model of the heating station equipment; perform component geometry correction and topology closure processing on the initial three-dimensional structural model of the heating station equipment to generate a three-dimensional model of the heating station equipment.

[0015] This invention constructs a high-fidelity 3D model of a heating station structure through a complete process from structural data acquisition, unit extraction, connection identification to spatial modeling and geometric closure. This effectively avoids component disconnection, topological errors, and geometric incompleteness issues present in structural modeling, ensuring the accuracy of subsequent simulations and analyses. By identifying structural components and extracting equipment unit data, fine-grained decomposition and classification of the complex equipment system of the heating station are achieved, improving the model's operability and maintainability and providing reliable data support for modeling and optimizing structural logical relationships. An initial structural diagram is constructed using equipment connection relationships, and equipment topology data is generated through graph structure analysis. This accurately describes the spatial and functional relationships between each equipment unit, providing a logical constraint basis for subsequent applications such as path planning and heat flow simulation. Spatial relationship modeling using structural mapping data ensures a high degree of consistency between equipment logical relationships and physical structures, thereby enhancing the model's expressive and interactive capabilities and supporting simulation calculations and system integration operations in multiple scenarios. After 3D modeling, component geometric correction and topology closure processing effectively solve problems such as boundary breaks and void misalignments that occur in the initial modeling, significantly improving the integrity, accuracy, and visualization quality of the structural model. High-quality 3D structural models provide a reliable foundation for accurate region localization, structural feature extraction, sensor data mapping, and analysis model construction in subsequent steps, thus building an integrated technical path from "structural understanding" to "functional analysis".

[0016] Preferably, step S2 includes the following steps:

[0017] Step S21: Divide the 3D model of the heating station equipment into regions to obtain the heat exchanger region structure data; identify the heat exchanger network based on the heat exchanger region structure data to obtain the heat exchanger topology data;

[0018] Step S22: Perform path tracing analysis on the heat exchange pipeline topology data, calculate the fluid path length and distribution, and generate heat exchange flow data;

[0019] Step S23: Simulate the deployment of surface acoustic wave sensors based on the heat exchanger area structure data to generate sensor sampling layout data; perform surface acoustic wave acquisition control on the sensor sampling layout data to obtain raw surface acoustic wave response data;

[0020] Step S24: Extract group velocity and correct time drift from the raw surface acoustic wave response data to generate heat transfer surface wave group velocity change data.

[0021] This invention segmentes the 3D equipment model of a thermal power station and extracts structural data from the heat exchanger regions, enabling rapid and accurate location of key heat exchange component areas. This provides a clear and structured regional model for subsequent analysis, improving modeling efficiency and accuracy. Based on the regional structural data, heat exchanger network identification generates topology data that fully reflects the spatial paths, connections, and structural levels of fluid channels, providing a realistic and usable topological basis for heat exchange path analysis and optimization. By tracing the heat exchanger topology and calculating the fluid path length and its distribution, quantifiable and simulable heat exchange flow data is obtained, providing fundamental parameter support for thermal stress analysis and dynamic simulation calculations. By simulating sensor deployment in the heat exchanger region, an optimal sensor sampling layout is generated, and the acquisition process of the surface acoustic wave (SAW) sensor is controlled, significantly improving the scientific nature of sensor deployment and the representativeness of response data, reducing blind spots, and increasing monitoring coverage. Group velocity extraction and time drift correction of the raw SAW response data effectively suppress system errors and environmental noise. The generated group velocity change data more realistically reflects the microstructural changes on the heat exchanger surface, laying a data foundation for micro-damage detection. This step integrates multi-dimensional data such as three-dimensional structure, topology path, flow parameters, and sensing response into a single system, forming a multi-source coupled model data chain for the heat exchanger, providing systematic support for subsequent thermal stress analysis and path optimization.

[0022] Preferably, step S22 includes the following steps:

[0023] Step S221: Filter the node connectivity of the heat exchange pipeline topology data, extract the effective heat exchange path segments, and generate path structure network data; perform geometric reconstruction and curvature analysis on the path structure network data, extract the geometric constraint information of the path segments, and generate geometric feature data of the heat exchange path.

[0024] Step S222: Spatial path expansion is performed on the geometric feature data of the heat exchange path, and flow direction vector calculation is performed on the expanded spatial path to generate the initial flow path vector field;

[0025] Step S223: Measure the resistance coefficient of the internal structure of the heat exchanger; use the resistance coefficient of the internal structure of the heat exchanger to weight the path resistance of the initial flow path vector field to generate an equivalent flow resistance path diagram; perform fluid path length integration and flow velocity dynamic simulation on the equivalent flow resistance path diagram to calculate the path-level fluid transmission delay and generate flow delay matrix data.

[0026] Step S224: Estimate the path-level heat exchange efficiency of the flow delay matrix data to generate heat transfer data.

[0027] This invention utilizes node connectivity screening and geometric reconstruction analysis. Step S221 eliminates invalid or redundant pipe sections, extracting the true and effective heat exchange paths. Combined with curvature analysis, it generates complete path structure network data and geometric feature information, providing a solid structural foundation for accurate modeling and subsequent simulation. Expanding the geometric path to a spatial scale and calculating the initial flow vector based on path direction information reflects the true direction and flow trend of each fluid path in the heat exchange system, laying a physical basis for flow behavior modeling and heat conduction simulation. By measuring the drag coefficient of the heat exchanger structure and weighting the flow vector field, an equivalent flow resistance path diagram is generated, accurately reflecting the flow impedance differences of different path segments, which helps identify flow bottlenecks and inefficient areas in the heat exchange station. Based on the equivalent flow resistance diagram, fluid path length integration and velocity simulation can dynamically calculate the fluid transmission delay on each path segment and generate flow delay matrix data, providing key time variable support for subsequent thermal efficiency evaluation and start-up / shutdown strategies. Estimating heat transfer efficiency based on the time delay matrix allows for quantitative assessment of differences in heat exchange efficiency along the path dimension, thereby identifying inefficient heat transfer pathways in the structure and providing a strong technical reference for optimized design and local modifications.

[0028] Preferably, step S222 includes the following steps:

[0029] Step S2221: Perform three-dimensional structural noise reduction projection on the geometric feature data of the heat exchange path to eliminate high-order micro-curvature interference and generate a set of path principal axis fitting lines; perform piecewise spline reconstruction and unfolding based on the set of path principal axis fitting lines to transform the complex spatial path into a regularized path segment sequence and generate a set of spatial unfolded paths.

[0030] Step S2222: Perform geometric continuity weighting on the spatially unfolded path set, calibrate the directional consistency and structural influence coefficient of each micro-segment, and generate a path direction weighting matrix;

[0031] Step S2223: Perform directional flow vector decomposition based on the path direction weighted matrix, extract the mainstream vector and disturbance vector of the path micro-unit, and generate a local flow direction distribution map;

[0032] Step S2224: Reconstruct the local flow direction distribution map by stitching together the entire path scale to generate a unified initial flow path vector field.

[0033] This invention generates a path principal axis fitting line by denoising projection of the three-dimensional structure, effectively removing disturbance information caused by high-order micro-curvature and eliminating errors caused by microstructures such as warping and concavity of pipeline components. Spline reconstruction transforms complex paths into regularized segment sequences, significantly improving the consistency of geometric modeling and the stability of numerical computation. Geometric continuity weighting quantifies the directional consistency and geometric structure influence of each micro-segment, establishing a path direction weighting matrix. This matrix accurately represents the contribution of each path segment to the overall flow direction consistency, effectively improving the local accuracy and global coherence of subsequent vector field modeling. The executed directional flow vector decomposition decomposes the flow trend of each path micro-element into a mainstream vector and a disturbance vector. The generated local flow direction distribution map provides crucial physical support for analyzing the influence of local structures on flow deflection, vortex formation, or turbulence formation, demonstrating high-resolution modeling capabilities. By splicing local flow maps to generate a unified flow path vector field, a seamless connection between local structural changes and global flow trends is achieved. The resulting vector field has good physical consistency, directional continuity, and structural correlation, providing a solid foundation for subsequent fluid simulation, flow resistance analysis, and heat exchange efficiency calculation.

[0034] Preferably, step S3 includes the following steps:

[0035] Step S31: Perform multi-scale thermodynamic inversion on the wave group velocity variation data of the heat transfer surface to generate transient thermal gradient distribution data; perform material thermal response modeling on the transient thermal gradient distribution data and calculate the corresponding transient thermal stress data;

[0036] Step S32: Perform unsteady-state boundary modeling on the heat transfer flow data to generate dynamic boundary condition data for the heat transfer process; perform coupled analysis on the transient thermal stress data and dynamic boundary condition data to implement unsteady-state damage accumulation modeling and generate micro-damage evolution curve data;

[0037] Step S33: Perform region clustering on the micro-damage evolution curve data, and extract the damage accumulation threshold from the clustered regions to generate heat exchanger micro-damage accumulation data;

[0038] Step S34: Based on the cumulative data of micro-damage to the heat exchanger, perform path loss sensitivity analysis on the heat exchange pipeline path, identify high-risk path segments, and generate a set of candidate paths for heat exchange pipeline improvement; reconstruct the flow efficiency and evaluate the heat exchange performance of the candidate paths for heat exchange pipeline improvement, and generate optimized heat exchange pipeline data.

[0039] This invention achieves high-precision transient thermal stress data by performing multi-scale thermodynamic inversion on group velocity change data to obtain transient thermal gradient distribution. Combined with a material thermal response model, this results in refined quantitative modeling from response data to thermodynamic behavior, providing a scientific basis for damage analysis. Unsteady-state boundary modeling is performed using heat transfer flow data to obtain dynamic boundary conditions, which are then coupled with thermal stress data to establish an unsteady-state damage accumulation model. This effectively improves the simulation and prediction capabilities of micro-damage evolution under actual operating conditions. Through the construction of micro-damage evolution curves and regional clustering analysis, the local and global damage change trends of the heat exchanger structure under thermal stress can be intuitively displayed, and high-damage areas can be identified, providing support for structural risk assessment. By extracting damage accumulation thresholds from clustered regions, quantifiable cumulative micro-damage data of the heat exchanger is obtained, providing digital input conditions for decision-making and control systems and promoting the formulation of data-driven damage early warning and maintenance strategies. Path loss sensitivity analysis based on damage data can accurately locate high-risk pipeline segments, generate targeted improvement candidate path sets, avoid ineffective optimization, and significantly improve the targeting of path reconstruction. Based on the improved candidate path set, flow efficiency is reconstructed and heat exchange performance is evaluated to generate the final heat exchange optimization pipeline data, thereby achieving dual optimization of structural health and system performance, and promoting the improvement of heat exchange efficiency and the extension of life of the heating station.

[0040] Preferably, step S34, which involves reconstructing the flow efficiency and evaluating the heat exchange performance of the candidate path set for heat exchange pipeline improvement, includes:

[0041] Finite volume fluid simulation is performed on the candidate path set for heat exchange pipeline improvement to generate path flow velocity distribution data; based on the path flow velocity distribution data, the turbulent structure of the candidate path set for heat exchange pipeline improvement is identified and pressure drop is calculated to generate flow resistance distribution map.

[0042] By combining the flow resistance distribution map with the thermodynamic boundary conditions, local heat transfer coefficient inversion is performed to generate heat transfer efficiency distribution data; the heat transfer efficiency distribution data is then used to conduct inter-path thermal performance comparison analysis, high-performance path units are selected, and a path performance optimization set is generated.

[0043] The structural safety of the optimized path performance set is reviewed by combining micro-damage accumulation data, potential fatigue segments are eliminated, and a structural safety optimized path set is generated.

[0044] Multi-objective thermo-mechanical co-optimization calculations are performed on the structural safety optimization path set to comprehensively evaluate thermal efficiency, structural integrity, and flow stability, ultimately generating heat exchange optimization pipeline data.

[0045] This invention generates fine-grained path flow velocity distribution data by performing finite volume method (FVM) fluid simulation on an improved path set. This provides realistic boundary conditions for subsequent turbulent structure identification and pressure drop calculation, achieving a precise mapping from geometric paths to the physical flow field. Based on the flow velocity distribution data, local turbulent structural features are automatically identified and pressure drop distribution is quantified, forming a flow resistance distribution map. This comprehensively reveals the flow bottlenecks and resistance gradients of the pipeline, providing a decision-making basis for optimizing fluid dynamics performance. Combining the flow resistance map and boundary conditions, the local heat transfer coefficient field is obtained through inversion, and heat transfer efficiency distribution data is further constructed. This enables spatial distribution modeling of the heat transfer capacity of different paths, providing a quantitative basis for evaluating path thermal performance. By performing performance comparison analysis between paths based on the heat transfer efficiency distribution data, high thermal efficiency regions are screened and a path performance optimization set is constructed, achieving efficient path selection based on thermal performance indicators and improving energy efficiency. The path performance optimization set is coupled with micro-damage cumulative data for analysis, identifying and eliminating potential fatigue sections, resulting in a structural safety optimized path set. This ensures that path optimization pursues efficiency while considering structural reliability and lifespan safety. Based on the structural safety optimization path set, multi-objective collaborative optimization calculations are performed, comprehensively considering three factors: maximizing thermal efficiency, maintaining structural integrity, and enhancing flow stability. This generates a globally optimal heat exchange path structure, achieving system-level collaborative improvement. The final output heat exchange optimization pipeline data is characterized by strong feasibility, high energy efficiency, and excellent safety, driving the thermal system towards a highly reliable and efficient evolution system driven by digital twins, and improving the operating life and maintenance economy of the heat exchange system.

[0046] Preferably, step S4 includes the following steps:

[0047] Step S41: Obtain the operating data of the heating station;

[0048] Step S42: Extract the heat exchange start-up and shutdown cycle and transient load change from the operating data of the heating station to obtain start-up and shutdown behavior sequence data; perform fluctuation frequency analysis and start-up peak statistics on the start-up and shutdown behavior sequence data to generate start-up and shutdown stress characteristic data;

[0049] Step S43: Perform time-series coupled simulation of start-up and shutdown stress characteristic data and heat exchange optimization pipeline data to analyze the stress response distribution of different paths and generate stress distribution diagrams of start-up and shutdown paths;

[0050] Step S44: Based on the stress distribution map of the start-up and shutdown paths, calculate the damage accumulation function of each candidate path, screen the path with the minimum damage growth rate, and generate the start-up and shutdown path that minimizes the cumulative damage; use the start-up and shutdown path that minimizes the cumulative damage to optimize the start-up and shutdown control of the heat exchanger area of ​​the heating station and generate operation strategy adjustment parameters.

[0051] Step S45: Input the operation strategy adjustment parameters into the three-dimensional model of the heating station equipment to perform closed-loop monitoring of the equipment health status, and determine the damage risk threshold of the monitored equipment health status in order to perform damage risk early warning operation of the heat exchanger of the heating station.

[0052] This invention analyzes the operating data of a heating station to extract the heat exchange start-up and shutdown cycles and transient load changes, constructing a start-up and shutdown behavior sequence data model. This transforms the raw operating information into structured data suitable for time-series analysis, providing a data foundation for dynamic stress modeling. By performing fluctuation frequency analysis and start-up peak statistics on the start-up and shutdown behavior sequence data, start-up and shutdown stress characteristic data, including frequency, amplitude, and duration, are obtained, revealing the transient mechanical and thermal shock laws caused by start-up and shutdown operations on the heat exchange pipelines, providing physical driving forces for subsequent damage analysis. The start-up and shutdown stress characteristic data are time-coupled with heat exchange optimization pipeline data for simulation, generating a start-up and shutdown path stress distribution map, achieving path-level stress response modeling, and supporting integrated dynamic analysis of stress and path structure. Based on the start-up and shutdown path stress distribution map, a path damage accumulation function model is constructed to evaluate the life decay trend of different paths under start-up and shutdown cycles, selecting the path with the minimum damage growth rate, and improving the reliability and fatigue suppression capability of the system's start-up and shutdown operations. Based on the identification results of minimizing the damage path, the operating strategy adjustment parameters (such as start-stop rhythm, power ramp-up coefficient, temperature slope, etc.) are derived to achieve intelligent start-stop control optimization of the heat exchanger area of ​​the heating station, reducing the probability of fatigue triggering. The operating strategy adjustment parameters are fed back into the three-dimensional model of the heating station equipment, and combined with real-time operating data, virtual sensor-driven closed-loop monitoring of health status is implemented, and trend tracking and status assessment of multi-dimensional indicators are performed.

[0053] Preferably, step S44, which optimizes the start-up and shutdown control of the heat exchanger area of ​​the heating station by using a start-up and shutdown path that minimizes cumulative damage, includes:

[0054] High-frequency start-stop segments are identified in the heat exchanger area of ​​the heating station by using the start-stop path that minimizes cumulative damage, thereby obtaining high-frequency start-stop segment data.

[0055] Based on the high-frequency start-stop segment data, the start-stop path that minimizes cumulative damage is optimized for start-stop preheating, generating start-stop path preheating optimization data; based on the start-stop path preheating optimization data, a dynamic control temperature difference threshold is set, and the start-stop path that minimizes cumulative damage is optimized for first start-stop control through dynamic control of the temperature difference threshold, generating first start-stop control optimization data;

[0056] Based on the start-up and shutdown path that minimizes cumulative damage, the heat exchanger area of ​​the heating station is screened for static paths to obtain long static start-up and shutdown paths; low-flow-rate start-up control optimization is performed on the long static start-up and shutdown paths to generate second start-up and shutdown control optimization data;

[0057] The first start-stop control optimization data and the second start-stop control optimization data are integrated into the parameters for adjusting the operating strategy.

[0058] This invention employs a high-frequency start-stop segment identification algorithm within the start-stop path that minimizes cumulative damage. This algorithm accurately extracts time segments with abnormally concentrated start-stop frequencies, generating high-frequency start-stop segment data for subsequent differentiated control strategy deployment. This effectively avoids the rapid accumulation of thermal fatigue induced by frequent operations. Based on the high-frequency start-stop segment data, preheating control optimization is implemented on the start-stop path, generating start-stop path preheating optimization data. This controls the initial temperature gradient and heat transfer stress abrupt changes during the start-stop process, significantly reducing the initial amplitude of stress fluctuations and delaying the propagation of structural damage. Using the start-stop path preheating optimization data as a foundation, a dynamic control temperature difference threshold model is constructed. This automatically adjusts the allowable temperature difference range within the heat exchanger area, achieving a three-dimensional linkage adjustment mechanism of "temperature difference-path-stress," generating the first start-stop control optimization data and improving the intelligence level of the control system. For areas in the start-stop path that have not been activated for a long time, a static path screening mechanism is implemented to extract long static start-stop paths. A low-flow-rate start-up control optimization mechanism is introduced for these paths to control the fluid shear force and thermal shock rate in the initial stage of start-up, generating the second start-stop control optimization data and preventing the "cold start stress concentration" effect. By integrating the first start-stop control optimization data and the second start-stop control optimization data, a set of operation strategy adjustment parameters covering high-frequency segments and static paths is formed, supporting multi-path, multi-state, and adaptive scheduling control logic during the start-stop process, and improving the system's safety throughout its entire lifecycle.

[0059] This specification provides a health status monitoring system for heating station equipment, used to execute the above-described health status monitoring method for heating station equipment. The heating station equipment health status monitoring system includes:

[0060] The simulation modeling module is used to acquire the structural data of the heating station equipment; perform topological analysis on the heating station based on the structural data of the heating station equipment to generate the topological data of the heating station equipment; and use the topological data of the heating station equipment to perform virtual simulation modeling on the structural data of the heating station equipment to generate a three-dimensional model of the heating station equipment.

[0061] The heat exchange analysis module is used to extract the heat exchanger area from the 3D model of the heating station equipment, perform heat exchange pipeline path analysis on the heat exchanger area, and generate heat exchange flow data; based on the heat exchanger area, surface acoustic wave sensor data is collected to obtain heat exchange surface wave group velocity change data.

[0062] The heat exchange path analysis module is used to calculate the transient thermal stress based on the change data of wave group velocity on the heat exchange surface; to perform unsteady-state damage accumulation analysis on the transient thermal stress using heat exchange flow data, generating cumulative micro-damage data of the heat exchanger; and to optimize the heat exchange pipeline path using the cumulative micro-damage data of the heat exchanger, generating optimized heat exchange pipeline data.

[0063] The health assessment module is used to acquire the operating data of the heating station; extract the heat exchange start-up and shutdown data from the operating data of the heating station, and optimize the start-up and shutdown pipeline control based on the heat exchange start-up and shutdown data to obtain the start-up and shutdown path that minimizes cumulative damage; adjust the operating strategy based on the start-up and shutdown path that minimizes cumulative damage, and feed the adjustment results back to the control system to realize closed-loop monitoring of the health status of the heat exchanger of the heating station and early warning of damage risk.

[0064] The beneficial effects of this invention lie in the fact that it uses a simulation modeling module to perform topological analysis and virtual modeling of the structural data of the heating station equipment, generating a three-dimensional digital twin model with accurate physical semantics and spatial layout. This provides a structured foundation for subsequent heat transfer analysis and damage simulation, effectively improving modeling efficiency and accuracy. By using a surface acoustic wave sensor to collect real-time data on surface wave group velocity changes in the heat exchanger area, and combining this with path analysis results, the heat transfer process is dynamically visualized and reconstructed, providing accurate physical thermal response data and enhancing the real-time performance and sensitivity of micro-damage identification. The heat transfer flow data and wave group thermal stress calculation results are coupled and analyzed to construct an unsteady-state damage accumulation model, systematically depicting the dynamic evolution of micro-damage under the combined effects of thermal shock and flow disturbance, significantly improving the accuracy of early damage identification. Based on the accumulated micro-damage data and flow characteristics, path reconstruction and thermal efficiency optimization are implemented to generate optimized heat transfer pipeline data, achieving efficient path selection and low-fatigue section matching, effectively suppressing fatigue concentration, and improving overall thermal efficiency and service life. By extracting start-up and shutdown behavior characteristics from operational data and coupling them with the thermodynamic performance of the heat exchange path, a start-up and shutdown path control optimization model is constructed to obtain the start-up and shutdown path that minimizes cumulative damage, effectively slowing down the fatigue degradation rate and improving start-up and shutdown robustness. Through a health assessment module, operational strategy adjustment parameters are fed back to the control system, forming a closed-loop health monitoring system centered on micro-damage perception, path optimization control, and health status identification, enabling predictive maintenance and intelligent early warning of equipment operating status. Therefore, this invention comprehensively improves the accuracy of monitoring and risk warning capabilities for the health status of heating station equipment through structural topology modeling, precise heat exchange area monitoring, dynamic damage accumulation analysis, and closed-loop operation optimization. Attached Figure Description

[0065] Figure 1 A schematic diagram of the steps in a method for monitoring the health status of equipment in a heating station;

[0066] Figure 2 for Figure 1 A detailed flowchart illustrating the implementation steps of step S2.

[0067] Figure 3 for Figure 1 A detailed flowchart illustrating the implementation steps of step S3.

[0068] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0069] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0070] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0071] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0072] To achieve the above objectives, please refer to Figures 1 to 3 A method for monitoring the health status of heating station equipment, the method comprising the following steps:

[0073] Step S1: Obtain the structural data of the heating station equipment; perform topological analysis on the heating station based on the structural data of the heating station equipment to generate the topological data of the heating station equipment; use the topological data of the heating station equipment to perform virtual simulation modeling on the structural data of the heating station equipment to generate a three-dimensional model of the heating station equipment.

[0074] Step S2: Extract the heat exchanger area from the 3D model of the heating station equipment, and perform heat exchange pipeline path analysis on the heat exchanger area to generate heat exchange flow data; collect surface acoustic wave sensor data based on the heat exchanger area to obtain heat exchange surface wave group velocity change data.

[0075] Step S3: Calculate the transient thermal stress based on the wave group velocity change data of the heat exchange surface; perform unsteady-state damage accumulation analysis on the transient thermal stress using heat exchange flow data to generate cumulative micro-damage data of the heat exchanger; optimize the heat exchange pipeline path using the cumulative micro-damage data of the heat exchanger to generate optimized heat exchange pipeline data.

[0076] Step S4: Obtain the operating data of the heating station; extract the heat exchange start-up and shutdown data from the operating data of the heating station, and optimize the start-up and shutdown pipeline control based on the heat exchange start-up and shutdown data to obtain the start-up and shutdown path that minimizes cumulative damage; adjust the operating strategy based on the start-up and shutdown path that minimizes cumulative damage, and feed the adjustment results back to the control system to realize closed-loop monitoring of the health status of the heat exchanger of the heating station and early warning of damage risk.

[0077] This invention generates a highly realistic 3D model of the thermal power station equipment through topology analysis and virtual simulation modeling, providing a digital foundation for subsequent heat exchange pipeline analysis and sensor data mapping, effectively improving modeling efficiency and accuracy. By using surface acoustic wave (SAW) sensors to collect surface wave group velocity change data of the heat exchanger and combining this with heat transfer characteristics for transient thermal stress and unsteady-state damage accumulation analysis, it can identify micro-structural damage and achieve early warning of potential failures caused by thermal stress. Based on the accumulated micro-damage data, the heat exchange pipeline path is dynamically adjusted to generate optimal heat exchange optimization pipeline data, improving heat exchange efficiency while extending equipment life, exhibiting high adaptability and autonomous optimization capabilities. Through in-depth mining of start-up and shutdown data and pipeline control optimization, the risk of fatigue damage to equipment during frequent start-ups and shutdowns is effectively reduced, ensuring system operational stability and extending the service life of key components. By integrating simulation models, sensor data, operational data, and control feedback, a closed-loop monitoring system for the health status of the heat exchanger is constructed, realizing a shift from "response maintenance" to "predictive maintenance," improving equipment reliability and the level of intelligent management. This method integrates structural modeling, sensing, data fusion, path optimization, and control feedback, possessing strong versatility and scalability. It provides technical support for the construction of smart energy systems and promotes the transformation and upgrading of heating station equipment from traditional management to intelligent operation and maintenance. Therefore, this invention comprehensively improves the accuracy of monitoring and risk warning capabilities for the health status of heating station equipment through structural topology modeling, refined heat exchange area monitoring, dynamic damage accumulation analysis, and closed-loop operation optimization.

[0078] In this embodiment of the invention, reference is made to Figure 1 The diagram shown is a flowchart illustrating the steps of a method for monitoring the health status of heating station equipment according to the present invention. In this example, the method for monitoring the health status of heating station equipment includes the following steps:

[0079] Step S1: Obtain the structural data of the heating station equipment; perform topological analysis on the heating station based on the structural data of the heating station equipment to generate the topological data of the heating station equipment; use the topological data of the heating station equipment to perform virtual simulation modeling on the structural data of the heating station equipment to generate a three-dimensional model of the heating station equipment.

[0080] Step S2: Extract the heat exchanger area from the 3D model of the heating station equipment, and perform heat exchange pipeline path analysis on the heat exchanger area to generate heat exchange flow data; collect surface acoustic wave sensor data based on the heat exchanger area to obtain heat exchange surface wave group velocity change data.

[0081] Step S3: Calculate the transient thermal stress based on the wave group velocity change data of the heat exchange surface; perform unsteady-state damage accumulation analysis on the transient thermal stress using heat exchange flow data to generate cumulative micro-damage data of the heat exchanger; optimize the heat exchange pipeline path using the cumulative micro-damage data of the heat exchanger to generate optimized heat exchange pipeline data.

[0082] Step S4: Obtain the operating data of the heating station; extract the heat exchange start-up and shutdown data from the operating data of the heating station, and optimize the start-up and shutdown pipeline control based on the heat exchange start-up and shutdown data to obtain the start-up and shutdown path that minimizes cumulative damage; adjust the operating strategy based on the start-up and shutdown path that minimizes cumulative damage, and feed the adjustment results back to the control system to realize closed-loop monitoring of the health status of the heat exchanger of the heating station and early warning of damage risk.

[0083] In this embodiment of the invention, the geometric dimensions, connection relationships, and material parameters of each piece of equipment within the heating station are collected using sensors, CAD drawings, or a BIM system. The station contains 100 pieces of equipment, primarily 20 heat exchangers, with a total pipe length of approximately 500 meters and pipe diameters ranging from 20 to 200 mm. The collected equipment data is mapped to nodes and edges to form a topology graph. Graph theory algorithms (such as Depth-First Search, DFS) are used to identify the connectivity between heat exchangers and pipes, generating a topology matrix. A 20×20 adjacency matrix is ​​constructed to represent the direct connections between heat exchangers. Using the topology data and the geometric parameters of the equipment, a three-dimensional model is built in simulation software (such as ANSYS or COMSOL). The model includes detailed structures such as heat exchanger shells, heat exchange pipes, and connecting flanges, with a dimensional accuracy of ±1 mm. A three-dimensional model file format (STEP, IGES, etc.) is output for subsequent analysis. The main heat exchanger area is identified from the three-dimensional model, and the heat exchange pipe path area is then selected. Geometric segmentation algorithms (such as region segmentation based on surface curvature) are used to accurately locate the heat exchanger tube paths with an error ≤2mm. Path planning algorithms (such as Dijkstra's or A* algorithms) are employed to analyze the heat exchange medium flow path and fluid resistance, generating heat exchange flow data, including pipe length, pressure loss, and velocity distribution. The longest pipe length is 80 meters, with an average velocity of 1.5 m / s and a pressure loss of 0.12 MPa. Ten surface acoustic wave (SAW) sensors (approximately 8 meters apart) are deployed at key points along the heat exchanger tube path to collect changes in the tube wall velocity. The sampling frequency is 100 kHz, and the data time resolution is 10 ms. The collected data includes dynamic curves of the group velocity over time, ranging from 0.5 to 5 km / s. Based on the group velocity change data, the transient thermal stress σ(t) is calculated using a thermoelastic mechanics model, as shown in the following formula: ;in, The elastic modulus is (2×10^11 Pa). The coefficient of thermal expansion is (12×10^-6 / °C). The instantaneous temperature change was obtained by inversion based on the group velocity change. The calculated maximum thermal stress peak value σ_max ≈ 80 MPa, lasting for 10 seconds. The cumulative value of micro-damage to the heat exchanger was calculated using the Miner damage accumulation method. : ;in This represents the actual number of loops. The fatigue life cycle count of the material is determined based on the thermal stress spectrum. The cumulative micro-damage D=0.035 (less than 1, indicating that the equipment has not yet failed). Based on the cumulative micro-damage data, the pipeline path is adjusted to reduce the flow rate in stress-concentrated pipe sections and distribute the heat load. A genetic algorithm is used to optimize the pipeline flow velocity distribution, with the objective function minimizing the cumulative damage. After optimization, the pipeline pressure loss is reduced by 10%, and the damage value is reduced to D=0.025. Real-time data such as temperature, pressure, and flow rate are collected, with particular attention paid to heat exchanger start-up and shutdown signals. The sampling period is 1 second, and historical data is stored for 6 months. The number of heat exchanger start-ups and shutdowns, their duration, and time intervals are statistically analyzed. The average start-up and shutdown cycle is 4 hours, and the average number of start-ups and shutdowns is 12 per day. Based on the optimized pipeline data and the start-up and shutdown data, a start-up and shutdown path control model is constructed. The objective function is to minimize the cumulative damage of the heat exchange pipeline, constraining the start-up and shutdown frequency and thermal efficiency. A dynamic programming algorithm is used to solve for the optimal start-up and shutdown path. The optimal start-up and shutdown path is converted into control commands and fed back to the PLC or DCS control system. After implementation, the heat exchanger lifespan was extended by 10%, and the number of unplanned equipment downtimes was reduced by 30%. A closed-loop health monitoring system was established, and a risk warning was triggered if the cumulative micro-damage exceeded 0.05.

[0084] Preferably, step S1 includes the following steps:

[0085] Step S11: Obtain the structural data of the heating station equipment;

[0086] Step S12: Identify the structural components of the heating station equipment structure data to extract equipment unit data; construct an equipment connection diagram based on the equipment unit data to generate an initial structure diagram of the heating station;

[0087] Step S13: Perform graph structure analysis on the initial structure diagram of the heating station, extract node connection relationships, and generate heating station equipment topology data; use the heating station equipment topology data to perform spatial association modeling on the equipment unit data, and generate structure mapping data;

[0088] Step S14: Perform virtual simulation modeling based on the structural mapping data to generate an initial three-dimensional structural model of the heating station equipment; perform component geometry correction and topology closure processing on the initial three-dimensional structural model of the heating station equipment to generate a three-dimensional model of the heating station equipment.

[0089] In this embodiment of the invention, high-precision spatial coordinates and surface morphology are obtained by acquiring equipment point cloud data using an on-site laser scanner. Design drawings, construction blueprints, and manufacturer CAD models (common formats such as STEP, IGES, and DWG) are acquired. Equipment sensor and smart tag data are read to obtain equipment identification information, status, and location. Historical maintenance records and equipment specifications are integrated to assist in confirming equipment type and parameters. Multi-source heterogeneous data is converted into a unified structured format (e.g., point cloud to mesh, CAD file to standard model format) and the point cloud data is filtered to remove environmental noise and scanning errors. For scanning blind spots and missing data, interpolation or shape completion techniques are used to fill in the gaps. The equipment list is checked to confirm that all equipment has been collected and the data is complete. A structured and standardized raw dataset of the heating station equipment is output. Point cloud / mesh data is segmented using point cloud clustering algorithms (e.g., DBSCAN) or deep learning-based semantic segmentation models to identify different equipment components. Combining part numbers and attribute tags from the CAD model, equipment unit classification (e.g., pumps, valves, pipe sections) is completed. Output a device unit data table, including device ID, spatial location, size, and functional attributes. Based on spatial proximity and design specifications, determine the physical connections between devices (such as pipe interfaces and mechanical connections). Analyze pipeline flow direction and signal lines to generate directed connection edges. Verify connection validity and eliminate abnormal or indirect connections. Treat device units as graph nodes and connection relationships as graph edges to form a device connection graph. Visualize the structure graph and support querying node and connection attributes. Output a device unit data table + device connection graph (initial structure graph). Calculate the degree centrality, betweenness centrality, and other indices for each node to identify critical devices and bottleneck nodes. Use a graph traversal algorithm to detect connected components, ensuring the overall network is complete and without broken links. Apply a loop detection algorithm to find cyclic flow paths and optimize the topology. Form a topology matrix or adjacency list describing node connection relationships. Record node attributes (device function, operating status) and edge attributes (connection type, fluid direction). Associate the topology data with the three-dimensional spatial coordinates of device units to achieve a dual mapping between space and topology. Evaluate the spatial distance, azimuth, and interference area between devices. Output structural mapping data, including comprehensive information on the 3D location and topological connections of the equipment. Output topological data and structural mapping data of the heating station equipment. Import the structural mapping data into a 3D modeling platform (such as Revit, SolidWorks, CATIA). Construct the skeleton structure of the overall 3D model of the heating station according to the spatial location and topological connections of the equipment units. Gradually refine the equipment components, adding structural details (support frames, interface flanges, etc.). Automatically detect and repair geometric anomalies in the model (such as overlapping surfaces, voids, and broken edges). Optimize surface quality and improve model smoothness using mesh reconstruction technology. Correct model scale deviations to ensure consistency with actual equipment dimensions. Apply topology checking tools to ensure the model has no open boundaries and non-manifold geometry.The fluid channels and pipe interfaces are closed to ensure the continuity of the simulated fluid path. The overall structural strength and connection stability of the model are verified, and standard format 3D model files (STL, OBJ, IFC) are generated for subsequent simulations, virtual reality demonstrations, and maintenance management. Version management of the model is implemented, recording modification history and metadata.

[0090] As an example of the present invention, reference is made to... Figure 2 As shown, step S2 in this example includes:

[0091] Step S21: Divide the 3D model of the heating station equipment into regions to obtain the heat exchanger region structure data; identify the heat exchanger network based on the heat exchanger region structure data to obtain the heat exchanger topology data;

[0092] Step S22: Perform path tracing analysis on the heat exchange pipeline topology data, calculate the fluid path length and distribution, and generate heat exchange flow data;

[0093] Step S23: Simulate the deployment of surface acoustic wave sensors based on the heat exchanger area structure data to generate sensor sampling layout data; perform surface acoustic wave acquisition control on the sensor sampling layout data to obtain raw surface acoustic wave response data;

[0094] Step S24: Extract group velocity and correct time drift from the raw surface acoustic wave response data to generate heat transfer surface wave group velocity change data.

[0095] In this embodiment of the invention, a 3D model of the heating station equipment is input (output in step S14). A 3D image segmentation algorithm based on geometric features and material properties (such as voxel clustering or graph cut algorithms) is used to identify the spatial range of the heat exchanger. CAD metadata and equipment function tags are combined to assist in confirming the boundaries of the heat exchanger components. The heat exchanger area structure data is output, including the heat exchanger's geometry, spatial location, and component information. Based on the heat exchanger area structure data, the geometric morphology of the heat exchange pipeline (such as pipe diameter, bends, and branches) is detected using point clouds or mesh details. The node and pipe segment information of the heat exchange pipeline is automatically extracted by combining connection point features and interface shapes. A heat exchange pipeline topology diagram is constructed, where nodes represent pipe connection points, edges represent pipe segments, and flow direction information is included. The heat exchange pipeline topology data is output to prepare for subsequent fluid path analysis (output in step S21). All fluid paths are identified using a graph traversal algorithm (such as Depth-First Search (DFS) or Breadth-First Search (BFS). The fluid path length is calculated for each path, and the lengths of each pipe segment are accumulated. Based on pipe diameter and branch points, simulate fluid flow distribution and pressure distribution. Combine actual operating parameters (such as flow velocity, pressure, and temperature) to calculate fluid residence time and heat exchange efficiency in each path. Generate a heat exchange flow dataset containing key indicators such as path length, flow distribution, and fluid residence time. Output heat exchange flow data to support subsequent performance evaluation and optimization design. Input heat exchanger area structure data (output in step S21). Utilize the heat exchanger geometric model and acoustic wave propagation characteristics to simulate the propagation path of surface acoustic waves (SAWs) on the heat exchanger surface. Based on acoustic wave coverage and sensitivity requirements, design a sensor deployment scheme, determining sensor location, quantity, and spacing. Output sensor sampling layout data, including the spatial coordinates and coverage area of ​​each sensor. Based on the sensor sampling layout data, control the SAW sensor acquisition module to start the signal acquisition program. Acquire the raw response signal during acoustic wave propagation, including waveform, time delay, amplitude, and other multi-dimensional data. Simultaneously acquire environmental parameters (temperature, humidity, etc.) as a calibration reference. Output raw SAW response data to prepare for subsequent signal processing. Input the raw surface acoustic wave response data (output in step S23). Extract the group velocity characteristics of the acoustic wave signal using time-frequency analysis methods (such as wavelet transform and short-time Fourier transform, STFT). Calculate the change curve of the acoustic wave group velocity to reflect the surface structure and material state of the heat exchanger. Analyze environmental parameters and the time delay drift of the acquisition equipment to establish a time drift model. Apply a correction algorithm (such as dynamic time warping, DTW) to correct the time drift error in the group velocity curve. Ensure that the extracted group velocity change data accurately reflects the actual physical changes. Form a high-precision, real-time dataset of group velocity changes reflecting the propagation state of the heat exchanger surface waves. Output the data for heat exchanger condition monitoring, fault diagnosis, and health assessment.

[0096] Preferably, step S22 includes the following steps:

[0097] Step S221: Filter the node connectivity of the heat exchange pipeline topology data, extract the effective heat exchange path segments, and generate path structure network data; perform geometric reconstruction and curvature analysis on the path structure network data, extract the geometric constraint information of the path segments, and generate geometric feature data of the heat exchange path.

[0098] Step S222: Spatial path expansion is performed on the geometric feature data of the heat exchange path, and flow direction vector calculation is performed on the expanded spatial path to generate the initial flow path vector field;

[0099] Step S223: Measure the resistance coefficient of the internal structure of the heat exchanger; use the resistance coefficient of the internal structure of the heat exchanger to weight the path resistance of the initial flow path vector field to generate an equivalent flow resistance path diagram; perform fluid path length integration and flow velocity dynamic simulation on the equivalent flow resistance path diagram to calculate the path-level fluid transmission delay and generate flow delay matrix data.

[0100] Step S224: Estimate the path-level heat exchange efficiency of the flow delay matrix data to generate heat transfer data.

[0101] In this embodiment of the invention, the topological data of the heat exchange pipeline (usually a graph structure showing the connection relationships between nodes and pipelines) is used to filter effective connected paths using graph theory algorithms (such as Depth-First Search (DFS) or Breadth-First Search (BFS)). Isolated points, dead ends, or invalid loops are removed, and the path segments that actually participate in the heat exchange fluid flow are extracted to construct a path structure network. Each path segment in the path structure network is mapped to its spatial coordinates (such as path lines in a 3D point cloud or CAD model). Curvature analysis is performed on the path: the curvature value of each point on the path is calculated to determine the degree of pipe bending. Key bending points are filtered through curvature thresholds to form geometric constraint information for the path segments (such as maximum curvature and local curvature distribution), used for geometric constraints in subsequent flow simulations. The output "heat exchange path geometric feature data" includes: coordinates of the starting and ending nodes of the path segment, pipe diameter information, curvature parameters, connection relationships, etc. The three-dimensional path is unfolded into a two-dimensional or easily computed spatial coordinate system for convenient subsequent flow direction analysis. The unfolding method can be "flattened" according to the pipeline direction, maintaining length and connection order. This process can be implemented using geometric projection or path parameterization methods. Based on the fluid flow direction, a unit flow vector is calculated for each segment along the path, typically a unit vector along the path tangent. The vector field data structure includes the position vector of each sampling point on the path and its corresponding unit flow vector; the generated "initial flow path vector field" serves as the basis for subsequent flow resistance weighting and dynamic simulation. Local resistance coefficients corresponding to different structures within the heat exchanger (such as pipe wall roughness, elbows, joints, etc.) are obtained through experimental measurements or literature review. Resistance coefficient data can be in scalar or matrix form, covering the characteristics of different path segments. The resistance coefficients are assigned to the corresponding path segments in the initial flow path vector field, forming a weighted equivalent flow resistance path diagram. The equivalent resistance of each path segment is calculated, reflecting the ease with which the fluid passes through that segment. Path length integration is performed on the equivalent flow resistance path diagram, considering the influence of resistance on fluid velocity. Numerical simulations (such as CFD simulations or simplified flow velocity dynamic models) are used to calculate the velocity changes along the path, obtaining the fluid transport time delay. A "flow delay matrix data" is generated, where rows and columns correspond to path nodes, and elements represent path transmission delays. Based on the flow delay matrix and the geometric and physical characteristics of the paths, a heat transfer efficiency estimation model is established. The model can refer to basic heat transfer principles, such as the relationship between convective heat transfer coefficients, heat transfer area, temperature difference, and fluid residence time. For each path, the heat exchange efficiency of that path is estimated based on the flow delay (reflecting fluid residence time). The efficiency can be calculated using empirical formulas or numerical simulations, and combined with the path's heat transfer characteristics, the path's heat exchange efficiency value is output. The heat exchange efficiencies of all paths are summarized to form overall "heat transfer flow data," which is used to guide pipeline design optimization and performance evaluation.

[0102] Preferably, step S222 includes the following steps:

[0103] Step S2221: Perform three-dimensional structural noise reduction projection on the geometric feature data of the heat exchange path to eliminate high-order micro-curvature interference and generate a set of path principal axis fitting lines; perform piecewise spline reconstruction and unfolding based on the set of path principal axis fitting lines to transform the complex spatial path into a regularized path segment sequence and generate a set of spatial unfolded paths.

[0104] Step S2222: Perform geometric continuity weighting on the spatially unfolded path set, calibrate the directional consistency and structural influence coefficient of each micro-segment, and generate a path direction weighting matrix;

[0105] Step S2223: Perform directional flow vector decomposition based on the path direction weighted matrix, extract the mainstream vector and disturbance vector of the path micro-unit, and generate a local flow direction distribution map;

[0106] Step S2224: Reconstruct the local flow direction distribution map by stitching together the entire path scale to generate a unified initial flow path vector field.

[0107] In this embodiment of the invention, geometric feature data of the heat exchange path (including the point set of the path in three-dimensional space and its curvature information) is input. Spatial filtering algorithms (such as Gaussian filtering, wavelet denoising, etc.) are used to eliminate high-order micro-curvature interference in the curvature data, removing noise caused by measurement errors or local microstructures. The path point cloud is projected along its principal direction, and principal component analysis (PCA) is used to extract the principal axis direction of the path, obtaining a vector set of the principal axis direction. Based on the principal axis direction, the path point set is fitted using multi-segment Bezier curves or B-spline curves to fit the principal axis of the path, generating a fitted line group. During the fitting process, the curve is kept smooth and the fitting error is minimized, and the order of the fitted curve is appropriately controlled to avoid overfitting or underfitting. The fitted line group is divided into several regular path segments, and a piecewise spline curve unfolding algorithm (such as uniformly parameterized B-splines) is used to transform the complex spatial path into a regular, continuous sequence of path segments. Each path segment maintains uniform length and curvature to facilitate subsequent flow vector calculations, generating a spatially expanded path set containing segmented spline path sequences and their spatial coordinate data. For each path micro-segment in the spatially expanded path set, the angle between its tangent direction and that of adjacent micro-segments is calculated to assess directional consistency. Smaller angles result in higher directional consistency weights, and vice versa. Structural influence coefficients are assigned based on the path's geometric characteristics (e.g., curvature magnitude, path segment length, local bending complexity). Segments with larger curvatures have relatively larger structural influence coefficients, reflecting their impact on flow direction disturbances. The directional consistency weight and structural influence coefficients are weighted and synthesized to form a comprehensive weight for each path micro-segment. The weights of all micro-segments are summarized to construct a path direction weighted matrix, where matrix elements represent the strength of directional relationships and influence weights between micro-segments. Based on the path direction weighted matrix, vector decomposition methods (e.g., Principal Component Analysis (PCA) or Singular Value Decomposition (SVD)) are used to decompose the flow direction vector of each path micro-element. This decomposition results in two components: a mainstream vector (representing the main flow direction) and a disturbance vector (representing local deviations from the flow direction). The main flow vectors and disturbance vectors of all path elements are spatially mapped to form a local flow direction distribution map. Each point in the flow direction distribution map contains vector information describing the directionality of fluid motion and the magnitude of disturbance at that point. The vector segments on the local flow direction distribution map are continuously spliced ​​together in path order to ensure directional continuity and a smooth transition of the vector field. The vector directions at the splicing points are interpolated to eliminate discontinuities or abrupt changes in direction caused by segmentation. Through splicing and reconstruction, a unified initial flow path vector field at the full path scale is formed. This vector field describes the initial motion trend of the fluid on the heat transfer path and serves as the basis for subsequent flow resistance weighting and simulation.

[0108] As an example of the present invention, reference is made to... Figure 3 As shown, step S3 in this example includes:

[0109] Step S31: Perform multi-scale thermodynamic inversion on the wave group velocity variation data of the heat transfer surface to generate transient thermal gradient distribution data; perform material thermal response modeling on the transient thermal gradient distribution data and calculate the corresponding transient thermal stress data;

[0110] Step S32: Perform unsteady-state boundary modeling on the heat transfer flow data to generate dynamic boundary condition data for the heat transfer process; perform coupled analysis on the transient thermal stress data and dynamic boundary condition data to implement unsteady-state damage accumulation modeling and generate micro-damage evolution curve data;

[0111] Step S33: Perform region clustering on the micro-damage evolution curve data, and extract the damage accumulation threshold from the clustered regions to generate heat exchanger micro-damage accumulation data;

[0112] Step S34: Based on the cumulative data of micro-damage to the heat exchanger, perform path loss sensitivity analysis on the heat exchange pipeline path, identify high-risk path segments, and generate a set of candidate paths for heat exchange pipeline improvement; reconstruct the flow efficiency and evaluate the heat exchange performance of the candidate paths for heat exchange pipeline improvement, and generate optimized heat exchange pipeline data.

[0113] In this embodiment of the invention, heat transfer surface wave group velocity variation data (multi-scale spatial and temporal resolution) is input. A heat conduction inversion algorithm (such as an inverse problem solution method based on finite element or finite difference) is used to calculate the transient thermal gradient distribution. During inversion, the material's thermal diffusion equation and boundary conditions are combined to perform spatial scale decomposition, capturing local and macroscopic thermal gradient changes. A thermodynamic response model of the material is established based on its thermophysical parameters (thermal expansion coefficient, elastic modulus, thermal conductivity, etc.). Transient thermal gradient data are input, and transient thermal stress caused by the temperature gradient is calculated. A thermoelastic model (linear elastic or nonlinear elastic) is used to simulate the stress field, generating transient thermal gradient distribution data and corresponding transient thermal stress data. A dynamic boundary condition model is established based on heat transfer flow data, considering the time-varying boundary characteristics of temperature, pressure, and flow velocity. Time-varying boundary conditions are input into the thermodynamic model to form dynamic boundary condition data for the heat transfer process. The transient thermal stress data and dynamic boundary condition data are coupled to construct a joint unsteady-state thermodynamic boundary problem. The cumulative analysis of material micro-damage under thermal stress is performed using damage mechanics models (such as continuous damage mechanics (CDM) or fracture mechanics models). By integrating damage variables over time and combining them with material fatigue life theory, the degree of damage evolution over time is calculated. Micro-damage evolution curve data is output, describing the trend of damage change over time. The spatial distribution of the micro-damage evolution curve data is clustered (using algorithms such as K-means, DBSCAN, or hierarchical clustering) to identify regions with similar evolution trends. The clustering goal is to divide the damage evolution of different parts of the material into several typical regions. Statistical analysis is performed on the damage curves of each clustered region to extract key damage thresholds (such as critical damage threshold, failure threshold, etc.). These thresholds are set according to material failure criteria (e.g., when the damage variable exceeds a certain value, it is considered severe micro-damage), generating cumulative micro-damage data for the heat exchanger, including the cumulative damage level and corresponding threshold for each region. The cumulative micro-damage data of the heat exchanger is then mapped to heat exchange pipeline path segments. Sensitivity analysis methods (such as local sensitivity analysis, Sobol index, etc.) are used to evaluate the contribution of each path segment to the overall damage impact. Identify high-risk pipeline segments (i.e., pipeline segments where damage accumulates significantly and affects overall performance). Based on the information of high-risk pipeline segments, design improvement strategies (such as route rearrangement, adding supports, material replacement, etc.) to generate a set of candidate routes for heat exchange pipeline improvement, including multiple optimization schemes. Perform flow simulation on each candidate route scheme (using CFD software or a custom fluid dynamics model) to calculate flow efficiency. Evaluate heat exchange performance indicators (such as thermal efficiency, thermal resistance, heat transfer, etc.), and conduct a comprehensive evaluation combined with structural safety to generate heat exchange optimized pipeline data, including recommended pipeline schemes and their performance comparisons.

[0114] Preferably, step S34, which involves reconstructing the flow efficiency and evaluating the heat exchange performance of the candidate path set for heat exchange pipeline improvement, includes:

[0115] Finite volume fluid simulation is performed on the candidate path set for heat exchange pipeline improvement to generate path flow velocity distribution data; based on the path flow velocity distribution data, the turbulent structure of the candidate path set for heat exchange pipeline improvement is identified and pressure drop is calculated to generate flow resistance distribution map.

[0116] By combining the flow resistance distribution map with the thermodynamic boundary conditions, local heat transfer coefficient inversion is performed to generate heat transfer efficiency distribution data; the heat transfer efficiency distribution data is then used to conduct inter-path thermal performance comparison analysis, high-performance path units are selected, and a path performance optimization set is generated.

[0117] The structural safety of the optimized path performance set is reviewed by combining micro-damage accumulation data, potential fatigue segments are eliminated, and a structural safety optimized path set is generated.

[0118] Multi-objective thermo-mechanical co-optimization calculations are performed on the structural safety optimization path set to comprehensively evaluate thermal efficiency, structural integrity, and flow stability, ultimately generating heat exchange optimization pipeline data.

[0119] In this embodiment of the invention, when reconstructing the flow efficiency and evaluating the heat exchange performance of a set of candidate improved heat exchange pipeline paths, the flow velocity distribution data at each spatial point within the path is obtained by first using finite volume numerical simulation technology, combined with the geometric model of the candidate path set and specific fluid inlet boundary conditions (such as flow velocity, pressure, and temperature). Appropriate turbulence models (such as the k-ε model, k-ω SST model, or Large Eddy Simulation (LES)) are used during the simulation to accurately capture the complex turbulent structural characteristics within the pipeline. Subsequently, based on the calculated velocity field, the spatial distribution characteristics of the turbulent structure in the path are identified. Simultaneously, combined with the local flow state, the pressure drop value of each path segment is calculated, thus forming a complete flow resistance distribution map. Next, this flow resistance distribution map is combined with the thermodynamic boundary conditions (including wall temperature distribution and heat exchange medium temperature field) during the heat exchange process. Using the local heat transfer coefficient inversion method, the local heat transfer coefficient of each path segment is calculated through the coupled equations of heat conduction and convection, obtaining the spatial distribution data of the heat transfer efficiency, thereby quantitatively describing the heat transfer performance of the heat exchange path. Based on the aforementioned heat transfer efficiency distribution data, a systematic comparative analysis of the thermal performance of each path in the candidate path set was conducted. Path units with excellent heat transfer performance were selected based on thermal efficiency indicators, forming a path performance optimization set. Subsequently, combined with previously obtained micro-damage cumulative data, structural safety was reviewed for the path segments in the path performance optimization set, focusing on eliminating path segments with potential fatigue failure risks to ensure the structural integrity and safety of the heat exchange pipeline in actual operation, resulting in a structural safety optimized path set. Finally, for this structural safety optimized path set, a multi-objective thermo-mechanical co-optimization calculation method was adopted to comprehensively evaluate and weigh the performance indicators of maximizing thermal efficiency, ensuring structural integrity, and flow stability. Genetic algorithms, particle swarm optimization, or other advanced multi-objective optimization algorithms were used to co-optimize the path geometry, fluid dynamics characteristics, and structural mechanical properties. Through iterative calculations and multi-dimensional performance indicator feedback, heat exchange optimized pipeline data that meets the requirements of efficient heat transfer, structural safety and robustness, and stable flow was finally generated, achieving comprehensive performance improvement and reliable operation assurance for the pipeline.

[0120] Preferably, step S4 includes the following steps:

[0121] Step S41: Obtain the operating data of the heating station;

[0122] Step S42: Extract the heat exchange start-up and shutdown cycle and transient load change from the operating data of the heating station to obtain start-up and shutdown behavior sequence data; perform fluctuation frequency analysis and start-up peak statistics on the start-up and shutdown behavior sequence data to generate start-up and shutdown stress characteristic data;

[0123] Step S43: Perform time-series coupled simulation of start-up and shutdown stress characteristic data and heat exchange optimization pipeline data to analyze the stress response distribution of different paths and generate stress distribution diagrams of start-up and shutdown paths;

[0124] Step S44: Based on the stress distribution map of the start-up and shutdown paths, calculate the damage accumulation function of each candidate path, screen the path with the minimum damage growth rate, and generate the start-up and shutdown path that minimizes the cumulative damage; use the start-up and shutdown path that minimizes the cumulative damage to optimize the start-up and shutdown control of the heat exchanger area of ​​the heating station and generate operation strategy adjustment parameters.

[0125] Step S45: Input the operation strategy adjustment parameters into the three-dimensional model of the heating station equipment to perform closed-loop monitoring of the equipment health status, and determine the damage risk threshold of the monitored equipment health status in order to perform damage risk early warning operation of the heat exchanger of the heating station.

[0126] In this embodiment of the invention, operational monitoring data of the heat exchanger is collected from the site. This data includes, but is not limited to, the operating temperature, pressure, flow rate, heat exchange power, start-up and shutdown command records, and timestamp information of the heat exchanger, forming a multi-dimensional time-series heat exchanger operation dataset. Feature extraction is performed on the collected heat exchanger operation data to identify the time periods corresponding to the heat exchanger start-up and shutdown control commands, and typical heat exchanger start-up and shutdown cycles and corresponding load response curves are extracted. Furthermore, time-series analysis is used to analyze the fluctuation frequency of the start-up and shutdown behavior sequence, identifying the frequency characteristics and periodic patterns of the start-up and shutdown behavior. Based on the peak response of the heat exchanger load change during the start-up and shutdown cycle, key parameters such as the temperature jump rate and pressure surge rate at startup are extracted to generate start-up and shutdown stress characteristic data reflecting the stress characteristics caused by the heat exchanger's start-up and shutdown process. Based on the aforementioned start-up and shutdown stress characteristic data and the obtained heat exchanger optimization pipeline data, a coupled thermo-mechanical time-series simulation model is constructed. The start-up and shutdown excitation signals of the heat load are applied to different optimized path segments to conduct path-level transient stress response simulation. By employing methods such as the finite element method and coupled multiphysics simulation, the stress distribution of each path segment under specific start-stop cycles is obtained, thereby generating a start-stop path stress distribution map to describe the stress concentration areas and dynamic stress amplitude distribution of the path segment over time. Based on the start-stop path stress distribution map, the damage accumulation function of each path segment under periodic start-stop loads is calculated using material fatigue models (such as the Miner linear cumulative damage model or nonlinear fracture mechanics model), and the damage growth rate of all path segments is compared and analyzed. According to the principle of minimizing the damage growth rate, the path with the mildest structural response and longest lifespan at the start-stop frequency is selected from multiple candidate paths, obtaining the start-stop path that minimizes cumulative damage. Subsequently, based on the thermo-mechanical response characteristics of this optimal path, the start-stop logic of the heat exchanger in the heating station is adjusted (such as adjusting the heating rate, pressure increase curve, or start-stop cycle rhythm), forming a set of operation strategy adjustment parameters based on the structural protection mechanism. The above operation strategy adjustment parameters are input into the three-dimensional digital twin model of the heating station, combined with actual monitoring sensor data (such as strain gauges, temperature sensors, and flow meter data), to implement real-time closed-loop monitoring of equipment status. The system utilizes health assessment algorithms to determine damage trends and health levels of thermal equipment, establishing a time-series mapping relationship between equipment status and structural load. Simultaneously, the system sets a dynamically adjustable damage risk threshold, which, by comparing it with the monitored status, triggers damage risk warnings for heat exchangers. This provides thermal station maintenance personnel with visualized, quantifiable, and controllable start-up and shutdown decision support, achieving intelligent operation and maintenance based on structural health perception.

[0127] Preferably, step S44, which optimizes the start-up and shutdown control of the heat exchanger area of ​​the heating station by using a start-up and shutdown path that minimizes cumulative damage, includes:

[0128] High-frequency start-stop segments are identified in the heat exchanger area of ​​the heating station by using the start-stop path that minimizes cumulative damage, thereby obtaining high-frequency start-stop segment data.

[0129] Based on the high-frequency start-stop segment data, the start-stop path that minimizes cumulative damage is optimized for start-stop preheating, generating start-stop path preheating optimization data; based on the start-stop path preheating optimization data, a dynamic control temperature difference threshold is set, and the start-stop path that minimizes cumulative damage is optimized for first start-stop control through dynamic control of the temperature difference threshold, generating first start-stop control optimization data;

[0130] Based on the start-up and shutdown path that minimizes cumulative damage, the heat exchanger area of ​​the heating station is screened for static paths to obtain long static start-up and shutdown paths; low-flow-rate start-up control optimization is performed on the long static start-up and shutdown paths to generate second start-up and shutdown control optimization data;

[0131] The first start-stop control optimization data and the second start-stop control optimization data are integrated into the parameters for adjusting the operating strategy.

[0132] In this embodiment of the invention, based on the start-up and shutdown path with minimized cumulative damage obtained through the aforementioned screening, time-series analysis is performed on the start-up and shutdown behaviors experienced during the operation of the heating station to identify local segments within the path that are frequently affected by start-up and shutdown operations. By statistically analyzing the start-up and shutdown frequency, duration, and corresponding temperature difference amplitude of the path within one operating cycle, path regions with frequent thermal excitation are delineated, generating high-frequency start-up and shutdown segment data reflecting the vulnerability of local areas. Subsequently, start-up and shutdown preheating optimization processing is carried out for the high-frequency start-up and shutdown segments. This processing mitigates the temperature surge phenomenon in the high-frequency path during the initial start-up phase by adjusting parameters such as the temperature rise rate, start-up flow rate, and preload power of the heat exchanger system during start-up and shutdown, reducing its transient thermal stress amplitude, thereby generating start-up and shutdown path preheating optimization data that includes preheating curves, temperature adjustment rates, and operating cycles. Based on this preheating optimization data, a dynamic control strategy for the start-up and shutdown path is customized, and a dynamic temperature difference control threshold is set. This temperature difference threshold is not a fixed value but is dynamically adjusted based on various factors such as ambient temperature, historical start-up and shutdown records, and system load to ensure that the instantaneous thermal excitation experienced by the path segment does not exceed the material's thermal response limit. The dynamic temperature difference threshold is applied to the start-stop control logic to form the first start-stop control optimization data, which guides the start-up conditions and rate control of each start-stop action during actual operation. Next, the start-stop paths that minimize cumulative damage and contain long-term non-operational segments are analyzed to identify those that have been in a cold or static state for extended periods, i.e., long static start-stop paths. These segments are prone to fatigue cracking upon restarting due to large temperature gradients and uneven stress release; therefore, a low-flow-rate start-up control strategy is required. By limiting the flow rate and pressure fluctuation range during the initial cold start, the heat diffusion process is smoothed, mitigating the thermo-mechanical impact on the path structure, thus generating the second start-stop control optimization data. Finally, the two sub-optimized data—the first start-stop control optimization data (dynamic temperature difference control) and the second start-stop control optimization data (low-speed cold start control)—are merged and integrated into a complete set of operational strategy adjustment parameters that includes path selection strategy, start-stop cycle time, start-up rate, and temperature control mechanisms. This parameter set can be used to configure the automatic control system of the heating station, adjust the start-up and shutdown strategy to adapt to different seasons, load conditions and path states, realize full life cycle optimization control of the start-up and shutdown operation of the heat exchanger area, effectively reduce the risk of frequent start-up and shutdown erosion of the structural integrity of critical path segments, and extend the service life of the equipment.

[0133] This specification provides a health status monitoring system for heating station equipment, used to execute the above-described health status monitoring method for heating station equipment. The heating station equipment health status monitoring system includes:

[0134] The simulation modeling module is used to acquire the structural data of the heating station equipment; perform topological analysis on the heating station based on the structural data of the heating station equipment to generate the topological data of the heating station equipment; and use the topological data of the heating station equipment to perform virtual simulation modeling on the structural data of the heating station equipment to generate a three-dimensional model of the heating station equipment.

[0135] The heat exchange analysis module is used to extract the heat exchanger area from the 3D model of the heating station equipment, perform heat exchange pipeline path analysis on the heat exchanger area, and generate heat exchange flow data; based on the heat exchanger area, surface acoustic wave sensor data is collected to obtain heat exchange surface wave group velocity change data.

[0136] The heat exchange path analysis module is used to calculate the transient thermal stress based on the change data of wave group velocity on the heat exchange surface; to perform unsteady-state damage accumulation analysis on the transient thermal stress using heat exchange flow data, generating cumulative micro-damage data of the heat exchanger; and to optimize the heat exchange pipeline path using the cumulative micro-damage data of the heat exchanger, generating optimized heat exchange pipeline data.

[0137] The health assessment module is used to acquire the operating data of the heating station; extract the heat exchange start-up and shutdown data from the operating data of the heating station, and optimize the start-up and shutdown pipeline control based on the heat exchange start-up and shutdown data to obtain the start-up and shutdown path that minimizes cumulative damage; adjust the operating strategy based on the start-up and shutdown path that minimizes cumulative damage, and feed the adjustment results back to the control system to realize closed-loop monitoring of the health status of the heat exchanger of the heating station and early warning of damage risk.

[0138] The beneficial effects of this invention lie in the fact that it uses a simulation modeling module to perform topological analysis and virtual modeling of the structural data of the heating station equipment, generating a three-dimensional digital twin model with accurate physical semantics and spatial layout. This provides a structured foundation for subsequent heat transfer analysis and damage simulation, effectively improving modeling efficiency and accuracy. By using a surface acoustic wave sensor to collect real-time data on surface wave group velocity changes in the heat exchanger area, and combining this with path analysis results, the heat transfer process is dynamically visualized and reconstructed, providing accurate physical thermal response data and enhancing the real-time performance and sensitivity of micro-damage identification. The heat transfer flow data and wave group thermal stress calculation results are coupled and analyzed to construct an unsteady-state damage accumulation model, systematically depicting the dynamic evolution of micro-damage under the combined effects of thermal shock and flow disturbance, significantly improving the accuracy of early damage identification. Based on the accumulated micro-damage data and flow characteristics, path reconstruction and thermal efficiency optimization are implemented to generate optimized heat transfer pipeline data, achieving efficient path selection and low-fatigue section matching, effectively suppressing fatigue concentration, and improving overall thermal efficiency and service life. By extracting start-up and shutdown behavior characteristics from operational data and coupling them with the thermodynamic performance of the heat exchange path, a start-up and shutdown path control optimization model is constructed to obtain the start-up and shutdown path that minimizes cumulative damage, effectively slowing down the fatigue degradation rate and improving start-up and shutdown robustness. Through a health assessment module, operational strategy adjustment parameters are fed back to the control system, forming a closed-loop health monitoring system centered on micro-damage perception, path optimization control, and health status identification, enabling predictive maintenance and intelligent early warning of equipment operating status. Therefore, this invention comprehensively improves the accuracy of monitoring and risk warning capabilities for the health status of heating station equipment through structural topology modeling, precise heat exchange area monitoring, dynamic damage accumulation analysis, and closed-loop operation optimization.

[0139] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0140] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for monitoring the health status of heating station equipment, characterized in that, Includes the following steps: Step S1: Obtain the structural data of the heating station equipment; Based on the structural data of the heating station equipment, a topology analysis of the heating station is performed to generate the topology data of the heating station equipment; the topology data of the heating station equipment is then used to perform virtual simulation modeling of the structural data of the heating station equipment to generate a three-dimensional model of the heating station equipment. Step S2: Extract the heat exchanger area from the 3D model of the heating station equipment, and perform heat exchange pipeline path analysis on the heat exchanger area to generate heat exchange flow data; collect surface acoustic wave sensor data based on the heat exchanger area to obtain heat exchange surface wave group velocity change data. Step S3: Calculate the transient thermal stress based on the wave group velocity variation data of the heat exchange surface; perform unsteady-state damage accumulation analysis on the transient thermal stress using heat transfer flow data to generate cumulative micro-damage data of the heat exchanger; The heat exchange piping path is optimized using accumulated micro-damage data of the heat exchanger, generating optimized heat exchange piping data; wherein, step S3 includes the following steps: Step S31: Perform multi-scale thermodynamic inversion on the wave group velocity variation data of the heat transfer surface to generate transient thermal gradient distribution data; perform material thermal response modeling on the transient thermal gradient distribution data and calculate the corresponding transient thermal stress data; Step S32: Perform unsteady-state boundary modeling on the heat transfer flow data to generate dynamic boundary condition data for the heat transfer process; perform coupled analysis on the transient thermal stress data and dynamic boundary condition data to implement unsteady-state damage accumulation modeling and generate micro-damage evolution curve data; Step S33: Perform region clustering on the micro-damage evolution curve data, and extract the damage accumulation threshold from the clustered regions to generate heat exchanger micro-damage accumulation data; Step S34: Based on the cumulative data of micro-damage to the heat exchanger, perform path loss sensitivity analysis on the heat exchange pipeline path, identify high-risk path segments, and generate a set of candidate paths for heat exchange pipeline improvement; reconstruct the flow efficiency and evaluate the heat exchange performance of the candidate paths for heat exchange pipeline improvement to generate optimized heat exchange pipeline data. Step S4: Obtain the operating data of the heating station; extract the heat exchange start-up and shutdown data from the operating data of the heating station, and optimize the start-up and shutdown pipeline control based on the heat exchange start-up and shutdown data to obtain the start-up and shutdown path that minimizes cumulative damage; adjust the operating strategy based on the start-up and shutdown path that minimizes cumulative damage, and feed the adjustment results back to the control system to realize closed-loop monitoring of the health status of the heat exchanger of the heating station and early warning of damage risk.

2. The method for monitoring the health status of heating station equipment according to claim 1, characterized in that, Step S1 includes the following steps: Step S11: Obtain the structural data of the heating station equipment; Step S12: Identify the structural components of the heating station equipment structure data to extract equipment unit data; construct an equipment connection diagram based on the equipment unit data to generate an initial structure diagram of the heating station; Step S13: Perform graph structure analysis on the initial structure diagram of the heating station, extract node connection relationships, and generate heating station equipment topology data; use the heating station equipment topology data to perform spatial association modeling on the equipment unit data, and generate structure mapping data; Step S14: Perform virtual simulation modeling based on the structural mapping data to generate an initial three-dimensional structural model of the heating station equipment; perform component geometry correction and topology closure processing on the initial three-dimensional structural model of the heating station equipment to generate a three-dimensional model of the heating station equipment.

3. The method for monitoring the health status of heating station equipment according to claim 1, characterized in that, Step S2 includes the following steps: Step S21: Divide the 3D model of the heating station equipment into regions to obtain the heat exchanger region structure data; identify the heat exchanger network based on the heat exchanger region structure data to obtain the heat exchanger topology data; Step S22: Perform path tracing analysis on the heat exchange pipeline topology data, calculate the fluid path length and distribution, and generate heat exchange flow data; Step S23: Simulate the deployment of surface acoustic wave sensors based on the heat exchanger area structure data to generate sensor sampling layout data; perform surface acoustic wave acquisition control on the sensor sampling layout data to obtain raw surface acoustic wave response data; Step S24: Extract group velocity and correct time drift from the raw surface acoustic wave response data to generate heat transfer surface wave group velocity change data.

4. The method for monitoring the health status of heating station equipment according to claim 3, characterized in that, Step S22 includes the following steps: Step S221: Filter the node connectivity of the heat exchange pipeline topology data, extract the effective heat exchange path segments, and generate path structure network data; perform geometric reconstruction and curvature analysis on the path structure network data, extract the geometric constraint information of the path segments, and generate geometric feature data of the heat exchange path. Step S222: Spatial path expansion is performed on the geometric feature data of the heat exchange path, and flow direction vector calculation is performed on the expanded spatial path to generate the initial flow path vector field; Step S223: Measure the resistance coefficient of the internal structure of the heat exchanger; use the resistance coefficient of the internal structure of the heat exchanger to weight the path resistance of the initial flow path vector field to generate an equivalent flow resistance path diagram; perform fluid path length integration and flow velocity dynamic simulation on the equivalent flow resistance path diagram to calculate the path-level fluid transmission delay and generate flow delay matrix data. Step S224: Estimate the path-level heat exchange efficiency of the flow delay matrix data to generate heat transfer data.

5. The method for monitoring the health status of heating station equipment according to claim 4, characterized in that, Step S222 includes the following steps: Step S2221: Perform three-dimensional structural noise reduction projection on the geometric feature data of the heat exchange path to eliminate high-order micro-curvature interference and generate a set of path principal axis fitting lines; perform piecewise spline reconstruction and unfolding based on the set of path principal axis fitting lines to transform the complex spatial path into a regularized path segment sequence and generate a set of spatial unfolded paths. Step S2222: Perform geometric continuity weighting on the spatially unfolded path set, calibrate the directional consistency and structural influence coefficient of each micro-segment, and generate a path direction weighting matrix; Step S2223: Perform directional flow vector decomposition based on the path direction weighted matrix, extract the mainstream vector and disturbance vector of the path micro-unit, and generate a local flow direction distribution map; Step S2224: Reconstruct the local flow direction distribution map by stitching together the entire path scale to generate a unified initial flow path vector field.

6. The method for monitoring the health status of heating station equipment according to claim 1, characterized in that, Step S34 involves reconstructing the flow efficiency and evaluating the heat exchange performance of the candidate path set for heat exchange pipeline improvement. Finite volume fluid simulation is performed on the candidate path set for heat exchange pipeline improvement to generate path flow velocity distribution data; based on the path flow velocity distribution data, the turbulent structure of the candidate path set for heat exchange pipeline improvement is identified and pressure drop is calculated to generate flow resistance distribution map. By combining the flow resistance distribution map with the thermodynamic boundary conditions, local heat transfer coefficient inversion is performed to generate heat transfer efficiency distribution data; the heat transfer efficiency distribution data is then used to conduct inter-path thermal performance comparison analysis, high-performance path units are selected, and a path performance optimization set is generated. The structural safety of the optimized path performance set is reviewed by combining micro-damage accumulation data, potential fatigue segments are eliminated, and a structural safety optimized path set is generated. Multi-objective thermo-mechanical co-optimization calculations are performed on the structural safety optimization path set to comprehensively evaluate thermal efficiency, structural integrity, and flow stability, ultimately generating heat exchange optimization pipeline data.

7. The method for monitoring the health status of heating station equipment according to claim 1, characterized in that, Step S4 includes the following steps: Step S41: Obtain the operating data of the heating station; Step S42: Extract the heat exchange start-up and shutdown cycle and transient load change from the operating data of the heating station to obtain start-up and shutdown behavior sequence data; perform fluctuation frequency analysis and start-up peak statistics on the start-up and shutdown behavior sequence data to generate start-up and shutdown stress characteristic data; Step S43: Perform time-series coupled simulation of start-up and shutdown stress characteristic data and heat exchange optimization pipeline data to analyze the stress response distribution of different paths and generate stress distribution diagrams of start-up and shutdown paths; Step S44: Based on the stress distribution map of the start-up and shutdown paths, calculate the damage accumulation function of each candidate path, screen the path with the minimum damage growth rate, and generate the start-up and shutdown path that minimizes the cumulative damage; use the start-up and shutdown path that minimizes the cumulative damage to optimize the start-up and shutdown control of the heat exchanger area of ​​the heating station and generate operation strategy adjustment parameters. Step S45: Input the operation strategy adjustment parameters into the three-dimensional model of the heating station equipment to perform closed-loop monitoring of the equipment health status, and determine the damage risk threshold of the monitored equipment health status in order to perform damage risk early warning operation of the heat exchanger of the heating station.

8. The method for monitoring the health status of heating station equipment according to claim 7, characterized in that, Step S44 involves optimizing the start-up and shutdown control of the heat exchanger area of ​​the heating station using a start-up and shutdown path that minimizes cumulative damage. High-frequency start-stop segments are identified in the heat exchanger area of ​​the heating station by using the start-stop path that minimizes cumulative damage, thereby obtaining high-frequency start-stop segment data. Based on the high-frequency start-stop segment data, the start-stop path that minimizes cumulative damage is optimized for start-stop preheating, generating start-stop path preheating optimization data; based on the start-stop path preheating optimization data, a dynamic control temperature difference threshold is set, and the start-stop path that minimizes cumulative damage is optimized for first start-stop control through dynamic control of the temperature difference threshold, generating first start-stop control optimization data; Based on the start-up and shutdown path that minimizes cumulative damage, the heat exchanger area of ​​the heating station is screened for static paths to obtain long static start-up and shutdown paths; low-flow-rate start-up control optimization is performed on the long static start-up and shutdown paths to generate second start-up and shutdown control optimization data; The first start-stop control optimization data and the second start-stop control optimization data are integrated into the parameters for adjusting the operating strategy.

9. A health status monitoring system for heating station equipment, characterized in that, For performing the method for monitoring the health status of heating station equipment as described in claim 1, the heating station equipment health status monitoring system comprises: The simulation modeling module is used to acquire the structural data of the heating station equipment; perform topological analysis on the heating station based on the structural data of the heating station equipment to generate the topological data of the heating station equipment; and use the topological data of the heating station equipment to perform virtual simulation modeling on the structural data of the heating station equipment to generate a three-dimensional model of the heating station equipment. The heat exchange analysis module is used to extract the heat exchanger area from the 3D model of the heating station equipment, perform heat exchange pipeline path analysis on the heat exchanger area, and generate heat exchange flow data; based on the heat exchanger area, surface acoustic wave sensor data is collected to obtain heat exchange surface wave group velocity change data. The heat exchange path analysis module is used to calculate the transient thermal stress based on the change data of wave group velocity on the heat exchange surface; to perform unsteady-state damage accumulation analysis on the transient thermal stress using heat exchange flow data, generating cumulative micro-damage data of the heat exchanger; and to optimize the heat exchange pipeline path using the cumulative micro-damage data of the heat exchanger, generating optimized heat exchange pipeline data. The health assessment module is used to acquire the operating data of the heating station; extract the heat exchange start-up and shutdown data from the operating data of the heating station, and optimize the start-up and shutdown pipeline control based on the heat exchange start-up and shutdown data to obtain the start-up and shutdown path that minimizes cumulative damage; adjust the operating strategy based on the start-up and shutdown path that minimizes cumulative damage, and feed the adjustment results back to the control system to realize closed-loop monitoring of the health status of the heat exchanger of the heating station and early warning of damage risk.

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