Thermal insulation layer health state assessment method based on thermal impedance monitoring

Abstract

The invention relates to the field of thermal insulation layer health state assessment, and discloses a thermal insulation layer health state assessment method based on thermal impedance monitoring, which comprises the following steps: S1, performing thermal impedance data acquisition on a thermal insulation layer by using a sensor array to generate original thermal impedance monitoring data of the thermal insulation layer; performing data preprocessing on the original thermal impedance monitoring data of the thermal insulation layer to obtain high-quality thermal impedance data of the thermal insulation layer; s2, performing thermal impedance spatial-temporal distribution characteristic analysis on the high-quality thermal impedance data of the thermal insulation layer to obtain thermal impedance spatial-temporal distribution characteristic data of the thermal insulation layer; according to the thermal impedance spatial-temporal distribution characteristic data of the thermal insulation layer, abnormal area preliminary positioning is carried out, and thermal insulation layer abnormal area preliminary positioning data is generated. Through multi-stage data processing and analysis, thermal impedance spatial-temporal distribution characteristic extraction, local thermal impedance change trend analysis and abnormal area refining are included; the abnormal area of the thermal insulation layer can be accurately positioned, the damage type of the thermal insulation layer is further analyzed, and finally all-around health state evaluation is achieved.

Description

Technical Field

[0001] This invention relates to the field of thermal insulation layer health status assessment technology, specifically a method for assessing the health status of thermal insulation layers based on thermal impedance monitoring. Background Technology

[0002] With the development of the construction industry, the application of thermal insulation materials has gradually increased, especially in fields such as heat transfer systems, pipe insulation, and building exterior wall insulation. The health of the insulation layer is directly related to the building's energy efficiency, service life, and safety. Traditional insulation layer health testing technologies mostly rely on manual inspection or external environmental monitoring, which has significant subjectivity and limitations, and the test results are difficult to fully reflect the actual health of the insulation layer.

[0003] Thermal impedance, as a physical quantity characterizing the thermal conductivity of a material, directly reflects the thermal performance and localized damage of the insulation layer. Utilizing thermal impedance for insulation layer health monitoring enables more accurate damage identification and timely repair and maintenance. Existing thermal impedance monitoring methods mostly focus on single data analysis or localized health assessments, lacking a comprehensive and systematic health assessment mechanism. Furthermore, they suffer from complex data processing and low accuracy in practical applications. Therefore, a new method is urgently needed that can comprehensively analyze the thermal impedance data of the insulation layer and provide comprehensive and accurate health status assessment results. Summary of the Invention

[0004] The purpose of this invention is to provide a method for assessing the health status of insulation layers based on thermal impedance monitoring, in order to solve the problems that most existing thermal impedance monitoring methods focus on single data analysis or local health assessment, lack a comprehensive and systematic health assessment mechanism, and suffer from complex data processing and low accuracy in practical applications.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a method for assessing the health status of insulation layers based on thermal impedance monitoring, comprising: S1. Use a sensor array to collect thermal impedance data of the insulation layer and generate raw thermal impedance monitoring data of the insulation layer; perform data preprocessing on the raw thermal impedance monitoring data of the insulation layer to obtain high-quality thermal impedance data of the insulation layer. S2. Perform spatiotemporal distribution characteristic analysis of thermal impedance data of high-quality thermal insulation layer to obtain spatiotemporal distribution characteristic data of thermal impedance of thermal insulation layer; perform preliminary location of abnormal area based on thermal impedance spatiotemporal distribution characteristic data of thermal insulation layer to generate preliminary location data of abnormal area of ​​thermal insulation layer. S3. Based on the preliminary location data of the abnormal area of ​​the insulation layer, perform local thermal impedance change trend analysis on the thermal impedance data of the high-quality insulation layer to obtain local thermal impedance change trend data; refine the boundary of the abnormal area according to the local thermal impedance change trend data to generate refined boundary data of the abnormal area of ​​the insulation layer. S4. Using the refined boundary data of the abnormal area of ​​the insulation layer, extract the thermal impedance parameters of the abnormal area from the thermal impedance data of the high-quality insulation layer to obtain the thermal impedance parameter data of the abnormal area; make a preliminary judgment on the damage type of the insulation layer based on the thermal impedance parameter data of the abnormal area to generate preliminary judgment data on the damage type of the insulation layer. S5. Obtain the thermal impedance characteristic database of the insulation layer material; use the thermal impedance characteristic database of the insulation layer material to perform precise matching of the damage type of the preliminary judgment data of the insulation layer damage type, and generate precise data of the insulation layer damage type; perform the insulation layer health status level assessment based on the precise data of the insulation layer damage type, and obtain the insulation layer health status level assessment data. S6. Generate a health status report for the insulation layer based on the health status assessment data of the insulation layer.

[0006] Furthermore, step S1 specifically includes: A distributed thermal impedance sensor array is used to collect thermal impedance data of the insulation layer at preset time intervals to generate raw thermal impedance monitoring data of the insulation layer. The original thermal impedance monitoring data of the insulation layer was checked for data integrity, and the data integrity check results were obtained. Based on the data integrity check results, the missing data interpolation process is performed on the original thermal resistance monitoring data of the insulation layer to generate interpolated thermal resistance data of the insulation layer. Anomaly detection was performed on the interpolated thermal resistance data of the insulation layer to obtain the anomaly detection results. Based on the outlier detection results, the interpolated thermal resistance data of the insulation layer is corrected for outliers, and the corrected thermal resistance data of the insulation layer is generated. The corrected thermal resistance data of the insulation layer is smoothed to obtain high-quality thermal resistance data of the insulation layer.

[0007] Furthermore, step S2 specifically includes: Time-series alignment processing is performed on the high-quality thermal resistance data of the insulation layer to obtain time-aligned thermal resistance data of the insulation layer. Spatial interpolation is performed on the time-aligned thermal resistance data of the insulation layer to generate a spatial distribution map of the thermal resistance of the insulation layer. The thermal impedance gradient of the insulation layer is calculated by analyzing the spatial distribution diagram of the thermal impedance of the insulation layer to obtain the thermal impedance gradient data of the insulation layer. Spatiotemporal distribution features of thermal impedance are extracted based on thermal impedance gradient data of thermal insulation layer to obtain spatiotemporal distribution feature data of thermal impedance of thermal insulation layer; Based on the spatiotemporal distribution characteristics of thermal impedance of the insulation layer, the abnormal area is initially located using a preset abnormal threshold, generating preliminary location data of the abnormal area of ​​the insulation layer.

[0008] Furthermore, step S3 specifically includes: Based on the preliminary location data of abnormal areas in the insulation layer, local thermal impedance data are extracted from the thermal impedance data of high-quality insulation layers. Time smoothing is performed on the local thermal impedance data to obtain smoothed local thermal impedance data; Trend analysis was performed on the smoothed local thermal impedance data to obtain the local thermal impedance change trend data; Based on the local thermal impedance change trend data, the boundary of the abnormal area is refined using a boundary detection algorithm, generating refined boundary data of the abnormal area of ​​the insulation layer.

[0009] Furthermore, step S4 specifically includes: By refining boundary data for abnormal regions of the insulation layer, thermal impedance data of abnormal regions can be extracted from high-quality thermal impedance data of the insulation layer. Statistical features are extracted from the thermal impedance data of the abnormal region to obtain statistical feature data of the thermal impedance of the abnormal region. Parameter fitting is performed based on the statistical characteristics of thermal impedance in the abnormal region to obtain the thermal impedance parameter data of the abnormal region. Probability distribution analysis was performed on the thermal impedance data in the abnormal region to obtain the thermal impedance probability distribution function; Calculate the mean and variance statistical parameters of the thermal impedance in the abnormal region based on the thermal impedance probability distribution function; The statistical parameters are fitted using a pre-defined parameter fitting model to obtain thermal impedance parameter data for the abnormal region. Based on the thermal impedance parameter data of the abnormal region, a preliminary judgment of the damage type of the insulation layer is made using a preset damage type feature library, and preliminary judgment data of the damage type of the insulation layer is generated.

[0010] Furthermore, step S4 specifically includes: Obtain a database of thermal impedance characteristics of insulation layer materials, which contains the range of thermal impedance parameters corresponding to damage types. The preliminary judgment data of the insulation layer damage type is compared with the thermal impedance characteristic database of the insulation layer material to perform precise matching of the damage type and generate precise data of the insulation layer damage type. Based on precise data on the damage type of the insulation layer, the health status of the insulation layer is assessed using a preset health status assessment standard, resulting in health status assessment data for the insulation layer.

[0011] Furthermore, the step of obtaining the thermal impedance gradient data of the insulation layer includes: Select several key points on the spatial distribution diagram of the thermal resistance of the insulation layer; Calculate the thermal impedance difference between each key point and its surrounding adjacent points; Calculate the magnitude and direction of the thermal impedance gradient at each key point based on the thermal impedance difference; By integrating the magnitude and direction of the thermal impedance gradient at all key points, the thermal impedance gradient data of the insulation layer is obtained.

[0012] Furthermore, the step of obtaining the refined boundary data of the abnormal area of ​​the insulation layer includes: Based on the local thermal impedance change trend data, determine the boundary change characteristics of the abnormal area; Edge detection algorithms are used to detect boundary change features in abnormal regions to obtain preliminary boundary points; The initial boundary points are fitted to generate a smooth boundary curve; Based on the boundary curve, the boundary data of the abnormal area of ​​the insulation layer is refined.

[0013] Furthermore, the steps for obtaining the health status assessment data of the insulation layer include: Collect thermal impedance data of different types of insulation materials under different damage states; The collected thermal impedance data were classified and organized, and grouped according to damage type; Determine the range of thermal impedance parameters corresponding to each damage type; Damage types and their corresponding thermal impedance parameter ranges are stored in a database to form a thermal impedance characteristic database of insulation layer materials. Based on precise data on the types of damage to the insulation layer, the corresponding health status assessment indicators are determined. Obtain the weight coefficient for each evaluation indicator; The overall health status score of the insulation layer is calculated based on the evaluation indicators and their weighting coefficients. The health status level of the insulation layer is determined based on the comprehensive health status score, thus obtaining the health status level assessment data of the insulation layer.

[0014] Furthermore, the steps for obtaining the health status assessment report of the insulation layer include: Based on the health status assessment data of the insulation layer, compile relevant information on the health status of the insulation layer; Organize relevant information on abnormal areas of the insulation layer; By integrating key information and information related to abnormal areas, information related to the health status of the insulation layer is formed; The relevant information on the health status of the insulation layer is formatted according to the preset report template to generate a health status assessment report for the insulation layer.

[0015] Compared with the prior art, the beneficial effects of the present invention are: (1) This invention uses multi-stage data processing and analysis, including extraction of thermal impedance spatiotemporal distribution features, analysis of local thermal impedance change trends and refinement of abnormal areas. It can not only accurately locate the abnormal areas of the insulation layer, but also further analyze its damage type, and finally achieve a comprehensive health status assessment. (2) By combining the thermal impedance characteristic database of the insulation layer material for accurate matching of damage types, the present invention can improve the accuracy of damage type judgment and reduce the possibility of misjudgment. (3) Through automated data preprocessing, outlier correction, smoothing and other technologies, the high quality of the data is ensured, making the subsequent analysis results more reliable and reducing the need for human intervention; (4) By adopting a distributed thermal impedance sensor array and collecting data at regular intervals, the health status of the insulation layer can be monitored and evaluated in real time, providing timely decision support for engineering maintenance; (5) The present invention provides a standardized and simple evaluation process through a preset algorithm and database, which is applicable to various insulation layer materials and different usage environments, and has strong universality and operability. Attached Figure Description

[0016] Figure 1 This is a flowchart of the overall process of the method of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] Please see Figure 1 This invention provides a technical solution: a method for assessing the health status of insulation layers based on thermal impedance monitoring, comprising: S1. Use a sensor array to collect thermal impedance data of the insulation layer and generate raw thermal impedance monitoring data of the insulation layer; perform data preprocessing on the raw thermal impedance monitoring data of the insulation layer to obtain high-quality thermal impedance data of the insulation layer. The sensor array is a collection of multiple sensors arranged according to specific layout and rules. These sensors can simultaneously measure thermal impedance-related parameters at different locations of the insulation layer. Compared to a single sensor, this provides more comprehensive and richer data, improving the accuracy and completeness of data acquisition and providing a more reliable foundation for subsequent analysis. The raw thermal impedance monitoring data of the insulation layer is the initial data on the thermal impedance of the insulation layer directly collected by the sensor array. It contains thermal impedance information at each location of the insulation layer at the time of collection, but may contain noise, interference, missing data, or outliers, requiring further processing before it can be used to accurately analyze the thermal characteristics of the insulation layer. Data preprocessing involves a series of processing operations on the raw thermal impedance monitoring data of the insulation layer, including data cleaning, data imputation, and data smoothing. The purpose is to improve data quality, making the data more suitable for the requirements of subsequent analysis, and obtaining high-quality thermal impedance data of the insulation layer. The high-quality thermal impedance data of the insulation layer, after data preprocessing, results in accurate, reliable, and complete data. S2. Perform spatiotemporal distribution characteristic analysis of thermal impedance data of high-quality thermal insulation layer to obtain spatiotemporal distribution characteristic data of thermal impedance of thermal insulation layer; perform preliminary location of abnormal area based on thermal impedance spatiotemporal distribution characteristic data of thermal insulation layer to generate preliminary location data of abnormal area of ​​thermal insulation layer. Among them, the analysis of the spatiotemporal distribution characteristics of thermal impedance studies the variation law and characteristics of the thermal impedance of the insulation layer in space and time; the spatiotemporal distribution characteristic data of thermal impedance of the insulation layer is the result data obtained after performing spatiotemporal distribution characteristic analysis of thermal impedance of high-quality insulation layer data; the preliminary location of abnormal areas is to determine the approximate range of areas in the insulation layer that may have abnormal thermal performance based on the spatiotemporal distribution characteristic data of thermal impedance of the insulation layer; the preliminary location data of abnormal areas of the insulation layer is the data that records the preliminary location results of abnormal areas of the insulation layer, which usually includes information such as the location coordinates and size of the abnormal area, providing a target area for subsequent analysis of local thermal impedance change trends; S3. Based on the preliminary location data of the abnormal area of ​​the insulation layer, perform local thermal impedance change trend analysis on the thermal impedance data of the high-quality insulation layer to obtain local thermal impedance change trend data; refine the boundary of the abnormal area according to the local thermal impedance change trend data to generate refined boundary data of the abnormal area of ​​the insulation layer. Among them, the local thermal impedance variation trend analysis further analyzes the variation trend of thermal impedance over time in the initially located abnormal area; the local thermal impedance variation trend data is the result data obtained after performing local thermal impedance variation trend analysis on the abnormal area; the abnormal area boundary refinement is based on the initially located abnormal area, and determines the boundary of the abnormal area more accurately according to the local thermal impedance variation trend data; the insulation layer abnormal area refinement boundary data is the data that records the boundary refinement results of the insulation layer abnormal area, which clarifies the specific boundary location and shape of the abnormal area, and provides an accurate area range for subsequent extraction of thermal impedance parameters of the abnormal area. S4. Using the refined boundary data of the abnormal area of ​​the insulation layer, extract the thermal impedance parameters of the abnormal area from the thermal impedance data of the high-quality insulation layer to obtain the thermal impedance parameter data of the abnormal area; make a preliminary judgment on the damage type of the insulation layer based on the thermal impedance parameter data of the abnormal area to generate preliminary judgment data on the damage type of the insulation layer. The extraction of thermal impedance parameters in abnormal areas involves extracting relevant parameters such as average thermal impedance, standard deviation of thermal impedance, and rate of change of thermal impedance from high-quality thermal impedance data of the insulation layer, based on refined boundary data of the abnormal areas. The extracted thermal impedance parameter data for abnormal areas is a collection of these parameters, reflecting the thermal performance of the abnormal areas and providing crucial information for preliminary assessment of insulation layer damage types. Preliminary assessment of insulation layer damage types involves using the thermal impedance parameter data of abnormal areas, combined with existing experience and knowledge, to make preliminary inferences about the possible damage types in the abnormal areas. Common types of insulation layer damage may include aging, dampness, breakage, and hollowing of the insulation material. Different damage types will lead to different changes in thermal impedance parameters. Preliminary assessment data for insulation layer damage types records the preliminary assessment results, typically including possible damage types and their corresponding confidence levels, providing an initial reference for subsequent accurate matching of damage types. S5. Obtain the thermal impedance characteristic database of the insulation layer material; use the thermal impedance characteristic database of the insulation layer material to perform precise matching of the damage type of the preliminary judgment data of the insulation layer damage type, and generate precise data of the insulation layer damage type; perform the insulation layer health status level assessment based on the precise data of the insulation layer damage type, and obtain the insulation layer health status level assessment data. The thermal impedance characteristic database for insulation materials is a database containing the thermal impedance characteristics of various insulation materials under different conditions. Accurate damage type matching utilizes this database to compare and analyze preliminary damage type assessment data with information in the database. Precise damage type data, obtained through accurate damage type matching, accurately describes the damage type of abnormal areas in the insulation layer, providing crucial information for subsequent insulation layer health status assessment. The insulation layer health status assessment, based on the precise damage type data and following specific assessment standards and rules, classifies the overall health status of the insulation layer. Common health status levels may include healthy, minor damage, moderate damage, and severe damage. This assessment provides a clear understanding of the current condition of the insulation layer, offering a basis for subsequent maintenance and repair decisions. The insulation layer health status assessment data records the assessment results, typically including the health status level and assessment criteria, providing core content for generating an insulation layer health status report. S6. Generate a health status report for the insulation layer based on the health status level assessment data of the insulation layer. The insulation layer health status report generation involves organizing and summarizing the insulation layer health status level assessment data and other relevant information, presenting it in the form of a report. The insulation layer health status assessment report is the final document that includes the insulation layer health status level, abnormal area information, damage type, etc. This report is a summary of the results of the entire insulation layer health status assessment process, providing an important basis for decision-making in the management and maintenance of the insulation layer.

[0019] It should be noted that during operation, raw data is collected first and then preprocessed to remove noise and other interference, ensuring data quality. Spatiotemporal distribution feature analysis is performed to initially locate abnormal areas, followed by local analysis to refine boundaries, gradually delving deeper to accurately pinpoint the problem area. Parameters of abnormal areas are extracted and damage types are initially determined. Precise matching using a database improves the accuracy of the judgment. Based on the damage type, the health status level is assessed and a report is generated, providing a clear and scientific basis for insulation layer maintenance decisions. The entire process is interconnected and progressive, from data acquisition to final report generation, comprehensively and accurately assessing the health status of the insulation layer, effectively improving assessment efficiency and reliability.

[0020] In one embodiment, step S1 specifically includes: A distributed thermal impedance sensor array is used to collect thermal impedance data of the insulation layer at preset time intervals to generate raw thermal impedance monitoring data of the insulation layer. The original thermal impedance monitoring data of the insulation layer was checked for data integrity, and the data integrity check results were obtained. Based on the data integrity check results, the missing data interpolation process is performed on the original thermal resistance monitoring data of the insulation layer to generate interpolated thermal resistance data of the insulation layer. Anomaly detection was performed on the interpolated thermal resistance data of the insulation layer to obtain the anomaly detection results. Based on the outlier detection results, the interpolated thermal resistance data of the insulation layer is corrected for outliers, and the corrected thermal resistance data of the insulation layer is generated. The corrected thermal resistance data of the insulation layer is smoothed to obtain high-quality thermal resistance data of the insulation layer.

[0021] This design utilizes distributed data collection from multiple locations, with preset intervals ensuring data timeliness. Integrity checks prevent missing data from affecting analysis, interpolation fills in missing values, outlier detection and correction eliminate interference, and smoothing reduces fluctuations. This multi-layered processing makes the data more accurate, stable, and reliable, providing a high-quality foundation for subsequent analysis, avoiding erroneous conclusions due to data issues, and improving the accuracy and credibility of the overall insulation layer health status assessment.

[0022] In one embodiment, step S2 specifically includes: Time-series alignment processing is performed on the high-quality thermal resistance data of the insulation layer to obtain time-aligned thermal resistance data of the insulation layer. Spatial interpolation is performed on the time-aligned thermal resistance data of the insulation layer to generate a spatial distribution map of the thermal resistance of the insulation layer. The thermal impedance gradient of the insulation layer is calculated by analyzing the spatial distribution diagram of the thermal impedance of the insulation layer to obtain the thermal impedance gradient data of the insulation layer. Thermal impedance gradient formula: in, and They represent The partial derivatives in the x-axis and y-axis directions are calculated using the following discrete difference formula: , ; Spatiotemporal distribution features of thermal impedance are extracted based on thermal impedance gradient data of thermal insulation layer to obtain spatiotemporal distribution feature data of thermal impedance of thermal insulation layer; Based on the spatiotemporal distribution characteristics of thermal impedance of the insulation layer, the abnormal area is initially located using a preset abnormal threshold, generating preliminary location data of the abnormal area of ​​the insulation layer.

[0023] This design first aligns the time series, then spatially interpolates to form a graph, calculates gradients to extract features, and finally locates anomalies. Time alignment ensures data consistency over time, spatial interpolation visually presents the thermal impedance distribution, and gradient calculation reflects the degree of change, facilitating anomaly detection. Preset thresholds locate anomalous regions. The operation is simple and efficient. By analyzing data from multiple dimensions of time and space, it comprehensively mines thermal impedance characteristics, enabling more accurate location of anomalous regions. This provides accurate targets for subsequent in-depth analysis of anomalous regions, improving assessment efficiency and accuracy.

[0024] In one embodiment, step S3 specifically includes: Based on the preliminary location data of abnormal areas in the insulation layer, local thermal impedance data are extracted from the thermal impedance data of high-quality insulation layers. Time smoothing is performed on the local thermal impedance data to obtain smoothed local thermal impedance data; Trend analysis was performed on the smoothed local thermal impedance data to obtain the local thermal impedance change trend data; Based on the local thermal impedance change trend data, the boundary of the abnormal area is refined using a boundary detection algorithm, generating refined boundary data of the abnormal area of ​​the insulation layer.

[0025] This design extracts local data, smooths it, analyzes trends, and then refines the boundaries using algorithms. It extracts local area data from high-quality data, focuses on abnormal areas, and reduces noise interference through time smoothing, making trend analysis more accurate. By using boundary detection algorithms to refine the boundaries, the range of abnormal areas can be determined more precisely. In-depth research on abnormal areas gradually narrows the range, improving the accuracy of boundary determination and avoiding misjudgments. This provides a precise area for accurately extracting thermal impedance parameters of abnormal areas and enhances the reliability of subsequent damage type judgment.

[0026] In one embodiment, step S4 specifically includes: By refining boundary data for abnormal regions of the insulation layer, thermal impedance data of abnormal regions can be extracted from high-quality thermal impedance data of the insulation layer. Statistical features are extracted from the thermal impedance data of the abnormal region to obtain statistical feature data of the thermal impedance of the abnormal region. Parameter fitting is performed based on the statistical characteristics of thermal impedance in the abnormal region to obtain the thermal impedance parameter data of the abnormal region. Probability distribution analysis was performed on the thermal impedance data in the abnormal region to obtain the thermal impedance probability distribution function; Calculate the mean and variance statistical parameters of the thermal impedance in the abnormal region based on the thermal impedance probability distribution function; The statistical parameters are fitted using a pre-defined parameter fitting model to obtain thermal impedance parameter data for the abnormal region. Based on the thermal impedance parameter data of the abnormal region, a preliminary judgment of the damage type of the insulation layer is made using a preset damage type feature library, and preliminary judgment data of the damage type of the insulation layer is generated.

[0027] This design extracts data from abnormal areas, obtains parameter data through statistics, fitting, and probability analysis, and preliminarily determines the damage type. It extracts abnormal area data from high-quality data to ensure data relevance. Statistical feature extraction and parameter fitting comprehensively describe thermal impedance characteristics. Probability distribution analysis calculates parameters such as mean and variance to further uncover data patterns. It uses a preset feature library to preliminarily determine the damage type, associates thermal impedance data with damage type, makes full use of data information, accurately extracts parameters, provides sufficient basis for damage type judgment, and improves the accuracy and scientific nature of the judgment.

[0028] In one embodiment, step S4 specifically includes: Obtain a database of thermal impedance characteristics of insulation layer materials, which contains the range of thermal impedance parameters corresponding to damage types. The preliminary judgment data of the insulation layer damage type is compared with the thermal impedance characteristic database of the insulation layer material to perform precise matching of the damage type and generate precise data of the insulation layer damage type. Based on precise data on the damage type of the insulation layer, the health status of the insulation layer is assessed using a preset health status assessment standard, resulting in health status assessment data for the insulation layer.

[0029] This design acquires a database containing thermal impedance parameter ranges for different damage types, compares preliminary judgment data to accurately match damage types, assesses health status levels, and establishes a database to provide reference standards, making damage type judgments based on evidence. Precise matching can accurately determine damage types and avoid misjudgments. Based on accurate data and assessment standards, the health status level is assessed, intuitively reflecting the condition of the insulation layer. This forms a complete judgment process, progressing step by step from data to damage type to health status level, improving the accuracy and systematic nature of the assessment, and providing a reliable basis for maintenance decisions.

[0030] In one embodiment, the step of obtaining the thermal impedance gradient data of the insulation layer includes: Select several key points on the spatial distribution diagram of the thermal resistance of the insulation layer; Calculate the thermal impedance difference between each key point and its surrounding adjacent points; Calculate the magnitude and direction of the thermal impedance gradient at each key point based on the thermal impedance difference; By integrating the magnitude and direction of the thermal impedance gradient at all key points, the thermal impedance gradient data of the insulation layer is obtained.

[0031] This design selects key points that represent the overall characteristics, reducing computational load. Calculating the difference between the key points and adjacent points reflects local changes, thereby obtaining the gradient magnitude and direction, accurately describing the spatial variation of thermal impedance. Integrating all key point data yields comprehensive gradient data, enabling efficient and accurate acquisition of thermal impedance gradient data. This provides an important basis for extracting spatiotemporal distribution features and locating abnormal areas, improving the accuracy and reliability of subsequent analysis.

[0032] In one embodiment, the step of obtaining refined boundary data for the abnormal area of ​​the insulation layer includes: Based on the local thermal impedance change trend data, determine the boundary change characteristics of the abnormal area; Edge detection algorithms are used to detect boundary change features in abnormal regions to obtain preliminary boundary points; The initial boundary points are fitted to generate a smooth boundary curve; Based on the boundary curve, the boundary data of the abnormal area of ​​the insulation layer is refined.

[0033] This design, when acquiring detailed boundary data of abnormal areas in the insulation layer, determines the boundary change characteristics, uses an edge detection algorithm to obtain preliminary boundary points, and fits a smooth curve to determine the boundary. Determining the boundary change characteristics focuses on key information. The edge detection algorithm quickly and accurately detects boundary points, and the fitting process makes the boundary smoother and more accurate. Combining the advantages of the algorithm, the boundary of the abnormal area is gradually and accurately determined, avoiding the subjectivity and error of manual judgment, improving the accuracy and efficiency of boundary determination, and providing reliable boundary data for accurate assessment of abnormal areas.

[0034] In one embodiment, the step of obtaining the health status assessment data of the insulation layer includes: Collect thermal impedance data of different types of insulation materials under different damage states; The collected thermal impedance data were classified and organized, and grouped according to damage type; Determine the range of thermal impedance parameters corresponding to each damage type; Damage types and their corresponding thermal impedance parameter ranges are stored in a database to form a thermal impedance characteristic database of insulation layer materials. Based on precise data on the types of damage to the insulation layer, the corresponding health status assessment indicators are determined. Obtain the weight coefficient for each evaluation indicator; The overall health status score of the insulation layer is calculated based on the evaluation indicators and their weighting coefficients. The health status level of the insulation layer is determined based on the comprehensive health status score, thus obtaining the health status level assessment data of the insulation layer. The weighted average method is used to assess the level of health status. Assume each health status assessment indicator is [value missing]. The corresponding weight Overall health status score The calculation formula is: in, The normalized evaluation indicators The weight coefficients of the indicators and satisfy the following conditions: =1, determined through training on historical data or the Analytic Hierarchy Process (AHP). The number of evaluation indicators.

[0035] This design allows for the collection and organization of data to form a database when acquiring data for assessing the health status of the insulation layer. It also involves determining assessment indicators and weights, calculating a comprehensive score to determine the level, and providing a reference standard for the assessment. This ensures a scientific basis for the evaluation. The determination of assessment indicators and weights allows for a comprehensive and reasonable assessment of the health status. The calculation of the comprehensive score and the determination of the level directly reflect the condition of the insulation layer, forming a complete assessment system. From data collection to level determination, each step is carefully monitored to improve the scientific rigor, accuracy, and objectivity of the assessment, providing reliable guidance for the maintenance of the insulation layer.

[0036] In one embodiment, the step of obtaining the health status assessment report of the insulation layer includes: Based on the health status assessment data of the insulation layer, compile relevant information on the health status of the insulation layer; Organize relevant information on abnormal areas of the insulation layer; By integrating key information and information related to abnormal areas, information related to the health status of the insulation layer is formed; The relevant information on the health status of the insulation layer is formatted according to the preset report template to generate a health status assessment report for the insulation layer.

[0037] This design allows for the compilation of health status and abnormal area information when obtaining the insulation layer health status assessment report. Key information is integrated and formatted according to a template. The compiled health status information comprehensively reflects the overall condition of the insulation layer, the compiled abnormal area information highlights key issues, and the integrated key information forms the core content. The template format makes the report standardized and clear, presenting complex data and analysis results in a concise and intuitive way. This facilitates relevant personnel to quickly understand the health status of the insulation layer, provides a clear basis for decision-making, and improves the practicality and readability of the report.

[0038] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A method for assessing the health status of a thermal insulation layer based on thermal impedance monitoring, characterized in that, include: S1. Use a sensor array to collect thermal impedance data of the insulation layer and generate original thermal impedance monitoring data of the insulation layer. Data preprocessing is performed on the original thermal resistance monitoring data of the insulation layer to obtain high-quality thermal resistance data of the insulation layer. S2. Perform spatiotemporal distribution characteristic analysis of thermal impedance data of high-quality thermal insulation layer to obtain spatiotemporal distribution characteristic data of thermal impedance of thermal insulation layer; perform preliminary location of abnormal area based on thermal impedance spatiotemporal distribution characteristic data of thermal insulation layer to generate preliminary location data of abnormal area of ​​thermal insulation layer. S3. Based on the preliminary location data of the abnormal area of ​​the insulation layer, perform local thermal impedance change trend analysis on the thermal impedance data of the high-quality insulation layer to obtain local thermal impedance change trend data; refine the boundary of the abnormal area according to the local thermal impedance change trend data to generate refined boundary data of the abnormal area of ​​the insulation layer. S4. Using the refined boundary data of the abnormal area of ​​the insulation layer, the thermal impedance parameters of the abnormal area are extracted from the thermal impedance data of the high-quality insulation layer to obtain the thermal impedance parameter data of the abnormal area. Based on the thermal impedance parameter data of the abnormal area, a preliminary judgment of the damage type of the insulation layer is made, and preliminary judgment data of the damage type of the insulation layer is generated. S5. Obtain the thermal impedance characteristic database of the insulation layer material; use the thermal impedance characteristic database of the insulation layer material to perform precise matching of the damage type of the preliminary judgment data of the insulation layer damage type, and generate precise data of the insulation layer damage type; perform the insulation layer health status level assessment based on the precise data of the insulation layer damage type, and obtain the insulation layer health status level assessment data. S6. Generate a health status report for the insulation layer based on the health status assessment data of the insulation layer.

2. The method for assessing the health status of insulation layers based on thermal impedance monitoring according to claim 1, characterized in that, Step S1 specifically includes: A distributed thermal impedance sensor array is used to collect thermal impedance data of the insulation layer at preset time intervals to generate raw thermal impedance monitoring data of the insulation layer. The original thermal impedance monitoring data of the insulation layer was checked for data integrity, and the data integrity check results were obtained. Based on the data integrity check results, the original thermal resistance monitoring data of the insulation layer is interpolated to generate interpolated thermal resistance data of the insulation layer. Anomaly detection was performed on the interpolated thermal resistance data of the insulation layer to obtain the anomaly detection results. Based on the outlier detection results, the interpolated thermal resistance data of the insulation layer is corrected for outliers, and the corrected thermal resistance data of the insulation layer is generated. The corrected thermal resistance data of the insulation layer is smoothed to obtain high-quality thermal resistance data of the insulation layer.

3. The method for assessing the health status of a thermal insulation layer based on thermal impedance monitoring according to claim 2, characterized in that, Step S2 specifically includes: Time-series alignment processing is performed on the high-quality thermal resistance data of the insulation layer to obtain time-aligned thermal resistance data of the insulation layer. Spatial interpolation is performed on the time-aligned thermal resistance data of the insulation layer to generate a spatial distribution map of the thermal resistance of the insulation layer. The thermal impedance gradient of the insulation layer is calculated by analyzing the spatial distribution diagram of the thermal impedance of the insulation layer to obtain the thermal impedance gradient data of the insulation layer. Spatiotemporal distribution features of thermal impedance are extracted based on thermal impedance gradient data of thermal insulation layer to obtain spatiotemporal distribution feature data of thermal impedance of thermal insulation layer; Based on the spatiotemporal distribution characteristics of thermal impedance of the insulation layer, the abnormal area is initially located using a preset abnormal threshold, generating preliminary location data of the abnormal area of ​​the insulation layer.

4. The method for assessing the health status of a thermal insulation layer based on thermal impedance monitoring according to claim 3, characterized in that, Step S3 specifically includes: Based on the preliminary location data of abnormal areas in the insulation layer, local thermal impedance data are extracted from the thermal impedance data of high-quality insulation layers. Time smoothing is performed on the local thermal impedance data to obtain smoothed local thermal impedance data; Trend analysis was performed on the smoothed local thermal impedance data to obtain the local thermal impedance change trend data; Based on the local thermal impedance change trend data, the boundary of the abnormal area is refined using a boundary detection algorithm, generating refined boundary data of the abnormal area of ​​the insulation layer.

5. The method for assessing the health status of a thermal insulation layer based on thermal impedance monitoring according to claim 4, characterized in that, Step S4 specifically includes: By refining boundary data for abnormal regions of the insulation layer, thermal impedance data of abnormal regions can be extracted from high-quality thermal impedance data of the insulation layer. Statistical features are extracted from the thermal impedance data of the abnormal region to obtain statistical feature data of the thermal impedance of the abnormal region. Parameter fitting is performed based on the statistical characteristics of thermal impedance in the abnormal region to obtain the thermal impedance parameter data of the abnormal region. Probability distribution analysis was performed on the thermal impedance data in the abnormal region to obtain the thermal impedance probability distribution function; Calculate the mean and variance statistical parameters of the thermal impedance in the abnormal region based on the thermal impedance probability distribution function; The statistical parameters are fitted using a pre-defined parameter fitting model to obtain thermal impedance parameter data for the abnormal region. Based on the thermal impedance parameter data of the abnormal region, a preliminary judgment of the damage type of the insulation layer is made using a preset damage type feature library, and preliminary judgment data of the damage type of the insulation layer is generated.

6. The method for assessing the health status of a thermal insulation layer based on thermal impedance monitoring according to claim 5, characterized in that, Step S4 specifically includes: Obtain a database of thermal impedance characteristics of insulation layer materials, which contains the range of thermal impedance parameters corresponding to damage types. The preliminary judgment data of the insulation layer damage type is compared with the thermal impedance characteristic database of the insulation layer material to perform precise matching of the damage type and generate precise data of the insulation layer damage type. Based on precise data on the damage type of the insulation layer, the health status of the insulation layer is assessed using a preset health status assessment standard, resulting in health status assessment data for the insulation layer.

7. The method for assessing the health status of an insulation layer based on thermal impedance monitoring according to claim 6, characterized in that, The steps for obtaining the thermal impedance gradient data of the insulation layer include: Select several key points on the spatial distribution diagram of the thermal resistance of the insulation layer; Calculate the thermal impedance difference between each key point and its surrounding adjacent points; Calculate the magnitude and direction of the thermal impedance gradient at each key point based on the thermal impedance difference; By integrating the magnitude and direction of the thermal impedance gradient at all key points, the thermal impedance gradient data of the insulation layer is obtained.

8. The method for assessing the health status of a thermal insulation layer based on thermal impedance monitoring according to claim 7, characterized in that, The steps for obtaining the refined boundary data of the abnormal area of ​​the insulation layer include: Based on the local thermal impedance change trend data, determine the boundary change characteristics of the abnormal area; Edge detection algorithms are used to detect boundary change features in abnormal regions to obtain preliminary boundary points; The initial boundary points are fitted to generate a smooth boundary curve; Based on the boundary curve, the boundary data of the abnormal area of ​​the insulation layer is refined.

9. The method for assessing the health status of a thermal insulation layer based on thermal impedance monitoring according to claim 8, characterized in that, The steps for obtaining the health status assessment data of the insulation layer include: Collect thermal impedance data of different types of insulation materials under different damage states; The collected thermal impedance data were classified and organized, and grouped according to damage type; Determine the range of thermal impedance parameters corresponding to each damage type; Damage types and their corresponding thermal impedance parameter ranges are stored in a database to form a thermal impedance characteristic database of insulation layer materials. Based on precise data on the types of damage to the insulation layer, the corresponding health status assessment indicators are determined. Obtain the weight coefficient for each evaluation indicator; The overall health status score of the insulation layer is calculated based on the evaluation indicators and their weighting coefficients. The health status level of the insulation layer is determined based on the comprehensive health status score, thus obtaining the health status level assessment data of the insulation layer.

10. The method for assessing the health status of an insulation layer based on thermal impedance monitoring according to claim 9, characterized in that, The steps for obtaining the health status assessment report of the insulation layer include: Based on the health status assessment data of the insulation layer, compile relevant information on the health status of the insulation layer; Organize relevant information on abnormal areas of the insulation layer; By integrating key information and information related to abnormal areas, information related to the health status of the insulation layer is formed; The relevant information on the health status of the insulation layer is formatted according to the preset report template to generate a health status assessment report for the insulation layer.

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