Online evaluation method for thermal insulation performance of pipeline

Abstract

The invention provides a pipeline thermal insulation performance online evaluation method, and belongs to the technical field of pipeline thermal insulation performance monitoring. The problems that in the prior art, judgment depends on personnel experience, and performance evaluation is not accurate are solved. The pipeline thermal insulation performance online evaluation method comprises the following steps: collecting the actually measured temperature of the outer surface of a thermal insulation layer, and collecting the environment temperature, the wind speed and the temperature of a medium in a pipeline; the comprehensive heat exchange coefficient of the outer surface of the pipeline and air in the current environment is obtained, and a thermal insulation layer outer surface temperature prediction model is constructed; the thermal resistance of the thermal insulation layer is adjusted through an iterative optimization algorithm, so that the root-mean-square error between the predicted temperature, obtained through the prediction model, of the outer surface of the thermal insulation layer and the actually measured temperature of the outer surface of the thermal insulation layer is minimum; when the root-mean-square error is smaller than a preset threshold value, the thermal resistance of the thermal insulation layer at the moment serves as the optimal thermal resistance of the thermal insulation layer to be compared with the designed thermal resistance, and the thermal insulation performance state of the corresponding thermal insulation layer position is evaluated according to the ratio. It can be ensured that the evaluation result of the pipeline heat preservation state under different climates and working conditions is accurate and reliable.

Description

Technical Field

[0001] This invention belongs to the field of pipeline insulation performance monitoring technology, and relates to an online evaluation method for pipeline insulation performance. Background Technology

[0002] In industries such as petroleum, chemical, power, and district heating, pipeline insulation performance directly affects system energy efficiency and safety. Currently, distributed fiber optic temperature sensing technology has been preliminarily applied in pipeline temperature monitoring. This technology, by laying temperature-sensing optical cables on the outer surface of the pipeline insulation layer, enables long-distance, continuous spatial temperature distribution measurement, providing the possibility of detecting localized overheating or temperature anomalies. However, existing monitoring schemes based on this technology still have significant limitations:

[0003] The value of the data has not been fully explored: it only achieves visualization of the temperature field, and the results rely on human experience to make judgments, and cannot be automatically converted into a quantitative assessment of the equivalent thermal resistance of the insulation layer.

[0004] Lack of fault tracing capability: It is difficult to distinguish whether the abnormal surface temperature is caused by water immersion, physical damage, insufficient thickness of the insulation layer, or simply by changes in environmental wind speed, leading to blind maintenance decisions.

[0005] Insufficient model adaptability: Most methods use simplified steady-state models, which result in large errors in heat loss calculation under varying operating conditions such as medium temperature fluctuations and diurnal temperature differences.

[0006] In summary, overcoming the limitations of existing technologies in terms of in-depth data utilization, intelligent fault tracing, and dynamic model adaptability, and developing an evaluation method that can integrate multi-source monitoring data, adapt to varying operating conditions, and achieve online quantitative inversion and automatic diagnosis of insulation performance parameters, has become a key technical challenge to ensure that pipeline insulation status evaluation results remain accurate and reliable under different climates and operating conditions. This is of great significance for improving the energy efficiency management level of pipeline systems and achieving predictive maintenance. Summary of the Invention

[0007] The purpose of this invention is to address the aforementioned problems in the existing technology by proposing an online evaluation method for pipeline insulation performance. The technical problem to be solved is: how to ensure the accuracy and reliability of pipeline insulation status evaluation results under different climates and operating conditions.

[0008] The objective of this invention can be achieved through the following technical solution: an online evaluation method for pipeline insulation performance, comprising the following steps:

[0009] S1. Data Acquisition: Collect the measured temperature of the outer surface of the insulation layer at multiple measuring points along the pipeline at preset intervals. And collect ambient temperature Wind speed and the temperature of the medium inside the pipeline ;

[0010] S2. Dynamic thermal resistance inversion calculation, the following sub-steps are performed at each measuring point:

[0011] S21, according to the wind speed Pipe insulation layer outer diameter and ambient temperature The kinematic viscosity of ambient air and air physical properties To obtain the comprehensive heat transfer coefficient h between the outer surface of the pipe and the air under the current environment;

[0012] S22. Construct a temperature prediction model for the outer surface of the insulation layer to establish the medium temperature. Ambient temperature Thermal resistance of insulation layer Pipe wall thermal resistance The overall heat transfer coefficient h and the predicted temperature of the outer surface of the insulation layer The calculation relationship between them;

[0013] S23. The measured temperature of the outer surface of the insulation layer at each measuring point. The thermal resistance of the insulation layer is adjusted using an iterative optimization algorithm to achieve the target value. The predicted temperature of the outer surface of the insulation layer obtained by the temperature prediction model of the outer surface of the insulation layer is thus achieved. The measured temperature of the outer surface of the insulation layer The root mean square error (RMSE) between them is the smallest;

[0014] S24. When the root mean square error (RMSE) is less than a preset threshold, stop the iterative optimization and adjust the thermal resistance of the insulation layer at this point. The output is the optimal thermal resistance of the insulation layer at this measuring point. ;

[0015] S3. Performance Evaluation: Based on the optimal thermal resistance value at each measuring point. With the preset design thermal resistance The ratio K is used to evaluate the thermal insulation performance of the corresponding insulation layer location.

[0016] When applying this online evaluation method for pipeline insulation performance, the data acquisition period is first preset. Based on this preset period, the measured temperature of the outer surface of the insulation layer at multiple measuring points along the pipeline is collected. And collect ambient temperature Wind speed and the temperature of the medium inside the pipeline Then, dynamic thermal resistance inversion calculations are performed on each measuring point to ensure that the predicted temperature of the outer surface of the insulation layer obtained through the insulation layer outer surface temperature prediction model is accurate. Measured temperature of the outer surface of the insulation layer The root mean square error (RMSE) between the values ​​is minimized, and the iterative optimization stops when the RMSE is less than a preset threshold. The thermal resistance of the insulation layer at this point is then determined. The output is the optimal thermal resistance of the insulation layer at this measuring point. Finally, based on the optimal thermal resistance value at each measuring point... With the preset design thermal resistance The ratio K is used to evaluate the insulation performance at the corresponding insulation layer location. This method uses an insulation layer outer surface temperature prediction model that integrates real-time climate parameters and an iterative optimization algorithm to inversely solve for the equivalent thermal resistance, which is stripped of all time-varying external disturbances and characterizes the insulation body performance. Using the ratio K as the evaluation index, the final evaluation conclusion depends only on the state of the insulation layer itself, and is completely decoupled from the external climate and operating conditions. This achieves the accuracy, consistency, and reliability of the evaluation results under any complex and variable operating conditions.

[0017] In the above-mentioned online evaluation method for pipeline insulation performance, step S21, the step of obtaining the comprehensive heat transfer coefficient h, includes:

[0018] The Reynolds number is obtained using Formula 1. Formula 1 is:

[0019]

[0020] in, The kinematic viscosity of ambient air is determined by ambient temperature. Obtained by looking up the table; The diameter of the pipe insulation layer;

[0021] Ambient temperature Air property parameters can be obtained by looking up the table. ;

[0022] The Nusselt number is obtained using Formula 2. Formula 2 is as follows:

[0023] ;

[0024] Nusel number Defined as: ;

[0025] The overall heat transfer coefficient h is obtained through deformation:

[0026]

[0027] in, is the thermal conductivity of air.

[0028] In this step, the Reynolds number Re and Nusselt number Nu are calculated by real-time wind speed, and the comprehensive heat transfer coefficient h is finally dynamically solved. This enables the temperature prediction model of the outer surface of the insulation layer to respond to the real environment at every moment, fundamentally solving the problem of unreliable diagnostic results caused by drastic changes in environmental conditions, and realizing accurate diagnosis under all weather and all working conditions.

[0029] In the above-mentioned online evaluation method for pipeline insulation performance, step S22, the step of predicting the temperature of the outer surface of the insulation layer includes:

[0030] The heat flux density is determined using Formula 3. ,in Formula three represents the heat transferred through the pipe wall per unit area and per unit time.

[0031] ;

[0032] ;

[0033] in, The temperature of the medium inside the pipe. For ambient temperature, The total thermal resistance of each layer, For the thermal resistance of the pipe wall, , For pipe wall thickness, The thermal conductivity of the pipe material; For external surface flow thermal resistance, ;

[0034] Based on the above heat flux density The following model is constructed to predict the temperature of the outer surface of the insulation layer:

[0035] ;

[0036] = ; ;

[0037] in, To reduce the pipe wall temperature, To reduce the temperature of the insulation layer, For heat flux density, The temperature of the outer surface of the insulation layer is predicted. The construction of the temperature prediction model for the outer surface of the insulation layer ensures the feasibility of the inversion and the reliability of the results.

[0038] In the above-mentioned online evaluation method for pipeline insulation performance, in step S23, the thermal resistance of the insulation layer is adjusted by an iterative optimization algorithm. The operations include:

[0039] Set thermal resistance update step size : ;

[0040] in, Let be the gradient of the k-th iteration. ; For the Hessian matrix approximation, The damping coefficient is dynamically adjusted. Predict the temperature of the outer surface of the insulation layer at the i-th measuring point. For the thermal resistance of the insulation layer, The deviation between the predicted temperature of the outer surface of the insulation layer at the i-th measuring point and the measured temperature of the outer surface of the insulation layer.

[0041] The thermal resistance of the insulation layer after each iteration of the optimization algorithm is set to :

[0042]

[0043] in, The thermal resistance of the insulation layer is adjusted by the k-th iteration optimization algorithm.

[0044] By introducing an iterative optimization algorithm to dynamically invert the thermal resistance of the insulation layer, online identification, dynamic updating, and spatially distributed analysis of the thermal resistance of the insulation layer are realized, enabling real-time diagnosis of the thermal insulation performance of the pipeline insulation layer.

[0045] In the above-described online evaluation method for pipeline insulation performance, step S3, the operation of stopping iterative optimization, further includes:

[0046] Calculate the thermal resistance of the insulation layer after each iteration of the optimization algorithm adjustment. Sum of squared residuals :

[0047] ;

[0048] Where n is the number of valid measurement points within the calculation unit. These are the predicted temperature and the measured temperature of the outer surface of the insulation layer at the i-th measuring point, respectively.

[0049] Calculate the sum of squared residuals for the current iteration step. Sum of squared residuals from the previous iteration step absolute value of change :

[0050] ;

[0051] when When the residual value is less than the preset residual threshold, the thermal resistance of the insulation layer at this time is... The output is the optimal thermal resistance of the insulation layer at this measuring point. .

[0052] The absolute value of the change in the sum of squared residuals in this step The judgment, combined with the judgment of root mean square error, forms a dual criterion for stopping when the result meets the target and when progress stalls. This ensures that the optimization algorithm can converge stably and reliably under various complex conditions, such as the presence of measurement noise and model approximation errors, avoiding invalid calculations and significantly improving the real-time performance of online evaluation.

[0053] In the above-mentioned online evaluation method for pipeline insulation performance, the thermal resistance of the insulation layer is adjusted through an iterative optimization algorithm. The operation also includes:

[0054] Compare the sum of squared residuals at the current iteration step Sum of squared residuals from the previous iteration step ;

[0055] like The iterative update is deemed effective, and the damping coefficient is reduced accordingly. This is used for the next iteration;

[0056] like Determine that the iterative update is invalid and keep it. At the same time, increase the damping coefficient This is used for the next iteration.

[0057] By performing this step, when the update is valid, the next iteration can converge quickly and preferentially; when the update is invalid, the step size can be reduced to ensure stability.

[0058] In the above-described online evaluation method for pipeline insulation performance, step S23, the operation of stopping iterative optimization, further includes:

[0059] Set the maximum number of iterations. ;

[0060] when When the time is right, stop iterative optimization.

[0061] Set a maximum number of iterations to avoid excessive iterations affecting real-time performance.

[0062] In the above-mentioned online evaluation method for pipeline insulation performance, in step S23, the root mean square error (RMSE) is calculated using formula four, which is:

[0063] RMSE=

[0064] Where n is the number of valid measurement points within the calculation unit. These represent the predicted temperature and the measured temperature of the outer surface of the insulation layer at the i-th measurement point, respectively. The calculation of the root mean square error (RMSE) provides an accurate mathematical optimization objective and an objective convergence criterion for thermal resistance inversion, ensuring the automatic convergence and reliability of the iterative optimization process and solving the problem of difficult convergence control in dynamic parameter estimation.

[0065] In the above-described online evaluation method for pipeline insulation performance, step S3 further includes:

[0066] Based on the optimal thermal resistance value at each measuring point Calculate real-time heat flux density :

[0067] ;

[0068] Calculate the real-time heat for each data collection cycle. : ,

[0069] in, For the heat exchange area of ​​the pipe section, , The diameter of the insulation layer; This refers to the length of the pipe section.

[0070] Cumulative heat loss energy during the statistical period :

[0071] ,

[0072] in, This represents the number of data collections within the statistical period. The data collection cycle is defined in this step. The calculation of accumulated heat loss energy enables online and automatic metering of energy waste caused by insulation failure, making invisible losses visible and measurable. Moreover, the metering results establish a unified quantitative benchmark for comparing the energy efficiency levels of different pipe sections and evaluating the effectiveness of maintenance measures.

[0073] In the above-mentioned online evaluation method for pipeline insulation performance, the online evaluation method further includes a defect type inference step:

[0074] Temperature measured from the outer surface of the insulation layer Ambient humidity (RH) and the optimal thermal resistance value In the process, at least two features are extracted from the following: spatial distribution pattern of temperature, spatial distribution characteristics of thermal resistance, rate of change of thermal resistance over time, average relative humidity value, and humidity-thermal resistance correlation.

[0075] The extracted features are matched with various defect types defined in a pre-defined defect feature library;

[0076] Based on the matching results, the defect type or defect probability is output. This step can automatically and accurately distinguish between different types of defects such as water immersion in the insulation layer, structural damage, material aging, and insufficient thickness, providing an intuitive, quantifiable, and decision-making ultimate indicator for insulation performance evaluation.

[0077] Compared with existing technologies, this online evaluation method for pipeline insulation performance has the following advantages:

[0078] 1. This invention introduces the dynamic calculation of the comprehensive heat transfer coefficient based on ambient wind speed and uses an iterative optimization algorithm for inversion, enabling the method to automatically adapt to the dynamic changes of pipeline medium and external environment, significantly improving the evaluation accuracy and reliability under varying operating conditions.

[0079] 2. This invention integrates multi-source information such as temperature, thermal resistance, and humidity and intelligently matches it with a pre-built defect feature library. It can automatically and accurately distinguish different types of defects such as water immersion in the insulation layer, structural damage, material aging, and insufficient thickness, and provide high-confidence inference results. This overcomes the shortcomings of existing methods, such as difficulty in identifying the cause of failure and lack of basis for maintenance decisions.

[0080] 3. This invention can not only evaluate the thermal insulation performance of the insulation layer in real time, but also dynamically calculate the real-time heat flux density and periodic cumulative heat loss energy of the pipeline based on the optimal thermal resistance distribution obtained by inversion. It directly quantifies the degree of thermal insulation performance degradation into economic losses of energy waste, providing direct and quantitative data basis for the operation and maintenance department to carry out precise maintenance and control of energy costs, and realizing a closed loop from condition monitoring to economic evaluation. Detailed Implementation

[0081] To make the objectives, technical solutions, and advantages of the present invention clearer, the embodiments of the present invention will be described in further detail below. The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the present invention or its application or use. 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.

[0082] When applying this online evaluation method for pipeline insulation performance to evaluate the pipeline insulation performance, the hardware equipment for implementing this online evaluation method is first configured, including a distributed temperature measurement unit: consisting of a distributed fiber optic temperature demodulator and a temperature-sensing optical cable laid axially close to the outer surface of the pipeline insulation layer, used to acquire time-synchronized and spatially continuous temperature sequence data of the outer surface of the insulation layer; a multi-source data sensing unit including a pipeline medium temperature sensor, an ambient temperature and humidity sensor, and a wind speed sensor; and an intelligent diagnostic analysis unit: which has a built-in prediction model for the outer surface temperature of the insulation layer and a defect feature library.

[0083] Subsequently, data is collected through distributed temperature measurement units and multi-source data sensing units: at preset intervals, such as 1 minute, the measured temperature of the outer surface of the insulation layer at multiple measuring points along the pipeline is collected. And collect ambient temperature Wind speed Relative humidity (RH) and temperature of the medium inside the pipeline ;

[0084] To proceed with the dynamic thermal resistance inversion calculation, perform the following steps at each measuring point:

[0085] According to wind speed Pipe insulation layer outer diameter and ambient temperature The kinematic viscosity of ambient air and air physical properties To obtain the overall heat transfer coefficient h between the outer surface of the pipe and the air under the current environment, the specific steps include:

[0086] The Reynolds number is obtained using Formula 1. Formula 1 is:

[0087]

[0088] in, The kinematic viscosity of ambient air is determined by ambient temperature. Obtained by looking up the table; The diameter of the pipe insulation layer;

[0089] Ambient temperature Air property parameters can be obtained by looking up the table. ;

[0090] The Nusselt number is obtained using Formula 2. Formula 2 is as follows:

[0091] ;

[0092] Nusel number Defined as: ;

[0093] The overall heat transfer coefficient h is obtained through deformation:

[0094]

[0095] in, is the thermal conductivity of air.

[0096] Construct a temperature prediction model for the outer surface of the insulation layer to establish the medium temperature. Ambient temperature Thermal resistance of insulation layer Pipe wall thermal resistance The overall heat transfer coefficient h and the predicted temperature of the outer surface of the insulation layer The calculation relationship between them, and the specific operations include:

[0097] The heat flux density is determined using Formula 3. ,in Formula three represents the heat transferred through the pipe wall per unit area and per unit time.

[0098] ;

[0099] ;

[0100] in, The temperature of the medium inside the pipe. For ambient temperature, The total thermal resistance of each layer, For the thermal resistance of the pipe wall, , For pipe wall thickness, The thermal conductivity of the pipe material; For external surface flow thermal resistance, ;

[0101] Based on the above heat flux density The following model is constructed to predict the temperature of the outer surface of the insulation layer:

[0102] ;

[0103] = ; ;

[0104] in, To reduce the pipe wall temperature, To reduce the temperature of the insulation layer, For heat flux density, Predict the temperature of the outer surface of the insulation layer.

[0105] After the model for predicting the temperature of the outer surface of the insulation layer is constructed, the predicted temperature of the outer surface of the insulation layer is performed using each computational unit, which is the smallest spatial unit divided along the pipe axis and used to independently perform thermal resistance iterative inversion. Prediction: Based on the actual measured temperature of the outer surface of the insulation layer at each measuring point. Let be the target value, where the measured temperature of the outer surface of the insulation layer at the i-th measuring point is set to . The predicted temperature of the outer surface of the insulation layer at each measuring point, obtained based on the temperature prediction model for the outer surface of the insulation layer, is: , i=1,2,...,n; n represents the number of consecutive distributed temperature measurement points contained in a computing unit.

[0106] Adjusting the thermal resistance of the insulation layer using an iterative optimization algorithm The predicted temperature of the outer surface of the insulation layer obtained by the temperature prediction model of the outer surface of the insulation layer is thus achieved. With the measured temperature The root mean square error (RMSE) is minimized between the two; among them, the thermal resistance of the insulation layer is adjusted through an iterative optimization algorithm. The operations include:

[0107] Set thermal resistance update step size : ;

[0108] Set the maximum number of iterations. It can be set to 10-20 times;

[0109] when Stop iterative optimization when the time comes;

[0110] in, Let be the gradient of the k-th iteration. ; For the Hessian matrix approximation, The damping coefficient is dynamically adjusted. Predict the temperature of the outer surface of the insulation layer at the i-th measuring point. The deviation between the predicted temperature of the outer surface of the insulation layer at the i-th measuring point and the measured temperature of the outer surface of the insulation layer.

[0111] The thermal resistance of the insulation layer after each iteration of the optimization algorithm is set to :

[0112]

[0113] in, The thermal resistance of the insulation layer is adjusted by the k-th iteration optimization algorithm.

[0114] The root mean square error (RMSE) is calculated using Formula 4, which is:

[0115] RMSE=

[0116] Where n is the number of valid measurement points within the calculation unit. These are the predicted temperature and the measured temperature of the outer surface of the insulation layer at the i-th measuring point, respectively.

[0117] When based on the thermal resistance of the insulation layer If the calculated root mean square error (RMSE) is less than a preset threshold, such as 0.5℃, the iterative optimization stops, and the thermal resistance of the insulation layer at this point is calculated. The output is the optimal thermal resistance of the insulation layer at the i-th measurement point. ;

[0118] This method, in addition to determining whether to stop iterative optimization by using the number of iterations k or the root mean square error, also includes:

[0119] Calculate the thermal resistance of the insulation layer after each iteration of the optimization algorithm adjustment. Sum of squared residuals :

[0120] ;

[0121] Where n is the number of valid measurement points within the calculation unit. These are the predicted temperature and the measured temperature of the outer surface of the insulation layer at the i-th measuring point, respectively.

[0122] Calculate the sum of squared residuals for the current iteration step. Sum of squared residuals from the previous iteration step absolute value of change :

[0123] ;

[0124] Compare the sum of squared residuals at the current iteration step Sum of squared residuals from the previous iteration step ;

[0125] like The iterative update is deemed valid, and the damping coefficient in the thermal resistance update step size is reduced. This is used for the next iteration;

[0126] like Determine that the iterative update is invalid and keep it. At the same time, increase the damping coefficient in the thermal resistance update step size. This is used for the next iteration;

[0127] when Less than the preset residual threshold, such as At this time, the thermal resistance of the insulation layer will be... The output is the optimal thermal resistance of the insulation layer at this measuring point. .

[0128] Finally, the performance evaluation step is performed: based on the optimal thermal resistance value at each measuring point. With the preset design thermal resistance The ratio K is used to evaluate the thermal insulation performance at the corresponding location of the insulation layer. Among these factors, the design thermal resistance... The maximum theoretical thermal resistance, which is the thermal performance index that the insulation layer should achieve, is preset during the design phase of the pipeline insulation system based on the medium temperature, environmental standards, energy-saving requirements, etc. This ratio directly reflects the actual thermal performance of the insulation layer and the design target thermal resistance. The deviation is such that when K is closer to 1, the thermal insulation performance is closer to the design standard; the smaller K is, the more serious the thermal insulation performance degradation.

[0129] The insulation performance level of the corresponding insulation layer location is evaluated based on a preset evaluation threshold.

[0130] When K≥0.9, that is, the actual thermal resistance reaches more than 90% of the design value, it means that the thermal insulation performance has basically no degradation, and it is evaluated as excellent.

[0131] When 0.7≤K<0.9, that is, the actual thermal resistance is 70%~90% of the design value, it indicates that the thermal insulation performance is slightly reduced and there is no obvious risk of heat loss, so the evaluation is qualified.

[0132] When 0.5≤K<0.7, that is, the actual thermal resistance is 50%~70% of the design value, it indicates that the thermal insulation performance is moderately reduced and there is a certain amount of heat loss, which is assessed as needing attention;

[0133] When K < 0.5, meaning the actual thermal resistance is less than 50% of the design value, it indicates a severe degradation in insulation performance and significant heat loss, which is assessed as deterioration and requires immediate repair.

[0134] The optimal thermal resistance value is obtained based on the inversion. Then, the real-time heat flux density and cumulative heat loss energy of each pipe section are calculated to provide direct data for energy efficiency auditing.

[0135] Calculate real-time heat flux density :

[0136] ;

[0137] Calculate the real-time heat for each data collection cycle. : ,

[0138] in, For the heat exchange area of ​​the pipe section, , The diameter of the insulation layer; This refers to the length of the pipe section.

[0139] Cumulative heat loss energy during the statistical period :

[0140] ,

[0141] in, This represents the number of data collections within the statistical period. The data collection period is specified, such as 1 minute.

[0142] To more accurately determine the defect type inference, the inference is based on measured data and thermal resistance inversion results. The specific process is as follows:

[0143] First, from the surface temperature data collected by the distributed temperature-sensing optical cable, the environmental parameters collected by the multi-source sensors, and the equivalent thermal resistance data obtained by inversion, five multi-dimensional features are extracted: spatial distribution morphology (the spatial distribution characteristics of the pipe axial temperature are obtained through spatial curve fitting and slope analysis, such as local sudden rises and banded high-temperature zones), thermal resistance time change rate (the linear regression slope is calculated based on the thermal resistance values ​​of multiple consecutive collection cycles to reflect the rate of thermal insulation performance decay), average relative humidity value (the average relative humidity value of the corresponding area of ​​the calculation unit is taken), thermal resistance spatial distribution characteristics (patterns such as local sudden drops and banded uniform decreases are extracted through spatial cluster analysis), and humidity-thermal resistance correlation (the correlation coefficient between the two time series is calculated to determine the synchronous change relationship). All features do not require additional data input and rely entirely on the system's own sensing and inversion results. The defect feature library is built based on a large number of experimental simulations and field failure cases. Each defect type in the library corresponds to a clear combination of features and threshold standards. For example, water seepage in the insulation layer corresponds to "a sudden drop in thermal resistance of ≥50% in a local area of ​​≤3 meters, and an ambient humidity of ≥85%, with a strong negative correlation between the two (correlation coefficient ≤-0.7)". Aging of the insulation material corresponds to "a continuous and uniform decrease in thermal resistance of ≥5 meters, with a slow rate of decrease and no obvious correlation with humidity". Structural damage corresponds to "a sudden drop in thermal resistance of ≥60% in a range of ≤1 meter, and a sudden increase in surface temperature of ≥5℃ in the corresponding area". Insufficient thickness corresponds to "a continuous and uniform decrease in thermal resistance of ≥10 meters at 50%-70% of the design value, with basically no time decay trend". During the matching and inference process, the specific values ​​of the multidimensional features of the pipe section to be diagnosed are first extracted, and then compared with the threshold values ​​of each pattern in the defect feature library one by one. The matching score is calculated according to the preset weights (thermal resistance spatial distribution 0.3, humidity-thermal resistance correlation 0.25, temperature distribution morphology 0.2, thermal resistance decrease rate 0.15, and ambient humidity 0.1). Finally, the defect pattern with the highest score is taken as the inference result. If the highest score is lower than 0.5, "suspected unknown defect, manual verification is recommended" is output to ensure the accuracy and practicality of defect identification.

[0144] Fault identification and tracing: Multi-dimensional signals such as the spatial morphology of thermal resistance decrease, time-varying rate, and local environmental humidity are matched with a defect feature knowledge base to obtain matching results. For example, when a sharp drop in the thermal resistance (insulation capacity) of the insulation layer at a certain location is detected within a very small spatial range (such as within 1 meter), but at the same time the environmental humidity in that area does not increase abnormally, it will be judged with a high degree of confidence that "structural damage to the insulation layer" has occurred, and the damage location will be accurately marked on the map (with an accuracy of up to the meter level).

[0145] If the matching result is an urgent defect such as "water seepage in the insulation layer" or "structural damage", it will be automatically marked as "high priority" and the location information will be pushed to the management platform simultaneously.

[0146] Early warning and reporting: Automatically generate diagnostic reports, trigger graded early warnings (prompt, warning, emergency) for "lesion" areas and specific defects identified, and push them to the management platform.

[0147] The system automatically generates diagnostic reports, including the k-ratio distribution, multidimensional feature analysis results, defect inference type and location, and cumulative heat loss data. It triggers graded early warnings (prompt: K=0.7~0.9, no urgent defect; warning: K=0.5~0.7, or suspected general defect; emergency: K<0.5, or confirmed water leakage / damage defect) for pipe sections with "deterioration" level and high priority defects, and pushes them to the management platform.

[0148] The specific embodiments described herein are merely illustrative of the spirit of the invention. Those skilled in the art to which this invention pertains may make various modifications or additions to the described specific embodiments or use similar methods to substitute them, without departing from the spirit of the invention or exceeding the scope defined by the appended claims.

Claims

1. A method for online evaluation of pipeline insulation performance, characterized in that, The online assessment method includes the following steps: S1. Data Acquisition: Collect the measured temperature of the outer surface of the insulation layer at multiple measuring points along the pipeline at preset intervals. And collect ambient temperature Wind speed and the temperature of the medium inside the pipeline ; S2. Dynamic thermal resistance inversion calculation, the following sub-steps are performed at each measuring point: S21, Based on the wind speed Pipe insulation layer outer diameter and ambient temperature The kinematic viscosity of ambient air and air physical properties To obtain the comprehensive heat transfer coefficient h between the outer surface of the pipe and the air under the current environment; S22. Construct a temperature prediction model for the outer surface of the insulation layer to establish the medium temperature. Ambient temperature Thermal resistance of insulation layer Pipe wall thermal resistance The overall heat transfer coefficient h and the predicted temperature of the outer surface of the insulation layer The calculation relationship between them; S23. The measured temperature of the outer surface of the insulation layer at each measuring point. The thermal resistance of the insulation layer is adjusted using an iterative optimization algorithm to achieve the target value. The predicted temperature of the outer surface of the insulation layer obtained by the temperature prediction model of the outer surface of the insulation layer is thus achieved. The measured temperature of the outer surface of the insulation layer The root mean square error (RMSE) between them is the smallest; S24. When the root mean square error (RMSE) is less than a preset threshold, stop the iterative optimization and adjust the thermal resistance of the insulation layer at this point. The output is the optimal thermal resistance of the insulation layer at this measuring point. ; S3. Performance Evaluation: Based on the optimal thermal resistance value at each measuring point. With the preset design thermal resistance The ratio K is used to evaluate the thermal insulation performance of the corresponding insulation layer location.

2. The online evaluation method for pipeline insulation performance according to claim 1, characterized in that, In step S21, the step of obtaining the comprehensive heat transfer coefficient h includes: The Reynolds number is obtained using Formula 1. Formula 1 is: in, The kinematic viscosity of ambient air is determined by ambient temperature. Obtained by looking up the table; The diameter of the pipe insulation layer; Ambient temperature Air property parameters can be obtained by looking up the table. ; The Nusselt number is obtained using Formula 2. Formula 2 is as follows: ； Nusel number Defined as: ; The overall heat transfer coefficient h is obtained through deformation: in, is the thermal conductivity of air.

3. The online evaluation method for pipeline insulation performance according to claim 1 or 2, characterized in that, In step S22, the steps of the thermal insulation layer outer surface temperature prediction model include: The heat flux density is determined using Formula 3. ,in Formula three represents the heat transferred through the pipe wall per unit area and per unit time. ； ； in, The temperature of the medium inside the pipe. For ambient temperature, The total thermal resistance of each layer, For the thermal resistance of the pipe wall, , For pipe wall thickness, The thermal conductivity of the pipe material; For external surface flow thermal resistance, ; Based on the above heat flux density The following model is constructed to predict the temperature of the outer surface of the insulation layer: ； = ； ； in, To reduce the pipe wall temperature, To reduce the temperature of the insulation layer, For heat flux density, Predict the temperature of the outer surface of the insulation layer.

4. The online evaluation method for pipeline insulation performance according to claim 1 or 2, characterized in that, In step S23, the thermal resistance of the insulation layer is adjusted by an iterative optimization algorithm. The operations include: Set thermal resistance update step size : ; in, Let be the gradient of the k-th iteration. ; For the Hessian matrix approximation, The damping coefficient is dynamically adjusted. Predict the temperature of the outer surface of the insulation layer at the i-th measuring point. For the thermal resistance of the insulation layer, The deviation between the predicted temperature of the outer surface of the insulation layer at the i-th measuring point and the measured temperature of the outer surface of the insulation layer. The thermal resistance of the insulation layer after each iteration of the optimization algorithm is set to : in, The thermal resistance of the insulation layer is adjusted by the k-th iteration optimization algorithm.

5. The online evaluation method for pipeline insulation performance according to claim 4, characterized in that, In step S3, the operation of stopping iterative optimization further includes: Calculate the thermal resistance of the insulation layer after each iteration of the optimization algorithm adjustment. Sum of squared residuals : ； Where n is the number of valid measurement points within the calculation unit. These are the predicted temperature and the measured temperature of the outer surface of the insulation layer at the i-th measuring point, respectively. Calculate the sum of squared residuals for the current iteration step. Sum of squared residuals from the previous iteration step absolute value of change : ； when When the residual value is less than the preset residual threshold, the thermal resistance of the insulation layer at this time will be... The output is the optimal thermal resistance of the insulation layer at this measuring point. .

6. The online evaluation method for pipeline insulation performance according to claim 5, characterized in that, Adjusting the thermal resistance of the insulation layer using an iterative optimization algorithm The operation also includes: Compare the sum of squared residuals at the current iteration step Sum of squared residuals from the previous iteration step ; like The iterative update is deemed effective, and the damping coefficient is reduced accordingly. This is used for the next iteration; like Determine that the iterative update is invalid and keep it. At the same time, increase the damping coefficient This is used for the next iteration.

7. The online evaluation method for pipeline insulation performance according to claim 1, characterized in that, In step S23, the operation of stopping iterative optimization further includes: Set the maximum number of iterations. ; when Stop iterative optimization when the time is right.

8. The online evaluation method for pipeline insulation performance according to claim 1, characterized in that, In step S23, the root mean square error (RMSE) is calculated using formula four, which is: RMSE= Where n is the number of valid measurement points within the calculation unit. These are the predicted temperature and the measured temperature of the outer surface of the insulation layer at the i-th measuring point, respectively.

9. The online evaluation method for pipeline insulation performance according to claim 1, characterized in that, In step S3, the performance evaluation also includes: Based on the optimal thermal resistance value at each measuring point Calculate real-time heat flux density : ； Calculate the real-time heat for each data collection cycle. : , in, For the heat exchange area of ​​the pipe section, , The diameter of the insulation layer; This refers to the length of the pipe section. Cumulative heat loss energy during the statistical period : ， in, This represents the number of data collections within the statistical period. This refers to the data collection cycle.

10. The online evaluation method for pipeline insulation performance according to claim 1 or 9, characterized in that, The online assessment method also includes a defect type inference step: Temperature measured from the outer surface of the insulation layer Ambient humidity (RH) and the optimal thermal resistance value In the process, at least two features are extracted from the following: spatial distribution pattern of temperature, spatial distribution characteristics of thermal resistance, rate of change of thermal resistance over time, average relative humidity value, and humidity-thermal resistance correlation. The extracted features are matched with multiple defect types defined in a pre-defined defect feature library; Based on the matching results, output the defect type or defect probability.

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