Early warning method and system for health state of long-distance heat supply pipeline

Abstract

The invention belongs to the technical field of pipeline state monitoring, and particularly relates to a health state early warning method and system for a long-distance heat supply pipeline, and the method comprises the steps: obtaining and storing distributed optical fiber temperature measurement data, point type sensor monitoring data and environment temperature data along the pipeline, and forming a historical data chain with a timestamp and a position index; dividing monitoring subareas by taking adjacent point type sensors as boundaries, comparing actual temperature drop with designed allowable temperature drop, and judging that the subareas are abnormal subareas if the deviation exceeds the standard; the abnormal partition is subdivided into monitoring point positions through optical fiber resolution, and a historical similar optical fiber temperature data set S is retrieved with the current working condition as the condition; and calculating a temperature difference delta T between the current optical fiber temperature and the S mean value, performing health state grading according to at least two preset temperature difference threshold values, and outputting an early warning. The problems of large monitoring blind area and inaccurate positioning in the prior art are solved, and accurate identification and graded early warning of the local failure point location of the pipeline thermal insulation layer are realized.

Description

Technical Field

[0001] This invention belongs to the field of pipeline condition monitoring technology, specifically a method and system for early warning of the health status of long-distance heating pipelines. Background Technology

[0002] The statements in this section merely refer to the background art related to this invention and do not necessarily constitute prior art.

[0003] Long-distance heating pipelines are the "main arteries" of urban centralized heating systems, and their safe and stable operation is crucial. Pre-insulated direct-buried polyurethane insulated pipes are widely used in long-distance heating projects due to their excellent insulation performance and mechanical strength. However, under long-term service environments of high temperature, high humidity, and complex loads, the polyurethane insulation layer is prone to aging, carbonization, and even detachment, leading to a decrease in insulation effectiveness and significant heat loss. In severe cases, it can even cause safety accidents due to localized overheating or stress concentration in the working steel pipe. Therefore, online monitoring and early warning of the health status of the pipeline insulation layer have become key requirements for ensuring the safety of heating networks and achieving intelligent operation and maintenance.

[0004] Currently, monitoring of long-distance heating pipelines mainly relies on point sensors installed at heat exchange stations, relay pump stations, and key nodes to determine pipeline anomalies by monitoring parameters such as the temperature, pressure, and flow rate of the medium. Alternatively, distributed optical fibers can be used to continuously monitor temperature and strain along the pipeline to identify anomalies.

[0005] However, the placement of point sensors is spaced out, and relying solely on sparse point sensors results in numerous monitoring blind spots. Even when combined with fiber optic data, there is a lack of an effective linkage analysis mechanism, making it impossible to accurately pinpoint specific microscopic damage locations after detecting macroscopic temperature drop anomalies. Summary of the Invention

[0006] This invention provides a method and system for early warning of the health status of long-distance heating pipelines, which solves the problem in the prior art that the sparse deployment of sensors and fragmented data make it impossible to accurately identify and locate the points of local insulation failure in pipelines.

[0007] The first aspect of this invention discloses a method for early warning of the health status of long-distance heating pipelines, comprising the following steps: Acquire and store distributed fiber optic temperature measurement data, point sensor monitoring data, and ambient temperature data along the pipeline, and add timestamps and location coordinate indexes to each data point to form a data chain containing historical operating conditions; among them, the point sensor monitoring data includes at least medium temperature data and medium flow data; The pipeline is divided into N monitoring zones based on adjacent point temperature sensors. The actual temperature drop is calculated based on the medium temperature of the upstream and downstream point sensors in each zone and compared with the design allowable temperature drop. When the deviation between the actual temperature drop and the design allowable temperature drop exceeds a preset threshold, the zone is determined to be a zone with abnormal temperature drop. Within the abnormal temperature drop zone, the zone is subdivided into M monitoring points using the resolution of distributed fiber optic temperature measurement data. For any abnormal point, the current pipe medium temperature, ambient temperature, and flow rate are used as matching conditions to retrieve the fiber optic monitoring temperature dataset S under similar historical operating conditions from the database. Calculate the temperature difference ΔT between the current fiber optic monitoring temperature of the abnormal location and the average fiber optic monitoring temperature in the historical similar dataset S. Classify the health status of the abnormal location according to at least two preset temperature difference thresholds and output the corresponding early warning information.

[0008] Furthermore, during the formation of the data chain, a unified data standardization interface is used to convert and collect data from the distributed fiber optic temperature measurement system, point sensors, and meteorological station. Using GPS / BeiDou time synchronization and pipeline length markers as dual indexes, the distributed fiber optic temperature measurement data, point sensor monitoring data, and ambient temperature data are spatiotemporally aligned to eliminate clock deviations between multiple data sources.

[0009] Furthermore, to eliminate clock skew, specifically: Meteorological station data with GPS / BeiDou time synchronization was selected as the UTC reference source, and a synchronization model was established by calculating the acquisition time difference between each sensor and the meteorological data. For high-frequency sensor data, a sliding window averaging method is used to compensate for time offset, while for low-frequency data, linear interpolation is used to match it to a unified time axis. Set a clock drift monitoring threshold, and automatically trigger resynchronization when the deviation exceeds the set value.

[0010] Furthermore, in the process of comparing the actual temperature drop with the design allowable temperature drop, the extreme value deviation of the actual temperature drop is corrected by the sensor monitoring accuracy, and the deviation between the corrected value and the design allowable temperature drop is calculated. The allowable temperature drop is calculated based on the pipe length, medium temperature, insulation layer structural parameters, and a thermodynamic model.

[0011] Furthermore, using the current pipe medium temperature, ambient temperature, and flow rate as matching conditions, a historical fiber optic monitoring temperature dataset S under similar operating conditions is retrieved from the database, specifically: Retrieve historical data chains that meet the set conditions, and extract the corresponding fiber optic monitoring temperature data to form dataset S; The conditions are set as follows: |Current medium temperature - Historical medium temperature| ≤ First temperature threshold; |Current ambient temperature - Historical ambient temperature| ≤ Second temperature threshold; |Current traffic - Historical traffic| ≤ Traffic threshold.

[0012] Furthermore, the preset at least two temperature difference thresholds include a first temperature difference threshold θ1, a second temperature difference threshold θ2, and a third temperature difference threshold θ3, where θ1 < θ2 < θ3; The health status of abnormal locations is classified based on the temperature difference ΔT, and corresponding early warning information is output. Specifically: If θ1≤ΔT<θ2, it is judged as a mild anomaly, the system records it and prompts for key detection in the next monitoring cycle; If θ2≤ΔT<θ3, it is determined to be a moderate anomaly, and a visual alarm message is pushed and the video surveillance is linked to confirm the status; If ΔT≥θ3, it is determined to be a severe abnormality or the insulation function is suspected to be malfunctioning, triggering a high-priority inspection plan.

[0013] Furthermore, the first temperature difference threshold θ1 is set according to the monitoring sensitivity of the point sensor; the third temperature difference threshold θ3 is set according to the temperature of the medium inside the pipe. When the temperature monitored by the optical fiber is close to the temperature of the medium inside the pipe within ±5℃, the insulation structure is determined to be basically ineffective.

[0014] Furthermore, the health status classification also introduces the annual aging rate α of the pipe insulation material to dynamically correct the temperature difference threshold. The annual aging rate α is predicted based on the annual relative increase rate of heat loss using the following formula: Δqt=q(t)-q(t-1)≈q0·(1+α)^(t-1)·α Where q(t) is the steady-state heat loss of the pipeline in year t, q0 is the initial heat loss, t is the number of years the pipeline has been in operation, and α is the annual aging rate of the thermal conductivity of the insulation material.

[0015] Furthermore, it also includes: Acquire vibration data from distributed optical fiber monitoring; When an abnormally large increase in vibration amplitude is detected, the results of the health status classification are combined to determine the risk of external mechanical damage and trigger a corresponding warning.

[0016] A second aspect of the present invention discloses a health status early warning system for long-distance heating pipelines, comprising: The multi-source data acquisition module is configured to acquire distributed fiber optic temperature measurement data, point sensor monitoring data, and ambient temperature data along the pipeline. The database module is configured to add a timestamp and location coordinate index to each received data and store it, forming a data chain containing historical operating conditions. The zoned temperature drop screening module is configured to: divide the pipeline into N monitoring zones based on adjacent point temperature sensors; calculate the actual temperature drop based on the medium temperature of the upstream and downstream point sensors of each zone; and compare it with the design allowable temperature drop; when the deviation between the actual temperature drop and the design allowable temperature drop exceeds a preset threshold, the zone is determined to be a zone with abnormal temperature drop. The abnormal point location and retrieval module is configured to: within the temperature drop abnormal zone, subdivide the zone into M monitoring points using the resolution of distributed fiber optic temperature measurement data; for any abnormal point, retrieve the historical fiber optic monitoring temperature dataset S under similar operating conditions from the database module using the current pipe medium temperature, ambient temperature and flow rate as matching conditions. The graded early warning module is configured to: calculate the temperature difference ΔT between the current fiber optic monitoring temperature of the abnormal point and the average fiber optic monitoring temperature in the historical similar dataset S; classify the health status of the abnormal point according to at least two preset temperature difference thresholds; and output the corresponding early warning information.

[0017] Compared with existing technologies, one or more of the above technical solutions have the following beneficial effects: 1. By employing a two-tiered linkage mechanism of "zoning coarse screening - precise point location," the shortcomings of existing technologies, such as sparse deployment of point sensors leading to large monitoring blind spots and inability to capture minute local deformations, are addressed. First, monitoring zones are divided using adjacent point sensors as boundaries. Abnormal zones are quickly identified through macroscopic temperature drop comparison. Then, utilizing the high spatial resolution of distributed fiber optic temperature measurement data, the abnormal zones are further subdivided into meter-level or even sub-meter-level monitoring points. Historical data chains of similar operating conditions are introduced for point-by-point temperature difference comparison. This overcomes the bottleneck of traditional methods that can only detect "whether there is an anomaly" but cannot pinpoint "where the anomaly is," enabling maintenance personnel to directly locate the specific coordinates of localized carbonization or failure in the insulation layer, thus improving the accuracy and efficiency of hazard identification.

[0018] 2. To address the data fragmentation issues caused by inconsistent formats and time synchronization of optical fiber, point sensors, and meteorological data in existing monitoring systems, a unified standardized data transfer interface was used to achieve compatible acquisition and spatiotemporal alignment of multi-source protocols. To address clock skew, a differentiated synchronization strategy was proposed, using meteorological station UTC time as the reference, employing sliding window compensation for high-frequency data, and linear interpolation matching for low-frequency data. This mechanism ensures that data from different sources and frequencies can be correlated and analyzed within a unified time and spatial coordinate system, giving historical data chain retrieval and comparison real physical meaning and avoiding misjudgments caused by data misalignment.

[0019] 3. An extreme value deviation correction mechanism was introduced in the comparison between the allowable temperature drop and the actual temperature drop. By incorporating the accuracy deviation of adjacent sensors into the calculation (e.g., combining the upper and lower deviations separately), the maximum possible deviation under the most unfavorable condition was used as the judgment criterion, avoiding the triggering of "false anomalies" or the underreporting of "true anomalies" caused by the superposition of individual sensor errors. This correction strategy enhances the robustness and reliability of the system in complex industrial environments, especially in scenarios where sensors drift during long-term operation.

[0020] 4. Based on the three core operating parameters of the current abnormal point—internal medium temperature, ambient temperature, and flow rate—multi-dimensional matching thresholds are set, and data chains with "same operating conditions but different times" are selected from historical data as comparison samples. This retrieval logic effectively isolates the impact of environmental changes and operating condition fluctuations on fiber optic temperature measurement data, allowing the temperature difference ΔT between the current fiber optic temperature and the historical average to directly reflect the degree of performance degradation of the insulation layer itself, achieving a leap from "looking at the temperature" to "looking at the quality of the insulation layer."

[0021] 5. By employing multi-level gradient temperature difference thresholds and matching differentiated emergency response plans, while also incorporating the annual aging rate α of the thermal conductivity of the insulation material to dynamically correct the temperature difference thresholds, the early warning sensitivity is matched with the actual aging curve of the pipeline. This effectively solves the problem of "false alarms easily occurring in new pipelines and missed alarms easily occurring in old pipelines," achieving adaptive and intelligent monitoring covering the entire life cycle of pipelines. Attached Figure Description

[0022] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0023] Figure 1 This is a schematic diagram of a health status early warning process for long-distance heating pipelines provided in one or more embodiments of the present invention. Detailed Implementation

[0024] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0025] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0026] As described in the background section, current monitoring of long-distance heating pipelines mainly relies on two types of technical methods: Point sensor monitoring: Point sensors are installed at heat exchange stations, relay pump stations, and key nodes to monitor parameters such as temperature, pressure, and flow rate of the medium in real time. This method is technically mature, but it has significant spatial limitations. The spacing between sensors is usually large (several kilometers), making it impossible to capture minute local deformations in the pipeline between two stations, local carbonization of the insulation layer, or the spread of hot spots. When significant anomalies appear in the monitoring data, local damage has often already developed to a certain extent, making early warning difficult.

[0027] Distributed fiber optic monitoring: In recent years, smart insulated pipes with built-in distributed fiber optic cables have been gradually applied, enabling continuous monitoring of temperature and strain along the pipes. However, most existing applications still remain at the level of displaying single data, lacking deep integration with point sensor data and environmental meteorological data.

[0028] The technical problems existing in the prior art are mainly reflected in the following aspects: Data heterogeneity and spatiotemporal fragmentation: Various monitoring systems (fiber optics, point sensors, weather stations) operate independently, with inconsistent data formats, sampling frequencies, and time bases. Historical and real-time data lack spatiotemporal alignment mechanisms, making it difficult to construct a data chain covering the entire lifecycle and resulting in an inability to effectively trace the evolution path of faults.

[0029] Monitoring blind spots and inaccurate positioning: Relying solely on sparse point sensors results in numerous monitoring blind spots. Even when combined with fiber optic data, there is a lack of effective linkage analysis mechanisms, making it impossible to accurately pinpoint specific microscopic damage locations after detecting macroscopic temperature drop anomalies.

[0030] The early warning mechanism has poor dynamic adaptability: Existing systems mostly use alarm rules based on fixed thresholds, which do not fully consider dynamic factors such as the performance degradation of insulation materials (e.g., aging rate) caused by the increasing service life of pipelines and drastic seasonal fluctuations in ambient temperature. When pipelines experience creep, corrosion, or gradual damage to the insulation layer, the fixed threshold cannot dynamically adjust its sensitivity, which can easily lead to early hidden danger signals being drowned out by normal operating condition fluctuation noise, resulting in missed or false alarms.

[0031] Therefore, this solution provides a method and system for early warning of the health status of long-distance heating pipelines. Through a two-level linkage mechanism of "regional coarse screening to locate abnormal intervals and fiber optic fine inspection to lock abnormal points", combined with comparative analysis of historical similar working condition data chains, it can achieve accurate identification of local failure points of pipeline insulation layer and graded early warning of health status.

[0032] like Figure 1 As shown, the health status early warning method for long-distance heating pipelines provided in this solution includes the following steps: The fiber optic temperature measurement system monitors temperature data at every location in the pipeline in real time; acquires local temperature data for the project area released by the local meteorological station; and obtains data from various point sensors on the pipeline, including the temperature and flow rate of the medium. Obtain a set of data, including the following at point A: outdoor temperature data TA1, pipe medium pressure PA1, medium temperature TA2, and fiber optic temperature TA3 at that location; At this point, the data chain for point A consists of: timestamp, pipeline location coordinates, and ambient temperature T outside the pipeline. 环境 Light monitoring temperature T 光纤 Upstream point sensor data (medium temperature T1, flow rate Q1), downstream point sensor data (medium temperature T2, flow rate Q2). The monitoring system has data storage and real-time retrieval functions. By establishing a complete database containing all the above data, it has the functions of traceability, review, and screening.

[0033] Setting thresholds: The thresholds for monitoring data at each point are set based on the sensor sensitivity range, or they can be calculated based on a thermal model.

[0034] Temperature loss analysis is performed on a section consisting of two point temperature sensors to determine whether the temperature loss meets the design requirements or the allowable temperature loss for pipeline operation. If it meets the requirements, routine monitoring is carried out at fixed intervals. If the requirements are not met, a precise pipeline health check is triggered; within the abnormal zone, precise points are marked according to the fiber optic temperature measurement sensitivity, and data corresponding to a random time is selected. Identify data points within the abnormal zone (specific location zone) and, based on pipe medium temperature (± threshold), ambient temperature (± threshold), and real-time flow rate (± threshold), screen for similar data chains in the database.

[0035] The acquired data was sorted along a timeline, and the temperature changes of the optical fiber at each location were compared and analyzed. (1) If the temperature of the optical fiber increases significantly, it means that the insulation layer at that point is decreasing, and the degree of decrease needs to be further assessed. By using a heating model, different temperature difference ranges θ1, θ2, and θ3 are set, with the temperature difference values ​​increasing sequentially, corresponding to different processing methods; ΔT 光纤异常点 =|T 光纤异常点- T 光纤监测均值 |; 1) If θ1≤ΔT 光纤异常点 If the value is less than θ2, it will be recorded in the system, filed as an early warning, and the location will be the focus of monitoring in the next monitoring cycle. 2) θ2≤ΔT 光纤异常点 If the value is less than θ3, a pop-up alarm will be triggered, and video surveillance or other inspection methods will be activated to confirm the pipeline status. 3) ΔT 光纤异常点 If the value is ≥θ3, a pop-up window and an app alarm will be triggered to indicate that the pipeline is suspected of being faulty, and manual or drone inspection will be initiated.

[0036] (2) If the temperature fluctuation of the optical fiber is not significant, it can be determined that the insulation structure at that point is in good working order.

[0037] This embodiment provides a method for monitoring, collecting, fusing, and constructing a corresponding database based on basic multi-source data for prefabricated direct-buried insulated pipelines.

[0038] Specifically, by designing a unified data standardization conversion interface, generally using Modbus RTU / ASCII / TCP or NB-IoT / LoRa protocols, it is necessary to support long-distance and multi-device connections, or low-power wide-area remote connections. The conversion interface should support the conversion of communication protocols between different sensors. At the same time, combined with the pipeline's built-in composite distributed optical fiber monitoring system, the temperature data of the entire pipeline is monitored. Based on the measured data, spatiotemporal alignment is performed using GPS time synchronization and pipeline length marking as dual indexes. The optical fiber temperature measurement data, point sensor data (medium temperature / flow rate), and meteorological station ambient temperature are integrated to form a complete data chain with timestamps and form a database.

[0039] To address clock skew in multi-source data, without modifying hardware, the system pre-diagnoses the time accuracy of each sensor and the timing method of the weather station, prioritizing data from weather stations with GPS / BeiDou timing as the UTC reference source. A synchronization model is established by calculating the acquisition time difference between each sensor and the weather data. For high-frequency sensor data, a sliding window averaging method can be used for time offset compensation; for low-frequency data, linear interpolation is suitable for matching to a unified time axis. Software configuration is performed for sensors supporting NTP / PTP, or a fixed deviation is calculated using known event timestamps during system debugging. All stored data is converted to UTC timestamps, and a clock drift monitoring threshold is set. When the deviation exceeds the set value, resynchronization is automatically triggered. The set value is confirmed according to customer requirements.

[0040] Multi-source data acquisition includes: distributed temperature measurement data from the optical fiber embedded in the insulation pipe (continuous monitoring along the pipeline location, with accuracy based on actual sensitivity) and vibration measurement data; point sensor data along the pipeline (medium temperature, flow rate); external environmental data (real-time local temperature obtained from the meteorological station interface); and basic pipeline parameters (design temperature drop model, zone boundary information).

[0041] By analyzing the above test data according to: "timestamp, pipe calibration length, and external ambient temperature T", 环境 Fiber optic monitoring of temperature T 光纤The database stores dynamic data chains in the order of "upstream point sensor data (medium temperature T1, flow rate Q1), downstream point sensor data (medium temperature T2, flow rate Q2)" to form a database. The database supports traceability, replayability, and screening capabilities: it stores historical data based on a time axis and allows for quick retrieval of similar data chains by calibration length, time range, and operating conditions (such as flow rate ± threshold, ambient temperature ± threshold).

[0042] This embodiment provides a comparative method driven by historical data on the effectiveness of the insulation layer of prefabricated direct-buried insulated pipelines.

[0043] Specifically, the current specifications stipulate the maximum allowable heat loss value for pipeline insulation structures. The pipeline temperature drop is calculated based on the calculation formula given in the specifications, combined with multiple parameters such as the average temperature of the heat medium, ambient temperature, mass flow rate, and specific heat capacity.

[0044] The heat loss per unit length can be calculated using the following formula: ; Where T0 is the external surface temperature of the equipment and pipeline, and the unit is Kelvin (K). T a The ambient temperature is expressed in Kelvin (K). q1 is the linear thermal resistance of the cylindrical insulation structure, expressed in Kelvin per square meter per watt [(m·K) / W]; q1 is the heat loss per unit length, expressed in watts per meter (W / m).

[0045] The temperature drop per unit length can be calculated using the following formula: ; in, This is the heat transfer coefficient per unit length of the pipe, expressed in W / m·℃. The average temperature of the heat transfer medium inside the pipe, in °C; This refers to the ambient temperature, expressed in °C. This refers to the mass flow rate of the heat transfer medium, expressed in kg / s. The specific heat capacity of hot water is expressed in J / kg·℃. Temperature drop, unit: °C / m; This represents the length of the pipe, in meters (m).

[0046] First, the pipeline is divided into N monitoring zones (each zone consists of 2 points between temperature sensors) using adjacent point temperature sensors as boundaries. For each zone, calculate the actual temperature drop ΔT = T max - T minAnd compare it with the design allowable temperature drop ΔT (the design allowable temperature drop ΔT is calculated by the design institute during the pipeline design stage, depending on the pipeline length, inlet and outlet water temperatures, insulation layer structure and thickness. The specific format of the design allowable temperature drop ΔT should be the allowable medium temperature loss per kilometer. The calculation needs to be based on the distance between the two-point sensors. When reading the parameters of the corresponding sensors, it is necessary to adjust them in combination with the sensor monitoring accuracy deviation. For example, if the design allowable temperature drop ΔT is 1.5℃ per kilometer, there are two sensors with a distance of 1 kilometer, the accuracy deviation of the two sensors is ±0.2℃, and the difference in the monitored medium temperature between the two sensors is 0.8℃. Take one upper deviation and one lower deviation, then the maximum deviation of the actual data of the two sensors is 1.2℃. The parameter with the larger value is used for subsequent calculations). If |actual temperature drop ΔT - design allowable temperature drop ΔT| ≤ θ1 (θ1 is the allowable temperature drop, and the threshold is set according to the sensor sensitivity), it is determined to be "normal" and continuous monitoring is required; If |actual temperature drop ΔT - design allowable temperature drop ΔT| > θ1, it is determined as "abnormal temperature drop" and the precise health detection process is triggered.

[0047] For precise location and data link screening within abnormal zones, the zone can be subdivided into M monitoring points based on the high spatial resolution (sensitivity 0.1m) of fiber optic temperature measurement data. In the database, using the current operating conditions of the abnormal points as a benchmark, historical similar data links are screened, prioritizing matches with similar external ambient temperatures T. 环境 Compare upstream point sensor data (medium temperature T1, flow rate Q1) and downstream point sensor data (medium temperature T2, flow rate Q2) with the fiber optic monitoring temperature T of this data chain. 光纤 Dataset.

[0048] This embodiment provides a graded early warning method for the health status of the insulation layer of prefabricated direct-buried insulated pipelines.

[0049] Specifically, for each monitoring point, the fiber optic monitoring temperature T is compared with that at a certain abnormal time point. 光纤异常点 Similar to historical data, fiber optic monitoring of temperature T in data link S. 光纤监测历史 Dataset: Judgment Logic A: The insulation layer's effectiveness has decreased.

[0050] By using a heating model (such as the Dittus-Boelter formula), different temperature difference ranges of θ1, θ2, and θ3 are set, with the temperature difference values ​​increasing sequentially.

[0051] θ1 represents the allowable temperature drop, and the threshold is set based on the sensor sensitivity. If the theoretically calculated allowable temperature drop of the pipeline should be 1℃, and the sensor's detection sensitivity range is ±0.2℃, then taking the limit deviation, θ1 should be set to 1.2℃.

[0052] For prefabricated direct-buried polyurethane insulated pipes, based on the good integrity of the outer protective pipe, excellent sealing at the pipe joints, and good pipe material quality, a typical annual aging rate α for the insulation structure is introduced. This α is based on the annual relative increase rate of heat loss, which is approximately equal to the annual aging rate of the thermal conductivity of the insulation material, and is predicted using the following formula: Δq t =q(t) q(t 1)≈q0·(1+α) t-1 ·α; Where q(t) is the steady-state heat loss of the pipeline, in W / m; t is the number of years the pipeline has been in operation, in years; α is the annual aging rate of the thermal conductivity of the insulation material (1 / year), in %; θ2 is the abnormal temperature drop value, which can be set with multiple data according to fixed temperature intervals, and the corresponding abnormal alarm level increases step by step.

[0053] θ3 is the abnormal temperature drop value, which is set within ±5℃ of the temperature of the medium inside the pipe. When the temperature at the optical fiber is close to the temperature of the medium inside the pipe, the insulation structure has basically failed.

[0054] If the temperature T at a certain point is monitored by fiber optic cable... 光纤异常点 Significantly higher than the mean T in S 光纤监测均值 If the insulation layer performance is reduced, it is determined that the insulation layer performance has decreased, according to ΔT. 光纤异常点 Graded processing, ΔT 光纤异常点 =|T 光纤异常点 -T 光纤监测均值 |

[0055] θ1≤ΔT 光纤异常点 <θ2: The system records and files the data, and will remind users to focus on monitoring this location in the next monitoring cycle.

[0056] θ2≤ΔT 光纤异常点 <θ3: Push visual alarm information, link video surveillance to confirm the status, and trigger the corresponding handling plan.

[0057] ΔT 光纤异常点 ≥θ3: Push visual alarm information, determine that the pipeline insulation function is suspected to be malfunctioning, and trigger the corresponding handling plan.

[0058] Judgment Logic B: The insulation structure is intact.

[0059] If ΔT 光纤异常点 If the value is ≤θ1, the insulation structure at that point is deemed to be functionally intact and should be monitored routinely.

[0060] At the same time, combined with vibration measurement data: if the vibration amplitude increases abnormally, it is determined to be a risk of external mechanical damage.

[0061] Taking a long-distance heating pipeline system as an example, this solution will be further explained: The main pipeline is DN500 (20km in length) and is equipped with intelligent insulation pipes: the outer side of the polyurethane layer has a pre-set optical fiber channel, and the built-in single-mode optical fiber realizes distributed temperature measurement (sampling interval 1m) and vibration monitoring; point sensors (measuring medium temperature and flow rate) are installed every 2km along the line; the system connects to the meteorological station API to obtain real-time air temperature.

[0062] The database uses a time-series approach to store structured data chains. For example, the data for point A (location coordinates K5+300) at time t0 is: [t0, K5+300, T...]. 环境 =5℃, T 光纤 =62℃, T1=85℃, Q1=150m 3 / h, T2=82℃, Q2=149m 3 / h).

[0063] Zoned temperature drop analysis and anomaly triggering.

[0064] The pipeline was divided into 10 monitoring zones (each 2km segment). For zone 3 (K4+000~K6+000): Calculate the actual temperature drop ΔT 实际 = T1- T2= 85℃ - 82℃ = 3℃; Design temperature drop ΔT 设计 = f(Q max ,T 环境） = 2.8℃ (based on thermodynamic model); Then the temperature difference ΔT = |ΔT 实际 -ΔT 设计 |=0.2℃; Set θ1=0.5℃ (derived from the accuracy of the point sensor ±0.2℃), and since |3-2.8|=0.2℃≤0.5℃, it is determined to be "normal".

[0065] For partition 7 (K12+000~K14+000): ΔT 光纤异常点 =4.5℃, ΔT 设计 =3.0℃, |4.5-3.0|=1.5℃>0.5℃, triggering precise detection.

[0066] Precise location and analysis of anomalies.

[0067] Within partition 7 (abnormal partition), the data is subdivided into 500 points (one point every 0.5m) using fiber optic data. Select point K12+500: Current operating conditions: T1 = 86℃, T 环境 =3℃, Q1=160m 3 / h, T 光纤 =70℃; Screening for similar historical data chains in the database (condition: |T1-T) max历史 |≤1℃, |T 环境 -T 环境历史 |≤0.5℃,|Q1-Q max历史 ≤5m 3 / h), retrieve set S containing 30 historical records, T 光纤监测均值 =65℃; Calculate ΔT 光纤异常点 =70℃-65℃=5℃; Set θ1=3℃, θ2=10℃, θ3=78℃ (medium temperature 80℃-2℃). Since 5℃ ∈ (3℃, 10℃), the insulation structure is suspected of failure, triggering a visual pop-up alarm, linking video monitoring, and triggering the corresponding handling plan.

[0068] Point K12+800 within the same partition: ΔT 光纤异常点 =2℃<θ1, indicating that the insulation structure has not failed.

[0069] This solution addresses the shortcomings of existing technologies, such as sparse deployment of point sensors leading to large monitoring blind spots and the inability to capture minute local deformations. It employs a two-tiered linkage mechanism: "zoning for coarse screening - precise point location." First, monitoring zones are divided using adjacent point sensors as boundaries. Macroscopic temperature drop comparisons quickly pinpoint abnormal zones, solving the initial screening challenge of "where the problem lies." Then, utilizing the high spatial resolution of distributed fiber optic temperature measurement data, the abnormal zones are further subdivided into meter-level or even sub-meter-level monitoring points. Historical data chains of similar operating conditions are introduced for point-by-point temperature difference comparisons. This completely overcomes the technical bottleneck of traditional methods, which can only detect "whether there is an anomaly" but cannot pinpoint "where the anomaly is." This allows maintenance personnel to directly locate the specific coordinates of localized carbonization or failure in the insulation layer, significantly improving the accuracy and efficiency of hazard identification.

[0070] To address the data fragmentation issues caused by inconsistent formats and time synchronization of fiber optics, point sensors, and meteorological data in existing monitoring systems, this solution achieves compatible acquisition of multi-source protocols through a unified standardized data transfer interface and uses GPS / BeiDou time synchronization and pipeline length markers as dual indexes for spatiotemporal alignment. In particular, to address clock deviation, a differentiated synchronization strategy is creatively proposed, using meteorological station UTC time as the reference, employing sliding window compensation for high-frequency data, and linear interpolation matching for low-frequency data, while setting a clock drift monitoring threshold. This mechanism ensures that data from different sources and frequencies can be correlated and analyzed within a unified time and spatial coordinate system, giving historical data chain retrieval and comparison real physical meaning and avoiding misjudgments caused by data misalignment.

[0071] This solution fully considers the measurement accuracy deviation problem commonly found in industrial sensors. In the comparison between the allowable temperature drop and the actual temperature drop, an extreme value deviation correction mechanism is introduced. By incorporating the accuracy deviations of adjacent sensors into the calculation (e.g., combining the upper and lower deviations separately), the maximum possible deviation under the most unfavorable condition is used as the judgment criterion, avoiding the triggering of "false anomalies" or the missed detection of "true anomalies" caused by the superposition of individual sensor errors. This correction strategy is particularly critical in scenarios where sensors drift during long-term operation, significantly enhancing the robustness and reliability of the system in complex industrial environments.

[0072] This solution departs from the traditional approach that relies solely on fixed thresholds, proposing a historical similarity retrieval mechanism based on multi-parameter coupling. Using the three core operating parameters—internal medium temperature, ambient temperature, and flow rate—at the current anomaly location as benchmarks, multi-dimensional matching thresholds are set to filter data chains from massive historical data that exhibit "identical operating conditions, differing only in time" as comparison samples. This retrieval logic effectively isolates the impact of environmental changes and operating condition fluctuations on fiber optic temperature measurement data, allowing the temperature difference ΔT between the current fiber optic temperature and the historical average to directly reflect the degree of performance degradation of the insulation layer itself. This represents a fundamental leap from "looking at temperature levels" to "looking at the quality of the insulation layer."

[0073] This solution abandons the traditional crude "one threshold, one alarm" approach and establishes a three-tiered temperature difference threshold system (θ1, θ2, θ3) corresponding to three health states: mild anomaly, moderate anomaly, and severe failure, each matched with a differentiated contingency plan. Mild anomalies are only recorded and filed by the system to avoid frequently disturbing maintenance personnel; moderate anomalies trigger pop-up alarms and video verification; and severe anomalies trigger high-priority inspections. This tiered processing mechanism avoids overwhelming real hazards with a massive number of invalid alarms while ensuring that serious hidden dangers are detected in a timely manner, achieving a smart leap from qualitative judgment of "presence or absence" to quantitative management of "mild, severe, slow, and urgent" risks.

[0074] Addressing the industry pain point that traditional fixed thresholds cannot adapt to the natural decline in insulation performance due to the increasing service life of pipelines, this solution innovatively introduces the annual aging rate α of the thermal conductivity of the insulation material to dynamically correct the temperature difference threshold. Based on a model of the annual relative increase rate of heat loss, the system can automatically adjust the warning threshold according to the pipeline's commissioning time, ensuring that the warning sensitivity matches the actual aging curve of the pipeline. This mechanism effectively solves the problem of "false alarms easily occurring in new pipelines and missed alarms easily occurring in old pipelines," achieving adaptive and intelligent monitoring covering the entire life cycle of pipelines.

[0075] This solution not only addresses the degradation of the thermal performance of the insulation layer but also incorporates vibration monitoring data from distributed optical fibers into the analysis system. When an abnormal increase in vibration amplitude is detected, the system can overlay this data with the results of temperature anomaly assessment to comprehensively determine whether there are complex risks such as external mechanical damage, third-party construction damage, or pipeline structural instability. This multimodal data fusion mechanism of "temperature + vibration" broadens the system's monitoring dimensions and provides a more comprehensive guarantee for pipeline safety.

[0076] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for early warning of the health status of long-distance heating pipelines, characterized in that, Includes the following steps: Acquire and store distributed fiber optic temperature measurement data, point sensor monitoring data, and ambient temperature data along the pipeline, and add timestamps and location coordinate indexes to each data point to form a data chain containing historical operating conditions; among them, the point sensor monitoring data includes at least medium temperature data and medium flow data; The pipeline is divided into N monitoring zones based on adjacent point temperature sensors. The actual temperature drop is calculated based on the medium temperature of the upstream and downstream point sensors in each zone and compared with the design allowable temperature drop. When the deviation between the actual temperature drop and the design allowable temperature drop exceeds a preset threshold, the zone is determined to be a zone with abnormal temperature drop. Within the abnormal temperature drop zone, the zone is subdivided into M monitoring points using the resolution of distributed fiber optic temperature measurement data. For any abnormal point, the current pipe medium temperature, ambient temperature, and flow rate are used as matching conditions to retrieve the fiber optic monitoring temperature dataset S under similar historical operating conditions from the database. Calculate the temperature difference ΔT between the current fiber optic monitoring temperature of the abnormal location and the average fiber optic monitoring temperature in the historical similar dataset S. Classify the health status of the abnormal location according to at least two preset temperature difference thresholds and output the corresponding early warning information.

2. The health status early warning method for long-distance heating pipelines as described in claim 1, characterized in that, During the formation of the data chain, data from the distributed fiber optic temperature measurement system, point sensors, and meteorological station are converted and collected through a unified data standardization interface. Using GPS / BeiDou time synchronization and pipeline length markers as dual indexes, the distributed fiber optic temperature measurement data, point sensor monitoring data, and ambient temperature data are spatiotemporally aligned to eliminate clock deviations between multiple data sources.

3. The health status early warning method for long-distance heating pipelines as described in claim 2, characterized in that, To eliminate clock skew, specifically: Meteorological station data with GPS / BeiDou time synchronization was selected as the UTC reference source, and a synchronization model was established by calculating the acquisition time difference between each sensor and the meteorological data. For high-frequency sensor data, a sliding window averaging method is used to compensate for time offset, while for low-frequency data, linear interpolation is used to match it to a unified time axis. Set a clock drift monitoring threshold, and automatically trigger resynchronization when the deviation exceeds the set value.

4. The health status early warning method for long-distance heating pipelines as described in claim 1, characterized in that, In the process of comparing the actual temperature drop with the design allowable temperature drop, the extreme value deviation of the actual temperature drop is corrected by the sensor monitoring accuracy, and the deviation between the corrected value and the design allowable temperature drop is calculated. The allowable temperature drop is calculated based on the pipe length, medium temperature, insulation layer structural parameters, and a thermodynamic model.

5. The health status early warning method for long-distance heating pipelines as described in claim 1, characterized in that, Using the current pipe medium temperature, ambient temperature, and flow rate as matching conditions, a dataset S of fiber optic temperature monitoring under similar historical operating conditions is retrieved from the database. Specifically: Retrieve historical data chains that meet the set conditions, and extract the corresponding fiber optic monitoring temperature data to form dataset S; The conditions are set as follows: |Current medium temperature - Historical medium temperature| ≤ First temperature threshold; |Current ambient temperature - Historical ambient temperature| ≤ Second temperature threshold; |Current traffic - Historical traffic| ≤ Traffic threshold.

6. The health status early warning method for long-distance heating pipelines as described in claim 1, characterized in that, The preset at least two temperature difference thresholds include a first temperature difference threshold θ1, a second temperature difference threshold θ2, and a third temperature difference threshold θ3, and θ1 < θ2 < θ3; The health status of abnormal locations is classified based on the temperature difference ΔT, and corresponding early warning information is output. Specifically: If θ1≤ΔT<θ2, it is judged as a mild anomaly, the system records it and prompts for key detection in the next monitoring cycle; If θ2≤ΔT<θ3, it is determined to be a moderate anomaly, and a visual alarm message is pushed and the video surveillance is linked to confirm the status; If ΔT≥θ3, it is determined to be a severe abnormality or the insulation function is suspected to be malfunctioning, triggering a high-priority inspection plan.

7. The health status early warning method for long-distance heating pipelines as described in claim 1, characterized in that, The first temperature difference threshold θ1 is set according to the monitoring sensitivity of the point sensor; the third temperature difference threshold θ3 is set according to the temperature of the medium inside the pipe. When the temperature monitored by the optical fiber is close to the temperature of the medium inside the pipe within ±5℃, the insulation structure is judged to be basically ineffective.

8. The health status early warning method for long-distance heating pipelines as described in claim 1, characterized in that, The health status classification also incorporates the annual aging rate α of the pipe insulation material to dynamically correct the temperature difference threshold. The annual aging rate α is predicted based on the annual relative increase rate of heat loss using the following formula: Δqt=q(t)-q(t-1)≈q0·(1+α)^(t-1)·α; Where q(t) is the steady-state heat loss of the pipeline in year t, q0 is the initial heat loss, t is the number of years the pipeline has been in operation, and α is the annual aging rate of the thermal conductivity of the insulation material.

9. The health status early warning method for long-distance heating pipelines as described in claim 1, characterized in that, It also acquires vibration data from distributed optical fiber monitoring; when an abnormal increase in vibration amplitude is detected, it combines the health status classification results to determine the risk of external mechanical damage and triggers a corresponding warning.

10. A health status early warning system for long-distance heating pipelines, used to implement the health status early warning method for long-distance heating pipelines as described in claim 1, characterized in that, include: The multi-source data acquisition module is configured to acquire distributed fiber optic temperature measurement data, point sensor monitoring data, and ambient temperature data along the pipeline. The database module is configured to add a timestamp and location coordinate index to each received data and store it, forming a data chain containing historical operating conditions. The zoned temperature drop screening module is configured to: divide the pipeline into N monitoring zones based on adjacent point temperature sensors; calculate the actual temperature drop based on the medium temperature of the upstream and downstream point sensors of each zone; and compare it with the design allowable temperature drop; when the deviation between the actual temperature drop and the design allowable temperature drop exceeds a preset threshold, the zone is determined to be a zone with abnormal temperature drop. The abnormal point location and retrieval module is configured to: within the temperature drop abnormal zone, subdivide the zone into M monitoring points using the resolution of distributed fiber optic temperature measurement data; for any abnormal point, retrieve the historical fiber optic monitoring temperature dataset S under similar operating conditions from the database module using the current pipe medium temperature, ambient temperature and flow rate as matching conditions. The graded early warning module is configured to: calculate the temperature difference ΔT between the current fiber optic monitoring temperature of the abnormal point and the average fiber optic monitoring temperature in the historical similar dataset S; classify the health status of the abnormal point according to at least two preset temperature difference thresholds; and output the corresponding early warning information.

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