A pipe coating thickness early warning system and method

Abstract

This application relates to the field of pipeline early warning technology, and discloses a pipeline coating thickness early warning system and method, comprising: a data acquisition module configured to acquire pipeline information of a monitoring point and obtain pipeline coating thickness data using an ultrasonic sensor; a data processing module configured to acquire and calculate the detection time based on the detection interval, establish a time-thickness curve between the detection time and the thickness data corresponding to the detection time, and calculate the slope of the time-thickness curve; a first reference module configured to obtain a first detection time based on the slope; a second reference module configured to obtain a second detection time when the thickness data equals a thickness threshold based on the time-thickness curve of the monitoring point; and an early warning module configured to determine the early warning time when there is a safety risk in the pipeline coating thickness based on the first and second detection times. This application improves the early warning efficiency of pipeline coating thickness.

Description

Technical Field

[0001] This application relates to the field of pipeline early warning technology, and more specifically, to a pipeline coating thickness early warning system and method. Background Technology

[0002] With the widespread application of pipeline transportation systems, especially in industries such as oil, natural gas, and chemicals, the safety and long-term stability of pipelines have become critical issues. Pipeline coatings, as effective measures for corrosion prevention, wear resistance, and improved pipeline durability, have become an important component of pipeline construction. The thickness of the coating directly affects the service life and corrosion resistance of the pipeline. However, as pipelines age, the coating may thin or break down to varying degrees due to environmental factors, the corrosiveness of the transported media, and mechanical wear. Damage to the coating not only affects pipeline safety but may also lead to major safety accidents such as leaks and corrosion. This risk is particularly severe for pipelines transporting flammable and explosive media, where a thin coating poses a significant risk.

[0003] In related technologies, monitoring pipeline coating thickness often relies on periodic manual inspections or localized testing methods (such as eddy current testing and ultrasonic testing) to detect coating thickness and promptly identify pipeline monitoring points with potential safety risks. However, due to varying levels of expertise among technicians, manual monitoring of pipeline coating thickness is inaccurate and cannot promptly identify pipelines with safety risks. Therefore, improving the early warning efficiency of pipeline coating thickness monitoring remains a technical problem to be solved. Summary of the Invention

[0004] In view of this, the present invention proposes a pipeline coating thickness early warning system and method, aiming to improve the early warning efficiency of pipeline coating thickness.

[0005] In a first aspect, the present invention provides a pipe coating thickness system, comprising:

[0006] The acquisition module includes an ultrasonic sensor installed at the monitoring point; the acquisition module is configured to acquire pipeline information at the monitoring point and obtain pipeline coating thickness data based on the ultrasonic sensor; the data processing module is configured to acquire and calculate the detection time based on the detection interval, establish a time-thickness curve by combining the detection time and the thickness data corresponding to the detection time, store the time-thickness curve according to the identifier of the monitoring point, and calculate the slope of the time-thickness curve; the first reference module is configured to determine whether to adjust the thickness threshold to obtain a correction threshold based on the slope; determine whether to adjust the detection interval to obtain a correction detection interval based on the slope; and fit the time-thickness curve of the monitoring point to obtain the time-thickness curve. The time-thickness function takes a correction threshold as input to obtain the first detection time. The second reference module is configured to determine similar monitoring points from the set of monitoring points other than the monitoring point when the slope of the monitoring point is negative, based on the pipeline information; calculate the similarity result between the slope of the monitoring point and the slope of each monitoring point in the similar monitoring points, and determine the comparison monitoring point from the similar monitoring points based on the similarity result; if the similarity result of the comparison monitoring point is greater than the first threshold, obtain the second detection time when the thickness data is equal to the thickness threshold based on the time-thickness curve of the comparison monitoring point; and the early warning module is configured to determine the early warning time when there is a safety risk in the pipeline coating thickness based on the first detection time and the second detection time.

[0007] Optionally, the slope threshold can be compared with the slope.

[0008] When the slope is less than or equal to the slope threshold, it is determined that the thickness threshold needs to be adjusted; when the slope is greater than the slope threshold, it is determined that the thickness threshold should not be adjusted.

[0009] The slope threshold satisfies the following formula:

[0010]

[0011] Where k0 represents the slope threshold, t i Let d represent the interval between the i-th detections. i Let λ represent the coating thickness in the i-th test, λ be the attenuation factor used to represent the rate at which the coating thickness is consumed over time, 0.01≤λ≤0.05, n be the total number of tests, t be the average value of the interval between n tests, and d be the average value of the coating thickness in the n tests.

[0012] Optionally, the correction threshold satisfies the following formula: m'=m(1+k0-k); where m' represents the correction threshold; k represents the slope; k0 represents the slope threshold; and m represents the thickness threshold.

[0013] Optionally, the first reference module determines whether to adjust the detection interval to obtain a corrected detection interval based on the slope, including: when the slope is less than or equal to a slope threshold, determining that the detection interval needs to be corrected; obtaining the minimum detection interval of the acquisition module, and determining the corrected detection interval based on the minimum detection interval; the corrected detection interval satisfies the following formula: Among them, t ′ This indicates a correction of the detection interval.

[0014] Optionally, the time thickness function satisfies the following formula:

[0015]

[0016] Where a represents the slope of the line and b represents the intercept.

[0017] Optionally, the second reference module determines similar monitoring points from a set of monitoring points other than the monitoring point based on the pipeline information, including: comparing the similarity between the pipeline information of the point to be monitored and the pipeline information of the monitoring points in the first set of monitoring points, and determining similar monitoring points from the first set; the first set of monitoring points is a set of monitoring points other than the point to be monitored, and the similarity between the pipeline information of the similar monitoring points and the pipeline information of the point to be monitored is greater than a second threshold; the pipeline information includes pipeline material, pipeline inner diameter, pipeline service life, transport medium type, and medium flow rate.

[0018] Optionally, the second reference module calculates the similarity between the slope of the point to be monitored and the slope of each monitoring point among similar monitoring points, and determines the comparison monitoring point from the similar monitoring points based on the similarity results, including:

[0019] Obtain N sets of continuous first thickness data with negative slopes from the monitoring points; obtain N sets of continuous second thickness data with negative slopes from similar monitoring points; calculate the first difference between the first thickness data and the second thickness data; calculate the second difference between the first slope corresponding to the first thickness data and the second slope corresponding to the second thickness data; determine the comparison monitoring point from the similar monitoring points; the comparison monitoring point is the monitoring point with the smallest first difference and second difference among the similar monitoring points.

[0020] Optionally, the second reference module obtains the detection time when the thickness data equals the thickness threshold based on the time-thickness curve of the comparison monitoring point, including: inputting the thickness threshold into the time-thickness curve of the comparison monitoring point to obtain the second detection time.

[0021] Optionally, the warning time includes the following formula: P = min(p1, p2); where P represents the warning time, p1 represents the first detection time, and p2 represents the second detection time.

[0022] Optionally, the detection time satisfies the following formula: Where T represents the detection time, t i This represents the interval between the i-th detections.

[0023] Secondly, a method for early warning of pipeline coating thickness is provided, comprising: acquiring pipeline information of the monitoring point using an ultrasonic sensor; obtaining pipeline coating thickness data based on the ultrasonic sensor; acquiring and calculating the detection time based on the detection interval; establishing a time-thickness curve by combining the detection time and the thickness data corresponding to the detection time; storing the time-thickness curve according to the identifier of the monitoring point; calculating the slope of the time-thickness curve; determining whether to adjust the thickness threshold based on the slope to obtain a corrected threshold; determining whether to adjust the detection interval based on the slope to obtain a corrected detection interval; and fitting the time-thickness curve of the monitoring point to obtain a time-thickness curve. The function takes a correction threshold as input to a time-thickness function to obtain the first detection time. When the slope of the monitoring point is negative, similar monitoring points are determined from the set of monitoring points other than the monitoring point based on the pipeline information. The similarity result between the slope of the monitoring point and the slope of each of the similar monitoring points is calculated, and a comparison monitoring point is determined from the similar monitoring points based on the similarity result. If the similarity result of the comparison monitoring point is greater than the first threshold, the detection time when the thickness data is equal to the thickness threshold is obtained from the time-thickness curve of the comparison monitoring point and recorded as the second detection time. The warning time for the safety risk of pipeline coating thickness is determined based on the first detection time and the second detection time.

[0024] Thirdly, a pipe coating thickness warning device is provided, including a memory and a processor; the memory is used to store computer-executed instructions, and the processor is connected to the memory via a bus; when the pipe coating thickness warning device is running, the processor executes the computer-executed instructions stored in the memory, so that the pipe coating thickness warning device performs the pipe coating thickness warning method of the second aspect.

[0025] The pipe coating thickness warning device can be a network device or a component of a network device, such as a chip system within the network device. This chip system supports the network device in implementing the functions involved in the first aspect and any possible implementation thereof, such as acquiring, determining, and transmitting the data and / or information involved in the aforementioned pipe coating thickness warning method. The chip system includes a chip, but may also include other discrete devices or circuit structures.

[0026] Fourthly, a computer-readable storage medium is provided, comprising computer-executable instructions that, when executed on a computer, cause the computer to perform the pipe coating thickness warning method of the second aspect.

[0027] Fifthly, a computer program product is also provided, which includes computer instructions that, when executed on a pipe coating thickness warning device, cause the pipe coating thickness warning device to perform the pipe coating thickness warning method as described in the second aspect above.

[0028] It should be noted that the aforementioned computer instructions can be stored, in whole or in part, on a computer-readable storage medium. This computer-readable storage medium can be packaged together with the processor of the pipe coating thickness warning device, or it can be packaged separately from the processor of the pipe coating thickness warning device; this application embodiment does not limit this.

[0029] The descriptions of the second, third, fourth, and fifth aspects of this application can be referenced to the detailed description of the first aspect.

[0030] In the embodiments of this application, the name of the aforementioned early warning device for pipe coating thickness does not limit the device or functional module itself. In actual implementation, these devices or functional modules may appear under other names. For example, the receiving unit may also be called a receiving module, receiver, etc. As long as the function of each device or functional module is similar to that of this application, it falls within the scope of the claims of this application and its equivalents.

[0031] This application provides a pipeline coating thickness early warning system and method. By collecting pipeline coating thickness data in real time and calculating the slope of the time-thickness curve, the system can accurately monitor the coating decay rate. This is significant for timely detection of potential problems such as excessively thin coatings and accelerated corrosion, thereby effectively improving pipeline safety and operational reliability. The pipeline coating thickness early warning system can automatically determine whether to adjust the preset thickness threshold based on changes in the slope and thickness data. When the coating consumption rate accelerates, the thickness threshold and detection interval are modified to monitor coating changes more frequently, ensuring timely risk detection. By filtering similar monitoring points based on pipeline information, the pipeline coating thickness early warning system can compare historical data from similar monitoring points to further optimize the early warning strategy and reduce the risk of false alarms or missed alarms. By employing a time-thickness curve fitting method, the pattern of coating thickness change is modeled as a function, which can more accurately predict the consumption trend of pipeline coatings. The pipeline coating thickness early warning system can predict the future state of the coating based on the thickness threshold and historical data, further improving the accuracy and intelligence of pipeline management. By analyzing, filtering, and comparing the slopes of monitoring points, the system can better identify pipeline monitoring points with rapid coating degradation, allowing for timely adjustments to detection strategies and concentrated maintenance resources. This not only helps extend the service life of pipelines but also reduces maintenance costs and the frequency of human intervention. This application improves pipeline coating thickness early warning efficiency, promptly detecting abnormal changes in the pipeline coating and preventing accidents such as corrosion and leakage caused by excessively thin coatings, thereby improving pipeline safety. Simultaneously, the automatic adjustment and prediction functions reduce the need for manual intervention, enhancing the efficiency and maintainability of pipeline management. Attached Figure Description

[0032] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the scope of this application. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0033] Figure 1 A structural block diagram of a pipe coating thickness early warning system provided in this application embodiment;

[0034] Figure 2 A flowchart illustrating a method for early warning of pipe coating thickness provided in an embodiment of this application;

[0035] Figure 3 This is a structural block diagram of a pipe coating thickness early warning device provided in an embodiment of this application. Detailed Implementation

[0036] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. The present application will now be described in detail with reference to the accompanying drawings and embodiments.

[0037] In some embodiments of this application, see Figure 1 As shown, a pipe coating thickness early warning system includes: a data acquisition module, a data processing module, a first reference module, a second reference module, and an early warning module.

[0038] The acquisition module includes an ultrasonic sensor installed at the monitoring point; the acquisition module is configured to acquire pipeline information at the monitoring point and obtain pipeline coating thickness data based on the ultrasonic sensor.

[0039] In one possible implementation, the acquisition module obtains coating thickness data from an ultrasonic sensor via a data acquisition unit, which is also used to configure the detection interval.

[0040] Optionally, the data acquisition unit is an intermediary device that connects the ultrasonic sensor and the data processing module, and is responsible for collecting, storing and transmitting the coating thickness data measured by the ultrasonic sensor.

[0041] Optionally, the ultrasonic sensor model is TMS-B or TMS-S, with a probe accuracy of 0.05mm and a resolution of 0.01mm. The sensor is a wireless passive sensor.

[0042] It's important to explain that the ultrasonic sensor is one of the core components of the data acquisition module; it's used for non-contact measurement of the pipe coating thickness. The ultrasonic sensor calculates the coating thickness by emitting ultrasonic signals and receiving the time difference between the signal return and the speed of sound wave propagation.

[0043] Understandably, this application uses the ultrasonic sensor in the acquisition module to monitor the pipe coating thickness in real time, and combines this with the detection interval configured in the acquisition unit to achieve accurate tracking and data acquisition of the pipe coating thickness. By continuously acquiring coating thickness data at monitoring points, the trend of coating consumption can be reflected in a timely manner, avoiding the problems of long cycles and untimely data in traditional manual inspections, and ensuring that the condition of the pipe coating is always under monitoring.

[0044] Furthermore, the data acquisition unit receives coating thickness measurement data from the ultrasonic sensor and temporarily stores this data in memory or storage. To ensure data accuracy and reliability, the data acquisition unit periodically activates the sensor to perform measurements and acquires a stable set of thickness data.

[0045] It's important to explain that the data acquisition unit not only collects data but also configures different detection intervals based on system settings. For example, the unit adjusts the detection interval based on factors such as pipe condition, ambient temperature, and coating type to ensure real-time detection results and maintain the system's low-power mode. The unit can also automatically adjust the detection interval based on slope changes to extend the detection cycle when coating wear is slow.

[0046] Optionally, the data acquisition device not only measures the coating thickness but also collects other pipeline-related information. This information includes, but is not limited to: pipeline material (e.g., steel, stainless steel, polyethylene, etc.), which may result in different coating consumption rates; pipeline inner diameter (the diameter affects the coating thickness distribution and consumption rate); pipeline service life (the coating consumption changes with the pipeline's service life); and the type and flow rate of the transported medium (e.g., natural gas, oil, water) and its flow rate also affect the coating consumption rate, especially since friction and corrosion can accelerate coating wear. This information is read by sensors and transmitted to the data acquisition device, which then analyzes and stores it to support subsequent data processing and analysis.

[0047] The data processing module is configured to acquire and calculate the detection time based on the detection interval, establish a time-thickness curve by combining the detection time and the thickness data corresponding to the detection time, store the time-thickness curve according to the identifier of the point to be monitored, and calculate the slope of the time-thickness curve.

[0048] The detection time satisfies the following formula: Where T represents the detection time, t i This represents the interval between the i-th detections.

[0049] The data processing module acquires coating thickness data and pipeline information collected by the acquisition unit via a wireless communication module (Wi-Fi, Zigbee, LoRa) or a wired communication interface (Ethernet, serial port). The data processing module further processes the data according to a preset algorithm, generates a time-thickness curve, performs slope analysis, and determines whether the thickness threshold and detection interval need to be adjusted.

[0050] The first reference module is configured to determine whether to adjust the thickness threshold based on the slope to obtain a corrected threshold; determine whether to adjust the detection interval based on the slope to obtain a corrected detection interval; fit the time-thickness curve of the point to be monitored to obtain a time-thickness function; input the corrected threshold into the time-thickness function to obtain the first detection time.

[0051] The correction threshold satisfies the following formula:

[0052] m' = m(1 + k0 - k);

[0053] Where m' represents the correction threshold; k represents the slope; k0 represents the slope threshold; and m represents the thickness threshold.

[0054] It should be noted that when the pipeline coating thickness early warning system determines that the thickness threshold needs adjustment based on the coating thickness change trend, it calculates the corrected thickness threshold based on the relationship between the slope and the slope threshold. This adjustment helps the system dynamically correct the thickness threshold according to the actual coating consumption rate, thereby improving the accuracy and response speed of the early warning system.

[0055] Specifically, the calculation process for the correction threshold considers the slope of the time-thickness curve corresponding to the current monitoring point, as well as a pre-set slope threshold. If, after comparing the current slope value with the set slope threshold, it is determined that the thickness threshold needs adjustment, the correction threshold will be calculated based on the difference between the two. In this way, the pipeline coating thickness early warning system can intelligently update the threshold according to the coating consumption rate and time change trend, so as to accurately reflect the current state and future trend of the coating. This ensures that, in practical applications, the coating thickness threshold can adapt to the actual working conditions and coating consumption of the pipeline, so that when the coating thickness approaches the preset threshold, the system can issue an early warning in a timely manner and take appropriate maintenance measures. At the same time, the corrected threshold is more flexible and can be dynamically adjusted according to the actual situation, further improving the reliability and intelligence level of the pipeline monitoring system.

[0056] The time-thickness function satisfies the following formula:

[0057]

[0058] Where a represents the slope of the line and b represents the intercept.

[0059] It should be explained that the data processing module, based on the slope analysis of the time-thickness curve, can intelligently determine whether the preset coating thickness threshold needs adjustment. When the coating consumption rate changes significantly, the pipeline coating thickness early warning system can automatically adjust the threshold or detection interval, avoiding the limitations of manual intervention. Through an intelligent slope calculation and adjustment mechanism, the system can achieve optimal monitoring frequency, improve monitoring accuracy, and reduce resource waste.

[0060] Understandably, the data processing module fits the time-thickness curves of the monitoring points to generate a time-thickness function, and then calculates the first detection time by substituting a correction threshold. This allows for accurate prediction of coating thickness changes at a future point in time, identifying in advance when the coating may reach a dangerous thickness, thus providing reliable decision support for pipeline maintenance and repair.

[0061] The second reference module is configured to, when the slope of the monitoring point is negative, determine similar monitoring points from the set of monitoring points other than the monitoring point based on pipeline information; calculate the similarity result between the slope of the monitoring point and the slope of each monitoring point in the similar monitoring points; determine the comparison monitoring point from the similar monitoring points based on the similarity result; if the similarity result of the comparison monitoring point is greater than a first threshold; and obtain the second detection time when the thickness data is equal to the thickness threshold based on the time-thickness curve of the comparison monitoring point.

[0062] It should be explained that when the slope of the monitoring point is negative, the second reference module can filter out similar monitoring points based on pipeline information and compare the slope of the similar monitoring points with the current monitoring point to find the closest comparison monitoring point. Through this comparison mechanism, the pipeline coating thickness early warning system can more accurately assess the trend of coating thickness changes, identify potential coating consumption problems, and provide targeted early warning information to maintenance personnel.

[0063] The early warning module is configured to determine the warning time when there is a safety risk in the thickness of the pipeline coating based on the first detection time and the second detection time.

[0064] The warning time is defined by the following formula: P = min(p1, p2); where P represents the warning time, p1 represents the first detection time, and p2 represents the second detection time.

[0065] It should be explained that the early warning system for pipeline coating thickness selects the detection time with the smallest value from the first detection time and the second detection time as the early warning time.

[0066] Optionally, the warning time is inversely proportional to the severity of the warning level. This ensures the earliest possible warning of coating problems, reducing the risk of pipeline damage and improving pipeline safety. The inverse relationship between warning time and warning level means that a shorter warning time indicates faster coating thickness loss, a higher warning level, and the need for early maintenance; conversely, a longer warning time indicates a lower coating loss rate and a lower warning level.

[0067] Optionally, the pipeline coating thickness early warning system can automatically adjust the alarm priority based on the actual changes in the coating by calculating the early warning time between the first detection time and the second detection time and setting the early warning level according to the early warning time.

[0068] It should be noted that after obtaining the first and second detection times, the pipeline coating thickness early warning system calculates these two times to obtain the early warning time. The early warning time is based on the coating thickness change trend and the corrected time prediction, enabling it to provide an early warning signal that the pipeline coating may reach a threshold.

[0069] Specifically, the pipeline coating thickness early warning system compares and calculates the first and second detection times to obtain the early warning time. This calculation not only reflects changes in pipeline coating thickness but also provides more targeted warning signals. Because the above calculation is based on historical data and time prediction models, pipeline maintenance and management can be more proactive and accurate.

[0070] It is understandable that by calculating the first detection time and the second detection time, the pipeline coating thickness early warning system can derive an early warning time that comprehensively considers factors such as coating consumption rate and attenuation factor. Furthermore, in some embodiments of this application, the detection time is obtained by summing all detection intervals. Specifically, each detection interval is automatically calculated by the system based on the time interval set by the acquisition module; these detection intervals are then accumulated to obtain a total detection time. The total detection time reflects the time span from the first monitoring to the current monitoring point, effectively helping to predict the trend of coating thickness changes over time.

[0071] It should be explained that the warning time is usually used to judge the future trend of the pipeline coating, and combined with the preset threshold, the coating's wear status is judged.

[0072] In some embodiments of this application, the first reference module determines whether to adjust the thickness threshold based on the slope, including:

[0073] The slope threshold is compared with the slope; when the slope is less than or equal to the slope threshold, it is determined that the thickness threshold needs to be adjusted; when the slope is greater than the slope threshold, it is determined that the thickness threshold should not be adjusted.

[0074] The slope threshold satisfies the following formula:

[0075]

[0076] Where k0 represents the slope threshold, t i Let d represent the interval between the i-th detections. i Let represent the coating thickness in the i-th test, λ be the attenuation factor representing the rate at which the coating thickness is consumed over time (0.01 ≤ λ ≤ 0.05), and n be the total number of tests. This represents the average value of n detection intervals. This represents the average coating thickness from n measurements.

[0077] In some embodiments of this application, the first reference module determines whether to adjust the detection interval to obtain a corrected detection interval based on the slope, including:

[0078] When the slope is less than or equal to the slope threshold, it is determined that the detection interval needs to be corrected; the minimum detection interval of the acquisition module is obtained, and the corrected detection interval is determined based on the minimum detection interval.

[0079] The corrected detection interval satisfies the following formula:

[0080] Among them, t ′ This indicates a correction of the detection interval.

[0081] It should be noted that the pipeline coating thickness early warning system will determine whether the detection interval needs to be adjusted based on the change in slope, so as to better adapt to the trend of coating thickness change and provide more accurate monitoring.

[0082] Specifically, when the slope of the calculated coating thickness is less than or equal to a preset slope threshold, the warning judgment of the pipeline coating thickness indicates a slow attenuation rate of the coating thickness, requiring adjustment of the detection interval. The purpose of this adjustment is to optimize the monitoring frequency, reducing unnecessary frequent detections when coating changes are relatively slow, thereby reducing energy consumption and extending sensor lifespan while maintaining sufficient monitoring accuracy.

[0083] When the system determines that the detection interval needs adjustment, the corrected detection interval will be calculated based on the minimum detection interval of the acquisition module. The determination of the corrected detection interval satisfies a certain relationship, enabling the system to dynamically adjust the detection frequency according to the actual coating decay rate. If the coating is consumed slowly, the corrected detection interval can be reduced to decrease redundant data during the acquisition process; conversely, if the slope is large (i.e., the coating is consumed quickly), the detection frequency will be increased accordingly to ensure that changes in coating thickness can be captured in a timely manner.

[0084] This flexible detection interval adjustment mechanism can improve monitoring efficiency and ensure that the system maintains optimal performance under different working environments and coating consumption rates, achieving more accurate and energy-saving monitoring results.

[0085] In some embodiments of this application, the second reference module determines similar monitoring points from a set of monitoring points other than the monitoring points based on pipeline information, including:

[0086] The pipeline information of the point to be monitored is compared with the pipeline information of the monitoring points in the first set of monitoring points to determine similar monitoring points.

[0087] The first set of monitoring points is the set of monitoring points excluding the point to be monitored. The similarity between the pipeline information of similar monitoring points and the pipeline information of the point to be monitored is greater than a second threshold. The pipeline information includes pipeline material, pipeline inner diameter, pipeline service life, type of transported medium, and medium flow rate.

[0088] It should be noted that in some embodiments of this application, when determining whether to adjust the thickness threshold based on the slope of the coating thickness change at monitoring points, the pipeline coating thickness early warning system sets a slope threshold. If the slope of the coating thickness change at a monitoring point is less than or equal to the set slope threshold, the pipeline coating thickness early warning system determines that the thickness threshold needs to be adjusted. Conversely, if the slope is greater than the slope threshold, it indicates that the coating thickness is changing rapidly, and the pipeline coating thickness early warning system determines that the thickness threshold does not need to be adjusted. In this way, the pipeline coating thickness early warning system can dynamically adjust the monitoring standard according to the actual coating consumption, ensuring that the monitoring threshold is more consistent with the actual consumption situation.

[0089] When adjustments to the thickness threshold are needed, the pipeline coating thickness early warning system also adjusts the detection frequency based on the coating consumption rate. If the slope is less than or equal to the set threshold, indicating slow coating consumption, the system will appropriately extend the detection interval to reduce energy consumption and system load caused by frequent detection. Conversely, if the slope is large, indicating rapid coating consumption, the system will correspondingly increase the detection frequency to ensure timely detection of coating thickness changes. This correction mechanism can flexibly adjust the monitoring frequency according to actual conditions, achieving energy saving and efficient monitoring.

[0090] It's important to explain that the monitoring time and coating thickness data are transformed into a time-thickness function using a fitting algorithm. The core of this process is to fit a straight line describing the coating thickness change over time using multiple monitoring data points. Through this fitted line, the pipeline coating thickness early warning system can predict the coating thickness change at a future point in time and further adjust the thickness threshold and detection strategy based on this trend, thereby improving the system's early warning capability and response efficiency.

[0091] Understandably, when the slope of the coating thickness change at a certain monitoring point is negative, it indicates that the coating at that monitoring point is being consumed rapidly. The system will then filter out other similar monitoring points based on relevant pipeline information. By comparing this pipeline information, the system can select monitoring points with similar consumption rates as "similar monitoring points." By comparing the time-thickness curves with these similar monitoring points, the system can more accurately determine the coating consumption trend at the current monitoring point and make corresponding adjustments.

[0092] Optionally, the system of this invention can dynamically adjust the monitoring frequency and threshold according to changes in coating consumption, avoiding overly frequent detection and reducing unnecessary energy consumption. By fitting time-thickness curves and screening similar monitoring points, the system not only improves prediction accuracy but also promptly detects anomalies in coating consumption changes. In summary, this system can optimize monitoring strategies, improve efficiency, and ensure the safety and service life of pipeline coatings while ensuring long-term stable operation.

[0093] This embodiment intelligently adjusts the coating thickness threshold and detection frequency by calculating a slope threshold and comparing it with the current slope to adapt to changes in different coating consumption rates. This method effectively addresses the gradual depletion of pipeline coating thickness, improving system flexibility and adaptability while maintaining monitoring accuracy. Furthermore, the slope threshold acquisition and thickness threshold adjustment mechanisms give the system strong adaptive capabilities.

[0094] Optionally, the criteria for judging similar monitoring points are: the same pipe material, the same type of transport medium, the current pipe inner diameter / the pipe inner diameter of other monitoring points ≥ 0.8, 0.8 ≤ the current pipe service life / the service life of other monitoring points ≤ 1.2 and 0.8 ≤ the current pipe flow rate / the pipe flow rate of other monitoring points ≤ 1.2.

[0095] In some embodiments of this application, the second reference module calculates the similarity result between the slope of the point to be monitored and the slope of each monitoring point among similar monitoring points, and determines the comparison monitoring point from the similar monitoring points based on the similarity result, including:

[0096] Obtain N sets of continuous first thickness data with negative slopes from the monitoring points; obtain N sets of continuous second thickness data with negative slopes from similar monitoring points; calculate the first difference between the first thickness data and the second thickness data; calculate the second difference between the first slope corresponding to the first thickness data and the second slope corresponding to the second thickness data; determine the comparison monitoring point from the similar monitoring points; the comparison monitoring point is the monitoring point with the smallest first difference and second difference among the similar monitoring points.

[0097] It should be noted that when the slope of the coating thickness change at a monitoring point is negative, it indicates that the coating thickness at that monitoring point is decreasing, and the pipeline coating thickness early warning system will conduct further analysis on that monitoring point.

[0098] Specifically, the pipeline coating thickness early warning system selects N consecutive sets of data from the current monitoring point, where the slopes of these data are all negative, meaning the coating thickness has been continuously decreasing during this period. To improve prediction accuracy, the system filters out other monitoring points that also have N consecutive sets of negative slopes as "similar monitoring points." The pipeline coating thickness early warning system then compares the slope and thickness data of these selected similar monitoring points with those of the current monitoring point.

[0099] Optionally, the early warning system for pipeline coating thickness calculates the differences in slope and thickness data between similar monitoring points and the current monitoring point, and selects the similar monitoring points with the smallest differences in slope and thickness data as "comparison monitoring points".

[0100] Through this comparison process, the system can find the monitoring point most similar to the current monitoring point in terms of coating consumption rate and trend, and predict the coating consumption trend of the current monitoring point based on the historical data of the comparison monitoring point. This not only helps improve the accuracy of coating thickness monitoring, but also helps the system better identify anomalies and provide early warnings of potential coating damage risks.

[0101] In some embodiments of this application, the second reference module obtains the detection time when the thickness data equals the thickness threshold based on the time-thickness curve of the comparison monitoring point, including: inputting the thickness threshold into the time-thickness curve of the comparison monitoring point to obtain the second detection time.

[0102] It should be noted that when the slope of the monitoring point is negative, the pipeline coating thickness early warning system will filter the data from other monitoring points to obtain a more accurate coating consumption trend.

[0103] Specifically, when acquiring comparative monitoring points, the pipeline coating thickness early warning system selects N sets of continuous thickness data with negative slopes, starting from the current monitoring point. It then filters out N sets of thickness data with continuously negative slopes from other monitoring points, forming a candidate monitoring point set.

[0104] Optionally, these N sets of continuous thickness data with negative slopes represent the attenuation trend of coating thickness over a certain period of time.

[0105] Specifically, to find the most suitable comparison monitoring point, the pipeline coating thickness early warning system filters by comparing the similarity between the current monitoring point and candidate monitoring points. The system calculates the slope difference and coating thickness data difference between the current monitoring point and the candidate monitoring points, selecting the candidate monitoring point with the smallest slope and thickness difference as the comparison monitoring point. This ensures that the selected comparison monitoring point is closest to the current monitoring point in terms of decay rate and coating thickness change trend, thereby improving the accuracy of the comparison analysis.

[0106] After obtaining the comparison monitoring point, the system will use the time-thickness curve of that comparison monitoring point for further calculations. Specifically, the system will substitute the set thickness threshold into the time-thickness curve of the comparison monitoring point to calculate the time point at which the coating thickness reaches the set threshold under that curve. This time point is the second detection time.

[0107] Because the calculation of the second detection time is based on historical data from comparative monitoring points using a precise fitting algorithm, the pipeline coating thickness early warning system can accurately predict when the pipeline coating will reach a set thickness threshold at a future point in time. This result can serve as a key basis for pipeline health management, providing early warning of potential coating wear and helping maintenance personnel to develop maintenance plans in advance. The second detection time, calculated by comparing the time-thickness curves of the comparative monitoring points, can effectively improve the accuracy and efficiency of pipeline management, prevent sudden failures, and ensure the safe and stable operation of the pipeline system.

[0108] In some embodiments, such as Figure 2 As shown in the embodiments of this application, a method for early warning of pipeline coating thickness is also provided, which is applied to the above-mentioned... Figure 1 An early warning system for pipe coating thickness includes:

[0109] S201. Collect pipeline information at the monitoring point using an ultrasonic sensor, and obtain pipeline coating thickness data based on the ultrasonic sensor.

[0110] S202. Obtain and calculate the detection time based on the detection interval, establish a time-thickness curve by combining the detection time and the thickness data corresponding to the detection time, store the time-thickness curve according to the identifier of the point to be monitored, and calculate the slope of the time-thickness curve.

[0111] The detection time satisfies the following formula: Where T represents the detection time, t i This represents the interval between the i-th detections.

[0112] S203. Determine whether to adjust the thickness threshold based on the slope to obtain the corrected threshold; determine whether to adjust the detection interval based on the slope to obtain the corrected detection interval; fit the time-thickness curve of the point to be monitored to obtain the time-thickness function, input the corrected threshold into the time-thickness function to obtain the first detection time.

[0113] The correction threshold satisfies the following formula: m'=m(1+k0-k); where m' represents the correction threshold; k represents the slope; k0 represents the slope threshold; and m represents the thickness threshold.

[0114] S204. When the slope of the monitoring point is negative, determine similar monitoring points from the set of monitoring points other than the monitoring point based on the pipeline information; calculate the similarity result between the slope of the monitoring point and the slope of each monitoring point in the similar monitoring points, and determine the comparison monitoring point from the similar monitoring points based on the similarity result; obtain the second detection time when the thickness data is equal to the thickness threshold based on the time-thickness curve of the comparison monitoring point.

[0115] Among them, the similarity results of the comparison monitoring points are greater than the first threshold.

[0116] Optionally, the time-thickness function satisfies the following formula:

[0117]

[0118] Where a represents the slope of the line and b represents the intercept.

[0119] S205. Determine the warning time when there is a safety risk in the thickness of the pipeline coating based on the first and second detection times.

[0120] The warning time is calculated using the following formula: P = min(p1, p2); where P represents the warning time, p1 represents the first detection time, and p2 represents the second detection time.

[0121] This application provides a pipeline coating thickness early warning system and method. By collecting pipeline coating thickness data in real time and calculating the slope of the time-thickness curve, the system can accurately monitor the coating decay rate. This is significant for timely detection of potential problems such as excessively thin coatings and accelerated corrosion, thereby effectively improving pipeline safety and operational reliability. The pipeline coating thickness early warning system can automatically determine whether to adjust the preset thickness threshold based on changes in the slope and thickness data. When the coating consumption rate accelerates, the thickness threshold and detection interval are modified to monitor coating changes more frequently, ensuring timely risk detection. By filtering similar monitoring points based on pipeline information, the pipeline coating thickness early warning system can compare historical data from similar monitoring points to further optimize the early warning strategy and reduce the risk of false alarms or missed alarms. By employing a time-thickness curve fitting method, the pattern of coating thickness change is modeled as a function, which can more accurately predict the consumption trend of pipeline coatings. The pipeline coating thickness early warning system can predict the future state of the coating based on the thickness threshold and historical data, further improving the accuracy and intelligence of pipeline management. By analyzing, filtering, and comparing the slopes of monitoring points, the system can better identify pipeline monitoring points with rapid coating degradation, allowing for timely adjustments to detection strategies and concentrated maintenance resources. This not only helps extend the service life of pipelines but also reduces maintenance costs and the frequency of human intervention. This application improves pipeline coating thickness early warning efficiency, promptly detecting abnormal changes in the pipeline coating and preventing accidents such as corrosion and leakage caused by excessively thin coatings, thereby improving pipeline safety. Simultaneously, the automatic adjustment and prediction functions reduce the need for manual intervention, enhancing the efficiency and maintainability of pipeline management.

[0122] In some embodiments of this application, in the above-described S204, determining whether to adjust the thickness threshold based on the slope includes:

[0123] The slope threshold is compared with the slope; if the slope is less than or equal to the slope threshold, it is determined that the thickness threshold needs to be adjusted; if the slope is greater than the slope threshold, it is determined that the thickness threshold does not need to be adjusted; the slope threshold satisfies the following formula:

[0124] Where k0 represents the slope threshold, t i Let d represent the interval between the i-th detections. i Let λ represent the coating thickness in the i-th test, λ be the attenuation factor used to represent the rate at which the coating thickness is consumed over time, 0.01≤λ≤0.05, n be the total number of tests, t be the average value of the interval between n tests, and d be the average value of the coating thickness in the n tests.

[0125] In some embodiments of this application, in step S204 above, determining whether to adjust the detection interval based on the slope to obtain a corrected detection interval includes:

[0126] When the slope is less than or equal to the slope threshold, it is determined that the detection interval needs to be corrected; the minimum detection interval of the acquisition module is obtained, and the corrected detection interval is determined based on the minimum detection interval; the corrected detection interval satisfies the following formula:

[0127] Among them, t ′ This indicates a correction of the detection interval.

[0128] In some embodiments of this application, in step S205 above, determining similar monitoring points from a set of monitoring points other than the monitoring points based on pipeline information includes:

[0129] The similarity of the pipeline information of the point to be monitored with the pipeline information of the monitoring points in the first set of monitoring points is compared to determine similar monitoring points from the first set. The first set of monitoring points is the set of monitoring points other than the point to be monitored. The similarity between the pipeline information of the similar monitoring points and the pipeline information of the point to be monitored is greater than the second threshold. The pipeline information includes pipeline material, pipeline inner diameter, pipeline service life, type of transport medium and medium flow rate.

[0130] In some embodiments of this application, in step S205 above, calculating the similarity result between the slope of the point to be monitored and the slope of each monitoring point among similar monitoring points, and determining the comparison monitoring point from the similar monitoring points based on the similarity result, includes:

[0131] Obtain N sets of continuous first thickness data with negative slopes from the monitoring points; obtain N sets of continuous second thickness data with negative slopes from similar monitoring points; calculate the first difference between the first thickness data and the second thickness data; calculate the second difference between the first slope corresponding to the first thickness data and the second slope corresponding to the second thickness data; determine the comparison monitoring points from the similar monitoring points.

[0132] Among them, the comparison monitoring point is the monitoring point with the smallest first and second difference among similar monitoring points.

[0133] In some embodiments of this application, in S205 above, obtaining the detection time when the thickness data equals the thickness threshold based on the time-thickness curve of the comparison monitoring point includes: inputting the thickness threshold into the time-thickness curve of the comparison monitoring point to obtain the second detection time.

[0134] like Figure 3 The diagram shown is a structural schematic of a pipe coating thickness warning device provided in an embodiment of this application. Figure 3 The early warning device for the thickness of the pipe coating shown includes: an acquisition unit 301 and a processing unit 302;

[0135] The acquisition unit 301 is used to collect pipeline information of the monitoring point and acquire pipeline coating thickness data based on the ultrasonic sensor. The processing unit 302 is used to acquire and calculate the detection time based on the detection interval, establish a time-thickness curve by combining the detection time and the corresponding thickness data, store the time-thickness curve according to the identifier of the monitoring point, and calculate the slope of the time-thickness curve. The processing unit 302 is used to determine whether to adjust the thickness threshold to obtain a correction threshold based on the slope; determine whether to adjust the detection interval to obtain a corrected detection interval based on the slope; fit the time-thickness curve of the monitoring point to obtain a time-thickness function; input the correction threshold into the time-thickness function to obtain the first detection time. Processing unit 302 is used to determine similar monitoring points from a set of monitoring points other than the monitoring point itself, based on pipeline information, when the slope of the monitoring point is negative; calculate the similarity result between the slope of the monitoring point and the slope of each of the similar monitoring points; determine a comparison monitoring point from the similar monitoring points based on the similarity result; if the similarity result of the comparison monitoring point is greater than a first threshold; and obtain a second detection time when the thickness data equals the thickness threshold based on the time-thickness curve of the comparison monitoring point. Processing unit 302 is also used to determine the warning time when there is a safety risk in the pipeline coating thickness based on the first detection time and the second detection time.

[0136] In some embodiments of this application, the processing unit 302 is specifically used for:

[0137] The slope threshold is compared with the slope; if the slope is less than or equal to the slope threshold, it is determined that the thickness threshold needs to be adjusted; if the slope is greater than the slope threshold, it is determined that the thickness threshold does not need to be adjusted; the slope threshold satisfies the following formula:

[0138]

[0139] Where k0 represents the slope threshold, t i Let d represent the interval between the i-th detections. i Let λ represent the coating thickness in the i-th test, λ be the attenuation factor used to represent the rate at which the coating thickness is consumed over time, 0.01≤λ≤0.05, n be the total number of tests, t be the average value of the interval between n tests, and d be the average value of the coating thickness in the n tests.

[0140] In some embodiments of this application, the correction threshold satisfies the following formula: m'=m(1+k0-k); where m' represents the correction threshold; k represents the slope; k0 represents the slope threshold; and m represents the thickness threshold.

[0141] In some embodiments of this application, the processing unit 302 is specifically used for: determining that the detection interval needs to be corrected when the slope is less than or equal to a slope threshold; obtaining the minimum detection interval of the acquisition module; and determining the corrected detection interval based on the minimum detection interval; the corrected detection interval satisfies the following formula:

[0142] Among them, t ′ This indicates a correction of the detection interval.

[0143] In some embodiments of this application, the time-thickness function satisfies the following formula:

[0144]

[0145] Where a represents the slope of the line and b represents the intercept.

[0146] In some embodiments of this application, the processing unit 302 is specifically used to: compare the similarity between the pipeline information of the point to be monitored and the pipeline information of the monitoring points in the first set of monitoring points, and determine similar monitoring points from the first set; the first set of monitoring points is a set of monitoring points other than the point to be monitored, and the similarity between the pipeline information of the similar monitoring points and the pipeline information of the point to be monitored is greater than a second threshold; the pipeline information includes pipeline material, pipeline inner diameter, pipeline service life, transport medium type and medium flow rate.

[0147] In some embodiments of this application, the processing unit 302 is specifically configured to: acquire N sets of continuous first thickness data with negative slopes from the monitoring point; acquire N sets of continuous second thickness data with negative slopes from similar monitoring points; calculate a first difference between the first thickness data and the second thickness data; calculate a second difference between the first slope corresponding to the first thickness data and the second slope corresponding to the second thickness data; determine a comparison monitoring point from the similar monitoring points; the comparison monitoring point is the monitoring point with the smallest first difference and second difference among the similar monitoring points.

[0148] In some embodiments of this application, the processing unit 302 is specifically used to: input the thickness threshold into the time-thickness curve of the comparison monitoring point to obtain the second detection time.

[0149] In some embodiments of this application, the warning time includes the following formula: P = min(p1, p2); where P represents the warning time, p1 represents the first detection time, and p2 represents the second detection time.

[0150] In some embodiments of this application, the detection time satisfies the following formula: Where T represents the detection time, t i This represents the interval between the i-th detections.

[0151] This application also provides a computer-readable storage medium, which includes computer-executable instructions that, when executed on a computer, cause the computer to perform the pipe coating thickness warning method provided in the above embodiments.

[0152] This application also provides a computer program product that can be directly loaded into a memory and contains software code. After being loaded and executed by a computer, the computer program product can implement the pipe coating thickness warning method provided in the above embodiments. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

[0153] The system provided in the above embodiments is only illustrated by the division of the above functional modules. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the modules or steps in the embodiments of the present invention can be further decomposed or combined. For example, the modules in the above embodiments can be merged into one module, or further divided into multiple sub-modules to complete all or part of the functions described above. The names of the modules and steps involved in the embodiments of the present invention are only for distinguishing the various modules or steps and are not considered as an improper limitation of the present invention.

[0154] Those skilled in the art will recognize that the modules and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. The programs corresponding to the software modules and method steps can be placed in random access memory (RAM), main memory, read-only memory (ROM), electrically erasable programmable ROM, registers, hard disks, removable disks, CD-ROMs, or any other form of storage medium known in the art. To clearly illustrate the interchangeability of electronic hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in electronic hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the invention.

Claims

1. A pipe coating thickness early warning system, characterized in that, include: The acquisition module includes an ultrasonic sensor installed at the monitoring point; the acquisition module is configured to acquire pipeline information at the monitoring point and obtain pipeline coating thickness data based on the ultrasonic sensor. The data processing module is configured to acquire and calculate the detection time according to the detection interval, establish a time-thickness curve with the detection time and the thickness data corresponding to the detection time, store the time-thickness curve according to the identifier of the monitoring point, and calculate the slope of the time-thickness curve. The first reference module is configured to determine whether to adjust the thickness threshold based on the slope to obtain a correction threshold; determine whether to adjust the detection interval based on the slope to obtain a correction detection interval; fit the time-thickness curve of the point to be monitored to obtain a time-thickness function; input the correction threshold into the time-thickness function to obtain a first detection time; The second reference module is configured to determine similar monitoring points from a set of monitoring points other than the monitoring point when the slope of the monitoring point is negative, based on the pipeline information. Calculate the similarity result between the slope of the point to be monitored and the slope of each monitoring point among the similar monitoring points, and determine the comparison monitoring point from the similar monitoring points based on the similarity result; the similarity result of the comparison monitoring point is greater than a first threshold; Based on the time-thickness curve of the comparative monitoring points, obtain the second detection time when the thickness data equals the thickness threshold; The early warning module is configured to determine an early warning time when there is a safety risk to the thickness of the pipe coating based on the first detection time and the second detection time; The first reference module determines whether to adjust the thickness threshold based on the slope, including: Compare the slope threshold with the slope; When the slope is less than or equal to the slope threshold, it is determined that the thickness threshold needs to be adjusted; when the slope is greater than the slope threshold, it is determined that the thickness threshold does not need to be adjusted. The slope threshold satisfies the following formula: ； in, Indicates the slope threshold. This represents the interval between the i-th detections. This represents the coating thickness measured in the i-th test. The attenuation factor represents the rate at which the coating thickness is consumed over time; 0.01 ≤ ≤0.05, where n is the total number of tests. This represents the average value of n detection intervals. This represents the average coating thickness from n measurements.

2. The system according to claim 1, characterized in that, The correction threshold satisfies the following formula: ； in, Indicates the correction threshold; k represents the slope; represents the slope threshold; m represents the thickness threshold.

3. The system according to claim 2, characterized in that, The first reference module determines whether to adjust the detection interval to obtain a corrected detection interval based on the slope, including: When the slope is less than or equal to the slope threshold, it is determined that the detection interval needs to be corrected. Obtain the minimum detection interval of the acquisition module, and determine the corrected detection interval based on the minimum detection interval; the corrected detection interval satisfies the following formula: ； in, This indicates a correction of the detection interval.

4. The system according to claim 3, characterized in that, The time-thickness function satisfies the following formula: ； ； Where a represents the slope of the line and b represents the intercept.

5. The system according to claim 4, characterized in that, The second reference module determines similar monitoring points from the set of monitoring points other than the monitoring points based on the pipeline information, including: The pipeline information of the point to be monitored is compared with the pipeline information of the monitoring points in the first set of monitoring points to determine the similar monitoring points from the first set; the first set of monitoring points is a set of monitoring points other than the point to be monitored, and the similarity between the pipeline information of the similar monitoring points and the pipeline information of the point to be monitored is greater than a second threshold; the pipeline information includes pipeline material, pipeline inner diameter, pipeline service life, transport medium type and medium flow rate.

6. The system according to claim 5, characterized in that, The second reference module calculates the similarity result between the slope of the point to be monitored and the slope of each monitoring point among the similar monitoring points, and determines the comparison monitoring point from the similar monitoring points based on the similarity result, including: Obtain N sets of continuous first thickness data with negative slopes from the monitoring points; obtain N sets of continuous second thickness data with negative slopes from the similar monitoring points; calculate the first difference between the first thickness data and the second thickness data; calculate the second difference between the first slope corresponding to the first thickness data and the second slope corresponding to the second thickness data; determine the comparison monitoring point from the similar monitoring points; the comparison monitoring point is the monitoring point with the smallest first difference and second difference among the similar monitoring points.

7. The system according to claim 6, characterized in that, The second reference module obtains the detection time when the thickness data is equal to the thickness threshold based on the time-thickness curve of the comparison monitoring point by: inputting the thickness threshold into the time-thickness curve of the comparison monitoring point to obtain the second detection time.

8. The system according to any one of claims 1-7, characterized in that, The warning time includes the following formula: ； in, Indicates the warning time. Indicates the first detection time. This indicates the second testing time.

9. The system according to any one of claims 1-7, characterized in that, The detection time satisfies the following formula: ； in, Indicates the detection time. This represents the interval between the i-th detections.

10. A method for early warning of pipeline coating thickness, characterized in that, The method includes: The pipe information of the monitoring point is collected by an ultrasonic sensor, and the thickness data of the pipe coating is obtained based on the ultrasonic sensor. The detection time is acquired and calculated based on the detection interval. A time-thickness curve is established by combining the detection time and the thickness data corresponding to the detection time. The time-thickness curve is stored according to the identifier of the point to be monitored, and the slope of the time-thickness curve is calculated. Based on the slope, determine whether to adjust the thickness threshold to obtain a corrected threshold; based on the slope, determine whether to adjust the detection interval to obtain a corrected detection interval; fit the time-thickness curve of the point to be monitored to obtain a time-thickness function, and input the corrected threshold into the time-thickness function to obtain the first detection time; When the slope of the point to be monitored is negative, similar monitoring points are determined from the set of monitoring points other than the point to be monitored based on the pipeline information; the similarity result between the slope of the point to be monitored and the slope of each of the similar monitoring points is calculated, and a comparison monitoring point is determined from the similar monitoring points based on the similarity result; if the similarity result of the comparison monitoring point is greater than a first threshold, a second detection time is obtained when the thickness data is equal to the thickness threshold based on the time-thickness curve of the comparison monitoring point; The warning time for a safety risk to the pipe coating thickness is determined based on the first detection time and the second detection time; The step of determining whether to adjust the thickness threshold based on the slope includes: Compare the slope threshold with the slope; When the slope is less than or equal to the slope threshold, it is determined that the thickness threshold needs to be adjusted; when the slope is greater than the slope threshold, it is determined that the thickness threshold does not need to be adjusted. The slope threshold satisfies the following formula: ； in, Indicates the slope threshold. This represents the interval between the i-th detections. This represents the coating thickness measured in the i-th test. The attenuation factor represents the rate at which the coating thickness is consumed over time; 0.01 ≤ ≤0.05, where n is the total number of tests. This represents the average value of n detection intervals. This represents the average coating thickness from n measurements.

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