Gas pipeline safety assessment early warning system and method based on data analysis

Abstract

The invention relates to the technical field of gas pipeline safety assessment, in particular to a gas pipeline safety assessment early warning system and method based on data analysis. The method comprises the following steps: collecting gas pressure data of a gas pipeline at multiple points at the same time, carrying out time-space synchronization processing to obtain aligned gas pressure data so as to construct a continuous gas pressure field of the pipeline, based on the gas pressure field, identifying the pressure distribution difference of the pipeline, estimating the transient flow velocity of the gas, continuously recording the transient flow velocity of the gas, and deducing the change of the flow velocity. Judging the deformation condition of the inner wall of the pipeline in combination with air pressure field data, positioning a deformation area, reconstructing a pipeline frame, analyzing deformation response, identifying a deformation type, identifying a corrosion evolution trend, performing safety evaluation, judging the gas circulation degree and performing physical safety evaluation. According to the invention, dynamic monitoring of the gas pipeline is realized, a data-driven safety management mode is promoted, the early warning capability of potential risks is improved, and a more intelligent safety assessment and early warning mechanism is formed.

Description

Technical Field

[0001] The present invention relates to the technical field of gas pipeline safety assessment, and in particular to a gas pipeline safety assessment and early warning system and method based on data analysis. Background Art

[0002] Traditional gas pipeline safety assessment technologies often rely on manual inspections and regular checks, resulting in insufficient timeliness in identifying and responding to potential risks. Many accidents occur without being detected. The limitations of traditional methods in data collection and analysis lead to blind spots in the assessment of pipeline status. Especially in complex environments, they cannot comprehensively reflect the actual operating conditions of pipelines, resulting in an increase in potential safety hazards. In the prior art, the collection and analysis of air pressure data usually lack timeliness and accuracy, and cannot achieve real-time monitoring of the gas flow state. The identification of pressure distribution differences and the calculation of flow rates often rely on static models, ignoring the important impact of transient changes on pipeline safety. The means for identifying and assessing the deformation of the inner wall of the pipeline are relatively single, and it is difficult to accurately classify corrosion and physical deformation types, thereby affecting the accuracy and effectiveness of safety early warning. The prior art is difficult to cope with the increasingly complex pipeline operating environment, resulting in a lag in safety management. Summary of the Invention

[0003] Based on this, it is necessary to provide a gas pipeline safety assessment and early warning system and method based on data analysis to solve at least one of the above technical problems.

[0004] To achieve the above object, a gas pipeline safety assessment and early warning method based on data analysis includes the following steps:

[0005] Step S1: Simultaneously collect air pressure data of the gas pipeline at multiple points; perform spatio-temporal synchronization enhancement on the air pressure data of the gas pipeline to obtain aligned air pressure data; construct a continuous air pressure field of the pipeline according to the aligned air pressure data;

[0006] Step S2: Identify the pressure distribution differences of the pipeline based on the continuous air pressure field of the pipeline; estimate the gas transient flow rate through the pressure distribution differences of the pipeline;

[0007] Step S3: Continuously record the gas transient flow rates at multiple time points, and perform inference on the flow rate changes to obtain gas flow rate change data; judge the inner wall deformation data of the pipeline through the continuous air pressure field of the pipeline and the gas flow rate change data;

[0008] Step S4: Locate the deformation area based on the inner wall deformation data of the pipeline, and reconstruct the deformed pipeline framework; perform deformation response analysis on the continuous air pressure field of the pipeline and the gas flow rate change data based on the deformed pipeline framework, and identify the pipeline deformation type;

[0009] Step S5: When the pipeline deformation type is pipeline corrosion deformation data, identify the corrosion evolution trend based on the pipeline corrosion deformation data, and conduct corrosion safety assessment and early warning based on the preset corrosion safety margin and corrosion evolution trend; when the pipeline deformation type is pipeline physical deformation data, judge the gas flow degree based on the pipeline physical deformation data, and conduct physical safety assessment and early warning based on the preset pressure tolerance threshold.

[0010] Through multi-point air pressure synchronous acquisition, the present invention realizes the comprehensive monitoring of the gas pipeline air pressure data, ensures the accuracy and consistency of the data, and lays a solid foundation for subsequent analysis. The application of the spatio-temporal synchronization enhancement technology improves the accuracy of data alignment, makes the construction of the pipeline continuous air pressure field more reliable, effectively reflects the pressure changes inside the pipeline, and promotes the accurate estimation of the gas transient flow velocity by the ability to identify the pipeline pressure distribution difference, enhances the understanding of the gas flow characteristics, and the continuously recorded gas transient flow velocity data provides the necessary information support for real-time monitoring and analysis. By combining the pipeline continuous air pressure field and the flow velocity change data, the deformation condition of the pipeline inner wall can be judged in time, ensuring the accurate positioning ability of the early identification of the deformation area of potential structural problems, improving the response analysis ability to pipeline deformation, helping to reconstruct the deformed pipeline framework, thus providing a comprehensive analysis of the subsequent pressure field and flow velocity change data. The function of identifying the pipeline deformation type further refines the basis for safety assessment and improves the monitoring ability of pipeline corrosion and physical deformation. The analysis of the corrosion deformation data can timely identify the development trend of corrosion, and conduct in-depth corrosion safety assessment in combination with the preset safety margin, providing a scientific basis for taking preventive measures. The judgment of the physical deformation data ensures the accurate assessment of the gas flow degree, and the physical safety assessment in combination with the pressure tolerance threshold effectively reduces the safety hazards caused by pipeline damage, realizes the dynamic monitoring of the gas pipeline, promotes the data-driven safety management mode, reduces the uncertainty brought by human judgment, improves the early warning ability of potential risks, promotes the intelligent development of the gas industry, forms a sustainable safety assessment and early warning mechanism, improves the overall safety of the industry, provides an effective guarantee for the long-term stable operation of the gas pipeline, finally creates conditions for the safe gas use environment of the society, promotes the maintenance of public safety, promotes the application and popularization of related technologies, provides important reference and reference for the future gas pipeline safety management, ensures the stability and safety of energy supply, and promotes the technological innovation and progress of the industry.

[0011] The present invention also provides a gas pipeline safety assessment and early warning system based on data analysis for executing the gas pipeline safety assessment and early warning method based on data analysis as described above. The gas pipeline safety assessment and early warning system based on data analysis includes:

[0012] Multi-point air pressure synchronization module, which is used to collect gas pipeline air pressure data simultaneously at multiple points; perform spatio-temporal synchronization enhancement on the gas pipeline air pressure data to obtain aligned air pressure data; construct a continuous air pressure field of the pipeline based on the aligned air pressure data;

[0013] Gas flow velocity calculation module, which is used to identify the pipeline pressure distribution difference based on the continuous air pressure field of the pipeline; estimate the gas transient flow velocity through the pipeline pressure distribution difference;

[0014] Deformation response detection module, which is used to continuously record the gas transient flow velocity at multiple time points, infer the change of the flow velocity, and obtain the gas flow velocity change data; judge the pipeline inner wall deformation data through the continuous air pressure field of the pipeline and the gas flow velocity change data;

[0015] Deformation type identification module, which is used to locate the deformation area based on the pipeline inner wall deformation data and reconstruct the deformed pipeline framework; perform deformation response analysis on the continuous air pressure field of the pipeline and the gas flow velocity change data based on the deformed pipeline framework, and identify the pipeline deformation type;

[0016] Safety warning decision module, which is used to, when the pipeline deformation type is pipeline corrosion deformation data, identify the corrosion evolution trend according to the pipeline corrosion deformation data, and perform corrosion safety assessment and warning based on the preset corrosion safety margin and corrosion evolution trend; when the pipeline deformation type is pipeline physical deformation data, judge the gas flow degree based on the pipeline physical deformation data, and perform physical safety assessment and warning based on the preset pressure bearing threshold.

[0017] Through the application of the multi-point air pressure synchronization module, the present invention realizes the comprehensive collection and spatio-temporal synchronization of gas pipeline air pressure data, ensures the consistency and accuracy of the data, provides a basis for subsequent analysis. The constructed continuous air pressure field of the pipeline can effectively reflect the pressure change inside the pipeline, which helps to identify potential pressure anomalies. The gas flow velocity calculation module can accurately estimate the transient flow velocity of the gas by analyzing the pressure distribution difference, providing an in-depth understanding of the gas flow characteristics. The deformation response detection module can continuously record the gas transient flow velocity, monitor the flow velocity change in real time, and provide important data support for judging the deformation of the inner wall of the pipeline. The introduction of the deformation type identification module enables the accurate positioning of the deformation area and the reconstruction of the deformed pipeline framework, laying a foundation for the subsequent data analysis of the pressure field and flow velocity change. The function of the safety warning decision module is that when detecting pipeline corrosion deformation, it can timely identify the corrosion evolution trend, conduct in-depth corrosion risk assessment in combination with the preset safety margin, ensure that measures can be taken before the corrosion problem worsens, and avoid potential safety hazards. For physical deformation, it can judge the gas flow-through degree and conduct physical safety assessment based on the pressure bearing threshold, effectively reducing the accident risk caused by pipeline physical damage. The whole system integrates a variety of data analysis technologies, realizes the comprehensive monitoring and dynamic assessment of gas pipelines, improves the intelligent level of pipeline safety management, enhances the real-time monitoring ability of pipeline status, also enhances the early warning ability of potential risks, reduces the influence of human factors on safety assessment, promotes the data-driven safety management mode, provides a scientific basis for the long-term stable operation of gas pipelines, ultimately realizes the all-round guarantee of gas pipeline safety, promotes the intelligent development of the gas industry, forms a sustainable safety assessment and early warning mechanism, provides an important reference for the application and promotion of future related technologies, improves the overall safety and economy of the industry, creates conditions for the safe gas use environment of society, and promotes the guarantee and improvement of public safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a schematic diagram of the step flow of a gas pipeline safety assessment and early warning method based on data analysis;

[0019] Figure 2 It is a schematic diagram of the detailed implementation step flow of step S2;

[0020] The realization, functional characteristics and advantages of the object of the present invention will be further described with reference to the embodiments and the accompanying drawings. DETAILED IMPLEMENTATION MANNER

[0021] The technical method of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative work belong to the scope of protection of the present invention.

[0022] In addition, the accompanying drawings are only schematic diagrams of the present invention and are not necessarily drawn to scale. The same reference numerals in the drawings represent the same or similar parts, and thus repeated descriptions thereof will be omitted. Some of the block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor methods and / or microcontroller methods.

[0023] It should be understood that although terms such as "first" and "second" may be used here to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, the first unit can be called the second unit, and similarly the second unit can be called the first unit. The term "and / or" used here includes any and all combinations of one or more of the listed related items.

[0024] To achieve the above object, please refer to Figures 1 to 2 , a gas pipeline safety assessment and early warning method based on data analysis, comprising the following steps:

[0025] Step S1: Simultaneously collect gas pipeline pressure data at multiple points; perform spatio-temporal synchronization enhancement on the gas pipeline pressure data to obtain aligned pressure data; construct a pipeline continuous pressure field based on the aligned pressure data;

[0026] Step S2: Identify the pipeline pressure distribution difference based on the pipeline continuous pressure field; estimate the gas transient flow velocity through the pipeline pressure distribution difference;

[0027] Step S3: Continuously record the gas transient flow velocity at multiple time points and perform flow velocity change inference on it to obtain gas flow velocity change data; judge the pipeline inner wall deformation data through the pipeline continuous pressure field and the gas flow velocity change data;

[0028] Step S4: Locate the deformation area based on the pipeline inner wall deformation data and reconstruct the deformed pipeline framework; perform deformation response analysis on the pipeline continuous pressure field and the gas flow velocity change data based on the deformed pipeline framework and identify the pipeline deformation type;

[0029] Step S5: When the pipeline deformation type is pipeline corrosion deformation data, identify the corrosion evolution trend based on the pipeline corrosion deformation data, and conduct corrosion safety assessment and early warning based on the preset corrosion safety margin and corrosion evolution trend; when the pipeline deformation type is pipeline physical deformation data, judge the gas flow degree based on the pipeline physical deformation data, and conduct physical safety assessment and early warning based on the preset pressure tolerance threshold.

[0030] Through multi-point air pressure synchronous acquisition, the present invention realizes the comprehensive monitoring of the air pressure data of the gas pipeline, ensures the accuracy and consistency of the data, and lays a solid foundation for subsequent analysis. The application of the space-time synchronous enhancement technology improves the accuracy of data alignment, makes the construction of the continuous air pressure field of the pipeline more reliable, effectively reflects the pressure change inside the pipeline, and promotes the accurate estimation of the gas transient flow velocity by the ability to identify the pipeline pressure distribution difference, enhances the understanding of the gas flow characteristics, and the continuously recorded gas transient flow velocity data provides the necessary information support for real-time monitoring and analysis. By combining the continuous air pressure field of the pipeline and the flow velocity change data, the deformation situation of the pipeline inner wall can be judged in time, ensuring the accurate positioning ability of the early identified deformation area of potential structural problems, improving the response analysis ability to pipeline deformation, helping to reconstruct the deformed pipeline framework, thus providing a comprehensive analysis for the subsequent pressure field and flow velocity change data. The function of identifying the pipeline deformation type further refines the basis for safety assessment, improves the monitoring ability of pipeline corrosion and physical deformation. The analysis of the corrosion deformation data can timely identify the development trend of corrosion, and conduct in-depth corrosion safety assessment in combination with the preset safety margin, providing a scientific basis for taking preventive measures. The judgment of the physical deformation data ensures the accurate assessment of the gas flow degree, and the physical safety assessment in combination with the pressure tolerance threshold effectively reduces the safety hazards caused by pipeline damage, realizes the dynamic monitoring of the gas pipeline, promotes the data-driven safety management mode, reduces the uncertainty brought by human judgment, improves the early warning ability of potential risks, promotes the intelligent development of the gas industry, forms a sustainable safety assessment and early warning mechanism, improves the overall safety of the industry, provides an effective guarantee for the long-term stable operation of the gas pipeline, finally creates conditions for the safe gas use environment of the society, promotes the maintenance of public safety, promotes the application and popularization of related technologies, provides important reference and reference for the future gas pipeline safety management, ensures the stability and safety of energy supply, and promotes the technological innovation and progress of the industry.

[0031] In the embodiment of the present invention, the method for gas pipeline safety assessment and early warning based on data analysis includes the following steps:

[0032] Step S1: Simultaneously collect gas pipeline pressure data at multiple points; perform spatio-temporal synchronization enhancement on the gas pipeline pressure data to obtain aligned pressure data; construct a continuous pipeline pressure field based on the aligned pressure data;

[0033] In this embodiment, when simultaneously collecting gas pipeline pressure data at multiple points, high-precision pressure sensors of model HPT500 are arranged, with a range of 0 to 5 MPa and an accuracy class of 0.1. A set of sensor nodes is set every 10 meters, and each set of nodes contains 3 independent channels, which respectively record the pressure value, ambient temperature, and measurement point timestamp. The sampling frequency is set to 10 Hz. The data acquisition terminal synchronously transmits the data to the central data server using the Modbus RTU protocol. Based on the distributed time synchronization protocol PTP (Precision Time Protocol), all measurement point timestamps are uniformly corrected, with the error limited within 0.01 seconds. The data with delays and packet losses is linearly interpolated through the bidirectional interpolation correction method, and the outlier points are removed. When the data is continuously missing for more than 1 second, a data missing mark is recorded and no interpolation compensation is performed. Finally, the aligned pressure data under the same time reference for the entire pipeline is obtained. Based on these data, bilinear interpolation is used to interpolate and fill the spatial dimension, generating a spatial continuous pressure field with an interval of 0.5 meters, and outputting a three-dimensional continuous pressure data matrix with a spatial resolution of 0.5 meters and a time resolution of 0.1 seconds.

[0034] Step S2: Identify the pipeline pressure distribution differences based on the continuous pipeline pressure field; estimate the gas transient flow velocity through the pipeline pressure distribution differences;

[0035] In this embodiment, based on the continuous pipeline pressure field, first extract the pressure values at each position at the same time point, calculate the pressure gradient difference between adjacent points, set the pressure difference threshold to 0.02 MPa, and the adjacent points exceeding this threshold are determined as pressure abnormal areas. Record the positions of the abnormal areas as pressure mutation points. According to the positions of the mutation points and the pressure change directions of the front and rear measurement points, use the first-order difference method to calculate the pressure change rate per unit time, and then adopt the transient flow velocity derivation formula. Substitute the pressure change rate, pipeline cross-sectional area, and gas medium density (taking a value of 0.72 kg / m 3 ) into it to obtain the gas transient flow velocity value, with a flow velocity resolution of 0.1 m / s. Record the flow velocity values corresponding to each position at the corresponding time, and generate a gas transient flow velocity matrix.

[0036] Step S3: Continuously record the gas transient flow velocities at multiple time points, and perform flow velocity change inference on them to obtain gas flow velocity change data; judge the pipeline inner wall deformation data through the continuous pipeline pressure field and the gas flow velocity change data;

[0037] In this embodiment, the transient gas flow velocity data sampled 5 times per second within 30 minutes is continuously recorded to form a transient gas flow velocity sequence containing 18,000 time points. Based on this velocity sequence, the maximum value, minimum value, mean value, and standard deviation within the time series are calculated for each sampling point respectively. The velocity change rate data is obtained by taking the first-order difference of the velocity sequence. Combining with the continuous pipeline pressure field data, based on the pressure-velocity coupling model, the local high-frequency velocity change region is compared and analyzed with the corresponding pressure field region. If the velocity fluctuation amplitude at a certain position is greater than 20% of the mean value and is accompanied by abnormal pressure, then this position is determined as the deformation influence region. Further, based on the duration and intensity of the velocity change, the inner wall deformation value of the pipeline in this region is deduced. The deformation threshold is set to 2%, and the region exceeding the threshold is marked as an abnormal deformation point.

[0038] Step S4: Locate the deformation region based on the inner wall deformation data of the pipeline and reconstruct the deformed pipeline framework; perform deformation response analysis on the continuous pipeline pressure field and gas flow velocity change data based on the deformed pipeline framework and identify the pipeline deformation type.

[0039] In this embodiment, according to the position information of the abnormal deformation points, all continuous deformation points are extracted to form a deformation region. The Cubic Spline interpolation is used to reconstruct the contour of the deformation region and establish the geometric framework of the deformed pipeline. Based on this framework, the continuous pipeline pressure field data and velocity change data are remapped, and the data in the original coordinate system is projected into the deformed framework to correct the spatial coordinates of each point. The pressure distribution and velocity distribution within the deformation region are calculated again. The included angle between the direction of the maximum deformation amount and the direction of the pressure gradient is used as the deformation response feature. If the included angle is less than 15°, it is determined as radial crushing deformation; if the included angle is greater than 75°, it is determined as corrosion wall thickness weakening deformation; otherwise, it is determined as comprehensive complex deformation.

[0040] Step S5: When the pipeline deformation type is pipeline corrosion deformation data, identify the corrosion evolution trend based on the pipeline corrosion deformation data, and perform corrosion safety assessment and early warning based on the preset corrosion safety margin and corrosion evolution trend; when the pipeline deformation type is pipeline physical deformation data, judge the gas flow degree based on the pipeline physical deformation data, and perform physical safety assessment and early warning based on the preset pressure bearing threshold.

[0041] In this embodiment, when the identified pipeline deformation type is corrosion deformation data, the maximum, minimum, and average inner wall thinning rates of the corrosion area are statistically calculated. The area with a thinning rate exceeding 25% is recorded as a high-risk corrosion point, and the corrosion area, corrosion depth, and distribution density are recorded. According to the preset corrosion safety margin limit value of 30%, if the fitting value of the thinning rate trend line in the corrosion area is expected to exceed this value within the next 12 months, it is determined that there is a risk, and a corrosion safety warning is triggered. If the identified pipeline deformation type is physical deformation data, the amount of deformation, deformation length, and deformation volume change rate are statistically calculated. According to the preset pressure-bearing threshold of 0.4 MPa, if the deformation causes the local pressure in this area to increase beyond the threshold, it is determined that there is a physical safety hazard in this area, and a physical deformation warning is triggered. All evaluation data are output in a graphical manner.

[0042] Preferably, step S1 includes the following steps:

[0043] Step S11: Deploy multiple pressure sensing arrays along the inside of the gas pipeline to collect gas pipeline pressure data. The number of collection points is set in the range of 5 to 50 measurement points, the pressure sampling frequency range is set from 1 Hz to 50 Hz, and the pressure value range is set from 0.05 MPa to 1.6 MPa;

[0044] Step S12: Mark the time stamps for the gas pipeline pressure data to obtain time-synchronized pressure, where the time synchronization accuracy is set from 0.01 s to 0.5 s;

[0045] Step S13: Correct the distributed node positions of the gas pipeline pressure data to generate spatially mapped pressure. The spatial matching distance error range is set from 0 m to 5 m; integrate the time-synchronized pressure and the spatially mapped pressure to generate aligned pressure data;

[0046] Step S14: Eliminate the random noise interference of the aligned pressure data and perform multi-dimensional spatio-temporal interpolation processing to obtain fully covered pressure data; construct a pipeline continuous pressure field based on the fully covered pressure data. The size of the pressure field grid unit is set from 0.5 m to 5 m, and the continuous interpolation smoothness parameter is set from 0.1 to 1.0.

[0047] In this embodiment, a pressure sensing array is arranged at equal intervals inside the gas pipeline. The number of measurement points is set to 20. The sensor model is selected as the GE UNIK5000 pressure sensor. The sensor range is set from 0 MPa to 2 MPa. The air pressure sampling frequency is set to 10 Hz, and 10 groups of air pressure values are collected per second. The collected air pressure data is transmitted to the edge computing unit in real time through the RS485 bus protocol. The collected air pressure value range is limited within the interval of 0.05 MPa to 1.6 MPa. Values below 0.05 MPa and above 1.6 MPa are automatically excluded and not included in the data set. The sensor deployment length range covers a 50 m gas pipeline section, ensuring uniform layout along the line and the number of sensors is not less than 5 and not more than 50. When deploying on site, a laser rangefinder is used for positioning, and the coordinate error of each sensor is controlled within ±0.5 m. For the collected gas pipeline air pressure data, a Unix timestamp is automatically appended to each piece of data in the data packet. The time accuracy is set to 0.01 s, and the timestamp is provided by a GPS synchronous time service module. The model is selected as the Ublox NEO-M8N module, and the time service error is less than 0.01 s. All data is connected to the same clock reference. During the time synchronization process, first extract the timestamps in the data packets of each measurement point and uniformly convert them into the standard UTC format. If there is a situation where the error exceeds 0.5 s, this group of data is discarded. After data synchronization, arrange them in the order of timestamps. Complete the generation of the time-synchronized air pressure data set. Based on the actual spatial coordinates of the sensors recorded during deployment, bind the corresponding air pressure data to the spatial positions. Adopt a distributed position correction method to map the positions of each sensing point to the standard pipeline coordinate system. The spatial matching error is limited between 0 m and 5 m. By correcting the deployment error, pipeline curvature, and coordinate measurement error, adjust the actual positions of the sensors using a three-dimensional interpolation method. Use the pipeline centerline measured by lidar scanning as the reference. The interpolation method uses the Inverse Distance Weighting method. Exclude the data of the measurement points with a matching error greater than 5 m, and control the exclusion rate within 5%. Complete the spatially mapped air pressure data set. Integrate the time-synchronized air pressure data and the spatially mapped air pressure data. Match the air pressure values of all measurement points according to the unified time series and spatial coordinates to generate an aligned air pressure data set. The aligned air pressure data is excluded from random noise interference using the local extreme value exclusion method. First, calculate the standard deviation of the air pressure values within 5 seconds for each measurement point. Data with a standard deviation exceeding the set threshold of 0.02 MPa is determined as random noise, and the noise data is replaced with the average value of the adjacent two seconds. Subsequently, use the Kriging interpolation method for multi-dimensional spatio-temporal interpolation. The interpolation interval is set from 0.5 m to 5 m to generate a fully covered air pressure data. The interpolation smoothness parameter is set to 0.5, and the interpolation smoothness coefficient is controlled between 0.1 and 1.In the 0 interval, by adjusting this coefficient to balance the smoothness and fitting accuracy of the interpolation surface, a continuous gas pipeline pressure field is finally constructed based on the fully covered barometric data. The size of the grid cells in the pressure field is set to 1 m, and the pressure field results are stored in the form of a three-dimensional matrix. The coordinate axes correspond to the spatial X, Y, Z positions and the barometric value respectively. Each grid cell in the three-dimensional matrix corresponds to a single-point barometric value, and the data unit is uniformly MPa.

[0048] Preferably, step S2 includes the following steps:

[0049] Step S21: Split the pressure gradient of the continuous gas pipeline pressure field to obtain the pipeline pressure gradient field, where the step range of the pressure gradient split is set to 0.5 m to 3 m;

[0050] Step S22: Conduct a local area clustering analysis on the pipeline pressure gradient field and identify the pressure anomaly area; perform a physical constraint decoupling process on the pressure anomaly area and identify the pipeline pressure distribution difference;

[0051] Step S23: Extract the pressure deviation characteristics of the pipeline pressure distribution difference; divide the deviation pipeline sections according to the pressure deviation characteristics, where the threshold range of the deviation characteristics is set to 0.01 MPa to 0.3 MPa, and determine the deviation change rate;

[0052] Step S24: Estimate the gas transient flow velocity based on the deviation pipeline sections and the deviation change rate.

[0053] In this embodiment, the three-dimensional matrix data of the continuous gas pressure field in the pipeline is subjected to segmented difference calculation in the order of spatial coordinates. The pressure value difference is calculated based on the positions of the center points of adjacent grid cells. The fixed splitting step method is adopted, and the step range is limited between 0.5 m and 3 m. In this embodiment, the splitting step is set to 1 m. The gas pressure difference between adjacent grid centers is calculated according to the splitting step, and the pressure difference between adjacent points is divided by the step using the central difference method to obtain the pressure gradient value in the corresponding direction. The gas pressure change rates are calculated respectively along the axial, radial, and vertical directions of the pipeline to generate a three-dimensional pressure gradient field. The data structure of the gradient field is consistent with the original continuous gas pressure field, and the gradient unit is uniformly MPa / m. All data formats use floating-point type, retaining three decimal places. Based on the generated pressure gradient field, the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is used to perform clustering analysis on the pressure gradient data in the local area. The minimum number of clustering units is set to 5, the radius threshold is set to 1.5 m, and the threshold of the pressure gradient difference between adjacent points is limited to 0.05 MPa / m. During the execution of the clustering algorithm, first calculate the number of neighbors of each point within the set radius range, and then determine whether the number of clustering units is satisfied. After clustering, the abnormal gradient area is marked. The local neighborhood is extracted according to the gradient spatial distribution of the identified abnormal area. The neighborhood range is set to a 2 m cube area around the center point. The pressure components in each direction within the abnormal area are independently decoupled, and the superposition effect of physical factors such as pipeline bending, branching, and valve disturbance is removed. The physical constraint conditions are determined by the actual pipeline layout diagram and on-site structure parameters. After confirming that there is no physical constraint interference, the residual pressure distribution difference area is identified. For the pressure distribution difference area after physical constraint decoupling, based on the gas pressure values of each measurement point within the difference area, the pressure deviation within this area is calculated. The deviation value is determined by the difference between the pressure at this point and the neighborhood mean value. The deviation threshold is limited to the range of 0.01 MPa to 0.3 MPa. In this embodiment, the deviation threshold is set to 0.05 MPa. All measurement points with deviations greater than 0.05 MPa are screened, and their deviation values and spatial positions are extracted. According to the spatial distribution characteristics of the deviation values, the pipeline is divided into multiple deviation sections, each deviation section is not less than 1 m in length and not more than the total length of the pipeline. The deviation change rate is calculated based on the change of the deviation values of adjacent measurement points. The calculation method of the change rate is the difference between the deviations of adjacent points divided by the distance between the two points, with the unit of MPa / m. The critical value of the deviation change rate is set to 0.03 MPa / m. The sections with values higher than this value are marked as abnormal deviation areas. According to the divided deviation pipeline sections and the corresponding deviation change rates, a one-dimensional unsteady flow theory model is used to estimate the transient gas velocity. The model input parameters include the length of the deviation section, the gas pressure values at both ends, the pipeline diameter, the gas density, and the pipeline friction coefficient. The gas density is obtained according to the gas composition and environmental temperature and pressure conditions. In this embodiment, the gas density is taken as 0.72 kg / m 3, the pipeline diameter is set to 0.3 m, the friction coefficient is taken as 0.015. According to the flow velocity change formula, the gas flow velocity in the deviation section is estimated, and finally the gas transient flow velocity value is output in m / s, and the estimation accuracy is controlled within 5%.

[0054] Preferably, continuously recording the gas transient flow velocities at multiple time points in step S3 and inferring the flow velocity change thereof includes:

[0055] Continuously record the gas transient flow velocities and mark the recording timestamps, wherein the continuous recording time interval is set to 0.1 s to 2 s, and the recording time period is set to 30 s to 300 s;

[0056] Integrate the gas transient flow velocities at multiple time points into a multi-period gas transient flow velocity set;

[0057] Screen the gas transient flow velocity change intervals in the multi-period gas transient flow velocity set, wherein the difference inference interval is limited to 0.05 m / s to 5 m / s;

[0058] Infer the gas flow velocity change data based on the gas transient flow velocity change interval.

[0059] In this embodiment, a high-frequency data acquisition device is used to continuously record the transient flow rate data of the gas. The data acquisition device uses a differential pressure flow rate sensor with a sampling frequency of 0.5Hz to 10Hz. The recording time interval is set in the range of 0.1s to 2s. In this embodiment, the time interval is set to 0.5s, and the recording time period is set to 120s. During the acquisition process, all transient flow rate data are attached with a timestamp accurate to milliseconds. The timestamp format adopts the YYYY-MM-DD HH:MM:SS:ms standard format. After the continuous acquisition is completed, all data are sorted by timestamp and organized into a two-dimensional matrix format. The first column is the timestamp, and the second column is the transient flow rate value at the corresponding time, in m / s. The data format uniformly retains three decimal places. The transient flow rate value comes from the value after the flow rate estimation result of the previous stage is fused with the real-time measurement data. The continuously recorded transient flow rate data are grouped according to the time period to form a multi-period gas transient flow rate set. Each flow rate set contains 240 consecutive data points. The naming rule of velocity set is "segment number + start time", for example "A01_20250415123000". Each velocity set data is arranged in chronological order, and the corresponding velocity value and timestamp are recorded. After the integration of all time period velocity sets is completed, data verification is performed to eliminate missing data, abnormal jump points and repeated timestamp data. The abnormal jump point judgment threshold is set to the difference between the previous and next moments greater than 20m / s. After all abnormal data are eliminated, the missing values are interpolated. The interpolation method uses linear interpolation. The interpolation is based on the missing data. The intermediate value of adjacent effective values before and after the loss point is calculated, and the intervals with significant changes in the transient gas flow rate concentration in multiple periods are screened. The difference method is used to calculate the difference in the transient flow rate between any two adjacent moments. The difference unit is unified as m / s. The screening condition is limited to the range of 0.05m / s to 5m / s. The continuous difference interval that meets this range is defined as the gas transient flow rate change interval. The length of the continuous interval is set to be no less than 1s. If there is an out-of-range value in the interval, it is automatically truncated. After the screening is completed, the start time, end time, maximum flow rate, minimum flow rate, average flow rate and change range of each change interval are recorded. All records are saved in the change interval table. The table format structure includes seven fields: "section number, start time, end time, maximum value, minimum value, mean value, change range", and the data unit is unified as m / s. Based on the screened gas transient flow rate change interval, the gas flow rate change data is inferred, and the moving average method is used to calculate the flow rate change rate in each change interval. The rate is defined as the interval flow rate difference divided by the interval duration, and the unit is unified as m / s 2 , summarize all the change interval rate values to obtain the gas flow rate change data in multiple time periods. The data format is in the form of a CSV file. The fields include "section number, change interval number, rate value, interval start time, interval end time". All values retain three decimal places, and the rate value unit is m / s 2 , and finally complete the inference of gas flow rate change data in multiple time periods.

[0060] Preferably, the determination of the deformation data of the inner wall of the pipeline based on the continuous air pressure field and the gas flow velocity change data in step S3 includes:

[0061] Identifying the pressure change fluctuations of the continuous pressure field of the pipeline;

[0062] Extracting the abnormal pressure fluctuations from the pressure change fluctuations;

[0063] Converting the gas flow velocity change data into a flow velocity change curve, and identifying the flow velocity change fluctuation characteristics based on the flow velocity change curve;

[0064] Reconstructing the stress tensor based on the flow velocity change fluctuation characteristics, and identifying the changes in the fluid shear characteristics;

[0065] Analyzing the fluid shear distribution data according to the changes in the fluid shear characteristics;

[0066] Performing sectional correlation matching on the abnormal pressure fluctuations and the fluid shear distribution data, and establishing a flow-pressure correlation mapping to obtain the flow-pressure coupling characteristics;

[0067] Analyzing the wall-fluid interaction data based on the flow-pressure coupling characteristics;

[0068] Performing anti-inversion of the avoidable roughness according to the wall-fluid interaction data to generate the wall roughness data;

[0069] Performing standard deviation ellipsoid fitting on the wall roughness data, and identifying the local mutation regions to obtain the deformation data of the inner wall of the pipeline.

[0070] In this embodiment, for the pressure data continuously collected by the high-frequency pressure sensor built into the pipeline, the sampling frequency is set to 20 Hz, the recording time period is set to 300 s, and the recording unit is Pa. All data are used to construct a one-dimensional pressure field data sequence in chronological order. The five-point smoothing average method is used to perform preliminary filtering on the original pressure sequence to eliminate high-frequency noise interference. The size of the filtering window is set to 5 data points. The smoothed pressure sequence is used to calculate the continuous pressure change rate by the second-order difference method. Based on the positive and negative alternation of the change rate and the amplitude mutation situation, the pressure change fluctuation is identified. The mutation threshold of the pressure change rate is set to 200 Pa / s. The fluctuation segments that continuously exceed this threshold are marked as pressure fluctuation intervals. The start and end times of the fluctuation and the maximum change rate value are recorded, and a pressure fluctuation interval list is generated. The list format includes four fields: start time, end time, maximum change rate, and average change rate. The unit of all fields is unified as Pa / s. Based on the identified pressure fluctuation intervals, the abnormal pressure fluctuations are extracted. The definition standard of abnormal fluctuations is that the maximum change rate of pressure within a single segment exceeds 300 Pa / s or the fluctuation duration exceeds 10 s. The fluctuation duration is determined by the time difference between the start and end times of the fluctuation interval. If either condition is met, it is determined as an abnormal fluctuation. The interval number, start and end times, maximum change rate, and duration of the abnormal fluctuation are recorded, and an abnormal fluctuation data table is constructed. The data table includes six fields: section number, abnormal number, start time, end time, maximum change rate, and duration. All numerical values are retained to two decimal places, and the unit is unified as Pa / s and seconds. The previously obtained gas flow velocity change data are used to draw a flow velocity change curve in chronological order. Linear interpolation is used to fill in the missing values, and the interpolation interval is set to 0.5 s. The unit of the vertical coordinate of the curve is m / s, and the unit of the horizontal coordinate is s. The curve smoothing process uses the moving average method, and the moving window is set to 5 data points. The smoothed flow velocity curve is used to calculate the flow velocity change rate by the difference method between adjacent points. The rate fluctuation threshold is set to 0.5 m / s 2 , the sections with rate fluctuations exceeding the threshold are screened out, the start and end times of the fluctuation and the extreme values of the change rate are recorded, and the extraction of the flow velocity change fluctuation characteristics is completed. The data table includes five fields: section number, start time, end time, maximum rate change, and average rate change. Based on the extracted flow velocity change fluctuation characteristics, the finite volume method is used to reconstruct the stress tensor distribution within the pipeline cross-section. The flow velocity fluctuation rate data and the pipeline cross-sectional area parameter are input. The pipeline cross-sectional area is set to 0.1256 m 2, based on the Newtonian fluid hypothesis, calculate the transient shear stress distribution. The unit of the shear stress distribution is Pa. According to the transient shear stress distribution, identify the section where the shear stress change rate exceeds 5 Pa / s, record the start and end times of the shear change, the maximum shear change rate, and the average shear change rate, and construct a shear change data set. The data format includes section number, start time, end time, maximum shear change rate, and average shear change rate. According to the shear change data, analyze the transient shear stress distribution at each position on the pipe cross-section. Project the shear stress distribution onto the cross-section in polar coordinates. The polar angle resolution is set to 10°. The cross-section radius is divided into equidistant rings with a spacing of 0.01 m. Calculate the average shear stress value in each ring, record the shear stress distribution in the θ direction, and draw a shear stress polar diagram. Compare the transient polar diagram with the steady-state polar diagram to identify the direction and magnitude of the shear stress mutation. The mutation judgment criterion is that the single-point transient shear stress exceeds 30% of the steady-state average value, and record the position and value of the mutation point. Construct a shear distribution feature table. The data fields include section number, polar angle, radius, and shear stress value. Match the aforementioned abnormal pressure fluctuation data and the shear distribution feature table based on the Pearson correlation coefficient matching method. Set the correlation coefficient threshold to 0.7. Calculate the Pearson correlation coefficient of the pressure change rate sequence and the shear stress change rate sequence in the corresponding time section. The sections that meet the threshold are extracted as the flow-pressure coupling sections, and record the section number, start time, end time, and correlation coefficient. Construct a flow-pressure coupling feature table. The data fields include section number, start time, end time, and correlation coefficient. Based on the flow-pressure coupling feature table, analyze the wall-fluid interaction behavior. According to the Couette flow model, substitute the pressure gradient value and the shear stress distribution value in the coupling feature section into the model to calculate the force value per unit area of the pipe wall, with the unit of Pa. Calculate the force distribution corresponding to each polar angle and radius position on the cross-section. The criterion for judging abnormal interaction is that the force per unit area exceeds 80% of the rated pressure-bearing value of the pipe. The rated pressure-bearing value is set to 1.6 MPa. Record the section number, start time, end time, maximum force value, and abnormal area ratio of the interaction abnormal section. Construct a fluid interaction data table. According to the interaction data table, instead of directly inverting the roughness data, use the residual analysis method to calculate the pressure and shear stress residual distributions in each section. The unit of the residual value is Pa. Set the residual threshold to 200 Pa. Screen the sections with residual values exceeding the threshold, and record the section number, start time, end time, maximum residual value, and average residual value. Estimate the corresponding pipe wall roughness value based on the residual change amplitude. The unit of the roughness value is mm. The judgment criterion is that the higher the residual value, the greater the roughness. Use a linear fitting model to map the residual value to the roughness, and record the roughness estimation value and the spatial position. For the pipe wall roughness data, apply the standard deviation ellipsoid fitting method. The deviation threshold is set to 0.2 mm, and the axis length ratio of the deviation ellipsoid is 1:1.5:2, Use the least squares method to fit the roughness point cloud data. After the fitting is completed, calculate the residual value of each point to the ellipsoid surface, with the unit of mm. Set the mutation determination criterion as the residual value being greater than 0.3 mm. Record the spatial coordinates of the mutation points, the mutation residual values, and the corresponding roughness values, and construct an inner wall deformation data table. The table structure includes section number, spatial position, roughness value, residual value, and mutation determination. Finally, complete the identification of the inner wall deformation data.

[0071] Preferably, the deformation area positioning based on the pipeline inner wall deformation data and the reconstruction of the deformed pipeline framework in step S4 include:

[0072] Extract the deformation area positioning of the pipeline inner wall deformation data;

[0073] Obtain the initial gas pipeline data;

[0074] Conduct a structural topology analysis on the initial gas pipeline data to obtain the initial pipeline topology structure;

[0075] Construct a three-dimensional initial pipeline based on the initial pipeline topology structure;

[0076] Perform a three-dimensional projection on the three-dimensional initial pipeline according to the deformation area positioning to determine the three-dimensional pipeline deformation area;

[0077] Conduct a deformation shape imprinting according to the pipeline inner wall deformation data to obtain the imprinted deformation shape;

[0078] Perform a deformation simulation on the three-dimensional initial pipeline based on the imprinted deformation shape and the three-dimensional pipeline deformation area to generate a deformed pipeline framework.

[0079] In this embodiment, when locating the deformation region for extracting the deformation data of the inner wall of the pipeline, first, the pipe wall roughness data and the fluid interaction data obtained from the previous steps are called. By setting the roughness change threshold ΔR to 0.3 mm and the fluid shear stress change threshold Δτ to 5 Pa, the roughness mutation region and the shear anomaly region are screened out. The position data of the two in the pipeline length direction are spatially superimposed, and the superimposed region is processed continuously using the linear interpolation method. Finally, the coordinate range data of the continuous deformation region are output, and the coordinate range is defined as from the starting point x1 to the ending point x2 of the deformation interval, with the unit of meter. During the superimposition process, an interpolation step size of 0.0.1 m. The interpolation formula is based on piecewise linear interpolation, which uniformly maps the numerical values in the deformation area to the three-dimensional space coordinate system. When obtaining the initial gas pipeline data, the gas pipeline completion archive data provided by the pipeline design institute is called. The data content includes pipeline segment number, start and end mileage, pipe diameter, wall thickness, material, layout method, burial depth, weld position, elbow and flange joint positions. The data format is a CSV file, and the fields include segment_id, start_km, end_km, diameter, wall_thickness, material, buried_depth, weld_position, elbow_position, flange_position. The file encoding format uses UTF-8. Each row record corresponds to a standard pipeline unit. All the data is imported into the PostgreSQL database to establish a structured table named pipeline_segment. The field types are the same as those of the CSV file fields, and the length unit is uniformly meters. The pipe diameter and wall thickness units are millimeters. When performing a structural topology analysis on the initial gas pipeline data, based on the data in the pipeline_segment table imported into the database, the NetworkX (Python graph theory library) is used to construct an undirected pipeline topology graph. The pipeline segment number segment_id is set as the node, and the connection points such as welds, elbows, and flanges are used as edges. The edge weights are calculated according to the pipeline length, and the weight unit is set to meters. Using the depth-first search (DFS) method, the main path of the pipeline is extracted, all branch paths are identified, and the start and end mileage and branch positions are recorded. The topology graph result is saved as a GraphML format file for subsequent three-dimensional modeling reference. The topology data includes the number of nodes, the number of edges, the path distribution, and the length attribute of each edge. When constructing a three-dimensional initial pipeline based on the initial pipeline topology structure, using the three-dimensional modeling platform ANSYS SpaceClaim, the GraphML format topology graph is imported to automatically generate the spatial coordinate relationship between nodes and edges. According to the start and end mileage and burial depth values of the nodes, they are distributed along the Z-axis direction, and the X and Y axes are mapped according to the layout method and the actual geographic coordinate system. The pipe diameter is defined according to the diameter field, and the wall thickness is assigned according to the wall_thickness field. The weld, elbow, and flange positions are marked in the model according to the weld_position, elbow_position, flange_position fields. The NURBS curve fitting method is used to connect adjacent nodes to generate a continuous and smooth three-dimensional pipeline entity. When performing a stereoscopic projection on the three-dimensional initial pipeline according to the deformation area positioning, the built-in spatial projection module of the three-dimensional modeling platform is called. The three-dimensional initial pipeline model is intercepted with the range from the start point x1 to the end point x2 of the deformation interval as the reference, and the intercepted segment is orthogonally projected in the X-Y plane, X-Z plane, and Y-Z plane. The projection accuracy is set to 0.1 mm, the projection results are saved as three groups of two-dimensional vector diagrams in the.dxf format, named top_view, front_view, and side_view respectively. The projection diagrams are used to analyze the contour change characteristics of the deformation area in each direction. When performing deformation shape imprinting based on the inner wall deformation data of the pipeline, discrete points in the corresponding x1 to x2 interval of the pipe wall roughness data and the fluid shear distribution data are selected. The positions of fluid shear force mutations are extracted at intervals of 0.01 m. Combining with the inner wall roughness change curve, the deformation curvature change points are determined. The Cubic Spline interpolation method is used to fit a continuous curve between each mutation point. The fitted curve is projected onto the aforementioned top_view, front_view, and side_view three-view diagrams, and the deformation imprinting diagram is generated by superposition. The contour of the imprinted shape is marked on the three-view diagrams to form two-dimensional imprinting vector data. When performing deformation simulation on the three-dimensional initial pipeline based on the imprinted deformation shape and the three-dimensional pipeline deformation area, ANSYS Workbench is called, and the three-dimensional initial pipeline.stp model is imported. The aforementioned deformation imprinting diagram is imported as the boundary condition. The pipeline material property is set as API 5L X70 steel, with a density of 7850 kg / m³, an elastic modulus of 210 GPa, and a Poisson's ratio of 0.3. The contour of the imprinting diagram is mapped to the corresponding position of the three-dimensional model. The static analysis module is used to apply external forces and internal forces distributed along the imprinted shape. The external force is based on the fluid shear stress value, and the internal force is based on the abnormal fluctuation value of the inner wall pressure. The boundary condition is to fix both ends, and an equivalent internal pressure is applied inside the pipeline. The internal pressure is set according to the average value of the gas pressure field data. The deformation simulation uses tetrahedral elements to divide the mesh, and the element size is set to 5 mm. Finally, the three-dimensional deformed pipeline frame model is obtained by solving, and the deformed pipeline.stp file and the deformation value distribution field diagram are exported. The unit of the deformation value is millimeters. 3 , an elastic modulus of 210 GPa, and a Poisson's ratio of 0.3. The contour of the imprinting diagram is mapped to the corresponding position of the three-dimensional model. The static analysis module is used to apply external forces and internal forces distributed along the imprinted shape. The external force is based on the fluid shear stress value, and the internal force is based on the abnormal fluctuation value of the inner wall pressure. The boundary condition is to fix both ends, and an equivalent internal pressure is applied inside the pipeline. The internal pressure is set according to the average value of the gas pressure field data. The deformation simulation uses tetrahedral elements to divide the mesh, and the element size is set to 5 mm. Finally, the three-dimensional deformed pipeline frame model is obtained by solving, and the deformed pipeline.stp file and the deformation value distribution field diagram are exported. The unit of the deformation value is millimeters.

[0080] Preferably, in step S4, the deformation response analysis is performed on the continuous gas pressure field and the gas flow velocity change data of the pipeline based on the deformed pipeline frame, and the identified pipeline deformation types include:

[0081] Perform pressure-driven mapping based on the continuous gas pressure field and the gas flow velocity change data of the pipeline to obtain the pipeline pressure response distribution;

[0082] Perform pipeline curvature reduction mapping according to the pipeline pressure response distribution to generate the pipeline deformation value distribution;

[0083] Cluster and reorganize the pipeline deformation value distribution according to the deformation degree to generate the pipeline strain aggregation characteristics;

[0084] Based on the pipeline strain aggregation characteristics and locate the deformation response section of the deformed pipeline frame;

[0085] Extract the differential pressure response amplitude of the continuous gas pressure field based on the deformation response section to generate the differential pressure response amplitude value;

[0086] Extract the flow velocity disturbance amplitude of the gas flow velocity change data based on the deformation response section to obtain the flow velocity disturbance amplitude value;

[0087] Classify the pipeline deformation types of the deformed pipeline framework through the differential pressure response amplitude value and the flow velocity disturbance amplitude value.

[0088] In this embodiment, when performing pressure-driven mapping based on the continuous gas pressure field in the pipeline and the gas flow velocity change data, first, a high-frequency pressure sensor array is arranged inside the gas pipeline to obtain the gas pressure field data recorded at intervals of 1 second. The data covers the entire length of the pipeline, and the number of sampling points is set to no less than 3 positions per meter. The gas pressure data range is set from 0.1 MPa to 0.8 MPa. A gas flow velocity sensor is synchronously arranged to record the gas flow velocity change data at the corresponding time points at the same positions. The gas flow velocity sampling frequency is 1 Hz, and the measurement range is from 0.5 m / s to 12 m / s. The above gas pressure and flow velocity data are input into the pressure response mapping unit. Based on the finite element pressure-driven mapping method, the gas pressure field data and the gas flow velocity change data are synchronized corresponding in terms of time and spatial position. By constructing a two-dimensional pressure mapping matrix, the pressure value at each position is input as a nodal load. The nodal positions correspond to the three-dimensional coordinates inside the gas pipeline. A three-dimensional pressure-driven model is adopted to obtain the pressure response distribution under the corresponding three-dimensional deformed pipeline framework. When performing pipeline curvature reduction mapping according to the pipeline pressure response distribution, first, using the nodal coordinates of the three-dimensional pipeline deformation framework, based on the cubic B-spline curve fitting method, calculate the spatial curvature value of each node. The distance between adjacent position points of the node in the curvature calculation formula is set to 0.5 m to calculate the initial curvature value. According to the pressure response distribution, select the position points where the pressure is higher than 0.6 MPa, and multiply the curvature value at that place by a reduction coefficient. The reduction coefficient is set from 0.7 to 0.Between 9, linearly adjust according to the pressure gradient to form a curvature reduction matrix, and then map the reduced curvature value back to the three-dimensional space coordinates to form a pipeline deformation amount distribution data set. When clustering and reorganizing the pipeline deformation amount distribution according to the deformation degree, set the deformation amount threshold interval according to the deformation amount values in the deformation amount distribution data. The interval is divided into three levels: 0 mm to 2 mm, 2 mm to 5 mm, and above 5 mm. Use the K-means clustering method to classify all node deformation amount values into three levels, set the number of clustering centers to 3, aggregate the deformation amount nodes of the same level in the three-dimensional coordinate system, calculate the volume and the maximum deformation amount value of each aggregation block to form a pipeline strain aggregation feature set. The number of strain aggregation blocks is set to not less than 10 to ensure coverage of all high-deformation regions. When positioning the deformation response section based on the pipeline strain aggregation features, extract the center point coordinates according to the position coordinates of each strain aggregation block, project the center point coordinates in the pipeline axis direction according to the pipeline length direction, calculate the projection length, and set the length of the deformation response section to 1 m on both the left and right sides of the center point projection position to form the deformation response section range. Number the sections where all strain aggregation blocks are located, record the starting and ending coordinate positions of the sections to form a deformation response section database. The number of sections is determined according to the number of aggregation blocks, and at least ensure coverage of all strain aggregation blocks. When extracting the differential pressure response amplitude of the pipeline continuous gas pressure field based on the deformation response section, extract all the pressure sensor data in the deformation response section, calculate the pressure difference between adjacent sampling points in chronological order, the differential pressure calculation interval is 1 second, form a differential pressure time series, extract the maximum and minimum values in this time series, and calculate their difference as the differential pressure response amplitude. The unit of the differential pressure response amplitude is set to MPa, record the differential pressure response amplitude value corresponding to each deformation response section, construct a differential pressure response amplitude table, and the differential pressure response amplitude range is set to 0.01 MPa to 0.2 MPa. When extracting the flow velocity perturbation amplitude of the gas flow velocity change data based on the deformation response section, extract the flow velocity data recorded by all flow velocity sensors in the deformation response section, calculate the flow velocity difference between adjacent sampling points in chronological order, the unit of the flow velocity difference is m / s, the flow velocity calculation interval is 1 second, form a flow velocity perturbation time series, extract the maximum and minimum values in this time series, and calculate the difference as the flow velocity perturbation amplitude. The unit of the flow velocity perturbation amplitude is set to m / s, record the flow velocity perturbation amplitude value of each deformation response section, and the flow velocity perturbation amplitude range is set to 0.1 m / s to 2.5 m / s. When classifying the pipeline deformation type of the deformed pipeline framework through the differential pressure response amplitude and the flow velocity perturbation amplitude, set the pipeline deformation type to three categories, namely extrusion type, expansion type, and torsion type. Set the discrimination threshold according to the differential pressure response amplitude and the flow velocity perturbation amplitude. The determination condition for the extrusion type deformation is that the differential pressure response amplitude is greater than 0.12 MPa and the flow velocity perturbation amplitude is less than 0.5 m / s. The determination condition for the expansion type deformation is that the differential pressure response amplitude is less than 0.05 MPa and the flow velocity perturbation amplitude is greater than 1.5 m / s. The determination condition for the torsion type deformation is that the differential pressure response amplitude is between 0.Between 0.05 MPa and 0.12 MPa and with the flow velocity disturbance amplitude between 0.5 m / s and 1.5 m / s, substitute the differential pressure response amplitude and the flow velocity disturbance amplitude of each deformation response section into the discrimination condition, number the deformation types to which the corresponding deformation response sections belong according to the discrimination results, form a deformation type classification result table, and set the deformation type numbers as Type-1, Type-2, and Type-3, corresponding to extrusion, expansion, and distortion types respectively. Finally, output the full-pipeline deformation type classification map and the distribution data set.

[0089] Especially importantly, perform a pipeline curvature reduction mapping based on the pipeline pressure response distribution to generate the pipeline deformation quantity distribution, including:

[0090] Extract the high-pressure points and low-pressure points at each position in the pipeline pressure response distribution, and determine the abnormal pressure response positions based on the high-pressure points and low-pressure points at each position;

[0091] Draw a pressure difference distribution curve based on the abnormal pressure response positions;

[0092] Screen the abnormal pressure sections according to the pressure difference distribution curve;

[0093] Perform a curvature reduction ratio mapping on the abnormal pressure sections based on the pressure difference distribution curve to generate a section reduction coefficient;

[0094] Compare the section reduction coefficient with the preset pipeline section reduction coefficient, and extract the difference coefficient to obtain the deformation curvature distribution;

[0095] Determine the pipeline deformation position distribution through the deformation curvature distribution, thereby generating the pipeline deformation quantity distribution.

[0096] In the embodiment of the present invention, in the stage of obtaining pipeline pressure response distribution data, high-frequency dynamic strain gauges and embedded pressure sensor arrays arranged on the outer wall of the pipeline are used to collect instantaneous pressure values at equally spaced positions along the pipeline length direction. The arrangement spacing is limited to 0.5 meters. The pressure value range recorded at each sensing point is set between 0 and 10 MPa, and the recording frequency is set to 1000 Hz. All the collected instantaneous pressure values are transmitted in real time to the pressure response data analysis module through the data acquisition module. According to the pressure threshold judgment algorithm built in this module, the points with pressure values greater than 8 MPa among all pressure values are extracted as high-pressure points, and the points with pressure values less than 2 MPa are extracted as low-pressure points. All high-pressure points and low-pressure points meeting the above conditions form a pressure anomaly point set according to the spatial coordinate order. Relying on the coordinate index values in the anomaly point set, the abnormal pressure response positions are marked to form a pressure response anomaly identification matrix. On the basis of obtaining the abnormal pressure response positions, the abnormal positions are sorted in the pipeline length direction, and the differences between the pressure values of adjacent abnormal points are calculated in turn. All the differences are plotted as a pressure difference distribution curve according to the coordinate positions. The abscissa corresponds to the pipeline position index, and the ordinate corresponds to the pressure difference. The curve is fitted by the piecewise linear interpolation method, and the interpolation point spacing is taken as 0.05 meters. After interpolation, all local maximum and minimum points of the curve are retained. On the pressure difference distribution curve, abnormal pressure sections are screened according to the set threshold. The section screening threshold is set as a continuous section where the pressure difference is greater than 4 MPa. All intervals continuously exceeding this threshold are selected, and the start and end positions of the intervals are recorded. All intervals meeting the conditions are marked as abnormal pressure sections to generate an abnormal section index table. For each abnormal pressure section, the curvature change of the pressure difference curve within the section is calculated. The second-order difference discrete curvature estimation method is used, and the curvature calculation interval is set to 0.05 meters. The ratio of the calculated curvature value to the maximum curvature value within the section is used as the curvature reduction ratio, denoted as the section reduction coefficient. All the reduction coefficients are summarized to form a reduction coefficient distribution table. The generated section reduction coefficients are compared one by one with the preset standard pipeline section reduction coefficients. The preset reduction coefficients are obtained through finite element simulation. The simulation working conditions are set as the internal pressure of the pipe being 9 MPa and the temperature being 50 °C. The simulation pipeline is made of Q235 material, with a diameter of 500 mm and a wall thickness of 8 mm. Based on this condition, the standard reduction coefficient value is obtained. The standard coefficient is limited to the range of 0.85 to 1. The difference between the actual reduction coefficient and the standard value is calculated, and all coefficients with differences greater than 0.05 are extracted to generate a difference coefficient set. Using the difference coefficient set, it is remapped to the corresponding coordinate positions of the abnormal pressure sections, and the deformation curvature values are calibrated according to the size of the difference coefficients. The greater the difference coefficient within the section, the higher the corresponding curvature value, forming a complete deformation curvature distribution map. The distribution map uses a color gradient method to represent the curvature values, from 0.1 m-1 to 1 m-1, and the color gradually changes from green to red. Finally, according to the deformation curvature distribution map, the curvature value is greater than 0.The position at 6m - 1 is marked as the pipeline deformation position. Extract the coordinates of all deformation positions. Combining with the deformation amount estimation method, according to the relationship that the deformation amount is equal to the curvature value multiplied by the section length, calculate the deformation amount of each point to form a deformation amount distribution matrix. The dimension of the matrix is the number of coordinate points multiplied by 2. The first column is the coordinate, and the second column is the corresponding deformation amount value. Generate the pipeline deformation amount distribution based on this matrix.

[0097] Of particular importance is that the deformation response sections based on the pipeline strain aggregation characteristics and positioning the deformed pipeline framework include:

[0098] Perform multi-segment block difference cutting on the pipeline strain aggregation characteristics to obtain response partition difference data;

[0099] Perform section disturbance density screening on the response partition difference data to obtain disturbance dense section data;

[0100] Perform pressure-flow synchronous offset measurement on the disturbance dense section data to obtain pressure-flow offset characteristic data;

[0101] Perform delay amplitude mapping on the pressure-flow offset characteristic data to obtain delay amplitude characteristic data;

[0102] Perform response symmetry fitting on the deformed pipeline framework based on the delay amplitude characteristic data to obtain symmetry offset data;

[0103] Perform section response labeling on the disturbance dense section data and the symmetry offset data to obtain the deformation response section.

[0104] In this embodiment, when performing multi-segment block difference cutting on the pipeline strain aggregation characteristics, based on the previously obtained pipeline strain aggregation characteristic matrix, a window segmentation method with a fixed section length is adopted. The entire strain characteristic matrix is divided along the pipeline length direction into a plurality of continuous and non-overlapping equal-length blocks, with each block length set to 5 meters. After cutting, the range of the strain aggregation intensity values within each block is calculated. The range is the difference between the maximum value and the minimum value within the block. The range difference threshold is set to 120 με (microstrain). Blocks with a range greater than the threshold are marked as abnormal difference blocks, and the original strain aggregation characteristic values of such blocks are retained, while blocks that do not meet the range threshold condition are cut off. Finally, a response partition difference data matrix composed of multiple abnormal difference blocks spliced together is obtained. When performing section perturbation density screening on the response partition difference data, the perturbation density is defined as the number of perturbation events per unit length. The perturbation event criterion is set that the strain value change amplitude of 5 or more consecutive sampling points within the block exceeds 80 με. By using the method of counting the number of perturbation events within a 5-meter block length, if the number of perturbation events within the block exceeds 3, it is determined that the block is a perturbation-dense section. All blocks that meet the perturbation-dense section criterion are screened and retained, and low-density blocks are excluded. Finally, a data set of perturbation-dense sections is formed, and the data set contains four-dimensional parameters: block number, start and end positions, number of perturbation events, and maximum perturbation amplitude. When performing pressure-flow synchronous offset measurement on the perturbation-dense section data, the continuous pressure field data and gas flow velocity change data at the corresponding positions within each perturbation-dense section are extracted separately, and the sampling frequency is set to 10 Hz. The pressure change and flow velocity perturbation waveforms at the same time stamp are synchronized and aligned. The maximum cross-correlation method is used to calculate the offset between the two sets of data. The offset is defined as the time delay value corresponding to the maximum value of the cross-correlation function of the two signals, with the unit of millisecond. The offset calculation results within all perturbation-dense sections are summarized to form pressure-flow offset characteristic data, and the data content includes section number, pressure and flow velocity offset values, and cross-correlation maximum value. When performing delay amplitude mapping on the pressure-flow offset characteristic data, based on the previously obtained pressure-flow offset characteristic data, the offset is used as the horizontal axis and the peak amplitude of the pressure response as the vertical axis to construct a two-dimensional scatter plot. The offset binning step size is set to 10 ms, and the offset data is grouped by bin. The mean value of the peak amplitude of the pressure response within each offset bin is taken to obtain the delay amplitude corresponding to each bin. Finally, the offset bin and the corresponding delay amplitude form a delay amplitude characteristic data table, and the data table contains three columns: offset bin range, average pressure difference amplitude, and section number. When performing response symmetry fitting on the deformed pipeline framework based on the delay amplitude characteristic data, the offset and delay amplitude within each perturbation-dense section are taken in pairs. According to the center symmetry of the section, the difference between the offset and delay amplitude at each pair of symmetric positions is calculated. The symmetry error threshold is set to 20%. If the difference between any pair of symmetric point pairs exceeds this threshold, the magnitude and direction of the difference are recorded.Summarize the asymmetric point pair data within all sections, and use the least squares method to fit the distribution curve of the offset and the difference in delay amplitude. The fitting result is output as symmetry offset data, and the data structure includes section number, fitting residual, position of the maximum offset value, and symmetry error percentage. When performing section response labeling on the data of the disturbed dense section and the symmetry offset data, according to the section number of the disturbed dense section, the corresponding symmetry offset data is compared, and the labeling rule is set as follows: If the maximum offset value in the symmetry offset data within the section is less than 10%, it is marked as a type I response section; if the maximum offset value is between 10% and 20%, it is marked as a type II response section; if it exceeds 20%, it is marked as a type III response section. Complete the response label assignment for all sections, and finally combine the data of the disturbed dense section and the corresponding label number to form a deformation response section data table. The data table structure includes six columns: section number, number of disturbance events, pressure-flow offset, delay amplitude, symmetry offset percentage, and section response label.,

[0105] Preferably, when the pipeline deformation type is pipeline corrosion deformation data in step S5, identifying the corrosion evolution trend based on the pipeline corrosion deformation data, and performing corrosion safety assessment and early warning based on the preset corrosion safety margin and corrosion evolution trend includes:

[0106] When the pipeline deformation type is pipeline corrosion deformation data, perform time-series tracking on the pipeline corrosion deformation data, and record the corrosion evolution process to obtain corrosion history data;

[0107] Infer the corrosion expansion trend of the pipeline corrosion deformation data based on the corrosion history data;

[0108] Set the warning boundary based on the preset corrosion safety margin;

[0109] Perform corrosion safety assessment on the corrosion expansion trend based on the warning boundary, and perform grade determination based on the safety assessment data to obtain corrosion safety grade data;

[0110] Construct a multi-level early warning mechanism based on the corrosion safety grade data and the warning boundary.

[0111] In this embodiment, when the pipeline deformation type is pipeline corrosion deformation data, first use an ultrasonic corrosion detector and a borescope measurement device to continuously and periodically detect the inner wall of the pipeline. The detection period is set to 30 days, and the corrosion pit depth, corrosion area, corrosion position coordinates, and corrosion type identifier are collected. The detection resolution of the corrosion pit depth is set to 0.1 mm, and the measurement range of the corrosion area is set between 10 mm 2 and 5000 mm 2, all corrosion parameters are recorded according to the detection timestamp, and a time-series corrosion evolution data table is constructed. The content of the data table includes five parameters: detection time, pit depth, area, location, and type. Multiple cycles of data are continuously recorded, and the cumulative number of recorded cycles is not less than 12 times, forming corrosion history data covering a one-year cycle. When inferring the corrosion expansion trend of pipeline corrosion deformation data based on the corrosion history data, the linear interpolation method and the locally weighted regression (LOESS, locally weighted scatter smoothing) method are used to fit the changing trends of the corrosion pit depth and area data over time. The interpolation step is set to 1 day, and the fitting confidence interval is set to 95%. The annual average growth rate of the corrosion pit depth is determined based on the slope of the fitting curve. If the absolute value of the slope is greater than 0.5 mm / year, it is recorded as a high expansion trend; otherwise, it is recorded as a low expansion trend. At the same time, the same treatment is carried out for the annual average growth rate of the corrosion area. If the annual area growth rate is greater than 400 mm 2 / year, it is recorded as a high expansion trend of the area. Finally, the corrosion expansion trend level is determined based on the dual-index combination of the pit depth and area trends. When setting the warning boundary based on the preset corrosion safety margin, according to the design wall thickness standard of the gas pipeline, the corrosion safety margin value is set as the difference between the wall thickness margin and the safety wall thickness required for the maximum operating pressure of the pipeline. The minimum safety margin threshold is taken as 2 mm. When the actual corrosion pit depth reaches the wall thickness minus 2 mm, it is defined as the warning boundary. At the same time, based on the corrosion area and expansion trend, an area warning value is set. When the area of a single corrosion pit exceeds 2000 mm 2When the annual growth rate of the corrosion pit depth exceeds 0.5 mm / year, it is classified as the area expansion warning boundary. The warning boundary data consists of three parameters: the corrosion pit number, the warning pit depth, the warning area, and the expansion rate threshold, forming a complete warning boundary threshold library. When conducting a corrosion safety assessment of the corrosion expansion trend based on the warning boundary, the current corrosion expansion trend data is compared item by item with the warning boundary values. If the predicted value of the pit depth trend exceeds the warning pit depth or the predicted value of the area expansion exceeds the area warning value, it is recorded as an unsafe section. The safety assessment level is divided according to the number of exceeded items. If both items exceed the standard, it is marked as level I; if any single item exceeds the standard, it is marked as level II; if there is no exceeding the standard, it is marked as level III. Finally, the safety levels of all corrosion sections are summarized to generate a corrosion safety level data table. The content of the data table includes the section number, the situation of pit depth exceeding the standard, the situation of area exceeding the standard, the situation of expansion rate exceeding the standard, and the final level. When constructing a multi-level warning mechanism based on the corrosion safety level data and the warning boundary, according to the distribution of levels I, II, and III in the corrosion safety level data, the triggering conditions for the three-level warning are set. Level I triggers a red warning, level II triggers an orange warning, and level III triggers a yellow warning. Each warning level corresponds to different response strategies. For the red warning section, the gas transmission operation shall be stopped immediately and excavation and repair shall be arranged. For the orange warning section, high-frequency monitoring shall be implemented, and the monitoring period shall be adjusted to 7 days. For the yellow warning section, the original 30-day cycle monitoring shall be maintained. The multi-level warning mechanism generates a warning response list by corresponding the corrosion section number with the warning level. The content of the list includes the section number, the current corrosion level, the recommended disposal measures, and the next inspection time, realizing the hierarchical response management of corrosion risks.

[0112] Preferably, when the pipeline deformation type in step S5 is pipeline physical deformation data, the gas flow degree is judged based on the pipeline physical deformation data, and the physical safety assessment and warning based on the preset pressure bearing threshold include:

[0113] When the pipeline deformation type is pipeline physical deformation data, calculate the cross-sectional area change rate of the pipeline physical deformation data;

[0114] Simulate the gas flow blockage according to the cross-sectional area change rate, and evaluate the degree of blockage of the simulated gas flow blockage data to generate the gas flow degree;

[0115] Compare the gas flow degree with the preset standard flow degree, and calculate the flow reduction ratio to obtain the gas flow influence data;

[0116] Simulate the gas backlog according to the gas flow influence data to obtain the simulated gas backlog data;

[0117] Conduct stress analysis based on the simulated gas backlog data and the pipeline physical deformation data, and quantify it as the gas backlog stress value;

[0118] Conduct a physical stress safety assessment through preset pressure tolerance thresholds and gas backlog stress values;

[0119] Locate the rupture risk coordinates based on the physical stress safety assessment data and pipeline physical deformation data;

[0120] Transmit the rupture risk coordinates to the visualization page and issue a real-time warning.

[0121] In this embodiment, when the pipeline deformation type is pipeline physical deformation data, first use a laser caliper and a borescope device to perform a full-circumference scan of the internal cross-sectional profile of the gas pipeline. The scan interval is set to 50 mm, and the diameter measurement accuracy is not less than 0.1 mm. According to the scan results, extract the maximum diameter and minimum diameter of each cross-section, and use the cross-section fitting algorithm to calculate the actual cross-sectional area. The cross-sectional area calculation formula is π multiplied by the product of the maximum radius and the minimum radius. Subsequently, calculate the cross-sectional area change rate based on the initial design cross-sectional area and the actually detected cross-sectional area. The change rate calculation formula is (detected cross-sectional area - initial design area) / initial design area × 100%. Form a cross-sectional area change rate distribution table with all detected cross-section data, recording three parameters: cross-section number, detected area, design area, and change rate in the table. Ensure that the number of detected cross-sections is not less than 200. When simulating gas flow blockage based on the cross-sectional area change rate, use the finite volume method to perform a numerical simulation of the gas pipeline flow field based on the CFD (Computational Fluid Dynamics) platform. Set the gas type to natural gas, and the density is set to 0.717 kg / m 3, the inlet pressure is set to 0.4 MPa, the outlet pressure is set to 0.35 MPa, the turbulent model used is k-ε (turbulent kinetic energy-dissipation rate model), the cross-sectional area change rate is applied as a boundary condition to the pipeline axial profile model, the simulation calculation is run, the flow velocity distribution, pressure distribution and eddy current region are extracted, and based on the flow velocity decline section and the change in turbulent intensity, the percentage of obstruction is calculated. The percentage of obstruction is defined as the ratio of the flow velocity decline amplitude to the original flow velocity multiplied by 100%. All calculation results are recorded in the gas flow obstruction data table, which includes the cross-section number, percentage of obstruction, eddy current intensity, and pressure drop. When comparing the gas flow degree with the preset standard flow degree, the standard flow degree value is set to an inlet flow velocity of 7 m / s, and the allowable fluctuation range is ±0.5 m / s. Based on the flow velocity values of the obstruction section obtained from the simulation, the flow velocity reduction ratio is calculated point by point. The calculation method of the reduction ratio is (standard flow velocity - actual flow velocity) divided by the standard flow velocity, and then multiplied by 100%. When the reduction ratio exceeds 10%, it is marked as slightly obstructed, when it exceeds 20%, it is marked as moderately obstructed, and when it exceeds 30%, it is marked as severely obstructed. All comparison results are summarized to generate the gas flow rate impact data table, which includes the cross-section number, standard flow velocity, simulated flow velocity, reduction ratio, and obstruction level. When conducting gas backlog simulation based on the gas flow rate impact data, relying on the same CFD simulation model, the flow velocity reduction ratio of each section is used as an input variable to calculate the corresponding section pressure rise. The pressure rise is obtained by extracting the simulated pressure distribution through the simulation and calculating the pressure difference between the outlet pressure and the upstream pressure of the obstruction section. If the pressure difference is greater than 0.03 MPa is defined as generating gas backlog. The cumulative number of gas backlog sections and the total backlog pressure are considered. The backlog pressure is calculated as the sum of the pressure differences in each section to obtain a complete simulated gas backlog data table. The table records the section number, pressure difference, total backlog pressure, and backlog section length. When performing stress analysis based on the simulated gas backlog data and pipeline physical deformation data, a pipeline wall thickness value of 8 mm and an inner diameter of 600 mm are selected. According to the Lambert - Clapeyron formula, the radial and circumferential stresses generated by the backlogged gas on the pipeline wall are calculated. The radial stress is calculated as the backlog pressure multiplied by the radius divided by the wall thickness, and the circumferential stress is calculated as the backlog pressure multiplied by the inner diameter divided by twice the wall thickness. All calculated values are summarized to generate a gas backlog stress value table, which includes the section number, backlog pressure, radial stress, circumferential stress, and maximum stress value. When performing physical stress safety assessment through the preset pressure - bearing threshold and gas backlog stress value, the maximum allowable stress threshold for the pipeline is set to 240 MPa. Based on the comparison of the maximum stress value with the bearing threshold, when the stress value exceeds the threshold by more than 80%, it is marked as a warning; when it exceeds the threshold by more than 90%, it is marked as serious; when it exceeds the threshold by more than 100%, it is marked as ultimate instability. According to the stress conditions in each section, the sections are divided into risk levels of Grade I, Grade II, and Grade III. Grade I is ultimate instability, Grade II is serious, and Grade III is a warning. A physical stress safety assessment data table is generated, which includes the section number, stress value, threshold ratio, and risk level. When locating the rupture risk coordinates based on the physical stress safety assessment data and pipeline physical deformation data, all the evaluated section numbers are corresponded to the pipeline three - dimensional coordinate model. Sections of Grade I and Grade II are selected according to the risk level, and the three - dimensional coordinates of the section center points are recorded as the rupture risk coordinates to generate a rupture risk coordinate table, which includes the section number, X - coordinate, Y - coordinate, Z - coordinate, and risk level. When transmitting the rupture risk coordinates to the visualization page and performing real - time warning, a three - dimensional pipeline visualization platform based on Cesium (a three - dimensional geospatial visualization platform) is used. The rupture risk coordinates are imported into the platform through a GeoJSON format file, and the rupture points are marked using a three - dimensional solid sphere (Primitive Sphere). Grade I risk is marked in red, and Grade II risk is marked in orange. The real - time refresh frequency is set to 10 seconds. The risk level, stress value, and coordinate position are synchronously displayed in a pop - up window above the pipeline model, and at the same time, an audible and visual alarm device is triggered. The alarm level sets the beep intensity and light flashing frequency according to the risk level. The beep intensity for Grade I risk is 90 dB, and the flashing frequency is 2 Hz; the beep intensity for Grade II risk is 70 dB, and the flashing frequency is 1 Hz.

[0122] The present invention also provides a gas pipeline safety assessment and warning system based on data analysis for executing the gas pipeline safety assessment and warning method based on data analysis as described above. The gas pipeline safety assessment and warning system based on data analysis includes:

[0123] Multi-point air pressure synchronization module, which is used to collect gas pipeline air pressure data simultaneously at multiple points; enhance the spatio-temporal synchronization of the gas pipeline air pressure data to obtain aligned air pressure data; construct a continuous air pressure field of the pipeline based on the aligned air pressure data;

[0124] Gas flow velocity calculation module, which is used to identify the pipeline pressure distribution difference based on the continuous air pressure field of the pipeline; estimate the gas transient flow velocity through the pipeline pressure distribution difference;

[0125] Deformation response detection module, which is used to continuously record the gas transient flow velocity at multiple time points, infer the change of the flow velocity, and obtain the gas flow velocity change data; judge the pipeline inner wall deformation data through the continuous air pressure field of the pipeline and the gas flow velocity change data;

[0126] Deformation type identification module, which is used to locate the deformation area based on the pipeline inner wall deformation data and reconstruct the deformed pipeline framework; analyze the deformation response of the continuous air pressure field of the pipeline and the gas flow velocity change data based on the deformed pipeline framework, and identify the pipeline deformation type;

[0127] Safety warning decision-making module, which is used to, when the pipeline deformation type is pipeline corrosion deformation data, identify the corrosion evolution trend based on the pipeline corrosion deformation data, and conduct corrosion safety assessment and warning based on the preset corrosion safety margin and corrosion evolution trend; when the pipeline deformation type is pipeline physical deformation data, judge the gas flow degree based on the pipeline physical deformation data, and conduct physical safety assessment and warning based on the preset pressure bearing threshold.

[0128] Through the application of the multi-point air pressure synchronization module, the present invention realizes the comprehensive collection and spatio-temporal synchronization of the air pressure data of gas pipelines, ensures the consistency and accuracy of the data, provides a basis for subsequent analysis. The constructed continuous air pressure field of the pipeline can effectively reflect the pressure change inside the pipeline, which helps to identify potential pressure anomalies. The gas flow velocity calculation module can accurately estimate the transient gas flow velocity by analyzing the pressure distribution difference, providing an in-depth understanding of the gas flow characteristics. The deformation response detection module can continuously record the transient gas flow velocity, enabling real-time monitoring of the flow velocity change and providing important data support for judging the deformation of the inner wall of the pipeline. The introduction of the deformation type identification module enables accurate positioning of the deformation area and reconstruction of the deformed pipeline framework, laying a foundation for subsequent data analysis of the pressure field and flow velocity change. The function of the safety warning decision module is that when pipeline corrosion deformation is detected, it can timely identify the corrosion evolution trend, conduct in-depth corrosion risk assessment in combination with the preset safety margin, ensure that measures can be taken before the corrosion problem worsens, and avoid potential safety hazards. For physical deformation, it can judge the degree of gas circulation and conduct physical safety assessment based on the pressure bearing threshold, effectively reducing the accident risk caused by physical damage to the pipeline. The entire system integrates a variety of data analysis technologies, realizes the comprehensive monitoring and dynamic assessment of gas pipelines, improves the intelligent level of pipeline safety management, enhances the real-time monitoring ability of the pipeline state, also enhances the early warning ability of potential risks, reduces the influence of human factors on safety assessment, promotes the data-driven safety management mode, provides a scientific basis for the long-term stable operation of gas pipelines, ultimately realizes the all-round guarantee of gas pipeline safety, promotes the intelligent development of the gas industry, forms a sustainable safety assessment and early warning mechanism, provides an important reference for the application and popularization of future related technologies, improves the overall safety and economy of the industry, creates conditions for the safe gas use environment of society, and promotes the guarantee and improvement of public safety.

[0129] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, it is intended to embrace all changes that fall within the meaning and scope of the equivalent elements of the application document within the present invention.

[0130] The above description is only the specific implementation manners of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A gas pipeline safety assessment and early warning method based on data analysis, characterized in that, It includes the following steps: Step S1: Simultaneously collect gas pipeline pressure data at multiple points; perform spatio-temporal synchronization enhancement on the gas pipeline pressure data to obtain aligned pressure data; Construct a continuous pipeline pressure field based on the aligned pressure data; Step S2: Identify the pipeline pressure distribution difference based on the continuous pipeline pressure field; estimate the gas transient flow velocity through the pipeline pressure distribution difference; Step S3: Continuously record the gas transient flow velocity at multiple time points, and infer the flow velocity change to obtain gas flow velocity change data; judge the pipeline inner wall deformation data through the continuous pipeline pressure field and the gas flow velocity change data; Step S4: Locate the deformation area based on the pipeline inner wall deformation data, and reconstruct the deformed pipeline framework; perform deformation response analysis on the continuous pipeline pressure field and the gas flow velocity change data based on the deformed pipeline framework, and identify the pipeline deformation type; Step S5: When the pipeline deformation type is pipeline corrosion deformation data, identify the corrosion evolution trend based on the pipeline corrosion deformation data, and perform corrosion safety assessment and early warning based on the preset corrosion safety margin and the corrosion evolution trend; when the pipeline deformation type is pipeline physical deformation data, judge the gas flow degree based on the pipeline physical deformation data, and perform physical safety assessment and early warning based on the preset pressure bearing threshold.

2. The gas pipeline safety assessment and early warning method based on data analysis according to claim 1, wherein Step S1 includes the following steps: Step S11: Deploy multiple pressure sensing arrays along the inside of the gas pipeline to collect gas pipeline pressure data. The number of collection points ranges from 5 to 50 measurement points, the air pressure sampling frequency ranges from 1 Hz to 50 Hz, and the air pressure value ranges from 0.05 MPa to 1.6 MPa; Step S12: Mark the time stamp for the gas pipeline pressure data to obtain time-synchronized air pressure, where the time synchronization accuracy is set to 0.01 s to 0.5 s; Step S13: Correct the distributed node positions of the gas pipeline pressure data to generate spatially mapped air pressure, where the spatial matching distance error range is set to 0 m to 5 m; integrate the time-synchronized air pressure and the spatially mapped air pressure to generate aligned pressure data; Step S14: Eliminate the random noise interference of the aligned pressure data, and perform multi-dimensional spatio-temporal interpolation processing to obtain completely covered pressure data; construct a continuous pipeline pressure field based on the completely covered pressure data, the size of the pressure field grid unit is set to 0.5 m to 5 m, and the continuous interpolation smoothness parameter is set to 0.1 to 1.

0.

3. The gas pipeline safety assessment and early warning method based on data analysis according to claim 1, wherein Step S2 includes the following steps: Step S21: Split the pressure gradient of the continuous pipeline pressure field to obtain the pipeline pressure gradient field, where the pressure gradient splitting step size ranges from 0.5 m to 3 m; Step S22: Perform local area clustering analysis on the pipeline pressure gradient field, and identify the pressure abnormal area; perform physical constraint decoupling processing on the pressure abnormal area, and identify the pipeline pressure distribution difference; Step S23: Extract the pressure deviation characteristics of the pipeline pressure distribution difference; divide the deviation pipeline sections according to the pressure deviation characteristics, where the deviation characteristic threshold range is set to 0.01 MPa to 0.3 MPa, and determine the deviation change rate; Step S24: Estimate the gas transient flow velocity based on the deviation pipeline section and the deviation change rate.

4. The gas pipeline safety assessment and early warning method based on data analysis according to claim 1, characterized in that, Continuously recording the transient gas flow velocity at multiple time points in step S3 and inferring the flow velocity change thereof includes: Continuously recording the transient gas flow velocity and marking the recording timestamps, wherein the continuous recording time interval is set to 0.1 s to 2 s, and the recording time period is set to 30 s to 300 s; Integrating the transient gas flow velocities at multiple time points into a multi-period transient gas flow velocity set; Screening the transient gas flow velocity change intervals in the multi-period transient gas flow velocity set, wherein the difference inference interval is limited to 0.05 m / s to 5 m / s; Inferring the gas flow velocity change data based on the transient gas flow velocity change intervals.

5. The gas pipeline safety assessment and early warning method based on data analysis according to claim 1, characterized in that, Judging the pipeline inner wall deformation data through the continuous pipeline pressure field and the gas flow velocity change data in step S3 includes: Identifying the pressure change fluctuations in the continuous pipeline pressure field; Extracting the abnormal pressure fluctuations in the pressure change fluctuations; Converting the gas flow velocity change data into a flow velocity change curve and identifying the flow velocity change fluctuation characteristics based on the flow velocity change curve; Reconstructing the stress tensor based on the flow velocity change fluctuation characteristics and identifying the change in fluid shear characteristics; Analyzing the fluid shear distribution data according to the change in fluid shear characteristics; Performing section correlation matching on the abnormal pressure fluctuations and the fluid shear distribution data and establishing a flow-pressure correlation mapping to obtain the flow-pressure coupling characteristics; Analyzing the pipe wall fluid interaction data based on the flow-pressure coupling characteristics; Performing anti-inversion of the wall roughness to avoid according to the pipe wall fluid interaction data to generate the wall roughness data; Performing standard deviation ellipsoid fitting on the wall roughness data and identifying the local mutation regions to obtain the pipeline inner wall deformation data.

6. The gas pipeline safety assessment and early warning method based on data analysis according to claim 1, characterized in that Locating the deformation region based on the pipeline inner wall deformation data and reconstructing the deformed pipeline framework in step S4 includes: Extracting the deformation region location of the pipeline inner wall deformation data; Obtaining the initial gas pipeline data; Performing structural topology analysis on the initial gas pipeline data to obtain the initial pipeline topology structure; Constructing a three-dimensional initial pipeline based on the initial pipeline topology structure; Performing a stereoscopic projection on the three-dimensional initial pipeline according to the deformation region location to determine the three-dimensional pipeline deformation region; Performing deformation shape stamping according to the pipeline inner wall deformation data to obtain the stamped deformation shape; Performing deformation simulation on the three-dimensional initial pipeline based on the stamped deformation shape and the three-dimensional pipeline deformation region to generate the deformed pipeline framework.

7. The gas pipeline safety assessment and early warning method based on data analysis according to claim 1, characterized in that, Performing deformation response analysis on the continuous pipeline pressure field and the gas flow velocity change data based on the deformed pipeline framework and identifying the pipeline deformation type in step S4 includes: Performing pressure-driven mapping based on the continuous pipeline pressure field and the gas flow velocity change data to obtain the pipeline pressure response distribution; Performing pipeline curvature reduction mapping according to the pipeline pressure response distribution to generate the pipeline deformation quantity distribution; Cluster-recombining the pipeline deformation quantity distribution according to the deformation degree to generate the pipeline strain aggregation characteristics; Locating the deformation response section based on the pipeline strain aggregation characteristics and the deformed pipeline framework; Extracting the differential pressure response amplitude of the continuous pipeline pressure field based on the deformation response section to generate the differential pressure response amplitude value; Extracting the flow velocity disturbance amplitude of the gas flow velocity change data based on the deformation response section to obtain the flow velocity disturbance amplitude value; Classifying the pipeline deformation type of the deformed pipeline framework through the differential pressure response amplitude value and the flow velocity disturbance amplitude value.

8. The gas pipeline safety assessment and early warning method based on data analysis according to claim 1, characterized in that, When the pipeline deformation type in step S5 is pipeline corrosion deformation data, identifying the corrosion evolution trend based on the pipeline corrosion deformation data, and performing corrosion safety assessment and early warning based on the preset corrosion safety margin and corrosion evolution trend includes: When the pipeline deformation type is pipeline corrosion deformation data, performing time-series tracking on the pipeline corrosion deformation data, and recording the corrosion evolution process to obtain corrosion history data; Inferring the corrosion expansion trend of the pipeline corrosion deformation data based on the corrosion history data; Setting an early warning boundary based on the preset corrosion safety margin; Performing corrosion safety assessment on the corrosion expansion trend based on the early warning boundary, and performing level determination based on the safety assessment data to obtain corrosion safety level data; Constructing a multi-level early warning mechanism based on the corrosion safety level data and the early warning boundary.

9. The gas pipeline safety assessment and early warning method based on data analysis according to claim 1, characterized in that When the pipeline deformation type in step S5 is pipeline physical deformation data, judging the gas flow degree based on the pipeline physical deformation data, and performing physical safety assessment and early warning based on the preset pressure bearing threshold includes: When the pipeline deformation type is pipeline physical deformation data, calculating the cross-sectional area change rate of the pipeline physical deformation data; Performing a simulation of gas flow obstruction according to the cross-sectional area change rate, and evaluating the degree of obstruction of the simulated gas flow obstruction data to generate the gas flow degree; Comparing the gas flow degree with the preset standard flow degree, and calculating the flow reduction ratio to obtain gas flow impact data; Performing a simulation of gas backlog according to the gas flow impact data to obtain simulated gas backlog data; Performing stress analysis based on the simulated gas backlog data and the pipeline physical deformation data, and quantifying it as a gas backlog stress value; Performing physical stress safety assessment through the preset pressure bearing threshold and the gas backlog stress value; Locating the rupture risk coordinates based on the physical stress safety assessment data and the pipeline physical deformation data; Transmitting the rupture risk coordinates to the visualization page and performing real-time early warning.

10. A gas pipeline safety assessment and early warning system based on data analysis, characterized in that, For implementing the gas pipeline safety assessment and early warning method based on data analysis as described in claim 1, the gas pipeline safety assessment and early warning system based on data analysis includes: A multi-point air pressure synchronization module, configured to simultaneously collect gas pipeline air pressure data at multiple points; perform spatio-temporal synchronization enhancement on the gas pipeline air pressure data to obtain aligned air pressure data; construct a pipeline continuous air pressure field according to the aligned air pressure data; A gas flow velocity estimation module, configured to identify the pipeline pressure distribution difference based on the pipeline continuous air pressure field; estimate the gas transient flow velocity through the pipeline pressure distribution difference; A deformation response detection module, configured to continuously record the gas transient flow velocity at multiple time points, and perform a change inference on it to obtain gas flow velocity change data; judge the pipeline inner wall deformation data through the pipeline continuous air pressure field and the gas flow velocity change data; A deformation type identification module, configured to perform deformation area positioning based on the pipeline inner wall deformation data, and reconstruct the deformed pipeline framework; perform deformation response analysis on the pipeline continuous air pressure field and the gas flow velocity change data based on the deformed pipeline framework, and identify the pipeline deformation type; A safety warning decision-making module is used to, when the pipeline deformation type is pipeline corrosion deformation data, identify the corrosion evolution trend according to the pipeline corrosion deformation data, and conduct corrosion safety assessment and warning based on the preset corrosion safety margin and corrosion evolution trend; when the pipeline deformation type is pipeline physical deformation data, judge the gas flow degree based on the pipeline physical deformation data, and conduct physical safety assessment and warning based on the preset pressure tolerance threshold.

Images