Large-burial-depth pipeline water leakage detection method and device based on electrode array

Abstract

The invention discloses a large-burial-depth pipeline water leakage detection method and device based on an electrode array, and the method comprises the steps: (1) dividing sub-regions according to a large-burial-depth pipeline line and peripheral geological conditions, collecting data, and constructing an initial measurement data set; (2) on the basis of the initial measurement data set, calculating apparent resistivity distribution data of the sub-regions and a leakage anomaly index of the ith sub-region, and establishing a leakage anomaly data set; (3) acquiring environment monitoring data obtained in the detection process, and constructing an environment influence coefficient of the ith sub-region; presetting an abnormal stability threshold value, and generating a first correction instruction to correct the initial measurement data set; and (4) in combination with the structural characteristic data of the large-burial-depth pipeline, extracting an abnormal sub-region and a pipeline state, calculating a correlation index of the ith sub-region, presetting a leakage risk threshold value, and correcting abnormal sub-region positioning. According to the two-stage correction mechanism, the robustness and reliability of leakage anomaly detection are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of pipeline leakage anomaly detection, in particular to a large-buried-depth pipeline leakage detection method and device based on electrode array. BACKGROUND

[0002] In a metallurgical plant, large-buried-depth water supply pipelines are usually laid along roads. Due to the complex production environment and large burial depth of the plant, water leakage detection faces great technical challenges. Existing detection methods mostly use acoustic detection technologies such as electronic listening for leakage and sound wave correlation analysis to determine the leakage position by collecting water leakage sound or pipeline vibration signals. However, in a metallurgical plant, there are a large number of high-intensity vehicle driving noises, mechanical equipment operation noises, crowd activity noises, and other underground facility leakage accompanying sound wave signals. These noises are highly overlapped with water leakage sound in frequency characteristics, and after ground propagation and superposition, they can easily mask the real water leakage signal, resulting in a decrease in the sensitivity of traditional sound wave detection methods and prominent misjudgment problems, especially in the small and micro leakage stage. Such leakage points can only be detected by non-ultrasonic detection methods.

[0003] In addition, the underground resistivity is significantly affected by multiple environmental factors. The soil moisture content changes with seasonal rainfall and underground water level fluctuations, which may cause large-scale resistivity deviation in the short term. The shallow gas content is easily affected by changes in underground pore gas, gas dissipation, and climate warming, which may produce false resistivity anomaly characteristics. In addition, factors such as changes in surface load and ground equipment operation vibration may also affect the underground electrical uniformity through gravity microchanges, forming misjudgment anomaly areas. Existing technologies often cannot collect these environmental change factors in real time, resulting in poor stability of resistivity anomaly characteristics, error accumulation, and high false alarm rate. SUMMARY

[0004] In view of the deficiencies of the prior art, the present application provides a large-buried-depth pipeline leakage detection method and device based on electrode array to solve the problems mentioned in the background.

[0005] To achieve the above purpose, the present application realizes the following technical scheme: a large-buried-depth pipeline leakage detection method based on electrode array, comprising the following steps:

[0006] S1, according to the large-buried-depth pipeline route and the surrounding geological conditions, the target detection area is divided into multiple sub-areas, and an electrode array device is arranged in each sub-area, the electrode array device is composed of several groups of electrodes arranged on the ground, and an initial electrode spacing L1 is set, a direct current is injected into the underground medium by a direct current instrument to form a stable current field, and the potential difference and injected current data between each electrode are collected to construct an initial measurement data set;

[0007] S2. Based on the initial measurement dataset, the apparent resistivity distribution data of each sub-region is calculated according to the preset resistivity calculation method. Based on the apparent resistivity variation characteristics and the burial depth correction coefficient, the leakage anomaly index Le of the i-th sub-region is calculated. i And establish a leakage anomaly dataset;

[0008] S3. Collect environmental monitoring data synchronously obtained during the detection process, including the surface soil moisture content variation coefficient Mw, the shallow gas content variation coefficient Ga, and the gravity micro-variation coefficient Gm of the detection sub-region, and construct the environmental impact coefficient EF of the i-th sub-region. i ; A preset abnormal stability threshold K is set when the leakage anomaly index Le of the i-th sub-region is... i When the value is below the abnormal stability threshold K, a first correction instruction is generated to correct the initial measurement dataset, forming a first corrected dataset.

[0009] S4. Combining the structural characteristic data of deep-buried pipelines, including pipeline material, burial depth, pipe diameter, and operating pressure, perform pipeline characteristic correction calculations on the first corrected dataset, extract abnormal sub-regions and pipeline states, and calculate the correlation index R of the i-th sub-region. i And preset the leakage risk threshold X, when the correlation index R of the i-th sub-region i When the leakage risk threshold X is greater than or equal to the threshold, a second correction instruction and corresponding risk level are generated to further correct the location of the abnormal sub-region, generate a final list of leakage anomaly coordinates after fine-tuning, and input it into the digital map.

[0010] Preferably, S1 includes:

[0011] S11. Obtain the route, burial depth data, pipe diameter, burial environment and surrounding geological data of the target deep-buried pipeline, and divide it into multiple sub-regions based on the pipeline length and geological distribution patterns, and determine the layout boundary of each sub-region.

[0012] S12. Set up baselines for electrode arrays along the pipe axis and transverse direction in each sub-region to determine the spatial layout of each group of electrodes.

[0013] S13. According to the electrode arrangement method of the Wenner device, the four electrode groups A, M, N and B are evenly arranged on each baseline. The initial electrode spacing L1 is set so that AM=MN=NB=AB / 3.

[0014] The A, M, N, B quadrupole group is a group of electrode arrangements, where A, M, N, and B are electrode symbols.

[0015] A represents the positive electrode, i.e., the electrode into which the current is injected;

[0016] B represents the current negative electrode, i.e. the current return electrode; A, B are used to inject direct current into the ground to form a current field; M, N are responsible for collecting the potential difference between two points in the current field;

[0017] M represents the point potential measurement positive electrode, i.e. the potential difference measurement point 1, and N represents the point potential measurement negative electrode, i.e. the potential difference measurement point 2;

[0018] S14, using a super digital direct current method instrument to inject stable direct current into the underground medium, while synchronously collecting the potential difference between each group of electrodes and the injected current value, moving the electrode array point by point to the right or forward to complete the data collection of the first layer profile line;

[0019] S15, after completing the scanning of the current electrode spacing L1, the electrode spacing is increased to L2, L3…Ln in turn, and step S14 is repeated to collect multi-layer resistivity measurement data in the form of an inverted trapezoid layer by layer to construct an initial measurement data set.

[0020] Preferably, S2 comprises:

[0021] S21, sorting the measurement data of each group of electrodes in the initial measurement data set obtained in step S1, extracting the potential difference U and the injected current I corresponding to each group of electrodes, and obtaining the spatial coordinate information of each electrode layout point;

[0022] S22, according to the electrode layout mode of the Wenner device, using the resistivity value Calculation formula: Wherein, is the apparent resistivity, L is the electrode spacing, U is the potential difference, and I is the injected current, and the apparent resistivity value of each measurement point is calculated;

[0023] S23, sorting the apparent resistivity values of each measurement point according to the sub-region number to form the apparent resistivity distribution data image in each sub-region, and extracting the low-resistance anomaly characteristic parameters, including the overall low-resistance anomaly amplitude of the i-th sub-region , the total low-resistance anomaly area Amj i of the i-th sub-region, and the anomaly continuity index C i ;

[0024] S24, extracting the overall low-resistance anomaly amplitude of the i-th sub-region , the total low-resistance anomaly area Amj i of the i-th sub-region, and the anomaly continuity index C i ; after normalization, the leakage anomaly index Le i of the i-th sub-region is calculated and obtained, and a leakage anomaly data set is established;

[0025] In the formula, ρref represents the background apparent resistivity of the sub-region, the maximum abnormal area of all sub-regions; 、 and represents the overall low-resistivity abnormal amplitude of the i-th sub-region as weight represents the strength of water seepage influence, the total low-resistivity abnormal area Amj of the i-th sub-region i represents the range of water seepage influence, the abnormal continuity index C of the i-th sub-region i represents the shape of water seepage influence.

[0026] Preferably, S23 comprises:

[0027] S2301, all apparent resistivity values calculated in step S22 are classified and arranged according to the corresponding sub-region number, and the apparent resistivity data set in each sub-region is established respectively;

[0028] S2302, in each sub-region, the background apparent resistivity pref of the sub-region is calculated, and the background apparent resistivity pref is determined according to the mean value, median value or average value of the selected stable area of the apparent resistivity statistical distribution in the sub-region;

[0029] And the low-resistivity determination threshold T is set according to the background apparent resistivity pref, T = 0.7 x pref;

[0030] S2303, all measurement points with resistivity values less than or equal to the low-resistivity determination threshold T are extracted and determined as low-resistivity abnormal measurement points, numbered as the j-th abnormal measurement point, and summarized as the first low-resistivity abnormal point group; for each low-resistivity abnormal measurement point, the corresponding spatial coordinate information is extracted to obtain the abnormal sub-region positioning coordinates ;

[0031] The measurement points with resistivity values greater than the low-resistivity determination threshold T are determined to be normal and continue to be monitored;

[0032] S2304, the minimum apparent resistivity value prefmin(j) of the first low-resistivity abnormal point group of the sub-region is searched, and the low-resistivity abnormal amplitude of the j-th abnormal measurement point of the i-th sub-region is calculated according to the following formula ;

[0033] S2305, the area Aa(i,j) of the j-th abnormal measurement point is counted, the area A(i,j) of the j-th abnormal measurement point in the i-th sub-region is added to obtain the total low-resistivity abnormal area Amj of the i-th sub-region i ;

[0034] S2306, the overall low-resistivity abnormal amplitude of the i-th sub-region is calculated according to the area weighting formula :

[0035] S2307, the longest connected path length Lmax of the first low-resistance anomaly point group in the sub-region is calculated by the following formula to obtain the anomaly continuity index C of the ith sub-region i ; .

[0036] Preferably, S3 comprises:

[0037] S31, during the detection operation, real-time collection of environmental monitoring data related to the target region, the environmental monitoring data comprising: surface soil moisture content variation coefficient Mw, shallow gas content variation coefficient Ga and detection sub-region gravity micro-variation coefficient Gm;

[0038] S32, extracting the surface soil moisture content variation coefficient Mw, the shallow gas content variation coefficient Ga and the detection sub-region gravity micro-variation coefficient Gm in the environmental monitoring data, converting into a standardized conversion function, mapping different indicators to a unified influence weight domain, dividing each item by the corresponding reference value, and obtaining the environmental influence coefficient EF of the ith sub-region after directional correction by the weight coefficient i ;

[0039] S33, according to historical detection data, leakage instance statistics of different buried depth intervals and inversion error analysis, determining an anomaly stability threshold K for judging the effectiveness of the current leakage anomaly index Le i ;

[0040] S34, for the leakage anomaly index Le i of the ith sub-region, judging whether it satisfies: Le i <K, if the leakage anomaly index Le i of the ith sub-region is less than K, it is considered that the sub-region has a leakage anomaly risk, and a first correction instruction is triggered; the first correction instruction comprises a correction target data set number, an environmental correction factor EF i required for correction and a corresponding correction coefficient γ i , forming a correction parameter group; Le i ≥K, it is considered that the sub-region does not have a leakage anomaly risk, and no correction is needed;

[0041] S35, according to the correction parameter group, correcting the leakage anomaly index Le i of the ith sub-region to obtain the first corrected permeation anomaly index of the ith sub-region ;

[0042] The following correction formula is used: wherein The i-th leakage anomaly index Le i In the case of a small comparison, the correction offset is prevented from being too large, and a first correction data set is formed.

[0043] Preferably, the surface soil moisture content variation coefficient Mw is obtained in the following manner:

[0044] S311, the portable soil moisture sensor or electromagnetic induction type soil moisture meter is laid out in the electrode array layout area of each sub-region synchronously, the soil moisture content Wnow of the detection day is collected, and the historical moisture content Wref before a period of time, including 72h, is collected; and the surface soil moisture content variation coefficient Mw is calculated: ;

[0045] The surface soil moisture content variation coefficient Mw reflects the dynamic change of soil moisture during the detection period, avoiding the misleading of the natural process of rainfall and evaporation on the resistivity anomaly;

[0046] The shallow gas content variation coefficient Ga is obtained in the following manner:

[0047] S312, in the shallow layer of each sub-region, including 0.5m~1m depth, a small shallow hole gas sampling device or shallow gas porosity sensor is used to measure the soil pore gas saturation Gnow; and the average gas saturation Gref of the reference area is obtained, and the gas content variation coefficient Ga is calculated: ;

[0048] The increase of the gas content variation coefficient Ga will increase the overall resistivity, which is easy to cover up the low resistance anomaly, so it needs to be corrected;

[0049] The measurement sub-region gravity micro-variation coefficient Gm is obtained in the following manner:

[0050] S313, the portable micro-gravity meter is used to collect the gravity value at each sub-region, and the current gravity value Znow is measured; and the initial or reference gravity value Zref of the measurement area is compared, and the gravity micro-variation coefficient Gm of the detection sub-region is calculated: .

[0051] The measurement sub-region gravity micro-variation coefficient Gm means that the increase of local pore water saturation caused by leakage increases the micro-gravity slightly;

[0052] The environmental influence coefficient EF of the i-th sub-region i The calculation formula is:

[0053] In the formula, Mw represents the reference value of the surface soil moisture content variation coefficient, Ga represents the reference value of the shallow gas content variation coefficient; Gm represents the reference value of the gravity micro-variation coefficient of the detection sub-region; , and is expressed as a weight, , , , and .

[0054] Preferably, S4 comprises:

[0055] S41, collecting the structural characteristic data of the large-buried-depth pipeline in each sub-region, including: pipeline material type, buried depth data H, pipe diameter D, and operating pressure;

[0056] S42, establishing a corresponding relationship between the collected structural characteristic data and the geological zoning data and the leakage anomaly data to form a pipeline structural characteristic data set;

[0057] S43, calculating the correlation degree index R of the i-th sub-region in the first correction data set in combination with its corresponding pipeline structural characteristic data set; i , the expression is as follows:

[0058] In the formula, M represents the material risk factor corresponding to different pipeline materials; reflecting the degree of abnormality under the unit pipe diameter, reflecting the joint leakage risk of buried depth-pressure; , and is expressed as a weight, , , , and .

[0059] Preferably, the material risk factor M corresponding to different pipeline materials in S43 comprises:

[0060] When the pipeline material is stainless steel material, M=0.2;

[0061] When the pipeline material is HDPE pipe material, M=0.5;

[0062] When the pipeline material is ductile cast iron pipe material, M=0.7;

[0063] When the pipeline material is ordinary steel pipe material, M=0.8;

[0064] When the pipeline material is PVC pipe material, M=1.0.

[0065] Preferably, S4 further comprises:

[0066] S44, a preset leakage risk threshold X is used to determine whether the current correlation degree index R reaches a high-risk leakage warning condition, if there is a sub-region satisfying: , it is determined that the sub-region has a high risk of structure-induced leakage;

[0067] and a corresponding risk level is generated, including:

[0068] When , a first high-risk area level is generated;

[0069] When , a second high-risk level is generated;

[0070] When , it is determined that the sub-region does not have a high risk of structure-induced leakage, a qualified area level is generated, and monitoring is continued;

[0071] S45, when it is determined that , the system generates a second correction instruction, and the instruction content includes: the sub-region number to be corrected, the abnormal positioning accuracy factor delta i to be adjusted and the corresponding correction range;

[0072] The corresponding correction range includes a fine adjustment range of plus or minus delta meters along the pipeline direction;

[0073] S46: execute abnormal sub-region positioning correction to form a second correction data set, according to the second correction instruction, fine-tune and correct the abnormal sub-region positioning coordinates to obtain the coordinates of the i-th sub-region after correction : ;

[0074] S47, the coordinates of the i-th sub-region after correction are summarized , a final leakage anomaly coordinate list after fine-tuning and correction is generated, accompanied by corresponding sub-region number, depth information and abnormal area, and a digital map is output, the boundary range and risk level of the abnormal sub-region are marked, and the sub-region of the first high-risk area level is marked in red, and the sub-region of the second high-risk level is marked in yellow.

[0075] Another technical solution provided by the application: a large-buried-depth pipeline leakage detection device based on an electrode array, comprising:

[0076] A detection area division module is used to obtain large-buried-depth pipeline line direction, buried depth data, pipeline diameter, buried environment and surrounding geological data, and divide the target detection area into a plurality of sub-regions according to the pipeline length and geological distribution law, and determine the layout boundary of each sub-region;

[0077] An electrode array arrangement module is arranged to arrange electrode array devices in each sub-region, wherein the electrode array device is composed of several groups of electrodes arranged on the ground surface, and an initial electrode spacing L1 is set according to the Wenner electrode arrangement mode;

[0078] A current field establishment and initial data acquisition module is arranged to control a direct current method instrument to inject a direct current into the underground medium to form a stable current field, and to synchronously acquire the potential difference between each electrode and the injected current data to construct an initial measurement data set;

[0079] A leakage anomaly evaluation module is arranged to calculate the apparent resistivity distribution data of each sub-region based on the initial measurement data set, to extract the low-resistance anomaly amplitude Δρi, the low-resistance anomaly area Ai and the anomaly continuity index Ci, to calculate the leakage anomaly index Lei of each sub-region according to a preset resistivity calculation method and a buried depth correction coefficient, and to establish a leakage anomaly data set;

[0080] An environmental influence correction module is arranged to acquire environmental monitoring data synchronously obtained during the detection process, including the surface soil moisture content variation coefficient Mw, the shallow gas content variation coefficient Ga and the detection sub-region gravity micro-variation coefficient Gm, to construct the environmental influence coefficient EF i of the i-th sub-region, and to generate a first correction instruction to correct the initial measurement data set to form a first corrected data set when the leakage anomaly index Lei i of the i-th sub-region is lower than the anomaly stability threshold K.

[0081] A pipeline characteristic correction module is arranged to acquire pipeline structure characteristic data of pipeline material, buried depth, pipe diameter and operating pressure, to perform pipeline characteristic correction calculation on the first corrected data set, to calculate the correlation index R i of the i-th sub-region, and to compare the correlation index R i of the i-th sub-region with the leakage risk threshold X, and to generate a second correction instruction and a corresponding risk level when the correlation index R i of the i-th sub-region is greater than or equal to the threshold X.

[0082] An anomaly positioning and output module is arranged to perform anomaly sub-region positioning coordinate fine-tuning correction according to the second correction instruction to form a final leakage anomaly coordinate list after fine-tuning correction, to attach the sub-region number, the depth information and the anomaly area, and to output to a digital map to mark the anomaly sub-region boundary range and the different risk level display.

[0083] The present application provides a large-buried-depth pipeline leakage detection method and device based on an electrode array, which has the following beneficial effects:

[0084] (1) The electrode array-based large-buried pipeline leakage detection method and device avoids the problem of reduced sensitivity of traditional acoustic detection methods in a strong noise background, especially in the early stages of small leakage and early-stage micro-leakage, and can still effectively identify the leakage area, thereby effectively supplementing the detection of leakage points that cannot be detected by acoustic methods. While collecting apparent resistivity data, the environmental factors (including soil moisture content change coefficient Mw, shallow gas content change coefficient Ga, and gravity micro-change coefficient Gm) highly correlated with electrical property changes are monitored in real time, effectively distinguishing between resistivity fluctuations caused by environmental factors and actual leakage anomalies, dynamically correcting resistivity errors caused by short-term environmental fluctuations, improving the accuracy of anomaly determination and the stability of long-term detection data, and significantly reducing false positives and false negatives.

[0085] In the determination of the abnormal sub-region, the material, buried depth, pipe diameter and operating pressure of the pipeline are further combined for comprehensive analysis, so that the positioning of the final abnormal sub-region not only depends on the change of single resistivity, but also fully considers the difference of leakage risk of different pipeline types in long-term operation. Through step-by-step correction and positioning optimization, accurate leakage abnormal sub-region coordinate information can be finally output, combined with sub-region number, depth and abnormal area, to form a digital visual leakage map, providing intuitive and accurate leakage position reference for maintenance units, shortening the on-site investigation time and improving the maintenance efficiency.

[0086] (2) The electrode array-based large-buried pipeline leakage detection method and device realizes accurate detection and dynamic correction of leakage anomalies in large-buried pipelines through the cooperative work of multiple modules. The environmental influence correction module calculates the environmental influence coefficient based on the real-time collected soil moisture content, shallow gas content and micro-gravity change data, and performs the first correction for the interference of natural environmental changes on resistivity measurement, effectively eliminates false positives caused by environmental factors, and improves the accuracy and stability of the anomaly index. The pipeline characteristic correction module combines the structural characteristics of pipeline material, buried depth, pipe diameter and operating pressure, and further corrects the abnormal positioning based on the first correction data, improving the spatial positioning accuracy of the abnormal sub-region and the scientificity of risk determination. The two-level correction mechanism not only significantly improves the robustness and reliability of leakage anomaly detection, but also realizes comprehensive compensation for complex environments and multiple factors. Finally, through the abnormal positioning and digital map output module, the leakage abnormal sub-region and its risk level are accurately marked, providing intuitive and timely risk warning information for pipeline management, significantly enhancing the safety protection capability and maintenance decision efficiency of underground pipelines. BRIEF DESCRIPTION OF DRAWINGS

[0087] Figure 1 The figure shows the steps of the electrode array-based large-buried pipeline leakage detection method of the present application;

[0088] Figure 2This is a schematic diagram of the process of a deep-buried pipeline leakage detection device based on an electrode array according to the present invention. Detailed Implementation

[0089] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0090] Example 1

[0091] Please see Figure 1 This invention provides a method for detecting water leakage in deeply buried pipelines based on an electrode array, comprising the following steps:

[0092] S1. Based on the deep-buried pipeline route and surrounding geological conditions, the target detection area is divided into multiple sub-areas. An electrode array device is deployed in each sub-area. The electrode array device consists of several groups of electrodes deployed on the ground surface. An initial electrode spacing L1 is set. A DC current is injected into the underground medium through a DC current method instrument to form a stable current field. The potential difference and injected current data between each electrode are collected to construct an initial measurement dataset.

[0093] S2. Based on the initial measurement dataset, the apparent resistivity distribution data of each sub-region is calculated according to the preset resistivity calculation method. Based on the apparent resistivity variation characteristics and the burial depth correction coefficient, the leakage anomaly index Le of the i-th sub-region is calculated. i And establish a leakage anomaly dataset;

[0094] S3. Collect environmental monitoring data synchronously obtained during the detection process, including the surface soil moisture content variation coefficient Mw, the shallow gas content variation coefficient Ga, and the gravity micro-variation coefficient Gm of the detection sub-region, and construct the environmental impact coefficient EF of the i-th sub-region. i ; A preset abnormal stability threshold K is set when the leakage anomaly index Le of the i-th sub-region is... i When the value is below the abnormal stability threshold K, a first correction instruction is generated to correct the initial measurement dataset, forming a first corrected dataset.

[0095] S4. Combining the structural characteristic data of deep-buried pipelines, including pipeline material, burial depth, pipe diameter, and operating pressure, perform pipeline characteristic correction calculations on the first corrected dataset, extract abnormal sub-regions and pipeline states, and calculate the correlation index R of the i-th sub-region. i And preset the leakage risk threshold X, when the correlation index R of the i-th sub-region iWhen the leakage risk threshold X is greater than or equal to the threshold, a second correction instruction and corresponding risk level are generated to further correct the location of the abnormal sub-region, generate a final list of leakage anomaly coordinates after fine-tuning, and input it into the digital map.

[0096] In this embodiment, by employing the resistivity detection principle, the sensitivity degradation of traditional acoustic detection methods in strong noise environments is avoided. Especially in the stages of minute and early micro-leakage, it can still effectively identify leakage areas, effectively supplementing the inability of acoustic methods to detect leaks. While collecting apparent resistivity data, environmental factors highly correlated with changes in leakage electrical properties (including soil moisture content variation coefficient Mw, shallow gas content variation coefficient Ga, and gravity micro-variation coefficient Gm) are monitored in real time. This effectively distinguishes resistivity fluctuations caused by environmental factors from actual leakage anomalies, dynamically corrects resistivity errors caused by short-term environmental fluctuations, improves the accuracy of anomaly detection and the stability of long-term detection data, and significantly reduces false alarms and false negatives.

[0097] This invention, when determining abnormal sub-regions, further incorporates a comprehensive analysis of characteristics such as pipe material, burial depth, pipe diameter, and operating pressure. This ensures that the final location of abnormal sub-regions does not rely solely on resistivity changes but fully considers the differences in leakage risk among different pipe types during long-term operation. Through step-by-step correction and positioning optimization, precise coordinate information of abnormal leakage sub-regions can be output. Combined with sub-region number, depth, and abnormal area, a digital and visualized leakage map is formed, providing maintenance units with an intuitive and accurate reference for leak location, shortening on-site investigation time, and improving maintenance efficiency.

[0098] Example 2

[0099] This embodiment is an explanation of Embodiment 1. Specifically, S1 includes:

[0100] S11. Obtain the route, burial depth data, pipe diameter, burial environment and surrounding geological data of the target deep-buried pipeline, and divide it into multiple sub-regions based on the pipeline length and geological distribution patterns, and determine the layout boundary of each sub-region.

[0101] S12. Set up baselines for electrode arrays along the pipe axis and transverse direction in each sub-region to determine the spatial layout of each group of electrodes.

[0102] S13. According to the electrode arrangement method of the Wenner device, the four electrode groups A, M, N and B are evenly arranged on each baseline. The initial electrode spacing L1 is set so that AM=MN=NB=AB / 3.

[0103] The A, M, N, B quadrupole group is a group of electrode arrangements, where A, M, N, and B are electrode symbols.

[0104] A represents the positive electrode, i.e., the electrode into which the current is injected;

[0105] B represents the negative electrode of the current, i.e., the current return electrode; A and B are responsible for injecting direct current into the ground to form a current field; M and N are responsible for collecting the potential difference between two points in the current field.

[0106] M represents the positive terminal of the potential measurement point, i.e., potential difference measurement point 1; N represents the negative terminal of the potential measurement point, i.e., potential difference measurement point 2.

[0107] S14. Use a super digital DC current meter to inject a stable DC current into the underground medium, and simultaneously collect the potential difference and injection current value between each group of electrodes. Move the electrode array to the right or forward point by point to complete the data acquisition of the first layer profile line.

[0108] S15. After completing the scan of the current electrode spacing L1, the electrode spacing is sequentially increased to L2, L3...Ln. Step S14 is repeated to collect multilayer resistivity measurement data in an inverted trapezoidal shape layer by layer to construct the initial measurement dataset.

[0109] In this embodiment, by scientifically dividing each sub-region based on the pipeline route, burial depth, pipeline diameter, burial environment, and surrounding geological conditions, and determining the layout boundaries, the complexity and geological variations of deep-buried pipelines can be effectively adapted, avoiding measurement blind spots or data distortion caused by blind layout. Simultaneously, electrode array baselines are set along the pipeline axis and transverse direction within each sub-region, and the four electrode groups (A, M, N, B) are evenly arranged using a Wenner device, ensuring that the electrode spacing AM=MN=NB=AB / 3. This guarantees uniform diffusion of the current field in the underground medium, contributing to improved spatial resolution and data stability of resistivity measurements. By using a super digital DC resistivity meter for stable DC current injection and simultaneously acquiring potential difference and current values, high signal-to-noise ratio raw resistivity data can be obtained. By employing multi-layer electrode spacing progressively extended measurements (L1 to Ln), an inverted trapezoidal multi-layer resistivity dataset is formed. This not only improves the detection depth resolution but also ensures the continuous identification of shallow and deep resistivity anomalies, providing high-precision and comprehensive raw data support for subsequent leakage anomaly analysis, correction calculations, and risk location.

[0110] Example 3

[0111] This embodiment is an explanation of Embodiment 1. Specifically, S2 includes:

[0112] S21. Organize the measurement data of each group of electrodes in the initial measurement dataset obtained in step S1, extract the potential difference U and injection current I corresponding to each group of electrodes, and obtain the spatial coordinate information of each electrode deployment point.

[0113] S22. Based on the electrode arrangement of the Wenner device, the resistivity value is adopted. Calculation formula:

[0114] in, Let L be the apparent resistivity, U be the electrode spacing, U be the potential difference, and I be the injection current. Calculate the apparent resistivity values ​​at each measurement point.

[0115] S23. Classify and organize the apparent resistivity values ​​of each measurement point according to the sub-region number to form an apparent resistivity distribution data image within each sub-region, and extract low-resistivity anomaly characteristic parameters, including the overall low-resistivity anomaly amplitude of the i-th sub-region. The total low-resistivity anomaly area Amj in the i-th sub-region i and the abnormal continuity index C i ;

[0116] S24. Extract the overall low-resistivity anomaly amplitude of the i-th sub-region. The total low-resistivity anomaly area Amj in the i-th sub-region i and the abnormal continuity index C i After normalization, the leakage anomaly index Le of the i-th sub-region is calculated and obtained. i A leakage anomaly dataset was then created. The calculation method is as follows:

[0117] In the formula, ρref represents the background apparent resistivity of the sub-region. The maximum anomaly area across all sub-regions; , and Represented as weights, , , ,and The overall low-resistivity anomaly amplitude of the i-th sub-region The total low-resistivity anomaly area Amj in the i-th sub-region represents the intensity of the seepage effect. i The anomalous continuity index C of the i-th sub-region represents the extent of the seepage effect. i Indicates the form of the effect of water seepage;

[0118] In this embodiment, by systematically organizing the initial measurement dataset collected in step S1, the potential difference U and injected current I of each group of electrodes are extracted, and combined with the spatial coordinate information of the electrode layout, high-precision spatial data reconstruction is achieved. Based on the electrode layout method of the Wenner device, the standard apparent resistivity calculation formula is used to accurately calculate the apparent resistivity values ​​of each measurement point, improving the quality of the basic data for electrical anomaly analysis. Furthermore, the apparent resistivity data is classified and organized according to sub-region numbers to construct the apparent resistivity distribution image of each sub-region, which helps to clearly identify local low-resistivity anomaly characteristics. Further, through the systematic extraction of low-resistivity anomaly characteristics, including the overall low-resistivity anomaly amplitude... Total low resistance anomaly area Amj i and the abnormal continuity index C i This comprehensively reflects the differences in intensity, extent, and morphology of leakage anomalies, avoiding the risk of misjudgment that may result from relying on a single indicator. In particular, the anomaly continuity index C... i The introduction of this method enables the identification of different leakage morphologies, such as elongated strips or dispersed clumps, providing morphological support for subsequent anomaly source analysis and risk assessment. Finally, by normalizing each feature parameter and setting reasonable weights, the leakage anomaly index Lei for the i-th sub-region is scientifically calculated, forming a leakage anomaly dataset. This not only improves the sensitivity of anomaly identification but also enhances the adaptability and stability of the overall model in complex geological environments, providing a reliable basic indicator system for subsequent correction analysis.

[0119] Example 4

[0120] This embodiment is an explanation of embodiment 3. Specifically, S23 includes:

[0121] S2301. All apparent resistivity values ​​calculated in step S22 are classified and organized according to the corresponding sub-region numbers, and apparent resistivity datasets are established for each sub-region.

[0122] S2302. Within each sub-region, calculate the background apparent resistivity ρref of that sub-region. The background apparent resistivity ρref is determined based on the mean, median, or average value of the statistical distribution of apparent resistivity within that sub-region.

[0123] The low resistance threshold T is set based on the background apparent resistivity ρref, where T = 0.7 × ρref;

[0124] S2303, Extract all values ​​that satisfy the resistivity value Measurement points with resistance below or equal to the low-resistivity threshold T are identified as low-resistivity anomaly measurement points, numbered as the j-th anomaly measurement point, and grouped into the first low-resistivity anomaly point group. For each low-resistivity anomaly measurement point, its corresponding spatial coordinate information is extracted to obtain the location coordinates of the anomaly sub-region. ;

[0125] Satisfy resistivity value Measurement points that exceed the low resistance threshold T are considered normal and will be continuously monitored.

[0126] S2304. Search for the minimum apparent resistivity value ρmin(j) of the first low-resistivity anomaly point group in the sub-region, and calculate the low-resistivity anomaly amplitude of the j-th anomaly measurement point in the i-th sub-region according to the following formula. ;

[0127] S2305. Calculate the area Aa(i,j) of the j-th abnormal measurement point. Add the areas A(i,j) of the j-th abnormal measurement points in the i-th sub-region to obtain the total low-resistivity abnormal area Amj of the i-th sub-region. i ;

[0128] S2306. Calculate the overall low-resistivity anomaly amplitude of the i-th sub-region according to the area-weighted formula. :

[0129] S2307. The longest connected path length Lmax of the first low-resistivity anomaly point group in this sub-region is calculated using the following formula to obtain the anomaly continuity index C of the i-th sub-region. i ; .

[0130] Among them, when the anomalies in the first low-resistivity anomaly group are connected by lines and appear in a cluster, the longest connected path length Lmax is close to Ci≈1; when the anomaly is significantly elongated (such as a leakage channel), Lmax is much greater than 1. Ci > 1;

[0131] The actual physical phenomena of water leakage: A leak point → generates water flow that seeps into the surrounding soil and rock; the seeping water migrates within the soil and rock along cracks, channels, or pore networks; therefore, low-resistivity anomaly sub-regions often possess certain connectivity, directionality, and scale characteristics. Characteristics of minimum-pitch array electrical imaging: It acquires a spatial resistivity field; in normal, homogeneous formations: the resistivity field is uniform; when leakage exists: low-resistivity anomalies appear as strip-shaped, banded, or clumpy anomaly images.

[0132] In this embodiment, by organizing the initial measurement dataset according to the sub-region numbering system, a complete apparent resistivity dataset was first established, providing accurate electrical data support for subsequent sub-regional leakage anomaly analysis. By calculating the background apparent resistivity ρref within the sub-region, a low-resistivity judgment threshold T (T=0.7×ρref) was adaptively determined using multiple statistical methods such as mean, median, or average value of stable regions. This effectively avoided the judgment offset problem caused by differences in background resistivity under different geological environments, improving the environmental adaptability and versatility of anomaly identification. For low-resistivity anomaly feature extraction, measurement points below the threshold T were selected as anomaly measurement points and numbered and classified into the first low-resistivity anomaly point group, ensuring that anomaly identification has fine-grained resolution capabilities. Combined with the electrode deployment location, the spatial coordinate information was extracted to further accurately calibrate the location coordinates of the anomaly sub-region, laying a spatial foundation for subsequent location correction.

[0133] In terms of extracting abnormal feature parameters, the amplitude of low-resistivity anomalies is calculated based on the minimum apparent resistivity within the anomaly point group, effectively characterizing the strength of leakage. The total anomaly area Amj is obtained by statistically accumulating the area point by point. i This reflects the extent of the abnormal spread; and an abnormal continuity index C is introduced. i Based on the longest connected path length Lmax, this method determines the morphological characteristics of leaks and can sensitively capture directional features such as strip-like and channel-like patterns. It is particularly well-suited to the physical characteristics of real seepage water migrating through fracture networks or pore zones. Compared to existing identification methods that rely solely on anomaly amplitude or area, this embodiment uses intensity, range, and morphology as three dimensions to characterize seepage features, achieving comprehensive identification and classification of seepage anomalies in complex geological environments, thus improving detection accuracy and stability.

[0134] Example 5

[0135] This embodiment is an explanation of embodiment 1. Specifically, S3 includes:

[0136] S31. During the exploration operation, environmental monitoring data related to the target area are collected in real time. The environmental monitoring data includes: surface soil moisture content variation coefficient Mw, shallow gas content variation coefficient Ga, and gravity micro-variation coefficient Gm of the exploration sub-region.

[0137] The method for obtaining the surface soil moisture content variation coefficient Mw is as follows:

[0138] S311. Portable soil moisture sensors or electromagnetic induction soil moisture meters are simultaneously deployed in the electrode array deployment area of ​​each sub-region to collect the soil moisture content Wnow on the day of detection, and the historical moisture content Wref for a period of time before detection, including 72 hours prior; the surface soil moisture content variation coefficient Mw is calculated. ;

[0139] The surface soil moisture content variation coefficient Mw reflects the dynamic changes in soil moisture during the detection period, avoiding the misleading influence of natural processes such as rainfall and evaporation on resistivity anomalies;

[0140] The method for obtaining the shallow gas content variation coefficient Ga is as follows:

[0141] S312. In the shallow layer of each sub-region, including a depth of 0.5m to 1m, the soil pore gas saturation Gnow is measured using a small shallow-hole gas sampling device or a shallow-layer gas porosity sensor; and the average gas saturation Gref of the reference area is obtained, and the gas content variation coefficient Ga is calculated. ;

[0142] An increase in the gas content variation coefficient Ga will increase the overall resistivity, which can easily mask low-resistivity anomalies, and therefore needs to be corrected.

[0143] The method for obtaining the gravity micro-change coefficient Gm in the measuring region is as follows:

[0144] S313. Use a portable microgravity meter to collect gravity values ​​at fixed points in each sub-region, measure the current gravity value Znow, and compare it with the initial or reference gravity value Zref of the test area to calculate the gravity micro-change coefficient Gm of the probe sub-region: .

[0145] The meaning of the gravity micro-change coefficient Gm in the measuring area is: leakage leads to an increase in local pore water saturation, resulting in a slight increase in microgravity;

[0146] S32. Extract the surface soil moisture content variation coefficient Mw, shallow gas content variation coefficient Ga, and gravity micro-variation coefficient Gm from the environmental monitoring data, convert them into a standardized transformation function, map different indicators to a unified influence weight domain, standardize each item by the corresponding reference value, and after directional correction through weight coefficients, obtain the environmental impact coefficient EF of the i-th sub-region. i ;

[0147] The calculation method is as follows:

[0148] In the formula, This represents the reference value for the coefficient of variation of surface soil moisture content. This is a reference value for the coefficient of variation of shallow gas content; This represents the reference value of the gravity micro-variance coefficient in the detected sub-region; the negative sign indicates the offsetting effect of gas content and other factors; when Ga increases, it indicates that there is more gas in the soil pores and the equivalent resistivity increases; without correction, the detection data may not show the low resistivity anomaly; the increase of Ga should have a "reverse weakening" effect on the correction factor EFi - that is, the larger Ga is, the smaller EFi is, to prevent the misjudgment of leakage anomaly from being masked. , and Represented as weights, , , ,and ;

[0149] S33. Based on historical detection data, statistical analysis of leakage cases at different burial depths, and inversion error analysis, determine the anomaly stability threshold K, which is used to judge the current leakage anomaly index Le. i Validity;

[0150] S34, For the leakage anomaly index Le of the i-th sub-region i Determine if the following condition is met: Le i <K, if the leakage anomaly index Le of the i-th sub-region i When the value is less than K, the sub-region is considered to have an abnormal leakage risk, and the first correction instruction is triggered; the first correction instruction includes the correction target dataset number and the environmental correction factor EF required for the correction. i and the corresponding correction factor γ i This forms a set of corrected parameters; Le i If the value is ≥K, it is considered that there is no risk of abnormal leakage in the sub-region, and no correction is required.

[0151] S35. Based on the correction parameter set, calculate the leakage anomaly index Le for the i-th sub-region. i Make corrections and obtain the first corrected penetration anomaly index for the i-th sub-region. ;

[0152] The following correction formula is adopted: in The 1 in the formula is used to prevent excessive correction offset when the leakage anomaly index Lei in the i-th sub-region is relatively small, and to form the first correction dataset.

[0153] In this embodiment, multiple environmental monitoring data within the detection area are collected in real time, including the surface soil moisture content variation coefficient Mw, the shallow gas content variation coefficient Ga, and the gravity micro-variation coefficient Gm, effectively reflecting the dynamic changes of environmental factors during the detection period. Various sensing devices, such as soil moisture sensors and shallow gas porosity sensors, are used to accurately measure soil moisture and gas content. Combined with microgravity monitoring of local gravity changes, environmental background support is provided for leakage detection. By standardizing and mapping each environmental indicator to a unified influence weight domain, a weighted correction model is used to reasonably eliminate the interference of non-leakage factors such as seasonal rainfall, evaporation, gas content changes, and surface load on resistivity anomaly determination, significantly improving anomaly stability and discrimination accuracy.

[0154] Further setting the abnormal stability threshold K, and adjusting the leakage anomaly index Lei Threshold determination is performed, combined with the environmental impact factor EF. i Dynamic correction is implemented using the corresponding correction coefficient γi, which effectively suppresses the risk of false alarms and missed detections caused by environmental noise. In particular, it enhances the sensitivity and stable identification capability of abnormal signals in the micro-leakage stage and can adapt to the complex and ever-changing underground environmental conditions of metallurgical plant areas.

[0155] Example 6

[0156] This embodiment is an explanation of embodiment 1. Specifically, S4 includes:

[0157] S41. Collect structural characteristic data of deep-buried pipelines in each sub-region, including: pipeline material type, burial depth H, pipe diameter D, and operating pressure;

[0158] S42. Establish a correspondence between the collected structural characteristic data and geological zoning data and leakage anomaly data to form a pipeline structural characteristic dataset.

[0159] S43. The first corrected penetration anomaly index of the i-th sub-region in the first corrected dataset. Based on the corresponding pipeline structure characteristic dataset, the correlation index R of the i-th sub-region is calculated. i The expression is as follows:

[0160] In the formula, M represents the material risk factor corresponding to different pipe materials; This reflects the degree of abnormality per unit pipe diameter. This reflects the combined leakage risk due to burial depth and pressure. , and Represented as weights, , , ,and The coefficients in the above formulas were set by those skilled in the art based on the actual situation.

[0161] The source of the material risk factor M corresponding to different pipe materials in S43 is as follows:

[0162] S431. Collect and establish a pipeline material information database, covering the common material types used in pipelines with large burial depths, and mark the material number and attribute label;

[0163] S432. Obtain historical operating data and leakage accident records of each material in a deep burial environment, extract the average leakage frequency, accident statistics and failure cause information of each material under long-term service conditions, and form an accident risk database.

[0164] S433. For different materials, collect their service performance indicators under high burial depth and high pressure, including key physical and chemical parameters such as impermeability, corrosion resistance, material strength, deformation tolerance and aging rate.

[0165] S434. Based on collected historical accident data and service performance parameters, combined with expert knowledge of leakage mechanisms, a material risk sensitivity scoring model is established using a multi-factor weighted method. Weighted scores are assigned to the leakage sensitivity of each material under service conditions to calculate the material risk factor M for each material. This M is then normalized to a preset risk scoring range, including the range of 0.2 to 1.0. Specifically, this includes:

[0166] When the pipe material is stainless steel, M=0.2;

[0167] When the pipe material is HDPE, M=0.5;

[0168] When the pipe material is ductile iron, M=0.7;

[0169] When the pipe material is ordinary steel pipe, M=0.8;

[0170] When the pipe material is PVC, M=1.0.

[0171] S44. A preset leakage risk threshold X is used to determine whether the current correlation index R reaches the high-risk leakage warning condition. If a sub-region meets the condition: If so, the sub-region is determined to have a high risk of structure-induced leakage;

[0172] And generate corresponding risk levels, including:

[0173] when This resulted in the creation of the highest-risk area.

[0174] when This generates the second highest risk level;

[0175] when If the sub-region does not pose a high risk of structurally induced leakage, a qualified region level is generated, and continuous monitoring is conducted.

[0176] S45, when determining When the system generates a second correction instruction, the instruction includes: the sub-region number to be corrected, the abnormal positioning accuracy factor δi to be adjusted, and the corresponding correction range;

[0177] The corresponding correction range includes a fine adjustment of ±δ meters along the pipeline route;

[0178] S46: Perform abnormal sub-region location correction to form a second correction dataset, and locate the coordinates of the abnormal sub-region according to the second correction instructions. Perform fine-tuning to obtain the coordinates of the i-th sub-region after correction. : ;

[0179] S47. Summarize the corrected coordinates of the i-th sub-region. The system generates a final list of leak anomaly coordinates after fine-tuning and correction, along with corresponding sub-region numbers, depth information, and anomaly area. It also outputs a digital map, marking the boundary range and risk level of the anomaly sub-regions. Sub-regions at the first high-risk level are marked in red, and sub-regions at the second high-risk level are marked in yellow.

[0180] In this embodiment, multi-dimensional structural characteristic data of the target deep-buried pipeline, including pipeline material type, burial depth, pipe diameter, and operating pressure, are collected. Combined with geological zoning information and first-corrected leakage anomaly data, a detailed pipeline structural characteristic dataset is constructed, enabling a multi-dimensional comprehensive analysis of leakage risk influencing factors. For different pipeline materials, the system extracts material service performance and leakage accident risk based on historical operating data and accident statistics, constructing a material risk sensitivity scoring model. This model normalizes and quantifies the leakage risk factors of different materials, achieving a scientific quantitative evaluation of the impact of material differences on leakage risk. By introducing the material risk factor M and weight parameters for burial depth, pipe diameter, and operating pressure, a correlation index Ri calculation model is designed to achieve accurate correlation analysis between the leakage anomaly index and pipeline structural characteristics, improving the scientific rigor and accuracy of leakage risk assessment. Using a preset leakage risk threshold X, the risk levels of abnormal sub-regions are differentiated, enabling tiered early warning and control. This effectively achieves rapid identification of high-risk areas and risk zoning management, ensuring the safety of pipeline operation. Based on the risk warning, the system further generates a second correction instruction. By adjusting the anomaly location accuracy factor δi, it fine-tunes the coordinates of the anomaly sub-region, improving the spatial location accuracy of the anomaly point and significantly enhancing the spatial accuracy and practical value of the leakage anomaly detection results. Finally, the corrected leakage anomaly coordinates and risk levels are intuitively displayed on a digital map, supporting pipeline network managers in quickly locating and classifying anomaly sub-regions for control. Furthermore, the use of color coding to identify high and medium risk areas enhances the visualization of risk information.

[0181] Example 7

[0182] Please refer to Figure 2 A leakage detection device for deep-buried pipelines based on an electrode array, comprising:

[0183] The detection area division module is used to acquire data on the route, depth, diameter, burial environment and surrounding geological conditions of deep-buried pipelines, and to divide the target detection area into multiple sub-regions based on the pipeline length and geological distribution patterns, and to determine the layout boundaries of each sub-region.

[0184] An electrode array deployment module is used to deploy an electrode array device in each sub-region. The electrode array device consists of several groups of electrodes deployed on the ground surface, and the initial electrode spacing L1 is set according to the Wenner electrode deployment method.

[0185] The current field establishment and initial data acquisition module is used to control the DC current instrument to inject DC current into the underground medium to form a stable current field, and simultaneously acquire the potential difference and injected current data between each electrode to construct the initial measurement dataset.

[0186] The leakage anomaly assessment module is used to calculate the apparent resistivity distribution data of each sub-region based on the initial measurement dataset, extract the low-resistivity anomaly amplitude Δρi, low-resistivity anomaly area Ai and anomaly continuity index Ci, calculate the leakage anomaly index Lei of each sub-region according to the preset resistivity calculation method and burial depth correction coefficient, and establish a leakage anomaly dataset.

[0187] The environmental impact correction module collects environmental monitoring data synchronously obtained during the detection process, including the surface soil moisture content variation coefficient Mw, the shallow gas content variation coefficient Ga, and the gravity micro-variation coefficient Gm of the detection sub-region, and constructs the environmental impact coefficient EF for the i-th sub-region. i ; A preset abnormal stability threshold K is set when the leakage anomaly index Le of the i-th sub-region is... i When the value is below the abnormal stability threshold K, a first correction instruction is generated to correct the initial measurement dataset, forming a first corrected dataset.

[0188] The pipeline characteristic correction module is used to collect pipeline structural characteristic data such as pipeline material, burial depth, pipe diameter, and operating pressure. It performs pipeline characteristic correction calculations on the first correction dataset to obtain the correlation index R of the i-th sub-region. i And compare it with the leakage risk threshold X, when the correlation index R of the i-th sub-region i When the threshold X is greater than or equal to the threshold, a second correction instruction and a corresponding risk level are generated;

[0189] The anomaly location and output module is used to perform fine-tuning of the location coordinates of the anomaly sub-region according to the second correction instruction, form a final list of leak anomaly coordinates after fine-tuning, attach the sub-region number, depth information and anomaly area, and output to the digital map to mark the boundary range of the anomaly sub-region and display different risk levels.

[0190] In this embodiment, through the collaborative work of multiple modules, accurate detection and dynamic correction of leakage anomalies in deeply buried pipelines are achieved. The environmental impact correction module calculates the environmental impact coefficient based on real-time collected data on soil moisture content, shallow gas content, and microgravity changes. It performs a first correction to address the interference of natural environmental changes on resistivity measurements, effectively eliminating misjudgments caused by environmental factors and improving the accuracy and stability of the anomaly index. The pipeline characteristic correction module, considering the structural characteristics of the pipeline, such as material, burial depth, diameter, and operating pressure, further corrects the anomaly location based on the first correction data, improving the spatial positioning accuracy of the anomaly sub-region and the scientific basis of risk assessment. This two-level correction mechanism not only significantly improves the robustness and reliability of leakage anomaly detection but also achieves comprehensive compensation for complex environments and the influence of multiple factors. Finally, through the anomaly location and digital map output module, the leakage anomaly sub-regions and their risk levels are accurately marked, providing intuitive and timely risk warning information for pipeline network management, significantly enhancing the safety assurance capabilities and maintenance decision-making efficiency of underground pipelines.

[0191] The threshold is set to facilitate comparison. The size of the threshold depends on the amount of sample data and the number of bases set by those skilled in the art for each set of sample data; as long as it does not affect the ratio between the parameter and the quantized value, it is acceptable.

[0192] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for detecting water leakage in deeply buried pipelines based on an electrode array, characterized in that, Includes the following steps: S1. Based on the deep-buried pipeline route and surrounding geological conditions, the target detection area is divided into multiple sub-areas. An electrode array device is deployed in each sub-area. The electrode array device consists of several groups of electrodes deployed on the ground surface. An initial electrode spacing L1 is set. A DC current is injected into the underground medium through a DC current resistivity instrument to form a stable current field. The potential difference and injected current data between each electrode are collected to construct an initial measurement dataset. S2. Based on the initial measurement dataset, the apparent resistivity distribution data of each sub-region is calculated according to the preset resistivity calculation method. Based on the apparent resistivity variation characteristics and the burial depth correction coefficient, the leakage anomaly index Le of the i-th sub-region is calculated. i And establish a leakage anomaly dataset; S3. Collect environmental monitoring data synchronously obtained during the detection process, including the surface soil moisture content variation coefficient Mw, the shallow gas content variation coefficient Ga, and the gravity micro-variation coefficient Gm of the detection sub-region, and construct the environmental impact coefficient EF of the i-th sub-region. i ; A preset abnormal stability threshold K is set when the leakage anomaly index Le of the i-th sub-region is... i When the value is below the abnormal stability threshold K, a first correction instruction is generated to correct the initial measurement dataset, forming a first corrected dataset. S4. Combining the structural characteristic data of deep-buried pipelines, including pipeline material, burial depth, pipe diameter, and operating pressure, perform pipeline characteristic correction calculations on the first corrected dataset, extract abnormal sub-regions and pipeline states, and calculate the correlation index R of the i-th sub-region. i And preset the leakage risk threshold X, when the correlation index R of the i-th sub-region i When the leakage risk threshold X is greater than or equal to the threshold, a second correction instruction and corresponding risk level are generated to further correct the location of the abnormal sub-region, generate a final list of leakage anomaly coordinates after fine-tuning, and input it into the digital map.

2. The method for detecting leakage in deep-buried pipelines based on an electrode array according to claim 1, characterized in that, S1 includes: S11. Obtain the route, burial depth data, pipe diameter, burial environment and surrounding geological data of the target deep-buried pipeline, and divide it into multiple sub-regions according to the pipeline length and geological distribution pattern, and determine the layout boundary of each sub-region. S12. Set up baselines for electrode arrays along the pipe axis and transverse direction in each sub-region to determine the spatial layout of each group of electrodes. S13. According to the electrode arrangement method of the Wenner device, the four electrode groups A, M, N and B are evenly arranged on each baseline. The initial electrode spacing L1 is set so that AM=MN=NB=AB / 3. The A, M, N, B quadrupole group is a group of electrode arrangements, where A, M, N, and B are electrode symbols. A represents the positive electrode, i.e., the electrode into which the current is injected; B represents the negative electrode of the current, i.e., the current return electrode; A and B are used to inject direct current into the ground to form a current field; M and N are responsible for collecting the potential difference between the two points in the current field; M represents the positive terminal of the potential measurement point, i.e., potential difference measurement point 1; N represents the negative terminal of the potential measurement point, i.e., potential difference measurement point 2. S14. Use a super digital DC current meter to inject a stable DC current into the underground medium, and simultaneously collect the potential difference and injection current value between each group of electrodes. Move the electrode array to the right or forward point by point to complete the data acquisition of the first layer profile line. S15. After completing the scan of the current electrode spacing L1, the electrode spacing is sequentially increased to L2, L3...Ln. Step S14 is repeated to collect multilayer resistivity measurement data in an inverted trapezoidal shape layer by layer to construct the initial measurement dataset.

3. The method for detecting leakage in a deep-buried pipeline based on an electrode array according to claim 1, characterized in that, S2 include: S21. Organize the measurement data of each group of electrodes in the initial measurement dataset obtained in step S1, extract the potential difference U and injection current I corresponding to each group of electrodes, and obtain the spatial coordinate information of each electrode deployment point. S22. Based on the electrode arrangement of the Wenner device, the resistivity value is adopted. Calculation formula: in, Let L be the apparent resistivity, U be the electrode spacing, U be the potential difference, and I be the injection current. Calculate the apparent resistivity values ​​at each measurement point. S23. Classify and organize the apparent resistivity values ​​of each measurement point according to the sub-region number to form an apparent resistivity distribution data image within each sub-region, and extract low-resistivity anomaly characteristic parameters, including the overall low-resistivity anomaly amplitude of the i-th sub-region. The total low-resistivity anomaly area Amj in the i-th sub-region i and the abnormal continuity index C i ; S24. Extract the overall low-resistivity anomaly amplitude of the i-th sub-region. The total low-resistivity anomaly area Amj in the i-th sub-region i and the abnormal continuity index C i After normalization, the leakage anomaly index Le of the i-th sub-region is calculated and obtained. i And establish a leakage anomaly dataset; In the formula, ρref represents the background apparent resistivity of the sub-region. The maximum anomaly area across all sub-regions; , and Represented as weight, the overall low-resistivity anomaly amplitude of the i-th sub-region. The total low-resistivity anomaly area Amj in the i-th sub-region represents the intensity of the seepage effect. i The anomalous continuity index C of the i-th sub-region represents the extent of the seepage effect. i This indicates the form of the effect of water seepage.

4. The method for detecting leakage in a deep-buried pipeline based on an electrode array according to claim 3, characterized in that, S23 includes: S2301. All apparent resistivity values ​​calculated in step S22 are classified and organized according to the corresponding sub-region numbers, and apparent resistivity datasets are established for each sub-region. S2302. Within each sub-region, calculate the background apparent resistivity ρref of that sub-region. The background apparent resistivity ρref is determined based on the mean, median, or average value of the statistical distribution of apparent resistivity within that sub-region. The low resistance threshold T is set based on the background apparent resistivity ρref, where T = 0.7 × ρref; S2303, Extract all values ​​that satisfy resistivity values Measurement points with resistance below or equal to the low-resistivity threshold T are identified as low-resistivity anomaly measurement points, numbered as the j-th anomaly measurement point, and grouped into the first low-resistivity anomaly point group. For each low-resistivity anomaly measurement point, its corresponding spatial coordinate information is extracted to obtain the location coordinates of the anomaly sub-region. ; Satisfy resistivity value Measurement points that exceed the low resistance threshold T are considered normal and will be continuously monitored. S2304. Search for the minimum apparent resistivity value ρmin(j) of the first low-resistivity anomaly point group in the sub-region, and calculate the low-resistivity anomaly amplitude of the j-th anomaly measurement point in the i-th sub-region according to the following formula. ; ； S2305. Calculate the area Aa(i,j) of the j-th abnormal measurement point. Add the areas A(i,j) of the j-th abnormal measurement points in the i-th sub-region to obtain the total low-resistivity abnormal area Amj of the i-th sub-region. i ; S2306. Calculate the overall low-resistivity anomaly amplitude of the i-th sub-region according to the area-weighted formula. : ； S2307. The longest connected path length Lmax of the first low-resistivity anomaly point group in this sub-region is calculated using the following formula to obtain the anomaly continuity index C of the i-th sub-region. i ; 。 5. The method for detecting leakage in deep-buried pipelines based on an electrode array according to claim 1, characterized in that, S3 includes: S31. During the exploration operation, environmental monitoring data related to the target area are collected in real time. The environmental monitoring data includes: surface soil moisture content variation coefficient Mw, shallow gas content variation coefficient Ga, and gravity micro-variation coefficient Gm of the exploration sub-region. S32. Extract the surface soil moisture content variation coefficient Mw, shallow gas content variation coefficient Ga, and gravity micro-variation coefficient Gm from the environmental monitoring data, convert them into a standardized transformation function, map different indicators to a unified influence weight domain, standardize each item by the corresponding reference value, and after directional correction through weight coefficients, obtain the environmental impact coefficient EF of the i-th sub-region. i ; S33. Based on historical detection data, statistical analysis of leakage cases at different burial depths, and inversion error analysis, determine the anomaly stability threshold K, which is used to judge the current leakage anomaly index Le. i The effectiveness; S34, For the leakage anomaly index Le of the i-th sub-region i Determine if the following condition is met: Le i <K, if the leakage anomaly index Le of the i-th sub-region i When the value is less than K, the sub-region is considered to have an abnormal leakage risk, and the first correction instruction is triggered; the first correction instruction includes the correction target dataset number and the environmental correction factor EF required for the correction. i and the corresponding correction factor γ i This forms a set of corrected parameters; Le i If the value is ≥K, it is considered that there is no risk of abnormal leakage in the sub-region, and no correction is required; S35. Based on the correction parameter set, calculate the leakage anomaly index Le for the i-th sub-region. i Make corrections and obtain the first corrected penetration anomaly index for the i-th sub-region. ; The following correction formula is adopted: in The 1 in the i-th leakage anomaly index Le i In cases where the offset is relatively small, this prevents the correction offset from becoming too large and forms the first correction dataset.

6. The method for detecting leakage in a deep-buried pipeline based on an electrode array according to claim 5, characterized in that, The method for obtaining the surface soil moisture content variation coefficient Mw is as follows: S311. Portable soil moisture sensors or electromagnetic induction soil moisture meters are simultaneously deployed in the electrode array deployment area of ​​each sub-region to collect the soil moisture content Wnow on the day of detection, and the historical moisture content Wref for a period of time before detection, including 72 hours prior; the surface soil moisture content variation coefficient Mw is calculated. ; The method for obtaining the shallow gas content variation coefficient Ga is as follows: S312. In the shallow layer of each sub-region, including a depth of 0.5m to 1m, the soil pore gas saturation Gnow is measured using a small shallow-hole gas sampling device or a shallow-layer gas porosity sensor; and the average gas saturation Gref of the reference area is obtained, and the gas content variation coefficient Ga is calculated. ; The method for obtaining the gravity micro-change coefficient Gm in the measuring region is as follows: S313. Use a portable microgravity meter to collect gravity values ​​at fixed points in each sub-region, measure the current gravity value Znow, and compare it with the initial or reference gravity value Zref of the test area to calculate the gravity micro-change coefficient Gm of the probe sub-region: ; The environmental impact coefficient EF of the i-th sub-region i The calculation formula is: In the formula, This represents the reference value for the coefficient of variation of surface soil moisture content. This is a reference value for the coefficient of variation of shallow gas content; This represents the reference value for the coefficient of gravity micro-variation in the probe sub-region; , and Represented as weights, , , ,and .

7. The method for detecting leakage in deep-buried pipelines based on an electrode array according to claim 1, characterized in that, S4 includes: S41. Collect structural characteristic data of deep-buried pipelines in each sub-region, including: pipeline material type, burial depth H, pipe diameter D, and operating pressure; S42. Establish a correspondence between the collected structural characteristic data and geological zoning data and leakage anomaly data to form a pipeline structural characteristic dataset. S43. The first corrected penetration anomaly index of the i-th sub-region in the first corrected dataset. Based on the corresponding pipeline structure characteristic dataset, the correlation index R of the i-th sub-region is calculated. i The expression is as follows: In the formula, M represents the material risk factor corresponding to different pipe materials; This reflects the degree of abnormality per unit pipe diameter. This reflects the combined leakage risk due to burial depth and pressure. , and Represented as weights, , , ,and .

8. The method for detecting leakage in a deep-buried pipeline based on an electrode array according to claim 7, characterized in that, The material risk factors M corresponding to different pipe materials in S43 include: When the pipe material is stainless steel, M=0.2; When the pipe material is HDPE, M=0.5; When the pipe material is ductile iron, M=0.7; When the pipe material is ordinary steel pipe, M=0.8; When the pipe material is PVC, M=1.

0.

9. A method for detecting leakage in a deep-buried pipeline based on an electrode array according to claim 7, characterized in that, S4 also includes: S44. A preset leakage risk threshold X is used to determine whether the current correlation index R reaches the high-risk leakage warning condition. If a sub-region meets the condition: If so, the sub-region is determined to have a high risk of structure-induced leakage; And generate corresponding risk levels, including: when This resulted in the creation of the highest-risk area. when This generates the second highest risk level; when If the sub-region does not pose a high risk of structurally induced leakage, a qualified region level is generated, and continuous monitoring is conducted. S45, when determining When the system generates a second correction instruction, the instruction includes: the sub-region number to be corrected, the abnormal positioning accuracy factor δi to be adjusted, and the corresponding correction range; The corresponding correction range includes a fine adjustment of ±δ meters along the pipeline route; S46: Perform abnormal sub-region location correction to form a second correction dataset, and locate the coordinates of the abnormal sub-region according to the second correction instructions. Perform fine-tuning to obtain the coordinates of the i-th sub-region after correction. : ; S47. Summarize the corrected coordinates of the i-th sub-region. The system generates a final list of leak anomaly coordinates after fine-tuning and correction, along with corresponding sub-region numbers, depth information, and anomaly area. It also outputs a digital map, marking the boundary range and risk level of the anomaly sub-regions. Sub-regions at the first high-risk level are marked in red, and sub-regions at the second high-risk level are marked in yellow.

10. A leakage detection device for deep-buried pipelines based on an electrode array, applied to the leakage detection method for deep-buried pipelines based on an electrode array as described in any one of claims 1-9, characterized in that, include: The detection area division module is used to acquire data on the route, depth, diameter, burial environment and surrounding geological conditions of deep-buried pipelines, and to divide the target detection area into multiple sub-regions based on the pipeline length and geological distribution patterns, and to determine the layout boundaries of each sub-region. An electrode array deployment module is used to deploy an electrode array device in each sub-region. The electrode array device consists of several groups of electrodes deployed on the ground surface, and the initial electrode spacing L1 is set according to the Wenner electrode deployment method. The current field establishment and initial data acquisition module is used to control the DC current instrument to inject DC current into the underground medium to form a stable current field, and simultaneously acquire the potential difference and injected current data between each electrode to construct the initial measurement dataset. The leakage anomaly assessment module is used to calculate the apparent resistivity distribution data of each sub-region based on the initial measurement dataset, extract the low-resistivity anomaly amplitude Δρi, low-resistivity anomaly area Ai and anomaly continuity index Ci, calculate the leakage anomaly index Lei of each sub-region according to the preset resistivity calculation method and burial depth correction coefficient, and establish a leakage anomaly dataset. The environmental impact correction module collects environmental monitoring data synchronously obtained during the detection process, including the surface soil moisture content variation coefficient Mw, the shallow gas content variation coefficient Ga, and the gravity micro-variation coefficient Gm of the detection sub-region, and constructs the environmental impact coefficient EF for the i-th sub-region. i ; A preset abnormal stability threshold K is set when the leakage anomaly index Le of the i-th sub-region is... i When the value is below the abnormal stability threshold K, a first correction instruction is generated to correct the initial measurement dataset, forming a first corrected dataset. The pipeline characteristic correction module is used to collect pipeline structural characteristic data such as pipeline material, burial depth, pipe diameter, and operating pressure. It performs pipeline characteristic correction calculations on the first correction dataset to obtain the correlation index R of the i-th sub-region. i And compare it with the leakage risk threshold X, when the correlation index R of the i-th sub-region i When the threshold X is greater than or equal to the threshold, a second correction instruction and a corresponding risk level are generated; The anomaly location and output module is used to perform fine-tuning of the location coordinates of the anomaly sub-region according to the second correction instruction, form a final list of leak anomaly coordinates after fine-tuning, attach the sub-region number, depth information and anomaly area, and output to the digital map to mark the boundary range of the anomaly sub-region and display different risk levels.

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