Oil pipeline leak point positioning system, method, electronic device and storage medium

Abstract

The application provides an oil pipeline leakage point positioning system, method, electronic device and storage medium, and the oil pipeline leakage point positioning system comprises an oil pipeline, the oil pipeline is distributed with a preset number of sampling points; a plurality of signal generation modules are arranged on the oil pipeline or around the oil pipeline, and are used for generating and emitting wireless signals at regular time; a signal acquisition module; a moving module is arranged on the oil pipeline, and is used for carrying the signal acquisition module to move to the sampling points to acquire the wireless signals in the case that the oil pipeline does not generate a leakage point; a processing module is used for receiving the wireless signals, constructing a sampling point position database based on the wireless signals and geographical position coordinates of the sampling points, and determining a leakage point position of the leakage point of the oil pipeline based on the sampling point position database. The leakage point of the oil pipeline can be quickly and accurately positioned.

Description

Technical Field

[0001] This invention relates to the field of oil pipeline technology, and in particular to an oil pipeline leak location system, method, electronic device, and storage medium. Background Technology

[0002] Oil pipeline transportation has become the preferred mode of transport for crude oil and refined oil products due to its advantages such as safety, speed, and economy. However, oil pipelines are characterized by high pressure, the flammability and explosiveness of the transported media, and environmental sensitivity. Pipeline leaks are frequent due to factors such as material corrosion, natural disasters, and third-party sabotage, and leaks in oil pipelines can cause significant economic losses.

[0003] According to relevant technologies, the current common method for detecting pipeline leaks is to install wireless sensors on the pipeline. However, this method is costly, and the wireless sensors are prone to failure and have low accuracy. Therefore, finding a high-precision system or method for locating leaks in oil pipelines has become a current research hotspot. Summary of the Invention

[0004] This invention provides a system, method, electronic device, and storage medium for locating leaks in oil pipelines, enabling rapid and accurate location of leaks in oil pipelines.

[0005] This invention provides a system for locating leaks in oil pipelines. The system includes: an oil pipeline with a predetermined number of sampling points distributed along it; multiple signal generation modules disposed on or around the oil pipeline for periodically generating and transmitting wireless signals; a signal acquisition module; a movement module disposed on the oil pipeline for carrying the signal acquisition modules to the sampling points to acquire the wireless signals when no leaks are observed; and a processing module for receiving the wireless signals, constructing a sampling point location database based on the wireless signals and the geographical coordinates of the sampling points, and determining the location of the leak in the oil pipeline based on the sampling point location database.

[0006] According to the present invention, an oil pipeline leak point location system further includes a recovery module, which is used to move to the leak point location and recover the oil products leaked from the leak point upon receiving a recovery oil product instruction issued by the processing module.

[0007] According to the present invention, a pipeline leak location system is provided, wherein the signal acquisition module acquires wireless signals including a preset number of wireless signals at each sampling point; the processing module constructs a sampling point location database based on the wireless signals and the geographical coordinates of the sampling points in the following manner: based on the preset number of wireless signals acquired at each sampling point, the expected signal strength vector and the signal strength covariance matrix of the wireless signals at each sampling point are determined; based on the geographical coordinates of the sampling points, the expected signal strength vector of the wireless signals at the sampling points, and the signal strength covariance matrix of the wireless signals at the sampling points, the processed sampling coordinates of the sampling points are constructed; and based on the processed sampling coordinates of each sampling point, the sampling point location database is constructed.

[0008] According to the oil pipeline leak location system provided by the present invention, the signal generation module is further configured to: in the event of a leak in the oil pipeline, collect the intensity value of the oscillation signal generated by the leaked oil product; the processing module is further configured to: obtain the intensity value of the oscillation signal; the processing module determines the leak location of the oil pipeline leak based on the sampling point location database in the following manner: constructing an oscillation signal intensity vector based on the intensity value of the oscillation signal; determining the distance value corresponding to each sampling point based on each processed sampling coordinate in the sampling point location database and the oscillation signal intensity vector, wherein the distance value is the distance between the processed sampling coordinate and the oscillation signal intensity vector; and determining the leak location of the oil pipeline leak based on the distance value corresponding to each sampling point.

[0009] According to the oil pipeline leak location system provided by the present invention, the processing module is further configured to: determine a first distance value among the distance values ​​corresponding to each of the sampling points, wherein the first distance value is the smallest distance value among the distance values; subtract the first distance value from each of the other distance values ​​except the first distance value to obtain a plurality of distance difference values; determine the average value of the plurality of distance difference values ​​based on the plurality of distance difference values, and determine the standard deviation of the distance difference values ​​based on the average value of the distance difference values; use the distance difference values ​​that are greater than the standard deviation as rejection values, and use the sampling points corresponding to the rejection values ​​as rejection sampling points; use the sampling points other than the rejection sampling points as retained sampling points; the processing module uses the following method to determine the leak location of the oil pipeline leak point based on the distance values ​​corresponding to each of the sampling points: determine the leak location of the oil pipeline leak point based on the distance values ​​corresponding to each of the retained sampling points.

[0010] According to the oil pipeline leak location system provided by the present invention, the processing module is further configured to: determine a first sampling point corresponding to a first distance value; determine a first geographical location coordinate of the first sampling point and a reserved sampling point location coordinate of each of the reserved sampling points; determine a filtering distance value corresponding to each of the reserved sampling points based on the first geographical location coordinate and the location coordinates of each of the reserved sampling points, wherein the filtering distance value is the distance between the first geographical location coordinate and the location coordinates of each of the reserved sampling points; use the filtering distance value that exceeds a preset distance value as the rejection distance value, and use the reserved sampling point corresponding to the rejection distance value as the secondary rejection sampling point; use the reserved sampling points other than the secondary rejection sampling points as the final reserved sampling points.

[0011] The present invention also provides a method for locating leak points in an oil pipeline, wherein the oil pipeline is distributed with a preset number of sampling points, and the method includes: periodically generating and transmitting wireless signals; collecting the wireless signals at the sampling points when no leak point is generated in the oil pipeline; constructing a sampling point location database based on the wireless signals collected at the sampling points and the geographical coordinates of the sampling points; and determining the location of the leak point in the oil pipeline based on the sampling point location database.

[0012] According to the method for locating leak points in an oil pipeline provided by the present invention, the oil pipeline is equipped with a recovery module. After determining the location of the leak point in the oil pipeline based on the sampling point location database, the method further includes: upon receiving an instruction to recover oil products, controlling the recovery module to move to the location of the leak point to recover the oil products leaked from the leak point.

[0013] According to the method for locating leak points in oil pipelines provided by the present invention, the step of collecting wireless signals at the sampling points specifically includes: collecting a preset number of wireless signals at each sampling point; the step of constructing a sampling point location database based on the wireless signals collected at the sampling points and the geographical coordinates of the sampling points specifically includes: determining the expected signal strength vector and the signal strength covariance matrix of the wireless signals at each sampling point based on the preset number of wireless signals collected at each sampling point; constructing the processed sampling coordinates of the sampling points based on the geographical coordinates of the sampling points, the expected signal strength vector of the wireless signals at the sampling points, and the signal strength covariance matrix of the wireless signals at the sampling points; and constructing the sampling point location database based on the processed sampling coordinates of each sampling point.

[0014] According to the method for locating leak points in oil pipelines provided by the present invention, the method further includes: when a leak occurs in the oil pipeline, collecting the intensity value of an oscillation signal generated by the leaked oil product; the step of determining the location of the leak point in the oil pipeline based on the sampling point location database specifically includes: constructing an oscillation signal intensity vector based on the oscillation signal intensity value; determining a distance value corresponding to each sampling point based on each processed sampling coordinate in the sampling point location database and the oscillation signal intensity vector, wherein the distance value is the distance between the processed sampling coordinate and the oscillation signal intensity vector; and determining the location of the leak point in the oil pipeline based on the distance value corresponding to each sampling point.

[0015] According to the method for locating a leak point in an oil pipeline provided by the present invention, the method further includes: determining a first distance value among the distance values ​​corresponding to each of the sampling points, wherein the first distance value is the smallest distance value among the distance values; subtracting the first distance value from each of the other distance values ​​to obtain a plurality of distance difference values; determining an average distance difference value based on the plurality of distance difference values, and determining a standard deviation of the distance difference value based on the average distance difference value; using distance difference values ​​greater than the standard deviation as rejection values, and using the sampling points corresponding to the rejection values ​​as rejection sampling points; using the sampling points other than the rejection sampling points as retained sampling points; the step of determining the leak point location of the oil pipeline based on the distance values ​​corresponding to each of the sampling points specifically includes: determining the leak point location of the oil pipeline based on the distance values ​​corresponding to each of the retained sampling points.

[0016] According to the method for locating leak points in oil pipelines provided by the present invention, the retained sampling points are determined in the following manner: determining a first sampling point corresponding to a first distance value; determining a first geographical location coordinate of the first sampling point and the location coordinates of each retained sampling point; determining a filtering distance value corresponding to each retained sampling point based on the first geographical location coordinate and the location coordinates of each retained sampling point, wherein the filtering distance value is the distance between the first geographical location coordinate and the location coordinates of each retained sampling point; using the filtering distance value exceeding a preset distance value as the elimination distance value, and using the retained sampling points corresponding to the elimination distance value as secondary elimination sampling points; using the retained sampling points other than the secondary elimination sampling points as the final retained sampling points.

[0017] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the oil pipeline leak location method as described above.

[0018] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the oil pipeline leak location method as described above.

[0019] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the oil pipeline leak location method as described above.

[0020] The present invention provides an oil pipeline leak point location system, method, electronic device, and storage medium. The oil pipeline leak point location system includes: an oil pipeline with a preset number of sampling points distributed along it; multiple signal generation modules disposed on or around the oil pipeline for periodically generating and transmitting wireless signals; a signal acquisition module; a moving module disposed on the oil pipeline for carrying the signal acquisition module to the sampling points to collect wireless signals when no leak point is found; and a processing module for receiving wireless signals, constructing a sampling point location database based on the wireless signals and the geographical coordinates of the sampling points, and determining the location of the leak point in the oil pipeline based on the sampling point location database, thereby enabling rapid and accurate location of leak points in oil pipelines. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 This is a schematic diagram of the structure of the oil pipeline leak location system provided by the present invention;

[0023] Figure 2 This is one of the flowcharts illustrating the method for locating leak points in oil pipelines provided by the present invention;

[0024] Figure 3 This is the second flowchart illustrating the method for locating leak points in oil pipelines provided by the present invention.

[0025] Figure 4 This is a schematic diagram of the process for determining the location of a leak in an oil pipeline based on a sampling point location database, provided by the present invention.

[0026] Figure 5 This is a flowchart illustrating the process of determining the location of a leak in an oil pipeline based on the distance values ​​corresponding to each sampling point, as provided by the present invention.

[0027] Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention.

[0028] Figure label:

[0029] 10: Oil pipeline leak location system; 110: Oil pipeline;

[0030] 120: Sampling point; 130: Signal generation module;

[0031] 140: Mobility module; 150: Signal acquisition module;

[0032] 160: Processing module. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0034] The oil pipeline leak location system provided by this invention has a simple structure, long service life, high reliability, and accurate and real-time positioning of the recovery system. It can save transportation costs, reduce environmental pollution, quickly locate pipeline leaks, and recover leaked crude oil, refined oil, etc.

[0035] Figure 1 This is a schematic diagram of the structure of the oil pipeline leak location system provided by the present invention.

[0036] The following will combine Figure 1 This document describes the system for locating leaks in oil pipelines.

[0037] In an exemplary embodiment of the present invention, combined with Figure 1 As can be seen, the oil pipeline leak location system 10 may include an oil pipeline 110, multiple signal generation modules 130, a movement module 140, a signal acquisition module 150, and a processing module 160. Each module will be described in detail below.

[0038] In one embodiment, the oil pipeline 110 may have a predetermined number of sampling points 120 distributed thereon, for example, Q sampling points. Multiple signal generation modules 130 may be disposed on or around the oil pipeline 110. Figure 1 The diagram shows a signal generation module 130 disposed around the oil pipeline 110. The signal generation module 130 can be used to periodically generate and transmit wireless signals. In one example, the wireless signal can be a Wi-Fi signal. The signal generation module 130 can be a Wi-Fi device.

[0039] In another embodiment, the mobile module 140 can be disposed on the oil pipeline 110 and, assuming no leaks occur in the oil pipeline 110, can carry the signal acquisition module 150 to Q sampling points 120 to collect wireless signals. In yet another example, the signal acquisition module 150 can be carried to Q sampling points 120 to collect q wireless signals emitted by q signal generation modules 130, thereby forming a signal strength vector based on the signal strength values ​​of the q wireless signals collected at each sampling point 120.

[0040] In another embodiment, the processing module 160 can be used to receive wireless signals. Furthermore, a sampling point location database can be constructed based on the wireless signals and the geographical coordinates of the sampling points 120, and the location of the leak in the oil pipeline 110 can be determined based on the sampling point location database. Through this embodiment, the leak in the oil pipeline can be located quickly and accurately at a low cost.

[0041] The oil pipeline leak location system 10 provided by the present invention includes: an oil pipeline 110, wherein a preset number of sampling points 120 are distributed on the oil pipeline 110; multiple signal generation modules 130, which are set on or around the oil pipeline 110, for periodically generating and transmitting wireless signals; a signal acquisition module 150; a moving module 140, which is set on the oil pipeline 110, for carrying the signal acquisition module 150 to the sampling points 120 to collect wireless signals when no leak point is generated in the oil pipeline 110; and a processing module 160, which is used to receive wireless signals, construct a sampling point location database based on the wireless signals and the geographical coordinates of the sampling points 120, and determine the location of the leak point of the oil pipeline 110 based on the sampling point location database, thereby realizing the ability to quickly and accurately locate the leak point of the oil pipeline 110.

[0042] In another exemplary embodiment of the present invention, the oil pipeline leak location system 10 may further include a recovery module. The recovery module can be used to move to the leak location and recover the leaked oil products upon receiving a recovery instruction from the processing module 160. Through this embodiment, without increasing additional costs, leaked oil products (e.g., crude oil, refined oil) can be quickly recovered by accurately locating the leak point, avoiding environmental pollution and enabling resource reuse.

[0043] In another exemplary embodiment of the present invention, the wireless signals acquired by the signal acquisition module 150 may include a preset number of wireless signals acquired at each sampling point, for example, q wireless signals acquired at Q sampling points. It should be noted that the preset number can be determined according to actual circumstances, and is not specifically limited in this embodiment; the preset number may be the same as the number of signal generation modules.

[0044] Furthermore, the processing module 160 can construct a sampling point location database based on the wireless signal and the geographic coordinates of the sampling points in the following manner:

[0045] Based on a preset number of wireless signals collected at each sampling point 120, the expected vector of the wireless signal strength and the covariance matrix of the wireless signal strength at each sampling point 120 are determined.

[0046] Based on the geographical coordinates of sampling point 120, the expected vector of the wireless signal strength at sampling point 120, and the covariance matrix of the wireless signal strength at sampling point 120, the processed sampling coordinates of sampling point 120 are constructed.

[0047] A sampling point location database is constructed based on the processed sampling coordinates of each sampling point (120).

[0048] In one embodiment, the processed sampling coordinates of sampling point 120 can be represented as (x t E (t) ,Σ t ), where t represents the current sampling point 120, and t = 1, 2, ..., Q. E (t) It is the expected vector (corresponding to the expected signal strength vector) of the q WiFi signal strengths (corresponding to the wireless signals) collected at sampling point t, Σ (t) It is the signal strength covariance matrix of the q WiFi signal strengths collected at sampling point t. In one example, the expected signal strength vector can be represented by formula (1):

[0049]

[0050] Among them, E(t) Let represent the expected signal strength vector of the wireless signal at sampling point t; n represents the total number of times the signal strength is sampled, where, continuing with the example mentioned earlier, n can be Q*q; P represents the number of times the signal strength is sampled in the Pth sampling. This represents the signal strength value collected for the first time at sampling point t.

[0051] Furthermore, the geographical coordinates x of the sampling point t can be used as a basis. t The expected vector E of the wireless signal strength at sampling point t (t) And the signal strength covariance matrix Σ of the wireless signal at sampling point t. t The processed sampling coordinates of each sampling point are constructed. A sampling point location database is then built based on these coordinates. This lays the foundation for determining the location of leaks in oil pipelines based on the sampling point location database.

[0052] In yet another exemplary embodiment of the present invention, the signal generation module 130 can also be used for:

[0053] In the event of a leak in oil pipeline 110, the intensity value of the oscillation signal generated by the leaking oil product is collected.

[0054] Processing module 160 can also be used for:

[0055] Obtain the oscillation signal strength value;

[0056] The processing module 160 can determine the location of the leak in the oil pipeline based on the sampling point location database in the following ways:

[0057] Construct an oscillation signal intensity vector based on the oscillation signal intensity value;

[0058] Based on the processed sampling coordinates and oscillation signal intensity vector in the sampling point location database, the distance value corresponding to each sampling point 120 is determined, where the distance value is the distance between the processed sampling coordinates and the oscillation signal intensity vector;

[0059] Based on the distance values ​​corresponding to each sampling point 120, the location of the leak point in the oil pipeline 110 is determined.

[0060] In one embodiment, the distance value can be a Mahalanobis distance value or other distance values. In this embodiment, the type of distance value is not specifically limited.

[0061] In another embodiment, the signal generation module 130, for example a WIFI device, can collect the intensity value of the oscillation signal generated by the oil leakage at the leak point and send the collected oscillation signal intensity value to the processing module 160. During application, the processing module 160 can construct an oscillation signal intensity vector s = (s1, s2, ..., s...) based on the collected oscillation signal intensity value. q Where s1 represents the intensity value of the oscillation signal acquired by the first signal generation module 130, s q This represents the intensity value of the oscillation signal acquired by the q-th signal generation module 130.

[0062] Furthermore, the Mahalanobis distance to each sampling point can be determined based on the processed sampled coordinates and oscillation signal intensity vector in the sampling point location database. In other words, the Mahalanobis distance can be used as a standard to measure the similarity between s and the sampling point location data. The smaller the Mahalanobis distance, the greater the similarity between the corresponding sampling point signal location data. Conversely, the larger the Mahalanobis distance, the smaller the similarity between the corresponding sampling point signal location data.

[0063] In another embodiment, the Mahalanobis distance can be expressed by formula (2):

[0064]

[0065] Where d represents the Mahalanobis distance corresponding to the sampling point t; s represents the oscillation signal intensity vector; E (t) Σ represents the expected signal strength vector of the wireless signal at sampling point t; t This represents the signal strength covariance matrix of the wireless signal at sampling point t.

[0066] Furthermore, the location of the leak point in the oil pipeline can be determined based on the distance values ​​corresponding to each sampling point.

[0067] In one example, the weights can be determined based on the Mahalanobis distance corresponding to each sampling point, and then the location of the leak point can be determined based on the geographical coordinates of each sampling point. In other words, the location data coordinates (geographical coordinates) of k sampling points are weighted and calculated. In one example, the location of the leak point can be determined using formula (3):

[0068]

[0069] Where x represents the location of the leak point; x t dt represents the geographical coordinates of the sampling point t; k represents the total number of sampling points; dt represents the distance to the sampling point t. It can be understood that dt and d in formula (2) are the same parameter. This can be understood as a weight.

[0070] In yet another exemplary embodiment of the present invention, the processing module 160 may also be used for:

[0071] Determine a first distance value from the distance values ​​corresponding to each sampling point, wherein the first distance value is the smallest distance value among the distance values;

[0072] Subtract the first distance value from each of the other distance values ​​except the first distance value to obtain multiple distance difference values;

[0073] Based on multiple distance differences, determine the average distance difference, and then determine the standard deviation of the distance difference based on the average distance difference.

[0074] Distance differences that are greater than the standard deviation are used as rejection values, and the sampling points corresponding to the rejection values ​​are used as rejection sampling points.

[0075] Sampling points other than those that were removed will be retained as sampling points;

[0076] Furthermore, the processing module 160 uses the following method to determine the location of the leak point in the oil pipeline based on the distance values ​​corresponding to each sampling point:

[0077] Based on the distance values ​​corresponding to each retained sampling point, the location of the leak point in the oil pipeline is determined.

[0078] In one embodiment, to reduce positioning errors, an improved adaptive K-value WENN algorithm can be incorporated. During application, the calculated Mahalanobis distances (corresponding to the distance values ​​for each sampling point) can be sorted in ascending order. The smallest Mahalanobis distance after sorting is d1 (corresponding to the first distance value), and the largest Mahalanobis distance is d... Q Where Q is the number of sampling points. d 1Q For d1 and d Q The Mahalanobis distance difference (corresponding distance difference value).

[0079] Furthermore, the average value of the Mahalanobis distance difference (corresponding to the average distance difference) is calculated, and the standard deviation of the distance difference is determined based on the average value of the distance difference. The average value of the Mahalanobis distance difference can be determined using formula (4), and the standard deviation can be determined using formula (5):

[0080]

[0081]

[0082] in, d represents the average value of the Mahalanobis distance difference; 1Q For d1 and d QMahalanobis distance; σ represents the standard deviation; d 1t For d1 and d t The difference in Mahalanobis distance.

[0083] In another embodiment, distance differences greater than the standard deviation can be used as rejection values, and the sampling points corresponding to the rejection values ​​can be used as rejection sampling points; the sampling points other than the rejection sampling points can be used as retained sampling points. Furthermore, the location of the leak point in the oil pipeline can be determined based on the distance values ​​corresponding to each retained sampling point.

[0084] In yet another embodiment, if d 1t >σ, then it will be with d 1t The data at the corresponding sampling point (corresponding to the discarded sampling point) is discarded. Conversely, it is retained, and the amount of data at the remaining sampling points (corresponding to the retained sampling points) is the K value.

[0085] To further improve the algorithm's accuracy, the location data of the K sampling points can be further eliminated.

[0086] In yet another exemplary embodiment of the present invention, the processing module 160 may also be used for:

[0087] Determine the first sampling point corresponding to the first distance value;

[0088] Determine the first geographical location coordinates of the first sampling point, and the location coordinates of the retained sampling points for each retained sampling point;

[0089] Based on the first geographic location coordinates and the location coordinates of each retained sampling point, a filtering distance value corresponding to each retained sampling point is determined, wherein the filtering distance value is the distance between the first geographic location coordinates and the location coordinates of each retained sampling point;

[0090] The filtering distance value that exceeds the preset distance value is used as the rejection distance value, and the retained sampling point corresponding to the rejection distance value is used as the secondary rejection sampling point;

[0091] The remaining sampling points, excluding those removed during the second round of sampling, will be used as the final retained sampling points.

[0092] In one embodiment, the filtering distance value can be Euclidean distance. During application, the location data of the aforementioned determined K sampling points can be subject to secondary filtering. First, the Euclidean distance (corresponding to the filtering distance value) between the coordinate x1 of the location data of the minimum Mahalanobis distance sampling point (corresponding to the first sampling point) and the coordinates of the K-1 sampling points (corresponding to the retained sampling points) is calculated, and denoted as di. 12 ,di 13 ,...,di 1k .

[0093] Furthermore, the filtering distance value exceeding the preset distance value can be used as the rejection distance value, and the retained sampling point corresponding to the rejection distance value can be used as the secondary rejection sampling point. The preset distance value can be adjusted according to actual conditions, and is not specifically limited in this embodiment. The preset distance value can be 1, and its setting can affect the final number of retained sampling points.

[0094] In another example, the Euclidean distance (corresponding to the filter distance value) can be determined using formula (6):

[0095]

[0096] Among them, di 1k This represents the coordinates (x1) of the minimum Mahalanobis distance sampling point (corresponding to the first sampling point) and the coordinates (x1) of the kth sampling point (corresponding to the reserved sampling point). k The Euclidean distance.

[0097] When di 1k When the value is greater than 1, the corresponding sampling point location data is removed from the K sampling point location data to obtain the final retained sampling point.

[0098] As can be seen from the above technical solution, the oil pipeline leak location system provided by this invention utilizes a Wi-Fi device (corresponding signal generation module) combined with an improved algorithm to calculate the leak point. Its system structure is simple, reacts quickly, is easy to use, has high positioning accuracy, high real-time performance, and low system setup cost. Furthermore, this invention can recover leaked crude oil and refined oil from oil pipelines, which can then be processed and reused, saving industrial costs, reducing waste, and minimizing environmental pollution.

[0099] As described above, the oil pipeline leak location system provided by this invention includes: an oil pipeline with a preset number of sampling points distributed along it; multiple signal generation modules located on or around the oil pipeline for periodically generating and transmitting wireless signals; a signal acquisition module; a moving module located on the oil pipeline for carrying the signal acquisition module to the sampling points to collect wireless signals when no leak occurs; and a processing module for receiving the wireless signals, constructing a sampling point location database based on the wireless signals and the geographical coordinates of the sampling points, and determining the location of the leak point in the oil pipeline based on the sampling point location database, thus enabling rapid and accurate location of leaks in the oil pipeline.

[0100] Based on the same inventive concept, the present invention also provides a method for locating leaks in oil pipelines. The method for locating leaks in oil pipelines provided by the present invention is described below, and the method described below can be referred to in correspondence with the system described above.

[0101] Figure 2 This is one of the flowcharts illustrating the method for locating leak points in oil pipelines provided by this invention.

[0102] The following will combine Figure 2 The method for locating leaks in oil pipelines is explained.

[0103] In an exemplary embodiment of the present invention, the oil pipeline is distributed with a predetermined number of sampling points, combined with Figure 2 As can be seen, the method for locating leaks in oil pipelines may include steps 210 to 240, and each step will be described below.

[0104] In step 210, wireless signals are generated and transmitted at regular intervals.

[0105] In one embodiment, a wireless signal can be generated and transmitted periodically based on a signal generation module disposed around the oil pipeline. In one example, the wireless signal can be a Wi-Fi signal. The signal generation module can be a Wi-Fi device.

[0106] In step 220, wireless signals are collected at the sampling point if there is no leak in the oil pipeline.

[0107] In one embodiment, assuming no leaks occur in the oil pipeline, a preset number of wireless signals can be collected at each sampling point, for example, q wireless signals collected at Q sampling points. It should be noted that the preset number can be determined based on actual conditions and is not specifically limited in this embodiment; the preset number can be the same as the number of signal generation modules.

[0108] In step 230, a sampling point location database is constructed based on the wireless signals collected at the sampling points and the geographical coordinates of the sampling points.

[0109] In step 240, the location of the leak point in the oil pipeline is determined based on the sampling point location database.

[0110] In one embodiment, a sampling point location database can be constructed based on wireless signals and the geographical coordinates of the sampling points, and the location of the leak in the oil pipeline can be determined based on the sampling point location database. This embodiment allows for the rapid and accurate location of leaks in oil pipelines at a low cost.

[0111] Figure 3This is the second flowchart illustrating the method for locating leak points in oil pipelines provided by this invention.

[0112] The following will combine Figure 3 The process of another method for locating leaks in oil pipelines is explained.

[0113] In an exemplary embodiment of the present invention, the oil pipeline may be equipped with a recovery module. Combined with... Figure 3 As can be seen, the method for locating the leak point of an oil pipeline may include steps 310 to 350, wherein steps 310 to 340 are the same as or similar to steps 210 to 240 respectively. For the specific implementation and beneficial effects, please refer to the previous description. In this embodiment, they will not be repeated. Step 350 will be introduced below.

[0114] In step 350, upon receiving an instruction to recover oil products, the recovery module is controlled to move to the location of the leak and recover the oil products leaking from the leak point.

[0115] In application, once the leak point is accurately located, upon receiving an instruction to recover the oil, the recovery module can be controlled to move to the leak point and recover the leaked oil. Through this embodiment, without incurring additional costs, leaked oil products (such as crude oil and refined oil) can be quickly recovered by accurately locating the leak point, avoiding environmental pollution and enabling resource reuse.

[0116] In yet another exemplary embodiment of the present invention, continuing with Figure 2 The above embodiment will be used as an example to illustrate how wireless signals can be collected at sampling points (corresponding to step 220) in the following way:

[0117] A preset number of wireless signals are collected at each sampling point;

[0118] Furthermore, based on the wireless signals collected at the sampling points and the geographical coordinates of the sampling points, the sampling point location database (corresponding to step 230) can be constructed in the following way:

[0119] Based on a preset number of wireless signals collected at each sampling point, the expected vector of wireless signal strength and the covariance matrix of wireless signal strength at each sampling point are determined.

[0120] Based on the geographical coordinates of the sampling point, the expected vector of the wireless signal strength at the sampling point, and the covariance matrix of the wireless signal strength at the sampling point, the processed sampling coordinates of the sampling point are constructed.

[0121] A sampling point location database is constructed based on the processed sampling coordinates of each sampling point.

[0122] In one embodiment, a preset number of wireless signals are collected at each sampling point, for example, q wireless signals collected at Q sampling points. It should be noted that the preset number can be determined according to actual circumstances, and is not specifically limited in this embodiment; the preset number can be the same as the number of signal generation modules.

[0123] In yet another embodiment, the processed sampling coordinates of the sampling points can be represented as (x t E (t) ,Σ t ), where t represents the current sampling point, and t = 1, 2, ..., Q. (t) It is the expected vector (corresponding to the expected signal strength vector) of the q WiFi signal strengths (corresponding to the wireless signals) collected at sampling point t, Σ (t) It is the signal strength covariance matrix of the q WiFi signal strengths collected at sampling point t. The expected signal strength vector can be represented by the formula (1) mentioned above.

[0124] Furthermore, the geographical coordinates x of the sampling point t can be used as a basis. t The expected vector E of the wireless signal strength at sampling point t (t) And the signal strength covariance matrix Σ of the wireless signal at sampling point t. t The processed sampling coordinates of each sampling point are constructed. A sampling point location database is then built based on these coordinates. This lays the foundation for determining the location of leaks in oil pipelines based on the sampling point location database.

[0125] Figure 4 This is a schematic diagram of the process for determining the location of a leak in an oil pipeline based on a sampling point location database, as provided by the present invention.

[0126] The following will combine Figure 4 The process of determining the location of a leak in an oil pipeline based on a database of sampling point locations is explained.

[0127] In an exemplary embodiment of the present invention, combined with Figure 4 As can be seen, determining the location of the leak point in an oil pipeline based on the sampling point location database can include steps 410 to 440, which will be described in detail below.

[0128] In step 410, in the event of a leak in the oil pipeline, the intensity value of the oscillation signal generated by the oil product leaking from the leak point is collected.

[0129] In step 420, an oscillation signal intensity vector is constructed based on the oscillation signal intensity value.

[0130] In step 430, the distance value corresponding to each sampling point is determined based on the processed sampling coordinates in the sampling point location database and the oscillation signal intensity vector.

[0131] In step 440, the location of the leak point in the oil pipeline is determined based on the distance values ​​corresponding to each sampling point.

[0132] The distance value is the distance between the processed sampled coordinates and the oscillation signal intensity vector. The distance value can be a Mahalanobis distance or other distance values; in this embodiment, the type of distance value is not specifically limited.

[0133] In another embodiment, the intensity value of the oscillation signal generated by the leaking oil at the leak point can be collected by a signal generation module, such as a WIFI device. During application, an oscillation signal intensity vector s = (s1, s2, ..., s...) can be constructed based on the collected oscillation signal intensity values. q Where s1 represents the intensity value of the oscillation signal acquired by the first signal generation module, s q This represents the intensity value of the oscillation signal acquired by the q-th signal generation module.

[0134] Furthermore, the Mahalanobis distance to each sampling point can be determined based on the processed sampled coordinates and oscillation signal intensity vector in the sampling point location database. In other words, the Mahalanobis distance can be used as a standard to measure the similarity between s and the sampling point location data. The smaller the Mahalanobis distance, the greater the similarity between the corresponding sampling point signal location data. Conversely, the larger the Mahalanobis distance, the smaller the similarity between the corresponding sampling point signal location data.

[0135] In another embodiment, the Mahalanobis distance can be expressed using the formula (2) described above.

[0136] Furthermore, the location of the leak point in the oil pipeline can be determined based on the distance values ​​corresponding to each sampling point.

[0137] In one example, the weights can be determined based on the Mahalanobis distance corresponding to each sampling point, and then the location of the leak point can be determined based on the geographical coordinates of each sampling point. In other words, the location data coordinates (geographical coordinates) of k sampling points are weighted and calculated. In one example, the location of the leak point can be determined using the formula (3) mentioned above.

[0138] Figure 5 This is a flowchart illustrating the process of determining the location of a leak in an oil pipeline based on the distance values ​​corresponding to each sampling point, as provided by the present invention.

[0139] The following will combine Figure 5The process of determining the location of a leak in an oil pipeline based on the distance values ​​corresponding to each sampling point is explained.

[0140] In an exemplary embodiment of the present invention, combined with Figure 5 As can be seen, determining the location of the leak point in the oil pipeline based on the distance values ​​corresponding to each sampling point can include steps 510 to 560, which will be described in detail below.

[0141] In step 510, a first distance value is determined from the distance values ​​corresponding to each sampling point.

[0142] In step 520, the distance values ​​other than the first distance value are subtracted from the first distance value to obtain multiple distance difference values.

[0143] In step 530, based on multiple distance differences, the average distance difference is determined, and the standard deviation of the distance difference is determined based on the average distance difference.

[0144] In step 540, the distance difference that is greater than the standard deviation is used as the rejection value, and the sampling point corresponding to the rejection value is used as the rejection sampling point.

[0145] In step 550, the sampling points other than the discarded sampling points are retained as sampling points.

[0146] In step 560, the location of the leak point in the oil pipeline is determined based on the distance values ​​corresponding to each retained sampling point.

[0147] The first distance value is the smallest distance value among all distance values.

[0148] In one embodiment, to reduce positioning errors, an improved adaptive K-value WENN algorithm can be incorporated. During application, the calculated Mahalanobis distances (corresponding to the distance values ​​for each sampling point) can be sorted in ascending order. The smallest Mahalanobis distance after sorting is d1 (corresponding to the first distance value), and the largest Mahalanobis distance is d... Q Where Q is the number of sampling points. d 1Q For d1 and d Q The Mahalanobis distance difference (corresponding distance difference value).

[0149] Furthermore, the average value of the Mahalanobis distance difference (corresponding to the average value of the distance difference) is calculated, and the standard deviation of the distance difference is determined based on the average value of the distance difference. The average value of the Mahalanobis distance difference can be determined using the formula (4) mentioned above, and the standard deviation can be determined using the formula (5) mentioned above.

[0150] In another embodiment, distance differences greater than the standard deviation can be used as rejection values, and the sampling points corresponding to the rejection values ​​can be used as rejection sampling points; the sampling points other than the rejection sampling points can be used as retained sampling points. Furthermore, the location of the leak point in the oil pipeline can be determined based on the distance values ​​corresponding to each retained sampling point.

[0151] In yet another embodiment, if d 1t >σ, then it will be with d 1t The data at the corresponding sampling point (corresponding to the discarded sampling point) is discarded. Conversely, it is retained, and the amount of data at the remaining sampling points (corresponding to the retained sampling points) is the K value.

[0152] To further improve the algorithm's accuracy, the location data of the K sampling points can be further eliminated.

[0153] In yet another exemplary embodiment of the present invention, the retained sampling points can be determined in the following manner:

[0154] Determine the first sampling point corresponding to the first distance value;

[0155] Determine the first geographical location coordinates of the first sampling point, and the location coordinates of the retained sampling points for each retained sampling point;

[0156] Based on the first geographic location coordinates and the location coordinates of each retained sampling point, a filtering distance value corresponding to each retained sampling point is determined, wherein the filtering distance value is the distance between the first geographic location coordinates and the location coordinates of each retained sampling point;

[0157] The filtering distance value that exceeds the preset distance value is used as the rejection distance value, and the retained sampling point corresponding to the rejection distance value is used as the secondary rejection sampling point;

[0158] The remaining sampling points, excluding those removed during the second round of sampling, will be used as the final retained sampling points.

[0159] In one embodiment, the filtering distance value can be Euclidean distance. During application, the location data of the aforementioned determined K sampling points can be subject to secondary filtering. First, the Euclidean distance (corresponding to the filtering distance value) between the coordinate x1 of the location data of the minimum Mahalanobis distance sampling point (corresponding to the first sampling point) and the coordinates of the K-1 sampling points (corresponding to the retained sampling points) is calculated, and denoted as di. 12 ,di 13 ,...,di 1k .

[0160] Furthermore, the filtering distance value exceeding the preset distance value can be used as the rejection distance value, and the retained sampling point corresponding to the rejection distance value can be used as the secondary rejection sampling point. The preset distance value can be adjusted according to actual conditions, and is not specifically limited in this embodiment. The preset distance value can be 1, and its setting can affect the final number of retained sampling points.

[0161] In yet another example, the Euclidean distance (corresponding to the filter distance value) can be determined using the formula (6) described above.

[0162] When di 1k When the value is greater than 1, the corresponding sampling point location data is removed from the K sampling point location data to obtain the final retained sampling point.

[0163] The method for locating oil pipeline leaks described above can quickly and accurately pinpoint the leak point. Furthermore, leaked crude oil and refined oil can be recovered and processed for reuse, saving industrial costs, reducing waste, and minimizing environmental pollution.

[0164] Figure 6 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 6 As shown, the electronic device may include a processor 610, a communications interface 620, a memory 630, and a communication bus 640. The processor 610, communications interface 620, and memory 630 communicate with each other via the communication bus 640. The processor 610 can call logical instructions in the memory 630 to execute a method for locating leak points in an oil pipeline. The oil pipeline has a preset number of sampling points. The method for locating leak points in an oil pipeline includes: periodically generating and transmitting wireless signals; collecting the wireless signals at the sampling points when no leak point has been found in the oil pipeline; constructing a sampling point location database based on the wireless signals collected at the sampling points and the geographical coordinates of the sampling points; and determining the location of the leak point in the oil pipeline based on the sampling point location database.

[0165] Furthermore, the logical instructions in the aforementioned memory 630 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, essentially, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0166] On the other hand, the present invention also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the oil pipeline leak point location method provided by the above methods. The oil pipeline is distributed with a preset number of sampling points. The oil pipeline leak point location method includes: periodically generating and transmitting wireless signals; collecting the wireless signals at the sampling points when no leak point is generated in the oil pipeline; constructing a sampling point location database based on the wireless signals collected at the sampling points and the geographical coordinates of the sampling points; and determining the location of the leak point in the oil pipeline based on the sampling point location database.

[0167] In another aspect, the present invention also provides a non-transitory computer-readable storage medium storing a computer program thereon. When executed by a processor, the computer program implements the oil pipeline leak point location method provided by the above methods. The oil pipeline is distributed with a preset number of sampling points. The oil pipeline leak point location method includes: periodically generating and transmitting wireless signals; collecting the wireless signals at the sampling points when no leak point has occurred in the oil pipeline; constructing a sampling point location database based on the wireless signals collected at the sampling points and the geographical coordinates of the sampling points; and determining the location of the leak point in the oil pipeline based on the sampling point location database.

[0168] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0169] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0170] It is further understood that although the operations are described in a specific order in the accompanying drawings in the embodiments of the present invention, this should not be construed as requiring these operations to be performed in the specific order or serial order shown, or requiring all the operations shown to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A system for locating leak points in oil pipelines, characterized in that, The oil pipeline leak location system includes: An oil pipeline, wherein a predetermined number of sampling points are distributed along the oil pipeline; Multiple signal generation modules are disposed on or around the oil pipeline for periodically generating and transmitting wireless signals. Signal acquisition module; A mobile module, installed in the oil pipeline, is used to carry the signal acquisition module to the sampling point to collect the wireless signal when there is no leak in the oil pipeline. The processing module is configured to receive the wireless signal, construct a sampling point location database based on the wireless signal and the geographical coordinates of the sampling points, and determine the location of the leak point in the oil pipeline based on the sampling point location database. The signal generation module is also used for: In the event of a leak in the oil pipeline, the intensity value of the oscillation signal generated by the oil product leaking from the leak point is collected; The processing module is also used for: Obtain the intensity value of the oscillation signal; The processing module uses the following method to determine the location of the leak point in the oil pipeline based on the sampling point location database: Based on the oscillation signal intensity value, an oscillation signal intensity vector is constructed; Based on the processed sampling coordinates and the oscillation signal intensity vector in the sampling point location database, a distance value corresponding to each sampling point is determined, wherein the distance value is the distance between the processed sampling coordinates and the oscillation signal intensity vector; Based on the distance values ​​corresponding to each of the sampling points, the location of the leak point of the oil pipeline is determined, wherein the wireless signal collected by the signal acquisition module includes a preset number of wireless signals collected at each of the sampling points; The processing module constructs a sampling point location database based on the wireless signal and the geographic coordinates of the sampling points in the following manner: Based on a preset number of wireless signals collected at each sampling point, the expected signal strength vector and the signal strength covariance matrix of the wireless signal at each sampling point are determined. Based on the geographical coordinates of the sampling point, the expected vector of the wireless signal strength at the sampling point, and the covariance matrix of the wireless signal strength at the sampling point, the processed sampling coordinates of the sampling point are constructed. A database of sampling point locations is constructed based on the processed sampling coordinates of each sampling point.

2. The oil pipeline leak location system according to claim 1, characterized in that, The oil pipeline leak location system also includes: The recovery module is used to move to the location of the leak point and recover the oil products leaked from the leak point when it receives a recovery instruction from the processing module.

3. The oil pipeline leak location system according to claim 1, characterized in that, The processing module is also used for: A first distance value is determined from the distance values ​​corresponding to each of the sampling points, wherein the first distance value is the smallest distance value among the distance values; Subtract the first distance value from each of the other distance values ​​except the first distance value to obtain multiple distance difference values; Based on the multiple distance differences, determine the average distance difference of the multiple distance differences, and determine the standard deviation of the distance difference based on the average distance difference; The distance difference that is greater than the standard deviation is used as the rejection value, and the sampling point corresponding to the rejection value is used as the rejection sampling point; Sampling points other than the discarded sampling points are retained as sampling points; The processing module determines the location of the leak point in the oil pipeline based on the distance value corresponding to each sampling point in the following manner: Based on the distance values ​​corresponding to each of the reserved sampling points, the location of the leak point in the oil pipeline is determined.

4. The oil pipeline leak location system according to claim 3, characterized in that, The processing module is also used for: Determine the first sampling point corresponding to the first distance value; Determine the first geographical location coordinates of the first sampling point, and the reserved sampling point location coordinates of each of the reserved sampling points; Based on the first geographic location coordinates and the location coordinates of each of the retained sampling points, a filtering distance value corresponding to each of the retained sampling points is determined, wherein the filtering distance value is the distance between the first geographic location coordinates and the location coordinates of each of the retained sampling points; The filtering distance value that exceeds the preset distance value is used as the rejection distance value, and the retained sampling point corresponding to the rejection distance value is used as the secondary rejection sampling point; The remaining sampling points, excluding those removed in the second round, are taken as the final remaining sampling points.

5. A method for locating a leak point in an oil pipeline, characterized in that, The oil pipeline has a predetermined number of sampling points, and the method for locating leak points in the oil pipeline includes: Generate and transmit wireless signals at regular intervals; The wireless signal is collected at the sampling point assuming there is no leak in the oil pipeline. A sampling point location database is constructed based on the wireless signals collected at the sampling points and the geographical coordinates of the sampling points. The location of the leak point in the oil pipeline was determined based on the sampling point location database; The method further includes: In the event of a leak in the oil pipeline, the intensity value of the oscillation signal generated by the oil product leaking from the leak point is collected; Determining the location of the leak point in the oil pipeline based on the sampling point location database specifically includes: Based on the oscillation signal intensity value, an oscillation signal intensity vector is constructed; Based on the processed sampling coordinates and the oscillation signal intensity vector in the sampling point location database, a distance value corresponding to each sampling point is determined, wherein the distance value is the distance between the processed sampling coordinates and the oscillation signal intensity vector; Based on the distance values ​​corresponding to each of the sampling points, the location of the leak point in the oil pipeline is determined, wherein the step of collecting the wireless signal at the sampling points specifically includes: A preset number of wireless signals are collected at each of the sampling points; The step of constructing a sampling point location database based on the wireless signals collected at the sampling points and the geographical coordinates of the sampling points specifically includes: Based on a preset number of wireless signals collected at each sampling point, the expected signal strength vector and the signal strength covariance matrix of the wireless signal at each sampling point are determined. Based on the geographical coordinates of the sampling point, the expected vector of the wireless signal strength at the sampling point, and the covariance matrix of the wireless signal strength at the sampling point, the processed sampling coordinates of the sampling point are constructed. A database of sampling point locations is constructed based on the processed sampling coordinates of each sampling point.

6. The method for locating leak points in oil pipelines according to claim 5, characterized in that, The oil pipeline is equipped with a recovery module. After determining the location of the leak point in the oil pipeline based on the sampling point location database, the method further includes: Upon receiving an instruction to recover oil products, the recovery module is controlled to move to the location of the leak and recover the oil products leaking from the leak point.

7. The method for locating leak points in oil pipelines according to claim 5, characterized in that, The method further includes: A first distance value is determined from the distance values ​​corresponding to each of the sampling points, wherein the first distance value is the smallest distance value among the distance values; Subtract the first distance value from each of the other distance values ​​except the first distance value to obtain multiple distance difference values; Based on the multiple distance differences, determine the average distance difference of the multiple distance differences, and determine the standard deviation of the distance difference based on the average distance difference; The distance difference that is greater than the standard deviation is used as the rejection value, and the sampling point corresponding to the rejection value is used as the rejection sampling point; Sampling points other than the discarded sampling points are retained as sampling points; Determining the location of the leak point in the oil pipeline based on the distance values ​​corresponding to each of the sampling points specifically includes: Based on the distance values ​​corresponding to each of the reserved sampling points, the location of the leak point in the oil pipeline is determined.

8. The method for locating leak points in oil pipelines according to claim 7, characterized in that, The reserved sampling points are determined in the following manner: Determine the first sampling point corresponding to the first distance value; Determine the first geographical location coordinates of the first sampling point, and the reserved sampling point location coordinates of each of the reserved sampling points; Based on the first geographic location coordinates and the location coordinates of each of the retained sampling points, a filtering distance value corresponding to each of the retained sampling points is determined, wherein the filtering distance value is the distance between the first geographic location coordinates and the location coordinates of each of the retained sampling points; The filtering distance value that exceeds the preset distance value is used as the elimination distance value, and the retained sampling point corresponding to the elimination distance value is used as the secondary elimination sampling point; The remaining sampling points, excluding those removed in the second round, are taken as the final remaining sampling points.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method for locating leak points in oil pipelines as described in any one of claims 5 to 8.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for locating leak points in oil pipelines as described in any one of claims 5 to 8.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the method for locating leak points in oil pipelines as described in any one of claims 5 to 8.

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