Detection method and system suitable for pipe network anomaly early warning

Abstract

This invention relates to the field of data processing technology, specifically proposing a detection method and system suitable for early warning of pipeline anomalies. The method includes establishing a pipeline network simulation model and obtaining the pipeline network topology based on the simulation model; determining the number of nodes in the pipeline network based on the topology, and determining the flow velocity and pressure information of all nodes at time t0, and establishing an initial matrix J0 based on the flow velocity and pressure information at time t0; obtaining the flow velocity and pressure information of all nodes again at time t1, and establishing a first matrix J1 based on the flow velocity and pressure information at time t1; comparing the flow velocity and pressure information of the corresponding nodes in the initial matrix J0 and the first matrix J1 to determine whether each node has an anomaly at time t1. This invention, by detecting the operating status parameters of each node at adjacent times in the pipeline network, can effectively determine the operating status of each node at different times.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically, to a detection method and system suitable for early warning of pipeline network anomalies. Background Technology

[0002] Currently, when detecting anomalies in natural gas pipeline networks, existing technologies rely on natural gas pipeline network management systems to monitor pipeline data. During the monitoring process, if abnormalities are detected in the pipeline's operational data, inspection instructions are issued to maintenance personnel, who then conduct on-site inspections to determine if leaks have occurred. Therefore, how to intelligently detect anomalies in the pipeline network's operational status based on existing pipeline network management systems has become an urgent problem to be solved. Summary of the Invention

[0003] In view of this, the present invention proposes a detection method and system suitable for early warning of pipeline anomalies, aiming to solve the problem of how to intelligently detect anomalies in the operating status of pipelines and accurately identify abnormal points in pipelines.

[0004] In one aspect, the present invention proposes a detection method suitable for early warning of pipeline network anomalies, comprising:

[0005] Establish a pipeline network simulation model and obtain the pipeline network topology based on the simulation model;

[0006] The number of nodes in the pipeline network is determined based on the topology, and the flow velocity and pressure information of all nodes at time t0 are determined. An initial matrix J0 is established based on the flow velocity and pressure information at time t0.

[0007] At time t1, the flow velocity and pressure information of all nodes are acquired again, and a first matrix J1 is established based on the flow velocity and pressure information at time t1.

[0008] The flow velocity and pressure information of the corresponding nodes in the initial matrix J0 are compared with those in the first matrix J1 to determine whether any abnormality occurs at time t1:

[0009] If an abnormality occurs, an abnormality alarm will be triggered.

[0010] If no abnormalities are found, proceed with subsequent testing.

[0011] Furthermore, when determining the number of nodes in the pipeline network based on the topology, determining the flow velocity and pressure information of all nodes at time t0, and establishing an initial matrix J0 based on the flow velocity and pressure information at time t0, the process includes:

[0012] Based on the topology, the number of nodes N in the pipeline network is determined. At time t0, the initial flow velocity and initial pressure information of each node are obtained, and an initial flow velocity set A0 (s1, s2, s3, ..., si, ..., sN) and an initial pressure set B0 (p1, p2, p3, ..., pi, ..., pN) are established, where si represents the initial flow velocity of the i-th node at time t0, and pi represents the initial pressure of the i-th node at time t0. Based on the obtained initial flow velocity set A0 and initial pressure set B0, an initial matrix J0 is established, J0[s1 / p1, s2 / p2, s3 / p3, ..., si / pi, ..., sN / pN];

[0013] When acquiring the flow velocity and pressure information of all nodes again at time t1, and establishing the first matrix J1 based on the flow velocity and pressure information at time t1, the process includes:

[0014] At time t1, obtain the first velocity set A1 (s11, s12, s13, ..., s1i, ..., s1N) and the first pressure set B1 (p11, p12, p13, ..., p1i, ..., p1N) for each node, where s1i represents the first velocity of the i-th node at time t1 and p1i represents the first pressure of the i-th node at time t1. Establish the first matrix J1, J1[s11 / p11, s12 / p12, s13 / p13, ..., s1i / p1i, ..., s1N / p1N].

[0015] Further, when comparing the flow velocity and pressure information of the corresponding nodes in the initial matrix J0 with those in the first matrix J1 to determine whether any node exhibits an anomaly at time t1, the following steps are taken:

[0016] The N elements in the initial matrix J0 and the first matrix J1 are compared one by one, and a reference difference C is preset:

[0017] When |si / pi-s1i / p1i|≤C, the i-th node is determined to be in a normal state at time t1.

[0018] When |si / pi-s1i / p1i|>C, then the i-th node is determined to be in an abnormal state to be confirmed at time t1.

[0019] Furthermore, the detection method applicable to pipeline network anomaly early warning also includes:

[0020] When the i-th node is determined to be in an abnormal state to be confirmed at time t1, the average values ​​of the flow velocity and pressure ratio of the i-th node at time t0 and time t1 are calculated respectively, and denoted as the initial average (Σsi / pi) / i and the first average (Σs1i / p1i) / i.

[0021] The initial mean (Σsi / pi) / i and the first mean (Σs1i / p1i) / i are compared, and the i-th node is determined to be abnormal based on the comparison result:

[0022] When (Σsi / pi) / i < (Σs1i / p1i) / i, the i-th node is determined to be in an abnormal state at time t1.

[0023] When (Σsi / pi) / i ≥ (Σs1i / p1i) / i, the detection of the i-th node continues, and the secondary flow velocity s2i and the secondary pressure p2i of the i-th node are obtained at time t21.

[0024] If |si / pi-s2i / p2i|>C, then the i-th node is determined to be in an abnormal state at time t21;

[0025] If |si / pi-s2i / p2i|≤C, then continue to detect the i-th node, and obtain the third flow velocity s3i and the third pressure p3i of the i-th node at time t22:

[0026] If |si / pi-s3i / p3i|>C, then the i-th node is determined to be in an abnormal state at time t22;

[0027] If |si / pi-s3i / p3i|≤C, then the i-th node is determined to be in a normal state at time t22.

[0028] Furthermore, the detection method applicable to pipeline network anomaly early warning also includes:

[0029] When |si / pi-s1i / p1i|>C, and (Σsi / pi) / i≥(Σs1i / p1i) / i, the interval between time t1 and time t21 is determined based on the difference between si / pi and s1i / p1i:

[0030] A first preset difference C1, a second preset difference C2, and a third preset difference C3 are preset, and C < C1 < C2 < C3 < 2C; a first preset duration T1, a second preset duration T2, a third preset duration T3, and a fourth preset duration T4 are preset, and (t1-t0) > T1 > T2 > T3 > T4;

[0031] When C < |si / pi - s1i / p1i| ≤ C1, the time interval between time t1 and time t21 is set to the first preset duration T1;

[0032] When C1 < |si / pi - s1i / p1i| ≤ C2, the time interval between time t1 and time t21 is set to the second preset duration T2;

[0033] When C2 < |si / pi - s1i / p1i| ≤ C3, the time interval between time t1 and time t21 is set to the third preset duration T3;

[0034] When C3 < |si / pi-s1i / p1i|, the time interval between time t1 and time t21 is set to the fourth preset duration T4.

[0035] After selecting the nth preset duration Tn as the time interval between t1 and t21, n = 1, 2, 3, 4, the time interval between time t21 and time t22 is set to 0.5Tn.

[0036] Furthermore, when determining that the i-th node is in an abnormal state at time t21 or t22, the method further includes:

[0037] Determine the upstream and downstream nodes of the i-th node, and represent the upstream node as the n-th node and the downstream node as the m-th node, where the values ​​of n and m range from 1 to N;

[0038] Calculate the flow difference Ln-i between the nth node and the i-th node, and calculate the flow difference Li-m between the i-th node and the m-th node, and determine the abnormal node interval based on the relationship between Ln-i and Li-m:

[0039] When Ln-i = Li-m, it is determined that there is an anomaly in the pipeline between the m-th node and the n-th node;

[0040] When Ln-i > Li-m, it is determined that there is an anomaly in the pipeline between the i-th node and the n-th node;

[0041] When Ln-i < Li-m, it is determined that there is an anomaly in the pipeline between the m-th node and the i-th node.

[0042] Furthermore, when Ln-i = Li-m, or when Ln-i > Li-m, or when Ln-i < Li-m, and when it is determined that there is an anomaly in the pipeline between the m-th node and the n-th node, between the i-th node and the n-th node, or between the m-th node and the i-th node, the following is included:

[0043] Based on distributed optical fiber detection technology, the temperature change signal around the pipe between the m-th node and the n-th node, between the i-th node and the n-th node, or between the m-th node and the i-th node at time t1 is obtained, and the first temperature curve at time t1 is established.

[0044] Identify the first temperature curve and determine whether there are any abnormal points in the first temperature curve;

[0045] If it exists, then it is determined that there is an anomaly in the pipeline between the m-th node and the n-th node, between the i-th node and the n-th node, or between the m-th node and the i-th node at time t21 or t22.

[0046] If it does not exist, then obtain the temperature change signal around the pipe between the m-th node and the n-th node, between the i-th node and the n-th node, or between the m-th node and the i-th node at time t0, and establish the initial temperature curve at time t0.

[0047] Compare the magnitude of change of the first temperature curve with that of the initial temperature curve:

[0048] When the change amplitude of the first temperature curve and the initial temperature curve is less than or equal to a threshold, a second temperature curve is acquired at time t11. The change amplitude of the second temperature curve is compared with that of the initial temperature curve. If the change amplitude is less than or equal to the threshold, the pipeline in that node section is determined to be in a normal state. If the change amplitude is greater than the threshold, a third temperature curve is acquired at time t12. When the change amplitude between the third temperature curve and the initial temperature curve is less than or equal to the threshold, the pipeline in that node section is determined to be in a normal state. When the change amplitude between the third temperature curve and the initial temperature curve is greater than the threshold, a leak point is determined in the pipeline in that node section.

[0049] When the change in the first temperature curve from the initial temperature curve is greater than the threshold, the velocity difference Δs between adjacent nodes is obtained:

[0050] When the velocity difference Δs is greater than or equal to the threshold, it is determined that there is a leak in the pipeline within that node interval.

[0051] When the flow velocity difference Δs is less than the threshold, the ambient temperature difference between time t0 and t1 is obtained. If the ambient temperature difference is less than the threshold, it is determined that there is a leak in the pipeline of that node interval.

[0052] If the ambient temperature difference is greater than or equal to the threshold, acoustic wave detection is performed on the pipeline in that node section, and the presence of a leak point in the pipeline in that node section is determined based on the results of the acoustic wave detection.

[0053] On the other hand, the present invention also proposes a detection system suitable for early warning of pipeline anomalies, comprising:

[0054] The simulation module is used to establish a pipeline network simulation model and obtain the pipeline network topology based on the simulation model.

[0055] The first data acquisition module is used to determine the number of nodes in the pipeline network based on the topology, determine the flow velocity and pressure information of all nodes at time t0, and establish an initial matrix J0 based on the flow velocity and pressure information at time t0.

[0056] The second data acquisition module is used to acquire the flow velocity and pressure information of all nodes again at time t1, and to establish a first matrix J1 based on the flow velocity and pressure information at time t1.

[0057] The data processing module is used to compare the flow velocity and pressure information of the corresponding nodes in the initial matrix J0 with those in the first matrix J1 to determine whether any abnormality occurs at each node at time t1.

[0058] If an abnormality occurs, an abnormality alarm will be triggered.

[0059] If no abnormalities are found, proceed with subsequent testing.

[0060] Furthermore, the first data acquisition module is also used to determine the number of nodes in the pipeline network based on the topology, determine the flow velocity and pressure information of all nodes at time t0, and establish an initial matrix J0 based on the flow velocity and pressure information at time t0, including:

[0061] The first data acquisition module is further configured to determine the number of nodes N of the pipeline network based on the topology, acquire the initial flow velocity and initial pressure information of each node at time t0, and establish an initial flow velocity set A0 (s1, s2, s3, ..., si, ..., sN) and an initial pressure set B0 (p1, p2, p3, ..., pi, ..., pN), where si represents the initial flow velocity of the i-th node at time t0, and pi represents the initial pressure of the i-th node at time t0. Based on the acquired initial flow velocity set A0 and initial pressure set B0, an initial matrix J0 is established, J0[s1 / p1, s2 / p2, s3 / p3, ..., si / pi, ..., sN / pN];

[0062] The second data acquisition module is also used to acquire the flow velocity and pressure information of all nodes again at time t1, and to establish the first matrix J1 based on the flow velocity and pressure information at time t1, including:

[0063] At time t1, obtain the first velocity set A1 (s11, s12, s13, ..., s1i, ..., s1N) and the first pressure set B1 (p11, p12, p13, ..., p1i, ..., p1N) for each node, where s1i represents the first velocity of the i-th node at time t1 and p1i represents the first pressure of the i-th node at time t1. Establish the first matrix J1, J1[s11 / p11, s12 / p12, s13 / p13, ..., s1i / p1i, ..., s1N / p1N].

[0064] Furthermore, the data processing module is also used to compare the flow velocity and pressure information of the corresponding nodes in the initial matrix J0 and the first matrix J1 to determine whether each node has an anomaly at time t1, including:

[0065] The N elements in the initial matrix J0 and the first matrix J1 are compared one by one, and a reference difference C is preset:

[0066] When |si / pi-s1i / p1i|≤C, the i-th node is determined to be in a normal state at time t1.

[0067] When |si / pi-s1i / p1i|>C, then the i-th node is determined to be in an abnormal state to be confirmed at time t1.

[0068] Compared with existing technologies, the beneficial effects of this invention are as follows: In the detection method and system applicable to pipeline network anomaly early warning, a pipeline network simulation model is established, the number of nodes in the pipeline network is determined based on the topology, and the flow velocity and pressure information of all nodes at time t0 is used to establish an initial matrix J0. At time t1, the flow velocity and pressure information of all nodes is acquired again to establish a first matrix J1. The flow velocity and pressure information of the corresponding nodes in the initial matrix J0 and the first matrix J1 are compared to determine whether any node exhibits an anomaly at time t1. This invention, by detecting the operating status parameters of each node at adjacent times in the pipeline network, can effectively determine the operating status of each node at different times. Furthermore, by establishing a simulation model and performing intelligent anomaly detection on each node based on the simulation model, it can not only accurately detect abnormal nodes in the pipeline network but also effectively improve detection efficiency.

[0069] Furthermore, in the aforementioned detection method and system applicable to pipeline network anomaly early warning, by comparing the N elements in the initial matrix J0 and the first matrix J1 one by one, and pre-setting a reference difference C, when |si / pi-s1i / p1i|≤C, the i-th node is determined to be in a normal state at time t1; when |si / pi-s1i / p1i|>C, the i-th node is determined to be in an abnormal state to be confirmed at time t1. This invention obtains the operating parameters of each node in the pipeline network at different times, promptly detects anomalies by observing changes in these parameters, and accurately identifies and judges these anomalies, thus enabling timely and effective detection of abnormal information in pipeline network nodes and greatly improving the accuracy of anomaly point judgment. Attached Figure Description

[0070] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0071] Figure 1 A flowchart illustrating a detection method for early warning of pipeline anomalies provided in an embodiment of the present invention;

[0072] Figure 2 This is a functional block diagram of a detection system for early warning of pipeline anomalies provided in an embodiment of the present invention. Detailed Implementation

[0073] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the disclosure to those skilled in the art. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0074] This invention is based on an existing natural gas pipeline network management system. By acquiring pipeline network data from the natural gas pipeline network management system, a pipeline network simulation model is established based on the pipeline network data. The pipeline network operation status is judged based on the pipeline network data displayed by the simulation model, which can effectively identify abnormal points in the pipeline network and improve the safety of pipeline network operation.

[0075] See Figure 1 As shown, this embodiment provides a detection method suitable for early warning of pipeline anomalies, including the following steps:

[0076] Step S100: Establish a pipeline network simulation model and obtain the pipeline network topology based on the pipeline network simulation model.

[0077] Specifically, in this embodiment, the pipeline network can be simulated using SPS (Stoner Pipeline Simulator) software to establish a pipeline network simulation model. Data on the operation of the pipeline network can be collected through the SCADA (Supervisory Control and Data Acquisition) system and fed back to the SPS software.

[0078] Specifically, after establishing the pipeline network simulation model, the topology of the pipeline network in the simulation model is obtained.

[0079] Step S200: Determine the number of nodes in the pipeline network based on the topology, determine the flow velocity and pressure information of all nodes at time t0, and establish an initial matrix J0 based on the flow velocity and pressure information at time t0.

[0080] Specifically, flow velocity and pressure information at pipeline nodes can be collected through a SCADA system or through the pipeline management system, depending on the actual situation.

[0081] Step S300: At time t1, acquire the flow velocity and pressure information of all nodes again, and establish the first matrix J1 based on the flow velocity and pressure information at time t1.

[0082] Step S400: Compare the flow velocity and pressure information of the corresponding nodes in the initial matrix J0 with those in the first matrix J1 to determine whether any abnormality occurs at each node at time t1.

[0083] If an abnormality occurs, an abnormality alarm will be triggered.

[0084] If no abnormalities are found, proceed with subsequent testing.

[0085] In this embodiment, a pipeline network simulation model is established, the number of nodes in the pipeline network is determined based on the topology, and the flow velocity and pressure information of all nodes at time t0 are used to establish an initial matrix J0. At time t1, the flow velocity and pressure information of all nodes are acquired again to establish a first matrix J1. The flow velocity and pressure information of the corresponding nodes in the initial matrix J0 and the first matrix J1 are compared to determine whether any abnormality occurs at time t1. This embodiment effectively determines the operating status of each node at different times by detecting the operating status parameters of each node at adjacent times. Furthermore, by establishing a simulation model and performing intelligent anomaly detection on each node based on the simulation model, not only can abnormal nodes in the pipeline network be accurately identified, but detection efficiency is also effectively improved.

[0086] Specifically, when determining the number of nodes in the pipeline network based on the topology, determining the flow velocity and pressure information of all nodes at time t0, and establishing an initial matrix J0 based on the flow velocity and pressure information at time t0, the process includes:

[0087] Based on the topology, the number of nodes N in the pipeline network is determined. At time t0, the initial flow velocity and initial pressure information of each node are obtained, and an initial flow velocity set A0 (s1, s2, s3, ..., si, ..., sN) and an initial pressure set B0 (p1, p2, p3, ..., pi, ..., pN) are established, where si represents the initial flow velocity of the i-th node at time t0, and pi represents the initial pressure of the i-th node at time t0. Based on the obtained initial flow velocity set A0 and initial pressure set B0, an initial matrix J0 is established, J0[s1 / p1, s2 / p2, s3 / p3, ..., si / pi, ..., sN / pN].

[0088] Specifically, when acquiring the flow velocity and pressure information of all nodes again at time t1, and establishing the first matrix J1 based on the flow velocity and pressure information at time t1, the process includes:

[0089] At time t1, obtain the first velocity set A1 (s11, s12, s13, ..., s1i, ..., s1N) and the first pressure set B1 (p11, p12, p13, ..., p1i, ..., p1N) for each node, where s1i represents the first velocity of the i-th node at time t1 and p1i represents the first pressure of the i-th node at time t1. Establish the first matrix J1, J1[s11 / p11, s12 / p12, s13 / p13, ..., s1i / p1i, ..., s1N / p1N].

[0090] Specifically, when comparing the flow velocity and pressure information of the corresponding nodes in the initial matrix J0 with those in the first matrix J1 to determine whether any node exhibits an anomaly at time t1, the following steps are included:

[0091] The N elements in the initial matrix J0 and the first matrix J1 are compared one by one, and a reference difference C is preset:

[0092] When |si / pi-s1i / p1i|≤C, the i-th node is determined to be in a normal state at time t1.

[0093] When |si / pi-s1i / p1i|>C, then the i-th node is determined to be in an abnormal state to be confirmed at time t1.

[0094] Specifically, the reference difference C is obtained by repeatedly collecting flow velocity and pressure ratio information at various nodes under stable operating conditions of the pipeline network, and through multiple data training iterations. It is understandable that the reference difference C can be determined based on actual circumstances.

[0095] Specifically, in the above embodiments, by comparing the N elements in the initial matrix J0 and the first matrix J1 one by one, and pre-setting a reference difference C, when |si / pi-s1i / p1i|≤C, the i-th node is determined to be in a normal state at time t1; when |si / pi-s1i / p1i|>C, the i-th node is determined to be in an abnormal state to be confirmed at time t1. The above embodiments, by acquiring the operating parameters of each node in the pipeline network at different times, promptly detect abnormal points through changes in operating parameters, and accurately identify and judge abnormal points, can effectively and timely detect abnormal information of pipeline network nodes, and can also greatly improve the accuracy of abnormal point judgment.

[0096] Specifically, the detection method applicable to pipeline network anomaly early warning also includes:

[0097] When the i-th node is determined to be in an abnormal state to be confirmed at time t1, the average values ​​of the flow velocity and pressure ratio of the i-th node at time t0 and time t1 are calculated respectively, and denoted as the initial average (Σsi / pi) / i and the first average (Σs1i / p1i) / i.

[0098] The initial mean (Σsi / pi) / i and the first mean (Σs1i / p1i) / i are compared, and the i-th node is determined to be abnormal based on the comparison result:

[0099] When (Σsi / pi) / i < (Σs1i / p1i) / i, the i-th node is determined to be in an abnormal state at time t1.

[0100] When (Σsi / pi) / i ≥ (Σs1i / p1i) / i, the detection of the i-th node continues, and the secondary flow velocity s2i and the secondary pressure p2i of the i-th node are obtained at time t21.

[0101] If |si / pi-s2i / p2i|>C, then the i-th node is determined to be in an abnormal state at time t21;

[0102] If |si / pi-s2i / p2i|≤C, then continue to detect the i-th node, and obtain the third flow velocity s3i and the third pressure p3i of the i-th node at time t22:

[0103] If |si / pi-s3i / p3i|>C, then the i-th node is determined to be in an abnormal state at time t22;

[0104] If |si / pi-s3i / p3i|≤C, then the i-th node is determined to be in a normal state at time t22.

[0105] Specifically, the detection method applicable to pipeline network anomaly early warning also includes:

[0106] When |si / pi-s1i / p1i|>C, and (Σsi / pi) / i≥(Σs1i / p1i) / i, the interval between time t1 and time t21 is determined based on the difference between si / pi and s1i / p1i:

[0107] A first preset difference C1, a second preset difference C2, and a third preset difference C3 are preset, and C < C1 < C2 < C3 < 2C; a first preset duration T1, a second preset duration T2, a third preset duration T3, and a fourth preset duration T4 are preset, and (t1-t0) > T1 > T2 > T3 > T4;

[0108] When C < |si / pi - s1i / p1i| ≤ C1, the time interval between time t1 and time t21 is set to the first preset duration T1;

[0109] When C1 < |si / pi - s1i / p1i| ≤ C2, the time interval between time t1 and time t21 is set to the second preset duration T2;

[0110] When C2 < |si / pi - s1i / p1i| ≤ C3, the time interval between time t1 and time t21 is set to the third preset duration T3;

[0111] When C3 < |si / pi-s1i / p1i|, the time interval between time t1 and time t21 is set to the fourth preset duration T4.

[0112] After selecting the nth preset duration Tn as the time interval between t1 and t21, n = 1, 2, 3, 4, the time interval between time t21 and time t22 is set to 0.5Tn.

[0113] Specifically, when determining that the i-th node is in an abnormal state at time t21 or t22, the method further includes:

[0114] Determine the upstream and downstream nodes of the i-th node, and represent the upstream node as the n-th node and the downstream node as the m-th node, where the values ​​of n and m range from 1 to N;

[0115] Calculate the flow difference Ln-i between the nth node and the i-th node, and calculate the flow difference Li-m between the i-th node and the m-th node, and determine the abnormal node interval based on the relationship between Ln-i and Li-m:

[0116] When Ln-i = Li-m, it is determined that there is an anomaly in the pipeline between the m-th node and the n-th node;

[0117] When Ln-i > Li-m, it is determined that there is an anomaly in the pipeline between the i-th node and the n-th node;

[0118] When Ln-i < Li-m, it is determined that there is an anomaly in the pipeline between the m-th node and the i-th node.

[0119] Specifically, when Ln-i = Li-m, or when Ln-i > Li-m, or when Ln-i < Li-m, and when it is determined that there is an anomaly in the pipeline between the m-th node and the n-th node, between the i-th node and the n-th node, or between the m-th node and the i-th node, the following applies:

[0120] Based on distributed optical fiber detection technology, the temperature change signal around the pipe between the m-th node and the n-th node, between the i-th node and the n-th node, or between the m-th node and the i-th node at time t1 is obtained, and the first temperature curve at time t1 is established.

[0121] Specifically, a distributed fiber optic detection system is set up to monitor the pipeline network. Simultaneously, this system communicates with the pipeline management system, enabling timely acquisition of data collected by the distributed fiber optic detection system.

[0122] Specifically, the first temperature curve is identified to determine whether there are any abnormal points in the first temperature curve;

[0123] If it exists, then it is determined that there is an anomaly in the pipeline between the m-th node and the n-th node, between the i-th node and the n-th node, or between the m-th node and the i-th node at time t21 or t22.

[0124] If it does not exist, then obtain the temperature change signal around the pipe between the m-th node and the n-th node, between the i-th node and the n-th node, or between the m-th node and the i-th node at time t0, and establish the initial temperature curve at time t0.

[0125] Compare the magnitude of change of the first temperature curve with that of the initial temperature curve:

[0126] When the change amplitude of the first temperature curve and the initial temperature curve is less than or equal to a threshold, a second temperature curve is acquired at time t11. The change amplitude of the second temperature curve is compared with that of the initial temperature curve. If the change amplitude is less than or equal to the threshold, the pipeline in that node section is determined to be in a normal state. If the change amplitude is greater than the threshold, a third temperature curve is acquired at time t12. When the change amplitude between the third temperature curve and the initial temperature curve is less than or equal to the threshold, the pipeline in that node section is determined to be in a normal state. When the change amplitude between the third temperature curve and the initial temperature curve is greater than the threshold, a leak point is determined in the pipeline in that node section.

[0127] When the change in the first temperature curve from the initial temperature curve is greater than the threshold, the velocity difference Δs between adjacent nodes is obtained:

[0128] When the velocity difference Δs is greater than or equal to the threshold, it is determined that there is a leak in the pipeline within that node interval.

[0129] When the flow velocity difference Δs is less than the threshold, the ambient temperature difference between time t0 and t1 is obtained. If the ambient temperature difference is less than the threshold, it is determined that there is a leak in the pipeline of that node interval.

[0130] If the ambient temperature difference is greater than or equal to the threshold, acoustic wave detection is performed on the pipeline in that node section, and the presence of a leak point in the pipeline in that node section is determined based on the results of the acoustic wave detection.

[0131] See Figure 2 As shown, in another preferred embodiment based on the above embodiments, this embodiment provides a detection system suitable for early warning of pipeline anomalies, including:

[0132] The simulation module is used to establish a pipeline network simulation model and obtain the pipeline network topology based on the simulation model.

[0133] The first data acquisition module is used to determine the number of nodes in the pipeline network based on the topology, determine the flow velocity and pressure information of all nodes at time t0, and establish an initial matrix J0 based on the flow velocity and pressure information at time t0.

[0134] The second data acquisition module is used to acquire the flow velocity and pressure information of all nodes again at time t1, and to establish a first matrix J1 based on the flow velocity and pressure information at time t1.

[0135] The data processing module is used to compare the flow velocity and pressure information of the corresponding nodes in the initial matrix J0 with those in the first matrix J1 to determine whether any abnormality occurs at each node at time t1.

[0136] If an abnormality occurs, an abnormality alarm will be triggered.

[0137] If no abnormalities are found, proceed with subsequent testing.

[0138] Specifically, the first data acquisition module is further configured to, when determining the number of nodes in the pipeline network based on the topology, determining the flow velocity and pressure information of all nodes at time t0, and establishing an initial matrix J0 based on the flow velocity and pressure information at time t0, include:

[0139] The first data acquisition module is further configured to determine the number of nodes N of the pipeline network based on the topology, acquire the initial flow velocity and initial pressure information of each node at time t0, and establish an initial flow velocity set A0 (s1, s2, s3, ..., si, ..., sN) and an initial pressure set B0 (p1, p2, p3, ..., pi, ..., pN), where si represents the initial flow velocity of the i-th node at time t0, and pi represents the initial pressure of the i-th node at time t0. Based on the acquired initial flow velocity set A0 and initial pressure set B0, an initial matrix J0 is established, J0[s1 / p1, s2 / p2, s3 / p3, ..., si / pi, ..., sN / pN];

[0140] The second data acquisition module is also used to acquire the flow velocity and pressure information of all nodes again at time t1, and to establish the first matrix J1 based on the flow velocity and pressure information at time t1, including:

[0141] At time t1, obtain the first velocity set A1 (s11, s12, s13, ..., s1i, ..., s1N) and the first pressure set B1 (p11, p12, p13, ..., p1i, ..., p1N) for each node, where s1i represents the first velocity of the i-th node at time t1 and p1i represents the first pressure of the i-th node at time t1. Establish the first matrix J1, J1[s11 / p11, s12 / p12, s13 / p13, ..., s1i / p1i, ..., s1N / p1N].

[0142] Specifically, the data processing module is further configured to compare the flow velocity and pressure information of the corresponding nodes in the initial matrix J0 and the first matrix J1 to determine whether any node exhibits an anomaly at time t1, including:

[0143] The N elements in the initial matrix J0 and the first matrix J1 are compared one by one, and a reference difference C is preset:

[0144] When |si / pi-s1i / p1i|≤C, the i-th node is determined to be in a normal state at time t1.

[0145] When |si / pi-s1i / p1i|>C, then the i-th node is determined to be in an abnormal state to be confirmed at time t1.

[0146] In this embodiment, a pipeline network simulation model is established, the number of nodes in the pipeline network is determined based on the topology, and the flow velocity and pressure information of all nodes at time t0 are used to establish an initial matrix J0. At time t1, the flow velocity and pressure information of all nodes are acquired again to establish a first matrix J1. The flow velocity and pressure information of the corresponding nodes in the initial matrix J0 and the first matrix J1 are compared to determine whether any abnormality occurs at time t1. This embodiment effectively determines the operating status of each node at different times by detecting the operating status parameters of each node at adjacent times. Furthermore, by establishing a simulation model and performing intelligent anomaly detection on each node based on the simulation model, not only can abnormal nodes in the pipeline network be accurately identified, but detection efficiency is also effectively improved.

[0147] Specifically, in the above embodiments, by comparing the N elements in the initial matrix J0 and the first matrix J1 one by one, and pre-setting a reference difference C, when |si / pi-s1i / p1i|≤C, the i-th node is determined to be in a normal state at time t1; when |si / pi-s1i / p1i|>C, the i-th node is determined to be in an abnormal state to be confirmed at time t1. The above embodiments, by acquiring the operating parameters of each node in the pipeline network at different times, promptly detect abnormal points through changes in operating parameters, and accurately identify and judge abnormal points, can effectively and timely detect abnormal information of pipeline network nodes, and can also greatly improve the accuracy of abnormal point judgment.

[0148] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0149] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowcharts (one or more blocks) and / or block diagrams (one or more blocks).

[0150] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means that implement the functions specified in a flowchart (one or more flowcharts) and / or a block diagram (one or more blocks).

[0151] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, such that the instructions, which execute on the computer or other programmable apparatus, provide steps for implementing the functions specified in the flowchart (one or more processes) and / or block diagram (one or more blocks).

[0152] 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 it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A detection method suitable for early warning of pipeline network anomalies, characterized in that, include: Establish a pipeline network simulation model and obtain the pipeline network topology based on the simulation model; The number of nodes in the pipeline network is determined based on the topology, and the flow velocity and pressure information of all nodes at time t0 are determined. An initial matrix J0 is established based on the flow velocity and pressure information at time t0. At time t1, the flow velocity and pressure information of all nodes are acquired again, and a first matrix J1 is established based on the flow velocity and pressure information at time t1. The flow velocity and pressure information of the corresponding nodes in the initial matrix J0 are compared with those in the first matrix J1 to determine whether any abnormality occurs at time t1: If an abnormality occurs, an abnormality alarm will be triggered. If no abnormalities are found, proceed with subsequent testing; When comparing the flow velocity and pressure information of the corresponding nodes in the initial matrix J0 with those in the first matrix J1 to determine whether any node exhibits an anomaly at time t1, the following steps are included: The N elements in the initial matrix J0 and the first matrix J1 are compared one by one, and a reference difference C is preset: When |si / pi-s1 i / p1 i|≤C, then the i-th node is determined to be in a normal state at time t1; When |si / pi-s1 i / p1 i|>C, then the i-th node is determined to be in an abnormal state to be confirmed at time t1. Where si represents the initial flow velocity of the i-th node at time t0, pi represents the initial pressure of the i-th node at time t0, s1 i represents the first flow velocity of the i-th node at time t1, and p1 i represents the first pressure of the i-th node at time t1. Also includes: When the i-th node is determined to be in an abnormal state to be confirmed at time t1, the average values ​​of the flow velocity and pressure ratio of the i-th node at time t0 and time t1 are calculated respectively, and denoted as the initial average (Σsi / pi) / i and the first average (Σs1 i / p1 i) / i. The initial mean (Σsi / pi) / i and the first mean (Σs1 i / p1 i) / i are compared, and the i-th node is determined to be abnormal based on the comparison result: When (Σsi / pi) / i < (Σs1 i / p1 i) / i, the i-th node is determined to be in an abnormal state at time t1. When (Σsi / pi) / i ≥ (Σs1 i / p1 i) / i, the detection of the i-th node continues, and the secondary flow velocity s2i and the secondary pressure p2i of the i-th node are obtained at time t21. If |si / pi-s2i / p2i|>C, then the i-th node is determined to be in an abnormal state at time t21; If |si / pi-s2i / p2i|≤C, then continue to detect the i-th node, and obtain the three-dimensional flow velocity s3i and the three-dimensional pressure p3i of the i-th node at time t22: If |si / pi-s3i / p3i|>C, then the i-th node is determined to be in an abnormal state at time t22; If |si / pi-s3i / p3i|≤C, then the i-th node is determined to be in a normal state at time t22.

2. The detection method for early warning of pipeline anomalies according to claim 1, characterized in that, When determining the number of nodes in the pipeline network based on the topology, determining the flow velocity and pressure information of all nodes at time t0, and establishing an initial matrix J0 based on the flow velocity and pressure information at time t0, the process includes: Based on the topology, the number of nodes N in the pipeline network is determined. At time t0, the initial flow velocity and initial pressure information of each node are obtained, and an initial flow velocity set A0 (s1, s2, s3, ..., si, ..., sN) and an initial pressure set B0 (p1, p2, p3, ..., pi, ..., pN) are established. Based on the obtained initial flow velocity set A0 and initial pressure set B0, an initial matrix J0 is established, J0[s1 / p1, s2 / p2, s3 / p3, ..., si / pi, ..., sN / pN]; When acquiring the flow velocity and pressure information of all nodes again at time t1, and establishing the first matrix J1 based on the flow velocity and pressure information at time t1, the process includes: At time t1, obtain the first velocity set A1 (s11, s12, s13, ..., s1i, ..., s1N) and the first pressure set B1 (p11, p12, p13, ..., p1i, ..., p1N) for each node, and establish the first matrix J1, J1[s11 / p11, s12 / p12, s13 / p13, ..., s1i / p1i, ..., s1N / p1N].

3. The detection method for early warning of pipeline anomalies according to claim 2, characterized in that, Also includes: When |si / pi-s1i / p1i|>C, and (Σsi / pi) / i≥(Σs1i / p1i) / i, the interval between time t1 and time t21 is determined based on the difference between si / pi and s1i / p1i: A first preset difference C1, a second preset difference C2, and a third preset difference C3 are preset, and C < C1 < C2 < C3 < 2C; a first preset duration T1, a second preset duration T2, a third preset duration T3, and a fourth preset duration T4 are preset, and (t1-t0) > T1 > T2 > T3 > T4; When C < |si / pi-s1 i / p1 i| ≤ C1, the time interval between time t1 and time t21 is set to the first preset duration T1; When C1 < |si / pi - s1 i / p1 i| ≤ C2, the time interval between time t1 and time t21 is set to the second preset duration T2; When C2 < |si / pi - s1 i / p1 i| ≤ C3, the time interval between time t1 and time t21 is set to the third preset duration T3; When C3 < |si / pi-s1 i / p1 i|, the time interval between time t1 and time t21 is set to the fourth preset duration T4; After selecting the nth preset duration Tn as the time interval between t1 and t21, n = 1, 2, 3, 4, the time interval between time t21 and time t22 is set to 0.5Tn.

4. The detection method for early warning of pipeline anomalies according to claim 3, characterized in that, When determining that the i-th node is in an abnormal state at time t21 or t22, the method further includes: Determine the upstream and downstream nodes of the i-th node, and represent the upstream node as the n-th node and the downstream node as the m-th node, where the values ​​of n and m range from 1 to N; Calculate the flow difference Ln-i between the nth node and the i-th node, and calculate the flow difference Li-m between the i-th node and the m-th node, and determine the abnormal node interval based on the relationship between Ln-i and Li-m: When Ln-i = Li-m, it is determined that there is an anomaly in the pipeline between the m-th node and the n-th node; When Ln-i > Li-m, it is determined that there is an anomaly in the pipeline between the i-th node and the n-th node; When Ln-i < Li-m, it is determined that there is an anomaly in the pipeline between the m-th node and the i-th node.

5. The detection method for early warning of pipeline anomalies according to claim 4, characterized in that, When Ln-i = Li-m, or when Ln-i > Li-m, or when Ln-i < Li-m, and when it is determined that there is an anomaly in the pipeline between the m-th node and the n-th node, between the i-th node and the n-th node, or between the m-th node and the i-th node, the following applies: Based on distributed optical fiber detection technology, the temperature change signal around the pipe between the m-th node and the n-th node, between the i-th node and the n-th node, or between the m-th node and the i-th node at time t1 is obtained, and the first temperature curve at time t1 is established. Identify the first temperature curve and determine whether there are any abnormal points in the first temperature curve; If it exists, then it is determined that there is an anomaly in the pipeline between the m-th node and the n-th node, between the i-th node and the n-th node, or between the m-th node and the i-th node at time t21 or t22. If it does not exist, then obtain the temperature change signal around the pipe between the m-th node and the n-th node, between the i-th node and the n-th node, or between the m-th node and the i-th node at time t0, and establish the initial temperature curve at time t0. Compare the magnitude of change of the first temperature curve with that of the initial temperature curve: When the change amplitude of the first temperature curve and the initial temperature curve is less than or equal to a threshold, a second temperature curve is acquired at time t11. The change amplitude of the second temperature curve is compared with that of the initial temperature curve. If the change amplitude is less than or equal to the threshold, the pipeline in that node section is determined to be in a normal state. If the change amplitude is greater than the threshold, a third temperature curve is acquired at time t12. When the change amplitude between the third temperature curve and the initial temperature curve is less than or equal to the threshold, the pipeline in that node section is determined to be in a normal state. When the change amplitude between the third temperature curve and the initial temperature curve is greater than the threshold, a leak point is determined in the pipeline in that node section. When the change in the first temperature curve from the initial temperature curve is greater than the threshold, the velocity difference Δs between adjacent nodes is obtained: When the velocity difference Δs is greater than or equal to the threshold, it is determined that there is a leak in the pipeline within that node interval. When the flow velocity difference Δs is less than the threshold, the ambient temperature difference between time t0 and t1 is obtained. If the ambient temperature difference is less than the threshold, it is determined that there is a leak in the pipeline of that node interval. If the ambient temperature difference is greater than or equal to the threshold, acoustic wave detection is performed on the pipeline in that node section, and the presence of a leak point in the pipeline in that node section is determined based on the results of the acoustic wave detection.

6. A detection system suitable for early warning of pipeline network anomalies, characterized in that, include: The simulation module is used to establish a pipeline network simulation model and obtain the pipeline network topology based on the simulation model. The first data acquisition module is used to determine the number of nodes in the pipeline network based on the topology, determine the flow velocity and pressure information of all nodes at time t0, and establish an initial matrix J0 based on the flow velocity and pressure information at time t0. The second data acquisition module is used to acquire the flow velocity and pressure information of all nodes again at time t1, and to establish a first matrix J1 based on the flow velocity and pressure information at time t1. The data processing module is used to compare the flow velocity and pressure information of the corresponding nodes in the initial matrix J0 with those in the first matrix J1 to determine whether any abnormality occurs at each node at time t1. If an abnormality occurs, an abnormality alarm will be triggered. If no abnormalities are found, proceed with subsequent testing; The data processing module is further configured to compare the flow velocity and pressure information of the corresponding nodes in the initial matrix J0 and the first matrix J1 to determine whether any node exhibits an anomaly at time t1, including: The N elements in the initial matrix J0 and the first matrix J1 are compared one by one, and a reference difference C is preset: When |si / pi-s1 i / p1 i|≤C, then the i-th node is determined to be in a normal state at time t1; When |si / pi-s1 i / p1 i|>C, then the i-th node is determined to be in an abnormal state to be confirmed at time t1. Where si represents the initial flow velocity of the i-th node at time t0, pi represents the initial pressure of the i-th node at time t0, s1 i represents the first flow velocity of the i-th node at time t1, and p1 i represents the first pressure of the i-th node at time t1. When the i-th node is determined to be in an abnormal state to be confirmed at time t1, the average values ​​of the flow velocity and pressure ratio of the i-th node at time t0 and time t1 are calculated respectively, and denoted as the initial average (Σsi / pi) / i and the first average (Σs1 i / p1 i) / i. The initial mean (Σsi / pi) / i and the first mean (Σs1 i / p1 i) / i are compared, and the i-th node is determined to be abnormal based on the comparison result: When (Σsi / pi) / i < (Σs1 i / p1 i) / i, the i-th node is determined to be in an abnormal state at time t1. When (Σsi / pi) / i ≥ (Σs1 i / p1 i) / i, the detection of the i-th node continues, and the secondary flow velocity s2i and the secondary pressure p2i of the i-th node are obtained at time t21. If |si / pi-s2i / p2i|>C, then the i-th node is determined to be in an abnormal state at time t21; If |si / pi-s2i / p2i|≤C, then continue to detect the i-th node, and obtain the three-dimensional flow velocity s3i and the three-dimensional pressure p3i of the i-th node at time t22: If |si / pi-s3i / p3i|>C, then the i-th node is determined to be in an abnormal state at time t22; If |si / pi-s3i / p3i|≤C, then the i-th node is determined to be in a normal state at time t22.

7. The detection system for early warning of pipeline anomalies according to claim 6, characterized in that, The first data acquisition module is further configured to, when determining the number of nodes in the pipeline network based on the topology, determining the flow velocity and pressure information of all nodes at time t0, and establishing an initial matrix J0 based on the flow velocity and pressure information at time t0, include: The first data acquisition module is also used to determine the number of nodes N of the pipeline network based on the topology, acquire the initial flow velocity and initial pressure information of each node at time t0, and establish an initial flow velocity set A0 (s1, s2, s3, ..., si, ..., sN) and an initial pressure set B0 (p1, p2, p3, ..., pi, ..., pN). Based on the acquired initial flow velocity set A0 and initial pressure set B0, an initial matrix J0 is established, J0[s1 / p1, s2 / p2, s3 / p3, ..., si / pi, ..., sN / pN]; The second data acquisition module is also used to acquire the flow velocity and pressure information of all nodes again at time t1, and to establish the first matrix J1 based on the flow velocity and pressure information at time t1, including: At time t1, obtain the first velocity set A1 (s11, s12, s13, ..., s1i, ..., s1N) and the first pressure set B1 (p11, p12, p13, ..., p1i, ..., p1N) for each node, and establish the first matrix J1, J1[s11 / p11, s12 / p12, s13 / p13, ..., s1i / p1i, ..., s1N / p1N].

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