Method, device and equipment for monitoring a gas network

Abstract

The application discloses a kind of gas pipe network monitoring method, device and equipment, wherein the method comprises: obtaining the monitoring information and prediction information of each gas valve well node in gas pipe network;Based on the correlation between monitoring information and prediction data, determine whether the gas leakage occurs in the gas pipe network;In the case where it is determined that gas leakage occurs in the gas pipe network, based on the monitoring information of each gas valve well node, determine the leakage point position information of the gas leakage in the gas pipe network.The correlation between monitoring information and prediction information can be analyzed by the application, and the abnormal operation conditions such as gas leakage in the gas pipe network can be quickly and accurately found, and by further analyzing the monitoring information, the abnormal operation pipe section can be located, to avoid large-scale gas leakage and explosion and other safety incidents.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of gas pipe network, and particularly relates to a gas pipe network monitoring method, device and equipment. BACKGROUND

[0002] With the acceleration of urbanization and industrialization, the scale of urban gas pipe network is getting larger and larger. As an important infrastructure and public service of the city, the safety of gas pipe network is of great importance.

[0003] At present, most of the gas safety monitoring is mainly for monitoring the combustible gas leakage in the gas valve well, monitoring whether the gas leakage monitoring sensing unit is invalid due to water flooding, monitoring whether the well cover of the gas valve well is displaced, and monitoring the node pressure of the gas valve well. However, based on the above gas safety detection, the combustible gas leakage in the gas pipe network cannot be discovered in time, and the monitoring data is not accurately and effectively analyzed and judged, so that the emergency and rescue are relatively lagging behind, affecting the safety of urban personnel. SUMMARY

[0004] The purpose of the embodiments of the present application is to provide a gas pipe network monitoring method, device and equipment to solve the problems of untimely discovery of gas leakage and inaccurate risk prediction in the prior art.

[0005] To solve the above technical problems, the first aspect of the present specification provides a gas pipe network monitoring method, comprising:

[0006] obtaining monitoring information and prediction information of each gas valve well node in the gas pipe network, wherein the monitoring information comprises pressure information monitored by each gas valve well during actual operation, and the prediction information is pressure information of each gas valve well node in the gas pipe network during normal operation, which is predicted based on a pre-trained pipe network data model;

[0007] determining whether the gas pipe network has gas leakage based on the correlation between the monitoring information and the prediction data;

[0008] In the case where it is determined that the gas pipe network has gas leakage, the position information of the leakage point in the gas pipe network where gas leakage occurs is determined based on the monitoring information of each gas valve well node.

[0009] In some embodiments, determining whether the gas pipe network has gas leakage based on the correlation between the monitoring information and the prediction information comprises:

[0010] determining the correlation between the monitoring information and the prediction information;

[0011] in the case where the correlation is lower than a preset correlation threshold, it is determined that the gas pipe network has gas leakage.

[0012] In a case where the correlation degree is not lower than a preset correlation threshold, it is determined that the gas pipeline network has not occurred gas leakage.

[0013] In some embodiments, the correlation degree between the monitoring information and the prediction information is determined by the following formula:

[0014]

[0015] Wherein, r represents the correlation degree between the monitoring information and the prediction information, a i represents the prediction information of the i-th gas valve well node in the gas pipeline network, b i represents the monitoring information of the i-th gas valve well node in the gas pipeline network, and n represents the number of gas valve well nodes in the gas pipeline network.

[0016] In some embodiments, based on the monitoring information of each gas valve well node, the leakage point position information of the gas pipeline network where gas leakage occurs is determined, comprising:

[0017] Based on the monitoring information, the pressure drop information of each gas valve well node is determined;

[0018] The gas valve node with the largest pressure drop in the pressure drop information is determined as a first target node;

[0019] The gas valve node with the smallest correlation degree between the monitoring information and the prediction information among at least one gas valve node connected to the target node is determined as a second target node;

[0020] Based on the first pressure drop of the target node, the second pressure drop of the second target, and the length of the gas pipe section between the first target node and the second target node, the leakage point position information is determined.

[0021] In some embodiments, the monitoring information of each gas valve well further includes at least one dimension of:

[0022] Combustible gas concentration information, temperature information, water level information, and well cover position information of the gas valve well.

[0023] In some embodiments, the above method further comprises:

[0024] Based on the data dependency relationship between the information of each dimension in the monitoring information of each gas valve well, the working state of the monitoring module corresponding to the information of each dimension is determined;

[0025] When any one of the monitoring modules is in an abnormal working state, a module abnormal alarm information is generated.

[0026] In some embodiments, the method further comprises:

[0027] determining whether the information of each dimension is abnormal based on the information of each dimension in the monitoring information and the safety threshold corresponding to the information of each dimension;

[0028] generating monitoring abnormality alarm information in a case where it is determined that the information of any one of the dimensions is abnormal.

[0029] The second aspect of the present specification provides a gas pipe network monitoring device, comprising:

[0030] an information acquisition module configured to acquire monitoring information and prediction information of each gas valve well node in a gas pipe network, wherein the monitoring information comprises pressure information monitored during actual operation of each gas valve well, and the prediction information is pressure information of each gas valve well node during normal operation of the gas pipe network, which is predicted based on a pre-trained pipe network data model;

[0031] a leakage determination module configured to determine whether gas leakage occurs in the gas pipe network based on a correlation degree between the monitoring information and the prediction data;

[0032] a position determination module configured to determine leakage point position information of a gas leakage point in the gas pipe network based on the monitoring information of each gas valve well node in a case where it is determined that gas leakage occurs in the gas pipe network.

[0033] The third aspect of the present specification provides a gas pipe network monitoring and early warning system, comprising a plurality of gas valve monitoring assemblies and a gas pipe network monitoring assembly, wherein a gas valve well corresponding to the gas valve monitoring assembly is a gas valve well node in a gas pipe network.

[0034] The gas valve monitoring assembly is configured to monitor information of a plurality of dimensions of the gas valve and upload the information of the plurality of dimensions as monitoring information to the gas pipe network monitoring assembly.

[0035] The gas pipe network monitoring assembly comprises:

[0036] an information acquisition module configured to acquire monitoring information and prediction information of each gas valve well node in a gas pipe network, wherein the monitoring information comprises pressure information monitored during actual operation of each gas valve well, and the prediction information is pressure information of each gas valve well node during normal operation of the gas pipe network, which is predicted based on a pre-trained pipe network data model;

[0037] a leakage determination module configured to determine whether gas leakage occurs in the gas pipe network based on a correlation degree between the monitoring information and the prediction data;

[0038] A position determining module is configured to, in a case where it is determined that a gas leakage occurs in the gas pipeline network, determine leakage point position information of the gas leakage in the gas pipeline network based on the monitoring information of each gas valve well node.

[0039] The fourth aspect of the present specification provides an electronic device, comprising a memory and a processor, which are communicatively connected with each other, the memory stores computer instructions, and the processor implements the steps of the method of the first aspect by executing the computer instructions.

[0040] The fifth aspect of the present specification provides a computer readable storage medium, which stores computer program instructions, and the computer program instructions implement the steps of the method of the first aspect when executed.

[0041] The monitoring method of the gas pipeline network provided by the embodiments of the present specification obtains the monitoring information and the prediction information of each gas valve well node in the gas pipeline network, and then determines whether a gas leakage occurs in the gas pipeline network based on the correlation between the monitoring information and the prediction information, and determines the leakage point position information of the gas leakage based on the monitoring information of each gas valve well node when it is determined that a gas leakage occurs in the gas pipeline network. By analyzing the correlation between the monitoring information and the prediction information, the present application can quickly and accurately find the operation abnormality such as gas leakage in the gas pipeline network, and by further analyzing the monitoring information, the operation abnormality pipeline section can be located, so as to avoid large-scale gas leakage and explosion safety events. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the description of the embodiments or the prior art will be briefly introduced. Obviously, the drawings in the following description are only some embodiments described in the present application, and those skilled in the art can also obtain other drawings according to these drawings without creative labor.

[0043] Figure 1 Fig. 1 shows a structure diagram of a monitoring and early warning system of a gas pipeline network provided by an embodiment of the present application;

[0044] Figure 2 Fig. 2 shows a structure diagram of a gas valve monitoring assembly provided by an embodiment of the present application;

[0045] Figure 3 Fig. 3 shows a structure diagram of a monitoring assembly of a gas pipeline network provided by an embodiment of the present application;

[0046] Figure 4Fig. 1 shows a flowchart of a method for monitoring a gas pipeline network according to an embodiment of the present application;

[0047] Figure 5 Fig. 2 shows a schematic diagram of a gas pipeline network model according to an embodiment of the present application;

[0048] Figure 6 Fig. 3 shows a flowchart of another method for monitoring a gas pipeline network according to an embodiment of the present application;

[0049] Figure 7 Fig. 4 shows a flowchart of another method for monitoring a gas pipeline network according to an embodiment of the present application;

[0050] Figure 8 Fig. 5 shows a schematic diagram of a monitoring device for a gas pipeline network according to an embodiment of the present application;

[0051] Figure 9 Fig. 6 shows a schematic diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to enable persons skilled in the art to better understand the technical solutions in the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by persons skilled in the art without creative labor should fall within the scope of protection of the present application.

[0053] As described above, the existing gas safety detection cannot timely find the combustible gas leakage in the gas pipeline network, and the monitoring data is not accurately and effectively analyzed and judged, so that the emergency and rescue are relatively lagging, which affects the safety of urban personnel.

[0054] In order to solve the above problems, the present application provides a method for monitoring a gas pipeline network, which can specifically include: obtaining monitoring information and prediction information of each gas valve well node in the gas pipeline network, wherein the monitoring information includes pressure information monitored by each gas valve well during actual operation, and the prediction information is pressure information of each gas valve well node in the gas pipeline network during normal operation, which is predicted based on a pre-trained pipeline data model; determining whether a gas leakage occurs in the gas pipeline network based on the correlation between the monitoring information and the prediction data; and determining leakage point position information of the gas leakage in the gas pipeline network based on the monitoring information of each gas valve well node, in the case that the gas leakage occurs in the gas pipeline network.

[0055] This application, through the analysis of the correlation between monitoring information and prediction information, can quickly and accurately detect operational anomalies such as gas leaks in gas pipeline networks. Furthermore, by further analyzing the monitoring information, it can locate the abnormal pipeline sections, thereby preventing large-scale gas leaks and explosions.

[0056] The method provided in this application can be executed by an electronic device, which is an electronic device with data computing, processing, and storage capabilities. This electronic device can be a terminal such as a personal computer (PC), tablet computer, smartphone, wearable device, or intelligent robot; or it can be a server. The server can be an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services.

[0057] Before introducing the gas pipeline network monitoring method in the embodiments of this application, we will first introduce a gas pipeline network monitoring and early warning system provided in the embodiments of this application.

[0058] Figure 1 The diagram shown is a structural diagram of a gas pipeline network monitoring and early warning system 100 provided in an embodiment of this application.

[0059] like Figure 1 As shown, the gas pipeline network monitoring and early warning system may include multiple gas valve monitoring components 101 and gas pipeline network monitoring components 102.

[0060] The gas valve monitoring component 101 is used to monitor multiple dimensions of information about the gas valve and upload this information as monitoring data to the gas pipeline network monitoring component 102. The gas valve monitoring component 101 may include a monitoring module for monitoring environmental data in multiple dimensions, as detailed in the reference [reference needed]. Figure 2 As shown.

[0061] The gas pipeline network monitoring component 102 is used to determine whether a gas pipeline network leak has occurred based on monitoring and prediction information from each gas valve well node in the gas pipeline network. If a gas leak is determined, it determines the location of the leak point in the gas pipeline network based on the monitoring information from each gas valve well node. The monitoring information includes the pressure information monitored during the actual operation of each gas valve well, and the prediction information is the pressure information of each gas valve well node in the gas pipeline network during normal operation, predicted based on a pre-trained pipeline network data model. The structure of the gas pipeline network monitoring component 102 can be as follows: Figure 3 As shown.

[0062] Figure 2Fig. 1 shows a structural schematic diagram of a gas valve monitoring assembly according to an embodiment of the present application. As shown in Fig. 1, the gas valve monitoring assembly 101 can include a gas monitoring module 201, a water level monitoring module 202, a pressure monitoring module 203, and a manhole cover monitoring module 204. Figure 2 As shown in Fig. 1, in some embodiments, the gas valve monitoring assembly 101 can include the gas monitoring module 201, the water level monitoring module 202, the pressure monitoring module 203, and the manhole cover monitoring module 204. The gas monitoring module 201 can be configured to monitor gas concentration data of safety indicators, and can specifically include safety data of combustible gas and operation state data of the gas monitoring module 201. The water level monitoring module 202 can be configured to monitor water level out-of-limit change state data, and can specifically include water level out-of-limit information of an installation environment or a water immersion state of the gas valve monitoring assembly. That is, the water level monitoring module 202 can be used to monitor a water level state in a gas valve well and protect gas equipment in the gas valve well, and can also be used to monitor a water immersion state of the gas valve monitoring assembly 101 and confirm whether the gas valve monitoring assembly 101 has a perfect combustible gas detection capability. The pressure monitoring module 203 can be configured to monitor gas pipe network pressure and pipe network temperature data. The manhole cover monitoring module 204 can be configured to monitor manhole cover opening state change data.

[0063] In some embodiments, the gas valve monitoring assembly 101 can further include a data buffer module 205, a clock module 206, a communication module 207, a service control module 208, a power module 209, and a theft prevention module 210. The data buffer module 205 can be configured to store periodically monitored multi-dimensional environmental data, threshold out-of-limit data, and state change data, and can store the data monitored by the monitoring module locally. The clock module 206 can be configured to calibrate a system time of the gas valve monitoring assembly 101 and add a time label to the monitored multi-dimensional environmental information. The service control module 208 can be configured to control low power consumption of each module in the gas valve monitoring assembly 101, execute a preset operation logic, process environmental data monitoring results, process information transmission of data, perform alarm services according to a preset alarm threshold and environmental state change, perform data buffer services, perform network diagnosis and data transmission integrity services, perform device state self-diagnosis, and perform local and remote management of the device. The communication module 207 can be configured to transmit periodically monitored multi-dimensional environmental information, threshold out-of-limit information, and state change information processed by the service control module 208 to a monitoring assembly 102 of a gas pipe network. The power module 209 can be configured to supply power to modules that need power supply under the control of the service control module 208. The theft prevention module 210 can be configured to monitor displacement changes of the gas valve monitoring assembly 101.

[0064] It can be understood that the business control module 208 can be specifically an embedded unit of a system integration program, which can be used to provide a working signal for the power supply module in the gas valve monitoring assembly 101, provide a power supply logic for the mainboard according to a preset opening interface, provide a voltage boosting signal for a module requiring voltage boosting, receive and process data collected by the monitoring module and the state of the monitoring module, process information according to a preset threshold and state judgment of the collected data, receive remote management information to correct the integrated system program and the threshold. The clock module 206 can add a time label to the collected data to determine the uniqueness of the data. The power module 209 is a lithium battery pack managed by the power management component, which can be used to supply power to the entire gas valve monitoring assembly 101 and meet the power supply safety requirements. The communication module 207 can also be used for information interaction with the monitoring assembly 102 of the gas pipeline network, receive and confirm the management information of the monitoring assembly 102 of the gas pipeline network and deliver it to the business control module 208.

[0065] In some embodiments, the gas monitoring module 201, the water level monitoring module 202, the data buffer module 205, the clock module 206, the communication module 207, the business control module 208, and the power module 209 can be used as a combustible gas monitoring part of the gas valve monitoring assembly 101. The combustible gas monitoring part of the gas valve monitoring assembly 101 and the well cover monitoring module 204 can be installed on the well wall of the gas valve well, inspection well, and three trenches and two wells related to gas. The pressure monitoring module 203 can be installed on the front valve and rear end of the gas pipeline diffuser valve. The anti-theft module 210 can be installed between the main body and the back plate of the gas valve monitoring assembly 101.

[0066] In some embodiments, the pressure monitoring module 203 and the anti-theft module 210 can be connected to the gas valve monitoring assembly 101 through the reserved interface of the gas valve monitoring assembly 101 by using external wires.

[0067] In some embodiments, based on the different installation environments of the gas valve well corresponding to the gas valve monitoring assembly 101, the gas valve monitoring assembly can include different monitoring modules for monitoring information in different dimensions. For example, Figure 1As shown, for example, the gas valve monitoring assembly can be applied to a steel cast iron pipe valve well, a polyethylene (PE) pipe valve well, a valve chamber, a three ditch two well, and other typical closed spaces of the gas pipe network. For the steel cast iron pipe valve well, the combustible gas concentration in the valve well, the valve well pressure, and water immersion can be sensed, and then the gas valve monitoring assembly corresponding to the steel cast iron pipe valve well can include a gas monitoring module, a water level monitoring module, and a pressure monitoring module. For the PE pipe valve well, the combustible gas concentration in the valve well and water immersion can be sensed, and then the gas valve monitoring assembly corresponding to the PE pipe valve well can include a gas monitoring module and a water level monitoring module. For the valve chamber, the combustible gas concentration in the valve chamber, the valve well pressure, water immersion, and the cover state can be sensed, and then the gas valve monitoring assembly corresponding to the valve chamber can include a gas monitoring module, a water level monitoring module, a pressure monitoring module, and a cover monitoring module. For the three ditch two well, the combustible gas concentration in the three ditch two well and water immersion can be sensed, and then the gas valve monitoring assembly corresponding to the three ditch two well can include a gas monitoring module and a water level monitoring module.

[0068] It can be understood that Figure 1 The gas valve monitoring assembly 101 in the gas valve monitoring device can be arranged in a gas valve well, and then the gas valve well corresponding to the gas valve monitoring device is a gas valve well node in the gas pipe network. In other embodiments, the gas valve monitoring assembly can be arranged in a pressure regulating box cabinet in the gas pipe network, and then the pressure regulating box cabinet where the gas valve monitoring device is located and the gas valve well corresponding to the pressure regulating box cabinet can be regarded as a gas valve well node. Further, based on the different positions of the gas valve monitoring assembly, the gas valve monitoring assembly can include different modules. For example, when the gas valve monitoring assembly is located in the gas valve well, it can include a gas detection module, a water level monitoring module, a data cache module, a clock module, a communication module, a service control module, a power module, a cover monitoring module, a pressure monitoring module, and an anti-theft module. When the gas valve monitoring assembly is located in the pressure regulating box cabinet, it can include a gas detection module, a data cache module, a clock module, a communication module, a service control module, a power module, two pressure monitoring modules, and an anti-theft module, wherein one pressure monitoring module is used to monitor the pressure of the input pipe of the pressure regulating box cabinet, and the other pressure monitoring module is used to monitor the pressure of the output pipe of the pressure regulating box cabinet.

[0069] Figure 3 Fig. 1 shows a structural schematic diagram of a gas pipe network monitoring assembly provided by an embodiment of the present application.

[0070] As Figure 3As shown, in some embodiments, the monitoring component 102 of the gas pipeline network can include a data analysis unit 301, which can analyze the monitoring information of each gas valve well node of the gas pipeline network, analyze the change rule of the pressure and other parameters of the gas pipeline network when a pipeline leakage occurs, determine the abnormal operation of the gas pipeline network due to gas leakage, locate the leakage point of the pipeline segment where the gas leakage occurs, and obtain the leakage point position information. Further, the pipeline leakage alarm information can be generated based on the leakage point position information, and the pipeline leakage alarm information can be pushed to support emergency repair operations and avoid large-scale leakage and explosion safety incidents.

[0071] In some embodiments, the data analysis unit 301 can include an information acquisition module, a leakage determination module, and a location determination module. The information acquisition module is configured to acquire monitoring information and prediction information of each gas valve well node in the gas pipeline network. The monitoring information includes pressure information monitored during actual operation of each gas valve well. The prediction information is pressure information of each gas valve well node during normal operation of the gas pipeline network, which is predicted based on a pre-trained pipeline data model. The leakage determination module is configured to determine whether a gas leakage occurs in the gas pipeline network based on a correlation between the monitoring information and the prediction information. The location determination module is configured to determine leakage point position information of the gas pipeline network where a gas leakage occurs based on the monitoring information of each gas valve well node when it is determined that a gas leakage occurs in the gas pipeline network. The determination of whether a gas leakage occurs in the gas pipeline network and the leakage point position by the data analysis unit 301 will be described in detail in the gas pipeline network monitoring method below, and will not be described here.

[0072] In some embodiments, the monitoring component 102 of the gas pipeline network can further include a monitoring and early warning unit 302 and a data analysis unit 303.

[0073] The monitoring and early warning unit 302 can determine whether the information of each dimension is abnormal based on the information of each dimension in the monitoring information and the safety threshold corresponding to the information of each dimension, and generate monitoring abnormal alarm information if it is determined that the information of any one of the dimensions is abnormal. Specifically, the monitoring and early warning unit 302 can set threshold values for multi-dimensional monitoring information such as combustible gas concentration information, water level information, pressure information, temperature information, manhole cover position information, and gas valve assembly state information, and determine that the monitoring information of any one dimension is abnormal when the monitoring information of any one dimension exceeds the corresponding threshold value, and generate monitoring abnormal alarm information for early warning. In some embodiments, multiple threshold values can be set for combustible gas concentration information, for example, a 25% high overrun alarm threshold value and a 50% high high overrun alarm threshold value, so that different warnings can be taken for combustible gas concentration information exceeding different threshold values, realizing the grading service of combustible gas information alarm. Among them, the high overrun alarm threshold value can be the local regulation and control of the gas valve assembly and the remote regulation and control of the monitoring and early warning unit 302, and the high high overrun alarm threshold value can be a fixed value.

[0074] The data analysis unit 303 can determine the working state of the monitoring module corresponding to each dimension of information based on the data dependency relationship between each dimension of information in the monitoring information of each gas valve well, and generate module abnormal alarm information when it is determined that any one monitoring module is in an abnormal working state. Specifically, the data analysis unit 303 can determine that the monitoring module that is abnormal or has no data is malfunctioning when it is determined that the individual monitoring information is suddenly abnormal or has no data, but other monitoring information is normal. Further, the data analysis unit 303 can determine that a part of the monitoring module in the gas valve assembly is malfunctioning when it is determined that the monitoring information of the part of the monitoring module cannot match the monitoring information of other monitoring modules, and generate module abnormal alarm information. For example, the combustible gas concentration data of the gas monitoring module in the gas valve assembly is A, the pressure data of the pressure monitoring module is B, and the water level data of the water level monitoring module is C, but the data analysis unit 303 analyzes that the combustible gas concentration data is A and the pressure data is B, and the corresponding water level data should be D, which does not match the actual water level data C, so it can be determined that the water level monitoring module is malfunctioning, and module abnormal alarm information can be generated. Through the setting of the data analysis unit 303, the fault of each monitoring module can be investigated and diagnosed, and the stability of the system can be improved.

[0075] In some embodiments, the gas pipeline network monitoring assembly 102 can further include a human-computer interaction unit 304, which can provide a visual monitoring interface and sound and light alarm information for the monitoring center. Specifically, the human-computer interaction unit 304 can provide analysis charts of monitoring information in different dimensions, and can generate different early warning processing procedures corresponding to different users. In addition, the data analysis unit 301 can display the pipe segment leakage alarm information, the monitoring anomaly alarm information generated by the monitoring early warning unit 302, or the module anomaly alarm information generated by the data analysis unit 302 on the visual monitoring interface.

[0076] In some embodiments, the gas pipeline network monitoring and early warning system 100 can further include a data management assembly 103. The gas valve monitoring assembly 101 can upload the monitoring information to the base station through the Internet of Things, and the base station can upload the monitoring information to the data management assembly 103 through the Internet of Things. The data management assembly 103 can interact with the gas pipeline network monitoring assembly 102, for example, to transmit monitoring information, alarm information, threshold control corresponding management information, and the like.

[0077] The data management assembly 103 can manage system data, realize analysis and storage of the monitoring information transmitted by the gas valve assembly, and classify and integrate the information according to functional requirements. Specifically, the data management assembly 103 can include operations such as data extraction, data validation, data cleaning, data deletion, data conversion, data loading, and metadata management. The main work of the data management assembly 103 is to operate the database, classify the data in combination with the business of the gas pipeline network monitoring assembly 102, and store the classified data in the comprehensive business database, the operation database, and the main database, respectively, to construct a gas valve well monitoring data model and a business model, provide data writing and reading services, and standardize the main data. The data management objects of the data management assembly 103 can include the received monitoring information and data generated by the components in the gas pipeline network monitoring assembly 102 during operation.

[0078] The following will be described in combination with Figure 4 The gas pipeline network monitoring method provided by the embodiments of the present application is introduced.

[0079] Figure 4 As shown in the figure, a flowchart of a gas pipeline network monitoring method provided by an embodiment of the present application is shown. As shown in the figure, Figure 4 The method can include:

[0080] S401: Obtain monitoring information and prediction information of each gas valve well node in the gas pipeline network.

[0081] The monitoring information includes pressure information monitored by each gas valve well during actual operation, and the prediction information is pressure information of each gas valve well node in the gas pipeline network during normal operation, which is predicted based on a pre-trained pipeline network data model.

[0082] In some embodiments, the monitoring information of each gas valve well further includes at least one of the following dimensions: combustible gas concentration information, temperature information, water level information, and well cover position information of the gas valve well. The monitoring information of each dimension can be collected by a corresponding monitoring module in the gas valve assembly 101.

[0083] In some embodiments, the pipeline structure texture of the gas pipeline network can be analyzed, a pipeline structure model can be constructed, and the pipeline structure model can be trained by monitoring information of each gas valve well node in the gas pipeline network monitored during normal operation of the gas pipeline network, and coefficients of the pipeline structure model can be optimized to obtain a pipeline data model. Further, when judging the leakage of the gas pipeline network, the pipeline data model can be simulated to obtain prediction information of each gas valve well node.

[0084] In some embodiments, the monitoring information obtained in step S401 can be subjected to Kalman filtering processing. Specifically, the gas pipeline network system with the gas valve well as the node can be regarded as a discrete dynamic system, and the recursive calculation of the optimal estimation of the system state can be performed through the observation data of the input and output of the gas pipeline network system, and the current pressure value can be corrected by the measured pipeline pressure value.

[0085] The Kalman recursive filtering algorithm can be represented by the following formula:

[0086]

[0087] X(k|k-1) represents the prediction result of the current state of the monitoring information, X(k-1|k-1) represents the optimal estimation value of the previous state of the monitoring information, U(k) represents the control amount of the current state of the monitoring information, P(k|k-1) represents the covariance corresponding to the prediction result of the current state of the monitoring information, P(k-1|k-1) represents the covariance corresponding to the optimal result of the previous state of the monitoring information, Q represents the covariance of the process of the gas pipeline network system, and A and B represent the state transition matrix and the input control matrix, respectively.

[0088] Based on the prediction result of the current state of the monitoring information and the measured value of the current state, the optimal estimation value of the current state can be obtained, wherein the optimal estimation value can be represented by the following formula:

[0089] X(k|k)=X(k|k-1)+Kg(k)(Z(k)-HX(k|k-1)) Formula (2);

[0090] wherein X(k|k) represents an optimal estimation value of the current state of the monitoring information, Kg(k) represents a Kalman filter gain of the current state of the monitoring information, Z(k) represents a measured value of the current state of the monitoring information, and H represents a state observation matrix.

[0091] wherein the Kalman filter gain can be determined based on a noise variance of the monitoring information, specifically by the following formula:

[0092] Kg(k) = P(k|k-1)H′ / (HP(k|k-1)H′+R) Formula (3);

[0093] wherein R represents a covariance of the noise of the monitoring information.

[0094] Based on the above formulas (1) to (3), the optimal estimation value X(k|k) of the current state of the monitoring information can be used to iteratively update the covariance of the current state, and finally the Kalman filter output can fully describe the operation of the gas pipe network system, supporting the subsequent data analysis unit 301 to analyze the pipe leakage event and identify the leakage position. That is, based on the measured value of the current state of the monitoring information and the prediction result of the previous sampling period, the state of the current state of the monitoring information can be estimated, the optimal estimation value of the current state can be predicted according to the system state equation, and the gain setting of the filter can be used to determine whether the measured value is more reliable or the estimation value is more reliable, or the optimal estimation value is more reliable. Finally, the data output can accurately describe the system, which is used for subsequent leakage analysis.

[0095] It can be understood that the process of Kalman filter processing of the monitoring information described above can be processed by the data analysis unit 301 or by the data management component 103.

[0096] S402: determining whether the gas pipe network has a gas leakage based on the correlation between the monitoring information and the predicted data.

[0097] In some embodiments, in step S402, determining whether the gas pipe network has a gas leakage based on the correlation between the monitoring information and the predicted information includes: determining the correlation between the monitoring information and the predicted information; in the case that the correlation is lower than a preset correlation threshold, determining that the gas pipe network has a gas leakage; and in the case that the correlation is not lower than the preset correlation threshold, determining that the gas pipe network does not have a gas leakage.

[0098] It can be understood that the smaller the correlation degree, the greater the difference between the monitoring information and the predicted information, and the further the monitoring information is from the information obtained under the normal working state of the gas pipe network. Conversely, the greater the correlation degree, the smaller the difference between the monitoring information and the predicted information, and the closer the monitoring information is to the information obtained under the normal working state of the gas pipe network. Whether the gas pipe network has a depression point of pipe pressure drop, i.e., whether the gas pipe network has a gas leakage, can be determined by comparing the correlation degree with a preset correlation degree threshold.

[0099] In some embodiments, the correlation degree between the monitoring information and the predicted information is determined by the following formula:

[0100]

[0101] wherein r represents the correlation degree between the monitoring information and the predicted information, a i represents the predicted information of the i-th gas valve well node in the gas pipe network, b i represents the monitoring information of the i-th gas valve well node in the gas pipe network, and n represents the number of gas valve well nodes in the gas pipe network.

[0102] S403: In the case where it is determined that the gas pipe network has a gas leakage, the leakage point position information of the gas pipe network where the gas leakage occurs is determined based on the monitoring information of each gas valve well node.

[0103] Wherein, determining that the gas pipe network has a gas leakage can be understood as determining that the correlation degree is lower than the preset correlation threshold.

[0104] In some embodiments, in step S403, based on the monitoring information of each gas valve well node, the leakage point position information of the gas pipe network where the gas leakage occurs is determined, including: determining the pressure drop information of each gas valve well node based on the monitoring information; determining the gas valve node with the maximum pressure drop in the pressure drop information as a first target node; determining the gas valve node with the minimum correlation degree between the monitoring information and the predicted information among at least one gas valve node connected to the target node as a second target node; determining the leakage point position information based on the first pressure drop of the target node, the second pressure drop of the second target, and the length of the gas pipe section between the first target node and the second target node.

[0105] It can be understood that the first target node is one end of the leakage pipe section where the gas leakage occurs, and the second target node is the other end of the leakage pipe section where the gas leakage occurs. Then, based on the radiation law of the leakage pressure drop, i.e., the node pressure drop is large near the leakage node, the upstream and downstream pressure drops of the pipe section are used for leakage point positioning. The upstream and downstream pressure drops can be understood as the pressure drop of the first target node and the pressure drop of the second target node.

[0106] In some embodiments, the location information of the leak point can be determined by the following formula:

[0107]

[0108] Where X represents the distance from the leak point to the first target node, L represents the total length of the pipe section between the first and second target nodes, ΔP represents the pressure drop at the first target node, and ΔP2 represents the pressure drop at the second target node.

[0109] In some embodiments, after performing step S403, the method may further include: generating pipe segment leakage alarm information based on the leakage point location information.

[0110] Figure 5 The diagram shown is a structural schematic of a gas pipeline network model provided in an embodiment of this application.

[0111] like Figure 5 As shown, the gas pipeline network model uses gas valve wells as nodes, and the pipe sections between gas valve wells are the connecting lines between nodes. Assuming that node 5 is determined to be the first target node in step S403 above, then further judging among nodes 2, 4, 8 and 6 connected to node 5, the correlation between the monitoring information and the prediction information in node 6 is the smallest. Therefore, the gas pipe section between node 5 and node 6 is determined to be the leaking pipe section, and the above formula (5) can be used to further determine the location information of the leak point.

[0112] Figure 6 The diagram shown is a flowchart illustrating another gas pipeline monitoring method provided in an embodiment of this application.

[0113] like Figure 6 As shown, in some embodiments, the above-mentioned gas pipeline network monitoring method further includes:

[0114] S404: Based on the data dependencies between the information in each dimension of the monitoring information of each gas valve well, determine the working status of the monitoring module corresponding to each dimension of information.

[0115] S405: When any one of the monitoring modules is determined to be in an abnormal working state, generate module abnormality alarm information.

[0116] It can be understood that the execution entity of the above steps S404 and S405 can be the data analysis unit 303 in the gas pipeline network monitoring component 102.

[0117] Figure 7 The diagram shown is a flowchart illustrating another gas pipeline monitoring method provided in an embodiment of this application.

[0118] like Figure 7As shown, in some embodiments, the above-mentioned gas pipeline network monitoring method further includes:

[0119] S406: determining whether the information of each dimension is abnormal based on the information of each dimension in the monitoring information and the safety threshold corresponding to the information of each dimension.

[0120] S407: generating monitoring abnormality alarm information in the case that the information of any one of the dimensions is determined to be abnormal.

[0121] It can be understood that the execution subject of the above-mentioned steps S406 and S407 can be the monitoring and early warning unit 303 in the gas pipeline network monitoring assembly 102.

[0122] The present application also provides a gas pipeline network monitoring device. Figure 8 As shown, the present application provides a structural schematic diagram of a gas pipeline network monitoring device 800. As shown in Figure 8 As shown, the device can include:

[0123] The information acquisition module 801 is configured to acquire monitoring information and prediction information of each gas valve well node in the gas pipeline network, wherein the monitoring information includes pressure information monitored during actual operation of each gas valve well, and the prediction information is pressure information of each gas valve well node during normal operation of the gas pipeline network, which is predicted based on a pre-trained pipeline network data model.

[0124] The leakage determination module 802 is configured to determine whether gas leakage occurs in the gas pipeline network based on the correlation between the monitoring information and the prediction data.

[0125] The position determination module 803 is configured to determine leakage point position information of the gas pipeline network in which gas leakage occurs based on the monitoring information of each gas valve well node in the case that it is determined that gas leakage occurs in the gas pipeline network.

[0126] It can be understood that the gas pipeline network monitoring device in the present application can be understood as the gas pipeline network monitoring assembly in Figure 1 The description and functions of the above-mentioned modules can be understood by referring to the content of the gas pipeline network monitoring method part, which will not be repeated here.

[0127] The present application also provides an electronic device, as shown in Figure 9 As shown, the electronic device can include a processor 901 and a memory 902, wherein the processor 901 and the memory 902 can be connected through a bus or other means, Figure 9 In the present application, the connection through the bus is taken as an example.

[0128] The processor 901 can be a central processing unit (CPU). The processor 901 can also be other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, or a combination of the above.

[0129] The memory 902, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs, non-transitory computer-executable programs and modules, such as program instructions / modules for monitoring a gas pipe network (for example, information acquisition module 801, leakage determination module 802, and position determination module 803) in the embodiments of the present application. The processor 901 performs various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory 902, that is, implements the monitoring method of the gas pipe network in the above method embodiments. Figure 8

[0130] The memory 902 can include a program storage area and a data storage area. The program storage area can store an operating system and at least one application required by a function; the data storage area can store data created by the processor 901 and the like. In addition, the memory 902 can include a high-speed random access memory, and can also include a non-transitory memory, such as at least one magnetic disk storage device, a flash memory device, or other non-transitory solid-state memory device. In some embodiments, the memory 902 can optionally include a memory disposed remotely with respect to the processor 901, and these remote memories can be connected to the processor 901 through a network. Examples of the above network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof.

[0131] The one or more modules are stored in the memory 902 and executed by the processor 901 to perform the monitoring method of the gas pipe network in the above embodiments. Figure 4

[0132] The above electronic device specific details can be understood by referring to the corresponding related descriptions and effects in the above method embodiments, which will not be repeated here.

[0133] ​​The specification also provides a computer storage medium storing computer program instructions, which, when executed, implement the steps of the above-mentioned gas pipe network monitoring method.

[0134] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing related hardware through a computer program. The program can be stored in a computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiment methods can be included. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), a random access memory (RAM), a flash memory, a hard disk drive (HDD) or a solid-state drive (SSD), etc. The storage medium can also include a combination of the above-mentioned types of memories.

[0135] Each embodiment in the specification is described in a progressive manner, and the same or similar parts between each embodiment can be referred to each other. Each embodiment focuses on the difference from other embodiments.

[0136] The system, device, module or unit described in the above embodiments can be specifically implemented by a computer chip or entity, or by a product with certain functions.

[0137] For the convenience of description, the above device is described as various units with functions. Of course, the functions of each unit can be implemented in the same or multiple software and / or hardware in the implementation of the present application.

[0138] From the above description of the embodiments, those skilled in the art can clearly understand that the present application can be implemented by means of software and necessary general hardware platforms. Based on this understanding, the technical solutions of the present application can be embodied in the form of a software product, which can be stored in a storage medium, such as a ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions to make a computer device (which can be a personal computer, a server, or a network device, etc.) execute the methods of some parts of the embodiments of the present application.

[0139] The application is operational with numerous other general purpose or special purpose computing system environments or configurations. Examples of well- known computing systems, environments, and / or configurations that can be suitable for use with the application include personal computers, server computers, handheld or laptop devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

[0140] The application can be described in the general context of computer- executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like, that perform particular tasks or implement particular abstract data types. The application can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in local and remote computer storage media including memory storage devices.

[0141] While this application has been depicted, described, and is to be understood in connection with specific example embodiments, it will be appreciated that modifications can be made by those skilled in the art without departing from the spirit and scope of the application. It is therefore intended that the application not be limited to the exact example embodiments described above, but should be construed to cover all suitable structures that are pointed out in the appended claims and their equivalents.

Claims

1. A method for monitoring a gas pipeline network, characterized in that, include: The monitoring information and prediction information of each gas valve well node in the gas pipeline network are obtained. The monitoring information includes the pressure information monitored during the actual operation of each gas valve well. The prediction information is the pressure information of each gas valve well node in the gas pipeline network during normal operation, which is predicted based on a pre-trained pipeline network data model. Based on the correlation between the monitoring information and the prediction information, it is determined whether a gas leak has occurred in the gas pipeline network; In the event that a gas leak has occurred in the gas pipeline network, the location information of the leak point in the gas pipeline network is determined based on the monitoring information of each gas valve well node. Based on the correlation between the monitoring information and the prediction information, determining whether a gas leak has occurred in the gas pipeline network includes: Determine the correlation between the monitoring information and the prediction information; If the correlation is lower than a preset correlation threshold, it is determined that a gas leak has occurred in the gas pipeline network. If the correlation is not lower than a preset correlation threshold, it is determined that no gas leak has occurred in the gas pipeline network; The correlation between the monitoring information and the prediction information is determined by the following formula: ； Where r represents the correlation between the monitoring information and the prediction information. This represents the predicted information for the i-th gas valve well node in the gas pipeline network. This represents the monitoring information of the i-th gas valve well node in the gas pipeline network, where n represents the number of gas valve well nodes in the gas pipeline network; Based on the monitoring information of each gas valve well node, the location information of the gas leak point in the gas pipeline network is determined, including: Based on the monitoring information, the pressure drop information of each gas valve well node is determined; The gas valve node with the largest pressure drop in the pressure drop information is identified as the first target node; The gas valve node with the lowest correlation between the monitoring information and the prediction information among at least one gas valve node connected to the target node is identified as the second target node. The location information of the leak point is determined based on the first pressure drop of the first target node, the second pressure drop of the second target node, and the length of the gas pipeline segment between the first target node and the second target node.

2. The method according to claim 1, characterized in that, The monitoring information for each gas valve well also includes at least one of the following dimensions: Information on combustible gas concentration, temperature, water level, and the location of the manhole cover for the gas valve well.

3. The method according to claim 2, characterized in that, Also includes: Based on the data dependencies between the information in each dimension of the monitoring information of each gas valve well, the working status of the monitoring module corresponding to each dimension of information is determined. When any one of the monitoring modules is determined to be in an abnormal working state, a module abnormality alarm message is generated.

4. The method according to claim 2, characterized in that, Also includes: Based on the information of each dimension in the monitoring information and the corresponding security threshold of each dimension, it is determined whether the information of each dimension is abnormal. If any one of the information dimensions is found to be abnormal, an alarm message for monitoring anomalies will be generated.

5. A monitoring device for a gas pipeline network, characterized in that, include: The information acquisition module is used to acquire monitoring information and prediction information of each gas valve well node in the gas pipeline network. The monitoring information includes the pressure information monitored during the actual operation of each gas valve well, and the prediction information is the pressure information of each gas valve well node in the gas pipeline network during normal operation, which is predicted based on a pre-trained pipeline network data model. The leakage determination module is used to determine whether a gas leak has occurred in the gas pipeline network based on the correlation between the monitoring information and the prediction information. The location determination module is used to determine the location information of the gas leak point in the gas pipeline network based on the monitoring information of each gas valve well node when a gas leak is determined to have occurred in the gas pipeline network. The leakage determination module is specifically used for: Determine the correlation between the monitoring information and the prediction information; If the correlation is lower than a preset correlation threshold, it is determined that a gas leak has occurred in the gas pipeline network. If the correlation is not lower than a preset correlation threshold, it is determined that no gas leak has occurred in the gas pipeline network; The correlation between the monitoring information and the prediction information is determined by the following formula: ； Where r represents the correlation between the monitoring information and the prediction information. This represents the predicted information for the i-th gas valve well node in the gas pipeline network. This represents the monitoring information of the i-th gas valve well node in the gas pipeline network, where n represents the number of gas valve well nodes in the gas pipeline network; The location determination module is specifically used for: Based on the monitoring information, the pressure drop information of each gas valve well node is determined; The gas valve node with the largest pressure drop in the pressure drop information is identified as the first target node; The gas valve node with the lowest correlation between the monitoring information and the prediction information among at least one gas valve node connected to the target node is identified as the second target node. The location information of the leak point is determined based on the first pressure drop of the first target node, the second pressure drop of the second target node, and the length of the gas pipeline segment between the first target node and the second target node.

6. A monitoring and early warning system for a gas pipeline network, characterized in that, It includes multiple gas valve monitoring components and gas pipeline network monitoring components, wherein the gas valve monitoring device corresponds to a gas valve well node in the gas pipeline network; The gas valve monitoring component is used to monitor multiple dimensions of information about the gas valve and upload the multiple dimensions of information as monitoring information to the monitoring component of the gas pipeline network. The monitoring components for the gas pipeline network include: The information acquisition module is used to acquire monitoring information and prediction information of each gas valve well node in the gas pipeline network. The monitoring information includes the pressure information monitored during the actual operation of each gas valve well, and the prediction information is the pressure information of each gas valve well node in the gas pipeline network during normal operation, which is predicted based on a pre-trained pipeline network data model. The leakage determination module is used to determine whether a gas leak has occurred in the gas pipeline network based on the correlation between the monitoring information and the prediction information. The location determination module is used to determine the location information of the gas leak point in the gas pipeline network based on the monitoring information of each gas valve well node when a gas leak is determined to have occurred in the gas pipeline network. The leakage determination module is specifically used for: Determine the correlation between the monitoring information and the prediction information; If the correlation is lower than a preset correlation threshold, it is determined that a gas leak has occurred in the gas pipeline network. If the correlation is not lower than a preset correlation threshold, it is determined that no gas leak has occurred in the gas pipeline network; The correlation between the monitoring information and the prediction information is determined by the following formula: ； Where r represents the correlation between the monitoring information and the prediction information. This represents the predicted information for the i-th gas valve well node in the gas pipeline network. This represents the monitoring information of the i-th gas valve well node in the gas pipeline network, where n represents the number of gas valve well nodes in the gas pipeline network; The location determination module is specifically used for: Based on the monitoring information, the pressure drop information of each gas valve well node is determined; The gas valve node with the largest pressure drop in the pressure drop information is identified as the first target node; The gas valve node with the lowest correlation between the monitoring information and the prediction information among at least one gas valve node connected to the target node is identified as the second target node. The location information of the leak point is determined based on the first pressure drop of the first target node, the second pressure drop of the second target node, and the length of the gas pipeline segment between the first target node and the second target node.

7. An electronic device, characterized in that, include: A memory and a processor, the processor and the memory being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to implement the steps of the method according to any one of claims 1 to 4.

8. A computer storage medium, characterized in that, The computer storage medium stores computer program instructions, which, when executed, implement the steps of the method according to any one of claims 1 to 4.

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