Gas pipe network monitoring method, device and equipment

Abstract

The invention discloses a gas pipe network monitoring method, device and equipment. The method comprises the steps that monitoring information and prediction information of all gas valve well nodes in a gas pipe network are obtained; based on the correlation degree between the monitoring information and the prediction data, whether gas leakage occurs in the gas pipe network or not is determined; and under the condition that it is determined that gas leakage occurs in the gas pipe network, based on the monitoring information of all the gas valve well nodes, position information of leakage points where gas leakage occurs in the gas pipe network is determined. By analyzing the correlation degree between the monitoring information and the prediction information, 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 pipe section with the abnormal operation can be positioned, and the occurrence of safety events such as large-scale gas leakage and explosion can be avoided.

Description

Technical Field

[0001] The present application relates to the technical field of gas pipeline networks, and in particular to a monitoring method, device and equipment for a gas pipeline network. Background Art

[0002] With the acceleration of urbanization and industrialization, the scale of urban gas pipeline networks is getting larger and larger. As an important infrastructure and public utility in cities and towns, the safety of urban gas pipeline networks is of great importance.

[0003] At present, most gas safety monitoring is to monitor the combustible gas leakage in the gas valve well, monitor whether the gas leakage monitoring sensor unit fails due to flooding, monitor whether the manhole cover of the gas valve well is displaced, and monitor the node pressure at the gas valve well. However, based on the above gas safety detection, the combustible gas leakage in the gas pipeline network cannot be discovered in time, and the monitoring data is not accurately and effectively analyzed and judged, which makes emergency response and rescue relatively lagging, affecting the safety of urban personnel. Summary of the invention

[0004] The purpose of the implementation mode of the present application is to provide a gas pipeline network monitoring method, device and equipment to solve the problems of untimely detection of gas leaks and inaccurate risk prediction in existing solutions.

[0005] In order to solve the above technical problems, the first aspect of this specification provides a method for monitoring a gas pipeline network, comprising:

[0006] 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 in the gas pipeline network during normal operation, which is predicted based on a pre-trained pipeline data model;

[0007] Determining whether a gas leak occurs in the gas pipeline network based on the correlation between the monitoring information and the prediction data;

[0008] When it is determined that a gas leak occurs in the gas pipeline network, the location information of the leakage point where the gas leak occurs in the gas pipeline network is determined based on the monitoring information of each gas valve well node.

[0009] In some embodiments, determining whether a gas leak occurs in the gas pipeline network based on the correlation between the monitoring information and the prediction information includes:

[0010] Determining a correlation between the monitoring information and the prediction information;

[0011] When the correlation is lower than a preset correlation threshold, determining that a gas leak occurs in the gas pipeline network;

[0012] When the correlation degree is not lower than a preset correlation threshold, it is determined that no gas leakage occurs in the gas pipeline network.

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

[0014]

[0015] Wherein, r represents the correlation 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, determining the location information of the leakage point where the gas leakage occurs in the gas pipeline network includes:

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

[0018] Determine the gas valve node with the largest pressure drop in the pressure drop information as the first target node;

[0019] Determine, as a second target node, a gas valve node having the smallest correlation between monitoring information and prediction information among at least one gas valve node connected to the target node;

[0020] The leakage point location information is determined 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.

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

[0022] Combustible gas concentration information, temperature information, water level information, and manhole cover location 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, determining the working state of the monitoring module corresponding to the information of each dimension;

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

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

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

[0028] When it is determined that any one dimension of the information in each dimension is abnormal, monitoring abnormality alarm information is generated.

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

[0030] An information acquisition module, used 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 in the gas pipeline network during normal operation, which is predicted based on a pre-trained pipeline data model;

[0031] A leakage determination module, used to determine whether a gas leakage occurs in the gas pipeline network based on the correlation between the monitoring information and the prediction data;

[0032] The location determination module is used to determine the location information of the leakage point where the gas leakage occurs in the gas pipeline network based on the monitoring information of each gas valve well node when it is determined that the gas leakage occurs in the gas pipeline network.

[0033] A third aspect of the present specification provides a monitoring and early warning system for a gas pipeline network, comprising a plurality of gas valve monitoring components and a monitoring component for a gas pipeline network, wherein the gas valve well corresponding to the gas valve monitoring device is a gas valve well node in the gas pipeline network;

[0034] The gas valve monitoring component is used to monitor information of multiple dimensions of the gas valve, and upload the information of multiple dimensions as monitoring information to the monitoring component of the gas pipe network;

[0035] The monitoring components of the gas pipeline network include:

[0036] An information acquisition module, used 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 in the gas pipeline network during normal operation, which is predicted based on a pre-trained pipeline data model;

[0037] A leakage determination module, used to determine whether a gas leakage occurs in the gas pipeline network based on the correlation between the monitoring information and the prediction data;

[0038] The location determination module is used to determine the location information of the leakage point where the gas leakage occurs in the gas pipeline network based on the monitoring information of each gas valve well node when it is determined that the gas leakage occurs in the gas pipeline network.

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

[0040] A fifth aspect of the specification provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed, the steps of the method described in the first aspect are implemented.

[0041] The gas pipeline network monitoring method provided in the embodiment of this specification obtains the monitoring information and prediction information of each gas valve well node in the gas pipeline network, and then determines whether a gas leak occurs in the gas pipeline network based on the correlation between the monitoring information and the prediction information, and when it is determined that a gas leak occurs in the gas pipeline network, the location information of the leakage point where the gas leak occurs is determined based on the monitoring information of each gas valve well node. This application can quickly and accurately detect abnormal operation such as gas leakage in the gas pipeline network by analyzing the correlation between the monitoring information and the prediction information, and can locate the abnormal operation pipe section by further analyzing the monitoring information, so as to avoid the occurrence of safety incidents such as large-scale gas leakage and explosion. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the implementation methods of the present application or the technical solutions in the prior art, the drawings required for use in the implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some implementation methods recorded in this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0043] Figure 1 The figure shows a structural diagram of a gas pipeline network monitoring and early warning system provided in an embodiment of the present application;

[0044] Figure 2 The figure is a schematic diagram of the structure of a gas valve monitoring assembly provided in an embodiment of the present application;

[0045] Figure 3 Shown is a schematic diagram of the structure of a gas pipe network monitoring component provided in an embodiment of the present application;

[0046] Figure 4The figure shows a flow chart of a gas pipeline network monitoring method provided in an embodiment of the present application;

[0047] Figure 5 The figure shows a schematic diagram of the structure of a gas pipe network model provided in an embodiment of the present application;

[0048] Figure 6 Shown is a flow chart of another gas pipeline network monitoring method provided in an embodiment of the present application;

[0049] Figure 7 Shown is a flow chart of another gas pipeline network monitoring method provided in an embodiment of the present application;

[0050] Figure 8 The figure shows a schematic diagram of the structure of a gas pipe network monitoring device provided in an embodiment of the present application;

[0051] Fig. 9 Shown is a principle block diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to enable those skilled in the art to better understand the technical solutions in this application, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0053] As mentioned above, existing gas safety detection cannot detect combustible gas leaks in the gas pipeline network in a timely manner, and the monitoring data is not accurately and effectively analyzed and judged, which makes emergency response and rescue relatively delayed, affecting the safety of urban personnel.

[0054] In order to solve the above problems, an embodiment of the present application provides a monitoring method for a gas pipeline network, which may 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 during actual operation of each gas valve well, and the prediction information is pressure information of each gas valve well node in the gas pipeline network during normal operation predicted based on a pre-trained pipeline data model; determining whether a gas leak occurs in the gas pipeline network based on the correlation between the monitoring information and the prediction data; and when it is determined that a gas leak occurs in the gas pipeline network, determining the location information of the leakage point where the gas leak occurs in the gas pipeline network based on the monitoring information of each gas valve well node.

[0055] By analyzing the correlation between monitoring information and prediction information, the present application can quickly and accurately detect abnormal operation such as gas leakage in the gas pipeline network, and through further analysis of the monitoring information, it can locate the pipe section with abnormal operation to avoid safety incidents such as large-scale gas leakage and explosion.

[0056] In the method provided in the embodiment of the present application, the execution subject of each step may be an electronic device, which refers to an electronic device with data calculation, processing and storage capabilities. The electronic device may be a terminal such as a personal computer (PC), a tablet computer, a smart phone, a wearable device, an intelligent robot, etc.; or it may be a server. Among them, the server may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud computing services.

[0057] Before introducing the gas pipeline network monitoring method in the embodiment of the present application, a gas pipeline network monitoring and early warning system provided in the embodiment of the present application is first introduced.

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

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

[0060] The gas valve monitoring component 101 is used to monitor information of multiple dimensions of the gas valve, and upload the information of multiple dimensions as monitoring information to the monitoring component 102 of the gas pipe network. The gas valve monitoring component 101 may include a monitoring module for monitoring environmental data of multiple dimensions, which can be specifically referred to in Figure 2 shown.

[0061] The monitoring component 102 of the gas pipeline network is used to determine whether a gas pipeline network leaks based on the monitoring information and prediction information of each gas valve well node in the gas pipeline network, and when it is determined that a gas leak occurs in the gas pipeline network, the location information of the leakage point where the gas leak occurs in the gas pipeline network is determined based on the monitoring information of each gas valve well node. Among them, 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 data model. The structure of the monitoring component 102 of the gas pipeline network can be as follows: Figure 3 shown.

[0062] Figure 2FIG. 1 is a schematic diagram of the structure of a gas valve monitoring component provided in an embodiment of the present application. Figure 2 As shown, in some embodiments, the gas valve monitoring component 101 may include a gas monitoring module 201, a water level monitoring module 202, a pressure monitoring module 203 and a manhole cover monitoring module 204. Among them, the gas monitoring module 201 can be used to monitor the gas concentration data of safety indicators, which may specifically include the safety data of combustible gas, and the operation status data of the gas monitoring module 201. The water level monitoring module 202 can be used to monitor the water level over-limit change state data, which may specifically include the water level over-limit information of the installation environment or the water immersion state of the gas valve monitoring component. That is, the water level monitoring module 202 can be used to monitor the water level height state in the gas valve well to protect the gas equipment in the gas valve well, and can also be used to monitor the water immersion state of the gas valve monitoring component 101 to confirm whether the combustible gas detection capability of the gas valve monitoring component 101 is intact. The pressure monitoring module 203 can be used to monitor the gas pipe network pressure and pipe network temperature data. The manhole cover monitoring module 204 can be used to monitor the manhole cover opening state change data.

[0063] In some embodiments, the gas valve monitoring component 101 may also include a data cache module 205, a clock module 206, a communication module 207, a service control module 208, a power module 209, and an anti-theft module 210. Among them, the data cache module 205 can be used to store the monitored periodic multi-dimensional environmental data, threshold crossing data and state change data, and can store the data monitored by the monitoring module locally. The clock module 206 can be used to calibrate the system time of the gas valve monitoring component 101 and attach time tags to the monitored multi-dimensional environmental information. The service control module 208 can be used to control the low power consumption of each module in the gas valve monitoring component 101, execute according to the preset operation logic, process the environmental data monitoring results, information processing of data transmission, alarm services according to preset alarm thresholds and environmental state changes, data cache services, network diagnosis and transmission data integrity services, equipment status self-diagnosis, and local and remote management of equipment. The communication module 207 can be used to transmit the periodic multi-dimensional environmental information, threshold crossing information and state change information processed by the service control module 208 to the monitoring component 102 of the gas pipeline network. The power supply module 209 can be used to supply power to the modules requiring power under the control of the service control module 208. The anti-theft module 210 can be used to monitor the displacement change of the gas valve monitoring component 101.

[0064] It can be understood that the business control module 208 can be specifically an embedded unit of the system integration program, which can be used to provide a working signal for the power supply module in the gas valve monitoring component 101, provide the mainboard with a power supply logic according to a preset opening interface, provide a boost signal for the module that needs to be boosted, receive and process the data collected by the monitoring module and the status of the monitoring module, perform information processing on the collected data according to the preset threshold and status judgment, and receive remote management information to correct the integrated system program and threshold. The clock module 206 can add a time tag to the collected data to determine the uniqueness of the data. The power supply module 209 is a lithium battery pack managed by the power management component, which can be used to power the gas valve monitoring component 101 as a whole and meet the power supply safety requirements. The communication module 207 can also be used to interact with the information of the monitoring component 102 of the gas pipeline network, receive and confirm the management information of the monitoring component 102 of the gas pipeline network and pass it to the business control module 208.

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

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

[0067] In some embodiments, based on the different installation environments of the gas valve wells corresponding to the gas valve monitoring component 101, the gas valve monitoring component may include different monitoring modules for monitoring information of different dimensions. Figure 1As shown, for example, the gas valve monitoring component can be applied to typical confined spaces of gas pipeline networks such as steel cast iron pipe valve wells, polyethylene (PE) pipe valve wells, valve chambers, and three trenches and two wells. For steel cast iron pipe valve wells, the combustible gas concentration, valve well pressure, and water immersion in the valve well can be sensed, and the gas valve monitoring component 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 PE pipe valve wells, the combustible gas concentration and water immersion in the valve well can be sensed, and the gas valve monitoring component corresponding to the PE pipe valve well can include a gas monitoring module and a water level monitoring module; for valve chambers, the combustible gas concentration, valve well pressure, water immersion, and well cover status of the valve chamber can be sensed, and the gas valve monitoring component corresponding to the valve chamber can include a gas monitoring module, a water level monitoring module, a pressure monitoring module, and a well cover monitoring module; for three trenches and two wells, the combustible gas concentration and water immersion in the three trenches and two wells can be sensed, and the gas valve monitoring component corresponding to the three trenches and two wells can include a gas monitoring module and a water level monitoring module.

[0068] Understandably, Figure 1 The gas valve monitoring component 101 in the embodiment 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 pipeline network. In other embodiments, the gas valve monitoring component can be arranged in a pressure regulating cabinet in the gas pipeline network, and then the pressure regulating cabinet where the gas valve monitoring device is located and the gas valve well corresponding to the pressure regulating cabinet can be regarded as a gas valve well node. Further, based on the different positions of the gas valve monitoring component, the gas valve monitoring component can include different modules. For example, when the gas valve monitoring component 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 business control module, a power module, a manhole cover monitoring module, a pressure monitoring module, and an anti-theft module; when the gas valve monitoring component is located in a pressure regulating cabinet, it can include a gas detection module, a data cache module, a clock module, a communication module, a business 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 port of the pressure regulating cabinet, and the other pressure monitoring module is used to monitor the pressure of the output pipe port of the pressure regulating cabinet.

[0069] Figure 3 Shown is a schematic structural diagram of a gas pipeline network monitoring component provided in an embodiment of the present application.

[0070] like Figure 3As shown, in some embodiments, the monitoring component 102 of the gas pipeline network may 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 law of the pressure and other parameters of the gas pipeline network when the pipeline segment leaks, identify the abnormal operation of the gas pipeline network when the gas leak occurs, and locate the leakage point of the pipeline segment where the gas leak is located to obtain the leakage point location information. Further, the pipeline segment leakage alarm information can be generated based on the leakage point location information, and the pipeline segment leakage alarm information can be pushed to support emergency maintenance operations and avoid large-scale leakage and explosion and other safety incidents.

[0071] In some embodiments, the data analysis unit 301 may include: an information acquisition module, a leakage determination module, and a location determination module. Among them, the information acquisition module is used to obtain 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 predicted based on the pre-trained pipeline data model. The leakage determination module is used to determine whether a gas leak occurs in the gas pipeline network based on the correlation between the monitoring information and the prediction data. The location determination module is used to determine the location information of the leakage point where the gas leak occurs in the gas pipeline network based on the monitoring information of each gas valve well node when it is determined that a gas leak occurs in the gas pipeline network. The process of determining whether a gas leak occurs in the gas pipeline network and the location of the leakage point by the data analysis unit 301 will be specifically introduced in the gas pipeline network monitoring method below, and will not be repeated here.

[0072] In some embodiments, the monitoring component 102 of the gas pipeline network may 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 when it is determined that the information of any one dimension in the information of each dimension is abnormal. Specifically, the monitoring and early warning unit 302 can set thresholds for monitoring information of multiple dimensions such as combustible gas concentration information, water level information, pressure information, temperature information, manhole cover position information, gas valve component status information, and determine that the monitoring information of any one dimension is abnormal when the monitoring information of the dimension exceeds the corresponding threshold, and generate monitoring abnormal alarm information for early warning. In some embodiments, multiple thresholds can be set for the combustible gas concentration information, for example, a 25% high over-limit alarm threshold and a 50% high-high over-limit alarm threshold, and then different warnings can be taken for the combustible gas concentration information exceeding different thresholds, so as to realize the alarm classification service for combustible gas information. Among them, the high over-limit alarm threshold can be locally regulated by the gas valve component and remotely regulated by the monitoring and early warning unit 302, and the high-high over-limit alarm threshold can be a fixed value.

[0074] The data analysis unit 303 can determine the working state of the monitoring module corresponding to the information of each dimension based on the data dependency relationship between the information of each dimension in the monitoring information of each gas valve well, and generate module abnormal alarm information when it is determined that any one of the monitoring modules is in an abnormal working state. Specifically, when the data analysis unit 303 determines that individual monitoring information is suddenly abnormal or has no data, but other monitoring information is normal, it can be determined that the abnormal or data-free monitoring module is faulty. Further, when the data analysis unit 303 determines that the monitoring information of some monitoring modules in the gas valve assembly cannot match the monitoring information of other monitoring modules, it can be determined that the part of the monitoring module is faulty, and then generate module abnormal alarm information. For example, for 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 when the data analysis unit 303 analyzes that the combustible gas concentration data is A and the pressure data is B, the corresponding water level data should be D, which does not match the actual water level data C, then it can be determined that the water level monitoring module is faulty, and module abnormal alarm information can be generated. The data analysis unit 303 can be used to troubleshoot and diagnose faults in each monitoring module, thereby improving system stability.

[0075] In some embodiments, the monitoring component 102 of the gas pipeline network may also include a human-computer interaction unit 304, which may provide a visual monitoring interface and sound and light alarm information for the monitoring center. Specifically, the human-computer interaction unit 304 may provide analysis charts of monitoring information of different dimensions, and may generate different early warning processing procedures for different users. When generating sound and light alarm information, the pipe leakage alarm information generated by the data analysis unit 301, the monitoring abnormality alarm information generated by the monitoring and early warning unit 302, or the module abnormality alarm information generated by the data analysis unit 302 may also be displayed on the visual monitoring interface.

[0076] In some embodiments, the monitoring and early warning system 100 of the gas pipeline network may further include a data management component 103. The gas valve monitoring component 101 may upload monitoring information to the base station via the Internet of Things, and the base station may upload the monitoring information to the data management component 103 via the Internet of Things. The data management component 103 may perform data interaction with the monitoring component 102 of the gas pipeline network, such as transmitting monitoring information, alarm information, and management information corresponding to threshold control.

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

[0078] Combine the following Figure 4 The gas pipeline network monitoring method provided in the embodiment of the present application is introduced.

[0079] Figure 4 FIG. 1 is a flow chart of a gas pipeline network monitoring method provided in an embodiment of the present application. Figure 4 As shown, the method may 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 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 data model.

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

[0083] In some embodiments, the pipe network structure texture of the gas pipe network can be analyzed to construct a pipe network structure model, and the pipe network structure model can be trained through the monitoring information of each gas valve well node in the gas pipe network monitored under normal working conditions of the gas pipe network, and the coefficient of the pipe network structure model can be optimized to obtain a pipe network data model. Furthermore, when judging the leakage of the gas pipe network, the pipe network data model can be simulated and run to obtain the prediction information of each gas valve well node.

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

[0085] Among them, the Kalman recursive filtering algorithm can be expressed by the following formula:

[0086]

[0087] Among them, X(k|k-1) represents the prediction result of the current state of the monitoring information, X(k-1|k-1) represents the optimized estimated 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 gas pipeline system process, A and B represent the state transfer 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 estimated value of the current state can be obtained, wherein the optimal estimated value can be expressed by the following formula:

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

[0090] Among them, X(k|k) represents the optimized estimated value of the current state of the monitoring information, Kg(k) represents the Kalman filter gain of the current state of the monitoring information, Z(k) represents the measured value of the current state of the monitoring information, and H represents the state observation matrix.

[0091] The Kalman filter gain can be determined based on the 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] Where R represents the covariance of the noise of the monitoring information.

[0094] Based on the above formulas (1) to (3), the optimal estimated value X(k|k) of the current state of the monitoring information can be used to iteratively update the covariance of the current state. Finally, the Kalman filter output can fully describe the operation of the gas pipe network system, and support the subsequent data analysis unit 301 to analyze the pipe leakage event and identify the leakage location. 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 current state of the monitoring information can be estimated. According to the system state equation, the optimal estimated value of the current state is predicted. According to the gain setting of the filter, it is determined whether the measured value is more reliable, the estimated value is more reliable, or the optimal estimated value is more reliable. Finally, the data that can accurately describe the system is output for subsequent leakage analysis.

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

[0096] S402: Determine whether a gas leak occurs in the gas pipeline network based on the correlation between the monitoring information and the prediction data.

[0097] In some embodiments, in step S402, based on the correlation between the monitoring information and the prediction information, determining whether a gas leakage occurs in the gas pipeline network includes: determining the correlation between the monitoring information and the prediction information; when the correlation is lower than a preset correlation threshold, determining that a gas leakage occurs in the gas pipeline network; when the correlation is not lower than a preset correlation threshold, determining that no gas leakage occurs in the gas pipeline network.

[0098] It can be understood that the smaller the correlation, the greater the difference between the monitoring information and the predicted information, and the farther the monitoring information is from the information obtained under the normal working state of the gas pipeline network. Conversely, the greater the correlation, 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 pipeline network. By comparing the correlation with the preset correlation threshold, it can be determined whether there is a depression point of pipeline pressure drop in the gas pipeline network, that is, whether a gas leak occurs in the gas pipeline network.

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

[0100]

[0101] Wherein, r represents the correlation 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.

[0102] S403: When it is determined that a gas leak occurs in the gas pipeline network, based on the monitoring information of each gas valve well node, the location information of the leakage point where the gas leak occurs in the gas pipeline network is determined.

[0103] Among them, determining that a gas leak occurs in the gas pipeline network can be understood as determining that the correlation is lower than a preset correlation threshold.

[0104] In some embodiments, in step S403, based on the monitoring information of each gas valve well node, the location information of the leakage point where the gas leakage occurs in the gas pipeline network is determined, including: based on the monitoring information, determining the pressure drop information of each gas valve well node; determining the gas valve node with the largest pressure drop in the pressure drop information as the first target node; determining the gas valve node with the smallest correlation between the monitoring information and the prediction information in at least one gas valve node connected to the target node as the second target node; 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, determining the leakage point location information.

[0105] It can be understood that the first target node is one end of the leaking pipe section where the gas leak occurs, and the second target node is the other end of the leaking pipe section where the gas leak occurs. Based on the radiation law of the leakage pressure drop, that is, the pressure drop of the node close to the leaking node is large, the upstream and downstream pressure drops of the pipe section can be used to locate the leakage point. 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 leakage point location information may be determined by the following formula:

[0107]

[0108] Wherein, X represents the distance between the leakage point and the first target node, L represents the total length of the pipe section between the first target node and the second target node, ΔP represents the pressure drop of the first target node, and ΔP2 represents the pressure drop of the second target node.

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

[0110] Figure 5 Shown is a structural schematic diagram of a gas pipeline network model provided in an embodiment of the present application.

[0111] like Figure 5 As shown, the gas pipe network model uses gas valve wells as nodes, and the pipe sections between gas valve wells are the connecting lines between nodes. Assuming that in the above step S403, node 5 is determined to be the first target node, it is further determined that 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, and the gas pipe section between node 5 and node 6 is determined to be a leaking pipe section, and the above formula (5) can be used to further determine the location information of the leaking point.

[0112] Figure 6 Shown is a flow chart of another gas pipeline network monitoring method provided in an embodiment of the present application.

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

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

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

[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 monitoring component 102 of the gas pipeline network.

[0117] Figure 7 Shown is a flow chart of another gas pipeline network monitoring method provided in an embodiment of the present application.

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

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

[0120] S407: When it is determined that any one dimension of the information of each dimension is abnormal, monitoring abnormality alarm information is generated.

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

[0122] The embodiment of the present application also provides a monitoring device for a gas pipeline network. Figure 8 FIG. 8 is a schematic diagram of a gas network monitoring device 800 provided in an embodiment of the present application. Figure 8 As shown, the device may include:

[0123] The information acquisition module 801 is used to obtain 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 the actual operation of each gas valve well, and the prediction information is based on the pressure information of each gas valve well node in the gas pipeline network during normal operation predicted by a pre-trained pipeline data model.

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

[0125] The location determination module 803 is used to determine the location information of the leakage point where the gas leakage occurs in the gas pipeline network based on the monitoring information of each gas valve well node when it is determined that the gas leakage occurs in the gas pipeline network.

[0126] It can be understood that the monitoring device of the gas network in this embodiment can be understood as Figure 1 The gas pipeline network monitoring components in the. The description and functions of the above 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, such as Fig. 9 As shown, the electronic device may include a processor 901 and a memory 902, wherein the processor 901 and the memory 902 may be connected via a bus or other means. Fig. 9 The example of connecting through bus is taken in the following.

[0128] The processor 901 may be a central processing unit (CPU). The processor 901 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or a combination of the above chips.

[0129] The memory 902 is a non-transient computer-readable storage medium that can be used to store non-transient software programs, non-transient computer executable programs and modules, such as program instructions / modules corresponding to the gas pipeline network monitoring method in the embodiment of the present invention (for example, Figure 8 The processor 901 executes various functional applications and data processing of the processor by running the non-transient software programs, instructions and modules stored in the memory 902, that is, the gas pipeline network monitoring method in the above method embodiment is implemented.

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

[0131] The one or more modules are stored in the memory 902, and when executed by the processor 901, the following is performed: Figure 4 A method for monitoring a gas pipeline network in the illustrated embodiment.

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

[0133] The present specification also provides a computer storage medium, wherein the computer storage medium stores computer program instructions, and when the computer program instructions are executed, the steps of the above-mentioned gas pipeline network monitoring method are implemented.

[0134] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the program can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the storage medium can be a 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 memory.

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

[0136] The systems, devices, modules or units described in the above embodiments may be implemented by computer chips or entities, or by products with certain functions.

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

[0138] It can be known from the description of the above implementation methods that those skilled in the art can clearly understand that the present application can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of the present application can be essentially or partly contributed to the prior art in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute certain parts of the methods of each implementation method of the present application.

[0139] The present application can be used in many general or special computer system environments or configurations, such as personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronic devices, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, etc.

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

[0141] Although the present application has been described through embodiments, those skilled in the art will appreciate that there are many modifications and variations to the present application without departing from the spirit of the present application, and it is intended that the appended claims include these modifications and variations without departing from the spirit of the present application.

Claims

1. A method for monitoring a gas pipeline network, characterized in that: include: 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 in the gas pipeline network during normal operation, which is predicted based on a pre-trained pipeline data model; Determining whether a gas leak occurs in the gas pipeline network based on the correlation between the monitoring information and the prediction data; When it is determined that a gas leak occurs in the gas pipeline network, the location information of the leakage point where the gas leak occurs in the gas pipeline network is determined based on the monitoring information of each gas valve well node.

2. The method according to claim 1, characterized in that Determining whether a gas leak occurs in the gas pipeline network based on the correlation between the monitoring information and the prediction information includes: Determining a correlation between the monitoring information and the prediction information; When the correlation is lower than a preset correlation threshold, determining that a gas leak occurs in the gas pipeline network; When the correlation degree is not lower than a preset correlation threshold, it is determined that no gas leakage occurs in the gas pipeline network.

3. The method according to claim 2, characterized in that The correlation between the monitoring information and the prediction information is determined by the following formula: Wherein, r represents the correlation 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.

4. The method according to claim 1, characterized in that Based on the monitoring information of each gas valve well node, the location information of the leakage point where the gas leakage occurs in the gas pipeline network is determined, including: Based on the monitoring information, determine the pressure drop information of each gas valve well node; Determine the gas valve node with the largest pressure drop in the pressure drop information as the first target node; Determine, as a second target node, a gas valve node having the smallest correlation between monitoring information and prediction information among at least one gas valve node connected to the target node; The leakage point location information is determined 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.

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

6. The method according to claim 5, characterized in that Also includes: Based on the data dependency relationship between the information of each dimension in the monitoring information of each gas valve well, determining the working state of the monitoring module corresponding to the information of each dimension; When it is determined that any one of the monitoring modules is in an abnormal working state, module abnormality alarm information is generated.

7. The method according to claim 5, characterized in that Also includes: Determine whether the information in each dimension is abnormal based on the information in each dimension in the monitoring information and the safety threshold corresponding to the information in each dimension; When it is determined that any one dimension of the information in each dimension is abnormal, monitoring abnormality alarm information is generated.

8. A gas network monitoring device, characterized in that: include: An information acquisition module, used 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 in the gas pipeline network during normal operation, which is predicted based on a pre-trained pipeline data model; A leakage determination module, used to determine whether a gas leakage occurs in the gas pipeline network based on the correlation between the monitoring information and the prediction data; The location determination module is used to determine the location information of the leakage point where the gas leakage occurs in the gas pipeline network based on the monitoring information of each gas valve well node when it is determined that the gas leakage occurs in the gas pipeline network.

9. A monitoring and early warning system for a gas pipeline network, characterized in that: It includes a plurality of gas valve monitoring components and a gas pipe network monitoring component, wherein the gas valve well corresponding to the gas valve monitoring device is a gas valve well node in the gas pipe network; The gas valve monitoring component is used to monitor information of multiple dimensions of the gas valve, and upload the information of multiple dimensions as monitoring information to the monitoring component of the gas pipe network; The monitoring components of the gas pipeline network include: An information acquisition module, used 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 in the gas pipeline network during normal operation, which is predicted based on a pre-trained pipeline data model; A leakage determination module, used to determine whether a gas leakage occurs in the gas pipeline network based on the correlation between the monitoring information and the prediction data; The location determination module is used to determine the location information of the leakage point where the gas leakage occurs in the gas pipeline network based on the monitoring information of each gas valve well node when it is determined that the gas leakage occurs in the gas pipeline network.

10. An electronic device, characterized in that: include: A memory and a processor, wherein the processor and the memory are communicatively connected to each other, the memory stores computer instructions, and the processor implements the steps of the method according to any one of claims 1 to 7 by executing the computer instructions.

11. A computer storage medium, characterized in that: The computer storage medium stores computer program instructions, and when the computer program instructions are executed, the steps of the method according to any one of claims 1 to 7 are implemented.

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