Gas pipe network safety on-line monitoring system and method

Abstract

The invention discloses a gas pipe network safety on-line monitoring system and method, and relates to the technical field of on-line monitoring. Internet of Things equipment is installed in a gas pipe network area needing to be monitored, an aggregation local area network is established based on the installed Internet of Things equipment, and meanwhile the operation mode of the Internet of Things equipment is configured; the gas state data in the gas pipe network area are collected and processed in real time through the running Internet of Things equipment, the gas state data, collected in real time, in the gas pipe network area are analyzed, and after analysis is completed, a fault risk identification model is constructed based on the analyzed gas state data; the method comprises the steps of analyzing gas state data, constructing a gas pipe network safety system based on a constructed fault risk identification model, finally performing prediction through a digital twinborn mode based on the analyzed gas state data, positioning a fault position and adjusting a gas pipe network safety monitoring mode according to a prediction result and the gas pipe network safety system, and improving the real-time performance of gas pipe network monitoring.

Description

Technical Field

[0001] This invention relates to the field of online monitoring technology, specifically to an online monitoring system and method for the safety of gas pipeline networks. Background Technology

[0002] As cities continue to expand and the number of gas users continues to grow, traditional manual inspection methods are no longer sufficient to meet the safety supervision needs of urban gas pipeline networks. The introduction of intelligent monitoring systems provides a new approach to solving the challenges of gas safety management. Summary of the Invention

[0003] The purpose of this invention is to provide an online monitoring system and method for the safety of gas pipeline networks, which solves the problems existing in the background art.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a method for online safety monitoring of gas pipeline networks, specifically including the following steps: S1. Install IoT devices in the gas pipeline network area that needs to be monitored, and build an aggregated local area network based on the installed IoT devices through wireless communication mode; S2. Configure the operating mode of the IoT device, and collect and process the gas status data in the gas pipeline area in real time through the running IoT device. S3. Analyze the gas status data collected in real time within the gas pipeline network area to obtain the analyzed gas status data; S4. Based on the analyzed gas status data, construct a fault risk identification model, and based on the constructed fault risk identification model, construct a gas pipeline network safety system; S5. Based on the analyzed gas status data, predictions are made using digital twins. At the same time, based on the prediction results and the gas pipeline safety system, the location of the fault is determined and the gas pipeline safety monitoring mode is adjusted.

[0005] Preferably, the steps of installing IoT devices in the gas pipeline network area requiring monitoring and establishing a converged local area network based on the installed IoT devices via wireless communication include the following steps: S11. Divide the gas pipeline network area that needs to be monitored into nodes and sections, and set up a monitoring station at each node. S12. Each monitoring station installs an IoT device at the beginning of the corresponding pipeline section, sets the distance interval for each pipeline section, and sets n monitoring points according to the distance interval threshold. S13. Collect and upload in real time the geographical location information of the corresponding gas pipeline network area based on the set monitoring points; S14. Construct and save the network topology based on the collected geographic location information and the location information of the monitoring points.

[0006] Preferably, configuring the operating mode of the IoT device and collecting and processing gas status data within the gas pipeline network area in real time through the operating IoT device includes the following steps: S21. Configure the operating mode of the IoT device and collect gas status data in the gas pipeline area in real time based on the operating mode of the IoT device. Configure two operating modes for IoT devices: manual monitoring and periodic monitoring; The manual monitoring refers to real-time monitoring of the gas pipeline network area via monitoring commands input by management personnel. The periodic monitoring: Based on the user's maintenance requirements for the gas pipeline network area, the monitoring time interval is set, the gas pipeline network area is monitored periodically, the monitoring results are automatically reported, and a performance analysis report is provided; Data, including gas pressure and gas flow, is collected and monitored in real time within the gas pipeline network area through operating IoT devices. S22. Process the gas status data collected in real time within the gas pipeline network area through data processing methods to obtain processed gas status data.

[0007] Preferably, the step of processing the real-time collected gas status data within the gas pipeline network area to obtain the processed gas status data includes the following steps: The dimensions of gas status data within the real-time gas pipeline network area are removed through data conversion. After the removal is completed, the gas status data collected in real time within the gas pipeline network area is standardized using data standardization methods to obtain the processed gas status data; The standardized calculation formula is as follows: ; in, This represents the minimum value among the real-time collected gas status data within the gas pipeline network area. This represents the maximum value among the real-time collected gas status data within the gas pipeline network area. This indicates that the s-th group collects real-time gas status data within the gas pipeline network area. This represents the gas state data after standardization and processing in the s-th group.

[0008] Preferably, the step of analyzing the gas status data collected in real time within the gas pipeline network area to obtain the analyzed gas status data includes the following steps: S31. Construct a model of the relationship between gas pressure and gas flow rate within the gas pipeline network area based on the processed gas parameters; set up ,in, This indicates the gas flow rate at the first monitoring point. Indicates the first Gas flow rate at each monitoring point This represents the gas flow rate set at each monitoring point; set up ,in, This indicates the gas flow rate of the first section. Indicates the first Gas flow rate in each section This represents the gas flow rate set for each section; The relationship between the gas flow rate at the monitoring point and the gas flow rate in the section is determined based on the connection relationship between the monitoring point and the section. The gas flow rate at the monitoring point is set as the sum of the gas flow rates at the monitoring point and those of all adjacent sections. Based on the gas flow equation, a model is established to represent the relationship between gas pressure and gas flow rate within the gas pipeline network area: ; in, Indicates gas flow rate, This indicates the change in gas pressure at both ends of the pipeline. Indicates the density of the gas. Indicates the gas flow rate; S32. Based on the constructed gas pressure and gas flow relationship model, analyze the gas status data collected in real time in the gas pipeline area to obtain the analyzed gas status data.

[0009] Preferably, the analysis of the gas status data collected in real time within the gas pipeline network area based on the constructed gas pressure and gas flow relationship model to obtain the analyzed gas status data includes the following steps: Based on the regional network topology of the gas pipeline network, the changes in gas pressure and gas flow rate of each monitoring point and section within the gas pipeline network area are recorded at different time periods. The changes in gas pressure and gas flow rate at each monitoring point and in each section within the gas pipeline network area are summarized, and gas pressure and gas flow rate change curves are established through data fitting. The fluctuation range of gas status data within the gas pipeline network area is determined based on the gas pressure and gas flow rate variation curves. The fluctuation range of gas status data within the gas pipeline network area is set to the analyzed gas status data.

[0010] Preferably, the process of constructing a fault risk identification model based on the analyzed gas status data, and constructing a gas pipeline network safety system based on the constructed fault risk identification model, includes the following steps: S41. Collect historical gas status data in real time, and use the analyzed gas status data as a standard to initially screen fault gas parameters. The faulty gas parameters are classified using 10% of the standard gas state data as the classification interval to obtain the classified faulty gas parameters; Calculate fault risk based on the classified faulty gas parameters; The risk level is calculated as follows: D = L × C; Where D represents the risk level, L represents the probability of gas failure, and C represents the severity index of gas failure; Where L represents the frequency of gas faults occurring under the corresponding fault gas parameters; S42. Summarize the calculated fault risk data to construct a fault risk identification model, and construct a gas pipeline safety system based on the fault risk identification model.

[0011] Preferably, the process of constructing a fault risk identification model from the aggregated and calculated fault risk data, and then constructing a gas pipeline network safety system based on the fault risk identification model, includes the following steps: Based on the calculated risk level, the safety risk level is divided into three levels from high to low: Level 1, Level 2, and Level 3. When the safety risk level is set to Level 1, indicating extreme danger, operations should be stopped immediately. The safety risk level is set at Level 2, indicating a high degree of danger; immediate rectification is required. Setting the safety risk level to 3 indicates that there are potential safety hazards that require inspection and rectification.

[0012] Preferably, the step of predicting the gas status based on the analyzed gas status data using a digital twin method, and simultaneously locating the fault location and adjusting the gas pipeline safety monitoring mode according to the prediction results and the gas pipeline safety system, includes the following steps: Based on the analyzed gas state data, the slope of the gas pressure and gas flow rate change curves in the analyzed gas state data is calculated by data differentiation. Based on the slope of the gas pressure and gas flow rate change curves calculated in real time, the digital twin prediction weights are set. Based on the set digital twin prediction weights, the probability of state transition of each monitoring point and each section of the gas pipeline network is calculated; A threshold for the probability of gas pipeline network state transition is set. When the calculated probability of gas pipeline network state transition exceeds the set threshold, it indicates that there is a risk in the gas pipeline network at the corresponding location. The corresponding location is uploaded in real time and monitored and alarmed in real time.

[0013] This invention also provides a gas pipeline network safety online monitoring system for implementing a gas pipeline network safety online monitoring method. The system includes: a data acquisition module, a data processing module, a data analysis module, a gas pipeline network risk identification module, and a gas pipeline network online monitoring module. The data acquisition module is used to collect real-time gas status data within the gas pipeline network area; The data processing module is used to process the gas status data collected in real time within the gas pipeline network area to obtain the processed gas status data. The data analysis module is used to analyze the processed gas state data to obtain the analyzed gas state data. The gas pipeline network risk identification module is used to construct a fault risk identification model based on the analyzed gas status data and to build a gas pipeline network safety system. The gas pipeline network online monitoring module is used to perform real-time online monitoring of the gas pipeline network based on the fault risk identification model and the gas pipeline network safety system using a digital twin approach.

[0014] The beneficial effects of this invention are as follows: (1) This invention improves the real-time performance of gas pipeline monitoring by installing Internet of Things (IoT) devices in the gas pipeline network area that needs to be monitored, building an aggregated local area network based on the installed IoT devices, configuring the operating mode of the IoT devices, and collecting and processing gas status data in the gas pipeline network area in real time through the operating IoT devices. Then, the gas status data collected in real time in the gas pipeline network area is analyzed. After the analysis is completed, a fault risk identification model is built based on the analyzed gas status data, and a gas pipeline network safety system is built based on the constructed fault risk identification model. Finally, based on the analyzed gas status data, a prediction is made through digital twin method. At the same time, according to the prediction results and the gas pipeline network safety system, the fault location is located and the gas pipeline network safety monitoring mode is adjusted.

[0015] This invention constructs a model of the relationship between gas pressure and gas flow rate within a gas pipeline network area based on processed gas parameters. It then analyzes the real-time gas status data within the gas pipeline network area based on the constructed model to determine the safe range of gas status data within the gas pipeline network area. Based on the determined safe range, it performs real-time online monitoring of the gas pipeline network area, thereby improving the accuracy of online monitoring of the gas pipeline network area. Attached Figure Description

[0016] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0017] Figure 1 This is a schematic diagram of the online monitoring method for gas pipeline safety according to the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] In a specific embodiment of the present invention, Reference Figure 1 As shown, the present invention provides an online safety monitoring system and method for gas pipeline networks, comprising the following steps: S1. Install IoT devices in the gas pipeline network area that needs to be monitored, and build an aggregated local area network based on the installed IoT devices through wireless communication mode; S2. Configure the operating mode of the IoT device, and collect and process the gas status data in the gas pipeline area in real time through the running IoT device. S3. Analyze the gas status data collected in real time within the gas pipeline network area to obtain the analyzed gas status data; S4. Based on the analyzed gas status data, construct a fault risk identification model, and based on the constructed fault risk identification model, construct a gas pipeline network safety system; S5. Based on the analyzed gas status data, make predictions using digital twins. At the same time, based on the prediction results and the gas pipeline safety system, locate the fault location and adjust the gas pipeline safety monitoring mode. Furthermore, referring to Figure 1 As shown, installing IoT devices in the gas pipeline network area that needs monitoring and building an aggregated local area network based on the installed IoT devices via wireless communication includes the following steps: S11. Divide the gas pipeline network area that needs to be monitored into nodes and sections, and set up a monitoring station at each node. S12. Each monitoring station installs an IoT device at the beginning of the corresponding pipeline section, sets the distance interval for each pipeline section, and sets n monitoring points according to the distance interval threshold. S13. Collect and upload in real time the geographical location information of the corresponding gas pipeline network area based on the set monitoring points; S14. Construct and save the network topology based on the collected geographic location information and the location information of the monitoring points; Furthermore, referring to Figure 1 As shown, configuring the operating mode of IoT devices and collecting and processing gas status data in the gas pipeline network area in real time through the operating IoT devices includes the following steps: S21. Configure the operating mode of the IoT device and collect gas status data in the gas pipeline area in real time based on the operating mode of the IoT device. Configure two operating modes for IoT devices: manual monitoring and periodic monitoring; The manual monitoring refers to real-time monitoring of the gas pipeline network area via monitoring commands input by management personnel. The periodic monitoring: Based on the user's maintenance requirements for the gas pipeline network area, the monitoring time interval is set, the gas pipeline network area is monitored periodically, the monitoring results are automatically reported, and a performance analysis report is provided; Data, including gas pressure and gas flow, is collected and monitored in real time within the gas pipeline network area through operating IoT devices. S22. Process the gas status data collected in real time within the gas pipeline network area through data processing methods to obtain the processed gas status data. The dimensions of gas status data within the real-time gas pipeline network area are removed through data conversion. After the removal is completed, the gas status data collected in real time within the gas pipeline network area is standardized using data standardization methods to obtain the processed gas status data; The standardized calculation formula is as follows: ; in, This represents the minimum value among the real-time collected gas status data within the gas pipeline network area. This represents the maximum value among the real-time collected gas status data within the gas pipeline network area. This indicates that the s-th group collects real-time gas status data within the gas pipeline network area. This represents the gas state data after standardization and processing in the s-th group. Furthermore, referring to Figure 1 As shown, the analysis of real-time collected gas status data within the gas pipeline network area yields the following steps: S31. Construct a model of the relationship between gas pressure and gas flow rate within the gas pipeline network area based on the processed gas parameters; set up ,in, This indicates the gas flow rate at the first monitoring point. Indicates the first Gas flow rate at each monitoring point This represents the gas flow rate set at each monitoring point; set up ,in, This indicates the gas flow rate of the first section. Indicates the first Gas flow rate in each section This represents the gas flow rate set for each section; Furthermore, the relationship between the gas flow rate at the monitoring point and the gas flow rate in the section is determined based on the connection between the monitoring point and the section; The gas flow rate at the monitoring point is set as the sum of the gas flow rates at the monitoring point and those of all adjacent sections. Furthermore, based on the gas flow equation, a model is established to represent the relationship between gas pressure and gas flow rate within the gas pipeline network area: ; in, Indicates gas flow rate, This indicates the change in gas pressure at both ends of the pipeline. Indicates the density of the gas. Indicates the gas flow rate; S32. Based on the constructed gas pressure and gas flow relationship model, analyze the gas status data collected in real time in the gas pipeline area to obtain the analyzed gas status data. Based on the regional network topology of the gas pipeline network, the changes in gas pressure and gas flow rate of each monitoring point and section within the gas pipeline network area are recorded at different time periods. Furthermore, the changes in gas pressure and gas flow rate at each monitoring point and in each section within the gas pipeline network area are summarized, and gas pressure and gas flow rate change curves are established through data fitting. Furthermore, the fluctuation range of gas status data within the gas pipeline network area is determined based on the gas pressure and gas flow rate change curves; The fluctuation range of gas status data within the gas pipeline network area is set to the analyzed gas status data. Furthermore, referring to Figure 1 As shown, the following steps are taken to construct a fault risk identification model based on the analyzed gas status data, and to build a gas pipeline network safety system based on the constructed fault risk identification model: S41. Collect historical gas status data in real time, and use the analyzed gas status data as a standard to initially screen fault gas parameters. The faulty gas parameters are classified using 10% of the standard gas state data as the classification interval to obtain the classified faulty gas parameters; Furthermore, the failure risk is calculated based on the classified failure gas parameters; The risk level is calculated as follows: D = L × C; Where D represents the risk level, L represents the probability of gas failure, and C represents the severity index of gas failure; Where L represents the frequency of gas faults occurring under the corresponding fault gas parameters; S42. Summarize the calculated fault risk data to construct a fault risk identification model, and construct a gas pipeline safety system based on the fault risk identification model; Based on the calculated risk level, the safety risk level is divided into three levels from high to low: Level 1, Level 2, and Level 3. When the safety risk level is set to Level 1, indicating extreme danger, operations should be stopped immediately. The safety risk level is set at Level 2, indicating a high degree of danger; immediate rectification is required. Setting the safety risk level to 3 indicates that there are safety hazards that need to be inspected and rectified. Furthermore, referring to Figure 1 As shown, based on the analyzed gas status data, predictions are made using a digital twin approach. Simultaneously, based on the prediction results and the gas pipeline safety system, the fault location is pinpointed, and the gas pipeline safety monitoring mode is adjusted. This includes the following steps: Based on the analyzed gas state data, the slope of the gas pressure and gas flow rate change curves in the analyzed gas state data is calculated by data differentiation. Based on the slope of the gas pressure and gas flow rate change curves calculated in real time, the digital twin prediction weights are set. Based on the set digital twin prediction weights, the probability of state transition of each monitoring point and each section of the gas pipeline network is calculated; A threshold for the probability of gas pipeline network state transition is set. When the calculated probability of gas pipeline network state transition exceeds the set threshold, it indicates that there is a risk in the gas pipeline network at the corresponding location. The corresponding location is uploaded in real time and monitored and alarmed in real time.

[0020] In one specific embodiment, the gas pipeline network safety online monitoring system is used to implement a gas pipeline network safety online monitoring method. The system includes: a data acquisition module, a data processing module, a data analysis module, a gas pipeline network risk identification module, and a gas pipeline network online monitoring module. The data acquisition module is used to collect real-time gas status data within the gas pipeline network area; The data processing module is used to process the gas status data collected in real time within the gas pipeline network area to obtain the processed gas status data. The data analysis module is used to analyze the processed gas state data to obtain the analyzed gas state data. The gas pipeline network risk identification module is used to construct a fault risk identification model based on the analyzed gas status data and to build a gas pipeline network safety system. The gas pipeline network online monitoring module is used to perform real-time online monitoring of the gas pipeline network based on the fault risk identification model and the gas pipeline network safety system using a digital twin approach.

[0021] It should be noted that, The above content is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined by the present invention, and all such modifications and additions should fall within the protection scope of the present invention.

Claims

1. A method for online monitoring of safety of a gas pipeline network, characterized in that, The method comprises the following steps: S1, installing an Internet of Things device in a gas pipeline network area that needs to be monitored, and establishing an aggregated local area network through a wireless communication mode based on the installed Internet of Things device; S2, configuring the operation mode of the Internet of Things device, and collecting and processing the gas state data in the gas pipeline network area in real time through the operating Internet of Things device; S3, analyzing the real-time collected gas state data in the gas pipeline network area to obtain analyzed gas state data; S4, constructing a fault risk identification model based on the analyzed gas state data, and constructing a gas pipeline network safety system based on the constructed fault risk identification model; S5, based on the analyzed gas state data, predicting by means of digital twinning, and positioning the fault location and adjusting the gas pipeline network safety monitoring mode according to the prediction result and the gas pipeline network safety system.

2. The method according to claim 1, characterized in that, The step of installing an Internet of Things device in a gas pipeline network area that needs to be monitored, and establishing an aggregated local area network through a wireless communication mode based on the installed Internet of Things device comprises the following steps: S11, dividing the gas pipeline network area that needs to be monitored, dividing the gas pipeline network into nodes and sections, and setting a monitoring station at each divided node; S12, installing an Internet of Things device at the starting end of each corresponding pipeline network section, setting the distance interval of each pipeline network section, and setting n monitoring points according to the distance interval threshold; S13, collecting the geographic location information of the corresponding gas pipeline network area based on the set monitoring points and uploading in real time; S14, constructing a network topology based on the collected geographic location information and the location information of the set monitoring points and saving.

3. The method according to claim 1, characterized in that, The step of configuring the operation mode of the Internet of Things device, and collecting and processing the gas state data in the gas pipeline network area in real time through the operating Internet of Things device comprises the following steps: S21, configuring the operation mode of the Internet of Things device, and collecting the gas state data in the gas pipeline network area in real time based on the operation mode of the Internet of Things device; Two operation modes of the Internet of Things device are set: manual monitoring and periodic monitoring; The manual monitoring: immediate monitoring of the gas pipeline network area by the monitoring command input by the management personnel; The periodic monitoring: setting the monitoring time interval according to the maintenance requirements of the user on the gas pipeline network area, periodically monitoring the gas pipeline network area, automatically reporting the monitoring result and providing a performance analysis report; The data collected and monitored in the gas pipeline network area in real time by the operating Internet of Things device includes gas pressure and gas flow; S22, processing the real-time collected gas state data in the gas pipeline network area by a data processing method to obtain processed gas state data.

4. The method according to claim 3, wherein, The step of processing the real-time collected gas state data in the gas pipeline network area by a data processing method to obtain processed gas state data comprises the following steps: Removing the dimension of the real-time collected gas state data in the gas pipeline network area by a data conversion method; After the removal is completed, the real-time collected gas state data in the gas pipeline network area is standardized by a data standardization method to obtain processed gas state data; The standardization calculation formula is as follows: ； wherein, represents a minimum value in the real-time collected gas state data in the gas pipe network region, represents a maximum value in the real-time collected gas state data in the gas pipe network region, represents the s-th group of real-time collected gas state data in the gas pipe network region, represents the s-th group of processed gas state data after standardization.

5. The method according to claim 1, wherein, The analysis of the real-time collected gas state data in the gas pipe network region includes the following steps: S31, based on the processed gas parameters, a gas pressure and gas flow relationship model in the gas pipe network region is constructed; Setting wherein, denotes the gas flow at the first monitoring point, denotes the gas flow at the first monitoring point, denotes the set of gas flows at the monitoring points; Setting wherein, denotes the gas flow of the first section, denotes the gas flow of the second section, denotes the gas flow of the third section, denotes the set of gas flows of the sections. The relationship between the gas flow of the monitoring point and the gas flow of the section is determined based on the connection relationship between the monitoring point and the section; The gas flow of the monitoring point is set as the sum of the gas flows of the connected sections; According to the gas flow equation, a gas pressure and gas flow relationship model in the gas pipe network region is established: ； wherein, represents the gas flow rate, represents the change in gas pressure at both ends of the pipe, represents the gas density, represents the gas flow rate; S32, based on the constructed gas pressure and gas flow relationship model, the real-time collected gas state data in the gas pipe network region is analyzed to obtain the analyzed gas state data.

6. A method of online monitoring of safety of a gas network according to claim 5, characterized in that, The analysis of the real-time collected gas state data in the gas pipe network region based on the constructed gas pressure and gas flow relationship model includes the following steps: Based on the network topology of the gas pipe network region, the change amount of the gas pressure and the gas flow of each monitoring point and each section in the gas pipe network region in each time period is recorded; The change amount of the gas pressure and the gas flow of each monitoring point and each section in the gas pipe network region is summarized, and a gas pressure and gas flow change curve is established by data fitting; Based on the gas pressure and gas flow change curve, the fluctuation range of the gas state data in the gas pipe network region is determined; The fluctuation range of the gas state data in the gas pipe network region is set as the analyzed gas state data.

7. The method according to claim 1, wherein, The construction of the fault risk identification model based on the analyzed gas state data, and the construction of the gas pipe network safety system based on the constructed fault risk identification model include the following steps: S41, real-time collection of historical gas state data, and preliminary screening of fault gas parameters based on the analyzed gas state data as the standard; Classify the fault gas parameters with 10% of the standard gas state data as the classification interval to obtain the classified fault gas parameters; Based on the classified fault gas parameters, the fault risk is calculated; The calculation method of the risk degree is: D=LxC; Wherein, D represents the risk degree, L represents the gas fault occurrence probability, and C represents the gas fault severity index; Wherein, L is the frequency data of the occurrence of gas fault corresponding to the classified fault gas parameters; S42, the fault risk identification model is constructed by summarizing the calculated fault risk data, and the gas pipe network safety system is constructed based on the fault risk identification model.

8. A method of online monitoring of safety of a gas network according to claim 7, characterized in that, The construction of the fault risk identification model based on the calculated fault risk data, and the construction of the gas pipe network safety system based on the fault risk identification model include the following steps: Based on the calculated risk degree, the safety risk level is divided into three levels from high to low, i.e. level 1, level 2 and level 3; When the safety risk level is level 1, it means extremely dangerous, and the work should be stopped immediately; When the safety risk level is level 2, it means high danger, and the rectification should be carried out immediately; When the safety risk level is level 3, it means that there is a security risk, which needs to be checked and rectified.

9. The method according to claim 1, wherein, The based on the analyzed gas state data, the prediction is carried out through the digital twin method, and the fault position is located and the gas pipe network safety monitoring mode is adjusted according to the prediction result and the gas pipe network safety system, including the following steps: Based on the analyzed gas state data, the change curve slope of gas pressure and gas flow in the analyzed gas state data is calculated by data derivation method; Based on the real-time calculated gas pressure and gas flow change curve slope, the digital twin prediction weight is set; And based on the set digital twin prediction weight, the gas pipe network state transition probability of each monitoring point and each section is calculated; Set the gas pipe network state transition probability threshold, when the calculated gas pipe network state transition probability exceeds the set threshold, it indicates that the corresponding position of the gas pipe network has risk, and the corresponding position is uploaded in real time and monitored and alarmed in real time.

10. A system for implementing the method for online monitoring of safety of a gas pipeline network according to claim 1, characterized in that, Including: Data acquisition module, data processing module, data analysis module, gas pipe network risk identification module and gas pipe network online monitoring module; The data acquisition module is used for real-time acquisition of gas state data in the gas pipe network area; The data processing module is used for processing the real-time collected gas state data in the gas pipe network area to obtain the processed gas state data; The data analysis module is used for analyzing the processed gas state data to obtain the analyzed gas state data; The gas pipe network risk identification module is used for constructing a fault risk identification model according to the analyzed gas state data, and constructing a gas pipe network safety system; The gas pipe network online monitoring module is used for real-time online monitoring of the gas pipe network according to the fault risk identification model and the gas pipe network safety system through the digital twin method.

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