A monitoring and inspection system for a gas station pipeline network and a leakage early warning method

Abstract

The application discloses a kind of monitoring and inspection systems and leakage early warning methods for gas station network, belong to gas pipeline inspection field, monitoring and inspection system includes routine monitoring module, for arranging fixed equipment to carry out monitoring to gas station network;Mobile inspection module, for directing mobile robot to carry out mobile inspection;Monitoring and inspection integrated platform, for realizing the function integration of routine monitoring and mobile inspection, when routine monitoring data or mobile inspection data detects gas leakage, result is uploaded to integrated platform to realize data sharing, by pre-constructing Gaussian plume diffusion model and hybrid genetic grey wolf algorithm, diffusion simulation and source term information inverse calculation are carried out to leakage area, provide leak point positioning, diffusion area prediction, hierarchical early warning and decision control measures for system;The application realizes the efficient monitoring in the area of gas station network, improves the accuracy of gas leakage source detection and early warning, guarantees the safe and reliable operation of gas station network.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of gas pipeline inspection, in particular to a monitoring and inspection system for gas station pipeline network and a leakage early warning method. BACKGROUND

[0002] Under the background of increasing demand for gas, a large number of gas pipelines have been laid, and complex urban gas pipeline networks have become an important part of urban development. The main component of urban gas is methane, which is a flammable and explosive gas. Once it leaks, it will spread rapidly in the air, forming a dangerous area of leakage and diffusion, causing suffocation if inhaled in excess, and even burning rapidly if it encounters a fire source, resulting in serious gas explosion accidents.

[0003] The pipelines of the gas station pipeline network are generally high-pressure gas pipelines, and at the same time, they are places with complex pipeline relationships and multiple process flows. Once a gas leakage and diffusion accident occurs, it may cause property loss, threaten the safety of life and property of workers, and also affect the urban power supply. Although the domestic gas station has arranged a leakage monitoring system in the initial stage of operation, under the influence of leakage point location, weather factors, etc., it may not be accurate in locating the leakage point and timely in predicting and warning, and the reliability of the gas pipeline network monitoring system still needs to be further improved. Therefore, in order to ensure the safety of gas use in the gas station pipeline network, daily monitoring and mobile inspection should be done, the diffusion law based on the spatio-temporal characteristics of gas leakage should be analyzed, and the monitoring and inspection collaborative early warning of the gas station pipeline network is imperative. Therefore, the present application proposes a monitoring and inspection system for the gas station pipeline network and a leakage early warning method. SUMMARY

[0004] The purpose of the present application is to provide a monitoring and inspection system for the gas station pipeline network and a leakage early warning method to overcome the above-mentioned deficiencies, to realize daily monitoring and mobile inspection of the gas station pipeline network, accurately find the leakage point and predict the leakage diffusion area, timely grade warning and prompt, and take control measures to ensure the stable operation of the gas station pipeline network.

[0005] To achieve the above purpose, the present application provides the following technical scheme:

[0006] A monitoring and inspection system for the gas station pipeline network, comprising:

[0007] A daily monitoring module for arranging fixed equipment to monitor the gas station pipeline network and transmitting the daily monitoring data collected by monitoring to an integrated database, and displaying the data in real time and calling the data during mobile inspection, the collected daily monitoring data including methane concentration, pressure, flow, wind direction and speed, and running state;

[0008] The mobile inspection module comprises a mobile inspection device, is used for commanding the mobile robot to conduct mobile inspection, and is used for uploading mobile inspection data collected in the inspection to an integrated database of a monitoring and inspection integrated platform in real time.

[0009] The monitoring and inspection integrated platform is used for realizing functional integration of daily monitoring and mobile inspection, uploading results to the integrated platform to realize data sharing when gas leakage is detected by daily monitoring data or mobile inspection data, simulating diffusion of a leakage area and inversely calculating source information by pre-establishing a Gaussian smoke plume diffusion model and a hybrid genetic grey wolf algorithm, and providing the system with leakage point positioning, diffusion area prediction, hierarchical early warning and decision control measures.

[0010] As a further technical solution of the present application, the integrated database comprises daily monitoring data, gas pipe network original data and mobile inspection data, the daily monitoring data is video monitoring and sensor fixed telemetry data in a station area, the gas pipe network original data is pipe network basic data, pipe network arrangement relationship and pipe network operation data, the pipe network basic data comprises pipe inner diameter and initial set flow pressure, and the pipe network operation data comprises flow and pressure change, and the mobile inspection data is image shooting in an inspection process and robot inspection data.

[0011] As a further technical solution of the present application, the gas station pipe network area is divided into grids and points, the grids comprise an air inlet grid, a cleaning grid, a metering network, a temperature control network, a pressure regulating grid and an air outlet grid, and gas pipes and auxiliary equipment in the grids are divided into different points.

[0012] The monitoring and inspection integrated platform further divides sub-areas according to importance of points on the basis of dividing grids and points in daily monitoring, the sub-areas comprise a key area, a critical area and a general area, the key area comprises a pipe and a pipe connection, a pipe and a tank connection and a weld; the critical area comprises a valve, a flange and an instrument and meter device; and the general area comprises a pipe body of a gas pipe and individual tank bodies.

[0013] As a further technical solution of the present application, the monitoring and inspection integrated platform comprises:

[0014] The integrated terminal is used for remotely monitoring a gas station pipe network and operation conditions, constructing an integrated database of the monitoring and inspection system, providing leakage point positioning, diffusion area prediction, leakage hierarchical early warning and a function of issuing control instructions;

[0015] The leakage diffusion simulation module is used for pre-establishing a Gaussian smoke plume diffusion model according to multi-element data, and updating and optimizing the model in real time through feedback data in a monitoring and inspection process, and the multi-element data comprises a leakage point height, a leakage aperture, a diffusion coefficient and atmospheric parameters.

[0016] a source item information inverse calculation module, which is used for inversely calculating the source item information of the gas pipeline network based on a hybrid genetic grey wolf algorithm, the source item information including a leakage point position and a leakage source strength;

[0017] an intelligent decision control module, which is used for formulating intelligent decision control measures according to the leakage point position and the diffusion area prediction and hierarchical early warning prompt when a leakage is found, the intelligent decision control measures including shutting off the gas pipeline network, adjusting the ventilation in the pollution area and evacuating the personnel in the influence area;

[0018] a server, which is used for data storage, data cleaning, deep mining, database establishment for managers and service provision for diffusion models and expert decision control.

[0019] As a further technical solution of the present application, the routine monitoring module includes a routine telemetry device and a normal monitoring equipment:

[0020] The routine telemetry device includes:

[0021] a laser sensor, which is used for emitting laser and receiving reflection, analyzing laser absorption spectrum and drawing a leakage gas concentration distribution on a laser path;

[0022] a high-definition camera, which is used for being installed near the gas pipeline network of a gas station, shooting, extracting and analyzing routine video images, monitoring the running state of each area, and facilitating the timely retrieval of video monitoring of the leakage diffusion area when a leakage occurs by marking the video address of the grid and the point;

[0023] an intelligent pan-tilt, which is used for installing the laser sensor and the camera, ensuring the angle rotation control of scanning and shooting according to the instruction, and realizing omnidirectional and dead-angle-free detection;

[0024] The normal monitoring equipment includes:

[0025] a pressure gauge, which is installed at a valve and a pipeline connection, and is used for monitoring the gas pressure in the pipeline;

[0026] a flow sensor, which is installed at the valve and the pipeline connection, and is used for monitoring the flow change in the pipeline;

[0027] a wind direction and speed sensor, which is arranged near the key area and the critical area of the grid and the point, detects the wind direction and speed meteorological parameter change in the area, transmits data to a monitoring and inspection integrated platform for storage, and provides data support for simulating the gas leakage diffusion under the influence of different factors.

[0028] As a further technical solution of the present application, the mobile inspection module includes:

[0029] a mobile robot, including a guide rail robot and a mobile trolley;

[0030] A laser sensor is used to emit laser and receive reflection, analyze laser absorption spectrum, and draw a concentration distribution of leaked gas on the laser path;

[0031] A high-definition camera is used to shoot high-definition images of gas pipelines during mobile inspection, so as to reduce the blurring problem caused by the movement of the inspection equipment;

[0032] An intelligent holder is used to install the laser sensor and the camera, so as to ensure the angle rotation control of scanning and shooting according to the instructions, and realize omnidirectional and dead-angle-free mobile inspection.

[0033] As a further technical solution of the present application, the formula of the Gaussian plume diffusion model established in advance according to the multi-element data is as follows:

[0034]

[0035] The formula of the isodensity curve of the Gaussian plume diffusion model is as follows:

[0036]

[0037] In the formula, C(x, y, z, H) is the gas concentration (unit: mg / m 3 ) at the downwind (x, y, z) of the leakage source; Q is the leakage source intensity (i.e., the leakage rate, unit: mg / s); is the average wind speed at the height of the leakage point (unit: m / s); y is the horizontal distance, and z is the vertical distance (unit: m); and σ y , σ z represent the diffusion parameters on the y-axis and the z-axis (unit: m); H is the effective height of the leakage point (unit: m); and the effective height H of the leakage point is H r + ΔH, wherein H r is the actual height of the leakage point, and ΔH is the lifting height of the leaked gas.

[0038] Another object of the embodiment of the present application is to provide a leakage early warning method for a gas station pipeline network, which comprises the following steps:

[0039] Daily monitoring, fixed equipment is arranged to monitor the gas station pipeline network, and the monitored and collected data are transmitted to the integrated database, and the data are displayed in real time and called when mobile inspection is performed;

[0040] Mobile inspection, the mobile robot is commanded to perform mobile inspection, and the mobile inspection data collected during the inspection are uploaded to the integrated database of the monitoring and inspection integrated platform in real time;

[0041] When the daily monitoring data or the mobile inspection data detects a gas leakage, the result is uploaded to the integrated platform to realize data sharing, and through the pre-constructed Gaussian plume diffusion model and the hybrid genetic wolf algorithm, diffusion simulation and source term information back calculation are performed on the leakage area, so as to provide the system with leak point positioning, diffusion area prediction, hierarchical early warning and decision control measures.

[0042] As a further technical solution of the present application, the daily monitoring, the step of arranging fixed equipment to monitor the gas station pipeline network and transmitting the monitored and collected data to the integrated database and displaying the data in real time and calling the data during mobile inspection includes:

[0043] Dividing the grid and the point in the gas station pipeline network area;

[0044] Arranging fixed equipment to monitor the gas station pipeline network;

[0045] And transmitting the monitored and collected data to the integrated database, displaying the data in real time and calling the data during mobile inspection;

[0046] When the grid has a facility damage leading to leakage, the management personnel can find abnormal data changes of the daily monitoring in real time through the displayed data, timely locate the leakage point area through different equipment, find the leakage point and take control measures, so as to ensure the daily operation safety of the gas station pipeline network.

[0047] As a further technical solution of the present application, the mobile inspection, the step of commanding the mobile robot to perform mobile inspection and uploading the mobile inspection data collected during the inspection to the integrated database of the monitoring and inspection integrated platform includes:

[0048] On the basis of dividing the grid and the point in the daily monitoring, further dividing the sub-area according to the importance of the point, wherein the sub-area includes a key area, a critical area and a general area;

[0049] According to the daily inspection task of the gas station pipeline network, pre-setting and managing the mobile inspection equipment, the coordinate and path planning, the scanning mode and the gas leakage diffusion model, wherein the gas leakage diffusion model is a Gaussian plume diffusion model and a correction model;

[0050] Remotely issuing a control instruction to command the mobile inspection equipment to approach and perform mobile inspection;

[0051] And uploading the mobile inspection data collected during the inspection to the integrated database of the monitoring and inspection integrated platform in real time.

[0052] As a further technical solution of the present application, the scanning mode includes directional scanning and reciprocating scanning, the directional scanning is performed on the key area and the critical area, and the reciprocating scanning is performed on the general area.

[0053] As a further technical solution of the present application, when the daily monitoring data or mobile inspection data detects gas leakage, the result is uploaded to the integrated platform to realize data sharing, and through the pre-constructed Gaussian smoke diffusion model and hybrid genetic wolf algorithm, the diffusion simulation and source term information inversion of the leakage area are carried out to provide the system with the steps of leak point positioning, diffusion area prediction, hierarchical early warning and decision control measures.

[0054] When the methane concentration is detected, the monitoring and inspection integrated platform judges the leakage hierarchical early warning according to the pre-set threshold value, if there is a leakage point at the point, the leakage diffusion simulation and source term information inversion are carried out quickly, if the result shows that the leakage point is not in the range of the point, the scanning and inspection of the next point are continued.

[0055] The methane concentration C(x, y, z, H) obtained through the scanning path is used to inverse the source term information of the leakage point, including the leakage source strength Q, the leakage point coordinates (x, y, z), the effective height H and the lifting height ΔH of the leakage gas, and the real height H is derived r , the transverse diffusion distance is calculated according to the equal concentration curve formula of the Gaussian smoke model, the leakage point of the gas pipeline is accurately positioned, and the timely and efficient leakage point detection work is completed;

[0056] The position coordinate point (x, y, z) generated by the source term information inversion in the previous step is used to further correct and adaptively adjust the moving position coordinates and scanning path of the mobile robot, the mobile robot is commanded to move towards the predicted coordinate point (x, y, z) according to the self-determined optimized coordinate point, the leakage gas diffusion concentration surface is drawn, and the diffusion area is predicted.

[0057] When the leakage concentration does not reach the set threshold value, the mobile inspection equipment makes a primary early warning reminder in the field within the range of the point, and uploads the data to the monitoring and inspection integrated platform, prompts that there is a leakage source in a point in the grid, suggests to take measures and carries out the next step of detecting the specific position and leakage rate;

[0058] When the maximum threshold value is exceeded, it is confirmed that leakage occurs, the leakage gas concentration diffusion range is drawn, the image of the point is taken, the leakage methane concentration distribution map is transmitted to the monitoring and inspection integrated platform, the platform makes a secondary early warning prompt, the early warning prompt can be specifically distinguished as the pipe body, flange connection, valve and weld of a point in a grid, and the video monitoring near the point is retrieved in the daily monitoring database, prompting the management personnel to pay attention to confirm the leakage point position, take control measures to stop leakage and timely ventilation.

[0059] When the detection concentration around the leakage point reaches the lower limit of methane explosion, the mobile inspection equipment and the monitoring inspection integrated platform make a linkage three-level early warning alarm to the leakage area, take control measures immediately to eliminate the explosion accident hidden danger, and try to repair the leakage point.

[0060] As a further technical solution of the present application, the step of the mobile inspection equipment performing mobile inspection includes;

[0061] When the mobile robot passes through a certain point of the inspection grid area, first, the laser sensor emits laser to the gas pipeline and the surrounding area and receives the reflection, analyzes the laser absorption spectrum, and draws the leakage gas concentration distribution on the laser path, and then compares with the daily monitoring database and the concentration threshold value at the place;

[0062] In order to improve the accuracy of mobile inspection, the concentration threshold value can be set to 0 at the lowest, and in order to weaken the interference of environmental factors, such as the gas inlet and outlet area of the gas station, which will inevitably cause methane concentration interference, therefore, the concentration threshold value of the inspection can be set to a special size according to the on-site environment, which is used to judge whether a leakage point appears.

[0063] As a further technical solution of the present application, the step of the source item information back calculation is:

[0064] Suppose that the gas leakage is detected for the first time, the robot moves to the downwind of the station pipeline to inspect, and the laser sensor collects n detection concentration values at different positions, which are represented by The corresponding calculation concentration value is calculated by using the Gaussian plume diffusion model The source item inversion problem is converted into solving the optimization problem of the objective function by using intelligent optimization algorithm, and the objective function of the source item information back calculation is In the formula: (x, y, z) is the position coordinate of the gas station pipeline leakage source, Q is the source intensity of the leakage source, and n is the number of all detections when the mobile inspection is performed.

[0065] As a further technical solution of the present application, the step of solving the leakage source item information based on the hybrid genetic grey wolf algorithm is:

[0066] S1, execute genetic algorithm, initialize population and related source item parameters: determine the first generation GA population, each individual in the population includes (Q, x, y, z), and the source item parameters include the gas leakage source intensity (rate) Q, the initial position (x, y, z), and the initialization state of the multi-parameter back calculation in three-dimensional space is formed;

[0067] S2, the sum of squares of the difference between the detection value and the calculation value is taken as the objective function, and the reciprocal of the objective function is determined as the fitness function f, that is

[0068] S3, the initial population and meteorological data and other into the Gaussian plume dispersion model, the calculation of each detection point concentration And the actual detection concentration Comparison; to determine the fitness value of each individual in the population, when the objective function reaches the minimum, the fitness f value reaches the maximum, the smaller the objective function, the smaller the difference between the calculated concentration And detection concentration The smaller, the higher the fitness, the greater the value of the individual in the GA algorithm, the greater the probability of passing to the next generation;

[0069] S4, the population is sorted by fitness size, and the individual with good fitness is subjected to genetic operation, including selection, crossover, mutation;

[0070] S5, generate a new population through genetic operation, realize the optimization of individuals in the GA population; repeat the above steps to update the population until the iteration stopping criteria is met, otherwise go back to S3;

[0071] S6, finally generate N [Q, x, y, z] of the new population, select the output with high fitness value in the final population as the best initial population of grey wolf algorithm;

[0072] S7, execute the grey wolf algorithm to determine the position information of the new grey wolf population and parameters a, A and C;

[0073] S8, calculate the fitness of the grey wolf individual, save the top three grey wolves α, β and δ with the best fitness;

[0074] S9, update the current grey wolf position and calculate the fitness of all grey wolves;

[0075] S10, update the fitness and position of α, β and δ;

[0076] S11, judge whether the termination condition is met, if not, go back to S4, if yes, output the optimal solution [Q, x, y, z] of the source term information back calculation, the back calculation process is ended, and the source intensity Q and the leakage point position coordinates (x, y, z) are output.

[0077] Compared with the prior art, the beneficial effects of the present application are: the present application constructs a new framework of monitoring and patrolling of gas station pipeline by integrating daily monitoring and mobile inspection detection data, first, fixed monitoring equipment is arranged on the basic pipeline network to carry out real-time monitoring of flow and pressure, to achieve collection, storage and analysis of historical data, to generate basic pipeline data and operation data of the pipeline, to provide support for data matching and calling of mobile inspection, secondly, based on the diffusion law of space-time characteristics of gas leakage, a Gaussian plume diffusion model is established to provide pre-simulation for mobile inspection equipment to enter the inspection, further, the methane laser sensor carried by the mobile inspection equipment performs dead angle-free laser scanning detection in different inspection areas, directional scanning is performed on key areas and critical areas, and reciprocating scanning is performed on general areas, which has efficient detection function of gas concentration of suspicious leakage points in the area, and can effectively avoid the possibility of missed detection and false detection, the system can meet the requirements of daily monitoring and leak detection, and after finding the leakage point, the leakage point coordinate and leakage source strength are calculated according to the Gaussian plume diffusion model and the hybrid genetic grey wolf algorithm, the mobile inspection path is optimized, the concentration surface of the leaked gas is drawn, the diffusion area is predicted, and instant hierarchical early warning is performed, so as to facilitate the management personnel to accurately locate the leakage point and quickly take measures to eliminate the hidden danger, and to ensure the safety of gas station pipeline. BRIEF DESCRIPTION OF DRAWINGS

[0078] Figure 1 The system framework diagram of the monitoring and inspection system for the gas station pipeline and the leakage early warning method of the present application

[0079] Figure 2 The integrated database and function schematic diagram of the monitoring and inspection system for the gas station pipeline and the leakage early warning method of the present application.

[0080] Figure 3 The area division schematic diagram of the monitoring and inspection system for the gas station pipeline and the leakage early warning method of the present application.

[0081] Figure 4 The flowchart of the leakage early warning method for the gas station pipeline of the present application.

[0082] Figure 5 The hybrid genetic grey wolf algorithm flowchart for source item information back calculation in the embodiment of the present application. DETAILED DESCRIPTION

[0083] In order to make the purpose, technical scheme and advantages of the present application more clear, the present application is further described in detail below in combination with the drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application, and are not used to limit the present application.

[0084] As Figure 1As shown, the present application provides a monitoring and inspection system for gas station pipe network, comprising:

[0085] A daily monitoring module is configured to arrange fixed equipment to monitor the gas station pipe network, and transmit daily monitoring data collected by monitoring to the integrated database, and display the data in real time and call the data when mobile inspection, wherein the collected daily monitoring data includes methane concentration, pressure, flow, wind direction and speed, and running state;

[0086] A mobile inspection module includes mobile inspection equipment, which is configured to command the mobile robot to conduct mobile inspection, and upload mobile inspection data collected by inspection to the integrated database of the monitoring and inspection integrated platform in real time;

[0087] A monitoring and inspection integrated platform is configured to realize the functional integration of daily monitoring and mobile inspection, upload the results to the integrated platform to realize data sharing when the daily monitoring data or mobile inspection data detects gas leakage, and provide leak point positioning, diffusion area prediction, hierarchical early warning and decision control measures by pre-constructing Gaussian plume diffusion model and hybrid genetic grey wolf algorithm to simulate diffusion and inverse calculate source term information in the leakage area.

[0088] Optionally, as shown in Figure 2 The integrated database includes daily monitoring data, gas pipe network original data, and mobile inspection data, wherein the daily monitoring data is video monitoring and sensor fixed telemetry data in the station area, the gas pipe network original data is pipe network basic data, pipe network arrangement relationship, and pipe network operation data, the pipe network basic data includes pipe diameter and initial set flow pressure, and the pipe network operation data includes flow and pressure change, and the mobile inspection data is image shooting and robot inspection data in the inspection process.

[0089] Optionally, as shown in Figure 3 The gas station pipe network area is divided into grids and points, wherein the grids include gas inlet grid, cleaning grid, metering network, temperature control network, pressure regulating grid, and gas outlet grid, and the gas pipes and auxiliary equipment in the grids are divided into different points.

[0090] The monitoring and inspection integrated platform further divides sub-areas according to the importance of the points on the basis of dividing grids and points in daily monitoring, wherein the sub-areas include key areas, critical areas, and general areas; the key areas include pipe and pipe connection, pipe and tank connection, and weld; the critical areas include valves, flanges, and instrument equipment; and the general areas include pipe body of gas pipe and individual tank.

[0091] Optionally, the monitoring and inspection integrated platform includes:

[0092] The integrated terminal is used for remotely monitoring a gas station pipeline and an operation state, constructing an integrated database of a monitoring and inspection system, providing a leakage point positioning, a diffusion area prediction, a leakage grading early warning work, and having a function of issuing a control instruction.

[0093] The leakage diffusion simulation module is used for establishing a Gaussian plume diffusion model in advance according to multi-element data, and updating and optimizing the model in real time through feedback data of a monitoring and inspection process, wherein the multi-element data includes a leakage point height, a leakage aperture, a diffusion coefficient and atmospheric parameters.

[0094] The source item information back calculation module is used for inversely calculating leakage source item information of the gas pipeline network based on a hybrid genetic grey wolf algorithm, wherein the source item information includes a leakage point position and a leakage source strength.

[0095] The intelligent decision control module is used for formulating intelligent decision control measures according to the leakage point position, the diffusion area prediction and the grading early warning prompt when the leakage occurs, wherein the intelligent decision control measures include cutting off a pipeline gas inlet, adjusting ventilation in a pollution area and evacuating personnel in an influence area.

[0096] The server is used for data storage, data cleaning and deep mining, and provides services for a database, a diffusion model and expert decision control for a manager.

[0097] Optionally, the daily monitoring module includes a daily remote measurement device and a normal monitoring equipment.

[0098] The daily remote measurement device includes:

[0099] The laser sensor is used for emitting laser and receiving reflection, analyzing laser absorption spectrum and drawing a leakage gas concentration distribution on a laser path.

[0100] The high-definition camera is used for being installed near the gas station pipeline, shooting, extracting and analyzing daily video images, daily monitoring of operation states of each area, and timely calling video monitoring of a leakage diffusion area through a video address of a marked grid and a point when the leakage occurs.

[0101] The intelligent cloud platform is used for installing the laser sensor and the camera, ensuring angle rotation control of scanning and shooting according to an instruction, and realizing omnibearing and dead angle free detection.

[0102] The normal monitoring equipment includes:

[0103] The pressure gauge is installed at a valve and a pipeline connection, and is used for monitoring gas pressure in the pipeline.

[0104] The flow sensor is installed at the valve and the pipeline connection, and is used for monitoring flow changes in the pipeline.

[0105] Wind direction and speed sensors are deployed near key and critical areas of grids and points to detect changes in wind direction and speed meteorological parameters within the area. The data is transmitted to the monitoring and inspection integration platform for storage, providing data support for simulating the spread of gas leaks under the influence of different factors.

[0106] Optionally, the mobile inspection module includes:

[0107] Mobile robots, including rail-guided robots and mobile carts;

[0108] A laser sensor is used to emit laser light and receive reflections, analyze the laser absorption spectrum, and plot the concentration distribution of leaked gas along the laser path.

[0109] High-definition cameras are used to capture high-definition images of gas pipelines during mobile inspections, reducing blurring caused by the movement of inspection equipment.

[0110] The intelligent gimbal is used to install laser sensors and cameras to ensure that the angle rotation control completes the scanning and shooting according to the instructions, realizing all-round mobile inspection without blind spots.

[0111] The mobile robot uses laser sensors and high-definition cameras to perform laser scanning of gas pipelines in the area, which improves the accuracy of locating leaks, especially under the interference of factors such as low-level pipelines and high obstacle layout.

[0112] Optionally, the gas leak diffusion model is a Gaussian plume diffusion model established based on the diffusion law of the spatiotemporal characteristics of gas leaks, and its basic formula is as follows:

[0113]

[0114] The formula for the isoconcentration curve of the Gaussian plume diffusion model is:

[0115]

[0116] In the formula, C(x,y,z,H) represents the gas concentration (unit: mg / m³) at the downwind (x,y,z) direction from the leak source. 3 Q represents the leakage source intensity (i.e., leakage rate, unit: mg / s); σ is the average wind speed at the height of the leak point (unit: m / s); y is the lateral distance, z is the vertical distance (unit: m); σ y σ z The diffusion parameters on the y-axis and z-axis are represented (unit: m); H is the effective height of the leak point (unit: m); the effective height of the leak point H = H r +ΔH, where H r ΔH represents the actual height of the leak point, and ΔH represents the rise height of the leaking gas.

[0117] Another purpose of the embodiment of the present application is to provide a leakage early warning method for a gas station pipe network, comprising the following steps:

[0118] Daily monitoring, fixed equipment is arranged to monitor the gas station pipe network, and the monitored data is transmitted to the integrated database, and real-time display and data calling during mobile inspection are provided;

[0119] Mobile inspection, the mobile robot is commanded to conduct mobile inspection, and the mobile inspection data collected during the inspection is uploaded to the integrated database of the monitoring and inspection integrated platform in real time;

[0120] When the daily monitoring data or the mobile inspection data detects gas leakage, the result is uploaded to the integrated platform to realize data sharing, and by constructing a Gaussian smoke plume diffusion model and a hybrid genetic grey wolf algorithm in advance, the leakage area is simulated and the source term information is calculated, so as to provide leakage point positioning, diffusion area prediction, hierarchical early warning and decision control measures for the system.

[0121] In one example of the technical scheme of the present application, as shown in Figure 4 A leakage early warning method for a gas station pipe network is specifically provided, and the steps are as follows:

[0122] Step 1, daily monitoring, the grid and point in the gas station pipe network area are divided, the grid includes the gas inlet grid, the cleaning grid, the metering network, the temperature control network, the pressure regulating grid, the gas outlet grid, etc., and the point includes the pipe body, the flange, the valve, the connection, etc. The daily remote measurement device and the normal monitoring equipment are arranged, the daily remote measurement device includes the intelligent holder, the laser sensor, the normal monitoring equipment includes the pressure gauge, the flow sensor, the weather sensor, the high-definition camera, etc., the methane concentration, the pressure, the flow, the wind direction and speed, and the running state in the area are monitored with high precision, the collected data is uploaded to the integrated database of the monitoring and inspection integrated platform, and real-time display and data calling during mobile inspection are provided. Especially when the leakage occurs due to the large flow pressure leakage, the large aperture corrosion leakage, or the damage of the valve, the flange and other key facilities in the grid, the management personnel can find the abnormal data change of the daily monitoring in time through the real-time display data, the leakage point area is located in time through different equipment, the leakage point is found and the control measures are taken, so as to ensure the safety of the daily operation of the gas station pipe network.

[0123] Step 2, mobile inspection, the monitoring inspection integrated platform divides the grid, point based on daily monitoring, according to the importance of point further divided into sub area, sub area including key area, key area, general area, according to the daily inspection task of gas station network, pre-set and management mobile inspection equipment, coordinate and path planning, scanning mode, gas leakage diffusion model, etc., the scanning mode includes but not limited to directional scanning, reciprocating scanning, etc., the key area and key area are scanned directionally, and the general area is scanned reciprocally, especially improve the accuracy of detecting leakage point under the influence of low level pipeline, high obstacle arrangement relationship and other coupling factors, the gas leakage diffusion model is Gaussian plume diffusion model and correction model, then remote control command is issued to command mobile inspection equipment to enter the execution of mobile inspection;

[0124] Step 2.1, when the mobile robot passes through a point of the inspection grid area, first, the laser sensor emits laser to the gas pipeline and the surrounding and receives the reflection, detects and analyzes the methane gas concentration distribution on the path, and then compares with the daily monitoring database and the concentration threshold value;

[0125] Step 2.2, in order to improve the accuracy of mobile inspection, the concentration threshold value can be set to 0, at the same time, in order to weaken the interference of environmental factors, such as the gas station, the inlet and outlet area will inevitably appear methane concentration interference, therefore, the concentration threshold value of the inspection can be set to a special size according to the field environment, which is used to judge whether there is a leakage point;

[0126] Optionally, when the leaked gas concentration is detected, the further specific steps are:

[0127] Step 3, when the methane concentration is detected, the monitoring inspection integrated platform judges the leakage classification warning according to the pre-set threshold value, if there is a leakage point in the point, the leakage diffusion simulation and source information inversion are carried out rapidly, if the result shows that the leakage point is not in the point range, the scanning inspection of the next point is continued;

[0128] Step 4, by intelligently identifying the methane concentration C(x, y, z, H) of the laser scanning path, using the pre-established Gaussian plume diffusion model and hybrid genetic grey wolf algorithm, the source information of the leakage point is inversely calculated, including the leakage source intensity Q, the leakage point coordinate (x, y, z), and the effective height H, the lifting height ΔH of the leakage gas, and then the real height H r is derived, and the transverse diffusion distance is calculated according to the equal concentration curve formula of Gaussian plume model. The leakage point of the gas pipeline is accurately positioned, and the timely and efficient leakage point detection work is completed.

[0129] Optionally, as shown in Figure 5 , the specific steps of the source information inversion are:

[0130] Step 4.1, assuming that the gas leak is detected for the first time, the robot moves to the downwind of the station field pipe network to patrol, and the laser sensor collects n detection concentration values at different positions, which are represented as , and the corresponding calculated concentration value is calculated by using the Gaussian plume diffusion model The source term inversion problem is converted into solving the optimization problem of the objective function by using intelligent optimization algorithm, and the objective function of the source term information inversion is , where (x, y, z) is the position coordinate of the gas station field pipe network leak source, Q is the source strength of the leak source, and n is the number of all detections during the mobile patrol.

[0131] Step 4.2, solving the leak source term information based on the hybrid genetic-gray wolf algorithm (GA-GWO algorithm):

[0132] Step 1, execute the genetic algorithm, initialize the population and related source term parameters: determine the first generation GA population, each individual in the population includes [Q, x, y, z], and the source term parameters include the gas leak source strength (rate) Q, the initial position (x, y, z), forming the initialization state of multi-parameter inversion in three-dimensional space;

[0133] Step 2, the sum of squares of the difference between the detection value and the calculated value is taken as the objective function, and the reciprocal of the objective function is determined as the fitness function f, that is

[0134] Step 3, put the initial population and meteorological data into the Gaussian plume diffusion model to get the calculated concentration of each detection point, and compare it with the actual detection concentration ; Determine the fitness value of each individual in the population, when the objective function reaches the minimum, the fitness f value reaches the maximum, the smaller the objective function, the smaller the difference between the calculated concentration and the detection concentration , the higher the fitness, the greater the probability of the individual with higher fitness value in the GA algorithm to be passed to the next generation;

[0135] Step 4, sort the population by fitness value, and perform genetic operations (selection, crossover, mutation) on individuals with good fitness;

[0136] Step 5, generate a new population by genetic operation, realize the optimization of individuals in the GA population; repeat the above steps to update the population until the iteration stopping criteria are met, otherwise go back to Step 3;

[0137] Step 6, finally generate N [Q, x, y, z] of the new population, and output the individual with the largest fitness value in the final population as the best initial population of the GWO algorithm;

[0138] Step 7, execute the GWO algorithm to determine the position information of the new gray wolf population and the parameters a, A and C;

[0139] Step 8, calculate the fitness of the gray wolf individuals and save the top three gray wolves a, β and δ with the best fitness;

[0140] Step 9, update the position of the current gray wolf and calculate the fitness of all gray wolves;

[0141] Step 10, update the fitness and position of a, β and δ;

[0142] Step 11, determine whether the termination condition is met, if not, return to Step 4, if yes, output the optimal solution [Q, x, y, z] of the source information inversion, the inversion process is completed, and output the source intensity Q and the position coordinates (x, y, z) of the leakage point

[0143] Step 5, based on the position coordinates (x, y, z) generated by the source information inversion in the previous step, further correct and adapt the moving position coordinates and scanning path of the robot, command the robot to move towards the predicted coordinate point (x, y, z) based on the optimized coordinate point determined autonomously, draw the concentration surface of the leaked gas diffusion, and predict the diffusion area to make an alarm and timely warning to the management personnel that a gas leak has occurred.

[0144] Step 6, when the leakage concentration is not high, the mobile inspection equipment makes a preliminary warning reminder in the field within the point range, uploads the data to the monitoring and inspection integrated platform, and prompts that a leakage source has occurred at a certain point in the grid, suggests taking measures and conducting the next step of verification and detection of the specific position and leakage rate;

[0145] Step 7, when the maximum threshold is exceeded, confirm that a leak has occurred, draw the concentration diffusion range of the leaked gas, take pictures of the point, and transmit the leaked methane concentration distribution map to the monitoring and inspection integrated platform, which makes a secondary warning prompt, which can be specifically distinguished as a pipe body, flange connection, valve, weld, etc. at a certain point in a certain grid, and retrieves the nearby video monitoring in the daily monitoring database through the point to prompt the management personnel to pay attention to confirm the leakage point position and take control measures to stop the leakage and timely ventilation;

[0146] Step 8, when the detection concentration around the leakage point has reached the lower limit of methane explosion, the mobile inspection equipment and the monitoring and inspection integrated platform simultaneously make a linkage three-level warning alarm for the leakage area, immediately take control measures to close the pressure regulating station pipeline intake, evacuate nearby personnel, take high-efficiency ventilation measures to eliminate explosion accident hazards, and attempt to repair the leakage point.

[0147] It should be understood that, although the steps in the flowcharts of the embodiments of the present application are shown in a certain order according to the arrows, the steps are not necessarily executed in the order of the arrows. Unless otherwise specified herein, the execution of the steps is not strictly limited in order, and the steps can be executed in other orders. Moreover, at least some of the steps in the embodiments can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of the sub-steps or stages is not necessarily sequential, but can be round-robin or alternately executed with other steps or sub-steps or stages of other steps.

[0148] It can be understood by those skilled in the art that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware, and the program can be stored in a non-volatile computer readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments of the methods. Any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0149] The technical features of the above-mentioned embodiments can be combined in any way. In order to make the description concise, not all possible combinations of the technical features in the above-mentioned embodiments are described, but as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0150] The above embodiments only express several implementation manners of the present application, which are described in a more specific and detailed manner, but should not be understood as a limitation on the patent scope of the present application. It should be noted that, for ordinary skilled persons in the art, several modifications and improvements can be made without departing from the concept of the present application, which are all within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

[0151] The above merely describes the preferred embodiments of the present application and should not be used to limit the present application. Any modification, equivalent replacement and improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A monitoring and inspection system for a gas station pipeline network, characterized in that, The utility model relates to a kind of gas station leakage detection system, including: Daily monitoring module, for arranging fixed equipment to monitor gas station pipe network, and daily monitoring data collected by monitoring are transmitted to integrated database, and real-time display and call data when moving inspection, the daily monitoring data collected include methane concentration, pressure, flow, wind direction wind speed, operating state; Mobile inspection module, including mobile inspection equipment, for directing mobile robot to carry out mobile inspection, and mobile inspection data collected by inspection are uploaded to the integrated database of monitoring inspection integrated platform in real time; Monitoring inspection integrated platform, for realizing the function integration of daily monitoring and mobile inspection, when daily monitoring data or mobile inspection data detect gas leakage, result is uploaded to integrated platform to realize data sharing, by pre-constructing Gaussian plume diffusion model and hybrid genetic grey wolf algorithm, diffusion simulation and source information inverse calculation are carried out to leakage area, optimize inspection path, provide leak point positioning, diffusion area prediction, hierarchical early warning and decision control measures for system; The monitoring inspection integrated platform includes: Integrated terminal, for remotely monitoring gas station pipe network and operating condition, constructing the integrated database of monitoring inspection system, providing leak point positioning, diffusion area prediction, leakage hierarchical early warning work, and having the function of issuing control instruction; Leakage diffusion simulation module, for pre-establishing Gaussian plume diffusion model according to multiple data, and real-time updating optimization model by feedback data of monitoring inspection process, the multiple data include leakage point height, leakage aperture, diffusion coefficient, atmospheric parameter; Source information inverse calculation module, based on hybrid genetic grey wolf algorithm, inverse calculates and calculates the leakage source information of gas pipe network, the source information includes leakage point position, leakage source intensity; Intelligent decision control module, for formulating intelligent decision control measures according to leakage point position, diffusion area prediction and hierarchical early warning prompt when finding leakage, and intelligent decision control measures include cutting off pipe network air inlet, adjusting ventilation in pollution area, evacuating personnel in influence area; Server, for data storage, data cleaning, deep mining, establishing database for management personnel, and providing services for diffusion model and expert decision control; The formula that the leakage diffusion simulation module pre-establishes Gaussian plume diffusion model according to multiple data is as follows: ； The formula of isodensity curve of Gaussian plume diffusion model is as follows: ； wherein downwind of the leak source Q is the leak source strength; is the average wind speed at the height of the leak; y is the horizontal distance and z is the vertical distance; 、 denotes the dispersion parameters in the y and z directions; H is the effective height of the leak; the effective height of the leak wherein is the actual height of the leak, is the lift height of the leaking gas.

2. The monitoring and inspection system for gas station pipeline network according to claim 1, characterized in that, The integrated database includes daily monitoring data, gas pipe network original data, mobile inspection data, the daily monitoring data is the data of video monitoring, sensor fixed telemetry in station area, gas pipe network original data is pipe network basic data, pipe network arrangement relationship, pipe network operating data, the pipe network basic data includes pipe diameter and initial setting flow pressure, and pipe network operating data includes flow and pressure change, mobile inspection data is the data of image shooting in inspection process, robot inspection.

3. The monitoring and inspection system for gas station pipeline network according to claim 1, characterized in that, The gas station pipe network area is divided into grids and points, the grids include gas inlet grid, cleaning grid, metering network, temperature control network, pressure regulating grid, gas outlet grid, and the gas pipes and auxiliary equipment in the grids are divided into different points; the monitoring and inspection integrated platform further divides sub-regions according to the importance of the points on the basis of dividing the grids and points in daily monitoring, the sub-regions include key regions, critical regions and general regions; the key regions include pipe and pipe connection, pipe and tank connection and weld; the critical regions include valves, flanges and instrument equipment; and the general regions include pipe body and tank body of the gas pipe.

4. The monitoring and inspection system for gas station pipeline network according to claim 3, characterized in that, The daily monitoring module includes daily telemetry devices and normal monitoring equipment: The daily telemetry devices include: a laser sensor for emitting laser and receiving reflection, analyzing laser absorption spectrum and drawing concentration distribution of leaked gas on the laser path; a high-definition camera for being installed near the gas station pipe network, shooting, extracting and analyzing daily video images, monitoring the running state of each region, and facilitating timely retrieval of video monitoring of the leakage diffusion region when leakage occurs by marking the video address of the grid and point; an intelligent pan-tilt for installing the laser sensor and the camera, ensuring angle rotation control of scanning and shooting according to instructions, and realizing omnidirectional and dead-angle-free detection; The normal monitoring equipment includes: a pressure gauge installed at the valve and pipe connection for monitoring the gas pressure in the pipe; a flow sensor installed at the valve and pipe connection for monitoring the flow change in the pipe; a wind direction and speed sensor arranged near the key regions and critical regions of the grid and point for detecting changes in wind direction and speed meteorological parameters in the region, transmitting data to the monitoring and inspection integrated platform for saving, and providing data support for simulating gas leakage diffusion under the influence of different factors.

5. The monitoring and inspection system for gas station pipeline network according to claim 1, characterized in that, The mobile inspection module includes: a mobile robot including a guide rail robot and a mobile trolley; a laser sensor for emitting laser and receiving reflection, analyzing laser absorption spectrum and drawing concentration distribution of leaked gas on the laser path; a high-definition camera for shooting high-definition images of the gas pipe during mobile inspection to reduce the blurring problem caused by the movement of the inspection equipment; an intelligent pan-tilt for installing the laser sensor and the camera, ensuring angle rotation control of scanning and shooting according to instructions, and realizing omnidirectional and dead-angle-free mobile inspection.

6. A method for early warning of leakage of a gas station pipeline network, using the system of any one of claims 1-5, characterized in that, The following steps are included: daily monitoring, arranging fixed equipment to monitor the gas station pipe network, transmitting the collected data to the integrated database, and displaying the data in real time and calling the data during mobile inspection; mobile inspection, commanding the mobile robot to conduct mobile inspection, and uploading the mobile inspection data collected during the inspection to the integrated database of the monitoring and inspection integrated platform in real time; when the daily monitoring data or the mobile inspection data detects gas leakage, the result is uploaded to the integrated platform to realize data sharing, a Gaussian plume diffusion model and a hybrid genetic grey wolf algorithm are constructed in advance to simulate diffusion and inverse calculate the source term information of the leakage region, and the system is provided with leak point positioning, diffusion region prediction, hierarchical early warning and decision control measures.

7. The method for early warning of leakage of a gas station pipeline network according to claim 6, characterized in that, The daily monitoring, the fixed device is arranged to the gas station pipe network monitoring, and the data transmission to the integrated database is collected, and the real-time display and the data calling step of moving inspection includes: Divide the grid and point in the gas station pipe network area; Arrange fixed equipment to monitor the gas station pipe network; And the data transmission to the integrated database is collected, and the real-time display and the data calling step of moving inspection includes: When the grid is damaged and leaks, the manager finds the abnormal data change of daily monitoring in time through the real-time display data, and timely locates the point area of the leakage through different equipment, finds the leakage point and takes control measures, which is convenient for the safety of daily operation of gas station pipe network.

8. The method for early warning of leakage of a gas station pipeline network according to claim 7, characterized in that, The mobile inspection, the mobile robot is commanded to move and inspect, and the mobile inspection data collected by the inspection is uploaded to the integrated database of the monitoring and inspection integrated platform in real time. The step includes: On the basis of dividing the grid and point in daily monitoring, further sub-regions are divided according to the importance of the point, wherein the sub-regions include key areas, critical areas and general areas; According to the daily inspection task of gas station pipe network, the mobile inspection equipment, coordinate and path planning, scanning mode, gas leakage diffusion model are preset and managed, and the gas leakage diffusion model is Gaussian plume diffusion model and correction model; Remote control instruction is issued to command the mobile inspection equipment to enter the scene and perform mobile inspection; And the mobile inspection data collected by the inspection is uploaded to the integrated database of the monitoring and inspection integrated platform in real time.

9. The method for early warning of leakage of a gas station pipeline network according to claim 8, characterized in that, The scanning mode includes directional scanning and reciprocating scanning. The key areas and critical areas are scanned directionally, and the general areas are scanned reciprocally.

10. The method for early warning of leakage of a gas station pipeline network according to claim 7, characterized in that, When the daily monitoring data or mobile inspection data detects gas leakage, the result is uploaded to the integrated platform to realize data sharing, and the steps of constructing Gaussian plume diffusion model and hybrid genetic grey wolf algorithm in advance, simulating diffusion and inverse calculation of source information in the leakage area, providing leakage point positioning, diffusion area prediction, hierarchical early warning and decision control measures for the system include: When methane concentration is detected, the monitoring and inspection integrated platform judges the leakage hierarchical early warning according to the preset threshold value, such as the existence of leakage point in the point, then rapid leakage diffusion simulation and source information inverse calculation are carried out, such as the result shows that the leakage point is not in the point range, then the scanning inspection of the next point is continued; Methane concentration acquired by scanning a path , using a pre-established Gaussian plume diffusion model and a hybrid genetic grey wolf algorithm, the source term information of the leakage point is inversely calculated, including the leakage source strength Q, the leakage point coordinate , while calculating the effective height H, the lifting height of the leakage gas , and further deriving the present height , further calculating the lateral diffusion distance according to the equal concentration curve formula of the Gaussian plume model, realizing the accurate positioning of the gas pipeline leakage point, and completing the timely and efficient leakage point detection work; Position coordinate points generated by inverse calculation of the source information of the previous step Further correction and adaptive adjustment of the moving position coordinates and scanning path of the mobile robot, and commanding the mobile robot to move towards the predicted coordinate points according to the autonomously determined optimized coordinate points Moving and drawing a leakage gas diffusion concentration surface to predict the diffusion area; When the leakage concentration does not reach the set threshold value, the mobile inspection equipment makes a preliminary warning in the field in the point range, and uploads the data to the monitoring and inspection integrated platform, which prompts that there is a leakage source in the grid, suggests to take measures and carry out the next step of detecting the specific position and leakage rate; When the maximum threshold is exceeded, it is confirmed that a leak has occurred, the diffusion range of the leaked gas concentration is plotted, the image of the point where the leak occurred is photographed, and the distribution map of the leaked methane concentration is transmitted to the monitoring and inspection integrated platform, which makes a secondary early warning prompt. The early warning prompt can be specifically distinguished as a pipe body, a flange connection, a valve, and a weld at a certain point in a certain grid, and the video monitoring near the point can be retrieved in the daily monitoring database to prompt the management personnel to pay attention to the confirmation of the leak point position and take control measures to stop the leakage and timely ventilation; When the detection concentration around the leak point has reached the lower limit of methane explosion, the mobile inspection equipment and the monitoring and inspection integrated platform make a linkage three-level early warning alarm for the leakage area at the same time, and immediately take control measures to eliminate the explosion accident hazard and try to repair the leak point.

11. The method for early warning of leakage of a gas station pipeline network according to claim 8, characterized in that, The step of the mobile inspection equipment performing mobile inspection includes; When the mobile robot passes through a certain point in the inspection grid area, first, the laser sensor emits laser to the gas pipeline and the surrounding area and receives the reflection, analyzes the laser absorption spectrum, and plots the leakage gas concentration distribution on the laser path. Secondly, compare with the daily monitoring database and the concentration threshold value at this point; To improve the accuracy of mobile inspection, the concentration threshold value can be set to 0 at the lowest. The gas inlet and outlet areas of the gas station will inevitably have methane concentration interference, so the concentration threshold value for inspection can be set according to the on-site environment, which is used to judge whether a leak point has occurred.

12. The method for early warning of leakage of a gas station pipeline network according to claim 10, characterized in that, The step of source item information back calculation is: Assuming that the initial detection of gas leakage, the robot moves to the station network downwind inspection, laser sensor collected at different locations n detection concentration values are shown , the corresponding calculation concentration value is calculated by Gaussian plume dispersion model , the source inversion problem is converted into the optimization problem of solving the objective function by intelligent optimization algorithm, and the objective function of the source information inversion is , wherein is the position coordinate of the gas station network leakage source, Q is the source intensity of the leakage source, and n is the number of all detections during the mobile inspection.

13. The method for early warning of leakage of a gas station pipeline network according to claim 12, characterized in that, The steps of solving the source item information based on the hybrid genetic grey wolf algorithm are: S1, performing a genetic algorithm, initializing a population and related source term parameters: determining a first generation of GA population, each individual in the population including , source term parameters including gas leakage source intensity Q, initial position , forming an initial state of multi-parameter inverse calculation in three-dimensional space; S2, the square sum of the difference between the detection value and the calculation value is taken as the objective function, and the reciprocal of the objective function is determined as the fitness function f, i.e. ; S3, the initial population and meteorological data into the Gaussian plume dispersion model, the detection point of the calculated concentration , and with the actual detection concentration Comparison; determine the fitness value of each individual in the population, when the objective function reaches the minimum, the fitness f value reaches the maximum, the smaller the objective function, the smaller the difference between the calculated concentration and detection concentration , the higher the fitness, the greater the probability of the individual with greater fitness value in the GA algorithm to pass to the next generation. S4, sort the population according to the fitness size, and perform genetic operation on the individuals with good fitness, the genetic operation including selection, crossover and mutation; S5, generate a new population through genetic operation to realize the optimization of individuals in the GA population; repeat the above steps to update the population until the iteration stopping criteria are met, otherwise go back to S3; S6, finally, N new populations are generated In the final population, the output with the large fitness value is preferred as the best initial population of the grey wolf algorithm. S7, execute the grey wolf algorithm to determine the position information of the new population of grey wolves and parameters ; S8, calculate the fitness of gray wolf individual, save the top three gray wolf with the best fitness ; S9, update the current grey wolf position and calculate the fitness of all grey wolves; S10, update fitness and position; S11, judge whether the termination condition is met, if not, return to S4, if yes, output the optimal solution of the source item information inverse calculation , the inverse calculation process is finished, output the source intensity Q and the leakage point position coordinate .

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