CN116642140A - Monitoring and inspection system and leakage early warning method for gas station field pipe network - Google Patents
Monitoring and inspection system and leakage early warning method for gas station field pipe network Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/005—Protection or supervision of installations of gas pipelines, e.g. alarm
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D3/00—Arrangements for supervising or controlling working operations
- F17D3/01—Arrangements for supervising or controlling working operations for controlling, signalling, or supervising the conveyance of a product
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D3/00—Arrangements for supervising or controlling working operations
- F17D3/18—Arrangements for supervising or controlling working operations for measuring the quantity of conveyed product
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
- F17D5/06—Preventing, monitoring, or locating loss using electric or acoustic means
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Abstract
The invention discloses a monitoring and inspection system and a leakage early warning method for a gas station pipe network, which belong to the field of gas pipeline inspection, wherein the monitoring and inspection system comprises a daily monitoring module, a monitoring module and a monitoring module, wherein the daily monitoring module is used for arranging fixed equipment to monitor the gas station pipe network; the mobile inspection module is used for commanding the mobile robot to carry out mobile inspection; the monitoring and inspection integrated platform is used for realizing functional integration of daily monitoring and mobile inspection, when the daily monitoring data or the mobile inspection data detect gas leakage, the result is uploaded to the integrated platform to realize data sharing, and diffusion simulation and source item information back calculation are carried out on a leakage area by constructing a Gaussian smoke plume diffusion model and a mixed genetic gray wolf algorithm in advance, so that leakage point positioning, diffusion area prediction, grading early warning and decision control measures are provided for the system; the invention realizes the efficient monitoring in the gas station pipe network area, improves the accuracy of gas leakage source detection and early warning, and ensures the safe and reliable operation of the gas station pipe network.
Description
Technical Field
The invention relates to the field of gas pipeline inspection, in particular to a monitoring inspection system and a leakage early warning method for a gas station field pipeline network.
Background
Under the background of the increasing demand of gas, a large number of gas pipelines are laid, and a complex urban gas pipeline network becomes an important component of urban development. The main component of urban fuel gas is methane, and once the inflammable and explosive gas leaks, the inflammable and explosive gas rapidly diffuses in the air to form a dangerous area for leakage and diffusion, and the excessive inhalation causes choking, and the inflammable and explosive gas is burnt even rapidly when encountering a fire source, so that serious gas explosion accidents occur.
The pipeline of the gas station pipeline network is generally a high-pressure gas pipeline, and is used as a place with complex pipeline relation and multiple process flows, once gas leakage and diffusion accidents occur, property loss is caused by light weight, life and property safety of workers is threatened, and urban power supply is also influenced. Although the leakage monitoring system is arranged at the initial stage of operation of the domestic gas station, under the influence of the positions of leakage points, meteorological factors and the like, the situation that the positioning of the leakage points is inaccurate and the prediction and the early warning are not timely is likely to occur, and the reliability of the gas pipe network monitoring system still needs to be further improved. Therefore, in order to ensure the gas safety of the gas station pipe network, daily monitoring and mobile inspection are performed, the diffusion rule based on the gas leakage space-time characteristics is analyzed, and the monitoring inspection collaborative early warning of the gas station pipe network is carried out.
Disclosure of Invention
Aiming at the defects, the invention provides a monitoring and inspection system and a leakage early warning method for a gas station pipe network, which are used for realizing daily monitoring and mobile inspection of the gas station pipe network, accurately finding leakage points and predicting leakage diffusion areas, timely classifying early warning prompts, and taking control measures to ensure that the gas station pipe network operates stably.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a monitoring and inspection system for a gas station line network, comprising:
the daily monitoring module is used for arranging fixed equipment to monitor a gas station pipe network, transmitting daily monitoring data acquired by monitoring to the integrated database, displaying in real time and calling data when the mobile station is patrolled and examined, wherein the acquired daily monitoring data comprises methane concentration, pressure, flow, wind direction and wind speed and running state;
the mobile inspection module comprises mobile inspection equipment and is used for commanding the mobile robot to carry out mobile inspection and uploading the mobile inspection data acquired by the inspection to an integrated database of the monitoring inspection integrated platform in real time;
the monitoring and inspection integrated platform is used for realizing functional integration of daily monitoring and mobile inspection, when the daily monitoring data or the mobile inspection data detect gas leakage, the result is uploaded to the integrated platform to realize data sharing, and diffusion simulation and source item information back calculation are carried out on a leakage area by constructing a Gaussian smoke plume diffusion model and a mixed genetic gray wolf algorithm in advance, so that leakage point positioning, diffusion area prediction, grading early warning and decision control measures are provided for the system.
As a further technical scheme of the invention, the integrated database comprises daily monitoring data, gas pipe network original data and mobile inspection data, wherein the daily monitoring data are video monitoring and sensor fixed telemetry data in a station area, the gas pipe network original data are pipe network base data, pipe network arrangement relations and pipe network operation data, the pipe network base data comprise pipe inner diameters and initial set flow pressures, the pipe network operation data comprise flow and pressure changes, and the mobile inspection data are image shooting and robot inspection data in an inspection process.
As a further technical scheme of the invention, grids and points are divided in the gas station pipe network area, wherein 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 pipelines and auxiliary equipment in the grids are divided into different points;
the monitoring and inspection integrated platform is used for further dividing subareas according to importance in the points on the basis of daily monitoring grid division and point location division, wherein the subareas comprise key areas, key areas and general areas; the key areas comprise joints of pipelines, joints of the pipelines and the tank body and welding seams; the key areas comprise valves, flanges and instruments and meter equipment; the general area comprises a pipe body of a gas pipeline and an individual tank body.
As a further technical solution of the present invention, the monitoring and inspection integrated platform includes:
the integrated terminal is used for remotely monitoring the gas station pipeline network and the running condition, constructing an integrated database of the monitoring and inspection system, providing the work of positioning leakage points, predicting diffusion areas and warning leakage in a grading manner, and having the function of issuing control instructions;
the leakage diffusion simulation module is used for establishing a Gaussian smoke plume diffusion model in advance according to multivariate data, and updating an optimization model in real time by monitoring feedback data of the inspection process, wherein the multivariate data comprise leakage point height, leakage aperture, diffusion coefficient and atmospheric parameters;
the source item information back calculation module is used for inversely calculating leakage source item information of the gas pipe network based on a hybrid genetic gray wolf algorithm, wherein the source item information comprises leakage point positions and leakage source strengths;
the intelligent decision control module is used for making intelligent decision control measures according to the positions of the leakage points, the prediction of the diffusion areas and the hierarchical early warning prompt when the leakage is found, wherein the intelligent decision control measures comprise cutting off the air inlet of a pipe network, and adjusting the ventilation and evacuation of people in a pollution area;
and the server is used for data storage, data cleaning and deep mining and provides services for the establishment of a database, a diffusion model and expert decision control of management personnel.
As a further technical scheme of the invention, the daily monitoring module comprises a daily telemetry device and a normal monitoring device:
wherein the daily telemetry device comprises:
the laser sensor is used for emitting laser and receiving reflection, analyzing the laser absorption spectrum and drawing the concentration distribution of leakage gas on a laser path;
the high-definition camera is used for being installed near a gas station pipe network, shooting, extracting and analyzing daily video images, and daily monitoring the running state of each area, and the video monitoring of a leakage diffusion area can be conveniently and timely called when leakage occurs by marking the video addresses of grids and points;
the intelligent cradle head is used for installing a laser sensor and a camera, guaranteeing the angle rotation control of scanning and shooting according to instructions, and realizing omnibearing dead-angle-free detection;
wherein, the normality monitoring device includes:
the pressure gauge is arranged at the joint of the valve and the pipeline and is used for monitoring the pressure of the fuel gas in the pipeline;
the flow sensor is arranged at the joint of the valve and the pipeline and is used for monitoring the flow change in the pipeline;
the wind direction and wind speed sensor is used for being arranged near key areas and key areas of grids and points, detecting changes of wind direction and wind speed meteorological parameters in the areas, transmitting data to the monitoring and inspection integrated platform for storage, and providing data support for simulating gas leakage diffusion under the influence of different factors.
As a further technical solution of the present invention, the mobile inspection module includes:
the mobile robot comprises a guide rail robot and a mobile trolley;
the laser sensor is used for emitting laser and receiving reflection, analyzing the laser absorption spectrum and drawing the concentration distribution of leakage gas on a laser path;
the high-definition camera is used for shooting high-definition images of the gas pipeline in the moving inspection process, so that the problem of blurring caused by movement of inspection equipment is solved;
the intelligent cradle head is used for installing a laser sensor and a camera, guaranteeing to finish the angle rotation control of scanning and shooting according to instructions, and realizing omnibearing dead-angle-free mobile inspection.
As a further technical scheme of the invention, the leakage diffusion simulation module builds a Gaussian smoke plume diffusion model in advance according to the multivariate data according to the following formula:
the formula of the equal concentration curve of the Gao Siyan feather diffusion model is as follows:
wherein C (x, y, z, H) is the concentration of gas (unit: mg/m) in the downwind direction (x, y, z) of the leakage source 3 ) The method comprises the steps of carrying out a first treatment on the surface of the Q is the leakage source intensity (i.e. leakage rate, singlyBits: mg/s);average wind speed (unit: m/s) for the height of the leak; y is the lateral distance and z is the vertical distance (unit: m); sigma (sigma) y 、σ z Representing diffusion parameters (unit: m) in the y-axis and z-axis; h is the effective height (unit: m) of the leakage point; the effective height of the leakage point is H=H r +ΔH, where H r Δh is the elevation of the leaking gas, which is the actual height of the leak.
Another object of the embodiment of the present invention is to provide a leakage early warning method for a gas station pipe network, including the following steps:
daily monitoring, namely arranging fixed equipment to monitor a gas station pipe network, transmitting data acquired by monitoring to an integrated database, displaying in real time, and calling the data when mobile inspection is performed;
the mobile inspection directs the mobile robot to carry out mobile inspection, and the mobile inspection data acquired by the inspection is uploaded to an integrated database of the monitoring inspection integrated platform in real time;
when the daily monitoring data or the mobile inspection data detect gas leakage, the result is uploaded to an integrated platform to realize data sharing, and diffusion simulation and source information back calculation are carried out on a leakage area by constructing a Gaussian smoke plume diffusion model and a mixed genetic gray wolf algorithm in advance, so that leakage point positioning, diffusion area prediction, grading early warning and decision control measures are provided for the system.
As a further technical scheme of the invention, the step of daily monitoring, arranging the fixed equipment to monitor the gas station network, transmitting the data collected by monitoring to the integrated database, displaying in real time and calling the data when the mobile inspection is carried out comprises the following steps:
Dividing grids and points in a gas station pipe network area;
arranging fixed equipment to monitor a gas station pipe network;
transmitting the data collected by monitoring to an integrated database, displaying in real time and calling the data when the mobile inspection is performed;
when the damage of facilities occurs in the grid to cause leakage, a manager timely discovers abnormal data change of daily monitoring through data displayed in real time, timely locates the leaked point location area through different equipment, searches for leakage points and takes control measures, and the daily operation safety of a gas station pipeline network is conveniently guaranteed.
As a further technical scheme of the invention, the steps of moving the inspection, commanding the mobile robot to move the inspection, and uploading the moving inspection data acquired by the inspection to an integrated database of a monitoring inspection integrated platform in real time comprise the following steps:
dividing the subareas according to the importance degree in the points on the basis of dividing grids and the points by daily monitoring, wherein the subareas comprise key areas, key areas and general areas;
according to the daily inspection task of a gas station pipe network, mobile inspection equipment, coordinate and path planning, a scanning mode and a gas leakage diffusion model are preset and managed, wherein the gas leakage diffusion model is a Gaussian smoke plume diffusion model and a correction model;
Remotely issuing a control instruction to command the mobile inspection equipment to enter a field to execute mobile inspection;
and uploading the mobile inspection data acquired by inspection to an integrated database of the monitoring inspection integrated platform in real time.
As a further technical scheme of the invention, the scanning mode comprises directional scanning and reciprocating scanning, wherein the directional scanning is carried out on the heavy point area and the key area, and the reciprocating scanning is carried out on the general area.
As a further technical scheme of the invention, when the daily monitoring data or the mobile inspection data detect gas leakage, uploading the result to an integrated platform to realize data sharing, and carrying out diffusion simulation and source item information back calculation on a leakage area by constructing a Gaussian smoke plume diffusion model and a mixed genetic gray wolf algorithm in advance, thereby providing leakage point positioning, diffusion area prediction, grading early warning and decision control measures for a system, wherein the steps comprise:
when the methane concentration is detected, the monitoring and inspection integrated platform researches and judges leakage grading early warning according to a preset threshold value, if a leakage point exists in the point position, leakage diffusion simulation and source item information back calculation are rapidly carried out, and if a result shows that the leakage point is not in the range of the point position, scanning inspection of the next point position is continued;
Inversion calculation of source information of leakage points, including leakage source intensity Q and leakage point position coordinates (x, y, z), and calculation of effective height H and lifting height delta H of leakage gas to further derive actual height H by means of a pre-established Gaussian plume diffusion model and a mixed genetic gray wolf algorithm r Further, the transverse diffusion distance is calculated according to an equal concentration curve formula of the Gaussian smoke plume model, so that the accurate positioning of the leakage point of the gas pipeline is realized, and timely and efficient detection of the leakage point is completed;
the method comprises the steps that (1) position coordinate points (x, y, z) generated by back calculation of source item information in the previous step are subjected to further correction and self-adaptive adjustment on moving position coordinates and scanning paths of a mobile robot, the mobile robot is commanded to move towards predicted coordinate points (x, y, z) according to the coordinate points after optimization determined autonomously, a diffusion concentration plane of leaked gas is drawn, and prediction of a diffusion area is carried out;
when the leakage concentration does not reach the set threshold, the mobile inspection equipment makes primary early warning reminding in the range of the point location on site, uploads data to the monitoring inspection integrated platform, prompts that a leakage source appears at a certain point location in the grid, and recommends measures to be taken and carries out detection work of verifying the specific position and the leakage rate in the next step;
When the maximum threshold value is exceeded, leakage is confirmed, the concentration diffusion range of leakage gas is drawn, the point position image where the leakage gas is located is shot, a leakage methane concentration distribution map is transmitted to a monitoring and inspection integrated platform, a secondary early warning prompt is made by the platform, the early warning prompt can be specifically distinguished into a pipe body, a flange connection part, a valve part and a welding seam part of a certain point position of a certain grid, and nearby video monitoring is called through the point position in a daily monitoring database, management staff is prompted to pay attention to confirm the position of the leakage point, and control measures are taken to stop leakage and timely ventilation;
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 simultaneously make linkage three-stage early warning alarm for the leakage area, immediately take control measures, eliminate hidden danger of explosion accidents and attempt to repair the leakage point.
As a further technical scheme of the invention, the step of performing mobile inspection by the mobile inspection equipment in the approach comprises the following steps of;
when the mobile robot passes through a certain point of the inspection grid area, firstly, a laser sensor emits laser to a gas pipeline and the surrounding, receives reflection, analyzes the laser absorption spectrum, draws the concentration distribution of leakage gas on a laser path, and then compares the concentration distribution with a daily monitoring database and a concentration threshold value at the position;
In order to improve the accuracy of mobile inspection, the concentration threshold can be set to be 0 at the lowest, and meanwhile, in order to weaken the interference of environmental factors, such as methane concentration interference in the gas inlet and outlet areas of a gas station, the concentration threshold for inspection can be set to be of a special size according to the field environment and used for judging whether leakage points occur.
As a further technical scheme of the invention, the step of back calculation of the source item information comprises the following steps:
assuming that gas leakage is detected for the first time, the robot moves to the downwind position of the station field pipe network for inspection, and the laser sensor collects n detection concentration values at different positions for useIndicating that the corresponding calculated concentration value is calculated by using the Gaussian plume diffusion model>Converting the inversion problem of the source item into the solution of the optimization problem of the objective function by using the intelligent optimization algorithm, wherein the objective function of the back calculation of the source item information is +.>Wherein: (x, y, z) is the position coordinate of the leakage source of the gas station pipe network, Q is the source intensity of the leakage source, and n is all detection during mobile inspectionIs a point of (3).
As a further technical scheme of the invention, the leakage source item information solving step based on the hybrid genetic gray wolf algorithm is as follows:
s1, executing a genetic algorithm, initializing a population and related source item parameters: determining a first generation GA population, wherein each single individual in the population comprises (Q, x, y, z), the source item parameters comprise the GAs leakage source intensity (rate) Q, and the initial position (x, y, z) to form an initialization state of multi-parameter back calculation in a three-dimensional space;
S2, taking the sum of squares of the difference values of the detection value and the calculated value as an objective function, and determining the reciprocal of the objective function as a fitness function f, namely
S3, substituting the initial group, meteorological data and the like into a Gaussian plume diffusion model to obtain the calculated concentration of each detection pointAnd is equal to the actual detection concentration->Comparing; determining fitness value of each individual in the population, and when the objective function reaches the minimum, the fitness f value reaches the maximum, wherein the smaller the objective function is, the calculated concentration is indicated>And detection concentration->The smaller the difference is, the higher the fitness is, and the larger the fitness value is in the GA algorithm, the larger the probability of being transmitted to the next generation is;
s4, sorting the fitness of the population, and carrying out genetic operation on individuals with good fitness, wherein the genetic operation comprises selection, crossing and variation;
s5, generating a new population through genetic operation, and realizing optimization of individuals in the GA population; repeating the steps to update the population until the iteration stopping standard is met, otherwise, returning to the step S3;
s6, finally generating N [ Q, x, y, z ] of a new population, and preferably selecting an output with a large fitness value from the final population as an optimal initial population of the gray wolf algorithm;
s7, executing a wolf algorithm, and determining position information and parameters a, A and C of a new wolf population;
S8, calculating individual fitness of the wolves, and storing the first three wolves alpha, beta and delta with the best fitness;
s9, updating the current position of the gray wolves, and calculating the adaptability of all the gray wolves;
s10, updating the fitness and the position of alpha, beta and delta;
s11, judging whether a termination condition is met, returning to S4 if the termination condition is not met, outputting an optimal solution [ Q, x, y, z ] of source item information back calculation if the termination condition is met, ending the back calculation process, and outputting a source intensity Q and a leakage point position coordinate (x, y, z).
Compared with the prior art, the invention has the beneficial effects that: according to the invention, a new monitoring linkage frame of the gas station pipeline network is constructed by integrating daily monitoring and mobile inspection detection data, firstly, fixed monitoring equipment is arranged on a basic pipeline network, the real-time monitoring of flow and pressure is carried out, the collection, storage and analysis of historical data are realized, the basic pipeline network data and operation data of a pipeline are generated, and support is provided for the matching and calling of the mobile inspection data; secondly, based on a diffusion rule of gas leakage space-time characteristics, a Gaussian smoke plume diffusion model is established, and a pre-simulation is provided for the approach inspection of mobile inspection equipment; further, through the methane laser sensor that mobile inspection equipment carried, carry out no dead angle laser scanning detection in different inspection areas, carry out directional scanning to heavy spot area and key area, reciprocating type scanning is carried out to general area, have the gas concentration high-efficient detection function to the suspicious leak point in the area, can effectively avoid leaking and examine, the possibility of false detection, this system can satisfy daily monitoring and leak hunting requirement, can also after finding the leak source, according to gaussian smoke plume diffusion model and mixed genetic gray wolf algorithm, back calculate leak source position coordinate and leak source intensity, optimize the removal route of patrolling, draw out concentration face such as leaking gas, predict the diffusion area, and carry out the warning suggestion of classifying immediately, the convenience management personnel pinpoints leak source, take measures promptly and get rid of accident potential, ensure gas station pipe network gas safety.
Drawings
FIG. 1 is a system frame diagram of a monitoring and inspection system and a leakage early warning method for a gas station field pipe network according to the present invention
Fig. 2 is an integrated database and a functional schematic diagram of a monitoring and inspection system and a leakage early warning method for a gas station field pipe network.
Fig. 3 is a schematic view of area division of a monitoring inspection system and a leakage early warning method for a gas station field pipe network.
FIG. 4 is a flow chart of a leakage early warning method for a gas station pipe network.
FIG. 5 is a flowchart of a hybrid genetic gray wolf algorithm for back calculation of source item information in an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, the present invention provides a monitoring and inspection system for a gas station field pipe network, which includes:
the daily monitoring module is used for arranging fixed equipment to monitor a gas station pipe network, transmitting daily monitoring data acquired by monitoring to the integrated database, displaying in real time and calling data when the mobile station is patrolled and examined, wherein the acquired daily monitoring data comprises methane concentration, pressure, flow, wind direction and wind speed and running state;
The mobile inspection module comprises mobile inspection equipment and is used for commanding the mobile robot to carry out mobile inspection and uploading the mobile inspection data acquired by the inspection to an integrated database of the monitoring inspection integrated platform in real time;
the monitoring and inspection integrated platform is used for realizing functional integration of daily monitoring and mobile inspection, when the daily monitoring data or the mobile inspection data detect gas leakage, the result is uploaded to the integrated platform to realize data sharing, and diffusion simulation and source item information back calculation are carried out on a leakage area by constructing a Gaussian smoke plume diffusion model and a mixed genetic gray wolf algorithm in advance, so that leakage point positioning, diffusion area prediction, grading early warning and decision control measures are provided for the system.
Optionally, as shown in fig. 2, the integrated database includes daily monitoring data, gas pipe network raw data and mobile inspection data, the daily monitoring data is video monitoring in a site area and sensor fixed telemetry data, the gas pipe network raw data is pipe network base data, pipe network arrangement relation and pipe network operation data, the pipe network base data includes pipe inner diameter and initial set flow pressure, the pipe network operation data includes flow and pressure changes, and the mobile inspection data is image shooting in an inspection process and robot inspection data.
Optionally, as shown in fig. 3, the grid and the point locations are divided in the gas station pipe network area, wherein the grid comprises an air inlet grid, a cleaning grid, a metering network, a temperature control network, a pressure regulating grid and an air outlet grid, and the gas pipeline and the accessory equipment in the grid are divided into different point locations;
the monitoring and inspection integrated platform is used for further dividing subareas according to importance in the points on the basis of daily monitoring grid division and point location division, wherein the subareas comprise key areas, key areas and general areas; the key areas comprise joints of pipelines, joints of the pipelines and the tank body and welding seams; the key areas comprise valves, flanges and instruments and meter equipment; the general area comprises a pipe body of a gas pipeline and an individual tank body.
Optionally, the monitoring and inspection integrated platform includes:
the integrated terminal is used for remotely monitoring the gas station pipeline network and the running condition, constructing an integrated database of the monitoring and inspection system, providing the work of positioning leakage points, predicting diffusion areas and warning leakage in a grading manner, and having the function of issuing control instructions;
the leakage diffusion simulation module is used for establishing a Gaussian smoke plume diffusion model in advance according to multivariate data, and updating an optimization model in real time by monitoring feedback data of the inspection process, wherein the multivariate data comprise leakage point height, leakage aperture, diffusion coefficient and atmospheric parameters;
The source item information back calculation module is used for inversely calculating leakage source item information of the gas pipe network based on a hybrid genetic gray wolf algorithm, wherein the source item information comprises leakage point positions and leakage source strengths;
the intelligent decision control module is used for making intelligent decision control measures according to the positions of the leakage points, the prediction of the diffusion areas and the hierarchical early warning prompt when the leakage is found, wherein the intelligent decision control measures comprise cutting off the air inlet of a pipe network, and adjusting the ventilation and evacuation of people in a pollution area;
and the server is used for data storage, data cleaning and deep mining and provides services for the establishment of a database, a diffusion model and expert decision control of management personnel.
Optionally, the daily monitoring module comprises a daily telemetry device and a normalcy monitoring device:
wherein the daily telemetry device comprises:
the laser sensor is used for emitting laser and receiving reflection, analyzing the laser absorption spectrum and drawing the concentration distribution of leakage gas on a laser path;
the high-definition camera is used for being installed near a gas station pipe network, shooting, extracting and analyzing daily video images, and daily monitoring the running state of each area, and the video monitoring of a leakage diffusion area can be conveniently and timely called when leakage occurs by marking the video addresses of grids and points;
The intelligent cradle head is used for installing a laser sensor and a camera, guaranteeing the angle rotation control of scanning and shooting according to instructions, and realizing omnibearing dead-angle-free detection;
wherein, the normality monitoring device includes:
the pressure gauge is arranged at the joint of the valve and the pipeline and is used for monitoring the pressure of the fuel gas in the pipeline;
the flow sensor is arranged at the joint of the valve and the pipeline and is used for monitoring the flow change in the pipeline;
the wind direction and wind speed sensor is used for being arranged near key areas and key areas of grids and points, detecting changes of wind direction and wind speed meteorological parameters in the areas, transmitting data to the monitoring and inspection integrated platform for storage, and providing data support for simulating gas leakage diffusion under the influence of different factors.
Optionally, the mobile inspection module includes:
the mobile robot comprises a guide rail robot and a mobile trolley;
the laser sensor is used for emitting laser and receiving reflection, analyzing the laser absorption spectrum and drawing the concentration distribution of leakage gas on a laser path;
the high-definition camera is used for shooting high-definition images of the gas pipeline in the moving inspection process, so that the problem of blurring caused by movement of inspection equipment is solved;
the intelligent cradle head is used for installing a laser sensor and a camera, guaranteeing to finish the angle rotation control of scanning and shooting according to instructions, and realizing omnibearing dead-angle-free mobile inspection.
The laser sensor and the high-definition camera carried on the mobile robot are used for carrying out laser scanning on the gas pipeline in the area, and especially the accuracy of accurately finding the position of the leakage point under the interference of factors such as a low-level pipeline, a high-obstacle arrangement relation and the like is improved.
Optionally, the gas leakage diffusion model is based on a diffusion rule of gas leakage space-time characteristics, and the established Gaussian smoke plume diffusion model has the following basic formula:
the formula of the equal concentration curve of the Gao Siyan feather diffusion model is as follows:
c in the formula(x, y, z, H) is the concentration of gas (in mg/m) in the downwind direction (x, y, z) of the leakage source 3 ) The method comprises the steps of carrying out a first treatment on the surface of the Q is the leakage source intensity (i.e., leakage rate, in mg/s);average wind speed (unit: m/s) for the height of the leak; y is the lateral distance and z is the vertical distance (unit: m); sigma (sigma) y 、σ z Representing diffusion parameters (unit: m) in the y-axis and z-axis; h is the effective height (unit: m) of the leakage point; the effective height of the leakage point is H=H r +ΔH, where H r Δh is the elevation of the leaking gas, which is the actual height of the leak.
Another object of the embodiment of the present invention is to provide a leakage early warning method for a gas station pipe network, including the following steps:
daily monitoring, namely arranging fixed equipment to monitor a gas station pipe network, transmitting data acquired by monitoring to an integrated database, displaying in real time, and calling the data when mobile inspection is performed;
The mobile inspection directs the mobile robot to carry out mobile inspection, and the mobile inspection data acquired by the inspection is uploaded to an integrated database of the monitoring inspection integrated platform in real time;
when the daily monitoring data or the mobile inspection data detect gas leakage, the result is uploaded to an integrated platform to realize data sharing, and diffusion simulation and source information back calculation are carried out on a leakage area by constructing a Gaussian smoke plume diffusion model and a mixed genetic gray wolf algorithm in advance, so that leakage point positioning, diffusion area prediction, grading early warning and decision control measures are provided for the system.
In an example of the technical scheme of the present invention, as shown in fig. 4, a leakage early warning method for a gas station network is specifically provided, and the method comprises the following steps:
step 1, daily monitoring, namely dividing grids and points in a gas station field pipe network area, wherein the grids comprise an air inlet grid, a cleaning grid, a metering network, a temperature control network, a pressure regulating grid, an air outlet grid and the like, and the points comprise areas such as pipe bodies, flanges, valves and joints; the daily telemetering device comprises an intelligent cradle head and a laser sensor, the normal state monitoring device comprises a pressure gauge, a flow sensor, a meteorological sensor, a high-definition camera and the like, high-precision monitoring is carried out on the methane concentration, pressure, flow, wind direction and wind speed in an area, and the operation state is carried out, and collected data is uploaded to an integrated database of a monitoring and inspection integrated platform, displayed in real time and can be used for calling data when mobile inspection is carried out; especially when the leakage is caused by the leakage of the large flow pressure, the large aperture corrosion leakage or the damage of key facilities such as valves, flanges and the like at a certain position in the grid, a manager can timely find the abnormal data change of daily monitoring through the data displayed in real time, timely locate the leaking point position area through different equipment, search the leakage point and take control measures, and the daily operation safety of the gas station pipe network is conveniently ensured.
Step 2, mobile inspection, namely, further dividing subareas according to importance in points on the basis of daily monitoring grid division and point location by a monitoring inspection integrated platform, wherein the subareas comprise key areas, key areas and general areas, mobile inspection equipment, coordinates and path planning, a scanning mode, a gas leakage diffusion model and the like are preset and managed according to daily inspection tasks of a gas station pipe network, the scanning mode comprises but is not limited to directional scanning, reciprocating scanning and the like, the directional scanning is carried out on the heavy areas and the key areas, the reciprocating scanning is carried out on the general areas, the accuracy of detecting leakage points is influenced by coupling factors such as a low-level pipeline, a high-barrier arrangement relation and the like, the gas leakage diffusion model is a Gaussian smoke plume diffusion model and a correction model, and then a remote issuing control command directs the mobile inspection equipment to enter a field to execute mobile inspection;
step 2.1, when the mobile robot passes through a certain point of the inspection grid area, firstly, laser is emitted to a gas pipeline and the surrounding by a laser sensor, reflection is received, the concentration distribution of methane gas on an analysis path is detected, and then, the concentration distribution is compared with a daily monitoring database and a concentration threshold value at the position;
Step 2.2, in order to improve the accuracy of mobile inspection, the lowest concentration threshold value can be set to 0, and meanwhile, in order to weaken the interference of environmental factors, such as methane concentration interference in the gas inlet and outlet areas of a gas station, the inspection concentration threshold value can be set to a special size according to the field environment for judging whether leakage points occur or not;
optionally, the further specific steps when detecting the concentration of leaked gas are:
step 3, when the methane concentration is detected, the monitoring and inspection integrated platform researches and judges leakage grading early warning according to a preset threshold value, if a leakage point exists in the point, leakage diffusion simulation and source item information back calculation are rapidly carried out, and if a result shows that the leakage point is not in the range of the point, scanning inspection of the next point is continued;
step 4, reversely calculating source item information of the leakage point, including leakage source intensity Q and leakage point position coordinates (x, y, z), by intelligently identifying methane concentration C (x, y, z, H) of a laser scanning path and utilizing a pre-established Gaussian plume diffusion model and a mixed genetic gray wolf algorithm, and simultaneously calculating effective height H and lifting height delta H of leakage gas so as to deduce actual height H r And further calculating the transverse diffusion distance according to an equal concentration curve formula of the Gaussian plume model. The accurate positioning of the leakage point of the gas pipeline is realized, and the timely and efficient detection work of the leakage point is completed.
Optionally, as shown in fig. 5, the specific steps of back calculation of the source item information are:
step 4.1, assuming that when gas leakage is detected for the first time, the robot moves to the downwind position of the station pipe network for inspection, and the laser sensor collects n detection concentration values at different positions for useIndicating that the corresponding calculated concentration value is calculated by using the Gaussian plume diffusion model>Converting the inversion problem of the source item into the solution of the optimization problem of the objective function by using the intelligent optimization algorithm, wherein the objective function of the back calculation of the source item information is +.>Wherein: and (x, y, z) is the position coordinate of a leakage source of a gas station pipe network, Q is the source intensity of the leakage source, and n is the number of points of all detection during mobile inspection.
Step 4.2, solving leakage source item information based on a hybrid genetic-gray wolf algorithm (GA-GWO algorithm):
step1, executing a genetic algorithm, initializing population and related source item parameters: determining a first generation GA population, wherein each single individual in the population comprises [ Q, x, y, z ], the source item parameters comprise GAs leakage source intensity (rate) Q, and initial positions (x, y, z) to form an initialization state of multi-parameter back calculation in a three-dimensional space;
step2, taking the sum of squares of the difference between the detected value and the calculated value as an objective function, and determining the reciprocal of the objective function as a fitness function f, namely
Step3, substituting the initial population, meteorological data and the like into a Gaussian plume diffusion model to obtain the calculated concentration of each detection pointAnd is equal to the actual detection concentration->Comparing; determining fitness value of each individual in the population, and when the objective function reaches the minimum, the fitness f value reaches the maximum, wherein the smaller the objective function is, the calculated concentration is indicated>And detection concentration->The smaller the difference is, the higher the fitness is, and the larger the fitness value is in the GA algorithm, the larger the probability of being transmitted to the next generation is;
step4, sequencing the fitness of the population, and performing genetic operation (selection, crossing and variation) on individuals with good fitness;
step5, generating a new population through genetic operation, and realizing optimization of individuals in the GA population; repeating the steps to update the population until the iteration stopping standard is met, otherwise, returning to Step3;
step6, finally generating N [ Q, x, y, z ] of the new population, and preferably selecting an output with a large fitness value from the final population as an optimal initial population of a GWO algorithm;
step7, executing GWO algorithm, and determining position information of the new sirius population and parameters a, a and C;
step8, calculating individual fitness of the wolves, and storing the first three wolves alpha, beta and delta with the best fitness;
Step9, updating the current position of the gray wolves, and calculating the adaptability of all the gray wolves;
step10, updating the fitness and the position of alpha, beta and delta;
step11, judging whether the termination condition is met, returning to Step4 if the termination condition is not met, outputting an optimal solution [ Q, x, y, z ] of source item information back calculation if the termination condition is not met, ending the back calculation process, and outputting a source intensity Q and a leakage point position coordinate (x, y, z)
And 5, performing back calculation on the generated position coordinate points (x, y, z) by the information of the source item in the last step, further correcting and adaptively adjusting the moving position coordinates and the scanning path of the robot, commanding the robot to move towards the predicted coordinate points (x, y, z) according to the autonomously determined and optimized coordinate points, drawing concentration surfaces such as leakage gas diffusion and the like, predicting a diffusion area, giving an alarm on site, and timely sending an early warning prompt to a manager for gas leakage.
Step 6, when the leakage concentration is not high, the mobile inspection equipment makes primary early warning reminding in the point location range on site, and uploads data to the monitoring inspection integrated platform to prompt a certain point location in the grid to have a leakage source, and recommends measures to be taken and carries out detection work of verifying the specific position and the leakage rate in the next step;
step 7, when the maximum threshold value is exceeded, confirming leakage, drawing a leakage gas concentration diffusion range, shooting a point position image where the leakage gas concentration diffusion range is located, transmitting a leakage methane concentration distribution map to a monitoring and inspection integrated platform, making a secondary early warning prompt by the platform, wherein the early warning prompt can be specifically distinguished into a pipe body, a flange connection position, a valve position, a welding seam position and the like of a certain point position of a certain grid, and calling nearby video monitoring through the point position in a daily monitoring database to prompt a manager to notice and confirm the position of the leakage point, and taking control measures to stop leakage and timely ventilation;
And 8, when the detected concentration around the leakage point reaches the lower limit of methane explosion, moving the inspection equipment and the monitoring inspection integrated platform to simultaneously make a linkage three-stage early warning alarm for the leakage area, immediately taking control measures, closing the pipeline of the pressure regulating station, evacuating nearby personnel, taking high-efficiency ventilation measures and the like to remove hidden danger of explosion accidents, and attempting to repair the leakage point.
It should be understood that, although the steps in the flowcharts of the embodiments of the present invention are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in various embodiments may include multiple sub-steps or stages that are not necessarily performed at the same time, but may be performed at different times, nor do the order in which the sub-steps or stages are performed necessarily performed in sequence, but may be performed alternately or alternately with at least a portion of the sub-steps or stages of other steps or other steps.
Those skilled in the art will appreciate that all or part of the processes in the methods of the above embodiments may be implemented by a computer program for instructing relevant hardware, where the program may be stored in a non-volatile computer readable storage medium, and where the program, when executed, may include processes in the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile 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. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
The foregoing description of the preferred embodiments of the invention is not intended to be limiting, but rather is intended to cover all modifications, equivalents, and alternatives falling within the spirit and principles of the invention.
Claims (15)
1. Monitoring inspection system towards gas station field pipe network, characterized by, include:
the daily monitoring module is used for arranging fixed equipment to monitor a gas station pipe network, transmitting daily monitoring data acquired by monitoring to the integrated database, displaying in real time and calling data when the mobile station is patrolled and examined, wherein the acquired daily monitoring data comprises methane concentration, pressure, flow, wind direction and wind speed and running state;
The mobile inspection module comprises mobile inspection equipment and is used for commanding the mobile robot to carry out mobile inspection and uploading the mobile inspection data acquired by the inspection to an integrated database of the monitoring inspection integrated platform in real time;
the monitoring and inspection integrated platform is used for realizing functional integration of daily monitoring and mobile inspection, when the daily monitoring data or the mobile inspection data detect gas leakage, the result is uploaded to the integrated platform to realize data sharing, and diffusion simulation and source item information back calculation are carried out on a leakage area by constructing a Gaussian smoke plume diffusion model and a mixed genetic gray wolf algorithm in advance, so that leakage point positioning, diffusion area prediction, grading early warning and decision control measures are provided for the system.
2. The system for monitoring and inspecting a gas station field pipe network according to claim 1, wherein the integrated database comprises daily monitoring data, gas pipe network raw data and mobile inspection data, the daily monitoring data are video monitoring in a station field area, sensor fixed telemetry data, the gas pipe network raw data are pipe network base data, pipe network arrangement relation and pipe network operation data, the pipe network base data comprise pipe inner diameter and initial set flow pressure, the pipe network operation data comprise flow and pressure changes, and the mobile inspection data are image shooting in an inspection process and robot inspection data.
3. The monitoring and inspection system for a gas station field pipe network according to claim 1, wherein grids and points are divided in the gas station field pipe network area, 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 pipelines and auxiliary equipment in the grids are divided into different points; the monitoring and inspection integrated platform is used for further dividing subareas according to importance in the points on the basis of daily monitoring grid division and point location division, wherein the subareas comprise key areas, key areas and general areas; the key areas comprise joints of pipelines, joints of the pipelines and the tank body and welding seams; the key areas comprise valves, flanges and instruments and meter equipment; the general area comprises a pipe body and a tank body of the gas pipeline.
4. The system of claim 1, wherein the integrated monitoring and inspection platform comprises:
the integrated terminal is used for remotely monitoring the gas station pipeline network and the running condition, constructing an integrated database of the monitoring and inspection system, providing the work of positioning leakage points, predicting diffusion areas and warning leakage in a grading manner, and having the function of issuing control instructions;
The leakage diffusion simulation module is used for establishing a Gaussian smoke plume diffusion model in advance according to multivariate data, and updating an optimization model in real time by monitoring feedback data of the inspection process, wherein the multivariate data comprise leakage point height, leakage aperture, diffusion coefficient and atmospheric parameters;
the source item information back calculation module is used for inversely calculating leakage source item information of the gas pipe network based on a hybrid genetic gray wolf algorithm, wherein the source item information comprises leakage point positions and leakage source strengths;
the intelligent decision control module is used for making intelligent decision control measures according to the positions of the leakage points, the prediction of the diffusion areas and the hierarchical early warning prompt when the leakage is found, wherein the intelligent decision control measures comprise cutting off the air inlet of a pipe network, and adjusting the ventilation and evacuation of people in a pollution area;
and the server is used for data storage, data cleaning and deep mining and provides services for the establishment of a database, a diffusion model and expert decision control of management personnel.
5. The gas station-oriented grid monitoring and inspection system of claim 1, wherein the daily monitoring module comprises a daily telemetry device and a normal monitoring device:
wherein the daily telemetry device comprises:
The laser sensor is used for emitting laser and receiving reflection, analyzing the laser absorption spectrum and drawing the concentration distribution of leakage gas on a laser path;
the high-definition camera is used for being installed near a gas station pipe network, shooting, extracting and analyzing daily video images, and daily monitoring the running state of each area, and the video monitoring of a leakage diffusion area can be conveniently and timely called when leakage occurs by marking the video addresses of grids and points;
the intelligent cradle head is used for installing a laser sensor and a camera, guaranteeing the angle rotation control of scanning and shooting according to instructions, and realizing omnibearing dead-angle-free detection;
wherein, the normality monitoring device includes:
the pressure gauge is arranged at the joint of the valve and the pipeline and is used for monitoring the pressure of the fuel gas in the pipeline;
the flow sensor is arranged at the joint of the valve and the pipeline and is used for monitoring the flow change in the pipeline;
the wind direction and wind speed sensor is used for being arranged near key areas and key areas of grids and points, detecting changes of wind direction and wind speed meteorological parameters in the areas, transmitting data to the monitoring and inspection integrated platform for storage, and providing data support for simulating gas leakage diffusion under the influence of different factors.
6. The gas station-oriented grid management monitoring and inspection system of claim 1, wherein the mobile inspection module comprises:
the mobile robot comprises a guide rail robot and a mobile trolley;
the laser sensor is used for emitting laser and receiving reflection, analyzing the laser absorption spectrum and drawing the concentration distribution of leakage gas on a laser path;
the high-definition camera is used for shooting high-definition images of the gas pipeline in the moving inspection process, so that the problem of blurring caused by movement of inspection equipment is solved;
the intelligent cradle head is used for installing a laser sensor and a camera, guaranteeing to finish the angle rotation control of scanning and shooting according to instructions, and realizing omnibearing dead-angle-free mobile inspection.
7. The monitoring and inspection system for a gas station field pipe network according to claim 4, wherein the leakage diffusion simulation module builds a gaussian plume diffusion model in advance according to multivariate data as follows:
the formula of the equal concentration curve of the Gao Siyan feather diffusion model is as follows:
wherein C (x, y, z, H) is the concentration of the fuel gas in the downwind direction (x, y, z) of the leakage source; q is leakage source intensity; u is the average wind speed of the leakage point height; y is the lateral distance and z is the vertical distance; sigma (sigma) y 、σ z Representing diffusion parameters in the y-axis and z-axis; h is the effective height of the leakage point; the effective height of the leakage point is H=H r +ΔH, where H r Δh is the elevation of the leaking gas, which is the actual height of the leak.
8. A leakage early warning method for a gas station pipe network is characterized by comprising the following steps:
daily monitoring, namely arranging fixed equipment to monitor a gas station pipe network, transmitting data acquired by monitoring to an integrated database, displaying in real time, and calling the data when mobile inspection is performed;
the mobile inspection directs the mobile robot to carry out mobile inspection, and the mobile inspection data acquired by the inspection is uploaded to an integrated database of the monitoring inspection integrated platform in real time;
when the daily monitoring data or the mobile inspection data detect gas leakage, the result is uploaded to an integrated platform to realize data sharing, and diffusion simulation and source information back calculation are carried out on a leakage area by constructing a Gaussian smoke plume diffusion model and a mixed genetic gray wolf algorithm in advance, so that leakage point positioning, diffusion area prediction, grading early warning and decision control measures are provided for the system.
9. The leakage pre-warning method for a gas station pipe network according to claim 8, wherein the step of daily monitoring, arranging a fixed device to monitor the gas station pipe network, transmitting data collected by monitoring to an integrated database, displaying in real time, and calling the data when the mobile inspection is performed comprises the following steps:
Dividing grids and points in a gas station pipe network area;
arranging fixed equipment to monitor a gas station pipe network;
transmitting the data collected by monitoring to an integrated database, displaying in real time and calling the data when the mobile inspection is performed;
when the damage of facilities occurs in the grid to cause leakage, a manager timely discovers abnormal data change of daily monitoring through data displayed in real time, timely locates the leaked point location area through different equipment, searches for leakage points and takes control measures, and the daily operation safety of a gas station pipeline network is conveniently guaranteed.
10. The gas station pipe network-oriented leakage early warning method according to claim 8, wherein the steps of moving the inspection, commanding the mobile robot to move the inspection, and uploading the moving inspection data collected by the inspection to the integrated database of the monitoring inspection integrated platform in real time comprise:
dividing the subareas according to the importance degree in the points on the basis of dividing grids and the points by daily monitoring, wherein the subareas comprise key areas, key areas and general areas;
according to the daily inspection task of a gas station pipe network, mobile inspection equipment, coordinate and path planning, a scanning mode and a gas leakage diffusion model are preset and managed, wherein the gas leakage diffusion model is a Gaussian smoke plume diffusion model and a correction model;
Remotely issuing a control instruction to command the mobile inspection equipment to enter a field to execute mobile inspection;
and uploading the mobile inspection data acquired by inspection to an integrated database of the monitoring inspection integrated platform in real time.
11. The leakage early warning method for the gas station pipe network according to claim 10, wherein the scanning mode comprises directional scanning and reciprocating scanning, wherein the directional scanning is carried out on a heavy area and a key area, and the reciprocating scanning is carried out on a general area.
12. The leakage early warning method for the gas station pipe network according to claim 8, wherein when the daily monitoring data or the mobile inspection data detect the gas leakage, the result is uploaded to an integrated platform to realize data sharing, and the leakage area is subjected to diffusion simulation and source item information back calculation by constructing a Gaussian smoke plume diffusion model and a hybrid genetic gray wolf algorithm in advance, so that leakage point positioning, diffusion area prediction, grading early warning and decision control measures are provided for the system, and the method comprises the following steps:
when the methane concentration is detected, the monitoring and inspection integrated platform researches and judges leakage grading early warning according to a preset threshold value, if a leakage point exists in the point position, leakage diffusion simulation and source item information back calculation are rapidly carried out, and if a result shows that the leakage point is not in the range of the point position, scanning inspection of the next point position is continued;
Inversion calculation of source information of leakage points, including leakage source intensity Q and leakage point position coordinates (x, y, z), and calculation of effective height H and lifting height delta H of leakage gas to further derive actual height H by means of a pre-established Gaussian plume diffusion model and a mixed genetic gray wolf algorithm r Further, the transverse diffusion distance is calculated according to an equal concentration curve formula of the Gaussian smoke plume model, so that the accurate positioning of the leakage point of the gas pipeline is realized, and timely and efficient detection of the leakage point is completed;
the method comprises the steps that (1) position coordinate points (x, y, z) generated by back calculation of source item information in the previous step are subjected to further correction and self-adaptive adjustment on moving position coordinates and scanning paths of a mobile robot, the mobile robot is commanded to move towards predicted coordinate points (x, y, z) according to the coordinate points after optimization determined autonomously, a diffusion concentration plane of leaked gas is drawn, and prediction of a diffusion area is carried out;
when the leakage concentration does not reach the set threshold, the mobile inspection equipment makes primary early warning reminding in the range of the point location on site, uploads data to the monitoring inspection integrated platform, prompts that a leakage source appears at a certain point location in the grid, and recommends measures to be taken and carries out detection work of verifying the specific position and the leakage rate in the next step;
When the maximum threshold value is exceeded, leakage is confirmed, the concentration diffusion range of leakage gas is drawn, the point position image where the leakage gas is located is shot, a leakage methane concentration distribution map is transmitted to a monitoring and inspection integrated platform, a secondary early warning prompt is made by the platform, the early warning prompt can be specifically distinguished into a pipe body, a flange connection part, a valve part and a welding seam part of a certain point position of a certain grid, and nearby video monitoring is called through the point position in a daily monitoring database, management staff is prompted to pay attention to confirm the position of the leakage point, and control measures are taken to stop leakage and timely ventilation;
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 simultaneously make linkage three-stage early warning alarm for the leakage area, immediately take control measures, eliminate hidden danger of explosion accidents and attempt to repair the leakage point.
13. The leakage pre-warning method for a gas station pipe network according to claim 10, wherein the step of performing mobile inspection by the mobile inspection equipment entering the field comprises the steps of;
when the mobile robot passes through a certain point of the inspection grid area, firstly, a laser sensor emits laser to a gas pipeline and the surrounding, receives reflection, analyzes the laser absorption spectrum, draws the concentration distribution of leakage gas on a laser path, and then compares the concentration distribution with a daily monitoring database and a concentration threshold value at the position;
In order to improve the accuracy of mobile inspection, the concentration threshold can be set to be 0 at the lowest, and meanwhile, in order to weaken the interference of environmental factors, such as methane concentration interference in the gas inlet and outlet areas of a gas station, the concentration threshold for inspection can be set to be of a special size according to the field environment and used for judging whether leakage points occur.
14. The leakage pre-warning method for a gas station pipe network according to claim 12, wherein the step of back calculation of the source item information is as follows:
assuming that gas leakage is detected for the first time, the robot moves to the downwind position of the station field pipe network for inspection, and the laser sensor collects n detection concentration values at different positions for useIndicating that the corresponding calculated concentration value is calculated by using the Gaussian plume diffusion model>Converting the inversion problem of the source item into the solution of the optimization problem of the objective function by using the intelligent optimization algorithm, wherein the objective function of the back calculation of the source item information is +.>Wherein: and (x, y, z) is the position coordinate of a leakage source of a gas station pipe network, Q is the source intensity of the leakage source, and n is the number of points of all detection during mobile inspection.
15. The leakage early warning method for the gas station pipe network according to claim 12, wherein the leakage source item information solving step based on the hybrid genetic gray wolf algorithm is as follows:
S1, executing a genetic algorithm, initializing a population and related source item parameters: determining a first generation GA population, wherein each single individual in the population comprises (Q, x, y, z), the source item parameters comprise the GAs leakage source intensity Q and the initial position (x, y, z), and an initialization state of multi-parameter back calculation in a three-dimensional space is formed;
s2, taking the sum of squares of the difference values of the detection value and the calculated value as an objective function, and taking the objective function of the sum of squaresThe inverse of the number is determined as the fitness function f, i.e
S3, substituting the initial group, meteorological data and the like into a Gaussian plume diffusion model to obtain the calculated concentration of each detection pointAnd is equal to the actual detection concentration->Comparing; determining fitness value of each individual in the population, and when the objective function reaches the minimum, the fitness f value reaches the maximum, wherein the smaller the objective function is, the calculated concentration is indicated>And detection concentration->The smaller the difference is, the higher the fitness is, and the larger the fitness value is in the GA algorithm, the larger the probability of being transmitted to the next generation is;
s4, sorting the fitness of the population, and carrying out genetic operation on individuals with good fitness, wherein the genetic operation comprises selection, crossing and variation;
s5, generating a new population through genetic operation, and realizing optimization of individuals in the GA population; repeating the steps to update the population until the iteration stopping standard is met, otherwise, returning to the step S3;
S6, finally generating N [ Q, x, y, z ] of a new population, and preferably selecting an output with a large fitness value from the final population as an optimal initial population of the gray wolf algorithm;
s7, executing a wolf algorithm, and determining position information and parameters a, A and C of a new wolf population;
s8, calculating individual fitness of the wolves, and storing the first three wolves alpha, beta and delta with the best fitness;
s9, updating the current position of the gray wolves, and calculating the adaptability of all the gray wolves;
s10, updating the fitness and the position of alpha, beta and delta;
s11, judging whether a termination condition is met, returning to S4 if the termination condition is not met, outputting an optimal solution [ Q, x, y, z ] of source item information back calculation if the termination condition is met, ending the back calculation process, and outputting a source intensity Q and a leakage point position coordinate (x, y, z).
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| CN202310471259.3A CN116642140A (en) | 2023-04-27 | 2023-04-27 | Monitoring and inspection system and leakage early warning method for gas station field pipe network |
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Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117037455A (en) * | 2023-10-09 | 2023-11-10 | 奥德集团有限公司 | Gas leakage alarm system and method based on artificial intelligence |
| CN117236651A (en) * | 2023-11-13 | 2023-12-15 | 天津市德安圣保安全卫生评价监测有限公司 | Comprehensive management method and system for safe production |
| CN117628417A (en) * | 2024-01-25 | 2024-03-01 | 深圳市晶湖科技有限公司 | Intelligent safety control system for gas field |
| CN117688775A (en) * | 2023-12-21 | 2024-03-12 | 上海叁零肆零科技有限公司 | Method, device, equipment and storage medium for generating leakage data of gas pipe network |
| CN117789421A (en) * | 2024-02-23 | 2024-03-29 | 济南鼎诺科技有限公司 | Combustible gas alarm control method and system based on CAN bus |
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| CN117037455A (en) * | 2023-10-09 | 2023-11-10 | 奥德集团有限公司 | Gas leakage alarm system and method based on artificial intelligence |
| CN117037455B (en) * | 2023-10-09 | 2024-01-09 | 奥德集团有限公司 | Gas leakage alarm system and method based on artificial intelligence |
| CN117236651A (en) * | 2023-11-13 | 2023-12-15 | 天津市德安圣保安全卫生评价监测有限公司 | Comprehensive management method and system for safe production |
| CN117236651B (en) * | 2023-11-13 | 2024-02-20 | 天津市德安圣保安全卫生评价监测有限公司 | Comprehensive management method and system for safe production |
| CN117688775A (en) * | 2023-12-21 | 2024-03-12 | 上海叁零肆零科技有限公司 | Method, device, equipment and storage medium for generating leakage data of gas pipe network |
| CN117628417A (en) * | 2024-01-25 | 2024-03-01 | 深圳市晶湖科技有限公司 | Intelligent safety control system for gas field |
| CN117628417B (en) * | 2024-01-25 | 2024-03-26 | 深圳市晶湖科技有限公司 | Intelligent safety control system for gas field |
| CN117789421A (en) * | 2024-02-23 | 2024-03-29 | 济南鼎诺科技有限公司 | Combustible gas alarm control method and system based on CAN bus |
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