Gas leakage source identification system and method based on multiple sensors and storage medium

Abstract

The invention discloses a gas leakage source identification system and method based on multiple sensors and a storage medium, and relates to the technical field of probability analysis. Detection data of the sensors in records and effective relative diffusion coefficients of soil are collected; constructing a gas diffusion model when the underground pipeline leaks by using the collected data; the optimal monitoring distance when the underground pipeline is monitored is calculated through the historical leakage opening length, and a monitoring point is made on the underground pipeline within the diffusion area through the optimal monitoring distance; calculating the dangerousness of each region in the diffusion area, determining a high-risk region according to the dangerousness, and placing a gas detection sensor in the high-risk region; aiming at each formulated monitoring point, inputting all data into a gas diffusion model to generate an adjoint equation of each gas detection sensor; the accompanying probability equation of each monitoring point is obtained, the accompanying probability is calculated, and the position of the underground pipeline gas leakage source is searched according to the accompanying probability.

Description

Technical Field

[0001] The present invention relates to the technical field of probability analysis, and in particular to a gas leakage source identification system, method and storage medium based on multiple sensors. Background Art

[0002] With the rapid economic development and rapid urban construction, the use of land resources has become very tight, and underground space, pipeline connections, and tunnel construction have become increasingly complex. In addition, they are complexly intertwined with urban gas pipelines, resulting in insufficient safety distances, which is a very serious problem. Urban gas pipelines are very important lifelines for the entire city. During the operation of natural gas pipelines, there are more or less natural aging corrosion or human damage factors, which lead to leakage accidents. In addition, urban gas pipelines are mainly distributed in densely populated areas, and the surrounding environment is very complex. Once leakage, explosion, poisoning and other accidents occur, it will cause serious casualties, property losses and environmental damage.

[0003] At present, the main method of detecting gas leaks in engineering is through manual detection. Workers are arranged to patrol and use some specific detectors to detect whether the gas concentration in a certain place exceeds the standard to determine whether a gas leak has occurred in the pipeline. Then, large-scale excavation is carried out within the scope of the pipeline to find the leakage point. This will result in a large amount of manpower, material resources, time and economic losses.

[0004] The current underground pipeline leakage tracing methods generally have the problem of insufficient quantitative calculation means. The traditional current underground pipeline leakage tracing methods generally have the problem of insufficient quantitative calculation means. The traditional method mainly relies on trial and error, which is inefficient and time-consuming. During the detection, the placement of the sensor is not accurate, and the efficiency is affected by trying to find the source of the leak through continuous measurement. This patent innovatively proposes a high-precision quantitative calculation method based on a small amount of measurement data, which greatly improves the tracing efficiency. Traditional complex formulas are applicable to above-ground convection conditions, but not to pure diffusion conditions underground. In view of the underground diffusion characteristics, we proposed a method to simplify the adjoint equation, which is specially designed for underground diffusion and basically maintains the original calculation accuracy. Summary of the invention

[0005] The object of the present invention is to provide a gas leakage source identification system, method and storage medium based on multiple sensors to solve the problems raised in the prior art.

[0006] To achieve the above object, the present invention provides the following technical solutions: A gas leakage source identification method based on multiple sensors, the method comprising the following steps: S100, collecting historical records of gas leakage in underground pipelines, extracting sensor positions when using sensors to measure leaked gas concentrations in the records, collecting sensor detection data and effective relative diffusion coefficients of soil in the records; and constructing a gas diffusion model when underground pipelines leak using the collected data; Furthermore, the specific steps of constructing a gas diffusion model when underground pipeline leaks using the collected data are as follows: S101. Collect the records of gas leakage in underground pipelines in history, extract the sensor position (x, y) when the sensor is used to measure the gas concentration in the records, and the position of the underground pipeline leakage source in the records is (x1, y1); use the position coordinates to calculate the relative distance between the sensor and the underground pipeline leakage source in the records, the formula is: ; In the formula, r represents the relative distance between the sensor and the underground pipeline leakage source. According to the soil type in the record, the effective relative diffusion coefficient D of the leaked gas in the soil is obtained by looking up in the soil management knowledge. m ; S102. Using the relative distance between the sensor and the underground pipeline leakage source and the effective relative diffusion coefficient, a gas diffusion model for underground pipeline leakage is constructed. The formula is: ; In the formula, C(r, t) represents the gas diffusion concentration in the soil after the underground pipeline gas leaks, q represents the detection data of the sensor in the historical records, erf represents the Gaussian error function, r represents the calculated relative distance between the sensor and the leakage source, and t represents the gas leakage time measured by the sensor in the historical records; D m It represents the effective relative diffusion coefficient of leaked gas in soil.

[0007] S200, collecting the length of the leakage port when gas leakage occurs in the underground pipeline in history, using the historical leakage port length to calculate the optimal monitoring distance for monitoring the underground pipeline, and when the underground pipeline leaks in real time, observing and measuring the diffusion area after the gas leakage, and setting monitoring points on the underground pipeline within the diffusion area using the optimal monitoring distance; Furthermore, the specific steps for establishing monitoring points using the optimal monitoring distance on underground pipelines within the diffusion area are as follows: S201. Collect the length of the pipeline leakage port when the gas leakage occurred in the underground pipeline in history. , It represents the length of the pipeline leakage port when the 1st, 2nd, 3rd, ..., nth gas leakage occurred in the collected history of underground pipelines, where n is a positive integer; the length of the pipeline leakage port represents the length of the leakage port in the longitudinal direction of the pipeline; the average value Lp of the leakage port length collected during the n historical underground pipeline gas leakages is calculated, and the optimal monitoring distance for monitoring the underground pipeline is calculated based on the average value, and the formula is: ; In the formula, Lz represents the calculated optimal monitoring distance for underground pipeline monitoring, Lp represents the average length of the leakage port collected during n historical underground pipeline gas leaks, and L max It represents the maximum value of the length of the leak hole in the n historical underground pipeline gas leaks collected, L min It represents the minimum value of the leakage length of n historical underground pipeline gas leakages collected; S202. When a gas leak occurs in the underground pipeline in real time, observe and measure the diffusion area after the gas leak, extract the pipeline length S in the gas diffusion area, set monitoring points according to the calculated optimal monitoring distance, and calculate the total number of monitoring points. The formula is: , M is rounded up; in the formula, M represents the number of monitoring points that need to be set when there is a real-time underground pipeline gas leak, Lz represents the optimal monitoring distance, and the monitoring points set in the real-time underground pipeline where a gas leak occurs are , It represents the 1st, 2nd, 3rd, ..., Mth monitoring points set up in the real-time underground pipeline where gas leakage occurs, and M is a positive integer.

[0008] The optimal monitoring distance is calculated based on the length of the leak when the underground pipeline has historically leaked gas. The monitoring points are set according to the optimal monitoring distance to ensure that all locations in the pipeline are monitored. This not only improves the detection speed without missing any detections, but also reduces false alarms caused by environmental interference or local gas concentration fluctuations. If the monitoring points are too dense, they may be misjudged as pipeline leaks due to slight local gas concentration changes, resulting in false alarms. If the distance between the monitoring points is too large, the real leakage signal may be missed, resulting in missed alarms.

[0009] S300, collecting the number of leakages, environmental data and pipeline data corresponding to the leakage source in the history of each area in the diffusion area of ​​the real-time underground pipeline gas leakage, calculating the danger of each area in the diffusion area, determining the high-risk area according to the danger, and placing gas detection sensors in the high-risk area; Furthermore, the specific steps for placing gas detection sensors in high-risk areas are: S301. Collect the number of leakage times, environmental data, and pipeline data that were leakage sources in the corresponding history for each area within the diffusion area during real-time pipeline gas leakage. Let the number of leakage times for each area be denoted as F. The collected different types of environmental data form a set A, and the collected different types of pipeline data form a set B. Calculate the linear functions of each type of environmental data in the environmental data set A and the number of leakage times, and each type of pipeline data in the pipeline data set B and the number of leakage times respectively according to the linear regression algorithm. Extract the independent variable coefficients of all linear functions as , representing the independent variable coefficients of the 1st, 2nd, 3rd,... u + v linear functions calculated from the two extracted sets, where u is the number of types of environmental data in set A and v is the number of types of pipeline data in set B; S302. Use the independent variable coefficients of each linear function as the weights of each type of environmental data and pipeline data, and calculate the risk of each area when a gas leakage occurs in the underground pipeline. The formula is: ; In the formula, Dan represents the calculated risk of each area, sum represents summation, F represents the number of times each area was a leakage source in history, a represents the weight of each type of environmental data and pipeline data, A represents the environmental data set, and B represents the pipeline data set; S303. Calculate the risk of each area when a gas leakage occurs in the underground pipeline. Calculate the average value of all risks as Dan_p. Use the average value to judge each risk. When Dan ≥ Dan_p, judge the corresponding area as a high-risk area. When Dan < Dan_p, judge the corresponding area as a low-risk area. Place gas detection sensors in the judged high-risk areas.

[0010] Judge the risk of each area within the diffusion area after a gas leakage occurs in the underground pipeline, search for high-risk areas, and place gas detection sensors in high-risk areas. Setting sensors in high-risk areas can detect gas leakage situations more quickly. Setting sensors in these areas can avoid monitoring blind spots caused by too large a distance between monitoring points. The sensors can more accurately monitor the location and degree of gas leakage; Concentrating the sensors in high-risk areas can avoid over-setting sensors in low-risk areas, thus saving sensor resources and related installation and maintenance costs.

[0011] S400. For each monitoring point formulated, calculate the distance between each placed gas detection sensor and the monitoring point, collect the detection data and leakage time of each gas detection sensor, and input all the data into the gas diffusion model to generate the adjoint equation of each gas detection sensor; Furthermore, the specific steps of inputting all data into the gas diffusion model to generate the adjoint equation for each gas detection sensor are: S401, for the designated monitoring point J b , use the underground pipeline as the horizontal coordinate and the vertical coordinate of the pipeline center point to draw the coordinate system, assume that all monitoring points are on the pipeline, and the coordinates are expressed as (Xg, 0); the coordinates of the gas detection sensor placed in the high-risk area are (Xc, Yc); use the distance formula in S101 to calculate the real-time distance between each placed gas detection sensor and the monitoring point as R; and extract the real-time time T of gas leakage in the underground pipeline and the detection data Q of each gas detection sensor; S402, input all collected real-time data into the gas diffusion model to generate the adjoint equation of each gas detection sensor, specifically: ; In the formula, represents the calculated accompanying concentration of the i-th gas detection sensor, Q i represents the detection data of the i-th gas detection sensor, R i Represents the real-time distance between the i-th sensor and the monitoring point; all gas detection sensors are calculated to obtain the distance between all gas detection sensors and monitoring point J b The adjoint equation of .

[0012] Determine the leak location of the leak source through gas diffusion. This overcomes the low efficiency of existing methods for determining the leak source, simplifies the calculation process and improves efficiency compared to existing methods, thereby reducing environmental pollution and the harm caused by harmful gases; S500. For each designated monitoring point, the adjoint equation of each sensor is integrated to obtain the adjoint probability equation of each monitoring point, and the adjoint probability of each monitoring point is calculated using the data of each gas detection sensor; the location of the gas leakage source in the underground pipeline is found according to the adjoint probability.

[0013] Furthermore, the specific steps for finding the location of the underground pipeline gas leakage source according to the accompanying probability are as follows: S501, for the designated monitoring point J b , the adjoint equation of each sensor is integrated to obtain the adjoint probability equation of each monitoring point, which is: ; In the formula, f b represents the calculated accompanying probability of the bth monitoring point, b is from 1 to M, and G represents the total number of all gas detection sensors placed; after the accompanying probabilities of all detection points are calculated, the monitoring point corresponding to the maximum accompanying probability is selected as the leakage source location.

[0014] A gas leakage source identification system based on multiple sensors, the gas leakage source identification system includes a data collection module, a gas diffusion model construction module, a monitoring point determination module, a sensor placement module, an adjoint equation calculation module and a leakage source search module; The data collection module is used to collect sensor locations and detection data when gas leaks occurred in historical underground pipelines; The gas diffusion model building module is used to extract the effective relative diffusion coefficient of the soil according to the sensor position and detection data when gas leakage occurred in the underground pipeline in history, and build a gas diffusion model; The monitoring point determination module is used to calculate the length of the leakage port when gas leakage occurred in the underground pipeline in history, obtain the optimal monitoring distance, and set the monitoring point; The sensor placement module is used to calculate the danger of each area when a gas leak occurs in the underground pipeline, determine the high-risk area, and place a gas detection sensor in the high-risk area; The adjoint equation calculation module is used to input the data of the gas detection sensors placed in real time into the gas diffusion model to generate the adjoint equation of each gas detection sensor; The leakage source search module is used to integrate the adjoint equation of each sensor to obtain the adjoint probability equation of each monitoring point, calculate the adjoint probability of each monitoring point; and find the location of the gas leakage source in the underground pipeline according to the adjoint probability.

[0015] The monitoring point determination module includes an optimal monitoring distance calculation unit and a monitoring point determination unit; The optimal monitoring distance calculation unit is used to calculate the length of the leakage port when gas leakage occurred in the underground pipeline in the past, so as to obtain the optimal monitoring distance; The monitoring point determination unit is used to collect the diffusion area when gas leakage occurs in the underground pipeline, obtain the pipeline length within the diffusion area, and set the monitoring point according to the optimal monitoring distance.

[0016] The leakage source search module includes an accompanying probability calculation unit and a leakage source location search unit; The adjoint probability calculation unit is used to integrate the adjoint equation of each sensor to obtain the adjoint probability equation of each monitoring point, and calculate the adjoint probability using the adjoint probability equation; The leakage source search unit is used to compare the calculated accompanying probability of each monitoring point, and select the monitoring point with the largest accompanying probability as the leakage source position.

[0017] A computer-readable storage medium stores a computer program, wherein the computer program is used to execute the method in the above steps.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention calculates the optimal monitoring distance according to the length of the leakage port when gas leakage occurs in the underground pipeline in history, and sets the monitoring points according to the optimal monitoring distance, thereby ensuring that all positions in the pipeline are monitored. This not only improves the detection speed without causing detection omissions, but also reduces false alarms caused by environmental interference or local gas concentration fluctuations.

[0019] 2. The present invention places gas detection sensors in high-risk areas. Setting sensors in high-risk areas can detect gas leakage more quickly. Setting sensors in these areas can avoid monitoring blind spots caused by excessive spacing between monitoring points.

[0020] 3. The present invention determines the leakage location of the leakage source through gas diffusion, thereby overcoming the problem of low efficiency of the existing methods for determining the leakage source, simplifying the calculation process and improving efficiency compared with the existing methods, thereby reducing environmental pollution and the harm caused by harmful gases. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a module distribution diagram of the gas leakage source identification system based on multiple sensors of the present invention; Figure 2 A schematic diagram of the steps of the gas leakage source identification method based on multiple sensors of the present invention; Figure 3 It is a monitoring point accompanying probability diagram of the gas leakage source identification method based on multiple sensors of the present invention. DETAILED DESCRIPTION

[0022] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0023] Example: Figure 1-Figure 3 As shown, the present invention provides a technical solution. A gas leakage source identification method based on multiple sensors, the method comprising the following steps: S100, collecting historical records of gas leakage in underground pipelines, extracting sensor positions when using sensors to measure leaked gas concentrations in the records, collecting sensor detection data and effective relative diffusion coefficients of soil in the records; and constructing a gas diffusion model when underground pipelines leak using the collected data; The specific steps of using the collected data to construct a gas diffusion model for underground pipeline leakage are: S101. Collect the records of gas leakage in underground pipelines in history, extract the sensor position (x, y) when the sensor is used to measure the gas concentration in the records, and the position of the underground pipeline leakage source in the records is (x1, y1); use the position coordinates to calculate the relative distance between the sensor and the underground pipeline leakage source in the records, the formula is: ; In the formula, r represents the relative distance between the sensor and the underground pipeline leakage source. According to the soil type in the record, the effective relative diffusion coefficient D of the leaked gas in the soil is obtained by looking up in the soil management knowledge. m ; S102. Using the relative distance between the sensor and the underground pipeline leakage source and the effective relative diffusion coefficient, a gas diffusion model for underground pipeline leakage is constructed. The formula is: ; In the formula, C(r, t) represents the gas diffusion concentration in the soil after the underground pipeline gas leaks, q represents the detection data of the sensor in the historical records, erf represents the Gaussian error function, r represents the calculated relative distance between the sensor and the leakage source, and t represents the gas leakage time measured by the sensor in the historical records; D m It represents the effective relative diffusion coefficient of leaked gas in soil.

[0024] S200, collecting the length of the leakage port when gas leakage occurs in the underground pipeline in history, using the historical leakage port length to calculate the optimal monitoring distance for monitoring the underground pipeline, and when the underground pipeline leaks in real time, observing and measuring the diffusion area after the gas leakage, and setting monitoring points on the underground pipeline within the diffusion area using the optimal monitoring distance; The specific steps for establishing monitoring points using the optimal monitoring distance on underground pipelines within the diffusion area are: S201. Collect the length of the pipeline leakage port when the gas leakage occurred in the underground pipeline in history. , It represents the length of the pipeline leakage port when the 1st, 2nd, 3rd, ..., nth gas leakage occurred in the collected history of underground pipelines, where n is a positive integer; the length of the pipeline leakage port represents the length of the leakage port in the longitudinal direction of the pipeline; the average value Lp of the leakage port length collected during the n historical underground pipeline gas leakages is calculated, and the optimal monitoring distance for monitoring the underground pipeline is calculated based on the average value, and the formula is: ; In the formula, Lz represents the calculated optimal monitoring distance for underground pipeline monitoring, Lp represents the average length of the leakage port collected during n historical underground pipeline gas leaks, and L max It represents the maximum value of the length of the leak hole in the n historical underground pipeline gas leaks collected, Lmin It represents the minimum value of the leakage length of n historical underground pipeline gas leakages collected; S202. When a gas leak occurs in the underground pipeline in real time, observe and measure the diffusion area after the gas leak, extract the pipeline length S in the gas diffusion area, set monitoring points according to the calculated optimal monitoring distance, and calculate the total number of monitoring points. The formula is: , M is rounded up; in the formula, M represents the number of monitoring points that need to be set when there is a real-time underground pipeline gas leak, Lz represents the optimal monitoring distance, and the monitoring points set in the real-time underground pipeline where a gas leak occurs are , It represents the 1st, 2nd, 3rd, ..., Mth monitoring points set up in the real-time underground pipeline where gas leakage occurs, and M is a positive integer.

[0025] The optimal monitoring distance is calculated based on the length of the leak when the underground pipeline has historically leaked gas. The monitoring points are set according to the optimal monitoring distance to ensure that all locations in the pipeline are monitored. This not only improves the detection speed without missing any detections, but also reduces false alarms caused by environmental interference or local gas concentration fluctuations. If the monitoring points are too dense, they may be misjudged as pipeline leaks due to slight local gas concentration changes, resulting in false alarms. If the distance between the monitoring points is too large, the real leakage signal may be missed, resulting in missed alarms.

[0026] S300, collecting the number of leakages, environmental data and pipeline data corresponding to the leakage source in the history of each area in the diffusion area of ​​the real-time underground pipeline gas leakage, calculating the danger of each area in the diffusion area, determining the high-risk area according to the danger, and placing gas detection sensors in the high-risk area; The specific steps for placing gas detection sensors in high-risk areas are: S301, collect the number of leakages, environmental data and pipeline data corresponding to the leakage source in the history of each area within the diffusion area of ​​the real-time pipeline gas leakage; let the number of leakages in each area be represented by F, and the collected different types of environmental data constitute a set A, and the collected different types of pipeline data constitute a set B; according to the linear regression algorithm, calculate the linear function of each type of environmental data and the number of leakages in the environmental data set A, and each type of pipeline data and the number of leakages in the pipeline data set B, and extract the independent variable coefficients of all linear functions as follows: , represents the independent variable coefficients of the 1st, 2nd, 3rd, ...u+vth linear functions calculated on the two extracted sets, where u is the number of environmental data types in set A, and v is the number of pipeline data types in set B; S302. Use the independent variable coefficients of each extracted linear function as the weights of each environmental data and pipeline data, and calculate the risk of each area when a gas leak occurs in the underground pipeline. The formula is: ; In the formula, Dan represents the risk of each calculated area, sum represents summation, F represents the number of times each area has been a leakage source in history, a represents the weights of each environmental data and pipeline data, A represents the set of environmental data, and B represents the set of pipeline data; S303. Calculate the risk of each area when a gas leak occurs in the underground pipeline. Calculate the average value of all risks as Dan_p, and use the average value to judge each risk. When Dan ≥ Dan_p, judge the corresponding area as a high-risk area; when Dan < Dan_p, judge the corresponding area as a low-risk area. Place gas detection sensors in the judged high-risk areas.

[0027] Judge the risk of each area within the diffusion area after a gas leak occurs in the underground pipeline, find high-risk areas, and place gas detection sensors in high-risk areas. Setting sensors in high-risk areas can detect gas leak situations more quickly. Setting sensors in these areas can avoid monitoring blind spots caused by too large a distance between monitoring points. The sensors can monitor the location and degree of gas leaks more accurately. Concentrating the sensors in high-risk areas can avoid over-setting sensors in low-risk areas, thus saving sensor resources and related installation and maintenance costs.

[0028] S400. For each established monitoring point, calculate the distance between each placed gas detection sensor and the monitoring point, collect the detection data and leakage time of each gas detection sensor, and input all the data into the gas diffusion model to generate the adjoint equation of each gas detection sensor; The specific steps of inputting all the data into the gas diffusion model to generate the adjoint equation of each gas detection sensor are as follows: S401. For the established monitoring point J b , take the underground pipeline as the abscissa and set the ordinate at the center point of the pipeline to draw a coordinate system. Assume that all monitoring points are on the pipeline, and the coordinates are expressed as (Xg, 0); the coordinates of the gas detection sensors placed in the high-risk area are (Xc, Yc); use the distance formula in S101 to calculate the real-time distance R between each placed gas detection sensor and the monitoring point; and extract the real-time time T when a gas leak occurs in the underground pipeline and the detection data Q of each gas detection sensor. S402. Input all the collected real-time data into the gas diffusion model to generate the adjoint equation of each gas detection sensor, specifically: ; In the formula, represents the calculated accompanying concentration of the i-th gas detection sensor, Q i represents the detection data of the i-th gas detection sensor, R i Represents the real-time distance between the i-th sensor and the monitoring point; all gas detection sensors are calculated to obtain the distance between all gas detection sensors and monitoring point J b The adjoint equation of .

[0029] Determine the leak location of the leak source through gas diffusion. This overcomes the low efficiency of existing methods for determining the leak source, simplifies the calculation process and improves efficiency compared to existing methods, thereby reducing environmental pollution and the harm caused by harmful gases; S500. For each designated monitoring point, the adjoint equation of each sensor is integrated to obtain the adjoint probability equation of each monitoring point, and the adjoint probability of each monitoring point is calculated using the data of each gas detection sensor; the location of the gas leakage source in the underground pipeline is found according to the adjoint probability.

[0030] The specific steps to find the location of the underground pipeline gas leakage source according to the accompanying probability are: S501, for the designated monitoring point J b , the adjoint equation of each sensor is integrated to obtain the adjoint probability equation of each monitoring point, which is: ; In the formula, f b represents the calculated accompanying probability of the bth monitoring point, b is from 1 to M, and G represents the total number of all gas detection sensors placed; after the accompanying probabilities of all detection points are calculated, the monitoring point corresponding to the maximum accompanying probability is selected as the leakage source location.

[0031] A gas leakage source identification system based on multiple sensors, the gas leakage source identification system includes a data collection module, a gas diffusion model construction module, a monitoring point determination module, a sensor placement module, an adjoint equation calculation module and a leakage source search module; The data collection module is used to collect sensor locations and detection data when gas leaks occurred in historical underground pipelines; The gas diffusion model building module is used to extract the effective relative diffusion coefficient of the soil according to the sensor position and detection data when gas leakage occurred in the underground pipeline in history, and build a gas diffusion model; The monitoring point determination module is used to calculate the length of the leakage port when gas leakage occurred in the underground pipeline in history, obtain the optimal monitoring distance, and set the monitoring point; The sensor placement module is used to calculate the danger of each area when a gas leak occurs in the underground pipeline, determine the high-risk area, and place a gas detection sensor in the high-risk area; The adjoint equation calculation module is used to input the data of the gas detection sensors placed in real time into the gas diffusion model to generate the adjoint equation of each gas detection sensor; The leakage source search module is used to integrate the adjoint equation of each sensor to obtain the adjoint probability equation of each monitoring point, calculate the adjoint probability of each monitoring point; and find the location of the gas leakage source in the underground pipeline according to the adjoint probability.

[0032] The monitoring point determination module includes an optimal monitoring distance calculation unit and a monitoring point determination unit; The optimal monitoring distance calculation unit is used to calculate the length of the leakage port when gas leakage occurred in the underground pipeline in the past, so as to obtain the optimal monitoring distance; The monitoring point determination unit is used to collect the diffusion area when gas leakage occurs in the underground pipeline, obtain the pipeline length within the diffusion area, and set the monitoring point according to the optimal monitoring distance.

[0033] The leakage source search module includes an accompanying probability calculation unit and a leakage source location search unit; The adjoint probability calculation unit is used to integrate the adjoint equation of each sensor to obtain the adjoint probability equation of each monitoring point, and calculate the adjoint probability using the adjoint probability equation; The leakage source search unit is used to compare the calculated accompanying probability of each monitoring point, and select the monitoring point with the largest accompanying probability as the leakage source position.

[0034] A computer-readable storage medium stores a computer program, wherein the computer program is used to execute the method in the above steps.

[0035] Example: According to the data provided by the local land bureau, the effective relative diffusion coefficient of gas in soil is D m =5.2×10-6m2 / s; Select a monitoring point with coordinates (0, 0) and a constant source strength of 1 kg / s. The number of selected sensors is 2, named I1 and I2 respectively; set the coordinates of sensor I1 to (-2, 5), and set the coordinates of sensor 2 to (7, 5). It is measured that the gas leak reaches stability after 20s, and the concentrations detected by the two sensors are 100ppm and 130ppm respectively; Substitute the set area coordinates, the concentration recorded by the sensor, the time of gas leakage and other data into the gas diffusion model to obtain the adjoint equations of the two sensors respectively; The adjoint equation is integrated to obtain the adjoint probability equation, and then the experimental data information detected by sensors S1 and S2 is substituted into the adjoint probability of multi-sensor combination: The calculated accompanying probability of the monitoring point (0, 0) is 0, and then the remaining monitoring points are calculated to obtain the maximum accompanying probability position at (7, 0), and the location of the leakage source is determined to be (7, 0).

[0036] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A gas leakage source identification method based on multiple sensors, characterized in that: The method comprises the following steps: S100, collecting historical records of gas leakage in underground pipelines, extracting sensor positions when using sensors to measure leaked gas concentrations in the records, collecting sensor detection data and effective relative diffusion coefficients of soil in the records; and constructing a gas diffusion model when underground pipelines leak using the collected data; S200, collecting the length of the leakage port when gas leakage occurs in the underground pipeline in history, using the historical leakage port length to calculate the optimal monitoring distance for monitoring the underground pipeline, and when the underground pipeline leaks in real time, observing and measuring the diffusion area after the gas leakage, and setting monitoring points on the underground pipeline within the diffusion area using the optimal monitoring distance; S300, collecting the number of leakages, environmental data and pipeline data corresponding to the leakage source in the history of each area in the diffusion area of ​​the real-time underground pipeline gas leakage, calculating the danger of each area in the diffusion area, determining the high-risk area according to the danger, and placing gas detection sensors in the high-risk area; S400, for each monitoring point, calculate the distance between each gas detection sensor and the monitoring point, collect the detection data and leakage time of each gas detection sensor, and input all the data into the gas diffusion model to generate the adjoint equation of each gas detection sensor; S500. For each designated monitoring point, the adjoint equation of each sensor is integrated to obtain the adjoint probability equation of each monitoring point, and the adjoint probability of each monitoring point is calculated using the data of each gas detection sensor; the location of the gas leakage source in the underground pipeline is found according to the adjoint probability.

2. The multi-sensor based gas leakage source identification method according to claim 1, characterized in that: The specific steps of constructing a gas diffusion model when underground pipeline leaks using the collected data in S100 are: S101. Collect the records of gas leakage in underground pipelines in history, extract the sensor position (x, y) when the sensor is used to measure the gas concentration in the records, and the position of the underground pipeline leakage source in the records is (x1, y1); use the position coordinates to calculate the relative distance between the sensor and the underground pipeline leakage source in the records, the formula is: ； In the formula, r represents the relative distance between the sensor and the underground pipeline leakage source. According to the soil type in the record, the effective relative diffusion coefficient D of the leaked gas in the soil is obtained by looking up in the soil management knowledge. m ; S102. Using the relative distance between the sensor and the underground pipeline leakage source and the effective relative diffusion coefficient, a gas diffusion model for underground pipeline leakage is constructed. The formula is: ； In the formula, C(r, t) represents the gas diffusion concentration in the soil after the underground pipeline gas leaks, q represents the detection data of the sensor in the historical records, erf represents the Gaussian error function, r represents the calculated relative distance between the sensor and the leakage source, and t represents the gas leakage time measured by the sensor in the historical records; D m It represents the effective relative diffusion coefficient of leaked gas in soil.

3. The multi-sensor based gas leakage source identification method according to claim 1, characterized in that: The specific steps of setting monitoring points on the underground pipeline within the diffusion area using the optimal monitoring distance in S200 are: S201. Collect the length of the pipeline leakage port when the gas leakage occurred in the underground pipeline in history. , It represents the length of the pipeline leakage port when the 1st, 2nd, 3rd, ..., nth gas leakage occurred in the collected history of underground pipelines, where n is a positive integer; the length of the pipeline leakage port represents the length of the leakage port in the longitudinal direction of the pipeline; the average value Lp of the leakage port length collected during the n historical underground pipeline gas leakages is calculated, and the optimal monitoring distance for monitoring the underground pipeline is calculated based on the average value, and the formula is: ； In the formula, Lz represents the calculated optimal monitoring distance for underground pipeline monitoring, Lp represents the average length of the leakage port collected during n historical underground pipeline gas leaks, and L max It represents the maximum value of the length of the leak hole in the n historical underground pipeline gas leaks collected, L min It represents the minimum value of the leakage length of n historical underground pipeline gas leakages collected; S202. When a gas leak occurs in the underground pipeline in real time, observe and measure the diffusion area after the gas leak, extract the pipeline length S in the gas diffusion area, set monitoring points according to the calculated optimal monitoring distance, and calculate the total number of monitoring points. The formula is: , M is rounded up; in the formula, M represents the number of monitoring points that need to be set when there is a real-time underground pipeline gas leak, Lz represents the optimal monitoring distance, and the monitoring points set in the real-time underground pipeline where a gas leak occurs are , It represents the 1st, 2nd, 3rd, ..., Mth monitoring points set up in the real-time underground pipeline where gas leakage occurs, and M is a positive integer.

4. The multi-sensor based gas leakage source identification method according to claim 1, characterized in that: The specific steps of placing gas detection sensors in high-risk areas in S300 are: S301, collect the number of leakages, environmental data and pipeline data corresponding to the leakage source in the history of each area within the diffusion area of ​​the real-time underground pipeline gas leakage; let the number of leakages in each area be represented by F, and the different types of environmental data collected constitute a set A, and the different types of pipeline data collected constitute a set B; according to the linear regression algorithm, calculate the linear functions of each type of environmental data and the number of leakages in the environmental data set A, and each type of pipeline data and the number of leakages in the pipeline data set B, and extract the independent variable coefficients of all linear functions as follows: , represents the independent variable coefficients of the 1st, 2nd, 3rd, ...u+vth linear functions calculated on the two extracted sets, where u is the number of environmental data types in set A, and v is the number of pipeline data types in set B; S302, using the independent variable coefficient of each extracted linear function as the weight of each environmental data and pipeline data, the danger of each area when a gas leak occurs in the underground pipeline is calculated, and the formula is: ； In the formula, Dan represents the calculated danger of each area, sum represents the sum, F represents the number of times each area has been a leakage source in history, a represents the weight of each environmental data and pipeline data, A represents the environmental data set, and B represents the pipeline data set; S303. Calculate the risk level of each area when a gas leak occurs in the underground pipeline. Calculate the average value of all the risk levels as Dan_p. Use the average value to judge each risk level. When Dan ≥ Dan_p, judge the corresponding area as a high-risk area; when Dan < Dan_p, judge the corresponding area as a low-risk area. Place gas detection sensors in the judged high-risk areas.

5. The multi-sensor based gas leakage source identification method according to claim 2, characterized in that: The specific steps of generating the adjoint equation of each gas detection sensor by inputting all the data into the gas diffusion model in S400 are as follows: S401, for the designated monitoring point J b , use the underground pipeline as the horizontal coordinate and the vertical coordinate of the pipeline center point to draw the coordinate system, assume that all monitoring points are on the pipeline, and the coordinates are expressed as (Xg, 0); the coordinates of the gas detection sensor placed in the high-risk area are (Xc, Yc); use the distance formula in S101 to calculate the real-time distance between each placed gas detection sensor and the monitoring point as R; and extract the real-time time T of gas leakage in the underground pipeline and the detection data Q of each gas detection sensor; S402. Input all the collected real-time data into the gas diffusion model to generate the adjoint equation of each gas detection sensor, specifically: ； In the formula, represents the calculated accompanying concentration of the i-th gas detection sensor, Q i represents the detection data of the i-th gas detection sensor, R i Represents the real-time distance between the i-th sensor and the monitoring point; all gas detection sensors are calculated to obtain the distance between all gas detection sensors and monitoring point J b The adjoint equation of .

6. The multi-sensor based gas leakage source identification method according to claim 5, characterized in that: The specific steps of finding the location of the gas leak source in the underground pipeline according to the adjoint probability in S500 are as follows: S501, for the designated monitoring point J b , the adjoint equation of each sensor is integrated to obtain the adjoint probability equation of each monitoring point, which is: ； In the formula, f b represents the calculated accompanying probability of the bth monitoring point, b is from 1 to M, and G represents the total number of all gas detection sensors placed; after the accompanying probabilities of all detection points are calculated, the monitoring point corresponding to the maximum accompanying probability is selected as the leakage source location.

7. A multi-sensor based gas leak source identification system, characterized by: The gas leak source identification system includes a data collection module, a gas diffusion model construction module, a monitoring point determination module, a sensor placement module, an adjoint equation calculation module, and a leak source search module. The data collection module is used to collect the sensor locations and detection data when gas leaks occurred in historical underground pipelines. The gas diffusion model construction module is used to extract the effective relative diffusion coefficient of the soil according to the sensor locations and detection data when gas leaks occurred in historical underground pipelines, and construct a gas diffusion model. The monitoring point determination module is used to calculate the length of the leak opening when gas leaks occurred in historical underground pipelines to obtain the optimal monitoring distance, and set the monitoring points. The sensor placement module is used to calculate the risk level of each area when a gas leak occurs in the underground pipeline, judge the high-risk areas, and place gas detection sensors in the high-risk areas. The adjoint equation calculation module is used to input the data of the gas detection sensors placed in real time into the gas diffusion model to generate the adjoint equation of each gas detection sensor. The leak source search module is used to integrate the adjoint equations of each sensor to obtain the adjoint probability equation of each monitoring point, and calculate the adjoint probability of each monitoring point; find the location of the gas leak source in the underground pipeline according to the adjoint probability.

8. The multi-sensor based gas leakage source identification system according to claim 7, characterized in that: The monitoring point determination module includes an optimal monitoring distance calculation unit and a monitoring point determination unit. The optimal monitoring distance calculation unit is used to calculate the length of the leak opening when gas leaks occurred in historical underground pipelines to obtain the optimal monitoring distance. The monitoring point determination unit is used to collect the diffusion area when a gas leak occurs in the underground pipeline, obtain the pipeline length within the diffusion area, and set the monitoring points according to the optimal monitoring distance.

9. The multi-sensor based gas leakage source identification system according to claim 7, characterized in that: The leak source search module includes an adjoint probability calculation unit and a leak source location search unit. The adjoint probability calculation unit is used to integrate the adjoint equations of each sensor to obtain the adjoint probability equation of each monitoring point, and calculate the adjoint probability using the adjoint probability equation. The leak source location search unit is used to compare the adjoint probabilities of each calculated monitoring point, and select the monitoring point with the maximum adjoint probability as the leak source location.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and the computer program is used to execute the method described in any one of claims 1 to 6 above.

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