Gas leakage accurate positioning method based on vehicle-mounted platform

Abstract

The invention discloses a gas leakage accurate positioning method based on a vehicle-mounted platform, and relates to the technical field of gas leakage positioning. According to the gas leakage accurate positioning method based on the vehicle-mounted platform, leakage prior distribution of a to-be-evaluated pipe network is obtained through analysis by obtaining leakage evaluation data; continuously acquiring observed fuel gas concentration values of the vehicle-mounted platform at a plurality of path position points on the preset inspection path, and judging whether the observed fuel gas concentration values are higher than a preset fuel gas concentration threshold value or not; if the gas concentration is higher than the preset gas concentration threshold value, correspondingly marking the gas concentration as a leakage diffusion point, obtaining diffusion two-dimensional position coordinates and diffusion smoke plume data, constructing a leakage likelihood function, and performing recursive update processing in combination with leakage prior distribution to obtain leakage posterior distribution; according to the method, the two-dimensional position coordinate and the corresponding leakage intensity value of each leakage source are obtained by comprehensively analyzing the leakage posterior distribution, so that the leakage sources can be efficiently identified, and the safety and the management efficiency of the gas pipe network are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of gas leakage location, and particularly to a precise gas leakage location method based on a vehicle-mounted platform. Background Art

[0002] Gas is an important energy source for urban residents and industrial enterprises. However, due to its flammable and explosive characteristics, gas leakage may trigger serious safety accidents, threatening people's lives and property. Therefore, gas leakage inspection has become an important means to ensure gas safety. Traditional leakage detection technologies mainly rely on manual operation with a methane detector in hand, which is time-consuming and laborious. Modern advanced gas inspection technologies use vehicle-mounted mobile platforms equipped with high-precision gas analyzers, which can collect gas concentration data and geographical location data in real time, and perform data processing and analysis through intelligent algorithms to achieve precise location of gas leakage points, gradually becoming the main means of gas inspection. However, in the existing technology, it is difficult to cover the entire pipeline network, and it is easy to miss areas far from the conventional path. Moreover, the leakage location accuracy is relatively low, making it difficult to accurately determine the location of the leakage source, which is prone to false alarms or missed alarms.

[0003] Based on the above solutions, it is found that the limitations of the existing technology at least include the following problems. It is difficult for the existing technology to accurately locate the leakage source. For example, in the gas pipeline network of a city, if a leakage occurs, it is often difficult for the existing technology to accurately locate the specific location of the leakage, resulting in delays in repair work and increased safety risks. Secondly, the existing technology has poor adaptability to complex environments, especially lacking real-time and dynamic updates in changing weather or underground complex environments. For example, in an environment where factors such as wind speed and temperature are constantly changing, it is difficult for the existing technology to quickly update the leakage risk assessment, resulting in the possibility that the leakage may not be identified in time, increasing potential safety hazards. Summary of the Invention

[0004] Aiming at the deficiencies of the existing technology, the present invention provides a precise gas leakage location method based on a vehicle-mounted platform, which solves the problems that the existing technology is difficult to accurately locate the source, adapt to complex environments and lacks real-time updates in gas leakage detection, thus increasing safety risks.

[0005] To achieve the above objectives, the present invention is realized through the following technical solutions: A precise gas leakage positioning method based on a vehicle-mounted platform, comprising the following steps: obtaining leakage assessment data of several regions of the pipeline network to be evaluated, and performing predictive analysis to obtain the prior leakage distribution of the pipeline network to be evaluated; continuously obtaining the observed gas concentration values at several path position points on the preset inspection path of the vehicle-mounted platform, and determining whether they are higher than the preset gas concentration threshold; if the observed gas concentration values at each path position point along the preset inspection path of the vehicle-mounted platform are higher than the preset gas concentration threshold, they are correspondingly marked as leakage diffusion points, obtaining the diffusion two-dimensional position coordinates and diffusion plume data, constructing the leakage likelihood function between each leakage diffusion point of the vehicle-mounted platform and the corresponding set leakage region of the pipeline network to be evaluated, and performing recursive update processing in combination with the prior leakage distribution to obtain the posterior leakage distribution of the pipeline network to be evaluated; if the observed gas concentration values at each path position point on the preset inspection path of the vehicle-mounted platform are not higher than the preset gas concentration threshold, they are not marked; comprehensively analyzing the posterior leakage distribution of the pipeline network to be evaluated to obtain the two-dimensional position coordinates of each leakage source of the pipeline network to be evaluated and the corresponding leakage intensity values.

[0006] Further, the leakage assessment data includes the material corrosion rate value, service life value, leakage frequency value, node density value, and burial depth value. The specific steps for obtaining the prior leakage distribution of the pipeline network to be evaluated are as follows: normalizing the material corrosion rate value, service life value, leakage frequency value, node density value, and burial depth value of each region of the pipeline network to be evaluated; comprehensively analyzing the normalized material corrosion rate value, service life value, leakage frequency value, node density value, and burial depth value of each region of the pipeline network to be evaluated to obtain the gas leakage risk index of each region of the pipeline network to be evaluated, and performing comprehensive analysis to obtain the initial leakage probability value and leakage standard deviation of each region of the pipeline network to be evaluated; obtaining the two-dimensional position coordinates of each pipeline position point in each region of the pipeline network to be evaluated, and comprehensively analyzing in combination with the initial leakage probability value of each region to obtain the prior leakage distribution of the pipeline network to be evaluated.

[0007] Further, the specific expression of the prior leakage distribution of the pipeline network to be evaluated is as follows: ; where is the prior leakage distribution of the pipeline network to be evaluated, is the initial leakage probability value of the th region of the pipeline network to be evaluated, is the leakage standard deviation of the th region of the pipeline network to be evaluated, is pi, is the two-dimensional position coordinate of the th pipeline position point in the th region of the pipeline network to be evaluated, ​ is the number of regions, , is the number of pipeline location points.

[0008] Furthermore, the diffusion plume data includes wind speed value, wind direction angle value, diffusion occlusion factor, and atmospheric stability index. The specific steps for constructing the leakage likelihood function between each leakage diffusion point of the vehicle-mounted platform and each pipeline location point of the corresponding set leakage area of the pipeline network to be evaluated are as follows: Perform correction analysis on the diffusion two-dimensional position coordinates, wind direction angle value of each leakage diffusion point of the vehicle-mounted platform, and the two-dimensional coordinates of each pipeline location point of the corresponding set leakage area of the pipeline network to be evaluated, to obtain the corrected diffusion two-dimensional position coordinates of each leakage diffusion point of the vehicle-mounted platform and the corrected two-dimensional position coordinates of each pipeline location point of the corresponding set leakage area of the pipeline network to be evaluated; perform comprehensive analysis on the corrected diffusion two-dimensional position coordinates, diffusion plume data of each leakage diffusion point of the vehicle-mounted platform, and the corrected two-dimensional position coordinates of each pipeline location point of the corresponding set leakage area of the pipeline network to be evaluated, to obtain the horizontal diffusion coefficient and vertical diffusion coefficient between each leakage diffusion point of the vehicle-mounted platform and each pipeline location point of the corresponding set leakage area of the pipeline network to be evaluated. Based on the Gaussian diffusion model, perform comprehensive analysis to obtain the predicted concentration value between each leakage diffusion point of the vehicle-mounted platform and each pipeline location point of the corresponding set leakage area of the pipeline network to be evaluated; and perform comprehensive analysis on the observed gas concentration value of each leakage diffusion point of the vehicle-mounted platform and the predicted concentration value of each pipeline location point of the corresponding set leakage area of the pipeline network to be evaluated, to obtain the concentration error standard value between each leakage diffusion point of the vehicle-mounted platform and each pipeline location point of the corresponding set leakage area of the pipeline network to be evaluated, and construct the leakage likelihood function between each leakage diffusion point of the vehicle-mounted platform and each pipeline location point of the corresponding set leakage area of the pipeline network to be evaluated.

[0009] Furthermore, the specific steps for obtaining the horizontal diffusion coefficient and vertical diffusion coefficient between each leakage diffusion point of the vehicle-mounted platform and each pipeline location point of the corresponding set leakage area of the pipeline network to be evaluated are as follows: Obtain the standard deviation of the horizontal turbulent velocity difference and the standard deviation of the vertical turbulent velocity difference between each leakage diffusion point of the vehicle-mounted platform and each pipeline location point of the corresponding set leakage area of the pipeline network to be evaluated; read the wind speed value, diffusion occlusion factor, atmospheric stability index, and the horizontal axis value and vertical axis value of the corrected diffusion two-dimensional position coordinates of each leakage diffusion point of the vehicle-mounted platform, and the horizontal axis value and vertical axis value of the corrected two-dimensional position coordinates of each pipeline location point of the corresponding area of the pipeline network to be evaluated, and perform comprehensive analysis in combination with the standard deviation of the horizontal turbulent velocity difference and the standard deviation of the vertical turbulent velocity difference respectively, to obtain the horizontal diffusion coefficient and vertical diffusion coefficient between each leakage diffusion point of the vehicle-mounted platform and each pipeline location point of the corresponding set leakage area of the pipeline network to be evaluated.

[0010] Further, the specific formula for calculating the lateral diffusion coefficient between each leakage diffusion point of the vehicle-mounted platform and each pipeline position point in the corresponding area of the pipeline network to be evaluated is as follows: ; where is the lateral diffusion coefficient between the -th leakage diffusion point of the vehicle-mounted platform and the -th pipeline position point in the corresponding area of the pipeline network to be evaluated, is the standard deviation value of the lateral turbulent velocity between the -th leakage diffusion point of the vehicle-mounted platform and the -th pipeline position point in the corresponding area of the pipeline network to be evaluated, is the horizontal axis value in the two-dimensional diffusion position coordinates of the -th leakage diffusion point of the vehicle-mounted platform, is the horizontal axis value in the two-dimensional position coordinates of the -th leakage diffusion point of the vehicle-mounted platform and the -th pipeline position point in the corresponding area of the pipeline network to be evaluated, is the wind speed value of the -th leakage diffusion point of the vehicle-mounted platform, is the initial adjustment coefficient stored in the database, is the atmospheric stability index of the -th leakage diffusion point of the vehicle-mounted platform, is the atmospheric stability adjustment coefficient stored in the database, is the diffusion occlusion factor of the -th leakage diffusion point of the vehicle-mounted platform, is the diffusion occlusion adjustment coefficient stored in the database, is the collaborative adjustment coefficient stored in the database.

[0011] Further, the specific steps for obtaining the diffusion occlusion factor of each leakage diffusion point of the vehicle-mounted platform are as follows: Obtain the diffusion occlusion image data of each leakage diffusion point of the vehicle-mounted platform, where the diffusion occlusion image data includes the pixel value, two-dimensional coordinates, and corresponding depth information value of each pixel point in the diffusion occlusion image; Input the diffusion occlusion image data of each leakage diffusion point of the vehicle-mounted platform into a pre-trained occlusion recognition model for prediction and analysis to obtain the occlusion index set of each leakage diffusion point of the vehicle-mounted platform, including the occlusion density index and the terrain complexity index, and perform comprehensive analysis to obtain the diffusion occlusion factor of each leakage diffusion point of the vehicle-mounted platform.

[0012] Further, the specific expression of the leakage likelihood function between each leakage diffusion point of the vehicle-mounted platform and the corresponding set leakage area of the pipeline network to be evaluated is: ; where is the The likelihood function of leakage at a leakage diffusion point corresponding to a corresponding set leakage area of the pipeline network to be evaluated For the th leakage diffusion point of the vehicle-mounted platform and the standard value of concentration error of the corresponding set leakage area of the pipeline network to be evaluated is the pi For the th observed gas concentration value of the leakage diffusion point of the vehicle-mounted platform For the th leakage diffusion point of the vehicle-mounted platform and the predicted concentration value of the th pipeline location point of the corresponding set leakage area of the pipeline network to be evaluated , is the number of leakage diffusion points , is the number of pipeline location points

[0013] Furthermore, the specific steps to obtain the posterior distribution of leakage of the pipeline network to be evaluated are as follows: comprehensively analyze the likelihood function of leakage at each leakage diffusion point of the vehicle-mounted platform and the corresponding set leakage area of the pipeline network to be evaluated, as well as the prior distribution of leakage of the pipeline network to be evaluated, to obtain the boundary probability value of each leakage diffusion point of the vehicle-mounted platform and the corresponding set leakage area of the pipeline network to be evaluated; and comprehensively analyze the prior distribution of leakage of the vehicle-mounted platform, the boundary probability value of the corresponding set leakage area, and the likelihood function of leakage at each leakage diffusion point in turn to obtain the posterior distribution of leakage of the pipeline network to be evaluated

[0014] Furthermore, the specific steps to obtain the two-dimensional position coordinates and the corresponding leakage intensity value of each leakage source of the pipeline network to be evaluated are as follows: comprehensively analyze the posterior distribution of leakage of the pipeline network to be evaluated to obtain several leakage sources of the pipeline network to be evaluated; and generate corresponding confidence intervals based on each leakage source of the pipeline network to be evaluated, and comprehensively analyze them to obtain the two-dimensional position coordinates and the corresponding leakage intensity value of each leakage source of the pipeline network to be evaluated

[0015] The present invention has the following beneficial effects (1). The gas leakage precise positioning method based on a vehicle-mounted platform collects gas concentration data along a preset path and combines it with a gas diffusion model, so as to perform real-time analysis using high-concentration points. The gas concentration data collected in real time during the movement of the vehicle-mounted platform is combined with the preset prior distribution of leakage and diffusion point information, and the posterior distribution of the leakage source is calculated through recursive update, and finally the two-dimensional position and leakage intensity of the leakage source are determined, thereby improving the precise positioning of the leakage source, effectively avoiding the blind area of manual inspection, especially in the complex environment of urban pipe networks, being able to efficiently identify the leakage source, reducing the response time after a leakage accident occurs, and further improving the safety and management efficiency of the gas pipe network

[0016] (2) The gas leakage precise positioning method based on a vehicle-mounted platform corrects the Gaussian diffusion model by combining environmental factors, making the leakage prediction more accurate. Environmental factors such as wind speed and wind direction have a direct impact on gas diffusion. Therefore, environmental data is obtained in real time and incorporated into the model, improving the accuracy of leakage diffusion prediction. Since pipeline leaks occur in different geographical environments and climatic conditions, by considering factors such as wind speed, wind direction, and atmospheric stability index, the diffusion path and concentration distribution of gas can be more realistically simulated, enabling earlier identification of potential leakage areas and thus improving the level of pipeline network safety management.

[0017] (3) The gas leakage precise positioning method based on a vehicle-mounted platform generates a leakage prior distribution of the pipeline network to be evaluated from leakage assessment data, forming a more accurate risk assessment model. By combining data mining and intelligent algorithms, the safety of pipeline network operation can be significantly improved, the probability of accidents reduced, and the operation efficiency of the pipeline network can be enhanced by real-time updating of the leakage prior distribution, thereby reducing the probability of emergencies and ensuring the safe and stable operation of the urban gas system.

[0018] Of course, it is not necessary for any product implementing the present invention to achieve all of the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 It is a flowchart of a gas leakage precise positioning method based on a vehicle-mounted platform according to the present invention.

[0020] Figure 2 It is a flowchart of the specific steps for obtaining the leakage prior distribution of the pipeline network to be evaluated in a gas leakage precise positioning method based on a vehicle-mounted platform according to the present invention.

[0021] Figure 3 It is a flowchart of the specific steps for obtaining the diffusion occlusion factor of each leakage diffusion point of the vehicle-mounted platform in a gas leakage precise positioning method based on a vehicle-mounted platform according to the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0022] Please refer to Figure 1, an embodiment of the present invention provides a technical solution: a precise gas leakage positioning method based on a vehicle-mounted platform, including the following steps: obtaining leakage assessment data of several regions (rectangular ranges with a length of 200 m and a width of 3 m) of the pipeline network to be evaluated, and performing predictive analysis to obtain the prior leakage distribution of the pipeline network to be evaluated; continuously obtaining the observed gas concentration values at several path position points (i.e., the position of the vehicle at the current time on the preset inspection path) on the preset inspection path of the vehicle-mounted platform (which can include various platforms such as cars, electric vehicles, and sliding rails that can carry gas detection equipment and move along the existing path), and determining whether it is higher than the preset gas concentration threshold; if the observed gas concentration value at each path position point on the preset inspection path of the vehicle-mounted platform is higher than the preset gas concentration threshold, it is correspondingly marked as a leakage diffusion point, obtaining the diffusion two-dimensional position coordinates (the position of the vehicle-mounted platform), diffusion plume data, and constructing a leakage likelihood function between each leakage diffusion point of the vehicle-mounted platform and the corresponding set leakage region of the pipeline network to be evaluated (i.e., the leakage probability value and the corresponding leakage intensity value of each pipeline position point between each leakage diffusion point of the vehicle-mounted platform and the corresponding set leakage region of the pipeline network to be evaluated), and performing recursive update processing in combination with the prior leakage distribution to obtain the posterior leakage distribution of the pipeline network to be evaluated; if the observed gas concentration value at each path position point on the preset inspection path of the vehicle-mounted platform is not higher than the preset gas concentration threshold, it is not marked, and the inspection continues along the preset inspection path; comprehensively analyzing the posterior leakage distribution of the pipeline network to be evaluated to obtain the two-dimensional position coordinates and the corresponding leakage intensity values of each leakage source of the pipeline network to be evaluated.

[0023] Specifically, as Figure 2As shown, the leakage assessment data includes the material corrosion rate value, the service duration value (which can be obtained from the pipeline installation records stored in the database), the leakage frequency value, the node density value, and the burial depth value. The specific steps to obtain the prior distribution of leakage in the pipeline network to be evaluated are as follows: Normalize the material corrosion rate value, service duration value, leakage frequency value, node density value, and burial depth value for each area of the pipeline network to be evaluated; comprehensively analyze the normalized material corrosion rate value, service duration value, leakage frequency value, node density value, and burial depth value for each area of the pipeline network to be evaluated to obtain the gas leakage risk index for each area of the pipeline network to be evaluated, and conduct a comprehensive analysis to obtain the initial leakage probability value for each area of the pipeline network to be evaluated (first perform a summation process to obtain the risk sum value, and then perform a ratio process on the gas leakage risk index of each area with the risk sum value), and the leakage standard deviation (perform a standard deviation process on the gas leakage risk index of each area to obtain the benchmark standard deviation of the pipeline network to be evaluated and conduct a ratio analysis with the √ gas leakage risk index of the corresponding area); obtain the two-dimensional position coordinates of each pipeline location point (i.e., any position representing the area) in each area of the pipeline network to be evaluated, and comprehensively analyze it in combination with the initial leakage probability value of each area to obtain the prior distribution of leakage in the pipeline network to be evaluated.

[0024] Among them, the material corrosion rate value is the corrosion rate of the material used for the pipelines in this area, which can be obtained from the industry standards stored in the database.

[0025] The leakage frequency value is the number of leaks in this area since its construction, which can be obtained from the maintenance reports stored in the database.

[0026] The node density value is the number of key connection points (such as valves, branch points, flanges, etc.) of the pipelines in this area, which can be obtained from the acceptance reports during pipeline laying stored in the database.

[0027] The burial depth value is the burial depth value of the pipelines in this area, that is, the vertical height value from the ground, which can be obtained from the acceptance reports during pipeline laying stored in the database.

[0028] The specific formula for calculating the gas leakage risk index for each area of the pipeline network to be evaluated is as follows: ; where is the gas leakage risk index for the th area of the pipeline network to be evaluated, is the material corrosion rate value for the th area of the pipeline network to be evaluated after normalization, is the material corrosion adjustment coefficient stored in the database, is the service duration value for the th area of the pipeline network to be evaluated after normalization, is the usage duration adjustment coefficient stored in the database, is the leakage frequency value of the th area of the pipeline network to be evaluated after normalization, is the leakage frequency adjustment coefficient stored in the database, is the smoothing coefficient stored in the database, and its value is 0.1 in this embodiment, is the node density value of the th area of the pipeline network to be evaluated after normalization, is the node density adjustment coefficient stored in the database, is the burial depth value of the th area of the pipeline network to be evaluated after normalization, is the burial depth adjustment coefficient stored in the database, is the interaction coefficient stored in the database, , is the number of areas.

[0029] It should be noted that this term in the formula is used to adjust the superposition effect among the material corrosion rate value, usage duration value, leakage frequency value, node density value, and burial depth value, to avoid the gas leakage risk index being too high or too low.

[0030] , , , , , can be achieved through the following steps: Using historical data, combined with the material corrosion rate value, usage duration value, leakage frequency value, node density value, and burial depth value, conduct statistical regression analysis to quantify the specific impact of each factor on the gas leakage risk index, so as to fit the initial weight value. Secondly, adopt the sensitivity analysis method to adjust the value range of each coefficient, observe its impact on the environmental carrying capacity evaluation result, ensure the stability and rationality of the model, and based on the regional characteristics and actual situation, correct and optimize the initially fitted coefficients, and finally determine the coefficient values applicable to specific regions.

[0031] The specific implementation example of calculating the gas leakage risk index of each area of the pipeline network to be evaluated is as follows: There is the following data: Randomly select the material corrosion rate value, usage duration value, leakage frequency value, node density value, and burial depth value of 3 areas of the pipeline network to be evaluated, as shown in Table 1: Table 1 Example of leakage evaluation data for 3 areas of the pipeline network to be evaluated

[0032] Normalize the data in Table 1 to obtain Table 2: Table 2 Example of leakage assessment data for three regions of the pipeline network to be evaluated after normalization

[0033] Coefficient for material corrosion adjustment stored in the database Approximately: 0.361; Coefficient for service life adjustment stored in the database Approximately: 0.216; Coefficient for leakage frequency adjustment stored in the database Approximately: 0.247; Smoothing coefficient stored in the database Is: 0.1; Coefficient for node density adjustment stored in the database Approximately: 0.413; Coefficient for burial depth adjustment stored in the database Approximately: 0.327; Interaction coefficient stored in the database Approximately: 2.561; Substitute Table 2 and the above coefficients into the specific formula for calculating the gas leakage risk index of each region of the pipeline network to be evaluated, and we get: The gas leakage risk index of Region 1 of the pipeline network to be evaluated ≈ 0.356; The gas leakage risk index of Region 2 of the pipeline network to be evaluated ≈ 0.288; The gas leakage risk index of Region 3 of the pipeline network to be evaluated ≈ 0.201.

[0034] The specific expression of the prior distribution of leakage in the pipeline network to be evaluated is as follows: ; where Is the prior distribution of leakage in the pipeline network to be evaluated (i.e., the initial leakage probability at any position in the pipeline network to be evaluated), Is the initial leakage probability value of the th region of the pipeline network to be evaluated, Is the leakage standard deviation of the th region of the pipeline network to be evaluated, Is pi, and in this implementation example, it takes the value of 3.14, Is the two-dimensional position coordinate of the th pipeline position point in the th region of the pipeline network to be evaluated, , Is the number of regions, , Is the number of pipeline position points.

[0035] In this implementation plan, by comprehensively considering multiple factors such as the material corrosion rate, service life, leakage times, node density, and burial depth value of the pipeline, and normalizing these data, the data under different regions and conditions can be fairly compared. By weighted analyzing these parameters in each region, the finally obtained leakage risk index can more accurately reflect the actual risk status of the pipe networks in different regions, thus making the leakage assessment results more accurate. Secondly, through the ratio analysis of the standard deviation processing and the reference standard deviation, the assessment of the leakage risk in each region can be dynamically adjusted, so as to timely identify potential high-risk regions, enabling the pipe network managers to more precisely grasp the risk status of the pipe network. Finally, by combining the leakage probability and standard deviation in each region with the two-dimensional position coordinates, the leakage risk at each position of the pipe network can be visually displayed, providing a scientific basis for the maintenance and inspection of the pipe network.

[0036] Specifically, the diffusion plume data includes wind speed value, wind direction angle value, diffusion shielding factor, and atmospheric stability index. The specific steps for constructing the leakage likelihood function between each leakage diffusion point of the vehicle-mounted platform and each pipeline position point of the corresponding set leakage area of the pipeline network to be evaluated are as follows: respectively perform correction analysis (i.e., coordinate transformation based on the rotation matrix) on the diffusion two-dimensional position coordinates, wind direction angle value of each leakage diffusion point of the vehicle-mounted platform, and the two-dimensional coordinates of each pipeline position point of the corresponding set leakage area of the pipeline network to be evaluated (the set leakage area is a rectangular range with a length of 50 m and a width of 3 m, and is located in the pipeline network to be evaluated that is consistent with the wind direction angle), to obtain the corrected diffusion two-dimensional position coordinates of each leakage diffusion point of the vehicle-mounted platform and the corrected two-dimensional position coordinates of each pipeline position point of the corresponding set leakage area of the pipeline network to be evaluated; respectively perform comprehensive analysis on the corrected diffusion two-dimensional position coordinates, diffusion plume data of each leakage diffusion point of the vehicle-mounted platform, and the corrected two-dimensional position coordinates of each pipeline position point of the corresponding set leakage area of the pipeline network to be evaluated, to obtain the horizontal diffusion coefficient and vertical diffusion coefficient between each leakage diffusion point of the vehicle-mounted platform and each pipeline position point of the corresponding set leakage area of the pipeline network to be evaluated. Based on the Gaussian diffusion model for comprehensive analysis, obtain the predicted concentration value between each leakage diffusion point of the vehicle-mounted platform and each pipeline position point of the corresponding set leakage area of the pipeline network to be evaluated; and perform comprehensive analysis on the observed gas concentration value of each leakage diffusion point of the vehicle-mounted platform and the predicted concentration value of each pipeline position point of the corresponding set leakage area of the pipeline network to be evaluated (i.e., first perform difference processing, and perform standard deviation processing based on the difference processing result), to obtain the concentration error standard value between each leakage diffusion point of the vehicle-mounted platform and each pipeline position point of the corresponding set leakage area of the pipeline network to be evaluated, and construct the leakage likelihood function between each leakage diffusion point of the vehicle-mounted platform and each pipeline position point of the corresponding set leakage area of the pipeline network to be evaluated.

[0037] Among them, the wind speed value can be obtained through the wind speed sensor on the vehicle-mounted platform.

[0038] The wind direction angle value can be obtained through the wind speed sensor of the vehicle-mounted platform.

[0039] The atmospheric stability index represents the vertical mixing ability of the air layer in the atmosphere. Different stabilities directly affect the diffusion degree of gases in the atmosphere. It can be obtained by acquiring temperature values, humidity values, atmospheric pressure values, solar radiation values, and performing standardization processing, and then performing weighted processing based on the results of the standardization processing. The obtained result is the atmospheric stability index.

[0040] The diffusion distance value is the vertical distance between the leakage diffusion point and the leakage source location, and is calculated based on the Euclidean distance formula.

[0041] The specific expression of the leakage likelihood function between each leakage diffusion point of the vehicle-mounted platform and the corresponding set leakage area of the pipeline network to be evaluated is: ; where is the leakage likelihood function between the th leakage diffusion point of the vehicle-mounted platform and the corresponding set leakage area of the pipeline network to be evaluated, is the concentration error standard value between the th leakage diffusion point of the vehicle-mounted platform and the corresponding set leakage area of the pipeline network to be evaluated, is the pi, which takes the value of 3.14 in this embodiment, is the observed gas concentration value of the th leakage diffusion point of the vehicle-mounted platform, is the predicted concentration value of the th leakage diffusion point of the vehicle-mounted platform and the th pipeline location point of the corresponding set leakage area of the pipeline network to be evaluated, , is the number of leakage diffusion points, , is the number of pipeline location points.

[0042] In this implementation scheme, by correcting the coordinates of each leakage and diffusion point of the vehicle-mounted platform and combining diffusion plume data such as wind speed, wind direction, and atmospheric stability index, the diffusion path and concentration distribution of the leaked substance can be accurately simulated, so that the prediction of gas leakage and diffusion is more accurate, and optimized adjustment is carried out for different environmental conditions. Through the comprehensive analysis of the Gaussian diffusion model and combining the observed concentration and predicted concentration of each leakage and diffusion point of the vehicle-mounted platform, the accuracy of gas leakage can be effectively evaluated, and the reliability of the model can be further tested through the concentration error standard value, so as to reduce the prediction error and make timely corrections. Finally, by constructing the leakage likelihood function, the possibility and risk of leakage can be quantified, and a decision-making basis can be provided for the pipeline network management personnel. Combining the analysis of the concentration error standard value, the risk of each pipeline location point in the pipeline network can be evaluated more accurately, and important support is provided for early warning, rapid response, and effective response to possible leakage incidents.

[0043] Specifically, the specific steps to obtain the horizontal diffusion coefficient and vertical diffusion coefficient of each leakage and diffusion point of the vehicle-mounted platform and each pipeline location point of the corresponding set leakage area of the pipeline network to be evaluated are as follows: Obtain the standard deviation of the horizontal turbulent velocity difference and the standard deviation of the vertical turbulent velocity difference between each leakage and diffusion point of the vehicle-mounted platform and each pipeline location point of the corresponding set leakage area of the pipeline network to be evaluated; Read the wind speed value, diffusion shielding factor, atmospheric stability index, and the horizontal axis value and vertical axis value in the corrected diffusion two-dimensional position coordinates of each leakage and diffusion point of the vehicle-mounted platform and the horizontal axis value and vertical axis value in the corrected two-dimensional position coordinates of each pipeline location point of the corresponding area of the pipeline network to be evaluated, and conduct comprehensive analysis respectively in combination with the standard deviation of the horizontal turbulent velocity difference and the standard deviation of the vertical turbulent velocity difference to obtain the horizontal diffusion coefficient and vertical diffusion coefficient of each leakage and diffusion point of the vehicle-mounted platform and each pipeline location point of the corresponding set leakage area of the pipeline network to be evaluated.

[0044] Among them, the standard deviation of the horizontal turbulent velocity difference can be obtained through the three-dimensional ultrasonic anemometer of the vehicle-mounted platform.

[0045] The standard deviation of the vertical turbulent velocity difference can be obtained through the three-dimensional ultrasonic anemometer of the vehicle-mounted platform.

[0046] The specific formula for calculating the horizontal diffusion coefficient of each leakage and diffusion point of the vehicle-mounted platform and each pipeline location point of the corresponding area of the pipeline network to be evaluated is as follows: ; where is the horizontal diffusion coefficient of the th leakage and diffusion point of the vehicle-mounted platform and the th pipeline location point of the corresponding area of the pipeline network to be evaluated, is the horizontal diffusion coefficient of the th leakage and diffusion point of the vehicle-mounted platform and the The standard deviation of the lateral turbulent velocity at a pipeline location point is the horizontal axis value in the two-dimensional position coordinates of the th leakage diffusion point of the vehicle-mounted platform, is the horizontal axis value in the two-dimensional position coordinates of the th pipeline location point corresponding to the th leakage diffusion point of the vehicle-mounted platform and the corresponding area of the pipeline network to be evaluated, is the wind speed value of the th leakage diffusion point of the vehicle-mounted platform, is the initial adjustment coefficient stored in the database, is the atmospheric stability index of the th leakage diffusion point of the vehicle-mounted platform, is the atmospheric stability adjustment coefficient stored in the database, is the diffusion shielding factor of the th leakage diffusion point of the vehicle-mounted platform, is the diffusion shielding adjustment coefficient stored in the database, is the collaborative adjustment coefficient stored in the database (the collaborative adjustment of the atmospheric stability index and the diffusion shielding factor).

[0047] It should be noted that , , , can be obtained through the following steps: Based on historical data, determine the initial influence weights of each variable (wind speed value, diffusion shielding factor, atmospheric stability index) on the vertical diffusion coefficient through statistical regression analysis. Then, use the sensitivity analysis method to adjust the value range of the coefficient to evaluate the stability and applicability of these parameters to the formula output. Next, further fit the weights through model optimization (such as machine learning algorithms or multi-objective optimization) to ensure that the formula can accurately reflect the actual vertical diffusion coefficient. Fine-tune the coefficient based on the characteristics of different leakage diffusion points to ensure its applicability to the specific vertical diffusion coefficient.

[0048] And the calculation logic of the vertical diffusion coefficient and the lateral diffusion coefficient for each leakage diffusion point of the vehicle-mounted platform and each pipeline location point corresponding to the pipeline network to be evaluated is the same.

[0049] In this implementation scheme, by comprehensively considering multiple factors such as the wind speed, turbulence speed, diffusion occlusion factor, and atmospheric stability index at the leakage and diffusion points of the vehicle-mounted platform, a more detailed modeling and analysis of the leakage and diffusion process can be carried out, so that the lateral and vertical diffusion coefficients can be calculated more accurately, and then the diffusion behavior of the leaked gas under different environmental conditions can be better simulated, thereby significantly improving the accuracy of leakage and diffusion prediction. Especially in complex environments, it can reflect the diversity and complexity of actual diffusion. Secondly, in areas with frequent changes in meteorological conditions and complex terrain, the influence of environmental factors on gas diffusion is complex and variable. Therefore, it can flexibly respond to various complex environments and ensure the reliability and applicability of the evaluation results. By using historical data for regression analysis and sensitivity analysis, the weight adjustment of influencing factors such as wind speed value, diffusion occlusion factor, and atmospheric stability index can be carried out, and the model can be dynamically adjusted according to the characteristics of different leakage and diffusion points, so as to have strong adaptability and be able to adjust the calculation of the diffusion coefficient in a timely manner according to different environmental changes, thereby ensuring the stability and accuracy of the model and avoiding the deviation caused by the static model. Finally, when the vehicle-mounted platform encounters different environmental conditions during driving, the model can adjust the calculation in real time and provide instant leakage and diffusion prediction, which helps to optimize resource scheduling and emergency response, and then improve the efficiency of the overall monitoring and management system.

[0050] Specifically, as Figure 3 shown, the specific steps to obtain the diffusion occlusion factor of each leakage and diffusion point of the vehicle-mounted platform are as follows: Obtain the diffusion occlusion image data of each leakage and diffusion point of the vehicle-mounted platform (the viewing angle of this image is the wind direction), and the diffusion occlusion image data includes the pixel value, two-dimensional coordinates, and corresponding depth information value of each pixel point in the diffusion occlusion image; Input the diffusion occlusion image data of each leakage and diffusion point of the vehicle-mounted platform into a pre-trained occlusion recognition model for prediction and analysis to obtain the occlusion index set of each leakage and diffusion point of the vehicle-mounted platform, including the occlusion density index and the terrain complexity index (which measures the height and angle of each occluder in the environment), and conduct comprehensive analysis (i.e., weighted processing) to obtain the diffusion occlusion factor of each leakage and diffusion point of the vehicle-mounted platform.

[0051] Among them, the occlusion recognition model is specifically a target detection model. The target detection model includes an input layer, an occlusion object detection layer, an occlusion object feature layer, and a classification output layer. The specific steps to obtain the occlusion index set of each leakage diffusion point of the vehicle-mounted platform are as follows: In the input layer of the target detection model, receive the diffusion occlusion image data of each leakage diffusion point of the vehicle-mounted platform and perform preprocessing; in the occlusion object detection layer of the target detection model, perform detection processing on the diffusion occlusion image data of each leakage diffusion point of the vehicle-mounted platform (the occlusion object detection layer includes multiple recognition convolutional layers, pooling layers, and fully connected layers. Local features such as edges, textures, and shapes are extracted through multiple recognition convolutional layers, and then the features are downsampled through the recognition pooling layer to reduce the computational amount and help the model focus on higher-level features. Then, it is flattened into a feature vector and input into the recognition fully connected layer to map the feature vector features to a higher-dimensional space for class classification and bounding box regression. The coordinates of the candidate bounding boxes of each occlusion object are output, and regression is performed on each candidate box to predict the exact position of the box, such as the coordinates of the upper left corner and the lower right corner, so as to more accurately locate the occlusion object. Non-maximum suppression is used to remove redundant and overly overlapping candidate boxes, and only the most accurate box is retained, which is the actual bounding box of the occlusion object), and obtain the bounding boxes of several occlusion objects in the diffusion occlusion image of each leakage diffusion point of the vehicle-mounted platform; in the occlusion object feature layer of the target detection model, perform feature extraction on the bounding box of each occlusion object in the diffusion occlusion image of each leakage diffusion point of the vehicle-mounted platform (the occlusion object feature layer includes an extraction convolutional layer and an extraction fully connected layer. Convert the bounding box of each occlusion object into a feature map of a fixed size, that is, divide the bounding box of each occlusion object into a fixed number of grids and then perform pooling processing on each small grid area to finally obtain a feature map of a fixed size, and extract various high-level features about the occlusion object from the feature map of each occlusion object through the extraction convolutional layer, such as shape features, texture features, color features, edge features. In this process, the extraction convolutional layer is continuously stacked to gradually transform from low-level features such as edges and corner points into higher-level semantic features, and the extracted features are flattened and then input into the extraction fully connected layer for further feature learning and classification. Through the fully connected layer, the model can perform classification and regression based on the extracted features, such as outputting the attributes and relative distances of each occlusion object, such as height, angle, position, etc.), and obtain the feature set of each occlusion object in the diffusion occlusion image of each leakage diffusion point of the vehicle-mounted platform;In the classification output layer of the object detection model, predictive analysis is performed on the feature set of each occluder in the diffusion occlusion image of each leakage diffusion point on the vehicle platform. (For the occlusion density index, the bounding box and relative position of the occluder in the feature set are extracted, the bounding box is processed with the total image area and the reverse ratio, and standardized in combination with the relative position, and the output result is mapped to between 0 and 1 using the Sigmoid function. For the terrain complexity index, the height, angle, etc. of each occluder are extracted, standardized, and the output result is mapped to between 0 and 1 using the Sigmoid function), obtaining the occlusion density index and terrain complexity index of each leakage diffusion point on the vehicle platform, that is, the occlusion index set.

[0052] The input layer is used to standardize, resize, denoise, etc. the input diffusion occlusion image data so that the model can effectively process this data. The occluder detection layer is used to perform occluder detection processing, that is, to identify each occluder, such as trees, buildings, etc.

[0053] The occluder feature layer is used to extract the attributes of each occluder (such as height, angle, position, etc.) and provide a feature set for subsequent analysis.

[0054] The classification output layer is used to perform predictive analysis on the occluder feature set to obtain the occlusion density index and terrain complexity index.

[0055] And the pre-training process of the object detection model is as follows: Obtain a labeled data set, including several groups of labeled occlusion images, such as labeling each occluder and its corresponding attributes, and dividing the labeled data set into a training set and a validation set.

[0056] Initialize the object detection model, that is, weight initialization (using He initialization), bias initialization (initializing the bias term to zero), activation function (using the ReLU activation function to increase the non-linearity of the network).

[0057] Set the number of loops (for example, train 50 times or 100 times). In each training loop, perform forward propagation based on the training set (the input data is passed through the layers of the network, and each layer performs specific mathematical operations, such as convolution, matrix multiplication, activation function, etc., and finally outputs the prediction value of the network), calculate the loss (calculate the difference between the prediction value of the model and the actual label. This difference is measured by a loss function, such as mean square error, cross-entropy loss, smooth L1 loss for bounding box regression in object detection tasks), and backpropagation (calculate the derivative of each parameter, such as weight, bias with respect to the loss function, use the chain rule to backpropagate the error, calculate the gradient layer by layer, and update the weight according to the calculated gradient using an optimization algorithm, such as SGD, Adam, RMSProp, etc.).

[0058] After each training loop, evaluate based on the validation set, that is, use the images in the validation set for forward propagation, calculate the loss value between the predicted values (i.e., the predicted occlusion density index and terrain complexity index) and the true values (i.e., the true occlusion density index and terrain complexity index), and calculate the loss function value and accuracy of the model on the validation set to evaluate the performance of the model.

[0059] By evaluating on the validation set, the performance of the current model can be judged, and then decide whether to save the parameters of the current model as the optimal model. If the loss on the validation set is high or the performance is poor, it may be necessary to adjust the hyperparameters of the model (such as the learning rate, network structure) or conduct more training. If the performance of the model does not improve on the validation set, training can be stopped early, i.e., early stopping.

[0060] When the training is completed and the loss and accuracy on the validation set reach the expected standards, the training process ends and a trained network model is obtained.

[0061] In this implementation, by using the object detection model, the positions and features (such as height, angle, etc.) of each occluder in the diffusion occlusion image can be accurately identified and extracted, so as to accurately calculate the occlusion density and terrain complexity of each leakage diffusion point, and then provide higher-quality data support for the subsequent calculation of the diffusion coefficient. And by analyzing the image data and combining depth information, the model can dynamically adapt to different terrains and environmental conditions and fine-tune different leakage diffusion points, which can ensure that the diffusion analysis results are more accurate in complex environments. Secondly, the object detection model can not only identify occluders, but also deeply extract multi-level features of occluders, including their shapes, textures, colors, etc. This helps to further optimize the calculation of the diffusion occlusion factor and improve the sensitivity and adaptability to different occluders. Finally, through automatic analysis using the pre-trained model, the analysis speed can be increased, and the diffusion occlusion factor of each leakage diffusion point can be feedback in real time, which has significant advantages in terms of rapid response, optimized decision-making, and reducing the burden of manual operations, and improves the adaptability to different environmental conditions, so as to ensure that the model can work effectively in different situations.

[0062] Specifically, the specific steps to obtain the posterior distribution of leakage of the pipeline network to be evaluated are as follows: comprehensively analyze (i.e., perform integral processing, which can use the Monte Carlo method and approximate the integral through random sampling, especially when the parameter space is very large, use the Markov chain Monte Carlo method) the leakage likelihood function of each leakage diffusion point of the vehicle-mounted platform and the corresponding set leakage area of the pipeline network to be evaluated and the prior distribution of leakage of the pipeline network to be evaluated, to obtain the boundary probability value of each leakage diffusion point of the vehicle-mounted platform and the corresponding set leakage area of the pipeline network to be evaluated; and comprehensively analyze in turn the prior distribution of leakage of each leakage diffusion point of the vehicle-mounted platform and the pipeline network to be evaluated, the boundary probability value of the corresponding set leakage area, and the leakage likelihood function, to obtain the posterior distribution of leakage of the pipeline network to be evaluated.

[0063] Among them, the specific steps of the comprehensive analysis are as follows: after the vehicle-mounted platform arrives at the first leakage diffusion point, perform the first update processing on the first leakage diffusion point of the vehicle-mounted platform, the prior distribution of leakage of the pipeline network to be evaluated, the boundary probability value of the corresponding set leakage area, and the leakage likelihood function, to obtain the posterior distribution of the pipeline network to be evaluated at the first leakage diffusion point of the vehicle-mounted platform. (It should be noted here that if the leakage probability value of each pipeline position point of the first leakage diffusion point and the corresponding set leakage area of the pipeline network to be evaluated is higher than the preset leakage probability threshold, mark it and trigger an alarm, so as to take targeted pipeline maintenance), and gradually update after the vehicle-mounted platform arrives at the second leakage diffusion point until the vehicle-mounted platform completes the inspection, to obtain the posterior distribution of leakage of the pipeline network to be evaluated, that is, the leakage probability value and leakage intensity value of each pipeline position point of the pipeline network to be evaluated.

[0064] And the update formula for the posterior distribution of the pipeline network to be evaluated at each leakage diffusion point of the vehicle-mounted platform is as follows: , where, is the posterior distribution of the pipeline network to be evaluated at the th leakage diffusion point of the vehicle-mounted platform, is the leakage likelihood function of each leakage diffusion point of the vehicle-mounted platform and the corresponding set leakage area of the pipeline network to be evaluated, is the posterior distribution of the pipeline network to be evaluated at the th leakage diffusion point of the vehicle-mounted platform (when , it is the prior distribution of the pipeline network to be evaluated), is the boundary probability value of each leakage diffusion point of the vehicle-mounted platform and the corresponding set leakage area of the pipeline network to be evaluated, , is the number of leakage diffusion points.

[0065] In this implementation scheme, by comprehensively analyzing the leakage likelihood function, prior distribution, and boundary probability values, the posterior distribution of leakage of the pipeline network to be evaluated is gradually updated, so as to more accurately evaluate the leakage probability and intensity of each pipeline position, which helps to identify the areas in the pipeline network most likely to have leakage, improve the accuracy of risk warning, and update the posterior distribution at each leakage diffusion point, so that the prediction results of leakage can be adjusted in real time according to the data collected by the vehicle-mounted platform. Furthermore, it ensures that the model can reflect the latest leakage diffusion situation and respond in a timely manner. Secondly, by using the Monte Carlo method, especially the Markov chain Monte Carlo method, the posterior distribution can be efficiently approximated and calculated when the parameter space is very large, providing a feasible solution for complex leakage assessment problems, thus avoiding the difficulties of high-dimensional calculations. At the same time, the prior distribution of leakage of the pipeline network and environmental boundary conditions are also considered, and various information can be comprehensively integrated to provide higher reliability for the calculation of leakage probability. Finally, by gradually updating the posterior distribution of each leakage diffusion point during the inspection process of the vehicle-mounted platform, the model can continuously improve the assessment of the leakage status of the pipeline network according to the new diffusion plume data, ensuring that the assessment results are continuously refined throughout the inspection process, so as to adapt to different pipeline network conditions and environmental changes and improve the universality of the assessment.

[0066] Specifically, the specific steps to obtain the two-dimensional position coordinates of each leakage source of the pipeline network to be evaluated and the corresponding leakage intensity values are as follows: comprehensively analyze the posterior distribution of leakage of the pipeline network to be evaluated (sample the posterior distribution of leakage of the pipeline network to be evaluated based on the Monte Carlo method to obtain several leakage source positions, that is, the pipeline position points where the leakage probability value of each leakage diffusion point of the vehicle-mounted platform and each pipeline position point in the corresponding set leakage area of the pipeline network to be evaluated is higher than the preset leakage probability value are marked as leakage source positions), and obtain several leakage sources of the pipeline network to be evaluated; and generate corresponding confidence intervals based on each leakage source of the pipeline network to be evaluated (sort the sampling results based on the Monte Carlo method in descending order and calculate the confidence intervals of the leakage source positions. For example, for a 95% confidence interval, select the first 2.5% and 97.5% sampling values in the posterior distribution as the upper and lower bounds of the interval), and conduct comprehensive analysis to obtain the two-dimensional position coordinates of each leakage source of the pipeline network to be evaluated and the corresponding leakage intensity value (take the maximum leakage intensity corresponding to the leakage source position), and take repair measures.

[0067] In this implementation scheme, by sampling from the leakage posterior distribution based on the Monte Carlo method, the most likely leakage source can be identified from multiple possible leakage source locations. By selecting the pipeline location points where the leakage probability value is higher than the preset threshold, the identification of the leakage source can be ensured to be more accurate and reliable. Secondly, through the selection method based on the maximum leakage intensity, the intensity value of each leakage source can be ensured to reflect the strongest degree of actual leakage, providing strong support for subsequent risk assessment and emergency response. By generating the confidence interval (such as a 95% confidence interval) for each leakage source location, the uncertainty of the leakage source location can be quantified, providing a more reliable and credible leakage risk assessment. The upper and lower bounds of the confidence interval reflect the uncertainty of the model prediction, which can help decision-makers better understand the leakage risk range. Finally, by obtaining the two-dimensional position coordinates of the leakage source and the corresponding leakage intensity value, the pipeline network management personnel can make more scientific and accurate decisions based on the data and take repair measures.

[0068] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications to these embodiments once they learn the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0069] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalent technologies, the present invention is also intended to include these modifications and variations.

Claims

1. A method for accurately locating a gas leak based on a vehicle-mounted platform, characterized in that: The following steps are involved: Obtain leakage assessment data of several areas of the pipeline network to be assessed, and perform prediction analysis to obtain a priori distribution of leakage of the pipeline network to be assessed; Continuously obtain the observed gas concentration values ​​of the vehicle-mounted platform at several path position points on the preset inspection path, and determine whether they are higher than the preset gas concentration threshold; If the observed gas concentration value of each path position point along the preset inspection path of the vehicle-mounted platform is higher than the preset gas concentration threshold, it is marked as a leakage diffusion point, and the diffusion two-dimensional position coordinates and diffusion plume data are obtained. The leakage likelihood function of each leakage diffusion point of the vehicle-mounted platform and the corresponding set leakage area of ​​the pipeline network to be evaluated is constructed, and recursive update processing is performed in combination with the leakage prior distribution to obtain the leakage posterior distribution of the pipeline network to be evaluated; If the observed gas concentration value at each path position point on the preset inspection path by the vehicle-mounted platform is not higher than the preset gas concentration threshold, it will not be marked; A comprehensive analysis is performed on the leakage posterior distribution of the pipeline network to be evaluated to obtain the two-dimensional position coordinates of each leakage source of the pipeline network to be evaluated and the corresponding leakage intensity value.

2. The method for accurately locating a gas leak based on a vehicle-mounted platform according to claim 1 is characterized in that: The leakage assessment data includes material corrosion rate value, service time value, leakage frequency value, node density value, and burial depth value. The specific steps for obtaining the leakage prior distribution of the pipeline network to be assessed are as follows: Normalize the material corrosion rate value, service time value, leakage frequency value, node density value, and burial depth value of each area of ​​the pipeline network to be evaluated; Comprehensively analyze the normalized material corrosion rate value, service time value, leakage frequency value, node density value, and burial depth value of each area of ​​the pipeline network to be evaluated to obtain the gas leakage risk index of each area of ​​the pipeline network to be evaluated, and conduct a comprehensive analysis to obtain the initial leakage probability value and leakage standard deviation of each area of ​​the pipeline network to be evaluated; The two-dimensional position coordinates of each pipeline location point in each area of ​​the pipeline network to be evaluated are obtained, and a comprehensive analysis is performed in combination with the initial leakage probability value of each area to obtain the leakage prior distribution of the pipeline network to be evaluated.

3. The method for accurately locating a gas leak based on a vehicle-mounted platform according to claim 2 is characterized in that: The specific expression of the leakage prior distribution of the pipeline network to be evaluated is as follows: ； in, is the leakage prior distribution of the pipeline network to be evaluated, The first The initial leakage probability value of the area, The first The leakage standard deviation of the area is is the circumference of a circle, The first Region The two-dimensional position coordinates of the pipeline location points, , is the number of regions, , The number of pipeline location points.

4. The method for accurately locating a gas leak based on a vehicle-mounted platform according to claim 1 is characterized in that: The diffusion plume data includes wind speed value, wind direction angle value, diffusion shielding factor, atmospheric stability index, and the specific steps of constructing the leakage likelihood function of each leakage diffusion point of the vehicle-mounted platform and each pipeline position point of the corresponding set leakage area of ​​the pipeline network to be evaluated are as follows: Correction analysis is performed on the diffusion two-dimensional position coordinates, wind direction angle value of each leakage diffusion point of the vehicle-mounted platform, and the two-dimensional coordinates of each pipeline position point in the corresponding set leakage area of ​​the pipeline network to be evaluated, to obtain the corrected diffusion two-dimensional position coordinates of each leakage diffusion point of the vehicle-mounted platform and the corrected two-dimensional position coordinates of each pipeline position point in the corresponding set leakage area of ​​the pipeline network to be evaluated; Comprehensively analyze the corrected diffusion two-dimensional position coordinates of each leakage diffusion point on the vehicle-mounted platform, the diffusion plume data, and the corrected two-dimensional position coordinates of each pipeline position point in the corresponding set leakage area of ​​the pipeline network to be evaluated, and obtain the lateral diffusion coefficient and vertical diffusion coefficient of each leakage diffusion point on the vehicle-mounted platform and each pipeline position point in the corresponding set leakage area of ​​the pipeline network to be evaluated. Comprehensively analyze based on the Gaussian diffusion model to obtain the predicted concentration value of each leakage diffusion point on the vehicle-mounted platform and each pipeline position point in the corresponding set leakage area of ​​the pipeline network to be evaluated; The observed gas concentration value of each leakage diffusion point on the vehicle-mounted platform and the predicted concentration value of each pipeline location point in the corresponding set leakage area of ​​the pipeline network to be evaluated are comprehensively analyzed to obtain the standard value of the concentration error between each leakage diffusion point on the vehicle-mounted platform and the corresponding set leakage area of ​​the pipeline network to be evaluated, and the leakage likelihood function between each leakage diffusion point on the vehicle-mounted platform and the corresponding set leakage area of ​​the pipeline network to be evaluated is constructed.

5. The method for accurately locating a gas leak based on a vehicle-mounted platform according to claim 4 is characterized in that: The specific steps for obtaining the lateral diffusion coefficient and vertical diffusion coefficient of each leakage diffusion point on the vehicle platform and each pipeline position point in the corresponding set leakage area of ​​the pipeline network to be evaluated are as follows: Obtain the standard deviation value of the transverse turbulence velocity and the standard deviation value of the vertical turbulence velocity between each leakage diffusion point on the vehicle-mounted platform and each pipeline position point in the corresponding set leakage area of ​​the pipeline network to be evaluated; The wind speed value, diffusion shielding factor, atmospheric stability index and horizontal and vertical axis values ​​in the corrected diffusion two-dimensional position coordinates of each leakage diffusion point on the vehicle-mounted platform and the horizontal and vertical axis values ​​in the corrected two-dimensional position coordinates of each pipeline position point in the corresponding area of ​​the pipeline network to be evaluated are read, and combined with the standard deviation of the lateral turbulence velocity and the standard deviation of the vertical turbulence velocity, a comprehensive analysis is performed to obtain the lateral diffusion coefficient and vertical diffusion coefficient of each leakage diffusion point on the vehicle-mounted platform and each pipeline position point in the corresponding set leakage area of ​​the pipeline network to be evaluated.

6. The method for accurately locating a gas leak based on a vehicle-mounted platform according to claim 5 is characterized in that: The specific formula for calculating the lateral diffusion coefficient between each leakage diffusion point on the vehicle platform and each pipeline location point in the corresponding area of ​​the pipeline network to be evaluated is as follows: ； in, For the vehicle platform The leakage diffusion point and the corresponding area of ​​the pipeline network to be evaluated The lateral diffusion coefficient at each pipeline location is For the vehicle platform The leakage diffusion point and the corresponding area of ​​the pipeline network to be evaluated The standard deviation of the transverse turbulent velocity at each pipe location is: For the vehicle platform The horizontal axis value in the diffusion two-dimensional position coordinates of the leakage diffusion point, For the vehicle platform The leakage diffusion point and the corresponding area of ​​the pipeline network to be evaluated The horizontal axis value in the two-dimensional position coordinates of the pipeline location point, For the vehicle platform The wind speed value of the leakage diffusion point is is the initial adjustment coefficient stored in the database, For the vehicle platform The atmospheric stability index of the leakage diffusion point, is the atmospheric stability adjustment coefficient stored in the database, For the vehicle platform The diffusion occlusion factor of the leakage diffusion point, is the diffuse occlusion adjustment coefficient stored in the database, is the synergy adjustment coefficient stored in the database.

7. The method for accurately locating a gas leak based on a vehicle-mounted platform according to claim 4 is characterized in that: The specific steps for obtaining the diffusion occlusion factor of each leakage diffusion point of the vehicle platform are as follows: Acquire diffusion occlusion image data of each leakage diffusion point of the vehicle-mounted platform, wherein the diffusion occlusion image data includes a pixel value, a two-dimensional coordinate, and a corresponding depth information value of each pixel point in the diffusion occlusion image; The diffusion occlusion image data of each leakage diffusion point of the vehicle platform is input into the pre-trained occlusion recognition model for predictive analysis to obtain the occlusion index set of each leakage diffusion point of the vehicle platform, including the occlusion density index and the terrain complexity index. A comprehensive analysis is then performed to obtain the diffusion occlusion factor of each leakage diffusion point of the vehicle platform.

8. The method for accurately locating a gas leak based on a vehicle-mounted platform according to claim 4 is characterized in that: The specific expression of the leakage likelihood function between each leakage diffusion point of the vehicle-mounted platform and the corresponding set leakage area of ​​the pipeline network to be evaluated is: ； in, For the vehicle platform The leakage likelihood function of the leakage diffusion point and the corresponding set leakage area of ​​the pipeline network to be evaluated, For the vehicle platform The concentration error standard value between each leakage diffusion point and the corresponding set leakage area of ​​the pipeline network to be evaluated, is the circumference of a circle, For the vehicle platform The observed gas concentration value at each leakage diffusion point, For the vehicle platform The leakage diffusion point and the corresponding set leakage area of ​​the pipeline network to be evaluated The predicted concentration value at each pipeline location, , is the number of leakage diffusion points, , The number of pipeline location points.

9. The method for accurately locating a gas leak based on a vehicle-mounted platform according to claim 1, characterized in that: The specific steps to obtain the leakage posterior distribution of the pipeline network to be evaluated are as follows: Comprehensively analyze the leakage likelihood function of each leakage diffusion point of the vehicle-mounted platform and the corresponding set leakage area of ​​the pipeline network to be evaluated, as well as the leakage prior distribution of the pipeline network to be evaluated, and obtain the boundary probability value of each leakage diffusion point of the vehicle-mounted platform and the corresponding set leakage area of ​​the pipeline network to be evaluated; A comprehensive analysis is then performed on each leakage diffusion point of the vehicle-mounted platform, the leakage prior distribution of the pipeline network to be evaluated, the boundary probability value of the corresponding set leakage area, and the leakage likelihood function to obtain the leakage posterior distribution of the pipeline network to be evaluated.

10. The method for accurately locating a gas leak based on a vehicle-mounted platform according to claim 1, characterized in that: The specific steps to obtain the two-dimensional position coordinates of each leakage source of the pipeline network to be evaluated and the corresponding leakage intensity value are as follows: Comprehensively analyze the leakage posterior distribution of the pipeline network to be evaluated, and obtain several leakage sources of the pipeline network to be evaluated; And based on each leakage source of the pipeline network to be evaluated, a corresponding confidence interval is generated, and a comprehensive analysis is performed to obtain the two-dimensional position coordinates of each leakage source of the pipeline network to be evaluated and the corresponding leakage intensity value.

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