A precise gas leakage location method based on a vehicle-mounted platform

Abstract

The present invention discloses a precise positioning method for gas leakage based on a vehicle-mounted platform, which relates to the technical field of gas leakage positioning. The precise positioning method for gas leakage based on a vehicle-mounted platform obtains leakage evaluation data, analyzes and obtains the prior distribution of leakage of the pipeline network to be evaluated; continuously obtains the observed gas concentration values at a plurality of path position points on a preset inspection path of the vehicle-mounted platform, and determines whether it is higher than a preset gas concentration threshold; if it is higher than the preset gas concentration threshold, it is correspondingly marked as a leakage diffusion point, obtains the diffusion two-dimensional position coordinates and diffusion plume data, constructs a leakage likelihood function, and performs recursive update processing in combination with the prior distribution of leakage to obtain the posterior distribution of leakage; otherwise, it is not marked. The present invention comprehensively analyzes the posterior distribution of leakage to obtain the two-dimensional position coordinates of each leakage source and the corresponding leakage intensity value, so as to efficiently identify the leakage source, and further improve the safety and management efficiency of the gas pipeline network.

Description

Technical Field

[0001] The present invention relates to the technical field of gas leakage location, and specifically 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 pipe network, easily miss areas far from the conventional path, and the leakage location accuracy is relatively low, making it difficult to accurately determine the location of the leakage source, which easily leads 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 pipe 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 variable 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 implemented through the following technical solutions: A precise gas leakage positioning method based on a vehicle-mounted platform, comprising the following steps: Obtain leakage assessment data for several regions of the pipeline network to be evaluated, and conduct predictive analysis to obtain the prior leakage distribution of the pipeline network to be evaluated; Continuously obtain the observed gas concentration values at several path position points on the preset inspection path of the vehicle-mounted platform, and determine whether they are higher than the preset gas concentration threshold; If the observed gas concentration value at each path position point along 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, obtain the diffusion two-dimensional position coordinates and diffusion plume data, construct 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, and perform 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; Conduct comprehensive analysis on the posterior leakage distribution of the pipeline network to be evaluated to obtain the two-dimensional position coordinates of each leakage source and the corresponding leakage intensity value of the pipeline network to be evaluated.

[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 to obtain the prior leakage distribution of the pipeline network to be evaluated are as follows: Normalize 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; Conduct comprehensive analysis on 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 conduct comprehensive analysis to obtain the initial leakage probability value and leakage standard deviation of each region of the pipeline network to be evaluated; Obtain the two-dimensional position coordinates of each pipeline position point in each region of the pipeline network to be evaluated, and conduct comprehensive analysis 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 the pi, is the two-dimensional position coordinates 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:

[0009] Perform correction analysis on the diffusion two-dimensional position coordinates, wind direction angle values 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, perform comprehensive analysis based on the Gaussian diffusion model, 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.

[0010] 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 in 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 in 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.

[0011] 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 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 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 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.

[0012] 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 predictive 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.

[0013] 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 leakage likelihood function of a leakage diffusion point and the 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.

[0014] Further, the specific steps to obtain the posterior distribution of leakage 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 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 the prior distribution of leakage of the vehicle-mounted platform, the boundary probability value of the corresponding set leakage area, and the leakage likelihood function of each leakage diffusion point in turn to obtain the posterior distribution of leakage of the pipeline network to be evaluated.

[0015] Further, 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 conduct comprehensive analysis to obtain the two-dimensional position coordinates and the corresponding leakage intensity value of each leakage source of the pipeline network to be evaluated.

[0016] The present invention has the following beneficial effects:

[0017] (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 by 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 thus improving the safety and management efficiency of the gas pipe network.

[0018] (2) The gas leakage precise positioning method based on the vehicle-mounted platform corrects the Gaussian diffusion model by combining environmental factors, so as to make the leakage prediction more accurate. Environmental factors such as wind speed and wind direction have a direct impact on gas diffusion. Therefore, real-time environmental data is obtained and incorporated into the model, thereby improving the accuracy of leakage diffusion prediction. Moreover, pipeline leakage occurs under different geographical environments and climate 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, so as to identify potential leakage areas earlier, thereby improving the level of pipeline network safety management.

[0019] (3) The gas leakage precise positioning method based on the vehicle-mounted platform generates the leakage prior distribution of the pipeline network to be evaluated through leakage assessment data, thereby forming a more accurate risk assessment model. By combining data mining and intelligent algorithms, the safety of pipeline network operation can be greatly improved, the probability of accidents can be reduced, and through the real-time update of the leakage prior distribution, the operation efficiency of the pipeline network can be improved, thereby reducing the probability of emergencies, and then ensuring the safe and stable operation of the urban gas system.

[0020] Of course, it is not necessary for any product implementing the present invention to achieve all the above-mentioned advantages simultaneously. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 It is a flowchart of a gas leakage precise positioning method based on the vehicle-mounted platform of the present invention.

[0022] Figure 2 It is a specific step flowchart for obtaining the leakage prior distribution of the pipeline network to be evaluated in a gas leakage precise positioning method based on the vehicle-mounted platform of the present invention.

[0023] Figure 3 It is a specific step flowchart 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 the vehicle-mounted platform of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0024] 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 between each leakage diffusion point of the vehicle-mounted platform and each pipeline position point of 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.

[0025] 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 for the gas leakage risk index of each area with the risk sum value), and the leakage standard deviation (perform a standard deviation process for 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 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] The burial depth value is the buried 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.

[0030] The specific formula for calculating the gas leakage risk index for each area of the pipeline network to be evaluated is as follows: ; Among them, 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 th leakage times value of the pipeline network to be evaluated after normalization, is the leakage times adjustment coefficient stored in the database, is the smoothing coefficient stored in the database, and takes the value of 0.1 in this embodiment, is the th node density value of the pipeline network to be evaluated after normalization, is the node density adjustment coefficient stored in the database, is the th buried depth value of the pipeline network to be evaluated after normalization, is the buried depth adjustment coefficient stored in the database, is the interaction coefficient stored in the database, , is the number of regions.

[0031] 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 times value, node density value, and buried depth value, to avoid the gas leakage risk index being too high or too low.

[0032] , , , , , The following steps can be taken: Using historical data, combined with the material corrosion rate value, usage duration value, leakage times value, node density value, and buried 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, use the sensitivity analysis method to adjust the value range of each coefficient and observe its impact on the environmental carrying capacity evaluation result to ensure the stability and rationality of the model. 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.

[0033] The specific implementation example of calculating the gas leakage risk index of each region of the pipeline network to be evaluated is as follows. The following data is available: Randomly select the material corrosion rate value, usage duration value, leakage times value, node density value, and buried depth value of 3 regions of the pipeline network to be evaluated, as shown in Table 1:

[0034] Table 1 Example of leakage assessment data for 3 regions of the pipeline network to be evaluated

[0035]

[0036] Normalize the data in Table 1 to obtain Table 2:

[0037] Table 2 Example of leakage assessment data for three regions of the pipeline network to be evaluated after normalization

[0038]

[0039] Material corrosion adjustment coefficient stored in the database Approximately: 0.361;

[0040] Service life adjustment coefficient stored in the database Approximately: 0.216;

[0041] Leakage frequency adjustment coefficient stored in the database Approximately: 0.247;

[0042] Smoothing coefficient stored in the database Is: 0.1;

[0043] Node density adjustment coefficient stored in the database Approximately: 0.413;

[0044] Burial depth adjustment coefficient stored in the database Approximately: 0.327;

[0045] Interaction coefficient stored in the database Approximately: 2.561;

[0046] 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:

[0047] The gas leakage risk index of Region 1 of the pipeline network to be evaluated ≈ 0.356;

[0048] The gas leakage risk index of Region 2 of the pipeline network to be evaluated ≈ 0.288;

[0049] The gas leakage risk index of Region 3 of the pipeline network to be evaluated ≈ 0.201.

[0050] The specific expression of the prior distribution of leakage of the pipeline network to be evaluated is as follows: ; where Is the prior distribution of leakage of the pipeline network to be evaluated (i.e., the initial leakage probability at any position of the pipeline network to be evaluated), Is the th region of the pipeline network to be evaluated, the initial leakage probability value, Is the th region of the pipeline network to be evaluated, the leakage standard deviation, Is pi, and in this implementation example, it takes the value of 3.14, Is the The two-dimensional position coordinates of the -th pipeline position point in a region, where is the number of regions, and

[0051] In this implementation solution, 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, data under different regions and different conditions can be fairly compared. By weighted analyzing these parameters of each region, the finally obtained leakage risk index can more accurately reflect the actual risk status of the pipe network in different regions, thus making the leakage assessment result more accurate. Secondly, through the ratio analysis of the standard deviation processing and the reference standard deviation, the assessment of the leakage risk of each region can be dynamically adjusted, so as to timely identify potential high-risk regions, enabling the pipe network manager to more accurately grasp the risk status of the pipe network. Finally, by combining the leakage probability and standard deviation of each region with the two-dimensional position coordinates, the leakage risk at each position of the pipe network can be intuitively displayed, providing a scientific basis for the maintenance and inspection of the pipe network.

[0052] Specifically, 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 position point in the corresponding set leakage area of the pipeline network to be evaluated are as follows: 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 in 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 with the same 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 in 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 position point in the corresponding set leakage area of the pipeline network to be evaluated, to obtain the lateral diffusion coefficient and vertical diffusion coefficient between 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. 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 in 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 in the corresponding set leakage area of the pipeline network to be evaluated (i.e., first perform difference processing and then 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 in 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 in the corresponding set leakage area of the pipeline network to be evaluated.

[0053] Among them, the wind speed value can be obtained through the wind speed sensor of the vehicle-mounted platform.

[0054] The wind direction angle value can be obtained through the wind speed sensor of the vehicle-mounted platform.

[0055] The atmospheric stability index is the vertical mixing ability of the air layer in the atmosphere. Different stabilities directly affect the diffusion degree of gas in the atmosphere. It can be obtained by acquiring temperature value, humidity value, atmospheric pressure value, solar radiation value, and performing standardization processing, and performing weighted processing based on the standardization processing result. The obtained result is the atmospheric stability index.

[0056] The diffusion distance value is the vertical distance between the leakage diffusion point and the leakage source position, which is calculated and obtained based on the Euclidean distance formula.

[0057] The specific expression of the leakage likelihood function between 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: ; where is the leakage likelihood function of 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 of 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, and its value is 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.

[0058] In this implementation plan, by correcting the coordinates of each leakage 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 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 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 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 it provides important support for early warning, rapid response, and effective response to possible leakage events.

[0059] Specifically, the specific steps for obtaining the lateral diffusion coefficient and vertical diffusion coefficient between 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 are as follows: Obtain the standard deviation of the lateral turbulent velocity and the standard deviation of the vertical turbulent velocity between 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; Read the wind speed value, diffusion shielding factor, atmospheric stability index, and the horizontal axis value and vertical axis value in the modified diffusion two-dimensional position coordinates of each leakage diffusion point of the vehicle-mounted platform, as well as the horizontal axis value and vertical axis value in the modified two-dimensional position coordinates of each pipeline position point in the corresponding area of the pipeline network to be evaluated, and perform comprehensive analysis respectively in combination with the standard deviation of the lateral turbulent velocity and the standard deviation of the vertical turbulent velocity to obtain the lateral diffusion coefficient and vertical diffusion coefficient between 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.

[0060] Among them, the standard deviation of the lateral turbulent velocity can be obtained by the three-dimensional ultrasonic anemometer of the vehicle-mounted platform.

[0061] The standard deviation of the vertical turbulent velocity can be obtained by the three-dimensional ultrasonic anemometer of the vehicle-mounted platform.

[0062] 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: ; Among them, 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 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 diffusion 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 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 shielding 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 (collaborative adjustment of the atmospheric stability index and the diffusion occlusion factor).

[0063] It should be explained that 、 、 、 can be obtained through the following steps: Based on historical data, determine the initial influence weights of each variable (wind speed value, diffusion occlusion 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.

[0064] And the calculation logics of the vertical diffusion coefficient and the lateral diffusion coefficient for each leakage diffusion point of the vehicle-mounted platform and each pipeline position point in the corresponding area of the pipeline to be evaluated are the same.

[0065] In this implementation plan, by comprehensively considering multiple factors such as the wind speed, turbulence speed, diffusion occlusion factor, and atmospheric stability index of the leakage diffusion points of the vehicle-mounted platform, a more detailed modeling and analysis of the leakage 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 diffusion prediction. Especially in complex environments, it can reflect the diversity and complexity of actual diffusion. Secondly, in areas with frequent meteorological condition changes and complex terrains, the influence of environmental factors on gas diffusion is complex and variable. Therefore, various complex environments can be flexibly responded to, and the reliability and applicability of the evaluation results can be ensured. 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 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 a 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 diffusion prediction, which helps to optimize resource scheduling and emergency response, and then improve the efficiency of the overall monitoring and management system.

[0066] Specifically, such as Figure 3As shown in the figure, the specific steps to obtain the diffusion occlusion factor of each leakage diffusion point on the vehicle-mounted platform are as follows: Obtain the diffusion occlusion image data of each leakage diffusion point on the vehicle-mounted platform (the perspective 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 diffusion point on 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 on 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 diffusion point on the vehicle-mounted platform.

[0067] 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 amount of calculation and help the model focus on higher-level features. Then, they are flattened into feature vectors and input into the recognition fully connected layer to map the feature vectors 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 precise 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), obtaining 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. Finally, a feature map of a fixed size is obtained, and various high-level features about the occlusion object, such as shape features, texture features, color features, edge features, are extracted from the feature map of each occlusion object through the extraction convolutional layer. In this process, the extraction convolutional layer is continuously stacked, gradually transforming from low-level features such as edges and corners 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.), obtaining 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 sets 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 processing is performed 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 processing is performed, 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.

[0068] 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.

[0069] The occluder detection layer is used to perform occluder detection processing, that is, to identify each occluder, such as trees, buildings, etc.

[0070] 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.

[0071] 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.

[0072] And the pre-training process of the object detection model is as follows:

[0073] 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.

[0074] Initialize the object detection model, that is, weight initialization (using He initialization), bias initialization (initializing the bias term to zero), and activation function (using the ReLU activation function to increase the non-linearity of the network).

[0075] Set the number of training loops (e.g., train 50 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 functions, etc., and finally outputs the predicted values of the network), calculate the loss (calculate the difference between the predicted values of the model and the actual labels. This difference is measured by a loss function such as mean squared error, cross-entropy loss, smooth L1 loss for bounding box regression in object detection tasks), and perform backpropagation (calculate the derivatives of each parameter such as weights and biases with respect to the loss function, use the chain rule to backpropagate the error, calculate the gradients layer by layer, and update the weights using an optimization algorithm such as SGD, Adam, RMSProp, etc.).

[0076] And after each training loop ends, evaluate based on the validation set, that is, perform forward propagation using the images in the validation set, calculate the loss value between the predicted values (i.e., the predicted occlusion density index, terrain complexity index) and the true values (i.e., the true occlusion density index, 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.

[0077] Through the evaluation on the validation set, the performance of the current model can be judged, and then it can be decided 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 perform more training. If the performance of the model does not improve on the validation set, the training can be stopped early, i.e., early stopping.

[0078] 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.

[0079] 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 further provide higher-quality data support for the subsequent calculation of the diffusion coefficient. Moreover, by analyzing the image data and combining the depth information, the model can dynamically adapt to different terrain 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 fed back in real time. This has significant advantages in terms of rapid response, optimized decision-making, and reduction of manual operation burden, and improves the adaptability to different environmental conditions, thus ensuring that the model can work effectively in different scenarios.

[0080] 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., integral processing, which can use the Monte Carlo method and approximate the integral through random sampling, especially when the parameter space is very large, the Markov chain Monte Carlo method is used) 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 then comprehensively analyze 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 of each leakage diffusion point of the vehicle-mounted platform in turn to obtain the posterior distribution of leakage of the pipeline network to be evaluated.

[0081] Among them, the specific steps of the comprehensive analysis are as follows: after the vehicle-mounted platform arrives at the first leakage diffusion point, the first update process will be performed 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, it will be marked and an alarm will be triggered, 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.

[0082] The posterior distribution update formula for each leakage diffusion point of the pipeline network to be evaluated on the vehicle-mounted platform is as follows: , where is the posterior distribution of the th leakage diffusion point of the pipeline network to be evaluated on the vehicle-mounted platform, is 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 the posterior distribution of the th leakage diffusion point of the pipeline network to be evaluated on the vehicle-mounted platform (when , it is the prior distribution of the pipeline network to be evaluated), is the boundary probability value between 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.

[0083] In this implementation plan, by comprehensively analyzing the leakage likelihood function, prior distribution, and boundary probability value, the leakage posterior distribution 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 leak, 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, thus ensuring 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 under the condition of a very large parameter space, providing a feasible solution for complex leakage assessment problems, thus avoiding the difficulties of high-dimensional calculation. At the same time, the leakage prior distribution 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.

[0084] Specifically, the specific steps to obtain the two-dimensional position coordinates and corresponding leakage intensity values of each leakage source of the pipeline network to be evaluated are as follows: comprehensively analyze the leakage posterior distribution of the pipeline network to be evaluated (sample from the leakage posterior distribution of the pipeline network to be evaluated based on the Monte Carlo method to obtain several leakage source positions, that is, mark the pipeline position points where the leakage probability value between 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 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 and corresponding leakage intensity values of each leakage source of the pipeline network to be evaluated (take the maximum leakage intensity corresponding to the leakage source position), and take repair measures.

[0085] In this implementation scheme, by sampling from the leakage posterior distribution based on the Monte Carlo method, the most likely leakage sources can be identified from multiple possible leakage source positions, and by selecting pipeline position points with leakage probability values higher than the preset threshold, the identification of leakage sources can be ensured to be more accurate and credible. Secondly, through the selection method based on the maximum leakage intensity, it can be ensured that the intensity value of each leakage source reflects the strongest degree of actual leakage, providing strong support for subsequent risk assessment and emergency response. And by generating the confidence interval (such as a 95% confidence interval) for each leakage source position, the uncertainty of the leakage source position 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 and corresponding leakage intensity values of the leakage sources, pipeline network managers can make more scientific and accurate decisions based on the data and take repair measures.

[0086] Although the preferred embodiments of the present invention have been described, those skilled in the art can make additional changes and modifications once they know the basic creative concept. Therefore, the appended claims are intended to be construed to include the preferred embodiments and all changes and modifications falling within the scope of the present invention.

[0087] 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 also intends to include these changes and modifications.

Claims

1. A precise gas leakage positioning method based on a vehicle-mounted platform, characterized in that, It includes the following steps: Obtain the leakage assessment data of several areas of the pipeline network to be evaluated, and conduct predictive analysis to obtain the prior leakage distribution of the pipeline network to be evaluated; Continuously obtain the observed gas concentration values at several path position points on the preset inspection path of the vehicle-mounted platform, and determine whether they are higher than the preset gas concentration threshold; If the observed gas concentration values at each path position point on the preset inspection path of the vehicle-mounted platform are higher than the preset gas concentration threshold, then mark them as leakage diffusion points correspondingly, obtain the two-dimensional diffusion position coordinates and diffusion plume data, including wind speed values, wind direction angle values, diffusion occlusion factors, and atmospheric stability indices, construct 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, and perform recursive update processing in combination with the prior leakage distribution to obtain the posterior leakage distribution of the pipeline network to be evaluated; The specific steps to obtain 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 coordinate, 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 predictive 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 conduct comprehensive analysis to obtain the diffusion occlusion factor of each leakage diffusion point of the vehicle-mounted platform; The specific steps to obtain the posterior leakage distribution of the pipeline network to be evaluated are as follows: Conduct comprehensive analysis on 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 and the prior leakage distribution of the pipeline network to be evaluated to obtain the boundary probability value between each leakage diffusion point of the vehicle-mounted platform and the corresponding set leakage area of the pipeline network to be evaluated; And conduct comprehensive analysis on the prior leakage distribution of the pipeline network to be evaluated, the boundary probability value of the corresponding set leakage area, and the leakage likelihood function for each leakage diffusion point of the vehicle-mounted platform in sequence 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, then do not mark; Conduct comprehensive analysis on 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 value.

2. The gas leakage precise positioning method based on a vehicle-mounted platform according to claim 1, wherein 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 to obtain the prior leakage distribution of the pipeline network to be evaluated are as follows: Normalize the material corrosion rate value, service life value, leakage frequency value, node density value, and burial depth value of each area of the pipeline network to be evaluated; Conduct comprehensive analysis on the material corrosion rate value, service life value, leakage frequency value, node density value, and burial depth value of each area of the pipeline network to be evaluated after normalization to obtain the gas leakage risk index of each area of the pipeline network to be evaluated, and conduct comprehensive analysis to obtain the initial leakage probability value and leakage standard deviation of each area of the pipeline network to be evaluated; Obtain the two-dimensional position coordinates of each pipeline location point in each area of the pipeline network to be evaluated, and conduct comprehensive analysis in combination with the initial leakage probability value of each area to obtain the prior leakage distribution of the pipeline network to be evaluated.

3. The gas leakage precise positioning method based on a vehicle-mounted platform according to claim 2, characterized in that, The specific expression of the prior leakage distribution of the pipeline network to be evaluated is as follows: ； Among them, is the prior distribution of the leakage of the pipeline network to be evaluated, is the initial leakage probability value of the -th area of the pipeline network to be evaluated, is the leakage standard deviation of the -th area of the pipeline network to be evaluated, is pi, is the two-dimensional position coordinate of the -th area and the -th pipeline position point of the pipeline network to be evaluated, , is the number of areas, , is the number of pipeline position points.

4. The gas leakage precise positioning method based on a vehicle-mounted platform according to claim 1, characterized in that The specific steps for constructing the leakage likelihood function between each leakage diffusion point of the vehicle-mounted platform and each pipeline location point in the corresponding set leakage area of the pipeline network to be evaluated are as follows: Conduct correction analysis on the diffusion two-dimensional position coordinates, wind direction angle values of each leakage diffusion point of the vehicle-mounted platform, and the two-dimensional coordinates of each pipeline location 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 location point in the corresponding set leakage area of the pipeline network to be evaluated; Conduct 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 in 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 in the corresponding set leakage area of the pipeline network to be evaluated, and conduct comprehensive analysis based on the Gaussian diffusion model to obtain the predicted concentration value between each leakage diffusion point of the vehicle-mounted platform and each pipeline location point in the corresponding set leakage area of the pipeline network to be evaluated; And conduct 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 in 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 in 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 in the corresponding set leakage area of the pipeline network to be evaluated.

5. The gas leakage precise positioning method based on a vehicle-mounted platform according to claim 4, wherein, 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 in the corresponding set leakage area of the pipeline network to be evaluated are as follows: Obtain the standard deviation of the horizontal turbulent velocity and the standard deviation of the vertical turbulent velocity between each leakage diffusion point of the vehicle-mounted platform and each pipeline location point in the corresponding set leakage area of the pipeline network to be evaluated; Read the wind speed value, diffusion shielding factor, atmospheric stability index of each leakage diffusion point of the vehicle-mounted platform, and the horizontal axis value and vertical axis value in the corrected diffusion two-dimensional position coordinates, and the horizontal axis value and vertical axis value in the corrected two-dimensional position coordinates of each pipeline location point in the corresponding area of the pipeline network to be evaluated, and conduct comprehensive analysis in combination with the standard deviation of the horizontal turbulent velocity and the standard deviation of the vertical turbulent velocity 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 in the corresponding set leakage area of the pipeline network to be evaluated.

6. The method for precise positioning of gas leakage based on a vehicle-mounted platform according to claim 5, wherein The specific formula for calculating the horizontal diffusion coefficient between each leakage diffusion point of the vehicle-mounted platform and each pipeline location point in the corresponding area of the pipeline network to be evaluated is as follows: ； Among them, is the lateral diffusion coefficient of the th leakage diffusion point of the vehicle-mounted platform and the th pipeline position point in the corresponding area of the pipeline to be evaluated, is the standard deviation of the lateral turbulent velocity of the th leakage diffusion point of the vehicle-mounted platform and the th pipeline position point in the corresponding area of the pipeline 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 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.

7. The method for precise positioning of gas leakage based on a vehicle-mounted platform according to claim 4, 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: ； wherein, is the leakage likelihood function of 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 of 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, 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.

8. The method for precise positioning of gas leakage based on a vehicle-mounted platform according to claim 1, wherein 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: Conduct a comprehensive analysis of the posterior distribution of leakage of the pipeline network to be evaluated to obtain several leakage sources of the pipeline network to be evaluated; Based on each leakage source of the pipeline network to be evaluated, generate the corresponding confidence interval, and conduct a 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 values.

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