A vehicle, dog, and portable instrument three-linkage gas leakage detection method

Abstract

The application provides a car, dog, portable instrument three linkage gas leakage detection method, relates to the gas pipe network inspection technical field, and the research model of the relationship between the inspection vehicle speed, the chamber temperature, the chamber pressure, the chamber water vapor concentration and the spectral response data is established based on the multiple linear regression model analysis, the relationship is used for establishing the correction model, the correction model can correct the measured spectral response according to the real-time measured vehicle speed, temperature, pressure, particle, water vapor concentration, obtain the first evaluation threshold A and the second evaluation threshold B generated about the gas-liquid separation filter core and the external particle filter respectively, and the gas-liquid separation filter core state evaluation index QYPg and the external particle filter evaluation index KLPg distinguish the use state according to the first evaluation threshold A and the second evaluation threshold B respectively.

Description

Technical Field

[0001] This invention relates to the field of gas pipeline inspection technology, specifically a three-linkage gas leak detection method using a vehicle, a dog, and a portable instrument. Background Technology

[0002] Existing vehicle-mounted detection systems, when passing a natural gas leak location, detect an increase in methane / ethane concentration, reaching a peak and then decreasing. According to controlled experiments, when the peak methane / ethane concentration exceeds a set value, and multiple passes at similar locations at different times consistently show a simultaneous increase in methane / ethane concentration, a leak can be identified. Mid-infrared laser absorption spectroscopy is an effective technique for gas detection. In distinguishing different gases and eliminating interference, the selection of spectral characteristics and wavelength plays a crucial role. Methane and ethane have unique absorption spectral characteristics in the mid-infrared region; they absorb different amounts of energy under different wavelengths of light. This difference can be used to distinguish them. After detecting a gas leak area... Map marking is performed, followed by manual inspection or secondary confirmation by trained inspection dogs to avoid mismarking of gas leaks. Due to the high dust levels on urban roads, an external particulate filter is required for filtration. Water vapor has significant absorption characteristics in the mid-infrared band and alters the optical path, causing changes in the light propagation path. The gas-liquid separator filter element is used to reduce the water vapor concentration in the inhaled gas to a normal standard value. The usage status of the existing gas-liquid separator filter element and external particulate filter is observed visually. Specifically, the gas-liquid separator filter element should be replaced when it changes color or expires, and the external particulate filter should be replaced when there are obvious particles on the surface or expires.

[0003] The prior art, disclosed in CN107420745B, provides a method for determining the detection range and coverage rate of urban gas pipeline networks. The method for determining the detection range includes: detecting real-time wind direction and speed during the movement of the pipeline network detection equipment; calculating the real-time angle between the wind direction and the direction of movement of the equipment; obtaining the detection distances on both sides of the equipment under windless conditions; then calculating the actual detection distances on both sides of the detection vehicle during the actual detection process to determine the actual detection coverage range of the vehicle; and, in conjunction with an urban gas pipeline network map, determining the pipeline length covered by the detection coverage range. The method for determining the detection coverage rate includes using the pipeline length covered by the detection range and the total length of all pipelines to determine the detection coverage rate. This invention provides a scientific and reasonable method for determining the detection range and coverage rate of urban gas pipeline networks, and can quantitatively calculate the pipeline network detection coverage rate, thereby determining the detection range in a timely and effective manner and avoiding the generation of detection blind spots.

[0004] The shortcomings are as follows:

[0005] A patent with publication number CN107420745B, which describes a method for determining the detection range and coverage of urban gas pipeline networks, is used to measure the detection coverage of pipeline network detection equipment. For gas leak detection equipment using a mid-infrared laser cavity, blockage in either the external particulate filter or the gas-liquid separator will affect the inlet pressure of the mid-infrared laser cavity. Increased inlet resistance leads to increased gas pressure within the chamber (mid-infrared laser cavity, optical detection chamber). Furthermore, when the external particulate filter is blocked, the blockage prevents timely dissipation of heat generated by the spectral element, further impacting the chamber's pressure. The increase in internal temperature and water vapor concentration can affect the accuracy of the detection results of the spectral elements. Furthermore, for gas leak detection equipment using a mid-infrared laser cavity, there is no research on the relationship between inspection vehicle speed, chamber temperature, chamber pressure, chamber water vapor concentration, and spectral response data. The current direction for improvement is to establish a research model that studies the relationship between inspection vehicle speed, chamber temperature, chamber pressure, chamber water vapor concentration, and spectral response data, and to generate a first and second evaluation threshold based on the research model to identify the usage status of the gas-liquid separation filter element and the external particulate filter.

[0006] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The purpose of this invention is to provide a three-linkage gas leak detection method involving a vehicle, a dog, and a portable device to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A three-linked gas leak detection method involving a vehicle, a patrol dog, and a portable gas leak detector includes a three-linked gas leak detection device that acquires leak data of the gas pipeline network along the detection path via a detection chamber on a patrol vehicle, and performs re-inspection based on the leak data by a patrol dog and a portable gas leak detector. Specific steps include:

[0010] S1. The inspection vehicle's speed and positioning data are collected by the data acquisition card on the inspection vehicle while it is in operation; the chamber temperature, chamber particle concentration, chamber pressure, water vapor concentration, and spectral response data of the optical detection chamber are obtained while it is in operation. The spectral response data is the absorption spectrum characteristics of the CH4 / C2H6 ratio. The data of the inspection vehicle's working state and the optical detection chamber's operating state constitute the data acquisition module and are transmitted to the terminal data processing module.

[0011] S2. Obtain data on the working status of the inspection vehicle and the operating status of the optical detection chamber, and perform dimensional operations to normalize the data. Analyze and process the normalized values ​​of water vapor concentration and chamber pressure after dimensionality and generate the gas-liquid separation filter element status evaluation index QYPg. Analyze and process the normalized values ​​of chamber particle concentration, chamber temperature, and chamber pressure after dimensionality and generate the external particle filter evaluation index KLPg.

[0012] S3. Obtain the spectral response data of the inspection vehicle and the optical detection chamber under different working and operating states in the historical data, and perform dimensional operations to normalize them. Use the data of the inspection vehicle working state and the optical detection chamber operating state as independent variables and the spectral response data as dependent variables to construct a multiple linear regression model. Based on the relationship between the independent variables in the constructed multiple linear regression model, generate a correction model for result calibration.

[0013] S4. Acquire the absorption spectral characteristic data of the CH4 / C2H6 ratio as the spectral response data, as well as the data of the inspection vehicle's working status and the optical detection chamber's operating status. Based on the comprehensive information, determine the location of the suspected leak point.

[0014] S5. Use the correction model to calibrate the data of the inspection vehicle's working state and the optical detection chamber's operating state. Based on the performance changes of the gas-liquid separation filter element and the external particulate filter, through the analysis of historical data or experimental data, determine the first evaluation threshold A and the second evaluation threshold B corresponding to the gas-liquid separation filter element's state evaluation index QYPg and the external particulate filter's evaluation index KLPg, respectively. Based on the first evaluation threshold A and the second evaluation threshold B, judge the degree of blockage of the gas-liquid separation filter element and the external particulate filter, respectively.

[0015] Compared with the prior art, the beneficial effects of the present invention are:

[0016] A research model based on multiple linear regression analysis was established to study the relationship between inspection vehicle speed, chamber temperature, chamber pressure, chamber water vapor concentration and spectral response data. The extracted relationship was used to establish a correction model. This correction model can correct the measured spectral response based on the real-time measured vehicle speed, temperature, pressure, particle and water vapor concentration. After obtaining historical data or experimental data, a first evaluation threshold A and a second evaluation threshold B were generated for the gas-liquid separation filter element and the external particulate filter, respectively. The gas-liquid separation filter element status evaluation index QYPg and the external particulate filter evaluation index KLPg were used to identify the usage status based on the first evaluation threshold A and the second evaluation threshold B, respectively. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall method flow of the present invention;

[0018] Figure 2 This is a flowchart of the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0020] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0021] Please see Figure 1 and Figure 2 The present invention provides a technical solution:

[0022] Example 1:

[0023] A three-linked gas leak detection method involving a vehicle, a patrol dog, and a portable gas leak detector includes a three-linked gas leak detection device that acquires leak data of the gas pipeline network along the detection path via a detection chamber on a patrol vehicle, and performs re-inspection based on the leak data by a patrol dog and a portable gas leak detector. Specific detection steps include:

[0024] S1. The inspection vehicle's speed and positioning data are collected by the data acquisition card on the inspection vehicle while it is in operation; the chamber temperature, chamber particle concentration, chamber pressure, water vapor concentration, and spectral response data of the optical detection chamber are obtained while it is in operation. The spectral response data is the absorption spectrum characteristics of the CH4 / C2H6 ratio. The data of the inspection vehicle's working state and the optical detection chamber's operating state constitute the data acquisition module and are transmitted to the terminal data processing module.

[0025] S2. Obtain data on the working status of the inspection vehicle and the operating status of the optical detection chamber, and perform dimensional operations to normalize the data. Analyze and process the normalized values ​​of water vapor concentration and chamber pressure after dimensionality and generate the gas-liquid separation filter element status evaluation index QYPg. Analyze and process the normalized values ​​of chamber particle concentration, chamber temperature, and chamber pressure after dimensionality and generate the external particle filter evaluation index KLPg.

[0026] S3. Obtain the spectral response data of the inspection vehicle and the optical detection chamber under different working and operating states in the historical data, and perform dimensional operations to normalize them. Use the data of the inspection vehicle working state and the optical detection chamber operating state as independent variables and the spectral response data as dependent variables to construct a multiple linear regression model. Based on the relationship between the independent variables in the constructed multiple linear regression model, generate a correction model for result calibration.

[0027] S4. Acquire the absorption spectral characteristic data of the CH4 / C2H6 ratio as the spectral response data, as well as the data of the inspection vehicle's working status and the optical detection chamber's operating status. Based on the comprehensive information, determine the location of the suspected leak point.

[0028] S5. Use the correction model to calibrate the data of the inspection vehicle's working state and the optical detection chamber's operating state. Based on the performance changes of the gas-liquid separation filter element and the external particulate filter, through the analysis of historical data or experimental data, determine the first evaluation threshold A and the second evaluation threshold B corresponding to the gas-liquid separation filter element's state evaluation index QYPg and the external particulate filter's evaluation index KLPg, respectively. Based on the first evaluation threshold A and the second evaluation threshold B, judge the degree of blockage of the gas-liquid separation filter element and the external particulate filter, respectively.

[0029] Example 2:

[0030] Based on Example 1, the vehicle speed is the inspection vehicle speed parameter XJCs obtained by the vehicle speed sensor;

[0031] The location data refers to the real-time position and direction data obtained by the inspection vehicle through the positioning module;

[0032] The chamber temperature is the chamber temperature parameter QSWd obtained by the temperature sensor;

[0033] The chamber particle concentration is the chamber particle concentration parameter QSKl obtained by the particle concentration sensor;

[0034] The chamber pressure is the chamber pressure parameter QSYl obtained by the pressure sensor and the external air inlet pressure characteristic WQk.

[0035] The water vapor concentration is the chamber water vapor concentration parameter QSZq and the external air inlet water vapor concentration characteristic WSZq obtained by the water vapor sensor;

[0036] The spectral response data is the spectral response data GPSj acquired by the analyzer;

[0037] The hardware of the data acquisition module includes an analyzer, a water vapor sensor, a particle concentration sensor, a pressure sensor, a temperature sensor, a Beidou positioning module, an anemometer, and a data acquisition card. The data acquisition card collects data from the analyzer host, the Beidou positioning module, the water vapor sensor, the pressure sensor, the temperature sensor, the particle concentration sensor, and the anemometer in real time. The data acquisition card communicates with the sensors using a serial communication protocol.

[0038] The terminal data processing module includes a vehicle-mounted terminal and a dispatch center terminal. The vehicle-mounted terminal is used by inspection personnel in the inspection vehicle during inspections. The dispatch center terminal is used by dispatch personnel, who connect to the server via the gas company's intranet and manage the system on their office computers. The main functions of the vehicle-mounted terminal are to load GIS map pipeline data, receive sensor data in real time, calculate gas diffusion and leakage, search for pipeline leaks, and display the original sensor data (inspection route, gas concentration) and calculation results (diffusion concentration information, leak point information) on a navigation map, such as Gaode Map or Baidu Map. At the same time, it sends the sensor data, calculation data, and parameters used in the calculation process to a remote server. The vehicle-mounted terminal communicates with the data acquisition card using the TCP protocol, and the dispatch center terminal communicates with the remote server using the HTTPS protocol.

[0039] Example 3:

[0040] Based on Example 2, the dimensional operation of the normalization process is further explained as follows:

[0041] The acquired inspection vehicle speed parameter XJCs, chamber temperature parameter QSWd, chamber particle concentration parameter QSKl, spectral response data GPSj, chamber pressure parameter QSYl, chamber water vapor concentration parameter QSZq, external air inlet end pressure characteristic WQk, and external air inlet end water vapor concentration characteristic WSZq are normalized and mapped to the following value range:

[0042] 0≤XJCs≤1, 0≤QSWd≤1, 0≤QSKl≤1, 0≤QSYl≤1, 0≤QSZq≤1, 0≤WQk≤1, 0≤WSZq≤1, 0≤GPSj≤1, the normalization calculation formula is as follows:

[0043]

[0044] Where GY represents the numerical normalization target, X GYIn the formula, all X data are of the same type, Ys represents the original value, max represents the maximum value in the collected data, and min represents the minimum value. X can be any one of the following: inspection vehicle speed parameter XJCs, chamber temperature parameter QSWd, chamber particle concentration parameter QSKl, spectral response data GPSj, chamber pressure parameter QSYl, chamber water vapor concentration parameter QSZq, external air inlet end pressure characteristic WQk, and external air inlet end water vapor concentration characteristic WSZq. The corresponding normalized target XJCs is obtained sequentially. GY QSWd GY QSKl GY GPSj GY QSYl GY QSZq GY WQk GY WSZq GY ;

[0045] The formula for calculating the gas-liquid separation filter element condition assessment index QYPg is as follows:

[0046]

[0047] Where a1, a2, a3, a4 and a5 are weighting factors, and a1+a2+a3+a4+a5=1, ε1 is the influence factor, 0.15≤ε1≤0.87;

[0048] The formula for calculating the external particulate filter evaluation index KLPg is as follows:

[0049]

[0050] Where b1, b2, b3, and b4 are weighting factors, and b1+b2+b3+b4=1, ε2 is an influence factor, 0.25≤ε1≤0.95.

[0051] Example 4:

[0052] Based on Example 3, the specific details of performing multiple linear regression model analysis based on the detection results are as follows:

[0053] The normalized collection dataset XJCs GY QSWd GY QSKl GY GPSj GY QSYl GY QSZq GY The dataset is fitted using a multiple linear regression model, as shown in the following formula:

[0054] GPSj GY =β0+β1×XJCs GY +β2×QSWdGY +β3×QSKl GY +β4×QSYl GY +β5×QSZq GY +∈

[0055] Where β0, β1, β2, β3, β4, and β5 are regression coefficients, and ∈ represents the error term. Regression analysis tools (e.g., the Scikit-learn library in Python, the lm() function in R) are used to fit the selected regression model to the processed data, yielding the values ​​of β0, β1, β2, β3, β4, and β5. Each coefficient represents the response of each independent variable to the spectral response GPSj. GY The relative contribution, i.e., the degree of influence of each factor on the detection result, is used to establish a spectral response correction model based on the extracted relationships. The spectral response correction formula is GPSj. Jz As shown below:

[0056] GPSj Jz =GPSj GY -β1×XJCs GY -β2×QSWd GY -β3×QSKl GY -β4×QSYl GY -β5×QSZq GY

[0057] The corrected spectral response GPSj was obtained using the correction model. Jz Extract the normalized values ​​of chamber pressure parameter QSYl, chamber temperature parameter QSWd, chamber particle concentration parameter QSKl, and chamber water vapor concentration parameter QSZq from historical or experimental data. Set the number of observations for pressure, temperature, particle concentration, and water vapor concentration to n, and calculate the normalized mean of the corresponding parameters sequentially. The calculation formula is as follows:

[0058]

[0059] Where X represents the chamber pressure parameter QSYl GY Chamber temperature parameter QSWd GY Chamber particle concentration parameter QSKl GY Chamber water vapor concentration parameter QSZq GY Any dataset of type X i This represents the i-th observation in the dataset;

[0060] Next, calculate the population standard deviation using the following formula:

[0061]

[0062] The formula for calculating the first evaluation threshold A is:

[0063]

[0064] The formula for calculating the second evaluation threshold B is:

[0065]

[0066] K is used to determine the sensitivity of the corresponding threshold. Adjusting K can change the sensitivity of the threshold and the strictness of detection according to the needs of actual applications.

[0067] Example 5:

[0068] Based on Example 4, further explanation is provided regarding the condition assessment of the gas-liquid separation filter element, specifically by obtaining the water vapor concentration characteristic WSZq at the external air inlet. GY With the chamber water vapor concentration parameter QSZq GY The ratio is calculated and a comparison value C is obtained. C represents the filtration capacity of the gas-liquid separator filter element. The closer the value of C is to 0, the stronger the water vapor filtration capacity of the gas-liquid separator filter element. Simultaneously, QSZq is set. GY The upper limit of the threshold is Setting WSZq GY upper limit of oversaturation The upper limit of the judgment threshold for comparison value C is set as follows: Where m is a value less than 1, and is adaptively adjusted according to the type of optical detection element in the cavity;

[0069]

[0070] QSZq GY With WSZq GY All values ​​of are non-negative.

[0071] When the gas-liquid separator filter element condition assessment index QYPg is less than the first assessment threshold A, the corresponding... and This indicates that the gas-liquid separator filter element can effectively filter water vapor entrained in the gas discharged from the external air inlet. At the same time, a threshold set R is set for temperature and pressure values. If the temperature and pressure values ​​are within the threshold set R, the corresponding gas-liquid separator filter element is slightly clogged or not clogged.

[0072] When the gas-liquid separator filter element condition assessment index QYPg equals the first assessment threshold A, the corresponding... This indicates that the gas-liquid separator filter element can moderately filter water vapor entrained in the gas discharged from the external air inlet. The temperature and pressure values ​​are within the threshold set R, corresponding to a moderate level of clogging in the gas-liquid separator filter element, with half of its preset lifespan remaining.

[0073] When the gas-liquid separator filter element condition assessment index QYPg is greater than the first assessment threshold A, the corresponding... There are two possible scenarios at this point. The first is that the corresponding gas-liquid separator filter element is severely clogged, with temperature and pressure values ​​outside the threshold set R, resulting in poor water vapor filtration capacity. The second is that the water vapor concentration in the external environment is too high, causing the gas-liquid separator filter element to be in a supersaturated state, requiring dehumidification before normal use. The judgment is based on the following: when... and The initial assessment indicated that the gas-liquid separator filter element was in a supersaturated state. Afterwards, the liquid separator filter element was dehumidified and discharged into a system that met the required standards. If the gas is still and This indicates that the corresponding gas-liquid separator filter element is severely clogged.

[0074] Example 6:

[0075] Building upon Example 5, further explanation is provided regarding the assessment of the external particulate filter status, specifically obtaining the normalized value QSKl of the chamber particulate concentration parameter. GY and set QSKl GY The upper limit of the threshold is

[0076] When the external particulate filter evaluation index KLPg is less than the second evaluation threshold B, at this time... Temperature and pressure values ​​within the threshold set R indicate that the external particulate filter is slightly clogged or not clogged at all.

[0077] When the external particulate filter evaluation index KLPg equals the second evaluation threshold B, at this time... Temperature and pressure values ​​within the threshold set R indicate that the external particulate filter is moderately clogged.

[0078] When the external particulate filter evaluation index KLPg is greater than the second evaluation threshold B, at this time... Temperature and pressure values ​​outside the threshold set R indicate that the external particulate filter is severely clogged.

[0079] Example 7:

[0080] Based on Example 6, the output of the correction model is further connected to the functional parameter alarm module and the gas leak alarm module, respectively.

[0081] For the functional parameter alarm module: set the chamber pressure range, chamber temperature range, and chamber temperature range, configure the normal operating speed of the inspection vehicle, generate an alarm when the normal operating range is exceeded, and indicate that all parameters collected by the sensor at this time are invalid. The usage status of the gas-liquid separation filter and the external particulate filter is evaluated according to the first evaluation threshold A and the second evaluation threshold B respectively, and an alarm is generated when the evaluation is abnormal.

[0082] For the gas leak alarm module, a leak alarm is issued when the gas concentration corresponding to the spectral response data GPSj exceeds a preset threshold. The specific steps are as follows:

[0083] If a gas leak inspection vehicle travels to a location where the concentration of methane or ethane exceeds 110% of the standard concentration, it is considered an abnormal methane or ethane concentration. The baseline value is the median of the methane / ethane concentration values ​​measured within the distance range of [-2km, 0km]. Abnormal methane and ethane concentrations are alerted separately. All abnormal methane / ethane concentrations should be marked on the map and a corresponding data table should be generated. If an increase in methane concentration is detected, and the detected methane / ethane ratio is equivalent to or lower than that in the natural gas composition, it is considered a suspected natural gas leak.

[0084] If both methane and ethane concentrations are elevated, and the detected methane / ethane ratio is significantly higher than that in the natural gas composition, it is determined that both a suspected natural gas leak and a suspected biogas leak are present simultaneously.

[0085] If the methane concentration increases but the ethane concentration does not, it is judged to be a suspected biogas.

[0086] If neither the methane nor the ethane concentration reaches 110% of the baseline concentration, it is considered normal.

[0087] If the same location (where the direct distance between two concentration rise points is less than 30 meters, it is considered the same location) is found to meet the "suspected leak" condition only once, it can only be identified as a "suspected natural gas leak" and not a "natural gas leak". If the "suspected leak" condition is met twice or more (with an interval of more than 10 minutes between the two times), it can be identified as a "highly suspected natural gas leak". The fields that the highly suspected leak information table should include are as follows:

[0088] If a precise natural gas leak point (including the natural gas outlet on the ground and the location of the pipeline leak) is found using handheld instruments or trained gas detection dogs, it is considered a confirmed natural gas leak, and the confirmed natural gas leak information table contains the fields listed.

[0089] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0090] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by software, electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0091] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0092] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A three-linked gas leak detection method involving a vehicle, a patrol dog, and a portable gas leak detector, comprising a three-linked gas leak detection device that acquires leak data of the gas pipeline network along the detection path via a detection chamber on a patrol vehicle, and performs re-inspection based on the leak data by a patrol dog and a portable gas leak detector, characterized in that... The specific steps include: S1. The inspection vehicle's speed and positioning data are collected by the data acquisition card on the inspection vehicle while it is in operation; the chamber temperature, chamber particle concentration, chamber pressure, water vapor concentration, and spectral response data of the optical detection chamber are obtained while it is in operation. The spectral response data is the absorption spectrum characteristics of the CH4 / C2H6 ratio. The data of the inspection vehicle's working state and the optical detection chamber's operating state constitute the data acquisition module and are transmitted to the terminal data processing module. S2. Obtain data on the working status of the inspection vehicle and the operating status of the optical detection chamber, and perform dimensional operations to normalize the data. Analyze and process the normalized values ​​of water vapor concentration and chamber pressure after dimensionality and generate the gas-liquid separation filter element status evaluation index QYPg. Analyze and process the normalized values ​​of chamber particle concentration, chamber temperature, and chamber pressure after dimensionality and generate the external particle filter evaluation index KLPg. S3. Obtain the spectral response data of the inspection vehicle and the optical detection chamber under different working and operating states in the historical data, and perform dimensional operations to normalize them. Use the data of the inspection vehicle working state and the optical detection chamber operating state as independent variables and the spectral response data as dependent variables to construct a multiple linear regression model. Based on the relationship between the independent variables in the constructed multiple linear regression model, generate a correction model for result calibration. S4. Acquire the absorption spectral characteristic data of the CH4 / C2H6 ratio as the spectral response data, as well as the data of the inspection vehicle's working status and the optical detection chamber's operating status. Based on the comprehensive information, determine the location of the suspected leak point. S5. Use the correction model to calibrate the data of the inspection vehicle's working state and the optical detection chamber's operating state. Based on the performance changes of the gas-liquid separation filter element and the external particulate filter, through the analysis of historical data or experimental data, determine the first evaluation threshold A and the second evaluation threshold B corresponding to the gas-liquid separation filter element status evaluation index QYPg and the external particulate filter evaluation index KLPg, respectively. Based on the first evaluation threshold A and the second evaluation threshold B, judge the degree of blockage of the gas-liquid separation filter element and the external particulate filter, respectively. The specific analysis of the multiple linear regression model based on the test results is as follows: The collected dataset after normalization , , , , , The dataset is fitted using a multiple linear regression model, as shown in the following formula: ； in , , , , , For regression coefficients, For the error term, use regression analysis tools to fit the selected regression model to the cleaned data, and obtain... Numerical values, where each coefficient represents the spectral response of each independent variable. The relative contribution of each factor, i.e., the degree of influence of each factor on the detection result, is used to establish a spectral response correction model based on the extracted relationships. The spectral response correction formula is... As shown below: The corrected spectral response was obtained using the corrected model. In historical or experimental data, the number of observations for pressure, temperature, particle concentration, and water vapor concentration is set to n. The normalized mean of the corresponding parameters is calculated sequentially using the following formula: Where X represents the chamber pressure parameter Chamber temperature parameters Chamber particle concentration parameters Chamber water vapor concentration parameters Any of the datasets in the dataset, This represents the i-th observation in the dataset; Next, calculate the population standard deviation using the following formula: The formula for calculating the first evaluation threshold A is: The formula for calculating the second evaluation threshold B is: K is used to determine the sensitivity of the corresponding threshold. Adjusting K can change the sensitivity of the threshold and the strictness of detection according to the needs of actual applications.

2. The method for detecting gas leaks using a vehicle, dog, and portable device in a three-way linkage as described in claim 1, characterized in that: The vehicle speed is the inspection vehicle speed parameter XJCs obtained by the vehicle speed sensor; the positioning data is the real-time position and direction data obtained by the inspection vehicle through the positioning module; the chamber temperature is the chamber temperature parameter QSWd obtained by the temperature sensor; the chamber particle concentration is the chamber particle concentration parameter QSKl obtained by the particle concentration sensor; the chamber pressure is the chamber pressure parameter QSYl obtained by the pressure sensor and the external air inlet pressure characteristic WQk; the water vapor concentration is the chamber water vapor concentration parameter QSZq obtained by the water vapor sensor and the external air inlet water vapor concentration characteristic WSZq; the spectral response data is the spectral response data GPSj obtained by the analyzer. The hardware of the data acquisition module includes an analyzer, a water vapor sensor, a particle concentration sensor, a pressure sensor, a temperature sensor, a Beidou positioning module, an anemometer, and a data acquisition card. The data acquisition card collects data from the analyzer host, the Beidou positioning module, the water vapor sensor, the pressure sensor, the temperature sensor, the particle concentration sensor, and the anemometer in real time. The data acquisition card communicates with the sensors using a serial communication protocol. The terminal data processing module includes an in-vehicle terminal and a dispatch center terminal, which send sensor data, calculation data, and parameters used in the calculation process to a remote server. The in-vehicle terminal communicates with the data acquisition card using the TCP protocol, and the dispatch center terminal communicates with the remote server using the HTTPS protocol.

3. The method for detecting gas leaks using a vehicle, dog, and portable device in a three-way linkage as described in claim 2, characterized in that: The dimensional operation of the normalization process is as follows: The acquired inspection vehicle speed parameter XJCs, chamber temperature parameter QSWd, chamber particle concentration parameter QSKl, spectral response data GPSj, chamber pressure parameter QSYl, chamber water vapor concentration parameter QSZq, external air inlet end pressure characteristic WQk, and external air inlet end water vapor concentration characteristic WSZq are normalized and mapped to the following value range: 0≤XJCs≤1, 0≤QSWd≤1, 0≤QSKl≤1, 0≤QSYl≤1, 0≤QSZq≤1, 0≤WQk≤1, 0≤WSZq≤1, 0≤GPSj≤1, the normalization calculation formula is as follows: Where GY represents the numerical normalization standard, In the formula, all X data are of the same type, Ys represents the original value, max represents the maximum value in the collected data, and min represents the minimum value. X can be any one of the following: inspection vehicle speed parameter XJCs, chamber temperature parameter QSWd, chamber particle concentration parameter QSKl, spectral response data GPSj, chamber pressure parameter QSYl, chamber water vapor concentration parameter QSZq, external air inlet end pressure characteristic WQk, and external air inlet end water vapor concentration characteristic WSZq. The corresponding normalized targets are obtained sequentially. , , , , , , , ; The gas-liquid separator filter element condition assessment index The calculation formula is: Where a1, a2, a3, a4, and a5 are weighting factors, and , As the impact factor, ; The external particulate filter evaluation index The calculation formula is: Where b1, b2, b3, and b4 are weighting factors, and , Impact factor, 0.25 ≤ ≤0.

95.

4. The method for detecting gas leaks using a vehicle, dog, and portable device in a three-way linkage as described in claim 3, characterized in that: For the condition assessment of the gas-liquid separator filter element, the water vapor concentration characteristics at the external air inlet are obtained. With chamber water vapor concentration parameters The ratio is calculated and a comparison value C is obtained. C represents the filtration capacity of the gas-liquid separator filter element. The closer the value of C is to 0, the stronger the water vapor filtration capacity of the gas-liquid separator filter element. Simultaneously, the following settings are configured: The upper limit of the threshold is ,set up upper limit of oversaturation The upper limit of the judgment threshold for comparison value C is set as follows: , , where m is a value less than 1, and is adaptively adjusted according to the type of optical detection element in the cavity; in and All values ​​of are non-negative. When the gas-liquid separator filter element condition assessment index QYPg is less than the first assessment threshold A, the corresponding... ,and This indicates that the gas-liquid separator filter element can effectively filter water vapor entrained in the gas discharged from the external air inlet. At the same time, a threshold set R is set for temperature and pressure values. If the temperature and pressure values ​​are within the threshold set R, the corresponding gas-liquid separator filter element is slightly blocked or not blocked. When the gas-liquid separator filter element condition assessment index QYPg equals the first assessment threshold A, the corresponding C is... This indicates that the gas-liquid separator filter element can perform moderate filtration of water vapor entrained in the gas discharged from the external air inlet. The temperature and pressure values ​​are within the threshold set R, corresponding to a moderate level of clogging in the gas-liquid separator filter element, with half of its preset lifespan remaining. < ; When the gas-liquid separator filter element condition assessment index QYPg is greater than the first assessment threshold A, the corresponding... There are two possible scenarios at this point. The first is that the corresponding gas-liquid separator filter element is severely clogged, with temperature and pressure values ​​outside the threshold set R, resulting in poor water vapor filtration capacity. The second is that the water vapor concentration in the external environment is too high, causing the gas-liquid separator filter element to be in a supersaturated state, requiring dehumidification before normal use. The judgment is based on the following: when... ,and The initial assessment indicated that the gas-liquid separator filter element was in a supersaturated state. Afterwards, the liquid separator filter element was dehumidified and discharged into a system that met the required standards. If the gas is still ,and This indicates that the corresponding gas-liquid separator filter element is severely clogged.

5. The method for detecting gas leaks using a vehicle, dog, and portable device in a three-way linkage as described in claim 4, characterized in that: For external particulate filter status assessment, obtain the normalized value of the chamber particulate concentration parameter. and set The upper limit of the threshold is ； When the external particulate filter evaluation index KLPg is less than the second evaluation threshold B, at this time... If the temperature and pressure values ​​are within the threshold set R, it indicates that the external particulate filter is slightly clogged or not clogged at all. When the external particulate filter evaluation index KLPg equals the second evaluation threshold B, at this time... If the temperature and pressure values ​​are within the threshold set R, it indicates that the external particulate filter is moderately clogged. When the external particulate filter evaluation index KLPg is greater than the second evaluation threshold B, at this time... If the temperature and pressure values ​​are outside the threshold set R, it indicates that the external particulate filter is severely clogged.

6. The method for detecting gas leaks using a vehicle, dog, and portable device in a three-way linkage as described in claim 5, characterized in that: The output of the correction model is connected to the functional parameter alarm module and the gas leak alarm module respectively. Using a handheld instrument or a trained gas detection dog, the exact natural gas leak point was found.

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