Intelligent ground mobile high-precision gas leakage inspection and source tracking system

Abstract

The invention discloses an intelligent ground mobile high-precision gas leakage inspection and source tracking system, and relates to the technical field of gas pipe network leakage detection and leakage point positioning analysis. The system is mainly composed of a mobile inspection platform, a mid-infrared laser detection device, a three-dimensional anemograph, an inertial navigation module, a GIS system, a GNSS navigation positioning system, a wireless communication module and an intelligent detection comprehensive platform, and multi-source information fusion calculation, a dynamic adaptive analysis model and a maximum likelihood estimation method are combined. According to the intelligent gas leakage detection system, autonomous inspection can be achieved in a complex environment, abnormal gas component data can be processed in real time, detected gas space-time distribution information is transmitted to an intelligent comprehensive platform through a wireless communication module, data sharing and monitoring are completed, and efficient detection and accurate positioning of gas leakage are achieved. The method is suitable for gas leakage monitoring and component tracking in the fields of urban gas pipe networks, industrial parks and environmental protection, and the efficiency and precision of gas detection are remarkably improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of gas pipeline network leakage detection and leakage point location analysis, and in particular relates to an intelligent ground mobile high-precision gas leakage inspection and source tracking system. Background Art

[0002] With the acceleration of urbanization and the rapid development of industry, gas leakage has become a serious safety hazard. Frequent leakage accidents not only bring huge economic losses, but also pose a threat to human life safety and environmental sustainability. The diffusion law of gas leakage is affected by a variety of environmental factors (such as wind speed, temperature, etc.) and leakage source characteristics (such as leakage point height, pressure, etc.), showing complex spatiotemporal characteristics. Traditional gas leak detection technology mainly relies on a single sensor for local monitoring, which is difficult to achieve comprehensive coverage of a large range of leakage areas, and it is also difficult to accurately track the source of the leak in a complex environment. Due to the limited monitoring range, insufficient positioning accuracy, and lagging response speed of traditional methods, it is unable to meet the increasingly complex gas safety management needs. Therefore, it is necessary to design an intelligent gas leak detection and source tracking system that can realize real-time monitoring and precise positioning, effectively improve the inspection efficiency and accuracy of gas leaks, and provide reliable technical guarantees for emergency response and prevention of gas leak accidents. Summary of the invention

[0003] The purpose of the present invention is to provide an intelligent ground mobile high-precision gas leak inspection and source tracking system to solve the problems of insufficient coverage, inaccurate positioning and delayed data response in traditional methods of gas leak monitoring, thereby improving inspection efficiency and positioning accuracy and protecting the environment and public safety.

[0004] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0005] The present invention is an intelligent ground mobile high-precision gas leak inspection and source tracking system, comprising:

[0006] The mobile inspection platform has a multifunctional integrated interface, a mobile chassis adapted to complex terrain and a stabilized load frame, which is used to carry and coordinate the various functional modules of the system;

[0007] The mid-infrared laser detection device is fixedly installed on the front of the mobile inspection platform, and includes a mid-infrared laser transmitting unit, a receiving unit, and a high-sensitivity sensor array for detecting gas composition;

[0008] The three-dimensional anemometer is arranged on the top of the mobile inspection platform, including a wind speed measurement sensor, a wind direction sensing unit and a stable mounting bracket, and is used for all-round airflow data collection;

[0009] The GNSS navigation and positioning module is built into the mobile inspection platform and includes a high-precision satellite positioning receiving unit, a multi-band satellite signal receiver, a built-in signal amplifier, an anti-interference module and a real-time data calibration function, and is used to transmit positioning information in real time;

[0010] The inertial navigation module is built into the mobile inspection platform and consists of an inertial measurement unit, an accelerometer, a gyroscope, and a data fusion module to provide continuous position information;

[0011] GIS module, which integrates a high-precision geographic information database, includes a data storage unit and a map generation module, and has built-in path planning algorithms and obstacle avoidance algorithms for generating environmental geographic information data;

[0012] The central processing unit integrates the multi-source data acquisition and processing unit from the mid-infrared laser detector, three-dimensional anemometer, inertial navigation module and GIS module, and integrates the Gaussian diffusion model, source data inversion unit and intelligent decision-making and control unit;

[0013] The wireless communication module is designed as a multi-band module compatible with multiple communication modes, which is used to transmit the collected data to the intelligent detection integrated platform in real time and receive instructions from the intelligent detection integrated platform;

[0014] The intelligent detection integrated platform, consisting of a data acquisition unit, a data analysis module, a real-time display terminal and a remote control interface, can realize leak location, range prediction, level judgment and command issuance.

[0015] As a preferred technical solution of the present invention, the mobile inspection platform is a self-service navigation robot or an unmanned vehicle, and the mid-infrared laser detection device adopts tunable laser diode (TDLAS) technology.

[0016] As a preferred technical solution of the present invention, the GNSS navigation and positioning module is combined with the inertial navigation module data, and the noise and error are eliminated through the Kalman filtering algorithm to ensure the accuracy of long-term navigation; the GIS module adopts the A* algorithm or the Dijkstra algorithm for path planning, and quickly generates the optimal path through the heuristic search method.

[0017] As a preferred technical solution of the present invention, the central processing unit adopts a Gaussian diffusion model to perform real-time calculation of the diffusion path of the gas, utilizes the wind speed, wind direction data and gas concentration distribution data of the three-dimensional anemometer to dynamically calculate the three-dimensional distribution of the gas diffusion, locates the leakage source through the maximum likelihood estimation method, and uses a genetic algorithm to optimize the parameters of the leakage source position and emission intensity.

[0018] As a preferred technical solution of the present invention, the Kalman filter algorithm includes a prediction stage and an update stage. The prediction stage uses a state transfer matrix to predict the current position and predict the error covariance; the update stage calculates the Kalman gain based on the current measurement value and the predicted value, corrects the predicted value, and updates the error covariance to obtain an updated state estimate.

[0019] As a preferred technical solution of the present invention, when planning the path, the GIS module first uses sensors and geographic information system map data to perform a preliminary scan of the inspection area to generate an environmental map, then divides the candidate path nodes, calculates the path cost of each node, and selects the node with the smallest total cost to proceed to the next step until the target point is found, and the path is backtracked to generate an inspection path.

[0020] As a preferred technical solution of the present invention, the Gaussian diffusion model is used to predict the diffusion path of the gas, and the distribution of the gas concentration at the position is given by the following formula

[0021]

[0022] As a preferred technical solution of the present invention, the maximum likelihood estimation method finds the most likely location of the leakage source through iterative calculation, and continuously optimizes the location accuracy of the leakage source according to real-time data.

[0023] As a preferred technical solution of the present invention, the genetic algorithm optimization includes initializing the population, fitness evaluation, selection operation, crossover operation, mutation operation and replacement operation, and gradually converges to the global optimal solution through multiple generations of evolution to obtain the optimized leakage source location and emission intensity.

[0024] The present invention has the following beneficial effects:

[0025] The present invention solves the problems of insufficient coverage, inaccurate positioning and delayed data response in traditional methods in gas leak monitoring through an intelligent ground mobile high-precision gas leak detection and source tracking system. First, the system adopts a modularly designed ground mobile platform, equipped with a chassis adapted to complex terrain and a stabilized load frame, to achieve comprehensive inspection of a large area. Secondly, the system adopts a variety of sensing technologies such as mid-infrared laser detection devices, three-dimensional anemometers, inertial navigation modules and GIS modules. Through multi-source data fusion and Gaussian diffusion model calculation, it can accurately detect gas concentration, wind speed and direction, and locate the leakage source in real time, thereby improving positioning accuracy and response speed. Furthermore, the GIS system supports real-time path planning and optimization, and can maintain high-precision navigation in an environment without GPS signals. Finally, the system is equipped with a wireless communication module, supports multiple communication modes such as 4G, 5G, LoRa, etc., ensures the real-time and stability of data transmission, and realizes data visualization and remote control through an intelligent detection integrated platform. In summary, the present invention significantly improves the efficiency and accuracy of gas leak detection and source tracking, and provides a reliable technical guarantee for gas safety management.

[0026] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0028] Figure 1 It is a system framework diagram of the present invention;

[0029] Figure 2 It is the mobile inspection platform in the present invention;

[0030] Figure 3 is the path planning diagram in the present invention;

[0031] Figure 4 The leakage source intensity analysis diagram is for locating the leakage source in the present invention. DETAILED DESCRIPTION

[0032] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Specific embodiment 1

[0034] like Figure 1 As shown, the present invention provides an intelligent ground mobile high-precision gas leak detection and source tracking system, comprising:

[0035] The mobile inspection platform is designed as a modular structure with a multifunctional integrated interface. It is equipped with a mobile chassis and a stabilized load frame that can adapt to complex terrains. It is used to carry and coordinate the various functional modules of the system. It is equipped with a high-performance power supply system and a suspension shock-absorbing device to adapt to various terrain environments.

[0036] The mid-infrared laser detection device is fixedly installed on the front of the inspection platform, including a mid-infrared laser emitting unit, a receiving unit and a high-sensitivity sensor array, with an optical window for detecting gas components and a data output interface, which is used to accurately detect the leaking gas components. ;

[0037] The three-dimensional anemometer is arranged on the top of the platform. Its structure includes a wind speed measurement sensor, a wind direction sensing unit and a stable mounting bracket. It supports all-round airflow data collection and has a USB interface for real-time data transmission. It can be combined with a turbulence propagation simulation unit to analyze the leakage path.

[0038] GNSS navigation and positioning module: Built into the inspection platform, it includes a high-precision satellite positioning receiving unit, supports the GPS satellite system, and is equipped with a data output interface for real-time transmission of positioning information; the module also integrates a signal amplifier, an anti-interference module, and a real-time data calibration function to ensure accurate positioning data in complex environments; by combining GNSS data with INS data, the navigation system can effectively reduce the error caused by time accumulation in the INS system and ensure the accuracy of long-term navigation; GNSS can provide not only horizontal position information, but also height information, helping the system plan inspection routes in three-dimensional space;

[0039] The inertial navigation module (INS), built into the inspection platform, consists of an inertial measurement unit (IMU), an accelerometer, a gyroscope, and a data fusion module, which can measure the acceleration and angular velocity of the platform in real time. By integrating these data, the system can calculate the relative displacement and attitude change of the platform, and provide stable motion information even in the absence of external signals. The system has high sensitivity and high precision, and can maintain continuous tracking of the platform in complex environments (such as signal interference or occlusion areas), ensuring real-time positioning of the inspection platform in dynamic environments;

[0040] GIS module, which integrates a high-precision geographic information database, is equipped with a real-time update interface, contains a data storage unit and a map generation module, and has built-in path planning algorithms and obstacle avoidance algorithms, which are used to generate environmental geographic information data and assist navigation;

[0041] The central processing unit uses a high-performance multi-core processor, equipped with a data processing chipset, storage unit and interface circuit, integrating a Gaussian diffusion model, a source data inversion unit and an intelligent decision-making and control unit, capable of simulating and calculating turbulent propagation, and combining environmental data for real-time analysis and decision-making;

[0042] The wireless communication module is designed as a multi-band module compatible with various communication methods, supports 4G, 5G and LoRa protocols, and includes a signal transmitter, receiver and data encryption module to ensure the stability and security of data transmission;

[0043] The intelligent detection integrated platform consists of a data acquisition unit, a data analysis module, a real-time display terminal and a remote control interface. It has a multi-level information processing architecture and an expandable hardware interface design to achieve leak location, range prediction, level judgment and command issuance.

[0044] In an optional embodiment, if Figure 2 As shown in the figure, the mobile inspection platform integrates the inertial navigation system (INS) and the global navigation satellite system (GNSS) to achieve precise positioning and autonomous navigation of the platform. The specific implementation details are as follows:

[0045] (1) Initialization

[0046] 1) State definition: define the state vector Including the location information (x, y) and speed information (V x ,V y ).

[0047] 2) Initial value setting: Set the initial state estimate and the error covariance matrix P 0|0 .

[0048] Model parameter setting: define the state transfer matrix F k (describes the change of state over time), the observation matrix H k (maps the state to the measurement space), process noise covariance Q and measurement noise covariance R k

[0049] (2) Data Acquisition

[0050] Get the current measurement value Z from the lidar and ultrasonic sensors k ,These measurements will be used to update the position estimate of the mobile inspection vehicle.

[0051] INS system: It consists of accelerometers and gyroscopes, which measure the acceleration and angular velocity of the platform in real time. By integrating these data, the system calculates the relative displacement and attitude change of the platform, and can provide stable motion information even in the absence of external signals. The system has high sensitivity and high precision, and can maintain continuous tracking of the platform in complex environments (such as signal interference or occlusion areas), ensuring real-time positioning of the inspection platform in dynamic environments.

[0052] GNSS module:

[0053] Receive positioning signals from satellites to provide the platform’s absolute position coordinates. GNSS data enables high-precision positioning around the world and provides altitude information to help the system plan inspection paths in three-dimensional space.

[0054] Multi-source data fusion: The navigation and positioning module uses a multi-source data fusion algorithm to combine the data generated by the INS and GNSS systems. Through the Kalman filter algorithm, the noise and error in the data of each sensor are eliminated to ensure high accuracy and reliability of positioning. When the GNSS signal is unavailable (such as in tunnels or densely built areas), the system will automatically switch to INS-based positioning mode and continue navigation based on historical paths and inertial information to avoid interruption of inspection tasks.

[0055] (3) Kalman filter processing

[0056] 1) Prediction stage: Use the state transfer matrix to predict the current position: Among them U k is the control input (such as acceleration).

[0057] Forecast error covariance:

[0058] 2) Update phase:

[0059] Calculate the Kalman gain based on the current measured value and the predicted value:

[0060] According to the Kalman gain, the predicted value is corrected to obtain the updated state estimate:

[0061] Update error covariance: P k|k =(IK k H k ) k|k-1

[0062] Through the processing of Kalman filter, the mobile inspection vehicle can obtain filtered position information in real time. The positioning accuracy and robustness are significantly improved.

[0063] In an optional embodiment, if Figure 3 As shown in the figure, the GIS in the mobile inspection module not only provides basic geographic information support, but also integrates multiple functions such as path planning, real-time environment update, data visualization, historical data analysis, etc., providing comprehensive intelligent support for gas leak detection and source tracking system. The following are the core functions and technical implementations of the GIS module:

[0064] Accurate geographic information support: GIS provides the system with accurate two-dimensional and three-dimensional geographic information, and realizes high-precision navigation of the gas leak detection platform in complex terrain through high-resolution maps; map information includes terrain features, building distribution, road network, vegetation coverage, etc., which can provide the necessary geographic background for path planning and leakage source location; the data source of the GIS module can come from satellite remote sensing, geographic information database or field measurement data to ensure the accuracy and timeliness of geographic data;

[0065] Dynamic path planning and optimization: GIS uses classic path planning algorithms such as A* algorithm and Dijkstra algorithm in path planning, and quickly generates the optimal path through heuristic search methods. The system will generate the best driving path for the inspection platform based on the inspection needs and geographical feature data of the gas pipeline network to ensure coverage of all high-risk areas. The specific steps of path planning include: initializing environmental scanning, dividing candidate path nodes, calculating path costs, selecting the node with the smallest total cost to dynamically advance, and dynamic obstacle avoidance processing to cope with environmental changes;

[0066] 1) Initialize environmental scan: Before path planning, use sensors (such as lidar, ultrasonic sensors) and geographic information system (GIS) map data to perform a preliminary scan of the inspection area to generate an environmental map. The map contains information such as road networks, buildings, obstacles, and gas pipelines. This environmental map will also mark high-risk areas (such as areas with frequent historical leaks and areas with high current detection concentrations) to provide data support for subsequent path planning;

[0067] 2) Division of candidate path nodes

[0068] The inspection path is divided into a series of candidate nodes, each of which represents a potential location of the inspection platform on the map. These nodes form a grid or graph structure, in which each node is connected to adjacent nodes; the connection represents a feasible path for the platform to move from one node to the next; to improve efficiency, the density of nodes can be adaptively adjusted according to the risk level: more nodes are deployed in high-risk areas to ensure more detailed inspection coverage; in low-risk areas, the nodes can be sparsely distributed;

[0069] 3) Risk-weighted path cost calculation

[0070] The path cost f is calculated for each candidate node, which includes the following three parts: 1. Actual cumulative distance g: the path distance from the starting point to the current node. 2. Estimated distance h: the estimated distance from the current node to the target node, usually calculated by Euclidean distance or Manhattan distance. 3. Add risk factor R(x,y) to the traditional path cost calculation, and the weight factor is adjustable.

[0071] The cost formula is as follows:

[0072] f=α·g+β·h+γ·R(x,y)

[0073] α, β, γ: weight coefficients of control distance, estimated distance and risk factor, usually satisfying α+β+γ=1;

[0074] 4) Select the node with the smallest total cost for the next step: Each time the system selects the node with the smallest total cost from the candidate area as the location for the next move. This means that the device always tries to choose a shorter and more direct route;

[0075] 5) Dynamic obstacle avoidance: If the inspection device detects a new obstacle during the exploration process, the A* algorithm will immediately update the environmental information and recalculate the total cost of the current node. During this process, the algorithm will skip the obstacle point and reselect the path node with the lowest total cost;

[0076] 6) Continuously update the candidate area until the target point is found: the system will repeatedly select the node with the lowest total cost and expand the path until the inspection device reaches the target point. In this way, even in a complex inspection environment, the device can move along a safe and efficient path;

[0077] 7) Path backtracking to generate inspection path: Once the target point is reached, the A* algorithm will start from the target node and gradually backtrack to the starting point to mark the entire inspection path. The final inspection path not only avoids obstacles, but also is the shortest and most energy-efficient route.

[0078] In an optional embodiment, the specific steps of the present invention are to provide a gas leak location and source tracing system, such as Figure 4 As shown, the specific implementation steps are:

[0079] (1) Multi-source data reception and processing

[0080] Data reception: The data processing and control module receives real-time data from various sensors through an integrated communication interface, including the concentration data of the gas to be measured provided by the gas detection module, the three-dimensional wind speed and direction data provided by the environmental monitoring module, the obstacle information of the GIS module, and the location information of the navigation and positioning module. Through a unified data format, all data can be quickly and seamlessly integrated into the system.

[0081] Data preprocessing: In order to improve the efficiency and accuracy of data processing, the module has real-time data filtering and correction functions. After receiving the sensor data, the raw data is first preprocessed, including noise filtering, outlier removal and signal smoothing. For distorted data, the module can compensate and correct it based on historical data to ensure the reliability of the input data.

[0082] (2) Data fusion and diffusion model calculation

[0083] Data fusion: The data processing and control module uses multi-sensor data fusion technology to fuse gas detection, wind speed, wind direction, and platform location information, and improves the perception of gas diffusion paths by associating data sources from multiple sensors. The fused data can reduce the impact of single sensor errors and ensure the accuracy of leak source location.

[0084] Calculate diffusion path: This module calculates the diffusion path of gas in real time based on the Gaussian diffusion model to simulate the propagation law of leaked gas in the environment. Using the wind speed, wind direction data and gas concentration distribution data of the three-dimensional anemometer, the module can dynamically calculate the three-dimensional distribution of gas diffusion and accurately simulate the diffusion behavior of gas under different meteorological conditions.

[0085] Gaussian plume diffusion model: used to predict the diffusion path of gas. The distribution of gas concentration at position (x, y, z) is given by the following formula:

[0086]

[0087] Where x, y, and z represent the downwind distance, lateral distance, and vertical height from the leak source, respectively; c(x, y, z, H) is the gas concentration at the position (x, y, z) downwind from the leak source, in mg / m 3 ; Q is the leakage source intensity, u is the average wind speed at the leakage point height, σ y , σ z Represents the diffusion scale of the gas in the horizontal (y direction) and vertical (z direction), which increases with the distance x, indicating that the diffusion range widens with the increase of distance.

[0088] H=H r +H p

[0089] H is the effective height of the leak point, indicating the height at which the gas will eventually reach. It is the sum of the actual height and the lift height. In diffusion models such as the Gaussian plume model, the effective height is used to calculate the diffusion of gas in the air. r is the actual height of the leak point, indicating the actual location of the leak source; H pIt is the lift height of the leak point, indicating the distance the gas rises in the atmosphere due to thermal buoyancy and momentum, and is affected by factors such as gas temperature, emission velocity and atmospheric conditions.

[0090] Dynamic adjustment of parameters: For the horizontal diffusion scale, it increases with the downwind distance x from the leakage source, and is related to factors such as atmospheric turbulence and wind speed. In general, the typical estimation formula for the horizontal diffusion scale is:

[0091] σ y =a x gx p ;

[0092] a x and p are empirical coefficients that depend on the atmospheric stability category; for a stable atmosphere, a x ≈0.10; p≈0.5.

[0093] The vertical diffusion scale indicates the diffusion range of natural gas in the vertical direction (height direction). Natural gas is lighter than air and tends to rise when leaking, so its vertical diffusion is usually more significant. The vertical diffusion scale also increases with downwind distance:

[0094] σ z =a z gx q ;

[0095] a z and q are empirical coefficients related to atmospheric stability. For stable gases, a z ≈0.05;q≈0.3.

[0096] Calculation output: Output gas concentration distribution map to show the concentration distribution of leaked gas in the current environment; provide two-dimensional diffusion path to identify the direction and coverage of gas diffusion for further analysis of the leakage source;

[0097] Initial leak source location

[0098] Preliminary positioning based on diffusion results: Calculate the approximate location of the leak source based on the gas concentration distribution and diffusion path, combined with environmental geographic information. Preliminary estimation of the leak source location serves as the starting point for optimization calculations.

[0099] (3) Leakage source location and maximum likelihood estimation

[0100] Maximum likelihood estimation method: Based on the calculation of the gas diffusion path, the data processing module locates the leak source through the maximum likelihood estimation method. This method finds the most likely leak source location through iterative calculations and continuously optimizes the location accuracy of the leak source based on real-time data. Maximum likelihood estimation can effectively handle complex environmental variables, especially in the case of variable wind speed and direction, to ensure that the system accurately tracks the leak source.

[0101] Maximum likelihood function:

[0102] Assume that the sensors are located at different positions (x i ,y i ,z i ) The measured gas concentration is C m,i These observations can be viewed as being influenced by the model parameters (the location of the leak source (x 0 ,y 0 ,z 0 ) and emission intensity Q), and contains noise. To this end, it can be assumed that the observed value follows a normal distribution with the model prediction value as the mean:

[0103] C measured,i ~N(C model,i ,σ 2 )

[0104] Among them C model,i The gas concentration of the sensor at the i-th position is calculated based on the diffusion model, σ 2 is the variance of the measurement noise.

[0105] The total likelihood function is the product of the probability density functions at each observation point:

[0106]

[0107] The log-likelihood function is:

[0108]

[0109] Optimize the objective function: To estimate the parameters x of the leakage source 0 ,y 0 ,z 0 ,Q, we need to maximize the log-likelihood function. Maximizing the log-likelihood function is equivalent to minimizing the following objective function:

[0110]

[0111] Similar to the classical least squares method, by optimizing this objective function, the most likely leak source location (x 0 ,y 0 ,z 0 ) and emission rate Q.

[0112] Genetic algorithm optimization

[0113] Initialize the population: Each individual represents a possible combination of leakage source location and emission intensity parameters (x, y, z, Q). The population size is determined by N, which is usually dozens to hundreds of individuals. Based on the calculation results of the diffusion model and the initial leakage source location, a set of random initial solutions is generated to cover the possible parameter space.

[0114] Fitness evaluation: For each individual, calculate its fitness value F:

[0115] Where: C i The gas concentration observed by the i-th sensor.

[0116] The gas concentration value calculated by the diffusion model based on this individual parameter.

[0117] The higher the fitness value (the smaller the error), the more consistent the leakage source parameters corresponding to the individual are with the actual observation.

[0118] Selection operation: Use strategies such as roulette, sorting selection, or tournament selection to give priority to individuals with higher fitness as parents.

[0119] Crossover operation: In the selected parent generation, two individuals are randomly selected for crossover to generate new individuals (offspring).

[0120] For the parent P 1 (x 1 ,y 1 ,z 1 ,Q 1 ) and P 2 (x 2 ,y 2 ,z 2 ,Q 2 ), generating offspring C(x c ,y c ,z c ,Q c ):

[0121] x c =ax 1 +(1-a)x 2 .

[0122] y c =ay 1 +(1-a)y 2

[0123] Where a is a random weight factor, and its value range is [0,1].

[0124] Mutation operation: Randomly select a part of the genes of the offspring (leakage source parameters) and add a small random perturbation to it to enhance the diversity of the population.

[0125] Mutate the parameters (x, y, z, Q): x' = x + Δx y' = y + Δy z' = z + Δz Q' = Q + ΔQ

[0126] Among them, Δx, Δy, Δz, and ΔQ are random disturbances, which usually obey the normal distribution.

[0127] Replacement operation: Replace individuals with lower fitness with newly generated offspring to form the next generation population. To avoid the loss of excellent solutions, retain several individuals with the highest fitness in the current generation and directly enter the next generation.

[0128] Multiple iterative optimization: The genetic algorithm gradually converges to the global optimal solution after multiple generations of evolution (usually dozens to hundreds of generations). In each generation, the system dynamically adjusts the objective function and crossover and mutation probabilities based on real-time data to ensure that the algorithm adapts to environmental changes.

[0129] Output result: The individual with the highest fitness is finally obtained, and its parameters (x, y, z, Q) are the optimized leakage source location and emission intensity.

[0130] In the description of this specification, the description with reference to the terms "one embodiment", "example", "specific example", etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0131] The preferred embodiments of the present invention disclosed above are only used to help illustrate the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to the specific implementation methods described. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent ground mobile high-precision gas leak inspection and source tracking system, characterized in that: include: The mobile inspection platform has a multifunctional integrated interface, a mobile chassis adapted to complex terrain and a stabilized load frame, which is used to carry and coordinate the various functional modules of the system; The mid-infrared laser detection device is fixedly installed on the front of the mobile inspection platform, and includes a mid-infrared laser transmitting unit, a receiving unit, and a high-sensitivity sensor array for detecting gas composition; The three-dimensional anemometer is arranged on the top of the mobile inspection platform, including a wind speed measurement sensor, a wind direction sensing unit and a stable mounting bracket, and is used for all-round airflow data collection; The GNSS navigation and positioning module is built into the mobile inspection platform and includes a high-precision satellite positioning receiving unit, a multi-band satellite signal receiver, a built-in signal amplifier, an anti-interference module and a real-time data calibration function, and is used to transmit positioning information in real time; The inertial navigation module is built into the mobile inspection platform and consists of an inertial measurement unit, an accelerometer, a gyroscope, and a data fusion module to provide continuous position information; GIS module, which integrates a high-precision geographic information database, includes a data storage unit and a map generation module, and has built-in path planning algorithms and obstacle avoidance algorithms for generating environmental geographic information data; The central processing unit integrates the multi-source data acquisition and processing unit from the mid-infrared laser detector, three-dimensional anemometer, inertial navigation module and GIS module, and integrates the Gaussian diffusion model, source data inversion unit and intelligent decision-making and control unit; The wireless communication module is designed as a multi-band module compatible with multiple communication modes, which is used to transmit the collected data to the intelligent detection integrated platform in real time and receive instructions from the intelligent detection integrated platform; The intelligent detection integrated platform, consisting of a data acquisition unit, a data analysis module, a real-time display terminal and a remote control interface, can realize leak location, range prediction, level judgment and command issuance.

2. According to claim 1, an intelligent ground mobile high-precision gas leak inspection and source tracking system is characterized in that: The mobile inspection platform is a self-service navigation robot or an unmanned vehicle, and the mid-infrared laser detection device adopts tunable laser diode (TDLAS) technology.

3. According to claim 1, an intelligent ground mobile high-precision gas leak inspection and source tracking system is characterized in that: The GNSS navigation and positioning module is combined with the inertial navigation module data, and the Kalman filter algorithm is used to eliminate noise and errors to ensure the accuracy of long-term navigation; the GIS module uses the A* algorithm or the Dijkstra algorithm for path planning, and quickly generates the optimal path through the heuristic search method.

4. According to claim 1, an intelligent ground mobile high-precision gas leak inspection and source tracking system is characterized in that: The central processing unit adopts a Gaussian diffusion model to perform real-time calculations on the diffusion path of the gas, uses the wind speed, wind direction data and gas concentration distribution data of the three-dimensional anemometer to dynamically calculate the three-dimensional distribution of the gas diffusion, locates the leakage source through the maximum likelihood estimation method, and uses a genetic algorithm to optimize the parameters of the leakage source position and emission intensity.

5. According to claim 3, the intelligent ground mobile high-precision gas leak inspection and source tracking system is characterized in that: The Kalman filter algorithm includes a prediction phase and an update phase. The prediction phase uses a state transfer matrix to predict the current position and predict the error covariance. In the update phase, the Kalman gain is calculated based on the current measured value and the predicted value, the predicted value is corrected, and the error covariance is updated to obtain the updated state estimate.

6. According to claim 3, an intelligent ground mobile high-precision gas leak inspection and source tracking system is characterized in that: When planning the path, the GIS module first uses sensors and geographic information system map data to perform a preliminary scan of the inspection area to generate an environmental map, then divides the candidate path nodes, calculates the path cost of each node, and selects the node with the smallest total cost to proceed to the next step until the target point is found, and the path is backtracked to generate an inspection path.

7. The intelligent ground mobile high-precision gas leak inspection and source tracking system according to claim 4 is characterized in that: The Gaussian diffusion model is used to predict the diffusion path of the gas, and the distribution of the gas concentration at the location is given by the following formula:

8. The intelligent ground mobile high-precision gas leak inspection and source tracking system according to claim 4 is characterized in that: The maximum likelihood estimation method finds the most likely location of the leakage source through iterative calculation, and continuously optimizes the location accuracy of the leakage source according to real-time data.

9. The intelligent ground mobile high-precision gas leak inspection and source tracking system according to claim 8 is characterized in that: The genetic algorithm optimization includes initializing the population, fitness evaluation, selection operation, crossover operation, mutation operation and replacement operation, and gradually converges to the global optimal solution through multiple generations of evolution to obtain the optimized leakage source location and emission intensity.

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