Intelligent laser methane telemetering system based on unmanned aerial vehicle

Abstract

The invention relates to the technical field of unmanned aerial vehicle fuel gas safety detection, and discloses an intelligent laser methane telemetering system based on an unmanned aerial vehicle, and the intelligent laser methane telemetering system comprises a path planning module; a data acquisition module; an original methane concentration calculation module; a concentration calibration and correction module; and a leakage detection module. Through optimization of the path planning module, an optimal inspection path can be automatically generated according to specific requirements of an inspection task, the inspection efficiency is improved, manual intervention is reduced, the methane concentration can be accurately measured and monitored in real time in combination with multiple data acquisition and processing modules, early warning is performed in time through the leakage detection module, and the inspection efficiency is improved. The safety of an inspection area is effectively guaranteed, the flight trajectory is adjusted in real time in a dynamic environment through a trajectory perception consciousness control algorithm and a rolling optimization method, and the continuity and accuracy of tasks are guaranteed.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of unmanned aerial vehicle gas safety detection, and in particular to an intelligent laser methane remote measurement system based on an unmanned aerial vehicle. BACKGROUND

[0002] Methane, as a colorless and odorless gas, is flammable and explosive. Its leakage not only can cause serious fire and explosion accidents, but also can cause air pollution and aggravate the greenhouse effect. Therefore, in the scenes of urban gas pipeline network, chemical enterprises, storage and transportation facilities, and oil and gas fields, timely and accurate detection is particularly important. Especially in densely populated areas or production operation environments, if the leakage is not discovered and disposed in time, it will not only threaten the safety of personnel and property, but also may cause a wide range of secondary disasters.

[0003] Traditional methods rely on manual operation, such as handheld gas detectors, pipeline inspection and local environmental monitoring, which have problems such as low efficiency, limited detection range and safety hazards in the operation process. Manual detection is usually time-consuming and labor-intensive, and it is difficult to achieve all-weather and dead-angle coverage. In addition, when the operator approaches the leakage source, there is a high safety risk, and in areas where personnel are difficult to access, such as the top pipeline of urban buildings, remote field pipelines, pipelines crossing bridges and large storage tanks, the detection of methane leakage becomes particularly difficult, and it is difficult to achieve comprehensive coverage and efficient monitoring. SUMMARY

[0004] In order to solve the problem of low efficiency and insufficient coverage of methane leakage detection in indoor scenes and areas where personnel are not easy to reach in the prior art, the present application provides an intelligent laser methane remote measurement system based on an unmanned aerial vehicle, which comprises: A path planning module is used to set the inspection task parameters, carry the LMmin5 small laser methane remote measurement host on the unmanned aerial vehicle, and automatically generate the optimal inspection path based on the path planning algorithm and fly to the target point above the area to be measured. A data acquisition module emits a laser beam of a specific wavelength through the LMmin5 small laser methane remote measurement module, receives the laser transmission intensity reflected by the detection point in the area to be measured, and synchronously acquires the external environmental data of the detection point. An original methane concentration calculation module calculates the original methane concentration of the target area according to the laser transmission intensity and the known laser reference intensity. A concentration calibration and correction module is used to calibrate the original methane concentration by using a concentration correction model in combination with the external environmental data to obtain the actual methane concentration. A leakage detection module is used to compare the actual methane concentration with the preset safety threshold, transmit the comparison result to the monitoring center through the LMmin5 small laser methane communication terminal, record the inspection trajectory, and generate an inspection report.

[0005] Optionally, the path planning module comprises: a patrol task creation module configured to create a patrol task and set patrol task parameters including a patrol area boundary, a detection point coordinate and a priority weight, and a UAV dynamics constraint; a map loading and initialization module configured to load a preset map, initialize a semantic probability map, discretize the map into grids, and set an initial information entropy of each grid to a maximum value; a comprehensive cost function definition module configured to define a comprehensive cost function that integrates a path length, a collision risk, an information gain, and a detection point priority according to the patrol task parameters and the grids; a path planning and optimization module configured to sample a plurality of flight trajectories in real time according to a trajectory perception and consciousness control algorithm, evaluate a comprehensive cost of each flight trajectory based on the comprehensive cost function, and select an optimal flight trajectory by a rolling optimization method to minimize the comprehensive cost function.

[0006] Optionally, defining the comprehensive cost function that integrates the path length, the collision risk, the information gain, and the detection point priority according to the patrol task parameters and the grids comprises: obtaining the patrol task parameters including the patrol area boundary, the detection point coordinate and the priority weight, and the UAV dynamics constraint, and a grid representation after the map is discretized; calculating a total length of a UAV flight path, a collision risk between a trajectory and an obstacle, an information gain of an area covered by the trajectory under a sensor field of view, and a distance between the path and each detection point according to the patrol task parameters and the grid after the map is discretized, and integrating a priority weight of the detection point to obtain the integrated path length, the collision risk, the information gain, and the detection point priority; setting weights of the integrated path length, the collision risk, the information gain, and the detection point priority, and defining the comprehensive cost function in combination with the corresponding weights.

[0007] Optionally, an expression of the information gain is:

[0008] In the expression, represents the information gain of the trajectory ; represents a grid point in an i-th row and a j-th column after the map is discretized; represents a grid set covered by the trajectory ; represents a grid quotient before detection; represents a grid quotient after detection.

[0009] ​​Optionally, according to the trajectory-aware consciousness control algorithm, a plurality of flight trajectories are sampled in real time, and a comprehensive cost of each flight trajectory is evaluated based on a comprehensive cost function, and an optimal flight trajectory is selected by a rolling optimization method to minimize the comprehensive cost function, including: a plurality of candidate flight trajectories are generated by adding random perturbations on the control sequence and forward simulation based on a six-degree-of-freedom model of the UAV; for each candidate trajectory, a comprehensive cost value of the candidate trajectory is evaluated by a comprehensive cost function; according to the comprehensive cost value of each trajectory, the trajectory is assigned a weight by an exponential weighting method; the control sequences of all candidate trajectories are weighted and averaged to obtain the optimal control instruction at the current time; the UAV executes the current control instruction, updates its own state, and restarts the trajectory sampling and optimization cycle based on the updated state and new environmental perception information.

[0010] Optionally, the plurality of candidate flight trajectories are generated by adding random perturbations on the control sequence and forward simulation based on a six-degree-of-freedom model of the UAV, including: a six-degree-of-freedom state vector of the UAV at the current time is obtained, and an initial control sequence covering the entire prediction time domain is set; a random perturbation is added to each control quantity of the current initial control sequence to generate a plurality of perturbed control sequences; for each perturbed control sequence, forward simulation is performed based on the six-degree-of-freedom dynamics model of the UAV, starting from the current state, recursively calculating the future state at each time step until the end of the prediction time domain; the complete state sequence obtained in the forward simulation process is recorded as a candidate trajectory; the steps of generating a perturbed control sequence and forward simulation are repeated to form a set of candidate trajectories.

[0011] Optionally, the expression for calculating the original methane concentration of the target area is:

[0012] In the formula, represents the original methane concentration; represents the reference light intensity of the laser when there is no gas; represents the absorption cross section at the current wavelength; represents the optical path length; represents the transmitted light intensity of the laser.

[0013] Optionally, the expression for calculating the actual methane concentration is:

[0014] In the formula, represents the actual methane concentration; represents the original methane concentration; represents a compensation factor; represents temperature; represents humidity.

[0015] In summary, the present application includes at least one of the following beneficial technical effects: 1、The integrated intelligent platform of the present application is more efficient and accurate for real-time processing and linkage response of detection data, and can realize full-process digital management, can display methane data in real time, record alarm information exceeding the standard, can return scene images and detection data, alarm immediately after intelligent analysis, and automatically record patrol track, alarm event and generate patrol report; in addition, its low cost, easy to carry and high mobility can be widely adapted to various detection scenes such as indoor gas users and areas where personnel are not easy to reach, and improve the detection efficiency and intelligent level.

[0016] 2、The present application can automatically generate the optimal inspection path according to the specific requirements of the inspection task through the optimization of the path planning module, improve the inspection efficiency and reduce the manual intervention, combine with various data acquisition and processing modules, can accurately measure and real-time monitor the methane concentration, and timely alarm through the leakage detection module, effectively guarantee the safety of the inspection area, through the trajectory awareness consciousness control algorithm and the rolling optimization method, real-time adjust the flight trajectory in the dynamic environment, guarantee the continuity and accuracy of the task. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is the principle diagram of the system in the present application; Figure 2 is the front view of the LMmin5 small laser methane remote measurement host in the present application; Figure 3 is the side view of the LMmin5 small laser methane remote measurement host in the present application; Figure 4 is the assembly drawing of the LMmin5 small laser methane remote measurement host and the unmanned aerial vehicle in the present application; Figure 5 is the floating window effect drawing of the visual interface in the present application; Figure 6 is one of the setting effect drawings of the visual interface in the present application; Figure 7 is the second setting effect drawing of the visual interface in the present application. DETAILED DESCRIPTION

[0018] Embodiments of the present application are described in detail below, examples of which are shown in the accompanying drawings.

[0019] In the description of the present specification, the description of the terms "certain embodiments", "one embodiment", "some embodiments", "illustrative embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in connection with the described embodiments or examples are included in at least one embodiment or example of the present application. In the present specification, the illustrative description 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.

[0020] The embodiments of the present application disclose an unmanned aerial vehicle (UAV)-based intelligent laser methane remote sensing system, which refers to Figures 1-7 The intelligent laser methane remote sensing system comprises: A path planning module is configured to set a patrol task parameter, carry an LMmin5 small laser methane remote sensing host on a UAV, and automatically generate an optimal patrol path and fly to a target point in the air above a to-be-detected area based on a path planning algorithm.

[0021] Preferably, the path planning module comprises: A patrol task creation module is configured to create a patrol task and set a patrol task parameter comprising a patrol area boundary, a detection point coordinate, and a priority weight, and a UAV dynamics constraint.

[0022] A map loading and initialization module is configured to load a preset map, initialize a semantic probability map, discretize the map into a grid, and set an initial information entropy of each grid to a maximum value.

[0023] It should be explained that the patrol area boundary is set as follows: 1. Define the boundary shape: the boundary of the patrol task can usually be a geometric shape such as a rectangle, a circle, or a polygon; the setting of the boundary can be determined according to actual needs, for example, the UAV needs to perform patrol in a specific area. 2. Coordinates and range of the boundary: the coordinate system of the boundary needs to be clear, which is usually represented by a geographic coordinate system (such as latitude and longitude) or a Cartesian coordinate system; by setting the starting point and the dimension of the boundary, the task area can be accurately delimited.

[0024] The detection point coordinate and the priority are set as follows: 1. Detection point coordinate: according to the needs of the patrol task, a plurality of detection points are set in the patrol area; the detection points can be equipment, facilities, or target objects that need to be focused on. 2. Priority weight: Each detection point may have different priorities, and priority weight can be used to guide the UAV to complete the task more efficiently; detection points with higher priority may require the UAV to arrive first or require higher attention; the weight can usually be set according to the importance of the point, distance, task urgency, etc.

[0025] The UAV dynamics constraints are set as follows: 1. Flight speed and acceleration: Set the flight speed, acceleration, and other dynamic parameters of the UAV; dynamic parameters are usually set according to the actual performance of the UAV to ensure that the physical capabilities of the UAV are not exceeded during task execution; 2. Flight altitude and heading restrictions: Depending on the task requirements and environmental factors (such as obstacles, weather conditions, etc.), flight altitude and heading restrictions may also need to be set; the UAV needs to execute the task within the specified altitude range; 3. Battery life and flight time: If the inspection task needs to consider flight time and energy consumption, the battery endurance time may also need to be set to ensure that the UAV can complete the task and return safely; In addition, according to the task requirements, multiple targets can be set, such as completing the task in the shortest time, minimizing the path length, maximizing information collection (such as monitoring area coverage), or ensuring obstacle avoidance, etc.

[0026] Load the preset map data as follows: 1. Map type: According to the task requirements, different types of map data can be loaded, such as two-dimensional maps, three-dimensional maps, laser radar scanning data, etc.; map data may include obstacle information, road information, terrain undulations, facility distribution, etc.; 2. Data format: Map data can come from GIS (Geographic Information System), SLAM (Simultaneous Localization and Mapping) systems, etc., and is usually stored in formats such as images, grids, point clouds, etc.; by loading the data and converting it into a suitable format for subsequent processing.

[0027] Initialize the semantic probability map as follows: 1. Definition of semantic map: A semantic map is a map that combines environmental information with semantic labels (such as "building", "tree", "obstacle", etc.); through semantic labels, the map can provide more rich information to help the UAV make more intelligent decisions; 2. Probability map: Each region in the semantic map has a probability value associated with it, indicating the probability that the region is a certain category (such as an obstacle); during initialization, based on prior knowledge or existing map data, the probability of each region is set to a certain initial value, usually uniformly distributed or estimated according to actual circumstances.

[0028] Discretize the map into a grid as follows: 1. Grid discretization: To facilitate path planning and optimization, the map is usually discretized into grids; each grid represents a specific region, and grid discretization helps simplify calculations, as most path planning algorithms are based on grids; 2. Grid size: The size of the grid affects the accuracy and speed of the calculation; smaller grids can provide more detailed map information, but increase the computational load; larger grids are faster to calculate, but may result in reduced accuracy; the choice of grid size depends on the actual task requirements and computational resources.

[0029] The initial information entropy of each grid is initialized as follows: 1. Definition of information entropy: Information entropy is a measure of uncertainty in a state; setting the initial information entropy of the grid to the maximum value means that the state of the region is completely uncertain; during task execution, as the UAV's perception information is updated, the information entropy of the grid will gradually decrease.

[0030] 2. Information entropy initialization: During map initialization, the initial information entropy of all grids is usually set to a high value (such as 0.5 or 1), indicating uncertainty about the environment state; as the task progresses, the information provided by the perception sensors will gradually reduce the information entropy of some grids, representing an increasing understanding of the region.

[0031] The comprehensive cost function definition module defines a comprehensive cost function that integrates path length, collision risk, information gain, and detection point priority based on the inspection task parameters and the grid.

[0032] Preferably, defining a comprehensive cost function that integrates path length, collision risk, information gain, and detection point priority based on the inspection task parameters and the grid includes: Obtain the inspection task parameters including the boundary of the inspection area, the coordinates and priority weights of the detection points, and the dynamics constraints of the UAV, and the grid representation after map discretization; Calculate the total length of the UAV flight path, the collision risk between the trajectory and obstacles, the information gain of the region covered by the trajectory within the sensor's field of view, and the distance between the path and each detection point, and fuse their priority weights based on the inspection task parameters and the grid after map discretization, to obtain the integrated path length, collision risk, information gain, and detection point priority; Set the weights of the integrated path length, collision risk, information gain, and detection point priority, and define the comprehensive cost function based on the corresponding weights.

[0033] Preferably, the expression of information gain is:

[0034] where, information gain of a trajectory denotes the grid point at the i-th row and j-th column after map discretization information gain of a trajectory denotes the grid quotient before detection denotes the grid quotient after detection

[0035] It is to be explained that the total path length is the sum of all path segments, which can be calculated by accumulating the Euclidean distance between each two points on the path for discrete paths (a conventional means).

[0036] The collision risk is usually evaluated based on the grid map, in which if a grid contains an obstacle and the path passes through the grid, the collision risk of the path is higher. The collision risk can be quantified by the degree of overlap or the shortest distance between the path and the obstacle.

[0037]

[0038]

[0039] wherein, denotes the collision risk between the path and the grid denotes the minimum distance between the path segment and the grid denotes a small constant to prevent division by zero error denotes the collision risk

[0040] For each detection point, the distance of the path to the point can be obtained by calculating the nearest distance on the path to the point, and then the priority weight of the detection point is fused to obtain the weighted distance. In combination with the priority weight, the contribution of the detection point p j can be represented as:

[0041] wherein, denotes the priority weight of the detection point p j denotes the minimum distance of the path to the point denotes the total number of detection points denotes the priority weighted distance By setting the weight of each target, the respective targets are balanced to obtain an optimal path. The comprehensive cost function can generally be represented as the weighted sum of the respective targets.

[0042] ​​​​​

[0043] wherein, , , and denote the total length of the path, the collision risk, the information gain and the weight of the priority weighted distance of the detection point, respectively; denotes the integrated cost function; denotes the total length of the path; denotes the collision risk; denotes the information gain; denotes the priority weighted distance of the detection point.

[0044] The path planning and optimization module samples multiple flight trajectories in real time according to the trajectory perception-aware control algorithm, evaluates the integrated cost of each flight trajectory based on the integrated cost function, and selects the optimal flight trajectory through the rolling optimization method to minimize the integrated cost function.

[0045] Preferably, sampling multiple flight trajectories in real time according to the trajectory perception-aware control algorithm, evaluating the integrated cost of each flight trajectory based on the integrated cost function, and selecting the optimal flight trajectory through the rolling optimization method to minimize the integrated cost function according to the trajectory perception-aware control algorithm includes: generating multiple candidate flight trajectories by adding random perturbations on the control sequence and forward simulating based on the six-degree-of-freedom model of the UAV; for each candidate trajectory, evaluating the integrated cost value of the candidate trajectory through the integrated cost function; according to the integrated cost value of each trajectory, assigning a weight to the trajectory through an exponentially weighted manner; performing a weighted average on the control sequences of all candidate trajectories to obtain the optimal control instruction at the current time; the UAV executes the current control instruction, updates its own state, and restarts the trajectory sampling and optimization cycle based on the updated state and new environmental perception information.

[0046] Preferably, generating multiple candidate flight trajectories by adding random perturbations on the control sequence and forward simulating based on the six-degree-of-freedom model of the UAV includes: obtaining the six-degree-of-freedom state vector of the UAV at the current time, and setting an initial control sequence covering the entire prediction time domain; adding random perturbations on each control quantity of the current initial control sequence to generate multiple perturbed control sequences; for each perturbed control sequence, forward simulating based on the six-degree-of-freedom dynamics model of the UAV, starting from the current state, recursively calculating the future state at each time step until the end of the prediction time domain; recording the complete state sequence obtained in the forward simulation process as a candidate trajectory; The steps of generating the perturbation control sequence and forward simulation are repeatedly performed to form a set of several candidate trajectories.

[0047] A specific example is as follows: Suppose a UAV needs to perform a fixed task, which specifically includes: 1. Patrol area: a rectangular area containing several key detection points (facilities or equipment), each with different priorities; 2. Task requirements: shortest path priority and avoidance of obstacles while optimizing information collection; 3. UAV: equipped with a LMmin5 small laser methane remote sensing host, with certain flight constraints (such as maximum speed, acceleration, flight height, heading limit, etc.); The patrol task parameters are set as follows: 1. Boundary shape: rectangular area, defining the lower left corner as (0, 0) and the upper right corner as (100, 50) coordinates; the UAV needs to patrol within this area; 2. Coordinates and range: use the Cartesian coordinate system to represent the area coordinates (in meters), the length of the area is 100 meters and the width is 50 meters; Detection points: there are 4 detection points in the area, which are: Point P1: coordinates (20, 10), priority 5; Point P2: coordinates (40, 30), priority 3; Point P3: coordinates (70, 20), priority 4; Point P4: coordinates (80, 40), priority 2; The priority weight of each detection point determines whether the UAV needs to pass through this point first; for example, P1 has the highest priority (weight 5) and P4 has the lowest priority (weight 2); The UAV dynamics constraints are as follows: 1. Flight speed: maximum speed 15 m / s, maximum acceleration 2 m / s 2 ; 2. Flight altitude: the UAV flight altitude is limited between 30 meters and 100 meters; 3. Battery life: assuming the battery endurance time is 30 minutes, considering the flight distance and energy consumption; The map data is a two-dimensional grid map, with a grid size of 1 meter x 1 meter, covering the entire 100 x 50 meter patrol area; Each grid of the map has a label indicating the type of the area (such as buildings, trees, roads, etc.), and each grid has a probability value indicating whether the area is occupied by an object; The grid map is discretized into 100*50 grids, a total of 5000 grids, and the initial information entropy of each grid is set to 1 (completely uncertain); with the sensor data collection of the unmanned aerial vehicle, the information entropy will gradually decrease; The total length of the path is calculated by the above formula 105.24 meters; collision risk 0.4998; information gain 1.5; the priority weighted distance of the detection point is 94.86; and the comprehensive cost function is brought in 51.0319 is obtained; Assuming that the steps of trajectory sampling and optimization have been completed, the following comprehensive cost values of several candidate trajectories are obtained: Trajectory 1: = 51.0319; Trajectory 2: = 50.5; Trajectory 3: = 52.2; Then trajectory 2 is selected as the left and right path.

[0048] The data acquisition module emits a laser beam of a specific wavelength through the LMmin5 small laser methane remote sensing module, receives the laser transmission light intensity reflected by the detection point in the target area, and synchronously acquires the external environment data of the detection point.

[0049] The original methane concentration calculation module calculates the original methane concentration of the target area according to the laser transmission light intensity and the known laser reference light intensity.

[0050] Preferably, the expression for calculating the original methane concentration of the target area is:

[0051] In the formula, represents the original methane concentration; represents the laser reference light intensity when there is no gas; represents the absorption cross section at the current wavelength; represents the optical path length; represents the laser transmission light intensity.

[0052] The concentration calibration and correction module is used to combine the external environment data and use the concentration correction model to calibrate the original methane concentration to obtain the actual methane concentration.

[0053] Preferably, the expression for calculating the actual methane concentration is:

[0054] In the formula, represents the actual methane concentration; represents the original methane concentration; represents a compensation factor; represents temperature, and temperature change will cause changes in gas molecular density and also cause drift in infrared light source and detector sensitivity; represents humidity, and water vapor molecules also have absorption peaks in the infrared region, which will superimpose interference with the absorption signal of methane, especially in high humidity environments.

[0055] It is to be supplemented that the process of obtaining f(T, H) through experiment calibration is to establish a controllable constant temperature and humidity environment box in the laboratory, set multiple groups of known methane concentrations (such as 500 ppm, 1000 ppm, 2000 ppm, …), and collect sensor output at different temperature points (such as 0°C, 20°C, 40°C, 60°C) and humidity points (20%, 50%, 80% RH).

[0056] Table lookup + interpolation: store experimental data as a two-dimensional table, and the processor obtains f(T, H) by table lookup and bilinear interpolation according to the current T and H during operation.

[0057] It is to be supplemented that the principle of bilinear interpolation is as follows: Suppose that four nearest grid points f(T1, H1), f(T1, H2), f(T2, H1), and f(T2, H2) are known; T1 < T < T2; H1 < H < H2;

[0058] In the formula, represents the compensation factor for removing temperature and humidity.

[0059] The leakage detection module is used to compare the actual methane concentration with a preset safety threshold, and the result of the comparison is transmitted to the monitoring center through the LMmin5 small laser methane communication end, while the patrol trajectory is recorded and a patrol report is generated.

[0060] ​It needs to be explained that the leakage detection module judges whether there is a leakage risk by monitoring the methane concentration in real time and comparing the actual concentration with the preset safety threshold; when detecting that the methane concentration exceeds the safety threshold, the module will transmit data and alarm information to the monitoring center through the LMmin5 small laser methane sensor; at the same time, the module will also record the inspection track of the unmanned aerial vehicle, ensure that each inspection area and point is covered, and generate a detailed inspection report after the task is completed, providing a basis for subsequent safety management and decision-making.

[0061] It needs to be explained that the LMmin5 small laser methane telemetry module is connected to the processor through the uart (universal asynchronous receiver transmitter) serial port, the data transmission uses the Modbus protocol, the processor acts as the Modbus host and periodically sends a request frame to the LMmin5 small laser methane telemetry module, including device address, function code, register address and CRC (cyclic redundancy check) check, the LMmin5 small laser methane telemetry module reads the corresponding register after receiving the request and returns a response frame, and analyzes the data to obtain the actual methane concentration and other related information.

[0062] It is supplemented that the LMmin5 small laser methane telemetry module is composed of an indicating laser, a detection laser and a methane detection sensor. The LMmin5 small laser methane telemetry host is mainly composed of a control circuit board and a charging module, and when working, the methane concentration value can be clearly and intuitively seen.

[0063] It needs to be explained that the calculation formula and the parameters participating in the operation in the present application are all pre-processed by dimensionless processing, and the process of dimensionless processing is known in the industry, which will not be described here.

[0064] Although the embodiments of the present application have been shown and described above, it can be understood that the above-mentioned embodiments are exemplary and cannot be understood as limiting the present application, and those skilled in the art can make changes, modifications, replacements and modifications to the above-mentioned embodiments within the scope of the present application.

Claims

1. An unmanned aerial vehicle (UAV) based intelligent laser methane remote sensing system, comprising: The intelligent laser methane remote sensing system comprises: A path planning module is configured to set a patrol task parameter, carry the LMmin5 small laser methane remote sensing host on the unmanned aerial vehicle, and automatically generate an optimal patrol path and fly to a target point in the air above a to-be-detected area based on a path planning algorithm; A data acquisition module is configured to emit a laser beam of a specific wavelength through the LMmin5 small laser methane remote sensing module, receive laser transmission intensity reflected by a detection point in the to-be-detected area, and synchronously acquire external environmental data of the detection point; An original methane concentration calculation module is configured to calculate the original methane concentration of the target area according to the laser transmission intensity and a known laser reference intensity; A concentration calibration and correction module is configured to calibrate the original methane concentration by using a concentration correction model in combination with the external environmental data to obtain an actual methane concentration; A leakage detection module is configured to compare the actual methane concentration with a preset safety threshold, transmit the comparison result to a monitoring center through the LMmin5 small laser methane communication end, record a patrol trajectory, and generate a patrol report.

2. The drone-based intelligent laser methane remote sensing system according to claim 1, wherein, The path planning module comprises: A patrol task creation module is configured to create a patrol task and set a patrol task parameter including a patrol area boundary, a detection point coordinate and a priority weight, and a constraint of unmanned aerial vehicle dynamics; A map loading and initialization module is configured to load a preset map, initialize a semantic probability map, discretize the map into a grid, and set an initial information entropy of each grid to a maximum value; An integrated cost function definition module is configured to define an integrated cost function integrating path length, collision risk, information gain and detection point priority according to the patrol task parameter and the grid; A path planning and optimization module is configured to sample a plurality of flight trajectories in real time according to a trajectory perception consciousness control algorithm, evaluate an integrated cost of each flight trajectory based on the integrated cost function, and select an optimal flight trajectory by a rolling optimization method to minimize the integrated cost function.

3. The UAV-based intelligent laser methane remote sensing system according to claim 2, wherein, The integrated cost function integrating path length, collision risk, information gain and detection point priority according to the patrol task parameter and the grid comprises: acquiring the patrol task parameter including the patrol area boundary, the detection point coordinate and the priority weight, and the constraint of the unmanned aerial vehicle dynamics, and a grid representation after the map is discretized; calculating a total length of an unmanned aerial vehicle flight path, a collision risk between a trajectory and an obstacle, an information gain of an area covered by the trajectory in a sensor field of view, and a distance between the path and each detection point according to the patrol task parameter and the grid after the map is discretized, and integrating path length, collision risk, information gain and detection point priority; setting weights of the integrated path length, collision risk, information gain and detection point priority, and defining the integrated cost function in combination with the corresponding weights.

4. The UAV-based intelligent laser methane remote sensing system according to claim 3, wherein, An expression of the information gain is: wherein, represents the trajectory information gain; represents the grid point of the i-th row and j-th column after the map is discretized; represents the trajectory the grid set that can be covered by the trajectory represents the grid quotient before detection; represents the grid quotient after detection.

5. The drone-based smart laser methane remote sensing system of claim 4, wherein, The trajectory perception consciousness control algorithm, sampling a plurality of flight trajectories in real time, evaluating an integrated cost of each flight trajectory based on the integrated cost function, and selecting an optimal flight trajectory by a rolling optimization method to minimize the integrated cost function comprise: a plurality of candidate flight trajectories are generated by adding random perturbations on the control sequence and forward simulation based on a six-degree-of-freedom model of the UAV; for each candidate trajectory, a comprehensive cost value of the candidate trajectory is evaluated by a comprehensive cost function; according to the comprehensive cost value of each trajectory, a weight is assigned to the trajectory by an exponential weighting method; the control sequence of all candidate trajectories is weighted averaged to obtain the optimal control instruction at the current time; the UAV executes the current control instruction, updates its own state, and restarts the trajectory sampling and optimization cycle based on the updated state and new environment perception information.

6. The UAV-based intelligent laser methane remote sensing system according to claim 5, wherein, the generating a plurality of candidate flight trajectories by adding random perturbations on the control sequence and forward simulation based on a six-degree-of-freedom model of the UAV comprises: a six-degree-of-freedom state vector of the UAV at the current time is obtained, and an initial control sequence covering the entire prediction time domain is set; random perturbations are added to each control quantity of the current initial control sequence to generate a plurality of perturbed control sequences; for each perturbed control sequence, forward simulation is performed based on the six-degree-of-freedom dynamics model of the UAV, starting from the current state, recursively calculating the future state of each time step until the end of the prediction time domain; the complete state sequence obtained in the forward simulation process is recorded as a candidate trajectory; the steps of generating perturbed control sequences and forward simulation are repeated to form a set of candidate trajectories.

7. The drone-based smart laser methane remote sensing system of claim 1, wherein, the expression for calculating the original methane concentration of the target region is: wherein, represents the original methane concentration; Iref represents the laser reference light intensity in the absence of gas; denotes the absorption cross section at the current wavelength; represents the optical path length; represents the laser transmission light intensity.

8. The drone-based smart laser methane remote sensing system of claim 7, wherein, the expression for calculating the actual methane concentration is: wherein, represents the actual methane concentration; represents the original methane concentration; represents a compensation factor; represents temperature; represents humidity.

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