Gas leakage detection method and device, equipment, storage medium and program product

Abstract

The invention provides a gas leakage detection method and device, equipment, a storage medium and a program product. The method comprises the following steps: obtaining first light intensity attenuation data of a target area, and carrying out inversion reconstruction processing on the first light intensity attenuation data to obtain a laser concentration field; acquiring traffic data in a low peak period in the target area, and determining traffic abnormal data in the traffic data through an anomaly detection model; obtaining discrete pressure sensor data in the target area, and determining pressure topology low point data based on the discrete pressure sensor data; and then fusion processing is performed on the laser concentration field, the flow abnormal data and the pressure topology low point data to obtain a leakage confidence coefficient, an alarm notification and a leakage point position are output when the leakage confidence coefficient exceeds a preset threshold value, and the method is used for achieving the effects of reducing a gas detection blind area and improving the accuracy of a gas leakage detection result.

Description

Technical Field

[0001] The present application relates to the technical field of gas leakage inspection, and in particular to a gas leakage detection method, device, equipment, storage medium and program product. Background Art

[0002] Gas leakage is a major hidden danger to urban public safety. Therefore, it is necessary to conduct gas detection in residential areas, industrial and commercial users, gas pipelines, etc. to improve urban public safety.

[0003] The existing technology is to densely deploy catalytic combustion, electrochemical, thermal conductivity and other point sensors in the detection area, and realize real-time detection of gas in the detection area through multiple point sensors.

[0004] However, the effective detection radius of a single catalytic combustion sensor in the existing technology is 2 to 5 meters, so 200 to 500 point sensors need to be deployed in every square kilometer. This is costly and prone to a large number of blind spots, further reducing the accuracy of gas leak detection. Summary of the Invention

[0005] The embodiments of the present application provide a gas leak detection method, apparatus, equipment, storage medium, and program product to reduce gas detection blind spots and improve the accuracy of gas leak detection results.

[0006] In a first aspect, an embodiment of the present application provides a method for detecting a gas leak, comprising:

[0007] Acquire first light intensity attenuation data of the target area, flow data during a low-peak period in the target area, and discrete pressure sensor data in the target area;

[0008] The laser concentration field is obtained by inverting and reconstructing the first light intensity attenuation data; the flow anomaly data in the flow data is determined by the anomaly detection model; and the pressure topology low point data is determined based on the discrete pressure sensor data;

[0009] The laser concentration field, flow anomaly data and pressure topology low point data are fused and processed to obtain the leakage confidence; when the leakage confidence exceeds the preset threshold, an alarm notification and the location of the leakage point are output.

[0010] Optionally, the laser concentration field, flow anomaly data, and pressure topology low point data are fused to obtain leakage confidence, specifically including:

[0011] Performing spatiotemporal alignment processing on the laser concentration field, flow anomaly data and pressure topology low point data to obtain the spatiotemporally aligned laser concentration field, flow anomaly data and pressure topology low point data;

[0012] Determine laser evidence based on the laser scanning concentration value in the laser concentration field after time-space alignment; determine flow evidence based on the gas meter flow in the flow anomaly data after time-space alignment; determine pressure evidence based on the pressure topology low point data after time-space alignment;

[0013] When the target area does not conform to the preset complex terrain, evidence synthesis processing is performed based on laser evidence, flow evidence and pressure evidence to determine the local leakage confidence of the target area.

[0014] Optionally, after determining the local leakage confidence, the leakage point position corresponding to the local leakage confidence is obtained by performing weighted averaging processing on the laser scanning point position corresponding to the laser concentration field, the gas meter position corresponding to the flow anomaly data, and the pressure sensor position corresponding to the pressure topology low point data.

[0015] Optionally, when the target area meets the preset complex terrain, the drone leakage evidence is determined based on the drone concentration field;

[0016] Generate a global leakage confidence level for the target area based on drone leakage evidence, laser evidence, flow evidence, and pressure evidence.

[0017] Optionally, when the global leakage confidence exceeds a preset threshold, the leakage point position corresponding to the global leakage confidence of the target area is obtained by determining the spatial intersection of the leakage point position indicated by the drone leakage evidence and the leakage point position indicated by the laser evidence, flow evidence, and pressure evidence.

[0018] Optionally, the UAV concentration field is obtained by inverting and reconstructing the second light intensity attenuation data of the target area after the UAV extension unit collects the second light intensity attenuation data of the target area; wherein,

[0019] When the drone extension unit collects the second light intensity attenuation data of the target area, it includes:

[0020] Determine the scanning width of a single route based on the terrain constraint information of the target area, the preset flight altitude and the preset laser scanning field of view angle;

[0021] An obstacle avoidance route set is generated based on the single route scanning width, the coverage rate of the target area meeting a preset coverage rate; and second light intensity attenuation data is collected based on the obstacle avoidance route set.

[0022] In a second aspect, an embodiment of the present application provides a gas leakage detection device, comprising:

[0023] an acquisition module, configured to acquire first light intensity attenuation data of a target area, flow data during a low-peak period in the target area, and discrete pressure sensor data in the target area;

[0024] A processing module is configured to obtain a laser concentration field by inverting and reconstructing the first light intensity attenuation data; determine flow anomaly data in the flow data by using an anomaly detection model; and determine pressure topology low point data based on discrete pressure sensor data;

[0025] The processing module is also used to fuse the laser concentration field, flow anomaly data and pressure topology low point data to obtain the leakage confidence; when the leakage confidence exceeds the preset threshold, an alarm notification and the location of the leakage point are output.

[0026] Optionally, the processing module is further used to perform spatiotemporal alignment processing on the laser concentration field, flow anomaly data and pressure topology low point data to obtain spatiotemporally aligned laser concentration field, flow anomaly data and pressure topology low point data;

[0027] Determine laser evidence based on the laser scanning concentration value in the laser concentration field after time-space alignment; determine flow evidence based on the gas meter flow in the flow anomaly data after time-space alignment; determine pressure evidence based on the pressure topology low point data after time-space alignment;

[0028] When the target area does not conform to the preset complex terrain, evidence synthesis processing is performed based on laser evidence, flow evidence and pressure evidence to determine the local leakage confidence of the target area.

[0029] Optionally, the processing module is also used to obtain the leakage point position corresponding to the local leakage confidence after determining the local leakage confidence by performing weighted averaging processing on the laser scanning point position corresponding to the laser concentration field, the gas meter position corresponding to the flow abnormality data, and the pressure sensor position corresponding to the pressure topology low point data.

[0030] Optionally, the processing module is further configured to determine drone leakage evidence based on the drone concentration field when the target area meets a preset complex terrain;

[0031] The global leakage confidence of the target area is generated based on the UAV leakage evidence, laser evidence, flow evidence, and pressure evidence.

[0032] Optionally, the processing module is also used to obtain the leakage point position corresponding to the global leakage confidence of the target area by determining the spatial intersection of the leakage point position indicated by the drone leakage evidence and the leakage point position indicated by the laser evidence, flow evidence, and pressure evidence when the global leakage confidence exceeds a preset threshold.

[0033] Optionally, the UAV concentration field is obtained by inverting and reconstructing the second light intensity attenuation data of the target area after the UAV extension unit collects the second light intensity attenuation data of the target area; wherein,

[0034] The processing module is further used to determine the scanning width of a single route based on terrain constraint information of the target area, a preset flight altitude and a preset laser scanning field of view angle;

[0035] An obstacle avoidance route set is generated based on the single route scanning width, the coverage rate of the target area meeting a preset coverage rate; and second light intensity attenuation data is collected based on the obstacle avoidance route set.

[0036] In a third aspect, an embodiment of the present application provides an electronic device, comprising: a memory, a processor;

[0037] Memory stores computer-executable instructions;

[0038] The processor executes the computer-executable instructions stored in the memory, so that the processor executes the above first aspect and / or various possible implementations of the first aspect.

[0039] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, in which computer-executable instructions are stored. When the computer-executable instructions are executed by a processor, they are used to implement the first aspect above and / or various possible implementation methods of the first aspect.

[0040] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the above first aspect and / or various possible implementation methods of the first aspect.

[0041] The gas leakage detection method, apparatus, device, storage medium and program product provided in the embodiments of the present application obtain first light intensity attenuation data of the target area and perform inversion and reconstruction processing on the first light intensity attenuation data to obtain a laser concentration field; obtain flow data during a low-peak period in the target area and determine flow anomaly data in the flow data through an anomaly detection model; obtain discrete pressure sensor data in the target area and determine pressure topology low point data based on the discrete pressure sensor data; then fuse the laser concentration field, flow anomaly data and pressure topology low point data to obtain leakage confidence, and output an alarm notification and leakage point location when the leakage confidence exceeds a preset threshold, so as to reduce the gas detection blind spot and improve the accuracy of the gas leakage detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0043] Figure 1 A flow chart of the gas leakage detection method provided in this application;

[0044] Figure 2Schematic diagram of the structure of the gas leakage detection system provided in this application Figure 1 ;

[0045] Figure 3 Schematic diagram of the structure of the gas leakage detection system provided in this application Figure 2 ;

[0046] Figure 4 A schematic diagram of the structure of a gas leakage detection device provided in this application;

[0047] Figure 5 This is a schematic diagram of the structure of the electronic device provided in this application.

[0048] The above drawings illustrate specific embodiments of the present application, which will be described in more detail below. These drawings and the textual description are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0049] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0050] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with the relevant laws, regulations and standards of the relevant regions, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation entrances for users to choose to authorize or refuse.

[0051] Currently, gas leaks are a major hazard to urban public safety. Therefore, timely gas leak detection is necessary. Detection locations include, but are not limited to: urban gas infrastructure areas such as residential communities, industrial and commercial users, gas pipeline networks, and gas gate stations; complex terrain areas such as mountainous areas and rivers; large areas such as 10km² industrial parks; areas involved in emergency situations such as post-earthquake pipeline inspections; and underground areas where GPS (Global Positioning System) signals are missing, such as underground pipeline corridors.

[0052] Existing gas leak detection technologies are divided into contact detection technologies (such as catalytic combustion sensors, electrochemical sensors, thermal conductivity sensors) and non-contact detection technologies (such as infrared gas imaging, ultrasonic detection).

[0053] For example, in contact detection technology, catalytic combustion sensors are used for detection. The effective detection radius of a single catalytic combustion sensor is only 2 meters to 5 meters, and 200 to 500 sensors need to be deployed per square kilometer. This is expensive and has a large number of blind spots, and the coverage range is limited. In addition, since catalytic combustion sensors rely on the heat changes generated by the combustion of gas on the catalyst surface to detect concentration, this method is suitable for combustible gases such as methane and propane, but is easily affected by sulfides, resulting in poisoning or detection failure, reducing the safety factor and reliability of detection.

[0054] For example, non-contact detection technology uses infrared gas imaging equipment for detection. This method uses a thermal imager to generate gas cloud images based on the gas's absorption characteristics of specific wavelengths of infrared light. This method is suitable for visualizing leaks in industrial plants, but the effective detection range is less than 100 meters, which is limited and susceptible to weather conditions (such as rain and fog), making it difficult to meet the needs of large-area coverage. Furthermore, when manually inspecting gas leaks, complex pipeline networks (such as underground pipeline corridors and densely built-up areas) are prone to blind spots in the inspection path. Manual inspections are inefficient, rely on manual experience, and have a high rate of missed detections.

[0055] From the above scenarios, it can be seen that the existing technology has technical problems such as limited detection range, high missed detection rate, low detection efficiency and poor accuracy.

[0056] The gas leak detection method provided by the present application obtains first light intensity attenuation data, off-peak flow data, and discrete pressure sensor data within a target area, performs inversion and reconstruction processing on the obtained first light intensity attenuation data to obtain a laser concentration field, analyzes the flow data to determine flow anomaly data, and analyzes the discrete pressure sensor data to determine pressure topology low point data. Laser evidence is then determined based on the laser scanning concentration value in the laser concentration field, flow evidence is determined based on the gas meter flow in the spatiotemporally aligned flow anomaly data, and pressure evidence is determined based on the spatiotemporally aligned pressure topology low point data. If the target area does not conform to a preset complex terrain, a local leak confidence level for the target area is determined based on the above evidence. If the target area conforms to the preset complex terrain, a drone expansion unit further collects second light intensity attenuation data and flight attitude data for the target area based on an obstacle avoidance route set. A drone concentration field is generated based on the collected second light intensity attenuation data and flight attitude data. UAV leakage evidence is determined based on the drone concentration field, and the drone leakage evidence and the local leak confidence level are weighted averaged to obtain a global leak confidence level. When the confidence level exceeds a preset threshold, an alarm notification and the leak point location are output. This application reduces the gas detection blind area and improves the accuracy and reliability of gas leak detection results.

[0057] The following specific embodiments describe in detail the technical solution of the present application and how the technical solution of the present application solves the above-mentioned technical problems. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be repeated in some embodiments. The embodiments of the present application will be described below in conjunction with the accompanying drawings.

[0058] Figure 1 A flow chart of the gas leakage detection method provided in this application is shown as follows: Figure 1 As shown, the method includes:

[0059] S101 , acquiring first light intensity attenuation data of a target area, flow data during a low-peak period in the target area, and discrete pressure sensor data in the target area.

[0060] Optionally, when the target area (e.g., station, community, small campus) does not meet the preset complex scenario, Figure 2 Schematic diagram of the structure of the gas leakage detection system provided in this application Figure 1 ,like Figure 2 As shown, the gas leakage detection system for the above-mentioned target area includes a laser scanning unit, a gas meter unit, a pressure sensor unit and a data fusion unit.

[0061] Optionally, when the target area (e.g., mountainous area, river, industrial park) meets the preset complex scenario, Figure 3Schematic diagram of the structure of the gas leakage detection system provided in this application Figure 2 ,like Figure 3 As shown, the gas leakage detection system for the above-mentioned target area includes a laser scanning unit, a gas meter unit, a pressure sensor unit, a drone extension unit and a data fusion unit.

[0062] In one possible embodiment, based on tunable semiconductor laser absorption spectroscopy technology, a laser scanning unit is used to measure the absorption intensity of methane to a laser of a specified wavelength to obtain first light intensity attenuation data. For example, methane molecules have a strong absorption peak in the near-infrared region (e.g., 1653.7 nm). Based on the Lambert-Beer law, , it is determined that after the laser passes through the methane gas, the first light intensity attenuation data is related to the methane concentration and the optical path length. Determine first light intensity attenuation data. is the emission intensity (W), is the received light intensity (W), For methane at wavelength Absorption coefficient at (ppm⁻¹·m⁻¹), is the methane concentration (ppm), is the optical path length (m).

[0063] Optionally, the PTZ in the laser scanning unit is rotated horizontally (0° to 360°) and vertically (-30° to +90°) to perform spatial scanning of the target area. The PTZ is equipped with a laser transmitting / receiving module, and the scanning path during spatial scanning adopts a spiral coverage strategy to ensure that there is no blind spot within a fixed distance (e.g., 500 meters) radius. The spiral coverage strategy includes horizontal step angle, vertical step angle, and scanning period. The calculation formula for the horizontal step angle is: , is the laser beam diameter (e.g., 0.1m), is the scanning radius (e.g., 500m). The vertical step angle is calculated as , is the scanning height (e.g., 10m). The calculation formula for the scanning period is , , used to indicate the horizontal rotation speed, , used to indicate the vertical rotation speed.

[0064] In a possible embodiment, a gas meter unit is used to collect flow data during off-peak hours in the target area.

[0065] In a possible embodiment, a pressure sensor unit is used to collect discrete pressure sensor data from multiple pressure sensors in the target area. Since the pressure drops near the leakage point, pressure topology low point data is determined in the discrete pressure sensor data.

[0066] S102 , obtaining a laser concentration field by inverting and reconstructing the first light intensity attenuation data; determining flow anomaly data in the flow data through an anomaly detection model; and determining pressure topology low point data based on discrete pressure sensor data.

[0067] In a possible embodiment, a laser scanning unit is used to scan and obtain multiple optical paths within the target area ( ) concentration integral value (i.e., ), then the first light intensity attenuation data is calculated according to the concentration integral value by the data fusion unit, and then the first light intensity attenuation data is iteratively optimized (e.g., SIRT algorithm) to generate the laser concentration field within a fixed distance (e.g., 500 meters) radius. (For example, a methane concentration heat map with a resolution of 0.5m×0.5m). For example, the formula for iterative optimization solution is .

[0068] In a possible embodiment, after the flow data is obtained, the data fusion unit uses an anomaly detection model to determine the flow anomaly data in the flow data. The anomaly detection model includes a constant flow model and a micro flow model. , the off-peak period Continuous from time to time Hourly traffic The flow rate is determined to be a constant flow abnormal state, where For example, the low-peak period is 0:00-6:00 and the peak period is 18:00-20:00. Based on the micro-flow model , the average flow rate during the off-peak period T Lower than the minimum reference gas flow If the abnormal state of constant flow or small flow is detected by the abnormal detection model, the flow integration Estimate the amount of leakage, is the abnormal start time, The abnormal end time is determined based on the leakage amount (for example, there is continuous flow despite no gas being used, or the flow is lower than the preset normal flow threshold).

[0069] In one possible embodiment, after obtaining discrete pressure sensor data at multiple locations, Kriging interpolation technology is used , the discrete pressure sensor data Interpolation to a continuous pressure field , is the interpolation weight and , minimize the estimated variance , is the semivariogram. Then, based on the continuous pressure field, the gradient of the continuous pressure field is calculated. , and the gradient modulus Less than threshold And the pressure value Below average pressure in target area The discrete pressure sensor data in the area is determined as the pressure topology low point data, that is, , is the standard deviation of the pressure field, which is used to reflect the normal fluctuation range.

[0070] S103, fusing the laser concentration field, flow anomaly data, and pressure topology low point data to obtain leakage confidence; and outputting an alarm notification and the location of the leakage point when the leakage confidence exceeds a preset threshold.

[0071] The gas leakage detection method provided in the embodiment of the present application obtains the leakage confidence of the target area by fusing the laser concentration field, flow anomaly data and pressure topology low point data. When the leakage confidence exceeds a preset threshold, an alarm is issued and the leakage point location is output, thereby expanding the gas detection range and coverage, reducing detection blind spots, and improving the accuracy of gas leak detection results.

[0072] Optionally, the laser concentration field, flow anomaly data and pressure topology low point data are subjected to spatiotemporal alignment processing to obtain the spatiotemporally aligned laser concentration field, flow anomaly data and pressure topology low point data; laser evidence is determined based on the laser scanning concentration value in the spatiotemporally aligned laser concentration field; flow evidence is determined based on the gas meter flow in the spatiotemporally aligned flow anomaly data; pressure evidence is determined based on the spatiotemporally aligned pressure topology low point data; when the target area does not conform to the preset complex terrain, evidence synthesis processing is performed based on the laser evidence, flow evidence and pressure evidence to determine the local leakage confidence of the target area.

[0073] Optionally, after determining the local leakage confidence, the leakage point position corresponding to the local leakage confidence is obtained by performing weighted averaging processing on the laser scanning point position corresponding to the laser concentration field, the gas meter position corresponding to the flow anomaly data, and the pressure sensor position corresponding to the pressure topology low point data.

[0074] In a possible embodiment, a laser concentration field with a spatial resolution of 0.5 m, flow anomaly data located at a specific user, and pressure topology low point data with a spatial resolution of 5 m are timestamped respectively through a data fusion unit to ensure data time consistency; then, the laser scanning point position corresponding to the laser concentration field, the specific user gas meter position corresponding to the flow anomaly data, and the pressure sensor position corresponding to the pressure topology low point data are mapped in a grid coordinate system of a preset size (e.g., a WGS84 grid coordinate system with a grid size of 0.5 m × 0.5 m) to ensure data spatial consistency and obtain the laser concentration field, flow anomaly data, and pressure topology low point data that are aligned in time and space.

[0075] In a possible embodiment, the data fusion unit determines that the laser evidence indicates a concentration of Exceeds the preset concentration threshold (e.g., 5000ppm), then determine the weight of the laser evidence: , If it is determined that the flow evidence indicates a constant flow or a small flow anomaly at the location, the weight corresponding to the flow evidence is determined: , If it is determined that the pressure evidence indicates that the location is a pressure topology low point, the weight corresponding to the pressure evidence is determined: , .

[0076] In a possible embodiment, when the target area (such as a station, a community, or a small campus) does not meet the preset complex scenario, based on Figure 2 The gas leak detection system shown in the figure defines the recognition framework Θ = {normal, abnormal}, and determines the basic probability distribution of laser evidence A, flow evidence B, and pressure evidence C respectively as follows: 、 、 , for example, its values ​​are as follows Table 1:

[0077] Table 1

[0078]

[0079] Based on the above information, the three-source evidence (laser evidence A, flow evidence B, and pressure evidence C) is synthesized using the Dempster combination rule to calculate the local leakage confidence level in the target area. The specific process is as follows:

[0080] (1) Combine laser evidence A and traffic evidence B using the Dempster combination rule, i.e., the conflict coefficient calculation formula based on the Dempster combination rule: ,in, is the focal element of laser evidence A, is the focal element of flow evidence B, and the focal element and The intersection is empty, so , substituting the data into .

[0081] For the target focal element J ( or ),based on calculate , the formula is as follows:

[0082] .

[0083] Among them, based on the target focal element J , due to satisfaction The combination of , so we get:

[0084] .

[0085] Substituting the data 0.8×0.7=0.56 into the above formula, we get:

[0086] .

[0087] Based on the target focal element J , due to satisfaction The combination of , so we get:

[0088] .

[0089] Substituting the data into the above formula is , and then we get:

[0090] .

[0091] Since 0.9032+0.0968=1, the result is verified and meets the basic probability distribution requirements.

[0092] (2) Combined with the pressure evidence C, that is, based on the formula for calculating the conflict coefficient in step (1), the conflict coefficient calculation formula is obtained:

[0093] .

[0094] in, 、 、 They are the focal elements of laser evidence A, flow evidence B, and pressure evidence C, and the focal elements and The intersection is empty, so we get:

[0095] .

[0096] For the above Substitute the data into .

[0097] For the target focal element J ( or ),based on Synthesized , the formula is as follows:

[0098] .

[0099] Among them, based on the target focal element J , since the intersection is The combination of ,therefore:

[0100] .

[0101] Substituting the data into the above formula is .

[0102] Then determine .

[0103] Based on the target focal element J , since the intersection is The combination of ,therefore:

[0104] .

[0105] Substituting the data into the above formula is .

[0106] Then determine .

[0107] because , so the result is verified and meets the basic probability distribution requirements.

[0108] (3) The data in Table 2 below are obtained:

[0109] Table 2

[0110]

[0111] In a possible embodiment, if the local leakage confidence calculated by the data fusion unit exceeds a preset threshold (eg, 0.9), an alarm notification is output based on the following formula:

[0112] Calculate the leakage point location corresponding to the local leakage confidence More specifically, by comparing the laser scanning point position corresponding to the laser concentration field , Gas meter location corresponding to abnormal flow data And the pressure sensor position corresponding to the pressure topology low point data Perform weighted average calculation to obtain the leakage point location corresponding to the local leakage confidence .in, ,For example, 0.8, 0.15, is 0.05.

[0113] This embodiment performs spatiotemporal alignment on multidimensional data to ensure the spatiotemporal consistency of multi-source data in non-complex terrain, avoiding false alarms and response delays caused by data asynchrony in traditional technologies. It also synthesizes local leakage confidence based on laser concentration fields, flow anomaly data, and pressure topology low point data, reducing detection blind spots and improving the accuracy and reliability of local leakage confidence.

[0114] When determining that the local leak confidence exceeds a preset threshold, this embodiment determines the coordinates of the local leak point by weighted average of the laser scanning point, gas meter position, and pressure sensor position, thereby improving the accuracy and reliability of leak location positioning.

[0115] Optionally, when the target area meets the preset complex terrain, the drone leakage evidence is determined based on the drone concentration field; and the global leakage confidence of the target area is generated based on the drone leakage evidence, laser evidence, flow evidence and pressure evidence.

[0116] Optionally, the UAV concentration field is obtained by inverting and reconstructing the second light intensity attenuation data of the target area after the UAV extension unit collects the second light intensity attenuation data of the target area; wherein, when the UAV extension unit collects the second light intensity attenuation data of the target area, it includes: determining the scanning width of a single route based on the terrain constraint information of the target area, a preset flight altitude and a preset laser scanning field of view angle; generating an obstacle avoidance route set whose coverage of the target area meets the preset coverage rate based on the single route scanning width; and collecting the second light intensity attenuation data based on the obstacle avoidance route set.

[0117] Optionally, when the target area (such as mountainous areas, rivers, industrial parks) meets the preset complex scenarios, based on Figure 3 The gas leakage detection system shown in the figure controls the flight of the drone through the drone expansion unit, and the drone can fly according to the altitude of the drone. and laser scanning field of view (horizontally), calculate the sweep width of a single route , thereby determining the obstacle avoidance route set based on the obtained sweep width , so that the obstacle avoidance route set covers the above target area, the coverage rate of the target area meets the preset coverage rate, and the spatial intersection of the obstacle avoidance route set and the obstacle set in the target area is an empty set.

[0118] Optionally, after obtaining the obstacle avoidance route set, the UAV extension unit represents each route as a point sequence, i.e. ,in As waypoints, and establish a fitness function , the fitness function is used to analyze the fitness of each route and solve the optimal path, so that the path length of the optimal path is minimized, the coverage of the target area is maximized, and the obstacle avoidance score is the highest, among which, .

[0119] Optionally, the obstacle avoidance route generated in the above embodiment is updated and iterated through the operations of selection (roulette), crossover (single point exchange), and mutation (random adjustment of waypoints).

[0120] Optionally, the data fusion unit includes a drone subsystem deployed on the drone extension unit side, a local subsystem deployed on the ground where the laser scanning unit, the pressure sensor unit and the gas meter unit are located, and a cloud fusion platform deployed on the cloud.

[0121] In one possible embodiment, after obtaining the optimal path, the drone extension unit controls the flight of the drone based on the obstacle avoidance route corresponding to the optimal path, so that the drone scans the target area in real time during flight to obtain the integrated concentration of multiple optical paths. , UAV position and attitude data (such as UAV altitude, scanning angle, GPS, INS (Inertial Navigation System)) and meteorological data, among which, is the optical path length, , is the altitude of the drone, is the scanning angle.

[0122] In one possible embodiment, a drone subsystem (e.g., NVIDIA Jetson AGX) is used to calculate the integrated concentration based on multiple optical paths. , the drone's position and attitude data (e.g., drone altitude and scan angle) (GPS / INS) and meteorological data, and the Lambert-Beer law in the above embodiment is modified to: , is the attenuation coefficient of the atmosphere (e.g., fog, rain) to the laser. Based on the modified Lambert-Beer law, the second light intensity attenuation data is determined, and the drone concentration field is generated by iteratively optimizing and solving the second light intensity attenuation data. ,Right now:

[0123] .

[0124] in, is the number of iterations, For the The drone altitude of the scan, For the The drone subsystem determines the drone leakage evidence based on the drone concentration field. (e.g., the concentration gradient exceeds a preset concentration gradient threshold, the concentration change rate exceeds a preset concentration change rate threshold), and uploads the drone evidence to the cloud for global fusion processing through the cloud fusion platform.

[0125] In a possible embodiment, after the local subsystem obtains the first light intensity attenuation data of the target area, the flow data during the off-peak period, and the discrete pressure sensor data, the laser evidence, flow evidence, and pressure evidence are determined based on the first light intensity attenuation data of the target area, the flow data during the off-peak period, and the discrete pressure sensor data, and local leakage evidence is generated based on the laser evidence, flow evidence, and pressure evidence. , and upload it to the cloud fusion platform in the cloud.

[0126] In a possible embodiment, the cloud fusion platform synthesizes the drone leakage evidence and the local leakage evidence based on the Dempster rule to obtain the global leakage confidence of the target area, where the global leakage confidence = .

[0127] This embodiment enables the drone extension unit in complex terrain to control the drone to collect data to generate a drone concentration field, and fuses the drone concentration field with laser evidence, pressure evidence, and flow evidence to generate a global confidence level, thereby achieving air-ground collaboration, effectively expanding the detection range, and improving the accuracy and reliability of the detection results.

[0128] This embodiment enables drone data collection when the target area is a preset complex terrain, calculates the scanning width based on the flight altitude and field of view, and generates a set of obstacle avoidance routes to ensure that the drone scanning area covers the above-mentioned preset complex terrain, reduce detection blind spots, avoid obstacles in the target area, and improve the accuracy and reliability of detection results.

[0129] Optionally, when the global leakage confidence exceeds a preset threshold, the leakage point position corresponding to the global leakage confidence of the target area is obtained by determining the spatial intersection of the leakage point position indicated by the drone leakage evidence and the leakage point position indicated by the laser evidence, flow evidence, and pressure evidence.

[0130] In a possible embodiment, if the global leakage confidence calculated by the data fusion unit exceeds a preset threshold (e.g., 0.95), an alarm notification is output, and a weighted average calculation is performed on the drone leakage evidence, the laser scanning point position corresponding to the laser concentration field, the gas meter position corresponding to the flow anomaly data, and the pressure sensor position corresponding to the pressure topology low point data to obtain the leakage point position corresponding to the local leakage confidence. .

[0131] This embodiment locates the global leak point location by locating the spatial intersection of drone leakage evidence with laser evidence, flow evidence, and pressure evidence, thereby achieving the combined complementarity of air-ground data, eliminating the positioning deviation of a single data source, and achieving precise positioning of the leak point in complex terrain, thereby improving the accuracy and reliability of the detection results.

[0132] Figure 4 A schematic diagram of the structure of the gas leakage detection device provided in this application is shown as follows: Figure 4 As shown, the gas leakage detection device 40 provided in this embodiment includes:

[0133] An acquisition module 401 is configured to acquire first light intensity attenuation data of a target area, flow data during a low-peak period in the target area, and discrete pressure sensor data in the target area;

[0134] The processing module 402 is configured to obtain a laser concentration field by inverting and reconstructing the first light intensity attenuation data; determine flow anomaly data in the flow data using an anomaly detection model; and determine pressure topology low point data based on discrete pressure sensor data.

[0135] The processing module 402 is also used to fuse the laser concentration field, flow anomaly data and pressure topology low point data to obtain leakage confidence; and output an alarm notification and the location of the leakage point when the leakage confidence exceeds a preset threshold.

[0136] Optionally, the processing module 402 is further configured to perform spatiotemporal alignment processing on the laser concentration field, the flow anomaly data, and the pressure topology low point data to obtain spatiotemporally aligned laser concentration field, the flow anomaly data, and the pressure topology low point data.

[0137] Determine laser evidence based on the laser scanning concentration value in the laser concentration field after time-space alignment; determine flow evidence based on the gas meter flow in the flow anomaly data after time-space alignment; determine pressure evidence based on the pressure topology low point data after time-space alignment;

[0138] When the target area does not conform to the preset complex terrain, evidence synthesis processing is performed based on laser evidence, flow evidence and pressure evidence to determine the local leakage confidence of the target area.

[0139] Optionally, the processing module 402 is also used to obtain the leakage point position corresponding to the local leakage confidence after determining the local leakage confidence by performing weighted averaging processing on the laser scanning point position corresponding to the laser concentration field, the gas meter position corresponding to the flow abnormality data, and the pressure sensor position corresponding to the pressure topology low point data.

[0140] Optionally, the processing module 402 is further configured to determine drone leakage evidence based on the drone concentration field when the target area meets a preset complex terrain;

[0141] Generate a global leakage confidence level for the target area based on drone leakage evidence, laser evidence, flow evidence, and pressure evidence.

[0142] Optionally, the processing module 402 is also used to obtain the leakage point position corresponding to the global leakage confidence of the target area by determining the spatial intersection of the leakage point position indicated by the drone leakage evidence and the leakage point position indicated by the laser evidence, flow evidence, and pressure evidence when the global leakage confidence exceeds a preset threshold.

[0143] Optionally, the UAV concentration field is obtained by inverting and reconstructing the second light intensity attenuation data of the target area after the UAV extension unit collects the second light intensity attenuation data of the target area; wherein,

[0144] The processing module 402 is further configured to determine a single route scan width based on terrain constraint information of the target area, a preset flight altitude, and a preset laser scanning field of view angle;

[0145] An obstacle avoidance route set is generated based on the single route scanning width, the coverage rate of the target area meeting a preset coverage rate; and second light intensity attenuation data is collected based on the obstacle avoidance route set.

[0146] The gas leakage detection device provided in this embodiment can execute the method provided in the above method embodiment. Its implementation principle and technical effects are similar and will not be described in detail in this embodiment.

[0147] Figure 5 This is a schematic diagram of the structure of the electronic device provided in this application. Figure 5 As shown, the electronic device 50 provided in this embodiment includes: at least one processor 501 and a memory 502. Optionally, the device 50 further includes a communication component 503. The processor 501, the memory 502 and the communication component 503 are connected via a bus 504.

[0148] In a specific implementation process, at least one processor 501 executes the computer-executable instructions stored in the memory 502, so that the at least one processor 501 performs the above method.

[0149] The specific implementation process of the processor 501 can be found in the above method embodiment. Its implementation principle and technical effects are similar and will not be repeated here in this embodiment.

[0150] In the above embodiments, it should be understood that the processor may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASICs), etc. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the method disclosed in the present invention may be directly executed by a hardware processor or by a combination of hardware and software modules within the processor.

[0151] The memory may include random access memory (RAM) and may also include non-volatile memory (NVM), such as at least one disk storage.

[0152] A bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be categorized as address buses, data buses, and control buses. For ease of illustration, the buses in the drawings of this application are not limited to just one bus or just one type of bus.

[0153] The present application also provides a computer program product, including a computer program, which implements the above method when executed by a processor.

[0154] The present application also provides a computer-readable storage medium, in which computer-executable instructions are stored. When a processor executes the computer-executable instructions, the above method is implemented.

[0155] The readable storage medium may be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium may be any available medium that can be accessed by a general-purpose or special-purpose computer.

[0156] An exemplary readable storage medium is coupled to a processor so that the processor can read information from the readable storage medium and write information to the readable storage medium. Of course, the readable storage medium can also be an integral part of the processor. The processor and the readable storage medium can be located in an application specific integrated circuit (ASIC). Of course, the processor and the readable storage medium can also exist in the device as discrete components.

[0157] The division of units is merely a logical functional division; actual implementations may employ alternative divisions, such as combining or integrating multiple units or components into another system, or omitting or disabling certain features. Furthermore, any direct coupling or communication connection shown or discussed may be an indirect coupling or communication connection between devices or units, either through an interface, electrical, mechanical, or other means.

[0158] Units described as separate components may or may not be physically separate, and components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0159] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0160] If a function is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the various embodiments of the method of the present invention. The aforementioned storage medium includes various media that can store program code, such as USB flash drives, mobile hard drives, read-only memories (ROMs), random access memories (RAMs), magnetic disks, or optical disks.

[0161] Those skilled in the art will appreciate that all or part of the steps in the above-described method embodiments can be implemented using hardware associated with program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0162] Finally, it should be noted that those skilled in the art will readily identify other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. The present invention is intended to cover any variations, uses, or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art not disclosed herein. The present invention is not limited to the precise structure described above and illustrated in the accompanying drawings, and various modifications and variations may be made without departing from the scope thereof. The scope of the present invention is limited solely by the appended claims.

Claims

1. A method for detecting gas leakage, characterized in that: include: Acquire first light intensity attenuation data of a target area, flow data during a low-peak period within the target area, and discrete pressure sensor data within the target area; Obtaining a laser concentration field by inverting and reconstructing the first light intensity attenuation data; determining flow anomaly data in the flow data by using an anomaly detection model; Determining pressure topology low point data based on the discrete pressure sensor data; The laser concentration field, the flow anomaly data and the pressure topology low point data are fused and processed to obtain a leakage confidence; and an alarm notification and the leakage point location are output when the leakage confidence exceeds a preset threshold.

2. The method according to claim 1, characterized in that The laser concentration field, the flow anomaly data and the pressure topology low point data are fused to obtain leakage confidence, specifically including: Performing spatiotemporal alignment processing on the laser concentration field, the flow anomaly data, and the pressure topology low point data to obtain spatiotemporally aligned laser concentration field, flow anomaly data, and pressure topology low point data; Determine laser evidence based on the laser scanning concentration value in the laser concentration field after time-space alignment; determine flow evidence based on the gas meter flow in the flow anomaly data after time-space alignment; determine pressure evidence based on the pressure topology low point data after time-space alignment; When the target area does not conform to the preset complex terrain, evidence synthesis processing is performed based on the laser evidence, flow evidence and pressure evidence to determine the local leakage confidence of the target area.

3. The method according to claim 1 or 2, characterized in that Also includes: After determining the local leakage confidence, the leakage point position corresponding to the local leakage confidence is obtained by performing weighted averaging processing on the laser scanning point position corresponding to the laser concentration field, the gas meter position corresponding to the flow anomaly data, and the pressure sensor position corresponding to the pressure topology low point data.

4. The method according to claim 2, characterized in that Also includes: When the target area meets the preset complex terrain, determining the drone leakage evidence based on the drone concentration field; A global leakage confidence level of the target area is generated based on the drone leakage evidence, the laser evidence, the flow evidence, and the pressure evidence.

5. The method according to claim 4, characterized in that Also includes: When the global leakage confidence exceeds a preset threshold, the leakage point position corresponding to the global leakage confidence of the target area is obtained by determining the spatial intersection of the leakage point position indicated by the drone leakage evidence and the leakage point position indicated by the laser evidence, flow evidence, and pressure evidence.

6. The method according to claim 4, characterized in that The UAV concentration field is obtained by inverting and reconstructing the second light intensity attenuation data of the target area after the UAV extension unit collects the second light intensity attenuation data of the target area; wherein, When the drone extension unit collects the second light intensity attenuation data of the target area, it includes: Determine the scanning width of a single route based on the terrain constraint information of the target area, the preset flight altitude and the preset laser scanning field of view angle; An obstacle avoidance route set is generated based on the single route scanning width, the coverage rate of the target area meeting a preset coverage rate; and the second light intensity attenuation data is collected based on the obstacle avoidance route set.

7. A gas leakage detection device, characterized in that: include: an acquisition module, configured to acquire first light intensity attenuation data of a target area, flow data during a low-peak period within the target area, and discrete pressure sensor data within the target area; a processing module configured to obtain a laser concentration field by inverting and reconstructing the first light intensity attenuation data; determine flow anomaly data in the flow data by using an anomaly detection model; and determine pressure topology low point data based on the discrete pressure sensor data; The processing module is further used to fuse the laser concentration field, the flow anomaly data and the pressure topology low point data to obtain leakage confidence; and output an alarm notification and the leakage point location when the leakage confidence exceeds a preset threshold.

8. An electronic device, characterized in that: include: Memory, processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory, so that the processor performs the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer-executable instructions, which are used to implement the method according to any one of claims 1 to 6 when executed by a processor.

10. A computer program product, characterized in that The invention comprises a computer program, which implements the method according to any one of claims 1 to 6 when the computer program is executed by a processor.

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