Gas monitoring system and method based on unmanned aerial vehicle

Abstract

The invention belongs to the field of gas monitoring, relates to an unmanned aerial vehicle collection technology, and particularly relates to a gas monitoring system and method based on an unmanned aerial vehicle, which are used for solving the problem that the collection accuracy, integrity and timeliness of atmosphere data cannot be guaranteed in the prior art. Comprising a monitoring layout module, a data statistics module, a calibration analysis module and a data receiving module which are in communication connection in sequence, and the data statistics module is further in communication connection with a missed collection supervision module; the monitoring layout module is used for carrying out unmanned aerial vehicle detection layout analysis on a gas monitoring area: dividing the gas monitoring area into a plurality of sub-areas, setting a plurality of acquisition points in the sub-areas according to a GIS map, and generating a calibration route and a plurality of inspection routes according to the acquisition points and the number of unmanned aerial vehicles allocated to the sub-areas; according to the method, the data validity can be guaranteed by collecting the calibration point data through the calibration object according to the calibration route, and the data collection resources distributed in the sub-regions are reasonably allocated.

Description

Technical Field

[0001] This invention belongs to the field of gas monitoring and relates to UAV data collection technology, specifically a gas monitoring system and method based on UAVs. Background Technology

[0002] The UAV-based gas monitoring system is an advanced environmental monitoring solution that combines gas sensing technology with a UAV platform. This system utilizes the mobility of UAVs to carry various gas sensors, enabling three-dimensional monitoring of various gas components in the atmospheric environment.

[0003] The invention patent with patent number CN107422747B discloses an unmanned aerial vehicle (UAV) system for online monitoring of the atmospheric environment and controlled atmospheric sampling. This system can accurately draw a three-dimensional distribution map of the atmospheric environmental quality within the detection area, and can also complete the collection of atmospheric samples and the tracing of atmospheric pollutant emission sources within the monitoring area. However, the system lacks the data collection layout analysis function of the UAV, which makes it impossible to verify the atmospheric data collected by the UAV. At the same time, it cannot promptly supplement data when there are missed collection points, resulting in the inability to guarantee the accuracy, completeness and timeliness of atmospheric data collection.

[0004] To address the aforementioned technical problems, this application proposes a solution. Summary of the Invention

[0005] The purpose of this invention is to provide a gas monitoring system and method based on unmanned aerial vehicles (UAVs) to solve the problem that the accuracy, completeness, and timeliness of atmospheric data collection cannot be guaranteed in the prior art.

[0006] The technical problem to be solved by this invention is: how to provide a gas monitoring system and method based on UAVs that can ensure the accuracy, integrity and timeliness of atmospheric data collection.

[0007] The objective of this invention can be achieved through the following technical solutions:

[0008] The gas monitoring system based on UAVs includes a monitoring layout module, a data statistics module, a calibration analysis module, and a data receiving module that are connected in sequence. The data statistics module is also connected in communication with a leak monitoring module.

[0009] The monitoring layout module divides the gas monitoring area into several sub-areas, sets several collection points in each sub-area based on the GIS map, and generates a calibration route and several inspection routes based on the number of drones allocated to the collection points and sub-areas. Drones are allocated according to the inspection routes, and drones matching the inspection routes are marked as inspection objects. The remaining drones are marked as calibration objects.

[0010] The data statistics module performs statistical analysis on the data collected by the drone and determines whether the data collection status of the sub-area meets the requirements.

[0011] The calibration analysis module collects the atmospheric parameter i at the calibration point as the calibration object passes through the calibration point along the calibration route to obtain the calibration value JZi. The inspection object collects the atmospheric parameter i at the collection point as the inspection object passes through the collection point along the inspection route to obtain the collection value CJi. The uploaded value SCi of the atmospheric parameter i is marked by the collected value CJi and the calibration value JZi. The uploaded values ​​SCi of all atmospheric parameters i are sent to the data receiving module.

[0012] The missed sampling monitoring module monitors and analyzes the missed sampling status of drones, and conducts factor investigation and analysis on sub-areas when the missed sampling status does not meet the requirements.

[0013] Furthermore, the generated inspection routes satisfy the following characteristics: the number of inspection routes = the number of drones - 1, the inspection routes cover all collection points in the sub-region and there are no route intersections between inspection routes, and the difference between the maximum and minimum number of collection points covered by the inspection routes is less than K1.

[0014] Furthermore, the specific process of the monitoring layout module for analyzing the UAV detection layout of the gas monitoring area also includes: marking the ratio of the longest inspection route length to the UAV flight speed as the execution duration; generating a monitoring period when the execution object begins to perform the data collection task, with the duration of the monitoring period being equal to the execution duration; setting several monitoring time points within the monitoring period; obtaining the amount of data collected at each monitoring time point by calculating the number of collection points expected to be collected by each execution object at the monitoring time point; randomly selecting several collection points as calibration points on each inspection route; generating calibration routes for sub-areas through the calibration points; and after the execution object completes data collection at the first calibration point on the calibration route, controlling the calibration object to take off and collect calibration data according to the calibration route.

[0015] Furthermore, the specific process of statistical analysis of the data collected by the drone by the data statistics module includes: obtaining the sum of the actual data collected by all inspection objects at the monitoring time point and marking it as the actual data volume; determining whether the actual data volume is equal to the collected data volume; if so, determining that the data collection status of the sub-area meets the requirements; if not, determining that the data collection status of the sub-area does not meet the requirements; comparing the collected data with the predicted completed collection points; marking the collection points with missing data as supplementary collection points; and sending the supplementary collection points to the calibration analysis module.

[0016] Furthermore, the labeling process for atmospheric parameter SCi includes: if the collection point is labeled as a calibration point but not as a supplementary collection point, the average of the calibration value JZi and the collected value CJi is labeled as the uploaded value SCi of atmospheric parameter i; if the collection point is labeled as both a calibration point and a supplementary collection point, the calibration values ​​JZi of all atmospheric parameters i are labeled as the uploaded value SCi; if the collection point is not labeled as both a calibration point and a supplementary collection point, the collected values ​​CJi of all atmospheric parameters i are labeled as the uploaded value SCi; if the collection point is labeled as a supplementary collection point but not as a calibration point, the corresponding collection point is labeled as a calibration point and the calibration route is replanned.

[0017] Furthermore, the specific process of the omission monitoring module for monitoring and analyzing the omission status of drones includes: marking the number of supplementary sampling points as the omission value at the end of the monitoring period, and comparing the omission value with a preset omission threshold: if the omission value is less than the omission threshold, it is determined that the omission status of the sub-region during the monitoring period meets the requirements; if the omission value is greater than or equal to the omission threshold, it is determined that the omission status of the sub-region during the monitoring period does not meet the requirements.

[0018] Furthermore, the specific process of factor investigation and analysis for sub-regions includes: marking the number of supplementary sampling points on the inspection route as the route concentration value; calculating the variance of the route concentration values ​​of all inspection routes to obtain the concentration coefficient; comparing the concentration coefficient with a preset concentration threshold; if the concentration coefficient is less than the concentration threshold, generating an anti-interference optimization signal and sending it to the mobile terminal of the management personnel; if the concentration coefficient is greater than or equal to the concentration threshold, marking the inspection route with the largest route concentration value as the replacement route, marking the inspection object corresponding to the replacement route as the fault object, generating a fault repair signal, and sending the fault repair signal and the fault object to the mobile terminal of the management personnel; simultaneously controlling the calibration object to travel along the replacement route, re-collecting the atmospheric parameter i values ​​of all sampling points on the replacement route through the calibration object and sending them to the data receiving module for data replacement.

[0019] The gas monitoring method based on unmanned aerial vehicles (UAVs) includes the following steps:

[0020] Step 1: Conduct UAV detection layout analysis of the gas monitoring area;

[0021] Step 2: Perform statistical analysis on the data collected by the drone;

[0022] Step 3: Calibrate and analyze the data collected by the drone;

[0023] Step 4: Monitor and analyze the missed data collection status of drones.

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

[0025] 1. The monitoring layout module can perform UAV detection layout analysis on the gas monitoring area. Based on the collection points and the number of UAVs allocated in the sub-area, inspection routes and calibration routes are generated. The calibration objects collect calibration point data according to the calibration routes to ensure data validity. The data collection resources allocated in the sub-area are rationally allocated to improve resource utilization.

[0026] 2. The data statistics module can perform statistical analysis on the data collected by the drone. At the monitoring time point, the data collection status can be evaluated by comparing the number of collected data with the actual data volume. When the data collection status is abnormal, supplementary collection points can be selected. The calibration route can be dynamically optimized through supplementary collection points, so that the calibration object can perform calibration tasks and supplementary collection tasks at the same time, thereby improving the timeliness of data collection and uploading.

[0027] 3. The calibration and analysis module can perform calibration and analysis on the data collected by the UAV, and generate uploaded data by combining the marking status of the collection points, calibration points and supplementary collection points in the inspection route, thereby improving the accuracy of the uploaded atmospheric parameters.

[0028] 4. The missed data collection monitoring module can monitor and analyze the missed data collection status of drones. At the end of the monitoring period, the missed data collection status is evaluated. When the missed data collection status is abnormal, the factors are investigated and analyzed in a timely manner. Based on the results of the factor investigation and analysis, corresponding processing signals are generated to reduce the probability of drone missed data collection in subsequent data collection tasks. Attached Figure Description

[0029] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0030] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0031] Figure 2 This is a flowchart of the method in Embodiment 2 of the present invention. Detailed Implementation

[0032] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0033] Example 1: As Figure 1As shown, the gas monitoring system based on UAV includes a monitoring layout module, a data statistics module, a calibration analysis module, and a data receiving module that are connected in sequence. The data statistics module is also connected to a leak monitoring module.

[0034] The monitoring layout module is used for drone detection layout analysis of the gas monitoring area: The gas monitoring area is divided into several sub-areas. Based on the GIS map, several data collection points are set within each sub-area. Several inspection routes are generated based on the number of data collection points and the number of drones allocated to each sub-area: the number of inspection routes = number of drones - 1. Inspection routes cover all data collection points within each sub-area, and there are no intersections between routes. The difference between the maximum and minimum number of data collection points covered by each inspection route is less than K1, where K1 is a constant set by administrators. Drones are allocated according to the inspection routes, and drones matching the routes are marked as inspection targets. The remaining drones are marked as calibration targets. The ratio of the longest inspection route length to the drone's flight speed is marked as the execution time. A monitoring period is generated when the execution object begins its data collection task. The duration of the monitoring period is equal to the execution duration. Several monitoring time points are set within the monitoring period. The amount of data collected at each monitoring time point is obtained by calculating the number of data collection points that each execution object is expected to complete at each monitoring time point. Several data collection points are randomly selected as calibration points on each inspection route. Calibration routes are generated for sub-areas based on these calibration points. After the execution object completes data collection at the first calibration point on the calibration route, the calibration object is controlled to take off and collect calibration data according to the calibration route. Inspection routes and calibration routes are generated based on the number of data collection points and allocated drones within the sub-area. Data validity is ensured by having the calibration object collect calibration point data according to the calibration route. The data collection resources allocated within the sub-area are rationally allocated to improve resource utilization.

[0035] The data statistics module is used to perform statistical analysis on the data collected by the drones: at the monitoring time point, the sum of the actual data collected by all inspection objects is obtained and marked as the actual data volume, and it is determined whether the actual data volume is equal to the collected data volume: if so, the data collection status of the sub-area is determined to meet the requirements; if not, the data collection status of the sub-area is determined to not meet the requirements. The collected data is compared with the predicted completed collection points, and the collection points with missing data are marked as supplementary collection points and sent to the calibration analysis module; at the monitoring time point, the data collection status is evaluated by comparing the collected quantity with the actual data volume. When the data collection status is abnormal, supplementary collection points are selected, and the calibration route is dynamically optimized through supplementary collection points, so that the calibration object can perform calibration tasks and supplementary collection tasks at the same time, improving the timeliness of data collection and uploading.

[0036] The calibration analysis module is used to perform calibration analysis on the data collected by the UAV: ​​When the calibration target passes through the calibration point along the calibration route, the atmospheric parameter i at the calibration point is numerically collected to obtain the calibration value JZi. The atmospheric parameter i generally includes CO2, CO, SO2, NOx, VOCs, etc. When the inspection target passes through the collection point along the inspection route, the atmospheric parameter i at the collection point is numerically collected to obtain the collection value CJi. If the collection point is marked as a calibration point and not marked as a supplementary collection point, the average value of the calibration value JZi and the collection value CJi is marked as the uploaded value SCi of the atmospheric parameter i. If the collection point is marked as both a calibration point and a supplementary collection point, the calibration values ​​JZi of all atmospheric parameters i are marked as the uploaded value SCi. If the collection point is not marked as both a calibration point and a supplementary collection point, the average value of the atmospheric parameter i is marked as the uploaded value SCi. For sampling points, the collected values ​​CJi of all atmospheric parameters i are marked as uploaded values ​​SCi. If a sampling point is marked as a supplementary sampling point but not as a calibration point, the corresponding sampling point is marked as a calibration point and the calibration route is replanned. The uploaded values ​​SCi of all atmospheric parameters i are sent to the data receiving module. The atmospheric monitoring results can be generated by comparing the uploaded values ​​SCi with the alarm thresholds of the corresponding atmospheric parameters i. This process is a mature existing technology in the field of atmospheric monitoring. The core of this application lies in optimizing the effectiveness, timeliness, and completeness of data collection at the monitoring and acquisition end. Therefore, the existing technology of the comparison process will not be elaborated here. The uploaded data is generated by combining the marking status of sampling points, calibration points, and supplementary sampling points in the inspection route to improve the accuracy of the uploaded atmospheric parameters.

[0037] The missed sampling monitoring module is used to monitor and analyze the missed sampling status of drones: At the end of the monitoring period, the number of supplementary sampling points is marked as the missed sampling value, and the missed sampling value is compared with a preset missed sampling threshold: If the missed sampling value is less than the missed sampling threshold, the missed sampling status of the sub-area during the monitoring period is determined to meet the requirements; if the missed sampling value is greater than or equal to the missed sampling threshold, the missed sampling status of the sub-area during the monitoring period is determined to not meet the requirements, and factor investigation analysis is performed on the sub-area: The number of supplementary sampling points on the inspection route is marked as the route concentration value, and the variance of the route concentration values ​​of all inspection routes is calculated to obtain the concentration coefficient, which is compared with a preset concentration threshold: If the concentration coefficient is less than the concentration threshold, an anti-interference optimization signal is generated and sent. The data is transmitted to the mobile terminals of management personnel. If the concentration coefficient is greater than or equal to the concentration threshold, the inspection route with the largest concentration value is marked as the replacement route, and the inspection object corresponding to the replacement route is marked as the fault object. A fault repair signal is generated and sent to the mobile terminals of management personnel along with the fault object. At the same time, the calibration object is controlled to travel along the replacement route. The atmospheric parameter i values ​​of all collection points on the replacement route are re-collected by the calibration object and sent to the data receiving module for data replacement. At the end of the monitoring period, the missed collection status is evaluated. When the missed collection status is abnormal, the factor investigation and analysis are carried out in a timely manner. The corresponding processing signal is generated based on the factor investigation and analysis results to reduce the probability of drone missed collection in subsequent collection tasks.

[0038] Example 2: Figure 2 As shown, the gas monitoring method based on unmanned aerial vehicles (UAVs) includes the following steps:

[0039] Step 1: Analyze the layout of UAV detection in the gas monitoring area: Divide the gas monitoring area into several sub-areas, set up several collection points in the sub-areas according to the GIS map, and generate a calibration route and several inspection routes based on the number of UAVs allocated to the collection points and sub-areas.

[0040] Step 2: Statistical analysis of the data collected by the drone: At the monitoring time point, obtain the sum of the actual data collected from all inspection objects and mark it as the actual data volume. Based on the comparison between the actual data volume and the collected data volume, determine whether the data collection status of the sub-area meets the requirements.

[0041] Step 3: Perform calibration and analysis on the data collected by the UAV: ​​Generate the upload value SCi of atmospheric parameter i by the marking relationship between the collection point, calibration point, and supplementary collection point, and send the upload value SCi of all atmospheric parameter i to the data receiving module;

[0042] Step 4: Monitor and analyze the missed data collection status of drones: At the end of the monitoring period, mark the number of supplementary data collection points as the missed data collection value. Use the missed data collection value to determine whether the missed data collection status of the sub-area during the monitoring period meets the requirements. If the requirements are not met, conduct factor investigation and analysis.

[0043] The gas monitoring system and method based on unmanned aerial vehicles (UAVs) divides the gas monitoring area into several sub-regions during operation. Several data collection points are set within each sub-region based on a GIS map. A calibration route and several inspection routes are generated based on the number of UAVs allocated to the collection points and sub-regions. At the monitoring time point, the sum of the actual data collected from all inspected objects is obtained and marked as the actual data volume. The data collection status of the sub-region is judged based on the comparison between the actual data volume and the collected data volume. The uploaded value SCi of atmospheric parameter i is generated through the marking relationship between the collection points, calibration points, and supplementary collection points, and the uploaded values ​​SCi of all atmospheric parameter i are sent to the data receiving module. At the end of the monitoring period, the number of supplementary collection points is marked as the missed collection value. The missed collection value is used to judge whether the missed collection status of the sub-region during the monitoring period meets the requirements. If the requirements are not met, factor investigation and analysis are performed.

[0044] The above description is merely an example and illustration of the structure of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described, or use similar methods to replace them, as long as they do not deviate from the structure of the invention or exceed the scope defined in the claims, all of which should fall within the protection scope of the present invention.

[0045] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0046] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A gas monitoring system based on unmanned aerial vehicles (UAVs), characterized in that, It includes a monitoring layout module, a data statistics module, a calibration analysis module, and a data receiving module that are connected in sequence. The data statistics module is also connected in communication with a leak monitoring module. The monitoring layout module divides the gas monitoring area into several sub-areas, sets several collection points in each sub-area according to the GIS map, and generates a calibration route and several inspection routes based on the number of drones allocated to the collection points and sub-areas. Drones are assigned according to the inspection route, and drones that match the inspection route are marked as inspection targets; Mark the remaining drones as calibration targets; The data statistics module performs statistical analysis on the data collected by the drone and determines whether the data collection status of the sub-area meets the requirements. The calibration analysis module collects the atmospheric parameter i at the calibration point when the calibration object passes through the calibration point along the calibration route to obtain the calibration value JZi. When the inspection object passes through the collection point along the inspection route, it collects the atmospheric parameter i at the collection point to obtain the collection value CJi. The uploaded value SCi of atmospheric parameter i is marked by the collection value CJi and the calibration value JZi. Send the uploaded values ​​SCi of all atmospheric parameters i to the data receiving module; The missed sampling monitoring module monitors and analyzes the missed sampling status of drones, and conducts factor investigation and analysis on sub-areas when the missed sampling status does not meet the requirements.

2. The gas monitoring system based on unmanned aerial vehicles according to claim 1, characterized in that, The generated inspection routes meet the following characteristics: the number of inspection routes = the number of drones - 1, the inspection routes cover all collection points in the sub-region and there are no route intersections between inspection routes, and the difference between the maximum and minimum number of collection points covered by the inspection routes is less than K1.

3. The gas monitoring system based on unmanned aerial vehicles according to claim 2, characterized in that, The specific process of the monitoring layout module for analyzing the UAV detection layout of the gas monitoring area also includes: marking the ratio of the longest inspection route length to the UAV flight speed as the execution duration; generating a monitoring period when the execution object begins to perform the data collection task, with the duration of the monitoring period being equal to the execution duration; setting several monitoring time points within the monitoring period; obtaining the amount of data collected at each monitoring time point by calculating the number of collection points expected to be collected by each execution object at the monitoring time point; randomly selecting several collection points as calibration points on each inspection route; generating calibration routes for sub-areas using the calibration points; and after the execution object completes data collection at the first calibration point on the calibration route, controlling the calibration object to take off and collect calibration data according to the calibration route.

4. The gas monitoring system based on unmanned aerial vehicles according to claim 3, characterized in that, The specific process of statistical analysis of the data collected by the drone by the data statistics module includes: obtaining the sum of the actual data collected by all inspection objects at the monitoring time point and marking it as the actual data volume; determining whether the actual data volume is equal to the collected data volume; if so, determining that the data collection status of the sub-area meets the requirements; if not, determining that the data collection status of the sub-area does not meet the requirements; comparing the collected data with the predicted collected points, marking the collected points with missing data as supplementary collection points, and sending the supplementary collection points to the calibration analysis module.

5. The gas monitoring system based on an unmanned aerial vehicle (UAV) according to claim 4, characterized in that, The labeling process for atmospheric parameter SCi includes: if the collection point is labeled as a calibration point but not as a supplementary collection point, the average of the calibration value JZi and the collected value CJi is labeled as the uploaded value SCi of atmospheric parameter i; if the collection point is labeled as both a calibration point and a supplementary collection point, the calibration values ​​JZi of all atmospheric parameters i are labeled as the uploaded value SCi; if the collection point is not labeled as both a calibration point and a supplementary collection point, the collected values ​​CJi of all atmospheric parameters i are labeled as the uploaded value SCi; if the collection point is labeled as a supplementary collection point but not as a calibration point, the corresponding collection point is labeled as a calibration point and the calibration route is replanned.

6. The gas monitoring system based on unmanned aerial vehicles according to claim 5, characterized in that, The specific process of the omission monitoring module for monitoring and analyzing the omission status of drones includes: marking the number of supplementary sampling points as the omission value at the end of the monitoring period, and comparing the omission value with the preset omission threshold: if the omission value is less than the omission threshold, it is determined that the omission status of the sub-area meets the requirements during the monitoring period; if the omission value is greater than or equal to the omission threshold, it is determined that the omission status of the sub-area does not meet the requirements during the monitoring period.

7. The gas monitoring system based on an unmanned aerial vehicle (UAV) according to claim 6, characterized in that, The specific process of factor investigation and analysis for sub-regions includes: marking the number of supplementary sampling points on the inspection route as the route concentration value; calculating the variance of the route concentration values ​​of all inspection routes to obtain the concentration coefficient; comparing the concentration coefficient with a preset concentration threshold; if the concentration coefficient is less than the concentration threshold, generating an anti-interference optimization signal and sending it to the mobile terminal of the management personnel; if the concentration coefficient is greater than or equal to the concentration threshold, marking the inspection route with the largest route concentration value as the replacement route, marking the inspection object corresponding to the replacement route as the fault object, generating a fault repair signal, and sending the fault repair signal and the fault object to the mobile terminal of the management personnel; simultaneously controlling the calibration object to travel along the replacement route, re-collecting the atmospheric parameter i values ​​of all sampling points on the replacement route through the calibration object and sending them to the data receiving module for data replacement.

8. A gas monitoring method based on unmanned aerial vehicles (UAVs), characterized in that, Applied to the UAV-based gas monitoring system according to any one of claims 1-7, the system includes the following steps: Step 1: Conduct UAV detection layout analysis of the gas monitoring area; Step 2: Perform statistical analysis on the data collected by the drone; Step 3: Calibrate and analyze the data collected by the drone; Step 4: Monitor and analyze the missed data collection status of drones.

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