Unmanned aerial vehicle based gas monitoring system and method

Abstract

The application belongs to the field of gas monitoring, and relates to unmanned aerial vehicle collection technology, and is used for solving the problems that the collection accuracy, completeness and timeliness of atmospheric data cannot be guaranteed in the prior art, and specifically relates to a gas monitoring system and method based on an unmanned aerial vehicle, which comprises a monitoring layout module, a data statistics module, a calibration analysis module and a data receiving module which are sequentially and communicatively connected, and the data statistics module is further communicatively connected with a missing collection supervision module; the monitoring layout module is used for unmanned aerial vehicle detection layout analysis of a gas monitoring area; the gas monitoring area is divided into a plurality of sub-areas, a plurality of collection points are set in the sub-areas according to a GIS map, and a calibration route and a plurality of inspection routes are generated according to the number of unmanned aerial vehicles allocated to the collection points and the sub-areas; the application can guarantee data effectiveness by collecting calibration point data of a calibration object according to the calibration route, and reasonably allocate data collection resources allocated to the sub-areas.

Description

TECHNICAL FIELD

[0001] The application belongs to the field of gas monitoring, and relates to unmanned aerial vehicle collection technology, in particular to a gas monitoring system and method based on an unmanned aerial vehicle. BACKGROUND

[0002] The gas monitoring system based on an unmanned aerial vehicle is an advanced environmental monitoring solution combining gas sensing technology and an unmanned aerial vehicle platform; the system utilizes the mobility of the unmanned aerial vehicle, carries various gas sensors, and realizes three-dimensional monitoring of various gas components in the atmospheric environment.

[0003] The patent No. CN107422747B discloses a kind of unmanned aerial vehicle system for atmospheric environment online monitoring and atmospheric controlled sampling, which can accurately draw the three-dimensional distribution diagram of atmospheric environment quality in detection area, and can complete atmospheric sample collection and atmospheric environment pollutant emission source tracing in monitoring area;However, the system lacks data collection layout analysis function of unmanned aerial vehicle, so that the atmospheric data collected by unmanned aerial vehicle cannot be verified, and when the sampling point is missed, the data cannot be supplemented in time, so that the accuracy, completeness and timeliness of atmospheric data collection cannot be guaranteed.

[0004] In view of the above technical problems, a solution is proposed in the present application. SUMMARY

[0005] The present application aims to provide a gas monitoring system and method based on an unmanned aerial vehicle, 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 the present application is how to provide a gas monitoring system and method based on an unmanned aerial vehicle that can guarantee the accuracy, completeness and timeliness of atmospheric data collection.

[0007] The object of the present application can be achieved by the following technical solutions:

[0008] The gas monitoring system based on an unmanned aerial vehicle comprises a monitoring layout module, a data statistics module, a calibration analysis module and a data receiving module connected in sequence, and the data statistics module is further connected with a missing sampling supervision module;

[0009] The monitoring layout module divides the gas monitoring area into several sub-areas, sets several collection points in the sub-areas according to GIS map, and generates a calibration route and several inspection routes according to the number of unmanned aerial vehicles allocated to the collection points and sub-areas; according to the inspection route, the unmanned aerial vehicles are allocated and marked as inspection objects; the remaining unmanned aerial vehicles are marked as calibration objects;

[0010] a data statistics module configured to statistically analyze the collected data of the unmanned aerial vehicles and determine whether the data collection state of the sub-region meets the requirements;

[0011] a calibration analysis module, when the calibration object passes through the calibration point according to the calibration route, the calibration analysis module is configured to collect the value of the atmospheric parameter i of the calibration point to obtain a calibration value JZi, when the inspection object passes through the collection point according to the inspection route, the calibration analysis module is configured to collect the value of the atmospheric parameter i of the collection point to obtain a collection value CJi, and the upload value SCi of the atmospheric parameter i is marked by the collection value CJi and the calibration value JZi; the upload value SCi of all atmospheric parameters i is sent to the data receiving module;

[0012] a missing collection supervision module configured to supervise and analyze the missing collection state of the unmanned aerial vehicles, and perform factor analysis on the sub-region when the missing collection state does not meet the requirements.

[0013] Further, the generation result of the inspection route meets the following characteristics: the number of inspection routes = the number of unmanned aerial vehicles - 1, the inspection routes cover all collection points in the sub-region and there is no route intersection between the inspection routes, and the difference between the maximum and minimum number of collection points covered by the inspection routes is less than K1.

[0014] Further, the specific process of the monitoring layout module for unmanned aerial vehicle detection layout analysis of the gas monitoring region further includes: marking the ratio of the route length value of the longest inspection route to the flight speed of the unmanned aerial vehicle as the execution duration, generating a supervision period when the execution object starts to perform the collection task, the duration of the supervision period is equal to the execution duration, setting a plurality of supervision time points in the supervision period, and obtaining the collection data amount of the supervision time point by calculating the number of collection points expected to be completed by each execution object at the supervision time point; a plurality of collection points are randomly selected on each inspection route as calibration points, a calibration route is generated for the sub-region by the calibration points, and after the first calibration point on the calibration route completes the data collection of the execution object, the calibration object is controlled to take off and perform calibration data collection according to the calibration route.

[0015] Further, the specific process of the data statistics module for statistically analyzing the collected data of the unmanned aerial vehicles includes: obtaining the sum of the actual data amounts collected by all inspection objects at the supervision time point and marking it as the actual data amount, determining whether the actual data amount is equal to the collection data amount: if yes, it is determined that the data collection state of the sub-region meets the requirements; if not, it is determined that the data collection state of the sub-region does not meet the requirements, and the collection points with missing data are marked as supplement collection points by comparing the collected data and the collection points expected to be completed, and the supplement collection points are sent to the calibration analysis module.

[0016] Further, the marking process of the atmospheric parameter SCi includes: if the collection point is marked as a calibration point and is not marked as a supplementary collection point, marking the average of the calibration value JZi and the collection value CJi as the upload value SCi of the atmospheric parameter i; if the collection point is marked as a calibration point and a supplementary collection point, marking all the calibration values JZi of the atmospheric parameter i as the upload value SCi; if the collection point is not marked as a calibration point and a supplementary collection point, marking all the collection values CJi of the atmospheric parameter i as the upload value SCi; if the collection point is marked as a supplementary collection point and is not marked as a calibration point, marking the corresponding collection point as a calibration point and re-planning the calibration route.

[0017] Further, the specific process of the leakage monitoring module for monitoring and analyzing the leakage state of the unmanned aerial vehicle includes: marking the number of marked supplementary collection points as a leakage value at the end of the monitoring period, and comparing the leakage value with a preset leakage threshold value; if the leakage value is less than the leakage threshold value, it is determined that the leakage state of the sub-region in the monitoring period meets the requirements; if the leakage value is greater than or equal to the leakage threshold value, it is determined that the leakage state of the sub-region in the monitoring period does not meet the requirements.

[0018] Further, the specific process of the factor investigation analysis on the sub-region includes: marking the number of supplementary collection points on the inspection route as a route concentration value, calculating the variance of the route concentration values of all inspection routes to obtain a concentration coefficient, and comparing the concentration coefficient with a preset concentration threshold value; if the concentration coefficient is less than the concentration threshold value, an anti-interference optimization signal is generated and sent to the mobile terminal of the management personnel; if the concentration coefficient is greater than or equal to the concentration threshold value, the inspection route with the largest route concentration value is marked as a replacement route, the corresponding inspection object of the replacement route is marked as a fault object, a fault maintenance signal is generated and sent to the mobile terminal of the management personnel together with the fault object; at the same time, the calibration object is controlled to travel along the replacement route, and the calibration object re-collects the values of the atmospheric parameters i of all collection points on the replacement route and sends them to the data receiving module for data replacement.

[0019] The gas monitoring method based on the unmanned aerial vehicle includes the following steps:

[0020] Step one: unmanned aerial vehicle detection layout analysis on the gas monitoring area;

[0021] Step two: statistical analysis on the collection data of the unmanned aerial vehicle;

[0022] Step three: calibration analysis on the collection data of the unmanned aerial vehicle;

[0023] Step four: monitoring analysis on the leakage state of the unmanned aerial vehicle.

[0024] The present application has the following advantages:

[0025] 1、Through the monitoring layout module, the unmanned aerial vehicle detection layout analysis can be performed on the gas monitoring area, the inspection route and the calibration route are generated according to the collection points in the sub-area and the number of distributed unmanned aerial vehicles, the data validity is ensured by collecting point data of the calibration object according to the calibration route, the data collection resources distributed in the sub-area are reasonably allocated, and the resource utilization rate is improved;

[0026] 2、Through the data statistical module, the collection data of the unmanned aerial vehicle can be statistically analyzed, the data collection state is evaluated through the comparison result of the collection quantity and the actual data volume at the supervision time point, the supplement collection point is screened out when the data collection state is abnormal, the calibration route is dynamically optimized through the supplement collection point, the calibration object simultaneously performs the calibration task and the supplement task, and the timeliness of data collection uploading is improved;

[0027] 3、Through the calibration analysis module, the collection data of the unmanned aerial vehicle can be calibrated and analyzed, the uploaded data is generated in combination with the marking state of the collection points, the calibration points and the supplement collection points in the inspection route, and the numerical accuracy of the uploaded atmospheric parameters is improved;

[0028] 4、Through the missing collection supervision module, the missing collection state of the unmanned aerial vehicle can be supervised and analyzed, the missing collection state is evaluated at the end of the supervision period, factor analysis is performed in a timely manner when the missing collection state is abnormal, corresponding processing signals are generated according to the factor analysis result, and the probability of missing collection of the unmanned aerial vehicle in the subsequent collection task is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0029] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or the prior art description. Obviously, the drawings in the following description only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained without creative labor on the basis of these drawings.

[0030] Figure 1 The system block diagram of the first embodiment of the present application is shown in the figure.

[0031] Figure 2 The method flow chart of the second embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0032] The technical solutions of the present application will be described in detail below in combination with the embodiments. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0033] Embodiment one: as Figure 1As shown, the unmanned aerial vehicle-based gas monitoring system comprises, in sequence, a monitoring layout module, a data statistics module, a calibration analysis module and a data receiving module, and the data statistics module is further communicatively connected with a missing sampling supervision module.

[0034] The monitoring layout module is configured to perform unmanned aerial vehicle detection layout analysis on the gas monitoring area: the gas monitoring area is divided into a plurality of sub-areas, a plurality of collection points are set in the sub-areas according to a GIS map, and a plurality of inspection routes are generated according to the number of unmanned aerial vehicles allocated to the collection points and the sub-areas: the number of inspection routes = the number of unmanned aerial vehicles - 1, the inspection routes cover all the collection points in the sub-areas and there is no route intersection between the inspection routes, the difference between the maximum and minimum number of collection points covered by the inspection routes is less than K1, K1 is a numerical constant, and the specific value of K1 is set by the management personnel; the unmanned aerial vehicles are allocated according to the inspection routes and marked as inspection objects; the remaining unmanned aerial vehicles are marked as calibration objects; the ratio of the route length value of the longest inspection route to the flight speed of the unmanned aerial vehicle is marked as the execution duration, a supervision period is generated when the inspection objects start to perform the collection task, the length of the supervision period is equal to the execution duration, a plurality of supervision time points are set in the supervision period, and the collection data volume of the supervision time points is obtained by calculating the number of collection points that each execution object is expected to complete collection at the supervision time points; a plurality of collection points are randomly selected on each inspection route as calibration points, the calibration route for the sub-area is generated through the calibration points, and after the first calibration point on the calibration route completes the data collection of the execution object, the calibration object is controlled to take off and perform calibration data collection according to the calibration route; the inspection routes and the calibration routes are generated according to the collection points in the sub-area and the number of allocated unmanned aerial vehicles, the calibration point data collection by the calibration object according to the calibration route is used to ensure data validity, the allocated data collection resources in the sub-area are reasonably allocated, and the resource utilization rate is improved.

[0035] The data statistics module is configured to perform statistical analysis on the collection data of the unmanned aerial vehicles: the sum of the data actually collected by all the inspection objects at the supervision time points is obtained and marked as the actual data volume, and it is determined whether the actual data volume is equal to the collection data volume: if yes, it is determined that the data collection state of the sub-area meets the requirements; if no, it is determined that the data collection state of the sub-area does not meet the requirements, the collection points with missing data are marked as supplement collection points by comparing the collected data with the collection points that are expected to have completed collection, and the supplement collection points are sent to the calibration analysis module; the data collection state is evaluated through the comparison result of the collection volume and the actual data volume at the supervision time points, the supplement collection points are screened out when the data collection state is abnormal, the calibration route is dynamically optimized through the supplement collection points, the calibration object simultaneously performs the calibration task and the supplement collection task, and the timeliness of data collection uploading is improved.

[0036] The calibration analysis module is used for calibration analysis of the collected data of the unmanned aerial vehicle: when the calibration object passes through the calibration point according to the calibration route, the numerical value of the atmospheric parameter i of the calibration point is collected to obtain the calibration value JZi, and the atmospheric parameter i generally includes CO2, CO, SO2, NOx, VOCs and the like; when the inspection object passes through the collection point according to the inspection route, the numerical value of the atmospheric parameter i of the collection point is collected to obtain the collection value CJi; if the collection point is marked as a calibration point and is not marked as a supplementary collection point, the average value of the calibration value JZi and the collection value CJi is marked as the upload value SCi of the atmospheric parameter i; if the collection point is marked as a calibration point and a supplementary collection point, the calibration value JZi of all atmospheric parameters i is marked as the upload value SCi; if the collection point is not marked as a calibration point and a supplementary collection point, the collection value CJi of all atmospheric parameters i is marked as the upload value SCi; if the collection point is marked as a supplementary collection point and is not marked as a calibration point, the corresponding collection point is marked as a calibration point and the calibration route is re-planned; the upload value SCi of all atmospheric parameters i is sent to the data receiving module; the atmospheric monitoring result can be generated by comparing the upload value SCi with the alarm threshold of the corresponding atmospheric parameter i, and this process is a mature existing technology in the field of atmospheric monitoring; the core of the present application is to optimize the data collection effectiveness, timeliness and integrity of the monitoring collection end, so the existing technology in the comparison link is not described here; the upload data is generated in combination with the marking state of the collection point and the calibration point and the supplementary collection point in the inspection route, and the numerical accuracy of the uploaded atmospheric parameters is improved.

[0037] The leakage supervision module is used for monitoring and analyzing the leakage state of the unmanned aerial vehicle: at the end of the supervision period, the number of markers of the supplementary sampling points is marked as a leakage value, and the leakage value is compared with a preset leakage threshold value: if the leakage value is less than the leakage threshold value, it is determined that the leakage state of the sub-region in the supervision period meets the requirements; if the leakage value is greater than or equal to the leakage threshold value, it is determined that the leakage state of the sub-region in the supervision period does not meet the requirements, and factor analysis is performed on the sub-region: the number of supplementary sampling points on the inspection route is marked as a route central value, the route central values of all inspection routes are calculated to obtain a central coefficient, and the central coefficient is compared with a preset central threshold value: if the central coefficient is less than the central threshold value, an anti-interference optimization signal is generated and sent to the mobile terminal of the management personnel; if the central coefficient is greater than or equal to the central threshold value, the inspection route with the largest route central value is marked as a replacement route, and the inspection object corresponding to the replacement route is marked as a fault object, a fault maintenance signal is generated and sent to the mobile terminal of the management personnel; at the same time, the calibration object is controlled to drive along the replacement route, and the calibration object reacquires the values of the atmospheric parameters i of all sampling points on the replacement route and sends them to the data receiving module for data replacement; at the end of the supervision period, the leakage state is evaluated, and factor analysis is performed in time when the leakage state is abnormal, corresponding processing signals are generated according to the factor analysis results, and the probability of the unmanned aerial vehicle leakage phenomenon in the subsequent collection task is reduced.

[0038] Embodiment two: as shown in the figure, the gas monitoring method based on the unmanned aerial vehicle includes the following steps: Figure 2

[0039] Step one: unmanned aerial vehicle detection layout analysis of the gas monitoring area: the gas monitoring area is divided into a plurality of sub-regions, a plurality of sampling points are set in the sub-regions according to the GIS map, and a calibration route and a plurality of inspection routes are generated according to the number of unmanned aerial vehicles allocated to the sampling points and the sub-regions;

[0040] Step two: statistical analysis of the collection data of the unmanned aerial vehicle: the sum of the data actually collected by all inspection objects is obtained at the supervision time point and marked as the actual data amount, and whether the data collection state of the sub-region meets the requirements is determined according to the comparison result of the actual data amount and the collection data amount;

[0041] Step three: calibration analysis of the collection data of the unmanned aerial vehicle: the upload value SCi of the atmospheric parameter i is generated through the marker relationship of the sampling points, the calibration points and the supplementary sampling points, and the upload values SCi of all atmospheric parameters i are sent to the data receiving module;

[0042] ​Step four: monitoring and analyzing the missing sampling state of the unmanned aerial vehicle: at the end of the monitoring period, the number of markers of the supplementary sampling point is marked as the missing sampling value, and whether the missing sampling state of the sub-region in the monitoring period meets the requirements is determined by the missing sampling value, and factor analysis is performed when the requirements are not met.

[0043] The unmanned aerial vehicle-based gas monitoring system and method, when working, divides the gas monitoring area into a plurality of sub-regions, sets a plurality of collection points in the sub-regions according to a GIS map, generates a calibration route and a plurality of inspection routes according to the number of unmanned aerial vehicles allocated to the collection points and the sub-regions; obtains the sum of the actual data amounts collected by all inspection objects at a monitoring time point and marks it as the actual data amount, and determines whether the data collection state of the sub-region meets the requirements according to the comparison result of the actual data amount and the collection data amount; generates the upload value SCi of the atmospheric parameter i through the marker relationship of the collection point, the calibration point and the supplementary sampling point, and sends all the upload values SCi of the atmospheric parameters i to the data receiving module; at the end of the monitoring period, the number of markers of the supplementary sampling point is marked as the missing sampling value, and whether the missing sampling state of the sub-region in the monitoring period meets the requirements is determined by the missing sampling value, and factor analysis is performed when the requirements are not met.

[0044] The above content is only an example and description of the structure of the present application, and those skilled in the art can make various modifications or supplements or use similar ways to replace the described specific embodiments, as long as they do not deviate from the structure of the present application or exceed the scope defined by the claims.

[0045] In the description of the present application, the description of the terms "one embodiment", "example", "specific example" and the like means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In the present application, the illustrative description of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in any one or more embodiments or examples in a suitable manner.

[0046] The preferred embodiments of the present application disclosed above are only used to help explain the present application. The preferred embodiments do not describe all the details and limit the present application to the specific embodiments. Obviously, many modifications and changes can be made according to the content of the present application. The present application selects and describes these embodiments in order to better explain the principles and practical applications of the present application, so that those skilled in the art can well understand and utilize the present application. The present application is limited by the claims and their entire scope and equivalents.

Claims

1. A UAV-based gas monitoring system, characterized in that, The monitoring layout module, the data statistical module, the calibration analysis module and the data receiving module are sequentially connected, the data statistical module is further connected with the missing sampling supervision module; The monitoring layout module divides the gas monitoring area into several sub-regions, sets several collection points in the sub-regions according to the GIS map, and generates a calibration route and several inspection routes according to the number of unmanned aerial vehicles allocated to the collection points and the sub-regions; The unmanned aerial vehicles are distributed according to the inspection routes, and the unmanned aerial vehicles matched with the inspection routes are marked as inspection objects; The remaining unmanned aerial vehicles are marked as calibration objects; The data statistical module statistically analyzes the collection data of the unmanned aerial vehicles and determines whether the data collection state of the sub-regions meets the requirements; The calibration analysis module collects the calibration value JZi of the atmospheric parameter i of the calibration point when the calibration object passes the calibration point according to the calibration route, collects the collection value CJi of the atmospheric parameter i of the collection point when the inspection object passes the collection point according to the inspection route, and marks the upload value SCi of the atmospheric parameter i by the collection value CJi and the calibration value JZi; The upload values SCi of all atmospheric parameters i are sent to the data receiving module; The missing sampling supervision module supervises and analyzes the missing sampling state of the unmanned aerial vehicles, and analyzes the factors of the sub-regions when the missing sampling state does not meet the requirements; The specific process of the statistical analysis of the collection data of the unmanned aerial vehicles by the data statistical module includes: obtaining the sum of the actual data amounts actually collected by all the inspection objects at the supervision time point and marking the sum as the actual data amount, determining whether the actual data amount is equal to the collection data amount, if yes, determining that the data collection state of the sub-region meets the requirements, if no, determining that the data collection state of the sub-region does not meet the requirements, comparing the collected data and the collection points that are predicted to have completed collection, marking the collection points with missing data as supplement collection points, and sending the supplement collection points to the calibration analysis module; The marking process of the atmospheric parameter SCi includes: if the collection point is marked as a calibration point and is not marked as a supplement collection point, marking the average value of the calibration value JZi and the collection value CJi as the upload value SCi of the atmospheric parameter i; if the collection point is marked as a calibration point and a supplement collection point, marking the calibration value JZi of all atmospheric parameters i as the upload value SCi; if the collection point is not marked as a calibration point and a supplement collection point, marking the collection value CJi of all atmospheric parameters i as the upload value SCi; if the collection point is marked as a supplement collection point and is not marked as a calibration point, marking the corresponding collection point as a calibration point and re-planning the calibration route; The atmospheric parameters i include CO2, CO, SO2, NOx and VOCs.

2. The UAV-based gas monitoring system of claim 1, wherein, The generation result of the inspection route meets the following characteristics: the number of inspection routes = the number of unmanned aerial vehicles - 1, the inspection routes cover all collection points in the sub-regions and there is no route intersection between the 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 UAV-based gas monitoring system of claim 2, wherein, The specific process of the monitoring layout module for unmanned aerial vehicle detection layout analysis of the gas monitoring area further includes: marking the ratio of the route length value of the longest inspection route to the unmanned aerial vehicle flight speed as an execution duration, generating a supervision time period at the beginning of the execution object to collect the task, the duration of the supervision time period being equal to the execution duration, setting a plurality of supervision time points within the supervision time period, and obtaining the collection data amount of the supervision time point by calculating the collection point number expected to be completed by each execution object at the supervision time point; randomly selecting a plurality of collection points on each inspection route as calibration points, generating a calibration route for the sub-region through the calibration points, and controlling the calibration object to take off and perform calibration data collection according to the calibration route after the first calibration point on the calibration route completes the data collection of the execution object.

4. The UAV-based gas monitoring system of claim 3, wherein, The specific process of the missing collection supervision module for supervising and analyzing the missing collection state of the unmanned aerial vehicle includes: marking the number of the missing collection points at the end of the supervision time period as a missing collection value, comparing the missing collection value with a preset missing collection threshold value, if the missing collection value is less than the missing collection threshold value, it is determined that the missing collection state of the sub-region in the supervision time period meets the requirements, and if the missing collection value is greater than or equal to the missing collection threshold value, it is determined that the missing collection state of the sub-region in the supervision time period does not meet the requirements.

5. The UAV-based gas monitoring system of claim 4, wherein, The specific process of the factor investigation analysis of the sub-region includes: marking the number of the missing collection points on the inspection route as a route central value, calculating the variance of the route central values of all inspection routes to obtain a central coefficient, comparing the central coefficient with a preset central threshold value, if the central coefficient is less than the central threshold value, generating an anti-interference optimization signal and sending the anti-interference optimization signal to the mobile terminal of the management personnel, if the central coefficient is greater than or equal to the central threshold value, marking the inspection route with the largest route central value as a replacement route, marking the inspection object corresponding to the replacement route as a 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, and simultaneously controlling the calibration object to travel according to the replacement route, re-collecting the values of the atmospheric parameters i of all collection points on the replacement route by the calibration object and sending them to the data receiving module for data replacement.

6. A method of gas monitoring based on a drone, characterized in that, The unmanned aerial vehicle-based gas monitoring system is applied to any one of claims 1-5, comprising the following steps: Step one: unmanned aerial vehicle detection layout analysis of the gas monitoring area; Step two: statistical analysis of the collection data of the unmanned aerial vehicle; Step three: calibration analysis of the collection data of the unmanned aerial vehicle; Step four: supervising and analyzing the missing collection state of the unmanned aerial vehicle.

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