CN114778774A

CN114778774A - Greenhouse gas monitoring method based on artificial intelligence and related equipment

Info

Publication number: CN114778774A
Application number: CN202210423839.0A
Authority: CN
Inventors: 李镐炜
Original assignee: Ping An International Smart City Technology Co Ltd
Current assignee: Ping An International Smart City Technology Co Ltd
Priority date: 2022-04-21
Filing date: 2022-04-21
Publication date: 2022-07-22

Abstract

The application provides a greenhouse gas monitoring method and device based on artificial intelligence, an electronic device and a storage medium, wherein the greenhouse gas monitoring method based on artificial intelligence comprises the following steps: collecting concentration values of different greenhouse gases of a plurality of observation points on different elevation values to obtain observation data of the observation points; establishing observation vectors of all greenhouse gases at each observation point according to the observation data, and taking the observation vectors of the same greenhouse gases as an observation matrix of the greenhouse gases; calculating the spatial distribution characteristics of the greenhouse gases based on the observation matrix, and formulating a flight monitoring scheme to acquire concentration values of the greenhouse gases to obtain greenhouse gas data; and monitoring the spatial distribution characteristics of the greenhouse gases to obtain abnormal greenhouse gases, and formulating a new flight monitoring scheme to update greenhouse gas data. The accuracy of planning flight monitoring scheme in order to guarantee greenhouse gas data that this application can be pertinence to the spatial distribution of monitoring greenhouse gas in time updates greenhouse gas data, realizes greenhouse gas's accurate monitoring.

Description

Greenhouse gas monitoring method based on artificial intelligence and related equipment

Technical Field

The present application relates to the field of artificial intelligence technology, and in particular, to a greenhouse gas monitoring method and apparatus, an electronic device, and a storage medium based on artificial intelligence.

Background

Greenhouse gas monitoring refers to a carbon monitoring method for monitoring the emission of various greenhouse gases such as carbon dioxide, methane, nitrous oxide, hydrofluorocarbons, perfluorocarbons and sulfur hexafluoride, is a scale established according to greenhouse effect evaluation and emission reduction measures, and is an important guarantee for comprehensively mastering greenhouse gas emission and the environment and climate effect thereof and predicting future changes thereof.

At present, a ground fixed observation station or a mobile observation station is generally used for detecting greenhouse gas concentration data at different heights in a target area in real time by using a greenhouse gas sensor, however, due to the fact that greenhouse gas emission has large space-time variation characteristics, the reliability of monitoring data obtained in the mode is low, and accurate monitoring of greenhouse gas in the target area cannot be achieved.

Disclosure of Invention

In view of the above, there is a need for a greenhouse gas monitoring method based on artificial intelligence and related devices, so as to solve the technical problem of how to achieve accurate monitoring of greenhouse gases in an area, wherein the related devices include a greenhouse gas monitoring device based on artificial intelligence, an electronic device and a storage medium.

The application provides a greenhouse gas monitoring method based on artificial intelligence, which comprises the following steps:

selecting a plurality of observation points in a monitoring area, and acquiring concentration values of each greenhouse gas of the observation points on different elevation values to form observation data of each observation point;

establishing an observation vector of each greenhouse gas at all observation points based on concentration values of different greenhouse gases in the observation data, and arranging the observation vectors of the same greenhouse gas at different observation points to obtain an observation matrix of each greenhouse gas;

calculating a spatial distribution characteristic of each greenhouse gas based on the observation matrix of the greenhouse gas;

formulating a flight monitoring scheme based on the observation matrix of the greenhouse gases to draw a greenhouse gas spatial distribution map of the monitoring area, wherein the greenhouse gas spatial distribution map comprises a concentration value of each greenhouse gas at a spatial position;

monitoring the change of the spatial distribution characteristics of the greenhouse gases to obtain abnormal greenhouse gases and an abnormal observation matrix corresponding to the abnormal greenhouse gases;

when abnormal greenhouse gases are obtained, a new flight monitoring scheme is formulated based on the abnormal observation matrix so as to update the greenhouse gas spatial distribution map.

In some embodiments, said constructing an observation vector for each greenhouse gas at all observation points based on concentration values of different greenhouse gases in said observation data comprises:

acquiring observation data of a target observation point, wherein the target observation point is any one of a plurality of observation points in the monitoring area;

acquiring a concentration value of target greenhouse gas from observation data of a target observation point to form observation subdata of the target greenhouse gas, wherein the target greenhouse gas is any one of all greenhouse gases;

acquiring a maximum concentration value, a minimum concentration value, an elevation value corresponding to the maximum concentration value and an elevation value corresponding to the minimum concentration value from the observation subdata of the target greenhouse gas to construct an observation vector of the target greenhouse gas;

traversing concentration values of all greenhouse gases to obtain an observation vector of each greenhouse gas at the target observation point;

all observation points are traversed to obtain an observation vector for each greenhouse gas at each observation point.

In some embodiments, said calculating a spatial distribution characteristic of each greenhouse gas based on the observation matrix of greenhouse gases comprises:

calculating the inner product of any two observation vectors in an observation matrix of the target greenhouse gas, wherein the target greenhouse gas is any one of all greenhouse gases;

constructing a spatial distribution characteristic of the target greenhouse gas based on the inner product of the observation vectors, wherein the spatial distribution characteristic is a square matrix, and the value of the ith row and the jth column is the inner product of the ith observation vector and the jth observation vector;

and traversing the observation matrixes of all the greenhouse gases to obtain the spatial distribution characteristics of each greenhouse gas.

In some embodiments, said formulating an airborne monitoring plan based on said observation matrix of greenhouse gases to map a greenhouse gas spatial profile of said monitoring area, said greenhouse gas spatial profile comprising concentration values of each greenhouse gas at spatial locations, comprises:

acquiring a range to be monitored of each greenhouse gas based on the observation matrix of the greenhouse gas;

formulating a flight monitoring scheme according to the range to be monitored of the greenhouse gases so as to acquire concentration values of different greenhouse gases in the range to be monitored;

and creating a three-dimensional coordinate system, and setting different layers in the three-dimensional coordinate system based on the concentration values of the different greenhouse gases to draw a greenhouse gas spatial distribution map of the monitoring area, wherein the layers correspond to the types of the greenhouse gases one to one.

In some embodiments, the obtaining the range to be monitored for each greenhouse gas based on the observation matrix of greenhouse gases comprises:

extracting a row where an elevation value corresponding to the maximum concentration value is located from an observation matrix of target greenhouse gas to form a maximum elevation vector, wherein the target greenhouse gas is any one of all greenhouse gases;

acquiring a maximum elevation value and a minimum elevation value in the maximum elevation vector as an upper limit and a lower limit respectively to form a distribution range of the maximum concentration value of the target greenhouse gas;

extracting a row where an elevation value corresponding to the minimum concentration value is located from the observation matrix of the target greenhouse gas to obtain a distribution range of the minimum concentration value of the target greenhouse gas;

taking the distribution range of the maximum concentration value and the distribution range of the minimum concentration value of the target greenhouse gas as the range to be monitored of the target greenhouse gas;

and traversing the observation matrix of each greenhouse gas to obtain the range to be monitored of each greenhouse gas.

In some embodiments, the monitoring changes in spatial distribution characteristics of the greenhouse gases to obtain abnormal greenhouse gases and abnormal observation matrices corresponding to the abnormal greenhouse gases comprises:

collecting a real-time observation matrix and real-time spatial distribution characteristics of each greenhouse gas according to a fixed period;

calculating the similarity between the real-time spatial distribution characteristics and the historical spatial distribution characteristics of the greenhouse gases of the same species, wherein the historical spatial distribution characteristics are the latest greenhouse gas spatial distribution characteristics, and the similarity satisfies the following relation:

wherein the content of the first and second substances,

is the value of ith row and jth column, G (k) 'in the real-time spatial distribution characteristic of the greenhouse gas k'_i,jThe numerical value of the ith row and the jth column in the historical spatial distribution characteristic of the greenhouse gas k, M is the row number and the column number of the spatial distribution characteristic, and sim (k) is the similarity of the real-time spatial distribution characteristic and the historical spatial distribution characteristic of the greenhouse gas k;

and comparing the similarity of each greenhouse gas with a preset similarity to obtain abnormal greenhouse gases, and taking a real-time observation matrix corresponding to the abnormal greenhouse gases as an abnormal observation matrix.

In some embodiments, when abnormal greenhouse gases are acquired, formulating a new flight monitoring scheme based on the abnormal observation matrix to update the greenhouse gas spatial distribution map comprises:

when the abnormal greenhouse gases are obtained, a new flight monitoring scheme is formulated according to the abnormal observation matrix corresponding to the abnormal greenhouse gases;

executing the new flight monitoring scheme to acquire a concentration value of the abnormal greenhouse gas;

and drawing a map layer corresponding to the abnormal greenhouse gas based on the concentration value of the abnormal greenhouse gas so as to update the greenhouse gas spatial distribution map.

The embodiment of this application still provides a greenhouse gas monitoring devices based on artificial intelligence, the device includes:

the system comprises an acquisition unit, a monitoring unit and a control unit, wherein the acquisition unit is used for selecting a plurality of observation points in a monitoring area and acquiring concentration values of each greenhouse gas of the observation points on different elevation values to form observation data of each observation point;

the arrangement unit is used for constructing observation vectors of each kind of greenhouse gas at all observation points based on the concentration values of different greenhouse gases in the observation data, and arranging the observation vectors of the same kind of greenhouse gas at different observation points to obtain an observation matrix of each kind of greenhouse gas;

a calculation unit for calculating a spatial distribution characteristic of each greenhouse gas based on the observation matrix of the greenhouse gas;

a drawing unit, configured to formulate a flight monitoring plan based on the observation matrix of the greenhouse gases to draw a greenhouse gas spatial distribution map of the monitoring area, where the greenhouse gas spatial distribution map includes a concentration value of each greenhouse gas at a spatial position;

the monitoring unit is used for monitoring the change of the spatial distribution characteristics of the greenhouse gases to acquire abnormal greenhouse gases and abnormal observation matrixes corresponding to the abnormal greenhouse gases;

and the updating unit is used for making a new flight monitoring scheme based on the abnormal observation matrix to update the greenhouse gas spatial distribution map when abnormal greenhouse gas is obtained.

An embodiment of the present application further provides an electronic device, where the electronic device includes:

a memory storing at least one instruction;

a processor executing instructions stored in the memory to implement the artificial intelligence based greenhouse gas monitoring method.

The embodiment of the present application further provides a computer-readable storage medium, in which at least one instruction is stored, and the at least one instruction is executed by a processor in an electronic device to implement the artificial intelligence-based greenhouse gas monitoring method.

In conclusion, the monitoring method and the monitoring system can acquire observation data of different elevation values according to a plurality of observation points in a monitoring area, and then formulate the flight monitoring scheme of the unmanned aerial vehicle, acquire greenhouse gas data of different elevation values in a targeted manner so as to ensure the accuracy of the greenhouse gas data, establish the spatial distribution characteristics of the greenhouse gas based on the observation data, monitor the change of the greenhouse gas spatial distribution, acquire new data in time so as to ensure the real-time performance of the greenhouse gas data, and accordingly realize the accurate monitoring of the greenhouse gas.

Drawings

FIG. 1 is a flow chart of a preferred embodiment of an artificial intelligence based greenhouse gas monitoring method to which the present application relates.

FIG. 2 is a spatial distribution diagram of greenhouse gases to which the present application relates.

FIG. 3 is a functional block diagram of a preferred embodiment of an artificial intelligence based greenhouse gas monitoring apparatus according to the present application.

FIG. 4 is a schematic diagram of an electronic device according to a preferred embodiment of the method for monitoring greenhouse gases based on artificial intelligence.

Detailed Description

For a clearer understanding of the objects, features and advantages of the present application, reference is made to the following detailed description of the present application along with the accompanying drawings and specific examples. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict. In the following description, numerous specific details are set forth to provide a thorough understanding of the present application, and the described embodiments are merely a subset of the embodiments of the present application and are not intended to be a complete embodiment.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first" and "second" may explicitly or implicitly include one or more of the described features. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.

The embodiment of the present Application provides a greenhouse gas monitoring method based on artificial intelligence, which can be applied to one or more electronic devices, where the electronic device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and hardware of the electronic device includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.

The electronic device may be any electronic product capable of performing human-computer interaction with a client, for example, a Personal computer, a tablet computer, a smart phone, a Personal Digital Assistant (PDA), a game machine, an Internet Protocol Television (IPTV), an intelligent wearable device, and the like.

The electronic device may also include a network device and/or a client device. The network device includes, but is not limited to, a single network server, a server group consisting of a plurality of network servers, or a Cloud Computing (Cloud Computing) based Cloud consisting of a large number of hosts or network servers.

The Network where the electronic device is located includes, but is not limited to, the internet, a wide area Network, a metropolitan area Network, a local area Network, a Virtual Private Network (VPN), and the like.

FIG. 1 is a flow chart of a preferred embodiment of the method for monitoring greenhouse gases based on artificial intelligence. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.

S10, selecting a plurality of observation points in the monitoring area, and collecting concentration values of each greenhouse gas of the observation points on different elevation values to form observation data of each observation point.

In an optional embodiment, a monitoring area is divided into a plurality of sub-areas with equal areas, an observation point is arranged at the central point of each sub-area to obtain a plurality of observation points in the monitoring area, and the monitoring area can be all areas with greenhouse gas monitoring requirements, such as forests, factories, water and the like; furthermore, a laser sensor and a greenhouse gas sensor are arranged below the lift-off balloon, the laser sensor is used for measuring the elevation value of the lift-off balloon, the greenhouse gas sensor is used for collecting the concentration of greenhouse gases, and the greenhouse gases comprise carbon dioxide, nitrous oxide, Freon, methane and other greenhouse gases; the method comprises the steps that a lift-off balloon is deployed at each observation point, the lift-off balloon is controlled to vertically lift off to reach different elevation values, concentration values of all greenhouse gases at each elevation value in a preset elevation range are collected by means of a laser sensor and a greenhouse gas sensor which are deployed on the lift-off balloon, and the preset elevation range comprises all possible distributed elevation values of the greenhouse gases.

In this optional embodiment, for one observation point, the concentration value of each greenhouse gas collected on all elevation values within a preset elevation range is used as observation data of the observation point, and the observation data corresponds to the observation points one to one.

So, can acquire the observation data of each observation point in the monitoring area, the concentration value of observation data for all greenhouse gas on the different elevation values provides the data basis for the formulation of follow-up flight monitoring scheme, flight monitoring scheme is unmanned aerial vehicle's flight altitude scope for gather the concentration value of the greenhouse gas of different spatial position departments in the monitoring area.

S11, constructing observation vectors of each greenhouse gas at all observation points based on concentration values of different greenhouse gases in the observation data, and arranging the observation vectors of the same greenhouse gas at different observation points to obtain an observation matrix of each greenhouse gas.

In an optional embodiment, said constructing an observation vector for each greenhouse gas at all observation points based on concentration values of different greenhouse gases in said observation data comprises:

In this optional embodiment, observation data of a target observation point is obtained, where the target observation point is any one of multiple observation points in the monitoring area, one greenhouse gas is randomly selected from all greenhouse gases as a target greenhouse gas, and a concentration value of the target greenhouse gas at each elevation value is extracted from the observation data of the target observation point to construct observation subdata of the target greenhouse gas; further selecting a maximum concentration value max and a minimum concentration value min in observation subdata of the target greenhouse gas, obtaining a height value max (h) corresponding to the maximum concentration value and a height value min (h) corresponding to the minimum concentration value, and arranging max, min, max (h) and min (h) according to a fixed sequence to obtain an observation vector of the target greenhouse gas at the target observation point, wherein the observation vector is a column vector of 4 rows and 1 column, and can represent the longitudinal distribution characteristics of the target greenhouse gas at the target observation point in the vertical direction; and traversing all the greenhouse gases according to the method to obtain the observation vector of each greenhouse gas at the target observation point.

In this alternative embodiment, the above steps are repeatedly performed to obtain an observation vector of each kind of greenhouse gas at each observation point, and the number of kinds of greenhouse gases is denoted as N, so that each observation point can obtain N observation vectors, where the observation vectors correspond to the kinds of greenhouse gases in a one-to-one manner.

In the optional embodiment, observation vectors of the same greenhouse gas are extracted from different observation points, the number of the observation points in the monitoring area is recorded as M, each greenhouse gas corresponds to M observation vectors, the M observation vectors are arranged along the row direction according to a preset sequence, and an observation matrix of each greenhouse gas can be obtained, wherein the size of the observation matrix is 4 rows and M columns. Wherein the predetermined sequence is to ensure that the observation vectors of the same column in the observation matrix of each greenhouse gas come from the same observation point.

Therefore, the observation matrix of each greenhouse gas can be obtained based on the observation data of all the observation points, the longitudinal distribution characteristics of the greenhouse gases at each observation point in the vertical direction can be accurately represented by the observation matrix, and a data base is provided for the follow-up monitoring of the greenhouse gases.

S12, calculating the spatial distribution characteristics of each greenhouse gas based on the observation matrix of the greenhouse gas.

In an alternative embodiment, said calculating the spatial distribution characteristic of each greenhouse gas based on the observation matrix of greenhouse gases comprises:

constructing spatial distribution characteristics of the target greenhouse gas based on the inner product of the observation vectors, wherein the spatial distribution characteristics are square matrixes, and the numerical value of the ith row and the jth column is the inner product of the ith observation vector and the jth observation vector;

In this alternative embodiment, since the calculation processes of the spatial distribution characteristics of different greenhouse gases are the same, the calculation process of the spatial distribution characteristics of the target greenhouse gas is described by taking the target greenhouse gas as an example, wherein the target greenhouse gas is any one of all greenhouse gases.

In this alternative embodiment, the observation matrix of the target greenhouse gas includes M observation vectors, an inner product of any two observation vectors is calculated, where the inner product can reflect the correlation between the two observation vectors, and taking the jth observation vector and the ith observation vector as an example, the calculation formula of the inner product is:

wherein, g_jWhich represents the j-th observation vector and,

represents the transpose of the i-th observation vector, G_i,jIs a numerical value representing the inner product of the ith observation vector and the jth observation vector, reflecting the correlation between the ith observation vector and the jth observation vector; constructing the spatial distribution characteristics of the target greenhouse gas based on the inner product of observation vectors, wherein the spatial distribution characteristics are square matrixes of M rows and M columns, and the numerical value of the ith row and the jth column in the spatial distribution characteristics is the inner product G of the ith observation vector and the jth observation vector_i,j。

In this alternative embodiment, the spatial distribution characteristic of the target greenhouse gas may represent a correlation between longitudinal distribution characteristics of the target greenhouse gas at each observation point, and may reflect a distribution condition of the target greenhouse gas in a monitored area, and when the distribution condition of the target greenhouse gas changes, the spatial distribution characteristic of the target greenhouse gas also changes.

Therefore, the spatial distribution characteristics of each greenhouse gas can be obtained according to the same method, the distribution situation of each greenhouse gas in the monitoring area can be accurately quantified, and the method is used for monitoring the change of the distribution situation of the greenhouse gases.

S13, making a flight monitoring scheme based on the observation matrix of the greenhouse gases to draw a greenhouse gas spatial distribution map of the monitoring area, wherein the greenhouse gas spatial distribution map comprises the concentration value of each greenhouse gas at a spatial position.

In an optional embodiment, the developing an airborne monitoring scheme based on the observation matrix of the greenhouse gases to map a greenhouse gas spatial distribution map of the monitoring area, the greenhouse gas spatial distribution map including a concentration value of each greenhouse gas at a spatial position, comprises:

formulating a flight monitoring scheme according to the to-be-monitored range of the greenhouse gas to acquire concentration values of different greenhouse gases in the to-be-monitored range;

In this alternative embodiment, the acquiring the range to be monitored of each greenhouse gas based on the observation matrix of greenhouse gases includes:

The maximum elevation vector is 1 row and M columns and comprises elevation values corresponding to the maximum concentration values of the target greenhouse gas at M observation points in a monitoring area, and the range to be monitored of the greenhouse gas covers the maximum concentration value and the minimum concentration value of the greenhouse gas at each position in the monitoring area.

In the optional embodiment, a flying monitoring scheme is formulated according to the to-be-monitored ranges of all greenhouse gases, the flying monitoring scheme comprises the flying height range of the unmanned aerial vehicle in a monitoring area, and the flying height range comprises the to-be-monitored ranges of all greenhouse gases.

In this alternative embodiment, after acquiring the flight monitoring plan, a greenhouse gas sensor is deployed on the drone to acquire greenhouse gas concentration values within the range of flight heights. In the acquisition process, acquire unmanned aerial vehicle's flying height, contrast flying height and all greenhouse gas wait the monitoring range, work as when flying height is located greenhouse gas's monitoring range, store greenhouse gas's concentration value with unmanned aerial vehicle's spatial position reduces the data volume of storage, unmanned aerial vehicle's spatial position includes unmanned aerial vehicle in the positional information of monitoring area plane and unmanned aerial vehicle's flying height.

For example, it is known that the monitoring range of greenhouse gas 1 is [10,15], the monitoring range of greenhouse gas 2 is [8,12], the monitoring range of greenhouse gas 3 is [11,16 ]; the flight height range of the unmanned aerial vehicle in the flight monitoring scheme is [8,16], in the acquisition process, if the flight height of the unmanned aerial vehicle is 10, the greenhouse gas sensors deployed on the unmanned aerial vehicle can acquire the concentration values of all greenhouse gases at the flight height of 10, but because the flight height is only located in the monitoring ranges of the greenhouse gases 1 and 2, the unmanned aerial vehicle only stores the concentration values of the greenhouse gases 1 and 2 and the spatial position of the unmanned aerial vehicle.

In this optional embodiment, a z-axis, a y-axis, and a z-axis are created to construct a three-dimensional coordinate system, an xy plane in the three-dimensional coordinate system is a plane where a monitoring area is located, the z axis represents different elevation values, each point in the three-dimensional coordinate system corresponds to a spatial position in the monitoring area one to one, a greenhouse gas concentration value corresponding to all spatial positions acquired by an unmanned aerial vehicle is drawn in the three-dimensional coordinate system as a pixel value, the concentration value corresponds to the pixel value one to one, different greenhouse gases are drawn in different layers to be distinguished, it is ensured that the distribution conditions of the different greenhouse gases are mutually independent, and a greenhouse gas spatial distribution map of the monitoring area is obtained, wherein the greenhouse gas spatial distribution map includes spatial distribution of each greenhouse gas. It should be noted that each layer can be displayed separately, and the spatial distribution map of the greenhouse gas of only one layer is taken as an example, and the spatial distribution map of the greenhouse gas is shown in fig. 2.

So, can rationally formulate unmanned aerial vehicle flight navigation scheme according to each kind of greenhouse gas's monitoring matrix, realize the accurate control of important elevation value in the monitoring area, improve unmanned aerial vehicle and carry out the efficiency of greenhouse gas monitoring to obtain the distribution situation of greenhouse gas spatial distribution map in order to reflect each kind of greenhouse gas in the monitoring area.

S14, monitoring the change of the spatial distribution characteristics of the greenhouse gases to obtain abnormal greenhouse gases and an abnormal observation matrix corresponding to the abnormal greenhouse gases.

In an optional embodiment, the monitoring the change of the spatial distribution characteristic of the greenhouse gases to obtain abnormal greenhouse gases and an abnormal observation matrix corresponding to the abnormal greenhouse gases comprises:

calculating the similarity between the real-time spatial distribution characteristics and the historical spatial distribution characteristics of the greenhouse gases of the same kind, wherein the historical spatial distribution characteristics are the latest greenhouse gas spatial distribution characteristics;

In the optional embodiment, data acquisition is performed on all observation points in the monitoring area at regular intervals to obtain real-time observation data of each observation point, and a real-time observation matrix and real-time spatial distribution characteristics of each greenhouse gas can be obtained based on the real-time observation data.

In this alternative embodiment, a historical spatial distribution characteristic of each kind of greenhouse gas at the last time is obtained, and a similarity between the real-time spatial distribution characteristic and the historical spatial distribution characteristic of the same kind of greenhouse gas is calculated, taking greenhouse gas k as an example, and a calculation formula of the similarity is as follows:

wherein, the first and the second end of the pipe are connected with each other,

is the value of ith row and jth column, G (k) 'in the real-time spatial distribution characteristic of the greenhouse gas k'_i,jThe numerical value of the ith row and the jth column in the historical spatial distribution characteristic of the greenhouse gas k, M is the row number and the column number of the spatial distribution characteristic, sim (k) is the similarity of the real-time spatial distribution characteristic and the historical spatial distribution characteristic of the greenhouse gas k, and the similarity corresponds to the types of the greenhouse gas one to one.

In this alternative embodiment, the similarity of each greenhouse gas is compared with a preset similarity threshold to determine whether the distribution of the greenhouse gas changes, if the similarity of the greenhouse gas is greater than the preset similarity threshold, it is determined that the distribution of the greenhouse gas changes, the greenhouse gas is used as an abnormal greenhouse gas, a real-time observation matrix corresponding to the abnormal greenhouse gas is used as an abnormal observation matrix, and the value of the preset similarity threshold is 0.8. One or more abnormal greenhouse gases are contained at the same time.

Therefore, abnormal greenhouse gases and abnormal observation matrixes can be acquired based on observation data of the observation points, global monitoring of distribution conditions of each greenhouse gas is achieved, and the greenhouse gas spatial distribution map of a monitoring area is updated in time when the greenhouse gas distribution conditions change.

And S15, when abnormal greenhouse gases are obtained, making a new flight monitoring scheme based on the abnormal observation matrix to update the greenhouse gas spatial distribution map.

In an optional embodiment, when abnormal greenhouse gases are acquired, a new flight monitoring scheme is formulated based on the abnormal observation matrix to update the greenhouse gas spatial distribution map, and the method comprises the following steps:

and drawing a layer corresponding to the abnormal greenhouse gas based on the concentration value of the abnormal greenhouse gas so as to update the greenhouse gas spatial distribution map.

In this optional embodiment, when abnormal greenhouse gases are obtained, it is described that the distribution of the abnormal greenhouse gases changes, at this time, a new flight monitoring scheme needs to be formulated according to an abnormal observation matrix corresponding to the abnormal greenhouse gases, the new flight monitoring scheme may cover a to-be-monitored range of the abnormal greenhouse gases, and after a concentration value of the abnormal greenhouse gases in the to-be-monitored range is acquired, the updated greenhouse gas spatial distribution map can be obtained only by deleting and redrawing a map layer corresponding to the abnormal greenhouse gases in the greenhouse gas spatial distribution map.

Therefore, only the greenhouse gas with the changed distribution situation needs to be collected, the monitoring area greenhouse gas spatial distribution diagram is updated, the workload is reduced, and meanwhile the timeliness of the greenhouse gas spatial distribution diagram is guaranteed.

According to the technical scheme, the observation data of different elevation values can be acquired according to a plurality of observation points in the monitoring area, and then the flight monitoring scheme of the unmanned aerial vehicle is formulated, greenhouse gas data at different elevation values are acquired in a targeted manner to guarantee the accuracy of the greenhouse gas data, meanwhile, the spatial distribution characteristics of the greenhouse gas are constructed based on the observation data, the change of the greenhouse gas spatial distribution is monitored, new data are acquired in time to guarantee the real-time performance of the greenhouse gas data, and therefore the greenhouse gas is accurately monitored.

Referring to fig. 3, fig. 3 is a functional block diagram of a preferred embodiment of the artificial intelligence-based greenhouse gas monitoring device according to the present invention. The greenhouse gas monitoring device 11 based on artificial intelligence comprises a collecting unit 110, an arranging unit 111, a calculating unit 112, a drawing unit 113, a monitoring unit 114 and an updating unit 115. A module/unit as referred to herein is a series of computer readable instruction segments capable of being executed by the processor 13 and performing a fixed function, and is stored in the memory 12. In the present embodiment, the functions of the modules/units will be described in detail in the following embodiments.

In an alternative embodiment, the collecting unit 110 is configured to select a plurality of observation points in the monitored area, and collect concentration values of each greenhouse gas at different elevation values of the observation points to form observation data of each observation point.

In an optional embodiment, a monitoring area is divided into a plurality of sub-areas with equal areas, an observation point is arranged at the central point of each sub-area to obtain a plurality of observation points in the monitoring area, and the monitoring area can be all areas with greenhouse gas monitoring requirements, such as forests, factories, water and the like; further, a laser sensor and a greenhouse gas sensor are arranged below the lift-off balloon, the laser sensor is used for measuring the elevation value of the lift-off balloon, the greenhouse gas sensor is used for collecting the concentration of greenhouse gases, and the greenhouse gases comprise carbon dioxide, nitrous oxide, Freon, methane and other greenhouse gases; the method comprises the steps that a lift-off balloon is deployed at each observation point, the lift-off balloon is controlled to vertically lift off to reach different elevation values, concentration values of all greenhouse gases at each elevation value in a preset elevation range are collected by means of a laser sensor and a greenhouse gas sensor which are deployed on the lift-off balloon, and the preset elevation range comprises all possible distributed elevation values of the greenhouse gases.

In an alternative embodiment, the arranging unit 111 is configured to construct an observation vector for each greenhouse gas at all observation points based on the concentration values of different greenhouse gases in the observation data, and arrange the observation vectors for the same greenhouse gas at different observation points to obtain an observation matrix for each greenhouse gas.

In an optional embodiment, the constructing an observation vector for each greenhouse gas at all observation points based on concentration values of different greenhouse gases in the observation data comprises:

acquiring a concentration value of a target greenhouse gas from observation data of a target observation point to form observation subdata of the target greenhouse gas, wherein the target greenhouse gas is any one of all greenhouse gases;

In this optional embodiment, observation data of a target observation point is obtained, where the target observation point is any one of a plurality of observation points in the monitoring area, one greenhouse gas is randomly selected from all greenhouse gases as a target greenhouse gas, and a concentration value of the target greenhouse gas on each elevation value is extracted from the observation data of the target observation point to construct observation subdata of the target greenhouse gas; further selecting a maximum concentration value max and a minimum concentration value min in observation subdata of the target greenhouse gas, obtaining a height value max (h) corresponding to the maximum concentration value and a height value min (h) corresponding to the minimum concentration value, and arranging max, min, max (h) and min (h) according to a fixed sequence to obtain an observation vector of the target greenhouse gas at the target observation point, wherein the observation vector is a column vector of 4 rows and 1 column, and can represent the longitudinal distribution characteristics of the target greenhouse gas at the target observation point in the vertical direction; and traversing all greenhouse gases according to the method to obtain the observation vector of each greenhouse gas at the target observation point.

In an alternative embodiment, the calculation unit 112 is configured to calculate the spatial distribution characteristic of each greenhouse gas based on the observation matrix of the greenhouse gases.

wherein, g_jWhich represents the j-th observation vector,

In this alternative embodiment, the spatial distribution characteristic of the target greenhouse gas may represent a correlation between longitudinal distribution characteristics of the target greenhouse gas at each observation point, and may reflect a distribution of the target greenhouse gas in the monitored area, and when the distribution of the target greenhouse gas changes, the spatial distribution characteristic of the target greenhouse gas also changes accordingly.

In an optional embodiment, the mapping unit 113 is configured to formulate a flight monitoring plan based on the observation matrix of the greenhouse gases to map a greenhouse gas spatial profile of the monitoring area, the greenhouse gas spatial profile including a concentration value of each greenhouse gas at a spatial position.

and establishing a three-dimensional coordinate system, and setting different layers in the three-dimensional coordinate system based on the concentration values of the different greenhouse gases to draw a greenhouse gas spatial distribution map of the monitoring area, wherein the layers correspond to the greenhouse gas types one to one.

In this optional embodiment, a flying monitoring scheme is formulated according to the to-be-monitored ranges of all greenhouse gases, the flying monitoring scheme includes the flight height range of the unmanned aerial vehicle in the monitoring area, and the flight height range includes the to-be-monitored ranges of all greenhouse gases.

In this alternative embodiment, after acquiring the flight monitoring plan, a greenhouse gas sensor is deployed on the drone to acquire greenhouse gas concentration values within the range of flight heights. In the collection process, acquire unmanned aerial vehicle's flying height, contrast in real time flying height and all greenhouse gas wait the monitoring range, work as when flying height is located greenhouse gas's monitoring range, store greenhouse gas's concentration value with unmanned aerial vehicle's spatial position reduces the data volume of storage, unmanned aerial vehicle's spatial position includes unmanned aerial vehicle in the positional information of monitoring area plane and unmanned aerial vehicle's flying height.

As an example, it is known that the monitoring range of the greenhouse gas 1 is [10,15], the monitoring range of the greenhouse gas 2 is [8,12], and the monitoring range of the greenhouse gas 3 is [11,16 ]; the flight height range of the unmanned aerial vehicle in the flight monitoring scheme is [8,16], in the acquisition process, if the flight height of the unmanned aerial vehicle is 10, the concentration values of all greenhouse gases when the flight height is 10 can be acquired by the greenhouse gas sensors deployed on the unmanned aerial vehicle, but because the flight height is only located in the monitoring ranges of the greenhouse gases 1 and 2, the unmanned aerial vehicle only stores the concentration values of the greenhouse gases 1 and 2 and the spatial position of the unmanned aerial vehicle.

In this optional embodiment, a z-axis, a y-axis, and a z-axis are created to construct a three-dimensional coordinate system, an xy plane in the three-dimensional coordinate system is a plane where a monitoring area is located, the z axis represents different elevation values, each point in the three-dimensional coordinate system corresponds to a spatial position in the monitoring area one to one, a greenhouse gas concentration value corresponding to all spatial positions acquired by an unmanned aerial vehicle is drawn in the three-dimensional coordinate system as a pixel value, the concentration value corresponds to the pixel value one to one, different greenhouse gases are drawn in different layers to be distinguished, it is ensured that the distribution conditions of the different greenhouse gases are mutually independent, and a greenhouse gas spatial distribution map of the monitoring area is obtained, wherein the greenhouse gas spatial distribution map includes spatial distribution of each greenhouse gas. It should be noted that each layer can be separately displayed, and the spatial distribution map of the greenhouse gas in only one layer is taken as an example, and the spatial distribution map of the greenhouse gas is shown in fig. 2.

In an alternative embodiment, the monitoring unit 114 is configured to monitor the variation of the spatial distribution characteristic of the greenhouse gas to obtain the abnormal greenhouse gas and the abnormal observation matrix corresponding to the abnormal greenhouse gas.

acquiring a real-time observation matrix and real-time spatial distribution characteristics of each greenhouse gas according to a fixed period;

In the optional embodiment, data acquisition is performed on all observation points in the monitoring area once every fixed time to obtain real-time observation data of each observation point, and a real-time observation matrix and real-time spatial distribution characteristics of each greenhouse gas can be obtained based on the real-time observation data.

wherein the content of the first and second substances,

is the value of ith row and jth column, G (k) 'in the real-time spatial distribution characteristic of the greenhouse gas k'_i,jThe numerical value of the ith row and the jth column in the historical spatial distribution characteristic of the greenhouse gas k, M is the row number and the column number of the spatial distribution characteristic, sim (k) is the similarity of the real-time spatial distribution characteristic and the historical spatial distribution characteristic of the greenhouse gas k, and the similarity is in one-to-one correspondence with the types of the greenhouse gas.

In an optional embodiment, the updating unit 115 is configured to, when abnormal greenhouse gases are acquired, formulate a new flight monitoring scheme based on the abnormal observation matrix to update the greenhouse gas spatial distribution map.

In an optional embodiment, when abnormal greenhouse gases are acquired, making a new flight monitoring scheme based on the abnormal observation matrix to update the greenhouse gas spatial distribution map comprises:

when the abnormal greenhouse gases are obtained, a new flight monitoring scheme is made according to the abnormal observation matrix corresponding to the abnormal greenhouse gases;

In this optional embodiment, when abnormal greenhouse gases are obtained, it is indicated that the distribution of the abnormal greenhouse gases changes, at this time, a new flight monitoring scheme needs to be formulated according to an abnormal observation matrix corresponding to the abnormal greenhouse gases, the new flight monitoring scheme may cover a to-be-monitored range of the abnormal greenhouse gases, and after a concentration value of the abnormal greenhouse gases in the to-be-monitored range is acquired, an updated greenhouse gas spatial distribution map may be obtained only by deleting and redrawing a map layer corresponding to the abnormal greenhouse gases in the greenhouse gas spatial distribution map.

Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device 1 comprises a memory 12 and a processor 13. The memory 12 is used for storing computer readable instructions, and the processor 13 is used for executing the computer readable instructions stored in the memory to realize the artificial intelligence based greenhouse gas monitoring method described in any one of the above embodiments.

In an alternative embodiment, the electronic device 1 further comprises a bus, a computer program stored in said memory 12 and executable on said processor 13, such as an artificial intelligence based greenhouse gas monitoring program.

Fig. 4 only shows the electronic device 1 with the memory 12 and the processor 13, and it will be understood by a person skilled in the art that the structure shown in fig. 4 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or a combination of certain components, or a different arrangement of components.

In conjunction with fig. 1, the memory 12 in the electronic device 1 stores a plurality of computer readable instructions to implement an artificial intelligence based greenhouse gas monitoring method, and the processor 13 can execute the plurality of instructions to implement:

Specifically, the specific implementation method of the instruction by the processor 13 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1, which is not described herein again.

It will be understood by those skilled in the art that the schematic diagram is only an example of the electronic device 1, and does not constitute a limitation to the electronic device 1, the electronic device 1 may have a bus-type structure or a star-shaped structure, the electronic device 1 may further include more or less hardware or software than those shown in the figures, or different component arrangements, for example, the electronic device 1 may further include an input and output device, a network access device, etc.

It should be noted that the electronic device 1 is only an example, and other existing or future electronic products, such as those that may be adapted to the present application, should also be included in the scope of protection of the present application, and are included by reference.

Memory 12 includes at least one type of readable storage medium, which may be non-volatile or volatile. The readable storage medium includes flash memory, removable hard disks, multimedia cards, card type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 12 may in some embodiments be an internal storage unit of the electronic device 1, for example a removable hard disk of the electronic device 1. The memory 12 may also be an external storage device of the electronic device 1 in other embodiments, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, provided on the electronic device 1. The memory 12 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of an artificial intelligence based greenhouse gas monitoring program, etc., but also for temporarily storing data that has been output or is to be output.

The processor 13 may be composed of an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 13 is a Control Unit (Control Unit) of the electronic device 1, connects various components of the electronic device 1 by various interfaces and lines, and executes various functions and processes data of the electronic device 1 by running or executing programs or modules (for example, executing a greenhouse gas monitoring program based on artificial intelligence, etc.) stored in the memory 12 and calling data stored in the memory 12.

The processor 13 executes the operating system of the electronic device 1 and various installed application programs. The processor 13 executes the application program to implement the steps of the various artificial intelligence based greenhouse gas monitoring method embodiments described above, such as the steps shown in FIG. 1.

Illustratively, the computer program may be partitioned into one or more modules/units, which are stored in the memory 12 and executed by the processor 13 to accomplish the present application. The one or more modules/units may be a series of computer readable instruction segments capable of performing certain functions, which are used to describe the execution of the computer program in the electronic device 1. For example, the computer program may be divided into an acquisition unit 110, an arrangement unit 111, a calculation unit 112, a rendering unit 113, a monitoring unit 114, an update unit 115.

The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a computer device, or a network device, etc.) or a Processor (Processor) to execute parts of the artificial intelligence based greenhouse gas monitoring method according to the embodiments of the present application.

The integrated modules/units of the electronic device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the processes in the methods of the embodiments described above may be implemented by a computer program, which may be stored in a computer-readable storage medium and executed by a processor, to implement the steps of the embodiments of the methods described above.

Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, U.S. disk, removable hard disk, magnetic diskette, optical disk, computer Memory, Read-Only Memory (ROM), random access Memory and other Memory, etc.

Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.

The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

The bus may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one arrow is shown in FIG. 4, but this does not indicate only one bus or one type of bus. The bus is arranged to enable connection communication between the memory 12 and at least one processor 13 or the like.

The present application further provides a computer-readable storage medium (not shown), in which computer-readable instructions are stored, and the computer-readable instructions are executed by a processor in an electronic device to implement the artificial intelligence based greenhouse gas monitoring method according to any of the above embodiments.

In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

In addition, functional modules in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.

Furthermore, it will be obvious that the term "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the specification may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.

Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present application and not for limiting, and although the present application is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present application without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. An artificial intelligence based greenhouse gas monitoring method, characterized in that the method comprises:

and when the abnormal greenhouse gases are obtained, a new flight monitoring scheme is formulated based on the abnormal observation matrix so as to update the greenhouse gas spatial distribution map.

2. The artificial intelligence based greenhouse gas monitoring method of claim 1, wherein the constructing an observation vector for each greenhouse gas at all observation points based on concentration values of different greenhouse gases in the observation data comprises:

3. The artificial intelligence based greenhouse gas monitoring method according to claim 1, wherein said calculating the spatial distribution characteristic of each greenhouse gas based on the observation matrix of greenhouse gases comprises:

4. The artificial intelligence based greenhouse gas monitoring method according to claim 1, wherein said formulating a flight monitoring plan based on said observation matrix of greenhouse gases to map a greenhouse gas spatial profile of said monitoring area, said greenhouse gas spatial profile including concentration values of each greenhouse gas at spatial positions comprises:

5. The artificial intelligence based greenhouse gas monitoring method according to claim 4, wherein the obtaining the range to be monitored of each greenhouse gas based on the observation matrix of greenhouse gases comprises:

6. The artificial intelligence based greenhouse gas monitoring method according to claim 1, wherein the monitoring the variation of the spatial distribution characteristics of the greenhouse gases to obtain abnormal greenhouse gases and abnormal observation matrices corresponding to the abnormal greenhouse gases comprises:

wherein the content of the first and second substances,

7. The artificial intelligence based greenhouse gas monitoring method according to claim 1, wherein when abnormal greenhouse gases are obtained, making a new flight monitoring scheme based on the abnormal observation matrix to update the greenhouse gas spatial distribution map comprises:

executing the new flight monitoring scheme to acquire the concentration value of the abnormal greenhouse gas;

8. An artificial intelligence based greenhouse gas monitoring device, the device comprising:

a drawing unit, configured to formulate a flight monitoring scheme based on the observation matrix of the greenhouse gases to draw a greenhouse gas spatial distribution map of the monitoring area, where the greenhouse gas spatial distribution map includes a concentration value of each greenhouse gas at a spatial position;

the monitoring unit is used for monitoring the change of the spatial distribution characteristics of the greenhouse gases so as to obtain abnormal greenhouse gases and an abnormal observation matrix corresponding to the abnormal greenhouse gases;

and the updating unit is used for making a new flight monitoring scheme based on the abnormal observation matrix to update the greenhouse gas spatial distribution map when the abnormal greenhouse gas is obtained.

9. An electronic device, characterized in that the electronic device comprises:

a memory storing computer readable instructions; and

a processor executing computer readable instructions stored in the memory to implement the artificial intelligence based greenhouse gas monitoring method of any one of claims 1 to 7.

10. A computer readable storage medium having computer readable instructions stored thereon which, when executed by a processor, implement the artificial intelligence based greenhouse gas monitoring method of any one of claims 1 to 7.