CN114324780A

CN114324780A - Atmospheric pollutant emission flux processing method, storage medium and computer terminal

Info

Publication number: CN114324780A
Application number: CN202210200399.2A
Authority: CN
Inventors: 姚易辰; 陈国栋; 李展; 陈磊; 仲晓辉; 胡媛; 杜飞; 王志斌; 李�昊
Original assignee: Alibaba Damo Institute Hangzhou Technology Co Ltd
Current assignee: Alibaba Damo Institute Hangzhou Technology Co Ltd
Priority date: 2022-03-03
Filing date: 2022-03-03
Publication date: 2022-04-12
Anticipated expiration: 2042-03-03
Also published as: WO2023165336A1; CN114324780B

Abstract

The invention discloses an atmospheric pollutant emission flux processing method, a storage medium and a computer terminal. Wherein, the method comprises the following steps: acquiring an observed value of the concentration of the atmospheric pollutants and an initial emission flux of the concentration of the atmospheric pollutants, wherein the observed value is used for representing a value obtained by measuring the concentration of the collected concentration of the atmospheric pollutants by a target station; processing the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the concentration of the atmospheric pollutants, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the concentration of the atmospheric pollutants in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; and updating the initial emission flux based on the target derivative to obtain the target emission flux of the concentration of the atmospheric pollutants. The invention solves the technical problem of lower monitoring accuracy of the concentration of the atmospheric pollutants in the related art.

Description

Atmospheric pollutant emission flux processing method, storage medium and computer terminal

Technical Field

The invention relates to the field of atmospheric pollutant emission flux processing, in particular to an atmospheric pollutant emission flux processing method, a storage medium and a computer terminal.

Background

The greenhouse gas carbon dioxide plays a crucial role in the circulation of the earth's atmosphere. Carbon dioxide absorbs surface radiation, causing more heat to be retained in the atmosphere, which in turn results in an increase in air temperature. At present, the monitoring and consideration of the carbon dioxide emission reduction target achievement condition depend on an accurate carbon emission monitoring scheme, but due to the influence of factors such as weather, the carbon emission is difficult to be accurately monitored.

In view of the above problems, no effective solution has been proposed.

Disclosure of Invention

The embodiment of the invention provides an atmospheric pollutant emission flux processing method, a storage medium and a computer terminal, which at least solve the technical problem of low monitoring accuracy of atmospheric pollutant concentration in the related art.

According to an aspect of an embodiment of the present invention, there is provided an atmospheric pollutant discharge flux treatment method, including: acquiring an observed value of the concentration of the atmospheric pollutants and an initial emission flux of the concentration of the atmospheric pollutants, wherein the observed value is used for representing the concentration of the atmospheric pollutants acquired by a target station; processing the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the concentration of the atmospheric pollutants, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the concentration of the atmospheric pollutants in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; and updating the initial emission flux based on the target derivative to obtain the target emission flux of the concentration of the atmospheric pollutants.

According to another aspect of the embodiments of the present invention, there is provided an atmospheric pollutant discharge flux treatment method, including: acquiring an observed value of carbon concentration and an initial carbon emission flux in a carbon neutralization scene, wherein the observed value is used for representing a numerical value obtained by measuring the carbon concentration by a target station; processing the observed value and the initial carbon emission flux by using an atmospheric transmission model to obtain a target derivative, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing the dynamic transmission process of gas influencing the carbon concentration in the atmosphere, and the target derivative is used for representing the derivative of the observed value relative to the carbon emission flux; and updating the initial carbon emission flux based on the target derivative to obtain the target carbon emission flux.

According to another aspect of the embodiments of the present invention, there is provided an atmospheric pollutant discharge flux treatment method, including: displaying an observed value of the concentration of the atmospheric pollutants and an initial emission flux of the concentration of the atmospheric pollutants in an interactive interface, wherein the observed value is used for representing a numerical value obtained by measuring the concentration of the atmospheric pollutants by a target station; responding to touch operation detected in an interactive interface, processing an observed value and initial emission flux by using an atmospheric transmission model to obtain a target derivative of the concentration of the atmospheric pollutants, wherein the atmospheric transmission model is constructed through a deep learning framework and is used for representing a dynamic transmission process of the concentration of the atmospheric pollutants in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; and displaying the target emission flux of the atmospheric pollutant concentration in the interactive interface, wherein the target emission flux is obtained by updating the initial emission flux through a target derivative.

According to another aspect of the embodiments of the present invention, there is provided an atmospheric pollutant discharge flux treatment method, including: the method comprises the steps that a cloud server receives an observation value of the atmospheric pollutant concentration and an initial emission flux of the atmospheric pollutant concentration, wherein the observation value is used for representing a numerical value obtained by measuring the concentration of the atmospheric pollutant concentration by a target station; the cloud server processes the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the atmospheric pollutant concentration, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the atmospheric pollutant concentration in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; the cloud server updates the initial emission flux based on the target derivative to obtain a target emission flux of the atmospheric pollutant concentration; the cloud server returns the target emission flux to the client.

According to another aspect of the embodiments of the present invention, there is provided an atmospheric pollutant discharge flux treatment method, including: the cloud server receives an atmospheric pollutant emission flux processing request uploaded by a client, wherein the atmospheric pollutant emission flux processing request at least comprises: presetting a time period and atmospheric pollutant concentration; the method comprises the steps that a cloud server obtains an observed value of the concentration of the atmospheric pollutants in a preset time period and an initial emission flux of the concentration of the atmospheric pollutants, wherein the observed value is used for representing a numerical value obtained by measuring the concentration of the atmospheric pollutants by a target station; the cloud server processes the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the atmospheric pollutant concentration, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the atmospheric pollutant concentration in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; the cloud server updates the initial emission flux based on the target derivative to obtain a target emission flux of the atmospheric pollutant concentration; the cloud server returns the target emission flux to the client.

According to another aspect of the embodiments of the present application, there is also provided a storage medium, where the storage medium includes a stored program, and when the program is executed, the apparatus where the computer readable storage medium is located is controlled to execute the atmospheric pollutant discharge flux processing method in any one of the above embodiments.

According to another aspect of the embodiments of the present application, there is also provided a computer terminal, including: and the processor is used for operating the program stored in the memory, wherein the program is operated to execute the atmospheric pollutant emission flux treatment method in any one of the above embodiments.

Through the steps, firstly, an observation value of the concentration of the atmospheric pollutants and the initial emission flux of the concentration of the atmospheric pollutants are obtained, wherein the observation value is used for representing the concentration of the atmospheric pollutants collected by a target station; processing the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the concentration of the atmospheric pollutants, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the concentration of the atmospheric pollutants in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; and updating the initial emission flux based on the target derivative to obtain the target emission flux of the concentration of the atmospheric pollutants, thereby realizing the purpose of monitoring the target emission flux. It is easy to notice that, the observation value of the atmospheric pollutant concentration and the initial emission flux of the atmospheric pollutant concentration can be obtained first, then the observation value and the initial emission flux are processed according to the power transmission model of the atmospheric pollutant concentration of the atmospheric transmission model in the atmosphere, so as to determine the influence factors of the environmental factors in the atmosphere on the prediction accuracy, determine the observation error according to the influence factors, and update the initial emission flux according to the target derivative determined by the observation error, thereby improving the accuracy of the initial emission flux, and further solving the technical problem of lower monitoring accuracy of the atmospheric pollutant concentration in the related art.

Drawings

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the invention without limiting the invention. In the drawings:

fig. 1 is a block diagram of a hardware structure of a computer terminal (or a mobile device) for implementing a method for processing an atmospheric pollutant discharge flux according to an embodiment of the present invention;

FIG. 2 is a flow chart of a method for treating the flux of the discharged atmospheric pollutants according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of an inversion of surface carbon flux based on an automatic gradient method according to an embodiment of the present invention;

FIG. 4 is a flow chart of another method for treating the flux of the discharged atmospheric pollutants according to the embodiment of the invention;

FIG. 5 is a flow chart of another method for treating the flux of the discharged atmospheric pollutants according to the embodiment of the invention;

FIG. 6 is a flow chart of another method for treating the flux of the discharged atmospheric pollutants according to the embodiment of the invention;

FIG. 7 is a flow chart of another method for treating the flux of the discharged atmospheric pollutants according to the embodiment of the invention;

FIG. 8 is a schematic view of another atmospheric pollutant discharge flux treatment device according to an embodiment of the present invention;

FIG. 9 is a schematic view of another atmospheric pollutant discharge flux treatment device according to an embodiment of the present invention;

FIG. 10 is a schematic view of another atmospheric pollutant discharge flux treatment device according to an embodiment of the present invention;

FIG. 11 is a schematic view of another atmospheric pollutant discharge flux treatment device according to an embodiment of the present invention;

FIG. 12 is a schematic view of another atmospheric pollutant discharge flux treatment device according to an embodiment of the present invention;

fig. 13 is a block diagram of a computer terminal according to an embodiment of the present application.

Detailed Description

In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

It should be noted that the terms "first," "second," and the like in the description and claims of the present invention and in the drawings described above are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

First, some terms or terms appearing in the description of the embodiments of the present application are applicable to the following explanations:

automatic gradient: the principle of autodifferentiation is to decompose the mathematical operation into some basic operations and then use the chain rule to calculate the gradient of the load function. The existing common deep learning frameworks such as deep learning libraries (tensorflow), scientific computing libraries (pitcher) and the like have integrated automatic derivation functions. Usually, only the forward process is coded to construct a computation graph, i.e. the derivative of the output to the input can be obtained by using the automatic derivation capability of the framework.

And (3) inversion of atmospheric carbon flux: through an atmospheric transport mode and a corresponding inversion algorithm, the observed values of main greenhouse gases, such as concentration distribution of carbon dioxide, methane and the like, can be utilized to obtain information of global carbon sources and carbon sinks.

At present, for the inversion of surface carbon emissions from carbon dioxide concentration monitoring, common solutions are variational adjoint methods and solutions incorporating kalman filtering.

The variational adjoint method is characterized in that a nonlinear power mode is linearized to obtain a linear adjoint form, and a linearized state transition is constructed. The derivative of the error function with respect to the input parameter is then calculated using a variational approach. The disadvantage is that the tangent mode depends on the first order taylor expansion of the original numerical format, with a large numerical error.

And the ensemble Kalman filtering mode is used for completing the state transition of variables and the state transition of a covariance matrix in an ensemble forecasting mode. At the observation point, the state value and the corresponding covariance matrix are corrected in a Bayesian manner. The drawback of collective kalman filtering is that collective prediction usually consumes large computational resources and memory resources, and usually only one-way update of the timing is performed.

In order to solve the above problems, the present application provides an atmospheric pollutant emission flux processing method, which updates an initial emission flux of carbon dioxide by performing a reverse deduction on an observed value of a carbon dioxide concentration, so as to achieve a target emission flux with higher accuracy.

Example 1

There is also provided, in accordance with an embodiment of the present invention, an atmospheric pollutant discharge flux treatment method embodiment, it being noted that the steps illustrated in the flowchart of the drawings may be carried out in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be carried out in an order different than that presented herein.

The method provided by the first embodiment of the present application may be executed in a mobile terminal, a computer terminal, or a similar computing device. Fig. 1 shows a hardware block diagram of a computer terminal (or mobile device) for implementing the atmospheric pollutant discharge flux treatment method. As shown in fig. 1, the computer terminal 10 (or mobile device 10) may include one or more processors (shown as 102a, 102b, … …, 102n in the figures) which may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, a memory 104 for storing data, and a transmission module 106 for communication functions. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial BUS (USB) port (which may be included as one of the ports of the BUS), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the electronic device. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.

It should be noted that the one or more processors and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuit may be a single stand-alone processing module, or incorporated in whole or in part into any of the other elements in the computer terminal 10 (or mobile device). As referred to in the embodiments of the application, the data processing circuit acts as a processor control (e.g. selection of a variable resistance termination path connected to the interface).

The memory 104 may be configured to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the atmospheric pollutant emission flux processing method in the embodiment of the present invention, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory 104, so as to implement the above-mentioned atmospheric pollutant emission flux processing method. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 106 can be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.

The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computer terminal 10 (or mobile device).

It should be noted here that in some alternative embodiments, the computer device (or mobile device) shown in fig. 1 described above may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that fig. 1 is only one example of a particular specific example and is intended to illustrate the types of components that may be present in the computer device (or mobile device) described above.

Under the operating environment, the application provides an atmospheric pollutant emission flux treatment method as shown in fig. 2. Fig. 2 is a flow chart of an atmospheric pollutant discharge flux treatment method according to an embodiment of the present invention.

Step S202, obtaining an observed value of the concentration of the atmospheric pollutants and an initial emission flux of the concentration of the atmospheric pollutants.

And the observation value is used for representing the concentration of the atmospheric pollutant collected by the target site.

The above-mentioned atmospheric pollutant concentrations may be carbon dioxide, PM2.5, sulfides, suspended particulate matter (e.g., dust, smoke), and the like.

The initial emission flux may be an estimated emission flux of the atmospheric pollutant concentration, and may alternatively be an estimated emission flux of the atmospheric pollutant concentration based on historical emission conditions. The initial discharge flux described above may also be set to any value.

The target site may be one or more sites that are preset to collect the concentration of the atmospheric pollutant concentration. The target site can be a site set on the ground, and the target site can also be a satellite remote sensing device.

In an alternative embodiment, the observed value of the concentration of the atmospheric pollutants may be an observed value of the concentration of the atmospheric pollutants over a period of time, and the change of the concentration of the atmospheric pollutants over a period of time may be determined according to the observed value of the concentration of the atmospheric pollutants, which may be influenced by natural weather during the discharge process, such as carbon neutralization of plants, wind power, rain water, etc., and may be caused to change over a period of time. Therefore, the emission flux accuracy of the atmospheric pollutant concentration from the observed value is low. At this time, the factor influencing the emission flux can be obtained by analyzing the observed value of the concentration of the atmospheric pollutants in a period of time, and the initial emission flux can be updated according to the factor, so that the obtained target emission flux is more accurate.

And step S204, processing the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the concentration of the atmospheric pollutants.

The atmospheric transmission model is constructed through a deep learning framework, the atmospheric transmission model is used for representing the dynamic transmission process of atmospheric pollutant concentration in the atmosphere, and the target derivative is used for representing the derivative of an observation value relative to emission flux.

In an optional embodiment, an atmospheric transmission model is built through a deep learning framework, a dynamic transmission process of atmospheric pollutant concentration in the atmosphere can be shown, an operation rule of the atmospheric pollutant concentration in the atmosphere can be obtained by analyzing an observed value in a period of time, and the initial emission flux is adjusted based on the operation rule, so that the data of the initial emission flux is more accurate.

In another optional embodiment, the observation value at the beginning of observation and the initial discharge flux may be predicted, the observation value at the end of observation and the real observation value may be predicted, an error between the two may be determined, the error of the initial discharge flux may be determined according to the error, optionally, a relationship between the error between the observation values and the initial discharge flux may be expressed in a form of a derivative, and further, the initial discharge flux may be updated based on the target derivative, so as to obtain the target discharge flux with higher accuracy.

And step S206, updating the initial emission flux based on the target derivative to obtain the target emission flux of the concentration of the atmospheric pollutants.

The target derivative described above is used to convert the error in the appearance of the observed value into an error in the appearance of the emission flux.

In an alternative embodiment, since the target derivative is data obtained according to the observation error, the initial discharge flux may be updated according to the target derivative to reduce the observation error of the initial discharge flux, so that the target discharge flux with higher accuracy may be obtained.

In an alternative embodiment, the carbon transaction may be implemented by obtaining the target emission flux of the atmospheric pollutant concentration in a carbon transaction scenario, wherein the carbon transaction refers to a transaction in which a carbon dioxide emission right is taken as a commodity, and a buyer pays a certain amount to a seller so as to obtain a certain amount of carbon dioxide emission right, thereby forming the carbon dioxide emission right. The method comprises the steps of obtaining an observed value of carbon dioxide and an initial emission flux of the carbon dioxide, processing the observed value and the initial emission flux through an atmospheric transmission model to obtain a target derivative of the carbon dioxide, updating the initial emission flux according to the target derivative to obtain a target emission flux of atmospheric pollutant concentration, and performing reverse deduction on the initial emission flux through an actual observed value, so that the obtained target emission flux has high accuracy, and therefore, in a carbon transaction scene, carbon transaction can be performed according to the target emission flux with high accuracy, and related economic loss is reduced.

In an alternative embodiment, the carbon emission flux may be estimated in a new energy scenario, such as a new energy automobile scenario, the flux of carbon emission that can be reduced by using the new energy vehicle can be determined, and optionally, an observed value of carbon dioxide emitted in an operation area of the new energy vehicle and an initial flux of carbon dioxide emission can be obtained, then processing the observed value and the initial emission flux in the operation area of the new energy automobile by using an atmospheric transmission model to obtain a target derivative of the carbon dioxide, the initial emission flux can be updated according to the target derivative to obtain the target emission flux of the carbon dioxide in the operation area of the new energy automobile, the emission flux of the new energy automobile in the area in the historical time period can be obtained, and the emission reduction effect of the new energy automobile can be determined by comparing the emission flux of the historical time period with the target emission flux. In another alternative embodiment, the carbon emission flux may be estimated in an autonomous driving scenario, since the vehicle in the autonomous driving scenario is driven by a new energy source, it has a certain effect on reducing the carbon emission flux to obtain an observed value of carbon dioxide and an initial emission flux of carbon dioxide discharged in an operation area of the autonomous driving vehicle, and then the observed value and the initial emission flux in the operation area of the autonomous driving vehicle are processed using an atmospheric transfer model to obtain a target derivative of carbon dioxide, the initial emission flux may be updated according to the target derivative to obtain a target emission flux of carbon dioxide in the operation area of the autonomous driving vehicle, an emission flux of a historical period of time in which the autonomous driving vehicle is not used in the area may be obtained, by comparing the emission flux of the historical period of time with the target emission flux, the emission reduction effect of the autonomous vehicle can be determined.

In the above embodiments of the present application, the observed value includes: the method comprises the following steps of acquiring the concentration of the atmospheric pollutants in a preset time period, processing an observed value and an initial emission flux by using an atmospheric transmission model, and obtaining a target derivative of the concentration of the atmospheric pollutants, wherein the target derivative comprises the following steps: performing reverse deduction on the initial observation value and the initial emission flux by using an atmospheric transmission model to obtain a predicted value of the concentration of the atmospheric pollutants, wherein the initial observation value is used for representing the concentration of the atmospheric pollutants collected at the initial moment in a preset time period, and the predicted value is used for representing the concentration of the atmospheric pollutants obtained by deduction in the preset time period; determining an observation error of the concentration of the atmospheric pollutant based on the predicted value and the observed value; and acquiring a derivative of the observation error relative to the discharge flux to obtain a target derivative.

The preset time period can be set by itself.

In an optional embodiment, the initial observation value and the initial emission flux may be reversely derived by using the atmospheric transmission model to obtain a predicted value of the atmospheric pollutant concentration, optionally, the atmospheric transmission model may be derived by combining weather during the reverse derivation, so that factors affecting emission flux data may be considered during the derivation process, and the predicted value of the atmospheric pollutant concentration in a preset time period may be derived, where the predicted value may be a predicted value of the atmospheric pollutant concentration at a certain point in a preset time, and an observation error of the atmospheric pollutant concentration may be determined according to the predicted value obtained by the reverse derivation and a value at the certain point obtained by actual observation, so as to determine an error of the initial emission flux of the atmospheric pollutant concentration according to the observation error. Alternatively, a target derivative for updating the initial emission flux may be determined by taking a derivative of the observed error with respect to the emission flux.

In the above embodiments of the present application, the atmosphere transfer model includes: a derivative calculation layer, a differential equation construction layer and a time integration layer.

The derivative calculation layer is used for determining a spatial derivative according to the relevant data of the atmospheric pollutant concentration and the speed field of the atmosphere.

The above differential equation construction layer is used to construct a target differential equation based on the spatial derivative and the initial discharge flux.

The time integration layer is used for carrying out integration operation on the target differential equation to obtain a predicted value.

In the above embodiment of the present application, performing reverse deduction on the initial observed value and the initial emission flux by using the atmospheric transmission model, and obtaining the predicted value of the atmospheric pollutant concentration includes: carrying out differential operation on the density of the concentration of the atmospheric pollutants, the atmospheric velocity field and the initial observed value by using a derivative calculation layer to obtain a spatial derivative; carrying out equation construction on the spatial derivative and the initial discharge flux by using a differential equation construction layer to obtain a target differential equation, wherein the target differential equation is irrelevant to a time function; and carrying out integral operation on the target differential equation by utilizing the time integration layer to obtain a predicted value.

In an alternative embodiment, the predicted value may be obtained by the following formula:

wherein the content of the first and second substances,

is the density of the concentration of the atmospheric pollutants,

is a velocity field of the atmosphere and is,

in order to be a spatial derivative of the signal,

in order to be the initial discharge flux,

in order to predict the value of the target,

is the sign of the differential operation.

In another alternative embodiment, the density of the atmospheric pollutant concentration, the atmospheric velocity field and the initial observed value may be differentiated by using a derivative calculation layer to obtain a spatial derivative so as to determine a spatial distribution of the initial observed value in the atmosphere, then the spatial derivative and the initial emission flux may be equation-constructed by using a differential equation to obtain a target differential mode so as to determine a motion condition of the initial emission flux in the space according to the spatial derivative, and the target differential equation may be integrated by using a time integration layer so as to determine a predicted value of the atmospheric pollutant concentration obtained by reverse derivation within a preset time period. By comparing the predicted value with the actual observed value, the observation error can be determined.

In the above embodiment of the present application, after updating the initial emission flux based on the target derivative to obtain the target emission flux of the atmospheric pollutant concentration, the method further includes: processing the initial observation value and the target emission flux by using an atmospheric transmission model to obtain a target predicted value of the concentration of the atmospheric pollutants; determining whether the target discharge flux meets a preset condition based on the target predicted value and the target discharge flux; determining a first derivative of the atmospheric pollutant concentration based on the target predicted value in response to the target emission flux meeting a preset condition, wherein the first derivative is used for representing a derivative of an observation error relative to the target emission amount; the target emission flux is updated based on the first derivative.

The target predicted value may be a predicted value at a certain time among the predicted values. It should be noted that the prediction process may be performed continuously, and the initial observed value and the initial emission flux may be reversely deduced through the atmospheric transmission model, so as to obtain the predicted values at all times within the preset time period.

The preset condition may be a condition that the iteration does not converge. That is, the target discharge flux does not reach the required accuracy.

In an optional embodiment, after the target emission flux is obtained, the target emission flux may be re-used as an initial emission flux to perform reverse deduction so as to verify whether the target emission flux reaches a required accuracy, optionally, the initial observation value and the target emission flux may be reversely deduced by using an atmospheric transport model to obtain a target predicted value, an observation error may be determined according to the real initial observation value and the predicted target predicted value, if the observation error is large, it is indicated that the target emission flux satisfies a condition that iteration is not converged, and at this time, the target emission flux needs to be updated. Further, a first derivative of the atmospheric pollutant concentration may be determined according to a derivative of an observation error between the target predicted value and the true observed value with respect to the emission flux under a condition that the target emission flux satisfies iteration non-convergence, and the target emission flux may be updated according to the first derivative, thereby further improving accuracy of the target emission flux.

Further, if the target discharge flux does not satisfy the preset condition, it indicates that the accuracy of the target discharge flux has reached the required accuracy, and at this time, the target discharge flux does not need to be updated.

In the above embodiments of the present application, determining whether the target discharge flux satisfies the preset condition based on the target predicted value and the target discharge flux includes: constructing a first error function based on the target predicted value and the observed value; constructing a second error function based on the target emission flux and the initial emission flux; obtaining a weighted sum of the first error function and the second error function to obtain a loss function of the atmospheric pollutant concentration; in response to the loss function being greater than a preset threshold, determining that the target emission flux meets a preset condition; in response to the loss function being less than a preset threshold, determining that the target emission flux does not satisfy a preset condition.

The first error function described above is used to represent the error between the target predicted value and the true observed value.

The second error function described above is used to represent the error between the target and initial discharge fluxes, i.e., the error between the prior and final resulting discharge fluxes.

The preset threshold value can be set by itself.

In an optional embodiment, for the observed value and the emission flux, there is a corresponding error covariance matrix, that is, the first error function and the second error function, a loss function of the atmospheric pollutant concentration may be obtained by obtaining a weighted sum of the first error function and the second error function, and when the loss function is greater than a preset threshold, it is described that an error is larger, at this time, iterative updating may be further performed on the target emission flux until the loss function is less than or equal to the preset threshold, and if the loss function is less than the preset threshold, it may be determined that the target emission flux does not satisfy a preset condition, at this time, iterative updating may be stopped on the target emission flux, and it is determined that the target emission flux is the final emission flux.

In the above embodiment of the present application, updating the initial emission flux based on the target derivative, and obtaining the target emission flux of the atmospheric pollutant concentration includes: and acquiring a weighted sum of the initial emission flux and the target derivative to obtain the target emission flux.

In an alternative embodiment, the target emission flux of atmospheric pollutant concentration may be obtained by the following formula:

wherein the content of the first and second substances,

may be the initial discharge flux for the nth iteration,

the target emission flux may be iterated for step n +1,

it may be the target derivative or it may be,

may be an observation error.

In the above embodiment of the present application, after updating the initial emission flux based on the target derivative to obtain the target emission flux of the atmospheric pollutant concentration, the method further includes: outputting a target discharge flux; receiving first feedback information corresponding to the target emission flux, wherein the first feedback information is used for representing whether the target emission flux is updated or not; responding to the first feedback information to update the target emission flux, and processing the observed value and the target emission flux by using an atmospheric transmission model to obtain a second derivative of the concentration of the atmospheric pollutants; the target emission flux is updated based on the second derivative.

The first feedback information is used for representing whether the target emission flux needs to be updated or not by utilizing the atmosphere transmission model.

The second derivative is the derivative of the 2 nd iteration step.

In an optional embodiment, the target emission flux may be output to the client, and a worker at the client may determine whether iterative updating is needed according to the first feedback information, and if so, may feed back to continue updating the target emission flux, and may process the observed value and the target emission flux by using the atmospheric transmission model to obtain a second derivative of the atmospheric pollutant concentration, so as to continue updating the target emission flux based on the second derivative, and obtain the updated target emission flux.

In the above embodiment of the present application, after obtaining the observed value of the concentration of the atmospheric pollutant and the initial discharge flux of the concentration of the atmospheric pollutant, the method further includes: outputting the observed value and the initial discharge flux; receiving second feedback information, wherein the second feedback information is obtained by modifying the observed value and the initial discharge flux; processing the second feedback information by using an atmospheric transmission model to obtain a third derivative of the concentration of the atmospheric pollutants; and updating the initial discharge flux based on the third derivative to obtain the target discharge flux.

In an optional embodiment, after the observed value of the atmospheric pollutant concentration and the initial emission flux of the atmospheric pollutant concentration are obtained, the observed value and the initial emission flux may be output to a client of a worker, so that the worker can detect the accuracy of the observed value and the data of the initial emission flux, if the observed value or the data of the initial emission flux are deemed to be inaccurate, the observed value and the initial emission flux may be modified to obtain second feedback information, the second feedback information may be processed by using an atmospheric transmission model to obtain a third derivative of the atmospheric pollutant concentration, and the initial emission flux may be updated according to the third derivative to obtain a target emission flux.

In the above embodiment of the present application, after updating the initial emission flux based on the target derivative to obtain the target emission flux of the atmospheric pollutant concentration, the method further includes: determining a grid point emission flux of each grid point on the target map based on the target emission flux; and displaying the grid point emission flux on the target map.

The target map may be a map corresponding to a region where the emission flux needs to be detected, and the target map may also be a global map.

The target emission flux may be an emission flux of a plurality of plant atmospheric pollutant concentrations.

The grid point may be a position on the target map where a factory that emits the atmospheric pollutant concentration is located. The grid point emission flux described above may be an emission flux of a plant emitting a concentration of an atmospheric pollutant.

In an alternative embodiment, the grid point discharge flux of the grid point where the factory is located on the target map can be determined according to the target discharge flux, and the grid point discharge flux is displayed on the target map. Optionally, the data of the grid point emission flux may be displayed on the grid points corresponding to the target map, and the grid point emission flux may be displayed on the grid points corresponding to the target map in an air mass manner, where the larger the air mass, the more the emission flux is, and the smaller the air mass, the smaller the emission flux is; the grid point discharge flux corresponding to the grid points of the target map can be represented by other vector diagrams, wherein the larger the vector diagram is, the larger the discharge flux is, and the smaller the vector diagram is, the smaller the discharge flux is.

In the above embodiment of the present application, after the grid point discharge flux is displayed on the target map, the method further includes: receiving a selected target area on a target map; summarizing the grid point discharge fluxes of all grid points contained in the target area to obtain an area discharge flux corresponding to the target area; the output area discharges flux.

In an optional embodiment, the user may further select a total emission flux corresponding to the target area from the target map, optionally, the user may determine the target area on the target map, may determine the target area by clicking the corresponding area in the target area, may also determine the target area by selecting the area of the target map in a frame, and after the target area is determined, may collect the grid point emission fluxes of all grid points included in the target area to obtain an area emission flux corresponding to the target area, so that the area emission flux of the target area may be output, so that the user may analyze the area emission flux of the target area.

Fig. 3 is a schematic diagram illustrating surface carbon flux inversion based on an automatic gradient method. The carbon transmission equation can be coded through a deep learning framework, the automatic differentiation capability is solved by using the carbon transmission equation, a derivative term of the observation on the earth surface carbon source sink is obtained, and the earth surface carbon flux is updated. The whole inversion process mainly relates to three processes, namely a carbon dynamics transportation process, an error calculation process and a derivation updating process.

For the carbon kinetic transportation process, the carbon kinetic transportation process can comprise a kinetic module, wherein the kinetic module comprises a derivative calculation layer, a differential equation construction layer and a time integration layer, the derivative calculation layer comprises a space derivative layer used for determining the space derivative according to the density of the concentration of the atmospheric pollutants, the velocity field of the atmosphere and an initial observation value, the right-end term construction of the partial differential equation can be carried out on the space derivative and the initial discharge flux in the differential equation construction layer to obtain a target differential equation, after the target differential equation is obtained, the target differential equation can be subjected to time integration, optionally, the target differential equation can be subjected to single-step time integration firstly, and then multi-step time integration is carried out to obtain the predicted value of the concentration of the carbon dioxide. It should be noted that the dynamics module adopts a deep learning framework to encode, and builds an overall calculation graph. The deep learning framework provides automatic differentiation functions of all tensor operations, the gradient relation of any two groups of tensors in the calculation graph can be automatically obtained only by building a forward calculation graph, the initial carbon concentration and the earth surface carbon sink flux are realized in the carbon dynamics transportation process, and the predicted value of the carbon dioxide concentration space-time distribution is obtained in the power transmission process under the transportation of the atmospheric wind speed.

In the error calculation process, an observation error function, namely the first error function, can be determined after the predicted value of the carbon dioxide concentration and the observed value of the carbon dioxide concentration are obtained, a background error function, namely the second error function, can be determined according to the initial emission flux and the target emission flux, and a loss function of the atmospheric pollutant concentration can be obtained according to the weighted sum of the first error function and the second error function. Wherein, in the carbon inversion process, the carbon flux priors are generally obtained from a surface inventory mode. Where the carbon flux prior is the initial emission flux as described above.

In the derivation updating process, after the loss function is obtained, it may be determined whether the target carbon flux meets the condition that iteration is not converged, if yes, the carbon flux needs to be updated continuously, optionally, a derivative of the loss function with respect to any learnable parameter may be obtained according to automatic gradient calculation in the carbon kinetic transport module, and when the learnable parameter is the carbon flux, the derivative of the loss function with respect to the carbon flux may be possible, so that the carbon flux may be updated, and a carbon flux posterior is obtained. Wherein the carbon flux posteriori is the above-mentioned target emission flux. The processing steps of the above modules can be iterated repeatedly, i.e., the correction of the carbon flux according to the observed value is realized, and after iteration convergence, the carbon flux posterior can be used as the final result.

It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.

Through the above description of the embodiments, those skilled in the art can clearly understand that the atmospheric pollutant emission flux processing method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.

Example 2

There is also provided, in accordance with an embodiment of the present application, an atmospheric pollutant discharge flux treatment method embodiment, it being noted that the steps illustrated in the flowchart of the drawings may be carried out in a computer system, such as a set of computer-executable instructions, and that, although a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be carried out in an order different than that presented herein.

Fig. 4 is a flow chart of an atmospheric pollutant discharge flux treatment method according to an embodiment of the present invention, and as shown in fig. 4, the method may include the following steps:

step S402, obtaining an observed value of carbon concentration and an initial carbon emission flux in a carbon neutralization scene.

The observed value is used for representing a numerical value obtained by measuring the carbon concentration by the target station.

The carbon neutralization scene can be a scene for neutralizing the emission flux of the carbon dioxide in the forms of afforestation, energy conservation, emission reduction and the like.

The observed value of the carbon concentration in the carbon neutralization scenario described above may be an observed value of the carbon dioxide concentration that has undergone carbon neutralization.

And S404, processing the observed value and the initial carbon emission flux by using an atmospheric transmission model to obtain a target derivative of the carbon dioxide.

The atmospheric transmission model is constructed through a deep learning framework, the atmospheric transmission model is used for representing the dynamic transmission process of gas influencing carbon concentration in the atmosphere, and the target derivative is used for representing the derivative of an observed value relative to carbon emission flux.

Step S406, the initial carbon emission flux is updated based on the target derivative, resulting in a target carbon emission flux of the carbon concentration.

In the above embodiments of the present application, the observed value includes: processing the observed value and the initial carbon emission flux by using an atmospheric transmission model according to the carbon concentration collected in a preset time period to obtain a target derivative of the carbon concentration, wherein the target derivative comprises: performing reverse deduction on the initial observation value and the initial carbon emission flux by using an atmospheric transmission model to obtain a predicted value of the carbon concentration, wherein the initial observation value is used for representing the carbon concentration acquired at the initial moment in a preset time period, and the predicted value is used for representing the carbon concentration obtained by deduction in the preset time period; determining an observation error of the carbon concentration based on the predicted value and the observed value; and acquiring a derivative of the observation error relative to the discharge flux to obtain a target derivative.

In the above embodiments of the present application, the obtaining of the predicted value of the carbon concentration by performing reverse deduction on the initial observed value and the initial carbon emission flux using the atmospheric transport model includes: carrying out differential operation on the density of the carbon concentration, the velocity field of the atmosphere and the initial observed value by using a derivative calculation layer to obtain a spatial derivative; carrying out equation construction on the spatial derivative and the initial carbon emission flux by using a differential equation construction layer to obtain a target differential equation, wherein the target differential equation is irrelevant to a time function; and carrying out integral operation on the target differential equation by utilizing the time integration layer to obtain a predicted value.

In the above embodiment of the present application, after updating the initial carbon emission flux based on the target derivative to obtain the target carbon emission flux of the carbon concentration, the method further includes: processing the initial observation value and the target carbon emission flux by using an atmospheric transmission model to obtain a target predicted value of the carbon concentration; determining whether the target carbon emission flux satisfies a preset condition based on the target predicted value and the target carbon emission flux; determining a first derivative of the carbon concentration based on the target predicted value in response to the target carbon emission flux satisfying a preset condition, wherein the first derivative is used for representing a derivative of the observation error relative to the target emission amount; the target carbon emission flux is updated based on the first derivative.

In the above embodiments of the present application, determining whether the target carbon emission flux satisfies the preset condition based on the target predicted value and the target carbon emission flux includes: constructing a first error function based on the target predicted value and the observed value; constructing a second error function based on the target carbon emission flux and the initial carbon emission flux; obtaining a weighted sum of the first error function and the second error function to obtain a loss function of the carbon concentration; in response to the loss function being greater than a preset threshold, determining that the target carbon emission flux meets a preset condition; and determining that the target carbon emission flux does not satisfy a preset condition in response to the loss function being less than a preset threshold.

In the above embodiment of the present application, after updating the initial carbon emission flux based on the target derivative to obtain the target carbon emission flux of the carbon concentration, the method further includes: outputting a target carbon emission flux; receiving first feedback information corresponding to the target carbon emission flux, wherein the first feedback information is used for representing whether the target carbon emission flux is updated or not; responding to the first feedback information to update the target carbon emission flux, and processing the observed value and the target carbon emission flux by using an atmospheric transmission model to obtain a second derivative of the carbon concentration; the target carbon emission flux is updated based on the second derivative.

In the above embodiment of the present application, after obtaining the observed value of the carbon concentration and the initial carbon emission flux, the method further includes: outputting the observed value and the initial carbon emission flux; receiving second feedback information, wherein the second feedback information is obtained by modifying the observed value and the initial carbon emission flux; processing the second feedback information by using an atmospheric transmission model to obtain a third derivative of the carbon concentration; and updating the initial carbon emission flux based on the third derivative to obtain a target carbon emission flux.

It should be noted that the preferred embodiments described in the above examples of the present application are the same as the schemes, application scenarios, and implementation procedures provided in example 1, but are not limited to the schemes provided in example 1.

Example 3

Fig. 5 is a flow chart of an atmospheric pollutant discharge flux treatment method according to an embodiment of the present invention, and as shown in fig. 5, the method may include the following steps:

step S502, displaying the observed value of the concentration of the atmospheric pollutants and the initial emission flux of the concentration of the atmospheric pollutants in the interactive interface.

The observation value is used for representing a numerical value obtained by measuring the concentration of the atmospheric pollutant concentration by the target site.

Step S504, responding to the touch operation detected in the interactive interface, and processing the observed value and the initial emission flux by using the atmospheric transmission model to obtain a target derivative of the concentration of the atmospheric pollutants.

And step S506, displaying the target emission flux of the atmospheric pollutant concentration in the interactive interface.

Wherein the target discharge flux is obtained by updating the initial discharge flux through a target derivative.

In the above embodiment of the present application, after updating the initial emission flux based on the target derivative to obtain the target emission flux of the atmospheric pollutant concentration, the method further includes: processing the initial observation value and the target emission flux by using an atmospheric transmission model to obtain a target predicted value of the concentration of the atmospheric pollutants; determining whether the target discharge flux meets a preset condition based on the target predicted value and the target discharge flux; determining a first derivative of the atmospheric pollutant concentration based on the target predicted value in response to the target emission flux satisfying a preset condition; the target emission flux is updated based on the first derivative.

In the above embodiment of the present application, after displaying the target emission flux of the atmospheric pollutant concentration in the interactive interface, the method further includes: receiving first feedback information corresponding to the target emission flux, wherein the first feedback information is used for representing whether the target emission flux is updated or not; responding to the first feedback information to update the target emission flux, and processing the observed value and the target emission flux by using an atmospheric transmission model to obtain a second derivative of the concentration of the atmospheric pollutants; the target emission flux is updated based on the second derivative.

In the above embodiment of the present application, the observed value of the concentration of the atmospheric pollutant and the initial discharge flux of the concentration of the atmospheric pollutant are displayed in the interactive interface, and the method further includes: outputting the observed value and the initial discharge flux; receiving second feedback information, wherein the second feedback information is obtained by modifying the observed value and the initial discharge flux; processing the second feedback information by using an atmospheric transmission model to obtain a third derivative of the concentration of the atmospheric pollutants; and updating the initial discharge flux based on the third derivative to obtain the target discharge flux.

Example 4

Fig. 6 is a flow chart of an atmospheric pollutant discharge flux treatment method according to an embodiment of the present invention, and as shown in fig. 6, the method may include the following steps:

step S602, the cloud server receives the observation value of the atmospheric pollutant concentration and the initial emission flux of the atmospheric pollutant concentration uploaded by the client;

the observation value is used for representing a numerical value obtained by measuring the concentration of the atmospheric pollutant concentration by the target site;

step S604, the cloud server processes the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the concentration of the atmospheric pollutants;

the system comprises an atmospheric transmission model, a target derivative and a target derivative, wherein the atmospheric transmission model is constructed through a deep learning framework, the atmospheric transmission model is used for representing the dynamic transmission process of atmospheric pollutant concentration in the atmosphere, and the target derivative is used for representing the derivative of an observed value relative to emission flux;

step S606, the cloud server updates the initial emission flux based on the target derivative to obtain the target emission flux of the atmospheric pollutant concentration;

in step S608, the cloud server returns the target emission flux to the client.

In the above embodiments of the present application, the observed value includes: the method comprises the following steps that the concentration of the atmospheric pollutant collected in a preset time period is acquired, the cloud server processes an observed value and initial emission flux by using an atmospheric transmission model, and the obtaining of a target derivative of the concentration of the atmospheric pollutant comprises the following steps: the cloud server performs reverse deduction on the initial observation value and the initial emission flux by using an atmospheric transmission model to obtain a predicted value of the concentration of the atmospheric pollutants, wherein the initial observation value is used for representing the concentration of the atmospheric pollutants collected at the initial moment in a preset time period, and the predicted value is used for representing the concentration of the atmospheric pollutants obtained through deduction in the preset time period; the cloud server determines an observation error of the concentration of the atmospheric pollutants based on the predicted value and the observed value; the cloud server obtains a derivative of the observation error relative to the emission flux to obtain a target derivative.

In the above embodiment of the present application, the cloud server performs reverse deduction on the initial observed value and the initial emission flux by using the atmospheric transmission model, and obtaining the predicted value of the atmospheric pollutant concentration includes: the cloud server performs differential operation on the density of the concentration of the atmospheric pollutants, the atmospheric velocity field and the initial observed value by using the derivative calculation layer to obtain a spatial derivative; the cloud server utilizes a differential equation construction layer to perform equation construction on the spatial derivative and the initial discharge flux to obtain a target differential equation, and the target differential equation is irrelevant to a time function; and the cloud server performs integral operation on the target differential equation by using the time integration layer to obtain a predicted value.

In the above embodiment of the present application, after the cloud server updates the initial emission flux based on the target derivative to obtain the target emission flux of the atmospheric pollutant concentration, the method further includes: the cloud server processes the initial observation value and the target emission flux by using an atmospheric transmission model to obtain a target prediction value of the atmospheric pollutant concentration; the cloud server determines whether the target emission flux meets a preset condition or not based on the target predicted value and the target emission flux; in response to the target emission flux meeting a preset condition, the cloud server determines a first derivative of the atmospheric pollutant concentration based on the target predicted value, wherein the first derivative is used for representing a derivative of an observation error relative to the target emission amount; the cloud server updates the target emission flux based on the first derivative.

In the above embodiments of the present application, the determining, by the cloud server, whether the target emission flux satisfies the preset condition based on the target predicted value and the target emission flux includes: the cloud server constructs a first error function based on the target predicted value and the observed value; the cloud server constructs a second error function based on the target emission flux and the initial emission flux; the cloud server obtains a weighted sum of the first error function and the second error function to obtain a loss function of the atmospheric pollutant concentration; in response to the loss function being greater than a preset threshold, the cloud server determines that the target emission flux meets a preset condition; in response to the loss function being less than the preset threshold, the cloud server determines that the target emission flux does not satisfy a preset condition.

In the above embodiment of the present application, after the cloud server updates the initial emission flux based on the target derivative to obtain the target emission flux of the atmospheric pollutant concentration, the method further includes: the cloud server outputs a target emission flux; receiving first feedback information corresponding to the target emission flux, wherein the first feedback information is used for representing whether the target emission flux is updated or not; in response to the first feedback information that the target emission flux is updated, the cloud server processes the observed value and the target emission flux by using the atmospheric transmission model to obtain a second derivative of the atmospheric pollutant concentration; the cloud server updates the target emission flux based on the second derivative.

In the above embodiment of the present application, after the cloud server obtains the observed value of the atmospheric pollutant concentration and the initial emission flux of the atmospheric pollutant concentration, the method further includes: the cloud server outputs the observed value and the initial emission flux; the cloud server receives second feedback information, wherein the second feedback information is obtained by modifying the observed value and the initial emission flux; the cloud server processes the second feedback information by using the atmospheric transmission model to obtain a third derivative of the concentration of the atmospheric pollutants; and the cloud server updates the initial emission flux based on the third derivative to obtain a target emission flux.

Example 5

Fig. 7 is a flowchart of an atmospheric pollutant discharge flux treatment method according to an embodiment of the present invention, and as shown in fig. 7, the method may include the following steps:

step S702, the cloud server receives an atmospheric pollutant emission flux processing request uploaded by a client;

wherein the request for processing the emission flux of the atmospheric pollutants at least comprises: a preset time period and an atmospheric pollutant concentration.

Step S704, the cloud server obtains an observed value of the concentration of the atmospheric pollutants and an initial emission flux of the concentration of the atmospheric pollutants within a preset time period;

Step S706, the cloud server processes the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the atmospheric pollutant concentration;

step 708, updating the initial emission flux by the cloud server based on a target derivative to obtain a target emission flux of the atmospheric pollutant concentration;

step S710, the cloud server returns the target emission flux to the client.

In the above embodiments of the present application, the observed value includes: the method comprises the following steps that the concentration of the atmospheric pollutant collected in a preset time period is acquired, the cloud server processes an observed value and initial emission flux by using an atmospheric transmission model, and the obtaining of a target derivative of the concentration of the atmospheric pollutant comprises the following steps: the cloud server performs reverse deduction on the initial observation value and the initial emission flux by using an atmospheric transmission model to obtain a predicted value of the concentration of the atmospheric pollutants, wherein the initial observation value is used for representing the concentration of the atmospheric pollutants collected at the initial moment in a preset time period, and the predicted value is used for representing the concentration of the atmospheric pollutants obtained through deduction in the preset time period; the cloud server determines an observation error of the concentration of the atmospheric pollutants based on the predicted value and the observed value; and acquiring a derivative of the observation error relative to the discharge flux to obtain a target derivative.

In the above embodiment of the present application, after the cloud server updates the initial emission flux based on the target derivative to obtain the target emission flux of the atmospheric pollutant concentration, the method further includes: the cloud server processes the initial observation value and the target emission flux by using an atmospheric transmission model to obtain a target prediction value of the atmospheric pollutant concentration; the cloud server determines whether the target emission flux meets a preset condition or not based on the target predicted value and the target emission flux; in response to the target emission flux meeting a preset condition, the cloud server determines a first derivative of the atmospheric pollutant concentration based on the target predicted value, wherein the first derivative is used for representing a derivative of an observation error relative to the target emission amount; the target emission flux is updated based on the first derivative.

In the above embodiment of the present application, after the cloud server updates the initial emission flux based on the target derivative to obtain the target emission flux of the atmospheric pollutant concentration, the method further includes: the cloud server outputs a target emission flux; the cloud server receives first feedback information corresponding to the target emission flux, wherein the first feedback information is used for representing whether the target emission flux is updated or not; in response to the first feedback information that the target emission flux is updated, the cloud server processes the observed value and the target emission flux by using the atmospheric transmission model to obtain a second derivative of the atmospheric pollutant concentration; the cloud server updates the target emission flux based on the second derivative.

Example 6

According to an embodiment of the present application, there is also provided an atmospheric pollutant discharge flux processing apparatus for implementing the above atmospheric pollutant discharge flux processing method, as shown in fig. 8, the apparatus 800 includes: an obtaining module 802, a processing module 804, and an updating module 806.

The system comprises an acquisition module, a data processing module and a data processing module, wherein the acquisition module is used for acquiring an observed value of the concentration of the atmospheric pollutants and an initial emission flux of the concentration of the atmospheric pollutants, and the observed value is used for representing the concentration of the atmospheric pollutants acquired by a target station; the processing module is used for processing the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the atmospheric pollutant concentration, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the atmospheric pollutant concentration in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; the updating module is used for updating the initial emission flux based on the target derivative to obtain the target emission flux of the atmospheric pollutant concentration.

It should be noted here that the acquiring module 802, the processing module 804, and the updating module 806 correspond to steps S202 to S206 in embodiment 1, and the three modules are the same as the corresponding steps in the implementation example and application scenario, but are not limited to the disclosure in embodiment 1. It should be noted that the above modules may be operated in the computer terminal 10 provided in embodiment 1 as a part of the apparatus.

In the above embodiments of the present application, the observed value includes: the concentration of the atmospheric pollutant concentration of gathering in the preset time quantum, processing module includes: the device comprises a deduction unit, a first determination unit and an acquisition unit.

The deduction unit is used for reversely deducing the initial observation value and the initial emission flux by using the atmospheric transmission model to obtain a predicted value of the concentration of the atmospheric pollutants, wherein the initial observation value is used for representing the concentration of the atmospheric pollutants collected at the initial moment in a preset time period, and the predicted value is used for representing the concentration of the atmospheric pollutants obtained by deduction in the preset time period; the first determination unit is used for determining the observation error of the atmospheric pollutant concentration based on the predicted value and the observed value; the acquisition unit is used for acquiring a derivative of the observation error relative to the discharge flux to obtain a target derivative.

In the above embodiments of the present application, the deduction unit includes: the device comprises a differential operation subunit, a construction subunit and an integral operation subunit.

The difference operation subunit is used for carrying out difference operation on the density of the concentration of the atmospheric pollutants, the atmospheric velocity field and the initial observed value by using the derivative calculation layer to obtain a spatial derivative; the construction subunit is used for carrying out equation construction on the spatial derivative and the initial discharge flux by utilizing the differential equation construction layer to obtain a target differential equation, and the target differential equation is irrelevant to a time function; and the integral operation subunit is used for carrying out integral operation on the target differential equation by utilizing the time integration layer to obtain a predicted value.

In the above embodiment of the present application, the apparatus further includes: and determining a module.

The processing module is further used for processing the initial observation value and the target emission flux by using the atmospheric transmission model to obtain a target prediction value of the atmospheric pollutant concentration; the determining module is used for determining whether the target emission flux meets a preset condition or not based on the target predicted value and the target emission flux; the determining module is further used for determining a first derivative of the atmospheric pollutant concentration based on the target predicted value in response to the target emission flux meeting a preset condition, wherein the first derivative is used for representing a derivative of the observation error relative to the target emission amount; the update module is further to update the target emission flux based on the first derivative.

In the above embodiments of the present application, the determining module includes: the device comprises a construction unit, a weighting unit and a second determination unit.

The construction unit is used for constructing a first error function based on the target predicted value and the observed value; the construction unit is also used for constructing a second error function based on the target discharge flux and the initial discharge flux; the weighting unit is used for obtaining the weighted sum of the first error function and the second error function to obtain the loss function of the atmospheric pollutant concentration; the second determining unit is used for responding to the fact that the loss function is larger than a preset threshold value, and determining that the target discharge flux meets a preset condition; the second determination unit is further configured to determine that the target discharge flux does not satisfy a preset condition in response to the loss function being less than a preset threshold.

In the above embodiment of the present application, the apparatus further includes: the device comprises an output module and a receiving module.

Wherein the output module is used for outputting the target discharge flux; the receiving module is used for receiving first feedback information corresponding to the target discharge flux, wherein the first feedback information is used for representing whether the target discharge flux is updated or not; the updating module is used for responding to the first feedback information to update the target emission flux, and processing the observed value and the target emission flux by using the atmospheric transmission model to obtain a second derivative of the concentration of the atmospheric pollutants; the update module is further to update the target emission flux based on the second derivative.

In the above embodiments of the present application, the output module is further configured to output the observation value and the initial discharge flux; the receiving module is further used for receiving second feedback information, wherein the second feedback information is obtained by modifying the observed value and the initial discharge flux; the processing module is further used for processing the second feedback information by using the atmospheric transmission model to obtain a third derivative of the atmospheric pollutant concentration; the updating module is further used for updating the initial emission flux based on the third derivative to obtain a target emission flux.

Example 7

According to an embodiment of the present application, there is also provided an atmospheric pollutant discharge flux treatment apparatus for implementing the above atmospheric pollutant discharge flux treatment method, as shown in fig. 9, the apparatus includes: an acquisition module 902, a processing module 904, and an update module 906.

The acquisition module is used for acquiring an observed value of carbon concentration and an initial carbon emission flux in a carbon neutralization scene, wherein the observed value is used for representing a numerical value obtained by measuring the carbon concentration by a target station; the processing module is used for processing the observed value and the initial carbon emission flux by using an atmospheric transmission model to obtain a target derivative, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of gas influencing carbon concentration in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the carbon emission flux; the updating module is used for updating the initial carbon emission flux based on the target derivative to obtain a target carbon emission flux.

It should be noted here that the acquiring module 902, the processing module 904, and the updating module 906 correspond to steps S402 to S406 in embodiment 2, and the three modules are the same as the corresponding steps in the implementation example and application scenario, but are not limited to the disclosure in embodiment 2. It should be noted that the above modules may be operated in the computer terminal 10 provided in embodiment 1 as a part of the apparatus.

Example 8

According to an embodiment of the present application, there is also provided an atmospheric pollutant discharge flux treatment apparatus for implementing the above atmospheric pollutant discharge flux treatment method, as shown in fig. 10, the apparatus includes: a first display module 1002, a processing module 1004, and a second display module 1006.

The first display module is used for displaying an observed value of the concentration of the atmospheric pollutants and an initial emission flux of the concentration of the atmospheric pollutants in an interactive interface, wherein the observed value is used for representing a numerical value obtained by measuring the concentration of the atmospheric pollutants by a target station; the processing module is used for responding to touch operation detected in the interactive interface, processing the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the concentration of the atmospheric pollutants, wherein the atmospheric transmission model is constructed through a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the concentration of the atmospheric pollutants in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; the second display module is used for displaying the target emission flux of the atmospheric pollutant concentration in the interactive interface, wherein the target emission flux is obtained by updating the initial emission flux through a target derivative.

It should be noted that the first display module 1002, the processing module 1004, and the second display module 1006 correspond to steps S502 to S506 in embodiment 3, and the three modules are the same as the corresponding steps in the implementation example and application scenario, but are not limited to the disclosure in embodiment 3. It should be noted that the above modules may be operated in the computer terminal 10 provided in embodiment 1 as a part of the apparatus.

Example 9

According to an embodiment of the present application, there is also provided an atmospheric pollutant discharge flux treatment apparatus for implementing the above atmospheric pollutant discharge flux treatment method, as shown in fig. 11, the apparatus includes: a receiving module 1102, a processing module 1104, an updating module 1106, and an obtaining module 1108.

The receiving module is used for receiving an observed value of the atmospheric pollutant concentration and an initial emission flux of the atmospheric pollutant concentration uploaded by a client, wherein the observed value is used for representing a numerical value obtained by measuring the concentration of the atmospheric pollutant concentration by a target station; the processing module is used for processing the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the atmospheric pollutant concentration, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the atmospheric pollutant concentration in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; the updating module is used for updating the initial emission flux based on the target derivative to obtain the target emission flux of the concentration of the atmospheric pollutants; the acquisition module is used for returning the target emission flux to the client.

It should be noted here that the receiving module 1102, the processing module 1104, the updating module 1106, and the obtaining module 1108 correspond to steps S602 to S608 in embodiment 4, and the four modules are the same as the corresponding steps in the implementation example and the application scenario, but are not limited to the disclosure in embodiment 4. It should be noted that the above modules may be operated in the computer terminal 10 provided in embodiment 1 as a part of the apparatus.

Example 10

According to an embodiment of the present application, there is also provided an atmospheric pollutant discharge flux treatment apparatus for implementing the above atmospheric pollutant discharge flux treatment method, as shown in fig. 12, the apparatus includes: a receiving module 1202, an obtaining module 1204, a processing module 1206, an updating module 1208, and a feedback module 1210.

The receiving module is used for receiving an atmospheric pollutant emission flux processing request uploaded by a client, wherein the atmospheric pollutant emission flux processing request at least comprises: presetting a time period and atmospheric pollutant concentration; the acquisition module is used for acquiring an observed value of the concentration of the atmospheric pollutants in a preset time period and an initial emission flux of the concentration of the atmospheric pollutants, wherein the observed value is used for representing a numerical value obtained by measuring the concentration of the atmospheric pollutants by a target station; the processing module is used for processing the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the atmospheric pollutant concentration, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the atmospheric pollutant concentration in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; the updating module is used for updating the initial emission flux based on the target derivative to obtain the target emission flux of the concentration of the atmospheric pollutants; the feedback module is used for returning the target emission flux to the client.

It should be noted here that the receiving module 1202, the obtaining module 1204, the processing module 1206, the updating module 1208, and the feedback module 1210 correspond to steps S702 to S710 in embodiment 5, and the five modules are the same as the corresponding steps in the implementation example and the application scenario, but are not limited to the disclosure in embodiment 4. It should be noted that the above modules may be operated in the computer terminal 10 provided in embodiment 1 as a part of the apparatus.

Example 11

The embodiment of the invention can provide a computer terminal which can be any computer terminal device in a computer terminal group. Optionally, in this embodiment, the computer terminal may also be replaced with a terminal device such as a mobile terminal.

Optionally, in this embodiment, the computer terminal may be located in at least one network device of a plurality of network devices of a computer network.

In this embodiment, the computer terminal may execute the program code of the following steps in the method for processing the flux of the emitted atmospheric pollutants: acquiring an observed value of the concentration of the atmospheric pollutants and an initial emission flux of the concentration of the atmospheric pollutants, wherein the observed value is used for representing the concentration of the atmospheric pollutants acquired by a target station; processing the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the concentration of the atmospheric pollutants, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the concentration of the atmospheric pollutants in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; and updating the initial emission flux based on the target derivative to obtain the target emission flux of the concentration of the atmospheric pollutants.

Alternatively, fig. 13 is a block diagram of a computer terminal according to an embodiment of the present invention. As shown in fig. 13, the computer terminal a may include: one or more processors (only one shown), memory.

The memory may be configured to store software programs and modules, such as program instructions/modules corresponding to the method and apparatus for processing the atmospheric pollutant emission flux in the embodiment of the present invention, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, implements the above-described method for processing the atmospheric pollutant emission flux. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory may further include memory remotely located from the processor, and these remote memories may be connected to terminal a through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: acquiring an observed value of the concentration of the atmospheric pollutants and an initial emission flux of the concentration of the atmospheric pollutants, wherein the observed value is used for representing the concentration of the atmospheric pollutants acquired by a target station; processing the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the concentration of the atmospheric pollutants, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the concentration of the atmospheric pollutants in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; and updating the initial emission flux based on the target derivative to obtain the target emission flux of the concentration of the atmospheric pollutants.

Optionally, the processor may further execute the program code of the following steps: performing reverse deduction on the initial observation value and the initial emission flux by using an atmospheric transmission model to obtain a predicted value of the concentration of the atmospheric pollutants, wherein the initial observation value is used for representing the concentration of the atmospheric pollutants collected at the initial moment in a preset time period, and the predicted value is used for representing the concentration of the atmospheric pollutants obtained by deduction in the preset time period; determining an observation error of the concentration of the atmospheric pollutant based on the predicted value and the observed value; and acquiring a derivative of the observation error relative to the discharge flux to obtain a target derivative.

Optionally, the processor may further execute the program code of the following steps: the atmosphere transmission model comprises: a derivative calculation layer, a differential equation construction layer and a time integration layer.

Optionally, the processor may further execute the program code of the following steps: carrying out differential operation on the density of the concentration of the atmospheric pollutants, the atmospheric velocity field and the initial observed value by using a derivative calculation layer to obtain a spatial derivative; carrying out equation construction on the spatial derivative and the initial discharge flux by using a differential equation construction layer to obtain a target differential equation, wherein the target differential equation is irrelevant to a time function; and carrying out integral operation on the target differential equation by utilizing the time integration layer to obtain a predicted value.

Optionally, the processor may further execute the program code of the following steps: processing the initial observation value and the target emission flux by using an atmospheric transmission model to obtain a target predicted value of the concentration of the atmospheric pollutants; determining whether the target discharge flux meets a preset condition based on the target predicted value and the target discharge flux; determining a first derivative of the atmospheric pollutant concentration based on the target predicted value in response to the target emission flux meeting a preset condition, wherein the first derivative is used for representing a derivative of an observation error relative to the target emission amount; the target emission flux is updated based on the first derivative.

Optionally, the processor may further execute the program code of the following steps: constructing a first error function based on the target predicted value and the observed value; constructing a second error function based on the target emission flux and the initial emission flux; obtaining a weighted sum of the first error function and the second error function to obtain a loss function of the atmospheric pollutant concentration; in response to the loss function being greater than a preset threshold, determining that the target emission flux meets a preset condition; in response to the loss function being less than a preset threshold, determining that the target emission flux does not satisfy a preset condition.

Optionally, the processor may further execute the program code of the following steps: outputting a target discharge flux; receiving first feedback information corresponding to the target emission flux, wherein the first feedback information is used for representing whether the target emission flux is updated or not; responding to the first feedback information to update the target emission flux, and processing the observed value and the target emission flux by using an atmospheric transmission model to obtain a second derivative of the concentration of the atmospheric pollutants; the target emission flux is updated based on the second derivative.

Optionally, the processor may further execute the program code of the following steps: outputting the observed value and the initial discharge flux; receiving second feedback information, wherein the second feedback information is obtained by modifying the observed value and the initial discharge flux; processing the second feedback information by using an atmospheric transmission model to obtain a third derivative of the concentration of the atmospheric pollutants; and updating the initial discharge flux based on the third derivative to obtain the target discharge flux.

The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: acquiring an observed value of carbon concentration and an initial carbon emission flux in a carbon neutralization scene, wherein the observed value is used for representing a numerical value obtained by measuring the carbon concentration by a target station; processing the observed value and the initial carbon emission flux by using an atmospheric transmission model to obtain a target derivative, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing the dynamic transmission process of gas influencing the carbon concentration in the atmosphere, and the target derivative is used for representing the derivative of the observed value relative to the carbon emission flux; and updating the initial carbon emission flux based on the target derivative to obtain the target carbon emission flux.

The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: displaying an observed value of the concentration of the atmospheric pollutants and an initial emission flux of the concentration of the atmospheric pollutants in an interactive interface, wherein the observed value is used for representing a numerical value obtained by measuring the concentration of the atmospheric pollutants by a target station; responding to touch operation detected in an interactive interface, processing an observed value and initial emission flux by using an atmospheric transmission model to obtain a target derivative of the concentration of the atmospheric pollutants, wherein the atmospheric transmission model is constructed through a deep learning framework and is used for representing a dynamic transmission process of the concentration of the atmospheric pollutants in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; and displaying the target emission flux of the atmospheric pollutant concentration in the interactive interface, wherein the target emission flux is obtained by updating the initial emission flux through a target derivative.

The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: the method comprises the steps that a cloud server receives an observation value of the atmospheric pollutant concentration and an initial emission flux of the atmospheric pollutant concentration, wherein the observation value is used for representing a numerical value obtained by measuring the concentration of the atmospheric pollutant concentration by a target station; the cloud server processes the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the atmospheric pollutant concentration, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the atmospheric pollutant concentration in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; the cloud server updates the initial emission flux based on the target derivative to obtain a target emission flux of the atmospheric pollutant concentration; the cloud server returns the target emission flux to the client.

The processor can call the information and application program stored in the memory through the transmission device to execute the following steps: the cloud server receives an atmospheric pollutant emission flux processing request uploaded by a client, wherein the atmospheric pollutant emission flux processing request at least comprises: presetting a time period and atmospheric pollutant concentration; the method comprises the steps that a cloud server obtains an observed value of the concentration of the atmospheric pollutants in a preset time period and an initial emission flux of the concentration of the atmospheric pollutants, wherein the observed value is used for representing a numerical value obtained by measuring the concentration of the atmospheric pollutants by a target station; the cloud server processes the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the atmospheric pollutant concentration, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the atmospheric pollutant concentration in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; the cloud server updates the initial emission flux based on the target derivative to obtain a target emission flux of the atmospheric pollutant concentration; the cloud server returns the target emission flux to the client.

By adopting the embodiment of the invention, firstly, an observed value of the concentration of the atmospheric pollutants and the initial emission flux of the concentration of the atmospheric pollutants are obtained, wherein the observed value is used for representing the concentration of the atmospheric pollutants acquired by a target station; processing the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the concentration of the atmospheric pollutants, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the concentration of the atmospheric pollutants in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; and updating the initial emission flux based on the target derivative to obtain the target emission flux of the concentration of the atmospheric pollutants, thereby realizing the purpose of monitoring the target emission flux. It is easy to notice that, the observation value of the atmospheric pollutant concentration and the initial emission flux of the atmospheric pollutant concentration can be obtained first, then the observation value and the initial emission flux are processed according to the power transmission model of the atmospheric pollutant concentration of the atmospheric transmission model in the atmosphere, so as to determine the influence factors of the environmental factors in the atmosphere on the prediction accuracy, determine the observation error according to the influence factors, and update the initial emission flux according to the target derivative determined by the observation error, thereby improving the accuracy of the initial emission flux, and further solving the technical problem of lower monitoring accuracy of the atmospheric pollutant concentration in the related art.

It can be understood by those skilled in the art that the structure shown in fig. 13 is only an illustration, and the computer terminal may also be a terminal device such as a smart phone (e.g., an Android phone, an iOS phone, etc.), a tablet computer, a palmtop computer, a Mobile Internet Device (MID), a PAD, and the like. Fig. 13 is a diagram illustrating a structure of the electronic device. For example, the computer terminal 10 may also include more or fewer components (e.g., network interfaces, display devices, etc.) than shown in FIG. 13, or have a different configuration than shown in FIG. 13.

Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by a program instructing hardware associated with the terminal device, where the program may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.

Example 4

The embodiment of the invention also provides a storage medium. Optionally, in this embodiment, the storage medium may be configured to store the program code executed by the atmospheric pollutant discharge flux processing method provided in the first embodiment.

Optionally, in this embodiment, the storage medium may be located in any one of computer terminals in a computer terminal group in a computer network, or in any one of mobile terminals in a mobile terminal group.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: acquiring an observed value of the concentration of the atmospheric pollutants and an initial emission flux of the concentration of the atmospheric pollutants, wherein the observed value is used for representing the concentration of the atmospheric pollutants acquired by a target station; processing the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the concentration of the atmospheric pollutants, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the concentration of the atmospheric pollutants in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; and updating the initial emission flux based on the target derivative to obtain the target emission flux of the concentration of the atmospheric pollutants.

Optionally, the storage medium is further configured to store program code for performing the following steps: performing reverse deduction on the initial observation value and the initial emission flux by using an atmospheric transmission model to obtain a predicted value of the concentration of the atmospheric pollutants, wherein the initial observation value is used for representing the concentration of the atmospheric pollutants collected at the initial moment in a preset time period, and the predicted value is used for representing the concentration of the atmospheric pollutants obtained by deduction in the preset time period; determining an observation error of the concentration of the atmospheric pollutant based on the predicted value and the observed value; and acquiring a derivative of the observation error relative to the discharge flux to obtain a target derivative.

Optionally, the storage medium is further configured to store program code for performing the following steps: the atmosphere transmission model comprises: a derivative calculation layer, a differential equation construction layer and a time integration layer.

Optionally, the storage medium is further configured to store program code for performing the following steps: carrying out differential operation on the density of the concentration of the atmospheric pollutants, the atmospheric velocity field and the initial observed value by using a derivative calculation layer to obtain a spatial derivative; carrying out equation construction on the spatial derivative and the initial discharge flux by using a differential equation construction layer to obtain a target differential equation, wherein the target differential equation is irrelevant to a time function; and carrying out integral operation on the target differential equation by utilizing the time integration layer to obtain a predicted value.

Optionally, the storage medium is further configured to store program code for performing the following steps: processing the initial observation value and the target emission flux by using an atmospheric transmission model to obtain a target predicted value of the concentration of the atmospheric pollutants; determining whether the target discharge flux meets a preset condition based on the target predicted value and the target discharge flux; determining a first derivative of the atmospheric pollutant concentration based on the target predicted value in response to the target emission flux satisfying a preset condition; the target emission flux is updated based on the first derivative.

Optionally, the storage medium is further configured to store program code for performing the following steps: constructing a first error function based on the target predicted value and the observed value; constructing a second error function based on the target emission flux and the initial emission flux; obtaining a weighted sum of the first error function and the second error function to obtain a loss function of the atmospheric pollutant concentration; in response to the loss function being greater than a preset threshold, determining that the target emission flux meets a preset condition; in response to the loss function being less than a preset threshold, determining that the target emission flux does not satisfy a preset condition.

Optionally, the storage medium is further configured to store program code for performing the following steps: outputting a target discharge flux; receiving first feedback information corresponding to the target emission flux, wherein the first feedback information is used for representing whether the target emission flux is updated or not; responding to the first feedback information to update the target emission flux, and processing the observed value and the target emission flux by using an atmospheric transmission model to obtain a second derivative of the concentration of the atmospheric pollutants; the target emission flux is updated based on the second derivative.

Optionally, the storage medium is further configured to store program code for performing the following steps: outputting the observed value and the initial discharge flux; receiving second feedback information, wherein the second feedback information is obtained by modifying the observed value and the initial discharge flux; processing the second feedback information by using an atmospheric transmission model to obtain a third derivative of the concentration of the atmospheric pollutants; and updating the initial discharge flux based on the third derivative to obtain the target discharge flux.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: acquiring an observed value of carbon concentration and an initial carbon emission flux in a carbon neutralization scene, wherein the observed value is used for representing a numerical value obtained by measuring the carbon concentration by a target station; processing the observed value and the initial carbon emission flux by using an atmospheric transmission model to obtain a target derivative, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing the dynamic transmission process of gas influencing the carbon concentration in the atmosphere, and the target derivative is used for representing the derivative of the observed value relative to the carbon emission flux; and updating the initial carbon emission flux based on the target derivative to obtain the target carbon emission flux.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: displaying an observed value of the concentration of the atmospheric pollutants and an initial emission flux of the concentration of the atmospheric pollutants in an interactive interface, wherein the observed value is used for representing a numerical value obtained by measuring the concentration of the atmospheric pollutants by a target station; responding to touch operation detected in an interactive interface, processing an observed value and initial emission flux by using an atmospheric transmission model to obtain a target derivative of the concentration of the atmospheric pollutants, wherein the atmospheric transmission model is constructed through a deep learning framework and is used for representing a dynamic transmission process of the concentration of the atmospheric pollutants in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; and displaying the target emission flux of the atmospheric pollutant concentration in the interactive interface, wherein the target emission flux is obtained by updating the initial emission flux through a target derivative.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: the method comprises the steps that a cloud server receives an observation value of the atmospheric pollutant concentration and an initial emission flux of the atmospheric pollutant concentration, wherein the observation value is used for representing a numerical value obtained by measuring the concentration of the atmospheric pollutant concentration by a target station; the cloud server processes the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the atmospheric pollutant concentration, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the atmospheric pollutant concentration in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; the cloud server updates the initial emission flux based on the target derivative to obtain a target emission flux of the atmospheric pollutant concentration; the cloud server returns the target emission flux to the client.

Optionally, in this embodiment, the storage medium is configured to store program code for performing the following steps: the cloud server receives an atmospheric pollutant emission flux processing request uploaded by a client, wherein the atmospheric pollutant emission flux processing request at least comprises: presetting a time period and atmospheric pollutant concentration; the method comprises the steps that a cloud server obtains an observed value of the concentration of the atmospheric pollutants in a preset time period and an initial emission flux of the concentration of the atmospheric pollutants, wherein the observed value is used for representing a numerical value obtained by measuring the concentration of the atmospheric pollutants by a target station; the cloud server processes the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the atmospheric pollutant concentration, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the atmospheric pollutant concentration in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux; the cloud server updates the initial emission flux based on the target derivative to obtain a target emission flux of the atmospheric pollutant concentration; the cloud server returns the target emission flux to the client.

The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.

In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.

In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units 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 units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention 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, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.

The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims

1. An atmospheric pollutant discharge flux treatment method is characterized by comprising the following steps:

obtaining an observed value of the concentration of the atmospheric pollutants and an initial emission flux of the concentration of the atmospheric pollutants, wherein the observed value is used for representing the concentration of the atmospheric pollutants collected by a target station;

processing the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the atmospheric pollutant concentration, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the atmospheric pollutant concentration in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux;

updating the initial emission flux based on the target derivative to obtain a target emission flux of the atmospheric pollutant concentration.

2. The method of claim 1, wherein the observation comprises: processing the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the atmospheric pollutant concentration, wherein the concentration of the atmospheric pollutant concentration is acquired within a preset time period, and the step comprises the following steps:

performing reverse deduction on an initial observation value and the initial emission flux by using the atmospheric transmission model to obtain a predicted value of the atmospheric pollutant concentration, wherein the initial observation value is used for representing the concentration of the atmospheric pollutant concentration acquired at the initial moment in the preset time period, and the predicted value is used for representing the concentration of the atmospheric pollutant concentration deduced in the preset time period;

determining an observation error of the atmospheric pollutant concentration based on the predicted value and the observed value;

and acquiring a derivative of the observation error relative to the discharge flux to obtain the target derivative.

3. The method of claim 2, wherein the atmospheric transport model comprises: a derivative calculation layer, a differential equation construction layer and a time integration layer.

4. The method of claim 3, wherein the back-deriving the initial observed value and the initial emission flux using the atmospheric transport model to obtain the predicted value of the atmospheric pollutant concentration comprises:

carrying out differential operation on the density of the atmospheric pollutant concentration, the atmospheric velocity field and the initial observation value by using the derivative calculation layer to obtain a spatial derivative;

carrying out equation construction on the spatial derivative and the initial discharge flux by using the differential equation construction layer to obtain a target differential equation, wherein the target differential equation is irrelevant to a time function;

and carrying out integral operation on the target differential equation by utilizing the time integration layer to obtain the predicted value.

5. The method of claim 2, wherein after updating the initial emission flux based on the target derivative to obtain a target emission flux for the concentration of atmospheric pollutants, the method further comprises:

processing the initial observation value and the target emission flux by using the atmospheric transmission model to obtain a target predicted value of the atmospheric pollutant concentration;

determining whether the target emission flux meets a preset condition based on the target predicted value and the target emission flux;

determining a first derivative of the atmospheric pollutant concentration based on the target predicted value in response to the target emission flux satisfying the preset condition, wherein the first derivative is used for characterizing a derivative of an observation error relative to the target emission amount;

updating the target emission flux based on the first derivative.

6. The method of claim 5, wherein determining whether the target emission flux satisfies a preset condition based on the target predicted value and the target emission flux comprises:

constructing a first error function based on the target predicted value and the observed value;

constructing a second error function based on the target emission flux and the initial emission flux;

obtaining a weighted sum of the first error function and the second error function to obtain a loss function of the atmospheric pollutant concentration;

determining that the target emission flux satisfies the preset condition in response to the loss function being greater than a preset threshold;

determining that the target emission flux does not satisfy the preset condition in response to the loss function being less than the preset threshold.

7. The method according to any one of claims 1 to 6, wherein after updating the initial emission flux based on the target derivative to obtain a target emission flux for the concentration of atmospheric pollutants, the method further comprises:

determining a grid point emission flux of each grid point on a target map based on the target emission flux;

displaying the grid point emission flux on the target map.

8. The method of claim 7, wherein after displaying the grid point discharge flux on the target map, the method further comprises:

receiving a selected target area on the target map;

summarizing the grid point discharge fluxes of all grid points contained in the target area to obtain an area discharge flux corresponding to the target area;

outputting the zone discharge flux.

9. An atmospheric pollutant discharge flux treatment method is characterized by comprising the following steps:

acquiring an observed value of carbon concentration and an initial carbon emission flux in a carbon neutralization scene, wherein the observed value is used for representing a value obtained by measuring the carbon concentration by a target station;

processing the observed value and the initial carbon emission flux by using an atmospheric transmission model to obtain a target derivative, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing the dynamic transmission process of the gas influencing the carbon concentration in the atmosphere, and the target derivative is used for representing the derivative of the observed value relative to the carbon emission flux;

updating the initial carbon emission flux based on the target derivative to obtain a target carbon emission flux.

10. The method of claim 9, wherein the observed value comprises: processing the observed value and the initial carbon emission flux by using an atmospheric transmission model to obtain the target derivative according to the carbon concentration acquired in a preset time period, wherein the step of obtaining the target derivative comprises the following steps:

performing reverse deduction on an initial observation value and the initial carbon emission flux by using the atmospheric transport model to obtain a predicted value of the carbon concentration, wherein the initial observation value is used for representing the carbon concentration acquired at the initial moment in the preset time period, and the predicted value is used for representing the carbon concentration deduced in the preset time period;

determining an observation error of the carbon concentration based on the predicted value and the observed value;

and acquiring a derivative of the observation error relative to the carbon emission flux to obtain the target derivative.

11. An atmospheric pollutant discharge flux treatment method is characterized by comprising the following steps:

the method comprises the steps that a cloud server receives an observed value of the atmospheric pollutant concentration and an initial emission flux of the atmospheric pollutant concentration, wherein the observed value is uploaded by a client and is used for representing a numerical value obtained by measuring the concentration of the atmospheric pollutant concentration by a target station;

the cloud server processes the observed value and the initial emission flux by using an atmospheric transmission model to obtain a target derivative of the atmospheric pollutant concentration, wherein the atmospheric transmission model is constructed by a deep learning framework, the atmospheric transmission model is used for representing a dynamic transmission process of the atmospheric pollutant concentration in the atmosphere, and the target derivative is used for representing a derivative of the observed value relative to the emission flux;

the cloud server updates the initial emission flux based on the target derivative to obtain a target emission flux of the atmospheric pollutant concentration;

the cloud server returns the target emission flux to the client.

12. An atmospheric pollutant discharge flux treatment method is characterized by comprising the following steps:

the method comprises the steps that a cloud server receives an atmospheric pollutant emission flux processing request uploaded by a client, wherein the atmospheric pollutant emission flux processing request at least comprises the following steps: presetting a time period and atmospheric pollutant concentration;

the cloud server acquires an observed value of the atmospheric pollutant concentration and an initial emission flux of the atmospheric pollutant concentration within the preset time period, wherein the observed value is used for representing a numerical value obtained by measuring the concentration of the atmospheric pollutant concentration by a target station;

the cloud server returns the target emission flux to the client.

13. A storage medium, characterized in that the storage medium comprises a stored program, wherein when the program is run, the storage medium is controlled to execute the method for treating the emission flux of the atmospheric pollutants as claimed in any one of claims 1 to 12.

14. A computer terminal, comprising: a memory and a processor for executing a program stored in the memory, wherein the program when executed performs the method of emission flux treatment of any one of claims 1 to 12.