CN111739587B - Processing method and device of particulate matter monitoring data, storage medium and terminal - Google Patents
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Abstract
The invention discloses a processing method, a device, a storage medium and a terminal of particulate matter monitoring data, wherein the method comprises the following steps: extracting component correlation data associated with the current particulate contamination from the monitoring data; determining the contribution concentration of each indicated source of the current particulate pollutants through the first model according to the component correlation data; and analyzing the components of the current particulate pollutants through a second model according to the component correlation data and the contribution concentrations of all indication sources to obtain an analysis result, wherein the analysis result carries component correlation information related to all the components of the current particulate pollutants. Therefore, the processing method provided by the embodiment of the disclosure can: according to the component correlation data and the first model constructed based on the simple algorithm, the contribution concentration of each indicated source of the current particulate pollutants can be accurately determined, so that the condition of the total contribution concentration of each source of the particulate pollutants can be simply and quantitatively characterized.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a processing method and device of particulate matter monitoring data, a storage medium and a terminal.
Background
At present, air quality monitoring business is continuously developed towards refinement, particulate component observation stations are built in cities and counties, and chemical components of the particulate component observation stations are monitored so as to grasp local particulate pollution source conditions. However, these monitoring data can only indicate the concentration of the components of the particulate matter, and can determine the source of the particulate matter, but if quantitative analysis is performed on different sources, further technical means are required, such as receptor mode source analysis, and numerical mode source analysis using NAQPMS (Nested Air Quality Prediction System).
The existing analysis method for the particulate matters is complex and needs to consume a large amount of manpower and material resources. The specific analysis method for analyzing the particulate matter comprises the following steps:
firstly, acquiring particulate component observation data, wherein the acquired particulate component observation data can be inorganic ion observation data, or metal element observation data, or carbon component observation data;
secondly, before analyzing the chemical components of the particulate matter based on the above observed data of the particulate matter components, an analysis model capable of analyzing the chemical components of the particulate matter needs to be constructed. Generally, a large amount of sample data is often needed for constructing an analysis model for analyzing chemical components of particulate matters, and then training is performed based on the sample data to obtain a corresponding analysis model. The above process of constructing an analytical model tends to be complicated. Furthermore, different types of particulate matter have different chemical properties. For particulate matters with different chemical characteristics, different types of analysis models are often required to be constructed to analyze chemical components of the particulate matters, so that the existing analysis method for analyzing the particulate matters is often complex, and a large amount of manpower and material resources are required to be consumed to obtain a quantitative analysis result.
In the application results, the existing methods can only qualitatively express the source of the component indication, such as "K in coarse particles+Higher concentrations, which are highly correlated with the source of sea and dust salts ", but no K is given for sea and dust salts as a particulate matter+The concentrations each contribute to how much of the concentration.
At present, the processing method of the particle monitoring data is difficult to realize: the method is simple, and can quantitatively analyze various sources of the particulate matters.
At present, there is no technical method for quantitatively analyzing sources by simple calculation using only component monitoring results.
Disclosure of Invention
The embodiment of the application provides a processing method and device of particulate matter monitoring data, a storage medium and a terminal. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present application provides a method for processing particulate monitoring data, where the method includes:
acquiring monitoring data associated with current particulate pollutants;
extracting component correlation data associated with current particulate contaminants from the monitoring data;
determining the contribution concentration of each indicated source of the current particulate pollutants through a first model according to the component correlation data;
analyzing the components of the current particulate pollutants through a second model according to the component correlation data and the contribution concentrations of the indication sources to obtain an analysis result, wherein the analysis result carries component correlation information related to the components of the current particulate pollutants.
In a second aspect, an embodiment of the present application provides a device for processing particulate matter monitoring data, the device including:
the acquisition module is used for acquiring monitoring data related to the current particulate pollutants;
the extraction module is used for extracting component related data related to the current particulate pollutants from the monitoring data acquired by the acquisition module;
the determining module is used for determining the contribution concentration of each indication source of the current particulate pollutants through a first model according to the component correlation data extracted by the extracting module;
and the analysis module is used for analyzing the components of the current particulate pollutants through a second model according to the component correlation data extracted by the extraction module and the contribution concentrations of the indication sources determined by the determination module to obtain an analysis result, wherein the analysis result carries component correlation information related to the components of the current particulate pollutants.
In a third aspect, embodiments of the present application provide a computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the above-mentioned method steps.
In a fourth aspect, an embodiment of the present application provides a terminal, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the above-mentioned method steps.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
in an embodiment of the present application, monitoring data associated with a current particulate contamination is obtained; extracting component correlation data associated with the current particulate contamination from the monitoring data; determining the contribution concentration of each indicated source of the current particulate pollutants through the first model according to the component correlation data; and analyzing the components of the current particulate pollutants through a second model according to the component correlation data and the contribution concentrations of all indication sources to obtain an analysis result, wherein the analysis result carries component correlation information related to all the components of the current particulate pollutants. According to the processing method provided by the embodiment of the disclosure, the contribution concentration of each indication source of the current particulate pollutants can be accurately determined according to the component correlation data and the first model constructed based on the simple algorithm, so that the total contribution concentration condition of each source of the particulate pollutants can be simply and quantitatively characterized. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a schematic flow chart of a method for processing particle monitoring data according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of a device for processing particulate matter monitoring data according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of a terminal according to an embodiment of the present application.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the invention to enable those skilled in the art to practice them.
It should be understood that the described embodiments are only some embodiments of the invention, and not all 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.
When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the invention, as detailed in the appended claims.
In the description of the present invention, it is to be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art. In addition, in the description of the present invention, "a plurality" means two or more unless otherwise specified. "and/or" describes the association relationship of the associated objects, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.
The existing methods, which can only qualitatively describe the source of the component indication, such as "high K + concentration in the coarse particles, which is highly correlated with the source of sea and dust salts", do not give the concentration of how much sea and dust salts contribute to the K + concentration in the particles, respectively. At present, the processing method of the particle monitoring data is difficult to realize: the method is simple, and can quantitatively analyze various sources of the particulate matters. Therefore, the application provides a processing method, a processing device, a storage medium and a terminal of particulate matter monitoring data, so as to solve the problems in the related art. In the technical scheme provided by the application, component related data related to the current particulate pollutants are extracted from monitoring data; determining the contribution concentration of each indicated source of the current particulate pollutants through the first model according to the component correlation data; according to the component correlation data and the contribution concentration of each indication source, the components of the current particulate pollutants are analyzed through the second model to obtain an analysis result, and the analysis result carries component correlation information related to each component of the current particulate pollutants.
The following describes in detail a processing method of particulate matter monitoring data provided by an embodiment of the present application with reference to fig. 1. The processing method of the particle monitoring data can be realized by relying on a computer program and can be operated on a processing device of the particle monitoring data. The computer program may be integrated into the application or may run as a separate tool-like application. Wherein, the processing apparatus of particulate matter monitoring data in this application embodiment can be user terminal, including but not limited to: personal computers, tablet computers, handheld devices, in-vehicle devices, wearable devices, computing devices or other processing devices connected to a wireless modem, and the like. The user terminals may be called different names in different networks, for example: user equipment, access terminal, subscriber unit, subscriber station, mobile station, remote terminal, mobile device, user terminal, wireless communication device, user agent or user equipment, cellular telephone, cordless telephone, Personal Digital Assistant (PDA), terminal equipment in a 5G network or future evolution network, and the like.
Referring to fig. 1, a flow chart of a processing method of particulate matter monitoring data is provided for an embodiment of the present application. As shown in fig. 1, the method for processing particulate matter monitoring data according to the embodiment of the present application may include the following steps:
and S101, acquiring monitoring data related to the current particulate pollutants.
In this step, the monitoring data associated with the current particulate contamination includes at least one of:
the method comprises the following steps of monitoring data of the component characteristics of the current particulate pollutants, vertical observation monitoring data of the current particulate pollutants, particle size analysis monitoring data of the current particulate pollutants and optical characteristic monitoring data of the current particulate pollutants.
In practical applications, there may be other monitoring data associated with the current particulate contamination, and the details are not repeated herein.
And S102, extracting component related data related to the current particulate pollutants from the monitoring data.
And extracting component related data related to the current particulate pollutants from the monitoring data of S101.
In particular, the component association data comprises at least one of: the current individual components of the particulate matter contaminants, the concentrations of the individual components, and the respective indicated sources under the individual components, the contribution weight values of the respective indicated sources.
TABLE 1
Table 1 above shows the current particulate contamination for a particular application, the concentration of each component, and the indicated source of each component.
In this step, the indication source is used to indicate one or two and more sources where each different component may be present.
The following monitoring data can be obtained visually by acquiring and reading the monitoring data: the correspondence of the components to the indicated sources. In practical applications, a component may have only one unique indicating source, for example, as shown in table 1, component B has a unique indicating source a. Alternatively, a component may have two or more indicator sources, e.g., as shown in Table 1, component D has two indicator sources A and B; component a has three indicated sources a, b and c; component C has three indicated sources, b, C and d.
And S103, determining the contribution concentration of each indication source of the current particulate pollutants through the first model according to the component correlation data.
In this step, determining, from the component correlation data, the contribution concentrations of the respective indicated sources of the current particulate contaminants by the first model comprises the steps of:
analyzing the component correlation data to obtain the corresponding relation between each component of the current particulate pollutants and the corresponding component indication source quantity:
in one possible implementation, analyzing the component association data to obtain a corresponding relationship between each component of the current particulate pollutant and a corresponding component indication source quantity includes the following steps:
and analyzing the component correlation data to obtain a one-to-one mapping relation between the components of the current particulate pollutants and the corresponding component indication sources.
In this application scenario, the component of the particulate contaminant currently has a unique component indicative source.
In the case where the indication of the source of the component is unique, the calculation formula of the concentration of contribution of the source to the particulate matter is the following formula (1)
C ═ C formula (1)
In the above equation (1), C is the source contribution concentration and C is the component monitor concentration.
For example, analysis of the data in table 1 reveals that component B has a unique source of indication, namely: a. The source contribution concentration of the a source to component B is equal to the monitored concentration of component B40.
In another possible implementation manner, analyzing the component correlation data to obtain a corresponding relationship between each component of the current particulate pollutant and the corresponding component indication source number further includes the following steps:
analyzing the component correlation data to obtain the corresponding relation between the components of the current particulate pollutants and the corresponding component indication source quantity, wherein the corresponding relation is as follows: current components of particulate contaminants have two or more component indicative sources.
In the case where the indication of the component to the source is not unique, the calculation formula for confirming the source type indicated by the component, the contribution ratio of each source to the concentration of the component estimated based on the region, season, and the like is as follows:
Ci＝c×ηiformula (2)
As shown in the above formula (2), wherein CiContribution concentration for type i indicator source, c component monitor concentration, ηiA contribution weight value for the indicated source for category i. Eta can be taken by experience and can also be configured according to specific application scenarios. Generally, oneThe eta value of each region can be obtained by disassembling the source analysis result of the existing data, and can also be obtained from the research of a tracing method. If no data is available, the contribution of each indicated source may be defaulted to be equal (i.e.:
where there are at least two or more indicating sources for a component of the current contaminant and where the contributions from the indicating sources for each component are equal, n refers to the number of indicating sources for a component).
For example, assuming equal contributions from each of the indicated sources by default, analysis of the data in table 1 reveals that component a has 3 indicated sources, respectively: a, B and C. Then, the source contributions of the sources A, B, and C are equally concentrated and equal to the monitored concentration 30 of component A multiplied by 1/3, where 3 is the indicated number of sources for component A. Component C has 3 indicated sources, respectively: b, C and D. Then, the source contributions of b, C, and d sources to component C are equal in concentration, all equal to the monitored concentration 60 of component C multiplied by 1/3, where 3 is the indicated number of sources for component C. Component D has two indicated sources, a and b respectively. Then, the source contribution concentrations of the a and b sources to component D are equal, both being equal to the monitored concentration 100 of component D multiplied by 1/2, where 2 is the corresponding indicated number of sources for component D.
And determining the contribution concentration of each indication source of the current particulate pollutants through the first model according to the component correlation data and the corresponding relation.
In one possible implementation, determining, by the first model, the contribution concentrations of the respective indicated sources of the current particulate pollutants based on the component correlation data and the corresponding relationships comprises the steps of:
reading the corresponding relation;
the corresponding relation is as follows: under the condition that the components of the particulate pollutants and the corresponding component indicating sources have one-to-one mapping relation, the contribution concentration of the indicating sources is determined to be equal to the monitoring concentration of the components through the first model.
In one possible implementation, determining, by the first model, the contribution concentration of each indicated source of the current particulate pollutant according to the component correlation data and the corresponding relationship further includes:
reading the corresponding relation; and
reading the contribution weight value of each indication source under a certain component which is configured in advance;
the corresponding relation is as follows: in the case where the component of the particulate contamination currently has two or more indicated sources of the component, the contribution concentration of each indicated source is determined by the first model to be equal to the monitored concentration of the component multiplied by the contribution weight value of each indicated source.
And finally, adding the contribution concentrations of the same indication source in the components to obtain the contribution concentrations of the current particulate pollutants of all the indication sources.
In the processing method provided by the embodiment of the present disclosure, the contribution concentration of a certain indication source is the sum of the contribution concentrations of the indication source calculated based on each indication result, and the calculation formula is as follows:
in the above formula (3), CsourceIs the total contribution concentration of a certain source, CjThe resulting source contribution concentration was calculated for the jth component.
In one application scenario, a first model may be constructed based on the algorithm of equation (3) as shown above, the first model being used to determine the contributing concentrations of the various indicated sources of the current particulate contaminants. The method for constructing the first model based on the algorithm of the formula (3) is a conventional method, the sample data is trained to obtain an initial model, and then the initial model is continuously corrected to obtain a final first model, which is not described herein again.
The above-mentioned only provides one algorithm for constructing the first model, and other algorithms may be introduced to construct the first model according to the needs of different application scenarios, which are only examples herein and are not described again.
According to the data as above in table 1, and the contribution weight values (with respect to component concentration) for each indicated source are the same;
then from the data in table 1 it can be seen that:
the total contributing concentrations of the A source were: 30 × 1/3+40+100 × 1/2 ═ 100;
the total contribution concentration of the b source was: 30 × 1/3+60 × 1/3+100 × 1/2 ═ 80;
the total contribution concentration of the C source is: 30 × 1/3+60 × 1/3 ═ 30;
the total contribution concentration of the D sources is: 60 × 1/3 ═ 20.
The total contribution concentrations of the respective components calculated above are concentrations calculated on the assumption that "the contribution weight values of the respective indication sources are the same", and the concentrations are relative concentrations.
And S104, analyzing the components of the current particulate pollutants through a second model according to the component correlation data and the contribution concentrations of all indication sources to obtain an analysis result, wherein the analysis result carries component correlation information related to all the components of the current particulate pollutants.
In this step, the second model is an analytical model that resolves the composition of the current particulate contamination. The second model is a conventional analytic model and is not described herein again.
In practical applications, the second model is a conventional analytical model, and the analytical models used may be NAQPMS-OSAM, CAMx-PSAT/OSAT, PMF and CMB analytical models.
In this step, the component associated information includes first associated information, second associated information, third associated information, and fourth associated information;
the first related information is used for indicating component information of each component of the current particulate pollutant, the second related information is used for indicating concentration information of each component of the current particulate pollutant, the third related information is used for indicating source information corresponding to each component of the current particulate pollutant, and the fourth related information is used for identifying contribution weight value information of each indicating source.
Only common component related information is listed above, and other component related information may also be set according to different application scenarios, which is not described herein again.
In a possible implementation manner, after analyzing the components of the current particulate pollutants through the second model according to the component correlation data and the contribution concentrations of the various indication sources to obtain an analysis result, the method further includes the following steps:
reading the component associated information, and pushing the component associated information to user terminal equipment with a preset MAC (Media Access Control Address);
the component associated information comprises first associated information, second associated information, third associated information and fourth associated information:
the first related information is used for indicating component information of each component of the current particulate pollutant, the second related information is used for indicating concentration information of each component of the current particulate pollutant, the third related information is used for indicating source information corresponding to each component of the current particulate pollutant, and the fourth related information is used for identifying contribution weight value information of each indicating source.
Only common component related information is listed above, and other component related information may also be set according to different application scenarios, which is not described herein again.
It should be noted that the processing method provided by the embodiment of the present disclosure can accurately determine the contribution concentration of each indicated source of the current particulate pollutant based on the monitoring data of the particulate component (e.g., inorganic ions, metal elements, or carbon components) of the particulate pollutant, according to the component correlation data, and the first model constructed based on a simple algorithm, thereby implementing a simple and quantitative representation of the total contribution concentration condition of each source of the particulate pollutant.
In an embodiment of the present application, monitoring data associated with a current particulate contamination is obtained; extracting component correlation data associated with the current particulate contamination from the monitoring data; determining the contribution concentration of each indicated source of the current particulate pollutants through the first model according to the component correlation data; and analyzing the components of the current particulate pollutants through a second model according to the component correlation data and the contribution concentrations of all indication sources to obtain an analysis result, wherein the analysis result carries component correlation information related to all the components of the current particulate pollutants. According to the processing method provided by the embodiment of the disclosure, the contribution concentration of each indication source of the current particulate pollutants can be accurately determined according to the component correlation data and the first model constructed based on the simple algorithm, so that the total contribution concentration condition of each source of the particulate pollutants can be simply and quantitatively characterized.
The following is an embodiment of the processing apparatus for monitoring particulate matter according to the present invention, which can be used to execute the embodiment of the processing method for monitoring particulate matter according to the present invention. For details not disclosed in the embodiment of the device for processing particulate matter monitoring data of the present invention, refer to the embodiment of the method for processing particulate matter monitoring data of the present invention.
Referring to fig. 2, a schematic structural diagram of a device for processing particulate matter monitoring data according to an exemplary embodiment of the present invention is shown. The processing device for the particulate matter monitoring data provided by the embodiment of the disclosure can be realized by software, hardware or a combination of the two to be all or part of a terminal. The processing device of the particulate matter monitoring data provided by the embodiment of the disclosure comprises an acquisition module 10, an extraction module 20, a determination module 30 and an analysis module 40.
Specifically, the acquisition module 10 is configured to acquire monitoring data associated with a current particulate pollutant;
the extraction module 20 is configured to extract component related data related to the current particulate pollutant from the monitoring data acquired by the acquisition module 10;
a determining module 30, configured to determine, through the first model, contribution concentrations of the current particulate pollutants from the respective indicated sources according to the component correlation data extracted by the extracting module 20;
and the analysis module 40 is configured to analyze, according to the component association data extracted by the extraction module 20 and the contribution concentrations of the indication sources determined by the determination module 30, the components of the current particulate pollutant through the second model to obtain an analysis result, where the analysis result carries component association information associated with the components of the current particulate pollutant.
Optionally, the determining module 30 is configured to:
analyzing the component correlation data to obtain the corresponding relation between each component of the current particulate pollutants and the corresponding component indication source quantity:
and determining the contribution concentration of each indication source of the current particulate pollutants through the first model according to the component correlation data and the corresponding relation.
Optionally, the determining module 30 is specifically configured to:
and analyzing the component correlation data to obtain a one-to-one mapping relation between each component of the current particulate pollutants and the corresponding component indication source.
Optionally, the determining module 30 is further specifically configured to:
reading the corresponding relation;
the corresponding relation is as follows: under the condition that the components of the particulate pollutants and the corresponding component indicating sources have one-to-one mapping relation, the contribution concentration of each indicating source is determined to be equal to the monitoring concentration of each component through the first model.
Optionally, the determining module 30 is further specifically configured to:
analyzing the component correlation data to obtain the corresponding relation between each component of the current particulate pollutants and the corresponding component indication source quantity as follows: current particulate contaminants each have two or more component indicative sources of each component.
Optionally, the determining module 30 is further specifically configured to:
reading the corresponding relation; and
reading the contribution weight value of each indication source under a certain component which is configured in advance;
the corresponding relation is as follows: in the case where each component of the particulate contamination currently has two or more indicated sources of the component, the contributing concentration of each indicated source is determined by the first model.
Optionally, the apparatus further comprises:
a reading module (not shown in fig. 2) configured to read the component correlation information after the analyzing module 40 analyzes the components of the current particulate pollutants through the second model according to the component correlation data and the contribution concentrations of the respective indication sources to obtain an analysis result;
the group associated information read by the reading module comprises first associated information, second associated information, third associated information and fourth associated information;
the first related information is used for indicating component information of each component of the current particulate pollutant, the second related information is used for indicating concentration information of each component of the current particulate pollutant, the third related information is used for indicating source information corresponding to each component of the current particulate pollutant, and the fourth related information is used for identifying contribution weight value information of each indicating source.
A pushing module (not shown in fig. 2) configured to push the component related information read by the reading module to a user terminal device having a preset MAC address; thus, the user can conveniently view the component associated information on the user terminal equipment.
In an embodiment of the application, an acquisition module acquires monitoring data associated with a current particulate pollutant; the extraction module extracts component related data related to the current particulate pollutants from the monitoring data acquired by the acquisition module; the determining module determines the contribution concentration of each indication source of the current particulate pollutants through the first model according to the component correlation data; and the analysis module analyzes the components of the current particulate pollutants through the second model according to the component correlation data and the contribution concentrations of all indication sources to obtain an analysis result, wherein the analysis result carries component correlation information related to all the components of the current particulate pollutants. According to the processing method provided by the embodiment of the disclosure, the contribution concentration of each indication source of the current particulate pollutants can be accurately determined according to the component correlation data and the first model constructed based on the simple algorithm, so that the total contribution concentration condition of each source of the particulate pollutants can be simply and quantitatively characterized.
The present invention also provides a computer readable medium having stored thereon program instructions, which when executed by a processor, implement the method for processing particulate matter monitoring data provided by the various method embodiments described above. The present invention also provides a computer program product containing instructions which, when run on a computer, cause the computer to perform the method of processing particulate matter monitoring data as described in the various method embodiments above.
Please refer to fig. 3, which provides a schematic structural diagram of a terminal according to an embodiment of the present application. As shown in fig. 3, the terminal 1000 can include: at least one processor 1001, at least one network interface 1004, a user interface 1003, memory 1005, at least one communication bus 1002.
Wherein a communication bus 1002 is used to enable connective communication between these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may also include a standard wired interface and a wireless interface.
The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
The Memory 1005 may include a Random Access Memory (RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer-readable medium. The memory 1005 may be used to store an instruction, a program, code, a set of codes, or a set of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the various method embodiments described above, and the like; the storage data area may store data and the like referred to in the above respective method embodiments. The memory 1005 may optionally be at least one memory device located remotely from the processor 1001. As shown in FIG. 3, the memory 1005, which is one type of computer storage medium, can include an operating system, a network communication module, a user interface module, and a particulate monitoring data processing application.
In the terminal 1000 shown in fig. 3, the user interface 1003 is mainly used as an interface for providing input for a user, and acquiring data input by the user; the processor 1001 may be configured to invoke a processing application of the particle monitoring data stored in the memory 1005, and specifically perform the following operations:
acquiring monitoring data associated with current particulate pollutants;
extracting component correlation data associated with the current particulate contamination from the monitoring data;
determining the contribution concentration of each indicated source of the current particulate pollutants through the first model according to the component correlation data;
and analyzing the components of the current particulate pollutants through a second model according to the component correlation data and the contribution concentrations of all indication sources to obtain an analysis result, wherein the analysis result carries component correlation information related to all the components of the current particulate pollutants.
In one embodiment, the processor 1001, when executing the determining the contribution concentrations of the respective indicated sources of the current particulate pollutants by the first model based on the component correlation data, specifically executes the following operations:
analyzing the component correlation data to obtain the corresponding relation between each component of the current particulate pollutants and the corresponding component indication source quantity:
and determining the contribution concentration of each indication source of the current particulate pollutants through the first model according to the component correlation data and the corresponding relation.
In one embodiment, when the processor 1001 performs the analysis on the component related data to obtain the corresponding relationship between each component of the current particulate pollutant and the corresponding component indication source quantity, specifically perform the following operations:
and analyzing the component correlation data to obtain a one-to-one mapping relation between each component of the current particulate pollutants and the corresponding component indication source.
In one embodiment, the processor 1001, when executing the determining, according to the component correlation data and the corresponding relationship, the contribution concentration of each indicated source of the current particulate pollutant through the first model, specifically executes the following operations:
reading the corresponding relation;
the corresponding relation is as follows: under the condition that the components of the particulate pollutants and the corresponding component indicating sources have one-to-one mapping relation, the contribution concentration of each indicating source is determined to be equal to the monitoring concentration of each component through the first model.
In one embodiment, when the processor 1001 performs the analysis on the component related data to obtain the corresponding relationship between each component of the current particulate pollutant and the corresponding component indication source quantity, the following operations are specifically further performed:
analyzing the component correlation data to obtain the corresponding relation between each component of the current particulate pollutants and the corresponding component indication source quantity as follows: current particulate contaminants each have two or more component indicative sources of each component.
In one embodiment, when the processor 1001 determines the contribution concentration of each indicated source of the current particulate pollutant through the first model according to the component related data and the corresponding relationship, the following operations are specifically performed:
reading the corresponding relation; and
reading the contribution weight value of each indication source under a certain component which is configured in advance;
the corresponding relation is as follows: in the case where each component of the particulate contamination currently has two or more indicated sources of the component, the contributing concentration of each indicated source is determined by the first model.
In one embodiment, after the processor 1001 performs the analysis on the current component of the particulate pollutant through the second model according to the component related data and the contribution concentrations of the respective indicated sources to obtain an analysis result, the following operations are further performed:
reading the component associated information, and pushing the component associated information to user terminal equipment with a preset MAC address;
the component associated information comprises first associated information, second associated information, third associated information and fourth associated information:
the first related information is used for indicating component information of each component of the current particulate pollutant, the second related information is used for indicating concentration information of each component of the current particulate pollutant, the third related information is used for indicating source information corresponding to each component of the current particulate pollutant, and the fourth related information is used for identifying contribution weight value information of each indicating source.
In an embodiment of the present application, monitoring data associated with a current particulate contamination is obtained; extracting component correlation data associated with the current particulate contamination from the monitoring data; determining the contribution concentration of each indicated source of the current particulate pollutants through the first model according to the component correlation data; and analyzing the components of the current particulate pollutants through a second model according to the component correlation data and the contribution concentrations of all indication sources to obtain an analysis result, wherein the analysis result carries component correlation information related to all the components of the current particulate pollutants. According to the processing method provided by the embodiment of the disclosure, the contribution concentration of each indication source of the current particulate pollutants can be accurately determined according to the component correlation data and the first model constructed based on the simple algorithm, so that the total contribution concentration condition of each source of the particulate pollutants can be simply and quantitatively characterized.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above disclosure is only for the purpose of illustrating the preferred embodiments of the present application and is not to be construed as limiting the scope of the present application, so that the present application is not limited thereto, and all equivalent variations and modifications can be made to the present application.
Claims (8)
1. A method of processing particulate monitoring data, the method comprising:
acquiring monitoring data associated with current particulate pollutants;
extracting component correlation data associated with current particulate contaminants from the monitoring data;
determining the contribution concentration of each indicated source of the current particulate pollutants through a first model according to the component correlation data; determining, by the first model, the contribution concentrations of the respective indicated sources of the current particulate contaminants based on the component correlation data comprises:
analyzing the component correlation data to obtain the corresponding relation between each component of the current particulate pollutants and the corresponding component indication source quantity: analyzing the component correlation data to obtain the corresponding relationship between each component of the current particulate pollutants and the corresponding component indication source quantity comprises:
analyzing the component correlation data to obtain a one-to-one mapping relation between each component of the current particulate pollutants and the corresponding component indication source;
determining the contribution concentration of each indication source of the current particulate pollutants through a first model according to the component correlation data and the corresponding relation;
analyzing the components of the current particulate pollutants through a second model according to the component correlation data and the contribution concentrations of the indication sources to obtain an analysis result, wherein the analysis result carries component correlation information related to the components of the current particulate pollutants.
2. The method of claim 1, wherein determining, via the first model, the contributing concentrations of each indicated source of the current particulate contamination based on the constituent correlation data and the correspondence comprises:
reading the corresponding relation;
the corresponding relation is as follows: under the condition that the components of the particulate pollutants and the corresponding component indication sources have one-to-one mapping relation, determining that the contribution concentration of each indication source is equal to the monitoring concentration of each component through the first model.
3. The method of claim 1, wherein analyzing the component correlation data to obtain a correspondence between each component of the current particulate contamination and a corresponding indicated source quantity of the component further comprises:
analyzing the component correlation data to obtain the corresponding relation between each component of the current particulate pollutants and the corresponding component indication source quantity, wherein the corresponding relation is as follows: currently each component of particulate matter contaminants has more than two component indicative sources.
4. The method of claim 1, wherein determining, via the first model, the contributing concentrations of each indicated source of the current particulate contamination based on the constituent correlation data and the correspondence further comprises:
reading the corresponding relation; and
reading the contribution weight value of each indication source under a certain component which is configured in advance;
the corresponding relation is as follows: in the case where each component of the particulate contamination has more than two indicated sources, the contributing concentrations of each indicated source are determined by the first model.
5. The method of claim 1, wherein after said analyzing the composition of the current particulate contaminant via a second model based on said composition correlation data and said respective contribution concentrations indicative of the source, the method further comprises:
reading the component correlation information, and pushing the component correlation information to user terminal equipment with a preset MAC address;
the component associated information comprises first associated information, second associated information, third associated information and fourth associated information:
the first related information is used for indicating component information of each component of the current particulate pollutant, the second related information is used for indicating concentration information of each component of the current particulate pollutant, the third related information is used for indicating source information corresponding to each component of the current particulate pollutant, and the fourth related information is used for identifying contribution weight value information of each indicating source.
6. A device for processing particulate matter monitoring data, the device comprising:
the acquisition module is used for acquiring monitoring data related to the current particulate pollutants;
the extraction module is used for extracting component related data related to the current particulate pollutants from the monitoring data acquired by the acquisition module;
the determining module is used for determining the contribution concentration of each indication source of the current particulate pollutants through a first model according to the component correlation data extracted by the extracting module; the determination module is to: analyzing the component correlation data to obtain the corresponding relation between each component of the current particulate pollutants and the corresponding component indication source quantity: determining the contribution concentration of each indication source of the current particulate pollutants through a first model according to the component correlation data and the corresponding relation; the determining module is specifically configured to: analyzing the component correlation data to obtain a one-to-one mapping relation between each component of the current particulate pollutants and the corresponding component indication source;
and the analysis module is used for analyzing the components of the current particulate pollutants through a second model according to the component correlation data extracted by the extraction module and the contribution concentrations of the indication sources determined by the determination module to obtain an analysis result, wherein the analysis result carries component correlation information related to the components of the current particulate pollutants.
7. A computer storage medium, characterized in that it stores a plurality of instructions adapted to be loaded by a processor and to carry out the method steps according to any one of claims 1 to 5.
8. A terminal, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps of any of claims 1 to 5.
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Application publication date: 20201002 Assignee: Beijing Zhongke Sanqing Environmental Technology Co.,Ltd. Assignor: 3CLEAR TECHNOLOGY Co.,Ltd. Contract record no.: X2022980012305 Denomination of invention: Method, device, storage medium and terminal for processing particulate matter monitoring data Granted publication date: 20210202 License type: Common License Record date: 20220815 |