CN112487115A

CN112487115A - Method, device and equipment for determining pollution source and storage medium

Info

Publication number: CN112487115A
Application number: CN202011272511.0A
Authority: CN
Inventors: 郭银波; 宋洋; 陶友明
Original assignee: Beijing Softcom Smart City Technology Co ltd
Current assignee: Beijing Softcom Smart City Technology Co ltd
Priority date: 2020-11-13
Filing date: 2020-11-13
Publication date: 2021-03-12

Abstract

The embodiment of the invention discloses a method, a device, equipment and a storage medium for determining a pollution source. The method comprises the following steps: carrying out grid division on a target area to obtain a plurality of grid areas; acquiring static pollution indexes of all grid areas, the position relation between each grid area and a set monitoring station and air quality data monitored by the set monitoring station; determining the influence index of each grid area according to the static pollution index, the position relation and the air quality data; determining a dynamic pollution index according to the influence index and the static pollution index; and determining a pollution source according to the dynamic pollution index. According to the method for determining the pollution source, the target area is subjected to grid division, the pollution source is determined according to the dynamic pollution indexes of all grid areas, the road pollution source can be tracked, and the accuracy of determining the pollution source is improved.

Description

Method, device and equipment for determining pollution source and storage medium

Technical Field

The embodiment of the invention relates to the technical field of air quality prediction, in particular to a method, a device, equipment and a storage medium for determining a pollution source.

Background

In recent years, human activities have become the primary factors influencing the quality of air in China, and the emission of pollutants caused by traffic activities has become one of the important sources of urban air pollution. The motor vehicle emission pollution amount is large in the aspect of road sources, and the lag is insufficient along with the development of air prevention and control. If the emission control is insufficient, the treatment is not in place, and the living environment and physical and psychological health of people are seriously threatened. The method is limited by the defects that the existing micro station can only monitor the current pollution degree and the particulate matter data beside the traffic key road, but due to the particularity of real-time dynamic change of road traffic, data acquisition is inaccurate, and tracing to a person in charge cannot be realized, if people want to monitor a specific road pollution source, artificial auxiliary verification is needed, and the method is extremely high in loss of manpower, material resources and financial resources. Similarly, the complex road pollution source structure also makes the process of accurately positioning the pollution source difficult, and the judgment can be only carried out by means of accumulation of stage pollution data, and the treatment of the pollution source is only carried out in a 'only measure no matter' stage after the pollution is generated due to the technical means deficiency. Therefore, it is necessary to research the relationship between human activities and atmospheric pollution on a traffic road, evaluate the emission condition of a road pollution source, realize accurate excavation of the pollution source, and solve the problem of environmental decision for the last kilometer.

Disclosure of Invention

The embodiment of the invention provides a method, a device, equipment and a storage medium for determining a pollution source, so as to realize the positioning and tracking of the pollution source.

In a first aspect, an embodiment of the present invention provides a method for determining a pollution source, including:

carrying out grid division on a target area to obtain a plurality of grid areas;

acquiring static pollution indexes of all grid areas, the position relation between each grid area and a set monitoring station and air quality data monitored by the set monitoring station;

determining each grid according to the static pollution index, the position relation and the air quality data

An impact index of the region;

determining a dynamic pollution index according to the influence index and the static pollution index;

and determining a pollution source according to the dynamic pollution index.

Further, obtaining static contamination indexes for each grid area includes:

acquiring information point POI data of each grid area;

and determining the static pollution indexes of all grid areas according to the POI data.

Further, determining an influence index of each grid area according to the static pollution index, the position relation and the air quality data comprises:

and fitting the static pollution index, the position relation and the air quality data based on a first set machine learning algorithm to obtain the influence index of each grid area.

Further, determining a dynamic pollution index from the impact index and the static pollution index, comprising:

and fitting the influence index and the static pollution index based on a second set machine learning algorithm to obtain a dynamic pollution index.

Further, determining a pollution source according to the dynamic pollution index comprises:

sequencing the dynamic pollution indexes, and extracting grid areas with a set number in the front of the sequencing to serve as target grid areas;

a source of contamination is determined in the target grid area.

Further, determining a contamination source in the target grid area, comprising:

and determining a pollution source according to POI data in the target grid area.

In a second aspect, an embodiment of the present invention further provides a device for determining a pollution source, including:

the grid area acquisition module is used for carrying out grid division on the target area to obtain a plurality of grid areas;

the data acquisition module is used for acquiring the static pollution indexes of all grid areas, the position relation between each grid area and a set monitoring station and the air quality data monitored by the set monitoring station;

the influence index determining module is used for determining the influence index of each grid area according to the static pollution index, the position relation and the air quality data;

the dynamic pollution index determining module is used for determining a dynamic pollution index according to the influence index and the static pollution index;

and the pollution source determining module is used for determining a pollution source according to the dynamic pollution index.

Further, the data acquisition module is further configured to:

acquiring information point POI data of each grid area;

In a third aspect, an embodiment of the present invention further provides a computer device, where the computer device includes: comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method for determining a contamination source according to an embodiment of the present invention when executing the program.

In a fourth aspect, the embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processing device, implements the method for determining a pollution source according to the embodiment of the present invention.

The embodiment of the invention discloses a method, a device, equipment and a storage medium for determining a pollution source. Firstly, carrying out grid division on a target area to obtain a plurality of grid areas; then obtaining static pollution indexes of all grid areas, the position relation between each grid area and a set monitoring station and air quality data monitored by the set monitoring station; then determining the influence index of each grid area according to the static pollution index, the position relation and the air quality data; then determining a dynamic pollution index according to the influence index and the static pollution index; and finally determining a pollution source according to the dynamic pollution index. According to the method for determining the pollution source, the target area is subjected to grid division, the pollution source is determined according to the dynamic pollution indexes of all grid areas, the road pollution source can be tracked, and the accuracy of determining the pollution source is improved.

Drawings

FIG. 1 is a flow chart of a method for determining a source of contamination according to a first embodiment of the present invention;

FIG. 2 is a schematic structural diagram of a device for determining a contamination source according to a second embodiment of the present invention;

fig. 3 is a schematic structural diagram of a computer device in a third embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.

Example one

Fig. 1 is a flowchart of a pollution source determination method according to an embodiment of the present invention, where the embodiment is applicable to a case of determining a road pollution source, and the method may be executed by a pollution source determination device, which may be composed of hardware and/or software, and may be generally integrated in a device having a pollution source determination function, where the device may be an electronic device such as a server or a server cluster. As shown in fig. 1, the method specifically includes the following steps:

step 110, performing mesh division on the target area to obtain a plurality of mesh areas.

Wherein the target area may be a city or county, etc. The target area may be divided into a plurality of meshes of a predetermined size. The size of each grid may be preset, for example, may be 1km by 1km or 0.5km by 0.5 km.

And step 120, acquiring the static pollution indexes of the grid areas, the position relation between the grid areas and the set monitoring station, and the air quality data monitored by the set monitoring station.

Wherein the static contamination index can be determined by Point of Information (POI) data. In this embodiment, the POI data may include the vehicle model number of each grid area passing through within a set time period, the vehicle use duration, building information included in the grid area, and the like. The POI data may be obtained from a database of map APP pairs. The positional relationship of the grid area to the set monitoring station may include a distance between a center of the grid area and the set monitoring station and an orientation of the grid area with respect to the set monitoring station. The air quality data may include sulfur dioxide (SO2), nitrogen dioxide (NO2), inhalable particulate matter (IP), etc. information.

Specifically, the manner of obtaining the static contamination index of each grid area may be: acquiring information point POI data of each grid area; and determining the static pollution indexes of the grid areas according to the POI data.

The POI data comprise the passing vehicle model of each grid area in a set time period, the using time of the vehicle and the building information contained in the grid area, and the static pollution index of the grid area in the set time period can be determined according to the data. In this embodiment, the POI data may be fitted based on an AI machine learning algorithm to obtain the static pollution index of each grid area.

And step 130, determining the influence index of each grid area according to the static pollution index, the position relation and the air quality data.

The influence index can be understood as the influence index of the grid region on the air quality. Specifically, the manner of determining the influence index of each grid region according to the static pollution index, the position relationship and the air quality data may be as follows: and fitting the static pollution index, the position relation and the air quality data based on a first set machine learning algorithm to obtain the influence index of each grid area.

Wherein the first set machine learning algorithm may be obtained based on a large number of sample training.

And step 140, determining a dynamic pollution index according to the influence index and the static pollution index.

Specifically, the manner of determining the dynamic pollution index according to the influence index and the static pollution index may be: and fitting the influence index and the static pollution index based on a second set machine learning algorithm to obtain a dynamic pollution index.

Wherein the second set machine learning algorithm may be obtained based on a large number of sample training.

And 150, determining a pollution source according to the dynamic pollution index.

Specifically, the manner of determining the pollution source according to the dynamic pollution index may be: sequencing the dynamic pollution indexes, and extracting grid areas with a set number in the front of the sequencing to serve as target grid areas; a source of contamination is determined in the target grid area.

Wherein, the dynamic pollution indexes are sorted from big to small. Specifically, the manner of determining the pollution source in the target grid area may be: and determining a pollution source according to POI data in the target grid area.

The pollution source is determined according to POI data in the target grid area, the location and the type of the pollution source can be quickly positioned, important environmental hidden dangers or event clues are found in advance, and the public opinion dynamics of environmental pollution can be mastered in time.

According to the technical scheme of the embodiment, firstly, grid division is carried out on a target area to obtain a plurality of grid areas; then obtaining static pollution indexes of all grid areas, the position relation between each grid area and a set monitoring station and air quality data monitored by the set monitoring station; then determining the influence index of each grid area according to the static pollution index, the position relation and the air quality data; then determining a dynamic pollution index according to the influence index and the static pollution index; and finally determining a pollution source according to the dynamic pollution index. According to the method for determining the pollution source, the target area is subjected to grid division, the pollution source is determined according to the dynamic pollution indexes of all grid areas, the road pollution source can be tracked, and the accuracy of determining the pollution source is improved.

Example two

Fig. 2 is a schematic structural diagram of a device for determining a pollution source according to a second embodiment of the present invention. As shown in fig. 2, the apparatus includes:

a grid region obtaining module 210, configured to perform grid division on a target region to obtain multiple grid regions;

the data acquisition module 220 is configured to acquire the static pollution index of each grid area, the position relationship between each grid area and a set monitoring station, and set air quality data monitored by the monitoring station;

an influence index determining module 230, configured to determine an influence index of each grid area according to the static pollution index, the position relationship, and the air quality data;

a dynamic pollution index determining module 240 for determining a dynamic pollution index from the impact index and the static pollution index;

and a pollution source determining module 250 for determining a pollution source according to the dynamic pollution index.

Optionally, the data obtaining module 220 is further configured to:

acquiring information point POI data of each grid area;

and determining the static pollution indexes of the grid areas according to the POI data.

Optionally, the influence index determining module 230 is further configured to:

Optionally, the dynamic pollution index determining module 240 is further configured to:

Optionally, the pollution source determining module 250 is further configured to:

a source of contamination is determined in the target grid area.

The device can execute the methods provided by all the embodiments of the invention, and has corresponding functional modules and beneficial effects for executing the methods. For details not described in detail in this embodiment, reference may be made to the methods provided in all the foregoing embodiments of the present invention.

EXAMPLE III

Fig. 3 is a schematic structural diagram of a computer device according to a third embodiment of the present invention. FIG. 3 illustrates a block diagram of a computer device 312 suitable for use in implementing embodiments of the present invention. The computer device 312 shown in FIG. 3 is only an example and should not bring any limitations to the functionality or scope of use of embodiments of the present invention. The device 312 is a computing device that is typically a functional determination of the source of contamination.

As shown in FIG. 3, computer device 312 is in the form of a general purpose computing device. The components of computer device 312 may include, but are not limited to: one or more processors 316, a storage device 328, and a bus 318 that couples the various system components including the storage device 328 and the processors 316.

Bus 318 represents one or more of any of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures include, but are not limited to, an Industry Standard Architecture (ISA) bus, a Micro Channel Architecture (MCA) bus, an enhanced ISA bus, a Video Electronics Standards Association (VESA) local bus, and a Peripheral Component Interconnect (PCI) bus.

Computer device 312 typically includes a variety of computer system readable media. Such media may be any available media that is accessible by computer device 312 and includes both volatile and nonvolatile media, removable and non-removable media.

Storage 328 may include computer system readable media in the form of volatile Memory, such as Random Access Memory (RAM) 330 and/or cache Memory 332. The computer device 312 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 334 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 3, and commonly referred to as a "hard drive"). Although not shown in FIG. 3, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a Compact disk-Read Only Memory (CD-ROM), a Digital Video disk (DVD-ROM), or other optical media) may be provided. In these cases, each drive may be connected to bus 318 by one or more data media interfaces. Storage 328 may include at least one program product having a set (e.g., at least one) of program modules that are configured to carry out the functions of embodiments of the invention.

Program 336 having a set (at least one) of program modules 326 may be stored, for example, in storage 328, such program modules 326 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which may comprise an implementation of a network environment, or some combination thereof. Program modules 326 generally carry out the functions and/or methodologies of embodiments of the invention as described herein.

The computer device 312 may also communicate with one or more external devices 314 (e.g., keyboard, pointing device, camera, display 324, etc.), with one or more devices that enable a user to interact with the computer device 312, and/or with any devices (e.g., network card, modem, etc.) that enable the computer device 312 to communicate with one or more other computing devices. Such communication may occur via input/output (I/O) interfaces 322. Also, computer device 312 may communicate with one or more networks (e.g., a Local Area Network (LAN), Wide Area Network (WAN), etc.) and/or a public Network, such as the internet, via Network adapter 320. As shown, network adapter 320 communicates with the other modules of computer device 312 via bus 318. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the computer device 312, including but not limited to: microcode, device drivers, Redundant processing units, external disk drive Arrays, disk array (RAID) systems, tape drives, and data backup storage systems, to name a few.

Processor 316 executes various functional applications and data processing, such as implementing the method of determining a source of contamination provided by the above-described embodiments of the present invention, by executing programs stored in storage device 328.

Example four

Embodiments of the present invention provide a computer-readable storage medium having stored thereon a computer program, which when executed by a processing apparatus, implements a method for mapping a point of regard as in embodiments of the present invention. The computer readable medium of the present invention described above may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.

In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.

The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.

The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: carrying out grid division on a target area to obtain a plurality of grid areas; acquiring static pollution indexes of all grid areas, the position relation between each grid area and a set monitoring station and air quality data monitored by the set monitoring station; determining the influence index of each grid area according to the static pollution index, the position relation and the air quality data; determining a dynamic pollution index according to the influence index and the static pollution index; and determining a pollution source according to the dynamic pollution index.

Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).

The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.

The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.

In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.

Claims

1. A method for determining a source of contamination, comprising:

determining the influence index of each grid area according to the static pollution index, the position relation and the air quality data;

and determining a pollution source according to the dynamic pollution index.

2. The method of claim 1, wherein obtaining the static contamination index for each grid area comprises:

acquiring information point POI data of each grid area;

3. The method of claim 1, wherein determining an impact index for each grid area based on the static pollution index, the positional relationship, and the air quality data comprises:

4. The method of claim 1, wherein determining a dynamic pollution index from the impact index and the static pollution index comprises:

5. The method of claim 2, wherein determining a source of pollution based on the dynamic pollution index comprises:

a source of contamination is determined in the target grid area.

6. The method of claim 5, wherein determining a source of contamination in the target grid area comprises:

7. An apparatus for determining a source of contamination, comprising:

8. The apparatus of claim 7, wherein the data acquisition module is further configured to:

acquiring information point POI data of each grid area;

9. A computer device, the device comprising: comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method of determining a contamination source according to any one of claims 1-6 when executing the program.

10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processing device, carries out the method of determining a pollution source according to any one of claims 1 to 6.