CN115685394B - Data processing method, device and medium - Google Patents
Data processing method, device and medium Download PDFInfo
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- CN115685394B CN115685394B CN202211405187.4A CN202211405187A CN115685394B CN 115685394 B CN115685394 B CN 115685394B CN 202211405187 A CN202211405187 A CN 202211405187A CN 115685394 B CN115685394 B CN 115685394B
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Abstract
The application provides a data processing method, device and medium. The method comprises the following steps: periodically acquiring weather block data corresponding to the three-dimensional atmospheric space grid of each processor core by taking the first preset duration as a period; periodically acquiring pollutant discharge rate data by taking a first preset time period as a period, wherein the pollutant discharge rate data is average pollutant discharge rate data of a second preset time period stored in a memory of the terminal equipment, and the second preset time period is longer than the first preset time period; for each period, determining air quality parameters of each area in the atmosphere area to be predicted in the period according to weather block data corresponding to the three-dimensional atmosphere space grid of each processor core and pollutant discharge rate data, and predicting the air quality of the atmosphere area to be predicted according to the air quality parameters. According to the method, the data reading speed of the terminal equipment under the high-resolution condition is improved, so that the data processing efficiency in air quality prediction is improved.
Description
Technical Field
The present disclosure relates to the field of data processing, and in particular, to a data processing method, apparatus, and medium.
Background
With the development of economic technology and the progress of society, people pay more attention to air quality, and the requirements for air quality forecast are also higher.
When the existing air quality forecasting mode system is used for forecasting air quality, firstly, each processor core of the terminal equipment can read all data of an area to be forecasted at one time in each forecasting period, wherein all data comprise meteorological data, pollutant emission data and the like; the different processor cores then extract the data corresponding to the current processor core from the read data. In this process, the input/output (IO) of the processor core to the data directly affects the efficiency of the air quality prediction mode system on the terminal device for air quality prediction.
When the regional resolution to be forecasted is very high, a large amount of data such as meteorological data, pollutant emission data and the like can be generated, so that the data reading speed of the terminal equipment is very low, and the data processing efficiency in air quality forecasting is affected.
Disclosure of Invention
The application provides a data processing method, device and medium, which are used for solving the problem that when the resolution of an area is high, the data reading speed of terminal equipment is low, so that the data processing efficiency in air quality prediction is affected.
In a first aspect, the present application provides a data processing method, including:
periodically acquiring weather block data corresponding to a three-dimensional atmospheric space grid of each processor core by taking a first preset time length as a period, wherein the three-dimensional atmospheric space grid is determined according to the regional range and the regional resolution of an atmospheric region to be predicted;
periodically acquiring pollutant discharge rate data of the atmospheric region to be predicted by taking a first preset time period as a period, wherein the pollutant discharge rate data is average pollutant discharge rate data of a second preset time period stored in a memory of terminal equipment, and the second preset time period is longer than the first preset time period;
for each period, determining air quality parameters of each region in the to-be-predicted atmospheric region in the period according to weather block data corresponding to the three-dimensional atmospheric space grid of each processor core and pollutant discharge rate data, and predicting the air quality of the to-be-predicted atmospheric region according to the air quality parameters.
In one possible implementation manner, the acquiring weather block data corresponding to the three-dimensional atmospheric space grid of each processor core specifically includes:
Determining a three-dimensional atmospheric space grid corresponding to each processor core;
for each processor core, acquiring total meteorological data corresponding to a three-dimensional atmospheric space grid of the processor core;
determining the position of a preset meteorological variable type in the total meteorological data;
sequentially extracting variable data corresponding to the positions of all meteorological variable types in the total meteorological data according to a preset variable data extraction sequence;
and determining weather block data corresponding to the three-dimensional atmospheric space grid of the processor core according to each weather variable type and variable data corresponding to each weather variable type.
In one possible embodiment, the meteorological variable types include one or more of the following:
wind speed, 2 meters temperature, surface pressure, 10 meters wind speed, water-air mixing ratio, cloud-water mixing ratio, rainwater mixing ratio, soil temperature, relative soil humidity, sea ice marking, main soil classification, vegetation proportion, physical snow depth, accumulated rainfall, non-accumulated rainfall, ground down short wave flux, friction wind speed, reciprocal of the mollin-obhuff length, boundary layer height, cloud optical thickness of ice, cloud optical thickness of water, pressure, height, temperature, relative humidity, low cloud proportion, mid cloud proportion, high cloud proportion, 2 meters relative humidity.
In one possible implementation manner, the determining the three-dimensional air space grid corresponding to each processor core specifically includes:
determining the three-dimensional atmospheric space grids corresponding to the atmospheric region to be predicted and the number of the three-dimensional atmospheric space grids according to the region range and the region resolution of the atmospheric region to be predicted;
and determining the three-dimensional atmospheric space grids corresponding to each processor core according to the number of the three-dimensional atmospheric space grids and the number of the processor cores.
In one possible embodiment, after the determining the position of the preset meteorological variable type in the total meteorological data, the method further includes:
judging whether a first meteorological variable type exists in the preset meteorological variable types or not, wherein the first meteorological variable type does not exist in a corresponding position in the total meteorological data;
if yes, sequentially extracting variable data corresponding to the positions of all the meteorological variable types in the total meteorological data according to a preset variable data extraction sequence, wherein the variable data comprises the following specific steps:
for the second meteorological variable types with corresponding positions in the total meteorological data, sequentially extracting variable data corresponding to the positions of the second meteorological variable types in the total meteorological data according to a preset variable data extraction sequence;
And for the first meteorological variable type with no corresponding position in the total meteorological data, assigning the variable data corresponding to the first meteorological variable type as 0.
In one possible implementation manner, the determining the air quality parameter of each area in the to-be-predicted atmosphere area in the period according to the meteorological block data corresponding to the three-dimensional atmospheric space grid of each processor core and the pollutant discharge rate data specifically includes:
and calculating meteorological block data and pollutant discharge rate data corresponding to the three-dimensional atmospheric space grid of each processor core according to preset processor core parameters and communication parameters to obtain air quality parameters of each region in the atmospheric region to be predicted in the period.
In one possible implementation, the processor core parameters include: the node uses the proportion of the processor core number to the total processor core number of the node;
the communication parameters include: communication request time, communication response time, completion queue size in communication, a pair of work request queue sizes in communication.
In a second aspect, the present application provides a terminal device, including:
The receiving module is used for periodically acquiring weather block data corresponding to a three-dimensional atmospheric space grid of each processor core by taking a first preset duration as a period, wherein the three-dimensional atmospheric space grid is determined according to the regional range and the regional resolution of an atmospheric region to be predicted; periodically acquiring pollutant discharge rate data of the atmospheric region to be predicted by taking a first preset time period as a period, wherein the pollutant discharge rate data is average pollutant discharge rate data of a second preset time period stored in a memory of terminal equipment, and the second preset time period is longer than the first preset time period;
and the processing module is used for determining the air quality parameters of each region in the atmospheric region to be predicted in each period according to the meteorological block data corresponding to the three-dimensional atmospheric space grid of each processor core and the pollutant discharge rate data, and predicting the air quality of the atmospheric region to be predicted according to the air quality parameters.
In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
The processor executes the computer-executable instructions stored in the memory to implement the methods described above.
In a fourth aspect, the present application provides a computer-readable storage medium having stored therein computer-executable instructions for performing the method described above when executed by a processor.
In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements the method described above.
The data processing method, the device and the medium can periodically acquire weather block data corresponding to the three-dimensional atmospheric space grid of each processor core by taking the first preset time length as a period, wherein the three-dimensional atmospheric space grid is determined according to the regional range and the regional resolution of the atmospheric region to be predicted; periodically acquiring pollutant discharge rate data of an atmospheric region to be predicted by taking a first preset time period as a period, wherein the pollutant discharge rate data is average pollutant discharge rate data of a second preset time period stored in a memory of terminal equipment, and the second preset time period is longer than the first preset time period; for each period, determining air quality parameters of each area in the atmosphere area to be predicted in the period according to weather block data corresponding to the three-dimensional atmosphere space grid of each processor core and pollutant discharge rate data, and predicting the air quality of the atmosphere area to be predicted according to the air quality parameters. According to the method, when meteorological data are acquired in each data acquisition period, each processor core does not read the total meteorological data of the atmospheric region to be predicted at one time, but only acquires meteorological block data corresponding to the three-dimensional atmospheric space grid of the processor core. By the arrangement, the data reading speed of the terminal equipment can be improved, so that the data processing efficiency of the air quality parameters is improved. Furthermore, the terminal device only needs to read the pollutant discharge rate data once in each data acquisition period of the second preset duration, after the pollutant discharge rate data is acquired for the first time, the pollutant discharge rate data can be stored in the memory of the terminal device, and each data acquisition period can directly call the data from the memory of the terminal device without reading again. By the arrangement, the data reading speed of the terminal equipment is further improved, and therefore the data processing efficiency of the air quality parameters is improved.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a schematic diagram of a prior art method for reading weather data;
FIG. 2 is a schematic diagram of a prior art reading of contaminant discharge rate data;
FIG. 3 is a system architecture diagram of an embodiment of the present application;
FIG. 4 is a flow chart of a data processing method according to an embodiment of the present application;
FIG. 5 is a schematic diagram illustrating the reading of meteorological data according to an embodiment of the present application;
FIG. 6 is a schematic diagram of reading contaminant discharge rate data according to an embodiment of the present application;
FIG. 7 is a flow chart of a data processing method according to another embodiment of the present application;
fig. 8 is a schematic structural diagram of a terminal device according to an embodiment of the present application;
fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Reference numerals: 1. a terminal device; 2. a server; 81. a receiving module; 82. and a processing module.
Specific embodiments thereof have been shown by way of example in the drawings and will herein be described in more detail. These drawings and the written description are not intended to limit the scope of the inventive concepts in any way, but to illustrate the concepts of the present application to those skilled in the art by reference to specific embodiments.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims.
The terms referred to in this application are explained first:
processor cores, referred to as cores (Die) of a central processing unit (central processing unit, CPU), are core chips in the middle of the CPU, also called cores, are the most important components of the CPU, and the CPU may have multiple cores. The CPU core is made of single crystal silicon, is used to perform all calculations, accept/store commands, process data, etc., and is a digital processing core.
The information transfer interface (Message Passing Interface, MPI) refers to a cross-language communication protocol, and is also a parallel programming technique based on information transfer. The information delivery interface is a programming interface standard, not a specific programming language. Briefly, the MPI standard defines a set of portable programming interfaces for writing parallel computer programs, supporting point-to-point and broadcasting.
When the existing air quality forecasting mode system is used for forecasting air quality, firstly, each processor core of the terminal equipment can read all data of an area to be forecasted at one time in each forecasting period, wherein all data comprise meteorological data, pollutant emission data and the like; the different processor cores then extract the data corresponding to the current processor core from the read data. In this process, the input/output (IO) of the processor core to the data directly affects the efficiency of the air quality prediction mode system on the terminal device for air quality prediction. For example, the domestic air quality prediction mode system NAQPMS adopts a data reading mode that each core reads all meteorological and emission data at one time and then extracts the data which is responsible for the current core. The foreign air quality prediction mode systems CMAQ, WRF-Chem and the like generally adopt a data reading mode that a process group consisting of a single process or a small number of processes reads all weather and emission data and then distributes the data to other processes, or a bottom layer adopts libraries such as IO API and the like.
When the regional resolution to be forecasted is very high, a large amount of data such as meteorological data, pollutant emission data and the like can be generated, so that the data reading speed of the terminal equipment is very low, and the data processing efficiency in air quality forecasting is affected. For example, when the atmospheric area to be predicted is a chinese area, if the resolution is 3 km horizontal resolution in the country, the important area (jingjin, long triangle, bead triangle) is 1 km horizontal resolution, and the vertical layer number is 20, which is high resolution, the data reading speed of the terminal device is slow, so that the time for the terminal device to calculate the air quality parameter of one period is long.
Specifically, fig. 1 is a schematic diagram of reading meteorological data in a certain prior art, as shown in fig. 1, each core in a terminal device reads all meteorological data of an area to be predicted from a server, and then extracts data corresponding to a current processor core according to requirements, wherein the reading quantity of the data is large.
Fig. 2 is a schematic diagram showing the reading of contaminant discharge rate data in some prior art, as shown in fig. 2, in each data reading period, the terminal device reads the contaminant discharge rate data of the area to be predicted from the server. The pollutant discharge rate data in the server is typically pre-stored average pollutant discharge rate data for a period of time, i.e. the data read by the terminal device is actually the same during each period of time, and the data is read in a large number of repetitions.
Based on the technical problem, the invention concept of the application is as follows: how to provide a data processing method with high data reading speed and high air quality forecasting efficiency.
Specifically, weather block data corresponding to a three-dimensional atmospheric space grid of each processor core can be periodically acquired with a first preset time length as a period, wherein the three-dimensional atmospheric space grid is determined according to a region range and a region resolution of an atmospheric region to be predicted; periodically acquiring pollutant discharge rate data of an atmospheric region to be predicted by taking a first preset time period as a period, wherein the pollutant discharge rate data is average pollutant discharge rate data of a second preset time period stored in a memory of terminal equipment, and the second preset time period is longer than the first preset time period; for each period, determining air quality parameters of each area in the atmosphere area to be predicted in the period according to weather block data corresponding to the three-dimensional atmosphere space grid of each processor core and pollutant discharge rate data, and predicting the air quality of the atmosphere area to be predicted according to the air quality parameters. According to the method, when meteorological data are acquired in each data acquisition period, each processor core does not read the total meteorological data of the atmospheric region to be predicted at one time, but only acquires meteorological block data corresponding to the three-dimensional atmospheric space grid of the processor core. By the arrangement, the data reading speed of the terminal equipment can be improved, so that the data processing efficiency of the air quality parameters is improved. Furthermore, the terminal device only needs to read the pollutant discharge rate data once in each data acquisition period of the second preset duration, after the pollutant discharge rate data is acquired for the first time, the pollutant discharge rate data can be stored in the memory of the terminal device, and each data acquisition period can directly call the data from the memory of the terminal device without reading again. By the arrangement, the data reading speed of the terminal equipment is further improved, and therefore the data processing efficiency of the air quality parameters is improved.
The following describes the technical solutions of the present application and how the technical solutions of the present application solve the above technical problems in detail with specific embodiments. The following embodiments may be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments. Embodiments of the present application will be described below with reference to the accompanying drawings.
Fig. 3 is a system architecture diagram according to an embodiment of the present application, and as shown in fig. 3, 1 represents a terminal device, and 2 represents a server. The terminal device 1 may first determine a three-dimensional atmospheric space grid corresponding to each processor core according to the area range, the area resolution, and the number of processor cores of the atmospheric area to be predicted. Then, the terminal device 1 may periodically acquire weather block data corresponding to the three-dimensional atmospheric space grid of each processor core from the server 2 with the first preset duration as a period, and periodically call the average pollutant discharge rate data in the month from the terminal device memory of the terminal device with the first preset duration as a period, as pollutant discharge rate data of each period. Finally, the terminal device 1 may determine the air quality parameters of each area in the atmospheric area to be predicted in the period according to the weather block data and the pollutant discharge rate data corresponding to the three-dimensional atmospheric space grid of each processor core, and predict the air quality of the atmospheric area to be predicted according to the air quality parameters.
Example 1
Fig. 4 is a flowchart of a data processing method according to an embodiment of the present application, where an execution body is used as a terminal device to describe the data processing method. As shown in fig. 4, the data processing method may include the steps of:
s101: and taking the first preset time length as a period, periodically acquiring weather block data corresponding to a three-dimensional atmospheric space grid of each processor core, wherein the three-dimensional atmospheric space grid is determined according to the regional range and the regional resolution of the atmospheric region to be predicted.
In this embodiment, the step S101 is described in detail in the second embodiment for obtaining weather block data corresponding to the three-dimensional atmospheric space grid of each processor core.
Fig. 5 is a schematic diagram of reading meteorological data according to an embodiment of the present application, as shown in fig. 5, each core in the terminal device only reads meteorological block data corresponding to the three-dimensional atmospheric space grid processed by the core from the server, and does not read all meteorological data of the area any more, thereby reducing the data reading amount.
In this embodiment, the terminal device may periodically obtain data such as meteorological data and pollutant emission rate data corresponding to the to-be-predicted atmospheric region from the server, and calculate an air quality parameter of each period according to the obtained data, so as to predict the air quality of the to-be-predicted atmospheric region range according to the air quality parameter.
In this embodiment, the first preset duration may be flexibly set by those skilled in the art, for example, the first preset duration may be 1h or 0.5h, which is not limited herein. The area range of the atmospheric area to be predicted may be the distance in the east-west direction and the distance in the north-south direction of the area. The regional resolution can be flexibly set by those skilled in the art, for example, the regional resolution can be 1 km or 3 km, and no limitation is imposed herein.
S102: and periodically acquiring pollutant discharge rate data of the atmospheric region to be predicted by taking the first preset time period as a period, wherein the pollutant discharge rate data can be average pollutant discharge rate data of a second preset time period stored in a memory of the terminal equipment, and the second preset time period is longer than the first preset time period.
In this embodiment, the weather block data and the pollutant discharge rate data are obtained according to the same data acquisition period, that is, in a certain data acquisition period, the air quality parameter is calculated according to the weather block data and the pollutant discharge rate data obtained in the period.
In the prior art, the terminal device needs to read the pollutant discharge rate data from the server in each data acquisition period, but the pollutant discharge rate data is generally average pollutant discharge rate data of a period of time, that is, the data read by the terminal device is actually the same in each period of time. Therefore, in order to reduce the data reading frequency of the terminal equipment and avoid meaningless data reading, the terminal equipment can store the pollutant discharge rate data into the memory of the terminal equipment after the pollutant discharge rate data is acquired for the first time, and each data acquisition period can directly call the data from the memory of the terminal equipment, so that the data reading time is saved, and the data processing efficiency of the air quality parameters is improved.
In this embodiment, the second preset duration may be flexibly set by a person skilled in the art, for example, the second preset duration may be a natural month, that is, the pollutant emission rate data may be average pollutant emission rate data of the month stored in the memory of the terminal device.
Illustratively, the server (or disk) stores therein the monthly average pollutant discharge rate data, and during the first data acquisition period of the month, the terminal device may read the monthly average pollutant discharge rate data from the server as the pollutant discharge rate data, and store the monthly average pollutant discharge rate data in the memory of the terminal device, and each data acquisition period after the month may directly retrieve the monthly average pollutant discharge rate data from the memory of the terminal device without reading from the server. Under the condition that the mode effect is not affected, the pollutant discharge rate data which is averaged by months is used, the discharge rate data is read in the first hour (the first data acquisition period) of each month and is stored in the memory, and the data in the memory is directly used without taking time to read the data from the disk file or the server although the mode still uses the discharge rate data once per hour. In addition, the monthly variations in discharge rate may be reflected in the replenishment of the process performed in the program, with the increased processing time being less than the time per hour for reading the disk file.
Fig. 6 is a schematic diagram of reading pollutant discharge rate data according to an embodiment of the present application, as shown in fig. 6, the server stores average pollutant discharge rate data of a second preset duration, in a first data acquisition period (period 1) corresponding to the second preset duration, period 1 reads the average pollutant discharge rate data of the second preset duration from the server and stores the data in a memory of the terminal device, and in other periods corresponding to the second preset duration, each period only needs to retrieve data from the memory of the terminal device, without reading the data from the server, thereby reducing the data reading frequency.
S103: for each period, determining air quality parameters of each area in the atmosphere area to be predicted in the period according to weather block data corresponding to the three-dimensional atmosphere space grid of each processor core and pollutant discharge rate data, and predicting the air quality of the atmosphere area to be predicted according to the air quality parameters.
In this embodiment, the meteorological data and the pollutant emission rate data are data which are indispensable for calculating the air quality parameter, and of course, other data can be obtained to calculate the air quality parameter together on the basis of the meteorological data and the pollutant emission rate data.
In this embodiment, after the weather block data and the pollutant discharge rate data corresponding to the three-dimensional atmospheric space grid of each processor core are obtained, the air quality parameters of each region in the atmospheric region to be predicted in the period can be calculated according to the weather block data and the pollutant discharge rate data, and the air quality prediction is performed according to the air quality parameters. Specifically, the specific process of calculating the air quality parameter according to the weather block data corresponding to the three-dimensional atmospheric space grid of each processor core and the pollutant discharge rate data may refer to the prior art, and will not be described herein. For example, the terminal device may perform physical and chemical process calculations such as advection, diffusion, dry sedimentation, wet sedimentation, gas phase chemistry, liquid phase chemistry, aerosol chemistry, etc., based on the read-in meteorological data, pollutant emission rate data, and other necessary data (static topography data, etc.). Likewise, the specific process of predicting the air quality of the atmospheric region to be predicted according to the air quality parameter may refer to the prior art, and will not be described herein.
In this embodiment, the air quality parameter may be a parameter in air quality prediction in the prior art, for example, concentration of pollutants such as smoke, total suspended particulate matters, inhalable particulate matters (PM 10), fine particulate matters (PM 2.5), nitrogen dioxide, sulfur dioxide, carbon monoxide, ozone, volatile organic compounds, and the like; air quality index, diagnostic quantity, related analytical data, etc.
In one possible embodiment, the determining, in step S103, the air quality parameter of each of the atmospheric regions to be predicted in the period according to the weather block data corresponding to the three-dimensional atmospheric space grid of each processor core and the pollutant discharge rate data may include: and calculating meteorological block data and pollutant discharge rate data corresponding to the three-dimensional atmospheric space grid of each processor core according to preset processor core parameters and communication parameters to obtain air quality parameters of each region in the atmospheric region to be predicted in the period.
In this embodiment, in the prior art, when calculating the air quality parameter, the plurality of processor cores perform data processing in such a manner that the information transfer interface MPI operates in parallel. In the case of high resolution, the problem of jamming of the calculation program often occurs due to excessive data volume, thereby affecting the normal processing of the data. In order to avoid the problem that a program is easy to be blocked in a multi-core parallel running mode, the method and the device modify the core parameters and the communication parameters of the existing processor, and can process acquired data according to the preset core parameters and the communication parameters of the processor, so that a calculation program can be normally and efficiently carried out, and the data processing efficiency of air quality parameters is improved.
In one possible implementation, the processor core parameters may include: the node uses the proportion of the processor core number to the total processor core number of the node; the communication parameters may include: communication request time, communication response time, completion queue size in communication, a pair of work request queue sizes in communication.
In this embodiment, the ratio of the number of processor cores used by the node to the total number of processor cores of the node may be the ratio of the number of processor cores used by the current node to the total number of processor cores, and the specific ratio of the number of processor cores used by the node to the total number of processor cores of the node may be flexibly set by those skilled in the art according to the type and/or the number of grids of the processor, which is not limited in any way. In the case where the ratio of the number of processor cores used by the node to the total number of processor cores used by the node is determined, the number of processor cores used by the node to the total number of processor cores used by the node may be arbitrarily set. By taking a marine CPU (model: hygon C86 7185 32-core Processor) as an example, multiple experiments prove that when the ratio of the number of Processor cores used by a node to the total number of Processor cores of the node is 60/64, the program running effect is good, the calculation efficiency is high, and the multi-core running program using the MPI-IO library function cannot be blocked. In this case, the total processor core number may be 64, the node use processor core number may be 60, or the total processor core number may be 128, and the node use processor core number may be 120. Of course, the examples herein are merely illustrative, and the ratio of the number of processor cores used by the corresponding node to the total number of processor cores used by the node may be the same or different, and are not intended to be limiting.
In this embodiment, the communication request TIME (dapl_ucm_rep_time) and the communication response TIME (dapl_ucm_rtu_time) may control the information delivery interface communication synchronization latency; the completion queue SIZE in communication (dapl_cq_size) and the pair of work request queue SIZEs in communication (dapl_qp_size) may control the messaging interface communication data SIZE. The specific values of the communication request time and the communication response time are set to be the same, and the specific values of the completion queue size in communication and the pair of work request queue sizes in communication are also set to be the same. The specific values of the communication request time and the communication response time, as well as the specific values of the completion queue size in the communication and the pair of work request queue sizes in the communication, can be flexibly set by those skilled in the art according to the type of the processor and/or the number of grids, without any limitation. For example, specific values of the communication request time and the communication response time may be between 2000 milliseconds and 8000 milliseconds, and specific values of the completion queue size in communication and the pair of work request queue sizes in communication may be between 2000 and 10000. It should be noted that, the specific values of the completion queue size in the communication and the pair of work request queue sizes in the communication are not set too large, and do not exceed 10000.
By taking a marine CPU (model: hygon C86 7185 32-core Processor) as an example, multiple experiments prove that the communication request time and the communication response time are 4000 milliseconds respectively, and when the size of a completion queue in communication and the size of a pair of work request queues in communication are 10000 respectively, the program running effect is good, the calculation efficiency is high, and the multi-core running of the program using the MPI-IO library function cannot be blocked.
For example, a certain atmospheric region to be predicted is set to be a high resolution of two layers of nested regions, namely 3 km horizontal resolution (D1 region) of the whole country, 1 km horizontal resolution (D2 region) of a key region (Jing Ji, long triangle and Zhu triangle), and 20 layers of vertical layers. In this high resolution case, when the air quality parameter is calculated from the acquired data, the number of processor cores is generally large, for example, more than 1 ten thousand, in order to avoid the problem that the multi-core runs a program using the MPI-IO library function, where the program process is stuck, cannot run the next step, and is not reported wrong or broken, the ratio of the number of node use processor cores to the total number of node processor cores may be set to 60/64, the communication request TIME (dapl_ucm_rep_time) and the communication response TIME (dapl_ucm_rtu_time) may be set to 4000, the completion queue SIZE (dapl_cq_size) in communication and the pair of work request queue SIZEs (dapl_qp_size) in communication may be set to 10000, and under this parameter, the calculation program may run normally and efficiently.
In this embodiment, parameters such as a ratio of the number of processor cores used by the node to the total number of processor cores used by the node, a communication request time, a communication response time, a completion queue size in communication, a pair of work request queue sizes in communication, etc. are main parameters affecting data calculation efficiency in high resolution.
Through experiments, under the conditions of the acquisition mode of the weather block data in the step S101, the acquisition mode of the pollutant discharge rate data in the step S102, and the processing of the processor core parameters and the communication parameters in the step S103, the prediction of the air quality of the area to be predicted can be efficiently and smoothly completed for the 3 km horizontal resolution (D1 area) of the whole country, the 1 km horizontal resolution (D2 area) of the key area (jingjin, long triangle and bead triangle) and the same 72 mode hours are calculated by the calculation example of 20 layers of vertical layers. Specifically, the operation time of the calculation example of the method is about 36% of that of the prior art, the total acceleration ratio reaches more than 2.7, the data read-write acceleration ratio reaches 12.5, the data reading time is reduced from more than 70% to 16% in the whole operation process, and the data reading speed is obviously improved, so that the data processing efficiency in air quality prediction is improved.
In this embodiment, weather block data corresponding to a three-dimensional atmospheric space grid of each processor core may be periodically acquired with a first preset time period as a period, where the three-dimensional atmospheric space grid is determined according to a region range and a region resolution of an atmospheric region to be predicted; periodically acquiring pollutant discharge rate data of an atmospheric region to be predicted by taking a first preset time period as a period, wherein the pollutant discharge rate data is average pollutant discharge rate data of a second preset time period stored in a memory of terminal equipment, and the second preset time period is longer than the first preset time period; for each period, determining air quality parameters of each area in the atmosphere area to be predicted in the period according to weather block data corresponding to the three-dimensional atmosphere space grid of each processor core and pollutant discharge rate data, and predicting the air quality of the atmosphere area to be predicted according to the air quality parameters. According to the method, when meteorological data are acquired in each data acquisition period, each processor core does not read the total meteorological data of the atmospheric region to be predicted at one time, but only acquires meteorological block data corresponding to the three-dimensional atmospheric space grid of the processor core. By the arrangement, the data reading speed of the terminal equipment can be improved, so that the data processing efficiency of the air quality parameters is improved. Furthermore, the terminal device only needs to read the pollutant discharge rate data once in each data acquisition period of the second preset duration, after the pollutant discharge rate data is acquired for the first time, the pollutant discharge rate data can be stored in the memory of the terminal device, and each data acquisition period can directly call the data from the memory of the terminal device without reading again. By the arrangement, the data reading speed of the terminal equipment is further improved, and therefore the data processing efficiency of the air quality parameters is improved.
The following describes in detail the specific embodiment two the weather block data corresponding to the three-dimensional atmospheric space grid obtained in step S101 in the first embodiment.
Example two
Fig. 7 is a flowchart of a data processing method according to another embodiment of the present application, where an execution body is used as a terminal device to describe the data processing method. As shown in fig. 7, the data processing method may include the steps of:
s201: a three-dimensional atmospheric spatial grid corresponding to each processor core is determined.
In one possible implementation, the determining, in step S201, the three-dimensional air space grid corresponding to each processor core may include: determining a three-dimensional atmospheric space grid corresponding to the atmospheric region to be predicted and the number of the three-dimensional atmospheric space grids according to the region range and the region resolution of the atmospheric region to be predicted; and determining the three-dimensional atmospheric space grids corresponding to each processor core according to the number of the three-dimensional atmospheric space grids and the number of the processor cores.
Illustratively, if the area coverage of the atmospheric area to be predicted is: 3000 km in the east-west direction, 3000 km in the north-south direction and 3 km in the horizontal resolution, the example is simple, and only horizontal two-dimensional meshing is performed, so that the three-dimensional atmospheric space meshes are 3 km by 3 km, and the number of the three-dimensional atmospheric space meshes is 1000 by 1000. If the number of processor cores is 50×50, each processor core corresponds to 20×20 three-dimensional atmospheric space grids.
In this embodiment, the three-dimensional atmospheric space grid corresponding to each processor core may be simply and conveniently determined according to the area range and the area resolution of the atmospheric area to be predicted, the size of the three-dimensional atmospheric space grid, and the number of the three-dimensional atmospheric space grids, and then according to the number of the three-dimensional atmospheric space grids and the number of the processor cores.
S202: for each processor core, global weather data corresponding to the three-dimensional atmospheric spatial grid of the processor core is acquired.
In this embodiment, when determining the three-dimensional atmospheric space grid corresponding to each processor core, the total weather data corresponding to the corresponding three-dimensional atmospheric space grid may be obtained from all weather data of the server. For example, if a processor core corresponds to 20×20 three-dimensional atmospheric space grids, it need only acquire the total meteorological data corresponding to the 20×20 three-dimensional atmospheric space grids.
S203: and determining the position of the preset meteorological variable type in the total meteorological data.
In this embodiment, a text recognition algorithm may be used to identify the corresponding weather variable type in the total weather data and determine a specific location, e.g., a directory, an index, etc.
In one possible embodiment, the types of meteorological variables in step S203 may include one or several of the following: wind speed, 2 meters temperature, surface pressure, 10 meters wind speed, water-air mixing ratio, cloud-water mixing ratio, rainwater mixing ratio, soil temperature, relative soil humidity, sea ice marking, main soil classification, vegetation proportion, physical snow depth, accumulated rainfall, non-accumulated rainfall, ground down short wave flux, friction wind speed, reciprocal of the mollin-obhuff length, boundary layer height, cloud optical thickness of ice, cloud optical thickness of water, pressure, height, temperature, relative humidity, low cloud proportion, mid cloud proportion, high cloud proportion, 2 meters relative humidity.
In the present embodiment, the types of the weather variables are not limited to the above types, and those skilled in the art can flexibly adjust the types according to the actual situation, and are not limited in any way.
In this embodiment, the weather features can be comprehensively and accurately reflected by the data corresponding to the weather variable types, so as to facilitate the calculation of the air quality parameters.
S204: and sequentially extracting variable data corresponding to the positions of all the meteorological variable types in the total meteorological data according to a preset variable data extraction sequence.
In this embodiment, the variable data extraction sequence may be preset for each meteorological variable type, and the specific sequence may be flexibly set by a person skilled in the art, which is not limited in any way.
In one possible embodiment, after determining the position of the preset meteorological variable type in the total meteorological data in the step S203, the method may further include: judging whether a first meteorological variable type exists in preset meteorological variable types or not, wherein the first meteorological variable type does not exist in a corresponding position in total meteorological data.
If the first meteorological variable type exists, step S204 sequentially extracts variable data corresponding to the position of each meteorological variable type in the total meteorological data according to a preset variable data extraction sequence, which may include:
for the second meteorological variable types with corresponding positions in the total meteorological data, sequentially extracting variable data corresponding to the positions of the second meteorological variable types in the total meteorological data according to a preset variable data extraction sequence; and for the first meteorological variable type with no corresponding position in the total meteorological data, assigning the variable data corresponding to the first meteorological variable type to be 0.
In this embodiment, since the preset meteorological variable types may be preset by a person skilled in the art according to priori knowledge, the preset meteorological variable types are relatively comprehensive, and in fact, the total meteorological data corresponding to the three-dimensional atmospheric space grid of the processor core may not include all the meteorological variable types, and the corresponding data may not be extracted. Therefore, if the first meteorological variable type of the corresponding position does not exist in the total meteorological data, the corresponding variable data can be directly assigned to 0, and if the second meteorological variable type of the corresponding position exists in the total meteorological data, the corresponding data can be sequentially extracted according to the preset variable data extraction sequence. By the arrangement, each preset meteorological variable type can be ensured to have a corresponding value, so that the subsequent calculation of the air quality parameters is facilitated.
S205: and determining weather block data corresponding to the three-dimensional atmospheric space grid of the processor core according to each weather variable type and variable data corresponding to each weather variable type.
In this embodiment, according to each meteorological variable type and variable data corresponding to each meteorological variable type, meteorological block data corresponding to the three-dimensional atmospheric space grid of the processor core can be simply and accurately determined.
In this embodiment, a specific MPI-IO implementation method may be used, where only a single all weather data file is used, so that weather block data needed by each process in the air quality mode is directly read, and each process does not need to read a very large file first, then each process uses the respective block data, or a certain process reads a very large file first, and then sends the very large file to the block data needed by each process through MPI communication.
In this embodiment, when the weather block data corresponding to the three-dimensional atmospheric space grid of each processor core is acquired, the three-dimensional atmospheric space grid corresponding to each processor core may be determined first, and the total weather data corresponding to the three-dimensional atmospheric space grid may be acquired from the server. And then, sequentially extracting variable data corresponding to each meteorological variable type according to the preset meteorological variable type and the variable data extraction sequence. Finally, according to each meteorological variable type and variable data corresponding to each meteorological variable type, the meteorological block data corresponding to the three-dimensional atmospheric space grid of each processor core can be comprehensively and accurately determined. By presetting the type of the meteorological variable and the extraction sequence of the variable data, the accuracy and the efficiency of the variable data extraction can be improved, so that the accuracy and the efficiency of the acquisition of the meteorological block data are improved, and the air quality parameter can be calculated according to the meteorological block data corresponding to the three-dimensional atmospheric space grid of a processor core.
The data processing method of the present application is described in a specific embodiment below.
Example III
In a specific embodiment, a research unit wants to get an air quality forecast for a period of time after a certain area, and the specific data processing procedure is as follows:
in the first step, the terminal equipment determines a three-dimensional atmospheric space grid corresponding to each processor core according to the regional range, regional resolution and the number of the processor cores of the atmospheric region to be predicted.
And secondly, periodically acquiring weather block data corresponding to the three-dimensional atmospheric space grid of each processor core by the terminal equipment with 1h as a period.
And thirdly, periodically calling the average pollutant discharge rate data in the month from the memory of the terminal equipment by the terminal equipment with 1h as a period, and taking the average pollutant discharge rate data in the month as pollutant discharge rate data of each period.
And fourthly, the terminal equipment calculates weather block data corresponding to the three-dimensional atmospheric space grid of each processor core and pollutant discharge rate data according to the preset proportion of the number of processor cores used by the node to the total number of processor cores of the node, communication request time, communication response time, completion queue size in communication and a pair of work request queue sizes in communication, so as to obtain the air quality parameters of each area in the atmosphere area to be predicted in the period.
Fifthly, the terminal equipment predicts the air quality of the atmospheric region to be predicted according to the air quality parameters so as to obtain the air quality forecast of the atmospheric region to be predicted.
Fig. 8 is a schematic structural diagram of a terminal device according to an embodiment of the present application, as shown in fig. 8, where the terminal device includes: the receiving module 81 is configured to periodically obtain weather block data corresponding to a three-dimensional atmospheric space grid of each processor core with a first preset duration as a period, where the three-dimensional atmospheric space grid is determined according to a region range and a region resolution of an atmospheric region to be predicted; periodically acquiring pollutant discharge rate data by taking a first preset time period as a period, wherein the pollutant discharge rate data is average pollutant discharge rate data of a second preset time period stored in a memory of the terminal equipment, and the second preset time period is longer than the first preset time period; the processing module 82 is configured to determine, for each cycle, an air quality parameter of each of the atmospheric regions to be predicted in the cycle according to weather block data corresponding to the three-dimensional atmospheric space grid of each processor core and pollutant discharge rate data. In one embodiment, the description of the specific implementation function of the terminal device may refer to steps S101 to S103 in the first embodiment and steps S201 to S205 in the second embodiment, which are not described herein.
Fig. 9 is a schematic structural diagram of an electronic device according to an embodiment of the present application, as shown in fig. 9, the electronic device includes: a processor 101, and a memory 102 communicatively coupled to the processor 101; memory 102 stores computer-executable instructions; the processor 101 executes computer-executable instructions stored in the memory 102 to implement the steps of the data processing method in the method embodiments described above.
In the above electronic device, the memory 102 and the processor 101 are electrically connected directly or indirectly to realize transmission or interaction of data. For example, the elements may be electrically connected to each other via one or more communication buses or signal lines, such as through a bus connection. The memory 102 stores therein computer-executable instructions for implementing a data access control method, including at least one software functional module that may be stored in the memory 102 in the form of software or firmware, and the processor 101 executes the software programs and modules stored in the memory 102 to thereby perform various functional applications and data processing.
The Memory 102 may be, but is not limited to, random access Memory (Random Access Memory, RAM), read Only Memory (ROM), programmable Read Only Memory (PROM), erasable Read Only Memory (Erasable Programmable Read-Only Memory, EPROM), electrically erasable Read Only Memory (Electric Erasable Programmable Read-Only Memory, EEPROM), etc. The memory 102 is used for storing a program, and the processor 101 executes the program after receiving an execution instruction. Further, the software programs and modules within the memory 102 may also include an operating system, which may include various software components and/or drivers for managing system tasks (e.g., memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide an operating environment for other software components.
The processor 101 may be an integrated circuit chip with signal processing capabilities. The processor 101 may be a general-purpose processor, including a central processing unit (Central Processing Unit, abbreviated as CPU), a network processor (Network Processor, abbreviated as NP), and the like. The disclosed methods, steps, and logic blocks in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
An embodiment of the present application further provides a computer-readable storage medium, where computer-executable instructions are stored, where the computer-executable instructions, when executed by a processor, are configured to implement the steps of the method embodiments of the present application.
An embodiment of the present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of the method embodiments of the present application.
Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the application following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the application pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.
It is to be understood that the present application is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the application is limited only by the appended claims.
Claims (9)
1. A method of data processing, comprising:
periodically acquiring weather block data corresponding to a three-dimensional atmospheric space grid of each processor core by taking a first preset time length as a period, wherein the three-dimensional atmospheric space grid is determined according to the regional range and the regional resolution of an atmospheric region to be predicted;
periodically acquiring pollutant discharge rate data of the atmospheric region to be predicted from a second data acquisition period by taking a first preset time period as a period, wherein the pollutant discharge rate data is average pollutant discharge rate data of a second preset time period which is longer than the first preset time period and is extracted from a server and stored in a memory of terminal equipment in the first data acquisition period;
for each period, determining air quality parameters of each region in the to-be-predicted atmospheric region in the period according to weather block data corresponding to the three-dimensional atmospheric space grid of each processor core and pollutant discharge rate data, and predicting the air quality of the to-be-predicted atmospheric region according to the air quality parameters;
The acquiring weather block data corresponding to the three-dimensional atmospheric space grid of each processor core specifically comprises the following steps:
determining a three-dimensional atmospheric space grid corresponding to each processor core;
for each processor core, acquiring total meteorological data corresponding to a three-dimensional atmospheric space grid of the processor core;
determining the position of a preset meteorological variable type in the total meteorological data;
sequentially extracting variable data corresponding to the positions of all meteorological variable types in the total meteorological data according to a preset variable data extraction sequence;
and determining weather block data corresponding to the three-dimensional atmospheric space grid of the processor core according to each weather variable type and variable data corresponding to each weather variable type.
2. The method of claim 1, wherein the meteorological variable types include one or more of: 2 meters of temperature, surface pressure, 10 meters of wind speed, water-air mixing ratio, cloud-water mixing ratio, rainwater mixing ratio, soil temperature, relative soil humidity, sea ice marking, main soil classification, vegetation ratio, physical snow depth, accumulated rainfall, non-accumulated rainfall, ground down short wave flux, friction wind speed, reciprocal of the mollin-obhuff length, boundary layer height, ice cloud optical thickness, water cloud optical thickness, pressure, relative humidity, low cloud ratio, mid cloud ratio, high cloud ratio, 2 meters of relative humidity.
3. The method according to claim 1, wherein determining the three-dimensional atmospheric space grid corresponding to each processor core specifically comprises:
determining the three-dimensional atmospheric space grids corresponding to the atmospheric region to be predicted and the number of the three-dimensional atmospheric space grids according to the region range and the region resolution of the atmospheric region to be predicted;
and determining the three-dimensional atmospheric space grids corresponding to each processor core according to the number of the three-dimensional atmospheric space grids and the number of the processor cores.
4. The method of claim 1, further comprising, after said determining a location of a predetermined meteorological variable type in said aggregate meteorological data:
judging whether a first meteorological variable type exists in the preset meteorological variable types or not, wherein the first meteorological variable type does not exist in a corresponding position in the total meteorological data;
if yes, sequentially extracting variable data corresponding to the positions of all the meteorological variable types in the total meteorological data according to a preset variable data extraction sequence, wherein the variable data comprises the following specific steps:
for the second meteorological variable types with corresponding positions in the total meteorological data, sequentially extracting variable data corresponding to the positions of the second meteorological variable types in the total meteorological data according to a preset variable data extraction sequence;
And for the first meteorological variable type with no corresponding position in the total meteorological data, assigning the variable data corresponding to the first meteorological variable type as 0.
5. The method according to any one of claims 1-4, wherein determining the air quality parameter of each of the to-be-predicted atmospheric regions in the period according to meteorological block data corresponding to the three-dimensional atmospheric space grid of each processor core and pollutant discharge rate data specifically comprises:
and calculating meteorological block data and pollutant discharge rate data corresponding to the three-dimensional atmospheric space grid of each processor core according to preset processor core parameters and communication parameters to obtain air quality parameters of each region in the atmospheric region to be predicted in the period.
6. The method of claim 5, wherein the processor core parameters comprise: the node uses the proportion of the processor core number to the total processor core number of the node;
the communication parameters include: communication request time, communication response time, completion queue size in communication, a pair of work request queue sizes in communication.
7. A terminal device, comprising:
the receiving module is used for periodically acquiring weather block data corresponding to a three-dimensional atmospheric space grid of each processor core by taking a first preset duration as a period, wherein the three-dimensional atmospheric space grid is determined according to the regional range and the regional resolution of an atmospheric region to be predicted; periodically acquiring pollutant discharge rate data of the atmospheric region to be predicted from a second data acquisition period by taking a first preset time period as a period, wherein the pollutant discharge rate data is average pollutant discharge rate data of a second preset time period which is longer than the first preset time period and is extracted from a server and stored in a memory of terminal equipment in the first data acquisition period;
the processing module is used for determining air quality parameters of each region in the atmospheric region to be predicted in the period according to weather block data corresponding to the three-dimensional atmospheric space grid of each processor core and pollutant discharge rate data, and predicting the air quality of the atmospheric region to be predicted according to the air quality parameters;
The receiving module is specifically configured to determine a three-dimensional atmospheric space grid corresponding to each processor core when acquiring weather block data corresponding to the three-dimensional atmospheric space grid of each processor core; for each processor core, acquiring total meteorological data corresponding to a three-dimensional atmospheric space grid of the processor core; determining the position of a preset meteorological variable type in the total meteorological data; sequentially extracting variable data corresponding to the positions of all meteorological variable types in the total meteorological data according to a preset variable data extraction sequence; and determining weather block data corresponding to the three-dimensional atmospheric space grid of the processor core according to each weather variable type and variable data corresponding to each weather variable type.
8. An electronic device comprising a processor and a memory communicatively coupled to the processor;
the memory stores computer-executable instructions;
the processor executes computer-executable instructions stored in the memory to implement the method of any one of claims 1 to 6.
9. A computer readable storage medium having stored therein computer executable instructions which when executed by a processor are adapted to carry out the method of any one of claims 1 to 6.
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