CN116930909A - Air quality forecasting system and method based on weather radar dataset - Google Patents
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Abstract
The application discloses an air quality forecasting system and method based on a weather radar dataset, which relate to the technical field of air quality forecasting and comprise the following steps: acquiring relevant parameters of electromagnetic wave signals emitted by a radar; for electromagnetic wave signalError analysis is carried out to obtain an electromagnetic wave comprehensive stability coefficient Dc; establishing an air quality prediction model; obtaining a first air quality prediction valueAnd the actual air quality valuePerforming error analysis to obtain a predicted discrete degree value YLx, and judging whether the second air quality prediction model can be output and applied; applying the second air quality prediction model subjected to error analysis to obtain a second air quality prediction valueFuture air quality conditions are assessed. The application can realize the dynamic supervision of pollution sources by evaluating the future air quality condition and sending out relevant early warning information in time.
Description
Technical Field
The application relates to the technical field of air quality forecasting, in particular to an air quality forecasting system and method based on a weather radar dataset.
Background
The traditional air quality prediction is mainly based on an empirical formula and a statistical model, and can only roughly predict the air quality, but factors such as a complex atmospheric chemical reaction process, a pollutant diffusion rule and the like are not considered enough, the prediction accuracy is very limited, and the pollution source and the pollution diffusion condition cannot be predicted.
In recent years, also scholars have conducted detection studies on atmospheric particulate matters (such as PM 2.5) using radar technology. The method is mainly based on a laser radar technology, and the concentration and distribution conditions of the particulate matters are detected by emitting laser beams into the atmosphere and receiving scattered echo signals, so that compared with the traditional monitoring method, the method has the advantages of no need of sampling, good real-time performance, wide coverage range and the like.
The method is based on a weather radar data set, and the movement direction of the pollutant particles can be calculated by introducing a single-station radar Doppler speed calculation method into a weather quality prediction model, so that the pollutant diffusion condition in the atmosphere is monitored, and the method provides powerful support for environmental protection departments and personnel to carry out environmental monitoring and early warning.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects of the prior art, the application provides an air quality forecasting system and method based on a weather radar data set.
(II) technical scheme
In order to achieve the above purpose, the application is realized by the following technical scheme: an air quality forecasting system and method based on a weather radar dataset comprises the following steps:
acquiring related parameters of electromagnetic wave signals emitted by the radar in the ideal condition during working in a technical specification, and acquiring actual emitted electromagnetic wave data of the weather radar through a spectrum analyzer and corresponding antenna detection;
the amplitude A and the frequency F of the electromagnetic wave signal emitted by the weather radar during the working process under ideal condition and the amplitude of the electromagnetic wave signal actually emitted in each T timeFrequency->Performing error analysis to obtain an electromagnetic wave amplitude stability index Zf and an electromagnetic wave frequency stability index Ps, and further correlating the electromagnetic wave amplitude stability index Zf and the electromagnetic wave frequency stability index Ps to form an electromagnetic wave comprehensive stability coefficient Dc;
introducing a single-station radar Doppler velocity calculation method into a machine learning model, establishing a first air quality prediction model, introducing a weather radar data set and an electromagnetic wave comprehensive stability coefficient Dc as sample data into the first air quality prediction model, and obtaining a second air quality prediction model after sample data training and testing;
importing the electromagnetic wave parameter database into a second air quality prediction model to obtain a first air quality prediction valueAnd pre-heating the first air massMeasurement of->And the actual air quality value->Performing error analysis to obtain a predicted discrete degree value YLx, and judging whether the second air quality prediction model can be output and applied according to the relation between the predicted discrete degree value YLx of the second air quality prediction model and a preset threshold value;
applying the second air quality prediction model subjected to error analysis to obtain a second air quality prediction valueAnd according to the second air quality prediction value +.>And evaluating future air quality conditions according to the relation between the preset first threshold value and the preset second threshold value, and selecting a corresponding early warning strategy.
Further, the antenna is pointed to the transmitting direction of the weather radar, and the spectrum analyzer is started to start detection, so that the electromagnetic wave amplitudes of different time periods in the T time are obtainedAnd frequency->An electromagnetic wave parameter database is constructed, i represents the numbers of electromagnetic wave amplitudes and frequencies of different periods in T time, i=1, 2, 3, 4, … … and n, and n is a positive integer.
Further, the amplitude A and the frequency F of the electromagnetic wave signal emitted by the weather radar during operation under ideal conditions and the amplitude of the electromagnetic wave signal actually emitted in each T time period are calculatedFrequency->Performing error analysis to obtain electromagnetic waveAmplitude stability index Zf and electromagnetic wave frequency stability index Ps:
；
the corresponding electromagnetic wave amplitude stability index Zf and electromagnetic wave frequency stability index Ps are calculated as above.
Further, an electromagnetic wave amplitude stability index Zf and an electromagnetic wave frequency stability index Ps are obtained, after dimensionless treatment,
the electromagnetic wave comprehensive stability coefficient Dc is formed in a correlation manner, and the electromagnetic wave comprehensive stability coefficient Dc is obtained as follows:
；
wherein ,and->For the weights, the settings are adjusted by the user.
Further, a single-station radar Doppler speed calculation method is led into a machine learning model, and a first air quality prediction model is built, specifically:
the electromagnetic wave signal emitted by the weather radar is recorded asThe echo signal is marked as-> , wherein />For the transmission frequency +.>For the initial phase +.>For the delay of the echo signal relative to the transmit signal, +.>C is the speed of light, which is the distance between the target and the radar;
if the target is disabled, thenIs constant and let->With a fixed phase difference between the echo and the transmitted signal，/>Is wavelength;
if there is relative motion between the target and the weather radar, the distanceThe radial movement speed of the target relative to the radar is set as +.>Then->t, echo delay->Further, the phase difference between the echo and the transmission signal is +.>The corresponding frequency difference is doppler frequency:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein: if the doppler frequency is positive, the target flies toward the radar, and if the doppler frequency is negative, the target flies against the radar.
Further, the air quality monitoring station is used for obtaining a corresponding first air quality predicted valueActual air quality value of predicted time +.>And the first air quality predictive value +.>And the actual air quality value->And performing error analysis, and obtaining a predicted discrete degree value YLx of the second air quality prediction model after calculation and analysis, wherein the calculation formula is as follows:
；
wherein t represents the sequence number corresponding to the first air quality predicted value and the actual air quality value, and t=1, 2, 3, 4, … …, n is a positive integer.
Further, according to the relation between the predicted discrete degree value YLx of the second air quality prediction model and the preset threshold value, judging whether the second air quality prediction model can be output and applied, if soAnd outputting and applying the second air quality prediction model by a preset threshold value. If the predicted discrete degree value +.>And (3) a preset threshold value, training and testing the second air quality prediction model again until the predicted discrete degree value YLx is smaller than the preset threshold value.
Further, according to the second air quality prediction valueAnd evaluating future air quality conditions according to the relation between the preset first threshold and the preset second threshold, and selecting a corresponding early warning strategy, wherein the method comprises the following steps of:
when (when)When the second threshold value is smaller than the first threshold value, the future air quality is in a normal state, correspondingly, no early warning signal is sent outwards, and no measures are needed.
When the second threshold valueWhen the first threshold value indicates that the future air quality is in a secondary pollution state, correspondingly, a secondary early warning signal is sent outwards, the outdoor activity time can be reduced as much as possible by adopting a recommended sensitive crowd, and the pollution sources such as industrial enterprises, transportation and the like are subjected to enhanced supervision and management, and emission is limited.
When the second threshold value is less than the first threshold valueWhen the air quality is in the first-level pollution state in the future, correspondingly, a first-level early warning signal is sent outwards, all people can be recommended to avoid outdoor activities, production or running of pollution sources such as industrial enterprises, transportation and the like should be stopped immediately, and emergency measures are taken to treat the pollution sources.
An air quality prediction system based on a weather radar dataset, comprising:
the data acquisition module acquires technical specifications of the weather radar, acquires relevant parameters of electromagnetic wave signals emitted by the radar during working under ideal conditions in the technical specifications, prepares a spectrum analyzer and a corresponding antenna, and detects and acquires actual emitted electromagnetic wave data of the weather radar.
The electromagnetic wave analysis module is used for analyzing the amplitude A and the frequency F of the electromagnetic wave signal emitted by the weather radar in the ideal condition and the amplitude of each T time of the actually emitted electromagnetic wave signalFrequency->And performing error analysis to obtain an electromagnetic wave amplitude stability index Zf and an electromagnetic wave frequency stability index Ps, and further correlating the electromagnetic wave amplitude stability index Zf and the electromagnetic wave frequency stability index Ps to form an electromagnetic wave comprehensive stability coefficient Dc.
The prediction model construction module is used for guiding a single-station radar Doppler speed calculation method into the machine learning model, establishing a first air quality prediction model, guiding a weather radar data set and an electromagnetic wave comprehensive stability coefficient Dc as sample data into the first air quality prediction model, and obtaining a second air quality prediction model after sample data training and testing.
The prediction error analysis module is used for importing the electromagnetic wave parameter database into a second air quality prediction model to obtain a first air quality prediction valueAnd the first air quality predictive value +.>And the actual air quality value->And performing error analysis to obtain a predicted discrete degree value YLx, and judging whether the second air quality prediction model can be output and applied according to the relation between the predicted discrete degree value YLx of the second air quality prediction model and a preset threshold value.
The safety early warning module is used for applying a second air quality prediction model subjected to error analysis to obtain a second air quality prediction valueAnd according to the second air quality prediction value +.>Evaluating future air quality conditions according to the relation between the preset first threshold value and the preset second threshold valueAnd estimating, namely selecting different early warning strategies.
(III) beneficial effects
The application provides an air quality forecasting system and method based on a weather radar dataset, and the air quality forecasting system and method have the following beneficial effects:
1. in practical application, due to the influence of various factors, electromagnetic wave signals actually transmitted by the weather radar may deviate from electromagnetic wave signals transmitted under ideal conditions to a certain extent, the sources and the magnitudes of the deviations can be known through error analysis, further, system errors are determined, corresponding measures are taken for correction, problems in radar system design can be found, and improvement suggestions are provided. For example, when considering factors such as antenna gain, radiation direction, etc. in antenna design, performance thereof needs to be evaluated and optimized.
2. By combining the first air quality prediction valueAnd the actual air quality value->And (3) carrying out error analysis, evaluating the accuracy of the model, if the error is smaller, indicating that the model has higher prediction capability, and if the error is larger, further optimizing the model and pertinently adjusting model parameters. For example, the weight of the filter may need to be adjusted when considering different contaminant types, different weather conditions, and the like.
3. By applying the second air quality prediction model subjected to error analysis, future air quality conditions are evaluated, related early warning information is timely sent out, dynamic supervision of pollution sources can be achieved, and measures can be timely taken to limit or treat when some pollution sources are found to possibly influence the air quality. Moreover, the possible heavy pollution weather can be predicted in advance, so that emergency response capability is enhanced, for example, corresponding measures are taken before the arrival of the heavy pollution weather, pollutant emission can be reduced, the air pollution degree can be reduced, the public can be reminded of paying attention to the air quality condition, the public is guided to take corresponding protective measures, the exposure to pollutants is reduced, and the health is protected.
Drawings
FIG. 1 is a schematic flow chart of an air quality forecasting method based on a weather radar dataset;
FIG. 2 is a schematic diagram of an air quality prediction system based on a weather radar dataset according to the present application;
in the figure; 10. a data acquisition module; 20. an electromagnetic wave analysis module; 30. a prediction model construction module; 40. a prediction error analysis module; 50. and a safety early warning module.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
Referring to fig. 1, the application provides an air quality forecasting method based on a weather radar dataset, comprising the following steps:
step one, acquiring a technical specification of a weather radar, acquiring relevant parameters of electromagnetic wave signals emitted by the radar during working under ideal conditions in the technical specification, preparing a spectrum analyzer and a corresponding antenna, and detecting and acquiring actual emitted electromagnetic wave data of the weather radar.
Step 101, acquiring a technical specification of the weather radar, and acquiring related parameters of electromagnetic wave signals emitted by the radar during working under ideal conditions in the technical specification, wherein the related parameters comprise amplitude A and frequency F.
Step 102, preparing a spectrum analyzer and a corresponding antenna, placing the spectrum analyzer and the corresponding antenna at a position suitable for detecting the actual electromagnetic wave data emitted by the weather radar, and setting the relevant parameters of the spectrum analyzer according to the relevant parameters of the electromagnetic wave signals recorded in the technical specification. Including scan range, center frequency, and bandwidth.
The spectrum analyzer is a device specially used for analyzing the spectrum characteristics of signals, can decompose complex signals into different frequency components, and can display the different frequency components on a screen, and can obtain attribute information such as the frequency, the bandwidth and the like of electromagnetic waves by analyzing the frequency components.
Step 103, directing an antenna to the transmitting direction of the weather radar, starting a spectrum analyzer to start detection, and obtaining electromagnetic wave amplitudes of different time periods in T timeAnd frequency->An electromagnetic wave parameter database is constructed, i represents the numbers of electromagnetic wave amplitudes and frequencies of different periods in T time, i=1, 2, 3, 4, … … and n, and n is a positive integer.
In use, the contents of steps 101 to 103 are combined:
by collecting electromagnetic wave data of the weather radar in real time, possible problems or abnormal conditions in the weather radar system can be found, and the problems or abnormal conditions can be timely adjusted and optimized, so that the normal operation of the weather radar system is ensured, reference and basis are provided for later maintenance of the weather radar and improvement of the weather radar technology, and important information support is provided.
Step two, the amplitude A and the frequency F of the electromagnetic wave signal emitted by the weather radar in the ideal condition during working and the amplitude of the electromagnetic wave signal actually emitted in each T time are calculatedFrequency->And performing error analysis to obtain an electromagnetic wave amplitude stability index Zf and an electromagnetic wave frequency stability index Ps, and further correlating the electromagnetic wave amplitude stability index Zf and the electromagnetic wave frequency stability index Ps to form an electromagnetic wave comprehensive stability coefficient Dc.
Step 201, the amplitude A of the electromagnetic wave signal emitted by the weather radar in ideal condition and the actual electromagnetic wave signal emitted by the weather radar are each within T timeAmplitude of (2)Performing error analysis to obtain an electromagnetic wave amplitude stability index Zf:
；
the calculation formula of the corresponding electromagnetic wave amplitude stability index Zf is as above.
Step 202, the frequency F of the electromagnetic wave signal emitted by the weather radar under ideal condition and the frequency of the electromagnetic wave signal actually emitted in each T timePerforming error analysis to obtain an electromagnetic wave frequency stability index Ps:
；
the calculation formula of the corresponding electromagnetic wave frequency stability index Ps is as above.
Step 203, obtaining an electromagnetic wave amplitude stability index Zf and an electromagnetic wave frequency stability index Ps, and after dimensionless processing, correlating to form an electromagnetic wave comprehensive stability coefficient Dc, wherein the electromagnetic wave comprehensive stability coefficient Dc is obtained by the following manner:
wherein ,and->For the weights, the settings are adjusted by the user.
In use, the contents of steps 201 to 203 are combined:
in practical application, due to the influence of various factors, electromagnetic wave signals actually transmitted by the weather radar may deviate from electromagnetic wave signals transmitted under ideal conditions to a certain extent, the sources and the magnitudes of the deviations can be known through error analysis, further, system errors are determined, corresponding measures are taken for correction, problems in radar system design can be found, and improvement suggestions are provided. For example, when considering factors such as antenna gain, radiation direction, etc. in antenna design, performance thereof needs to be evaluated and optimized.
Step three, a single-station radar Doppler speed calculation method is led into a machine learning model, a first air quality prediction model is established, a weather radar data set and an electromagnetic wave comprehensive stability coefficient Dc are used as sample data to be led into the first air quality prediction model, and a second air quality prediction model is obtained after sample data training and testing.
Step 301, importing a single-station radar Doppler speed calculation method into a machine learning model, and establishing a first air quality prediction model, wherein the method specifically comprises the following steps:
the electromagnetic wave signal emitted by the weather radar is recorded asThe echo signal is marked as-> , wherein />For the transmission frequency +.>For the initial phase +.>For the delay of the echo signal relative to the transmit signal, +.>And c is the speed of light, which is the distance between the target and the radar.
If the target is disabled, thenIs constant and let->With a fixed phase difference between the echo and the transmitted signal，/>Is the wavelength.
If there is relative motion between the target and the weather radar, the distanceThe radial movement speed of the target relative to the radar is set as +.>Then->Echo delay->Further, the phase difference between the echo and the transmission signal is +.>The corresponding frequency difference is doppler frequency:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein: if the doppler frequency is positive, the target flies toward the radar, and if the doppler frequency is negative, the target flies against the radar.
The movement direction of the pollutant particles can be calculated through a single-station radar Doppler velocity calculation method, the pollutant diffusion condition in the atmosphere can be monitored, and the method provides powerful support for environmental protection departments and personnel to carry out environment monitoring and early warning, for example, in heavy pollution weather, the pollutant diffusion condition can be monitored in real time, and corresponding measures can be taken in time to reduce pollution.
Step 302, a weather radar data set and an electromagnetic wave comprehensive stability coefficient Dc are used as sample data to be imported into a first air quality prediction model, and a second air quality prediction model is obtained after sample data training and testing.
In use, the contents of steps 301 and 302 are combined:
by introducing a single-station radar Doppler velocity calculation method into a machine learning model, an air quality prediction model is established, dynamic supervision of pollution sources can be realized, and measures can be taken in time to limit or treat when some pollution sources are found to possibly influence the air quality.
Step four, importing an electromagnetic wave parameter database into a second air quality prediction model to obtain a first air quality prediction valueAnd the first air quality predictive value +.>And the actual air quality value->And performing error analysis to obtain a predicted discrete degree value YLx, and judging whether the second air quality prediction model can be output and applied according to the relation between the predicted discrete degree value YLx of the second air quality prediction model and a preset threshold value.
Step 401, acquiring an electromagnetic wave parameter database, and transmitting the amplitude of an electromagnetic wave signal in real time within each T timeFrequency->Introducing a second air quality prediction model to predict the current air quality change to obtain a first air quality prediction value +.>。
Step 402, obtaining a corresponding first air quality predicted value through an air quality monitoring stationActual air quality value of predicted time +.>And the first air quality predictive value +.>And the actual air quality value->And performing error analysis, and obtaining a predicted discrete degree value YLx of the second air quality prediction model after calculation and analysis, wherein the calculation formula is as follows:
；
wherein t represents the sequence number corresponding to the first air quality predicted value and the actual air quality value, and t=1, 2, 3, 4, … …, n is a positive integer.
Step 403, judging whether the second air quality prediction model can be output and applied according to the relation between the predicted discrete degree value YLx of the second air quality prediction model and the preset threshold value, if soAnd outputting and applying the second air quality prediction model by a preset threshold value. If the predicted discrete degree value +.>And (3) a preset threshold value, training and testing the second air quality prediction model again until the predicted discrete degree value YLx is smaller than the preset threshold value.
In use, the contents of steps 401 to 403 are combined:
by combining the first air quality prediction valueAnd the actual air quality value->And (3) carrying out error analysis, evaluating the accuracy of the model, if the error is smaller, indicating that the model has higher prediction capability, and if the error is larger, further optimizing the model and pertinently adjusting model parameters. For example, the weight of the filter may need to be adjusted when considering different contaminant types, different weather conditions, and the like.
Step five, applying a second air quality prediction model subjected to error analysis to obtain a second air quality prediction valueAnd according to the second air quality prediction value +.>And evaluating future air quality conditions according to the relation between the preset first threshold value and the preset second threshold value, and selecting a corresponding early warning strategy.
Step 501, applying a second air quality prediction model subjected to error analysis, and importing the amplitude of electromagnetic wave signals emitted in real time in each T timeFrequency->Obtaining a second air quality prediction value +.>。
Wherein t represents the sequence number corresponding to the second air quality predicted value, t=1, 2, 3, 4, … …, n is a positive integer.
Step 502, according to the second air quality prediction valueWith a preset first threshold value and a second threshold value, for future airThe quality condition is evaluated, and a corresponding early warning strategy is selected, specifically as follows:
when (when)When the second threshold value is smaller than the first threshold value, the future air quality is in a normal state, correspondingly, no early warning signal is sent outwards, and no measures are needed.
When the second threshold valueWhen the first threshold value indicates that the future air quality is in a secondary pollution state, correspondingly, a secondary early warning signal is sent outwards, the outdoor activity time can be reduced as much as possible by adopting a recommended sensitive crowd, and the pollution sources such as industrial enterprises, transportation and the like are subjected to enhanced supervision and management, and emission is limited.
When the second threshold value is less than the first threshold valueWhen the air quality is in the first-level pollution state in the future, correspondingly, a first-level early warning signal is sent outwards, all people can be recommended to avoid outdoor activities, production or running of pollution sources such as industrial enterprises, transportation and the like should be stopped immediately, and emergency measures are taken to treat the pollution sources.
In use, the contents of steps 501 and 502 are combined:
by applying the second air quality prediction model subjected to error analysis, future air quality conditions are evaluated, related early warning information is timely sent out, dynamic supervision of pollution sources can be achieved, and measures can be timely taken to limit or treat when some pollution sources are found to possibly influence the air quality. Moreover, the possible heavy pollution weather can be predicted in advance, so that emergency response capability is enhanced, for example, corresponding measures are taken before the arrival of the heavy pollution weather, pollutant emission can be reduced, the air pollution degree can be reduced, the public can be reminded of paying attention to the air quality condition, the public is guided to take corresponding protective measures, the exposure to pollutants is reduced, and the health is protected.
Referring to fig. 2, the present application provides an air quality prediction system based on a weather radar dataset, comprising:
the data acquisition module 10 acquires technical specifications of the weather radar, acquires relevant parameters of electromagnetic wave signals emitted by the radar during operation under ideal conditions in the technical specifications, prepares a spectrum analyzer and a corresponding antenna, and detects and acquires actual emitted electromagnetic wave data of the weather radar.
The electromagnetic wave analysis module 20 is used for analyzing the amplitude A and the frequency F of the electromagnetic wave signal emitted by the weather radar in ideal condition and the amplitude of the actually emitted electromagnetic wave signal in each T timeFrequency->And performing error analysis to obtain an electromagnetic wave amplitude stability index Zf and an electromagnetic wave frequency stability index Ps, and further correlating the electromagnetic wave amplitude stability index Zf and the electromagnetic wave frequency stability index Ps to form an electromagnetic wave comprehensive stability coefficient Dc.
The prediction model construction module 30 introduces a single-station radar doppler velocity calculation method into the machine learning model, establishes a first air quality prediction model, introduces a weather radar data set and an electromagnetic wave integrated stability coefficient Dc as sample data into the first air quality prediction model, and obtains a second air quality prediction model after sample data training and testing.
The prediction error analysis module 40 imports the electromagnetic wave parameter database into the second air quality prediction model to obtain the first air quality prediction valueAnd the first air quality predictive value +.>And the actual air quality value->Error analysis is performedAnd obtaining a predicted discrete degree value YLx, and judging whether the second air quality prediction model can be output and applied according to the relation between the predicted discrete degree value YLx of the second air quality prediction model and a preset threshold value.
The safety pre-warning module 50 applies the second air quality prediction model subjected to the error analysis to obtain a second air quality prediction valueAnd according to the second air quality prediction value +.>And evaluating future air quality conditions according to the relation between the preset first threshold value and the preset second threshold value, and selecting a corresponding early warning strategy.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided by the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and there may be additional divisions of actual implementations, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims. Finally: the foregoing description of the preferred embodiments of the application is not intended to limit the application, but to enable any modification, equivalent or improvement to be made without departing from the spirit and principles of the application.
Claims (9)
1. An air quality forecasting method based on a weather radar dataset is characterized in that: the method comprises the following steps:
acquiring related parameters of electromagnetic wave signals emitted by the radar in the ideal condition during working in a technical specification, and acquiring actual emitted electromagnetic wave data of the weather radar through a spectrum analyzer and corresponding antenna detection;
the amplitude A and the frequency F of the electromagnetic wave signal emitted by the weather radar during the working process under ideal condition and the amplitude of the electromagnetic wave signal actually emitted in each T timeFrequency->Performing error analysis to obtain an electromagnetic wave amplitude stability index Zf and an electromagnetic wave frequency stability index Ps, and further correlating the electromagnetic wave amplitude stability index Zf and the electromagnetic wave frequency stability index Ps to form an electromagnetic wave comprehensive stability coefficient Dc;
introducing a single-station radar Doppler velocity calculation method into a machine learning model, establishing a first air quality prediction model, introducing a weather radar data set and an electromagnetic wave comprehensive stability coefficient Dc as sample data into the first air quality prediction model, and obtaining a second air quality prediction model after sample data training and testing;
importing the electromagnetic wave parameter database into a second air quality prediction model to obtain a first air quality prediction valueAnd the first air quality predictive value +.>And the actual air quality value->Performing error analysis to obtain a predicted discrete degree value YLx, and judging whether the second air quality prediction model can be output and applied according to the relation between the predicted discrete degree value YLx of the second air quality prediction model and a preset threshold value;
applying the second air quality prediction model subjected to error analysis to obtain a second air quality prediction valueAnd according to the second air quality prediction value +.>And evaluating future air quality conditions according to the relation between the preset first threshold value and the preset second threshold value, and selecting a corresponding early warning strategy.
2. An air quality forecasting method based on a weather radar dataset as claimed in claim 1, wherein: the antenna is pointed to the transmitting direction of the weather radar, and the spectrum analyzer is started to start detection, so that electromagnetic wave amplitudes of different periods in T time are obtainedAnd frequency->An electromagnetic wave parameter database is constructed, i represents the numbers of electromagnetic wave amplitudes and frequencies of different periods in T time, i=1, 2, 3, 4, … … and n, and n is a positive integer.
3. An air quality forecasting method based on a weather radar dataset as claimed in claim 2, wherein: the amplitude A and the frequency F of the electromagnetic wave signal emitted by the weather radar during the working process under ideal condition and the amplitude of the electromagnetic wave signal actually emitted in each T timeFrequency->Performing error analysis to obtain an electromagnetic wave amplitude stability index Zf and an electromagnetic wave frequency stability index Ps:
;
the corresponding electromagnetic wave amplitude stability index Zf and electromagnetic wave frequency stability index Ps are calculated as above.
4. A method of air quality prediction based on a weather radar dataset as claimed in claim 3, wherein: the electromagnetic wave amplitude stability index Zf and the electromagnetic wave frequency stability index Ps are obtained, after dimensionless treatment, the electromagnetic wave comprehensive stability coefficient Dc is formed in a correlation manner, and the electromagnetic wave comprehensive stability coefficient Dc is obtained in the following manner:
;
wherein ,and->For the weights, the settings are adjusted by the user.
5. An air quality forecasting method based on a weather radar dataset as claimed in claim 1, wherein: the single-station radar Doppler speed calculation method is led into a machine learning model, and a first air quality prediction model is established, specifically:
the electromagnetic wave signal emitted by the weather radar is recorded asThe echo signal is marked as-> , wherein />For the transmission frequency +.>For the initial phase +.>For echo signals relative to transmitted signalsDelay of number->C is the speed of light, which is the distance between the target and the radar;
if the target is disabled, thenIs constant and let->With a fixed phase difference between the echo and the transmitted signal，/>Is wavelength;
if there is relative motion between the target and the weather radar, the distanceThe radial movement speed of the target relative to the radar is set as +.>Then->t, echo delay->Further, the phase difference between the echo and the transmission signal is +.>The corresponding frequency difference is doppler frequency:
；
wherein: if the doppler frequency is positive, the target flies toward the radar, and if the doppler frequency is negative, the target flies against the radar.
6. An air quality forecasting method based on a weather radar dataset as claimed in claim 1, wherein: acquiring a corresponding first air quality predicted value through an air quality monitoring stationActual air quality value of predicted time +.>And the first air quality predictive value +.>And the actual air quality value->And performing error analysis, and obtaining a predicted discrete degree value YLx of the second air quality prediction model after calculation and analysis, wherein the calculation formula is as follows:
;
wherein t represents the sequence number corresponding to the first air quality predicted value and the actual air quality value, and t=1, 2, 3, 4, … …, n is a positive integer.
7. The air quality forecasting method based on the weather radar dataset of claim 6, wherein: judging whether the second air quality prediction model can be output and applied according to the relation between the predicted discrete degree value YLx of the second air quality prediction model and a preset threshold value, if soOutputting and applying a second air quality prediction model if a threshold value is preset; if it isPredictive discrete degree value +.>And (3) a preset threshold value, training and testing the second air quality prediction model again until the predicted discrete degree value YLx is smaller than the preset threshold value.
8. An air quality forecasting method based on a weather radar dataset as claimed in claim 1, wherein: according to the second air quality predictive valueAnd evaluating future air quality conditions according to the relation between the preset first threshold and the preset second threshold, and selecting a corresponding early warning strategy, wherein the method comprises the following steps of:
when (when)When the second threshold value is smaller than the first threshold value, the future air quality is in a conventional state, and correspondingly, no early warning signal is sent out;
when the second threshold valueWhen the first threshold value is reached, the future air quality is in a secondary pollution state, and correspondingly, a secondary early warning signal is sent outwards;
when the second threshold value is less than the first threshold valueAnd when the air quality is in the first-level pollution state, correspondingly, sending out a first-level early warning signal.
9. An air quality forecasting system based on a weather radar data set is characterized in that: comprising the following steps:
the data acquisition module (10) acquires technical specifications of the weather radar, acquires relevant parameters of electromagnetic wave signals emitted by the radar during working under ideal conditions in the technical specifications, prepares a spectrum analyzer and a corresponding antenna, and detects and acquires actual emitted electromagnetic wave data of the weather radar;
an electromagnetic wave analysis module (20) for analyzing the amplitude A and the frequency F of the electromagnetic wave signal emitted by the weather radar in ideal condition and the amplitude of the actually emitted electromagnetic wave signal in each T timeFrequency->Performing error analysis to obtain an electromagnetic wave amplitude stability index Zf and an electromagnetic wave frequency stability index Ps, and further correlating the electromagnetic wave amplitude stability index Zf and the electromagnetic wave frequency stability index Ps to form an electromagnetic wave comprehensive stability coefficient Dc;
the prediction model construction module (30) is used for guiding a single-station radar Doppler speed calculation method into the machine learning model, establishing a first air quality prediction model, guiding a weather radar data set and an electromagnetic wave comprehensive stability coefficient Dc as sample data into the first air quality prediction model, and obtaining a second air quality prediction model after sample data training and testing;
a prediction error analysis module (40) for importing the electromagnetic wave parameter database into a second air quality prediction model to obtain a first air quality prediction valueAnd the first air quality predictive value +.>And the actual air quality value->Performing error analysis to obtain a predicted discrete degree value YLx, and judging whether the second air quality prediction model can be output and applied according to the relation between the predicted discrete degree value YLx of the second air quality prediction model and a preset threshold value;
safety early warning module(50) Applying a second air quality prediction model subjected to error analysis to obtain a second air quality prediction valueAnd according to the second air quality prediction value +.>And evaluating future air quality conditions according to the relation between the preset first threshold value and the preset second threshold value, and selecting different early warning strategies.
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| GB1500209A (en) * | 1974-12-24 | 1978-02-08 | Nissan Motor | Radar signal discrimination method and a radar system embodying the method |
| CN113987094A (en) * | 2021-09-26 | 2022-01-28 | 北京连山科技股份有限公司 | GIS map early warning method based on meteorological radar |
| CN115933008A (en) * | 2022-11-22 | 2023-04-07 | 广东电网有限责任公司广州供电局 | Strong convection weather forecast early warning method |
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| GB1500209A (en) * | 1974-12-24 | 1978-02-08 | Nissan Motor | Radar signal discrimination method and a radar system embodying the method |
| CN113987094A (en) * | 2021-09-26 | 2022-01-28 | 北京连山科技股份有限公司 | GIS map early warning method based on meteorological radar |
| CN115933008A (en) * | 2022-11-22 | 2023-04-07 | 广东电网有限责任公司广州供电局 | Strong convection weather forecast early warning method |
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