CN115980690A - Clear sky echo recognition model construction method, clear sky echo filtering method and clear sky echo filtering device - Google Patents
Clear sky echo recognition model construction method, clear sky echo filtering method and clear sky echo filtering device Download PDFInfo
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Abstract
The invention discloses a clear sky echo recognition model construction method, a clear sky echo filtering method and a clear sky echo filtering device, wherein the construction method comprises the following steps: acquiring sample data, wherein the sample data comprises clear sky echo data and non-clear sky echo data; extracting a plurality of characteristic factor data in sample data; screening optimal characteristic factor data in the plurality of characteristic factor data by adopting a statistical algorithm; and training the neural network recognition model by using the normalized optimal characteristic factor data to obtain a clear sky echo recognition model. Compared with the existing modes of filtering by adopting a threshold method and filtering by adopting a fuzzy logic algorithm, the clear sky echo identification model constructed by the construction method can directly identify clear sky echoes, so that the clear sky echoes are filtered, and the clear sky echo identification model is convenient and easy to operate and high in accuracy.
Description
Technical Field
The invention relates to the technical field of atmospheric science, in particular to a clear sky echo recognition model construction method, a clear sky echo filtering method and a clear sky echo filtering device.
Background
The millimeter wave cloud measuring instrument has many advantages in the aspect of detecting cloud information, can describe the micro physical structure in the cloud, and can continuously observe macroscopic parameters such as cloud height, cloud thickness and cloud amount, so that the millimeter wave cloud measuring instrument is widely regarded and applied in the field of cloud observation. However, the atmospheric boundary layer atmosphere directly acts on the underlying surface, so that the atmospheric movement of the layer has extremely strong turbulence characteristics, and meteorological elements have strong day-to-day variation characteristics, so that radar echoes, namely clear sky echoes, can be frequently detected on clear days without cloud or precipitation phenomena.
According to statistics, 70% -80% of observation time of boundary layer millimeter wave cloud measuring instrument echoes can be influenced by clear sky echoes, so that the detection accuracy of clouds and related information is greatly limited, and the problem is greater particularly under the complex weather background in which the clear sky echoes, precipitation and the clouds are mixed. How to eliminate the interference of clear sky echoes under the complex weather background, and extracting useful meteorological information such as cloud and precipitation from radar echoes has important research significance and practical value.
Disclosure of Invention
In view of this, embodiments of the present invention provide a clear sky echo identification model construction method, a clear sky echo filtering method, and a clear sky echo filtering device, so as to solve a technical problem that a millimeter wave nephelometer lacks clear sky echo filtering in the prior art.
The technical scheme provided by the invention is as follows:
the first aspect of the embodiments of the present invention provides a clear sky echo recognition model construction method, including: acquiring sample data, wherein the sample data comprises clear sky echo data and non-clear sky echo data; extracting a plurality of characteristic factor data in the sample data; screening optimal characteristic factor data in the plurality of characteristic factor data by adopting a statistical algorithm; and training the neural network recognition model by using the normalized optimal characteristic factor data to obtain a clear sky echo recognition model.
Optionally, the obtaining sample data comprises: acquiring weather data acquired by a millimeter wave ceilometer; performing isolated noise filtering and median filtering on the weather data to obtain weather data after quality control; and marking the quality-controlled weather data to obtain sample data containing clear sky echo data and non-clear sky echo data.
Optionally, screening the optimal feature factor data in the plurality of feature factor data by using a statistical algorithm, including: performing histogram statistics on the distribution of each characteristic factor in clear sky echo data and non-clear sky echo data; determining the distinguishing capability of each characteristic factor on clear sky echo data and non-clear sky echo data according to the histogram statistical result; and screening the optimal characteristic factor data in the clear air echo data and the non-clear air echo data according to the distinguishing capability.
Optionally, training the neural network recognition model by using the normalized optimal feature factor data to obtain a clear sky echo recognition model, including: normalizing each characteristic factor to a preset interval according to the value range of each optimal characteristic factor to obtain normalized optimal characteristic factor data; dividing the normalized optimal characteristic factor data into a training data set, a verification data set and a test data set; training a BP neural network model based on the training data set to obtain a clear sky echo recognition model; and verifying and testing the clear sky echo recognition model by adopting the verification data set and the test data set.
Optionally, obtaining weather data collected by the millimeter wave ceilometer includes: partitioning the areas with similar climate characteristics into the same area based on the climate characteristics; and acquiring weather data in the same area acquired by the millimeter wave ceilometer.
Optionally, the plurality of feature factor data comprises: reflectivity factor, radial velocity, spectral width, signal-to-noise ratio, linear depolarization ratio, echo height.
A second aspect of the embodiments of the present invention provides a clear sky echo filtering method, including: acquiring weather data to be detected; extracting optimal characteristic factor data in weather data to be detected; inputting normalized optimal characteristic factor data into a clear sky echo recognition model constructed by the clear sky echo recognition model construction method according to the first aspect and any one of the first aspect of the embodiment of the invention to obtain a recognition result of weather data to be detected; and filtering clear sky echo data in the weather data to be detected based on the identification result.
A third aspect of an embodiment of the present invention provides a clear sky echo recognition model building apparatus, including: the system comprises a sample acquisition module, a data acquisition module and a data processing module, wherein the sample acquisition module is used for acquiring sample data, and the sample data comprises clear sky echo data and non-clear sky echo data; the extraction module is used for extracting a plurality of characteristic factor data in the sample data; a screening model for screening the optimal characteristic factor data among the plurality of characteristic factor data by adopting a statistical algorithm; and the training module is used for training the neural network recognition model by adopting the normalized optimal characteristic factor data to obtain a clear sky echo recognition model.
A fourth aspect of the embodiments of the present invention provides a clear sky echo filtering device, including: the data acquisition module is used for acquiring weather data to be detected; the factor extraction module is used for extracting optimal characteristic factor data in the weather data to be detected; the identification module is used for inputting normalized optimal characteristic factor data into the clear sky echo identification model constructed by the clear sky echo identification model construction method in any one of the first aspect and the first aspect of the embodiment of the invention to obtain an identification result of weather data to be detected; and the filtering module is used for filtering clear sky echo data in the weather data to be detected based on the identification result.
A fifth aspect of the embodiments of the present invention provides a computer-readable storage medium, where computer instructions are stored, and the computer instructions are configured to enable the computer to execute the clear sky echo identification model building method according to any one of the first aspect and the first aspect of the embodiments of the present invention and the clear sky echo filtering method according to the second aspect of the embodiments of the present invention.
A sixth aspect of an embodiment of the present invention provides an electronic device, including: the clear sky echo identification model building method includes a memory and a processor, the memory and the processor are connected in a communication mode, the memory stores computer instructions, and the processor executes the computer instructions to execute the clear sky echo identification model building method and the clear sky echo filtering method according to the first aspect and the second aspect of the embodiments of the present invention.
The technical scheme provided by the invention has the following effects:
according to the clear sky echo identification model construction method and device provided by the embodiment of the invention, the sample data is obtained, the multiple characteristic factor data in the sample data are extracted, and the optimal characteristic factor data in the multiple characteristic factor data 5 are screened by adopting a statistical algorithm; and training the neural network recognition model by using the normalized optimal characteristic factor data to obtain a clear sky echo recognition model. Compared with the existing modes of filtering by adopting a threshold method and filtering by adopting a fuzzy logic algorithm, the clear sky echo recognition model constructed by the construction method can directly recognize clear sky echoes, so that the clear sky echoes are filtered, and the clear sky echo recognition model is convenient and easy to operate and high in accuracy.
The clear sky echo filtering method provided by the embodiment of the invention obtains weather data to be measured; extracting optimal characteristic factor data in the weather data to be measured 0; inputting the normalized optimal characteristic factor data into the constructed clear sky echo recognition model to obtain a recognition result of the weather data to be detected; and filtering clear sky echo data in the weather data to be detected based on the identification result. Therefore, the clear sky echo filtering method can identify and effectively remove clear sky echoes received by the boundary layer millimeter wave cloud measuring instrument in the detection process.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a clear sky echo identification model construction method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a clear sky echo identification model construction method according to another embodiment of the invention;
fig. 3 is a flow chart of a clear sky echo filtering method according to an embodiment of the invention;
fig. 4 is a block diagram of a clear sky echo recognition model building apparatus according to an embodiment of the present invention;
fig. 5 is a block diagram of a clear sky echo filtering apparatus according to an embodiment of the present invention;
FIG. 6 is a schematic structural diagram of a computer-readable storage medium provided according to an embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It will be appreciated that the data so used may be interchanged under appropriate circumstances such that the embodiments described herein may be practiced otherwise than as specifically illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
In accordance with an embodiment of the present invention, there is provided a clear sky echo recognition model building method, it is noted that the steps illustrated in the flowchart of the drawings may be performed in a computer system such as a set of computer executable instructions, and that while a logical order is illustrated in the flowchart, in some cases the steps illustrated or described may be performed in an order different than here.
In this embodiment, a clear sky echo recognition model construction method is provided, which may be used for electronic devices such as a computer, a mobile phone, a tablet computer, and the like, and fig. 1 is a flowchart of the clear sky echo recognition model construction method according to an embodiment of the present invention, and as shown in fig. 1, the method includes the following steps:
step S101: acquiring sample data, wherein the sample data comprises clear sky echo data and non-clear sky echo data; specifically, weather data collected by the millimeter wave cloud finder may be used as sample data, and the weather 5 data includes clear sky echo data and non-clear sky echo data. Can combine clear sky echo and other qi
The prior knowledge of the target echo characteristics classifies weather data into clear sky echoes and non-clear sky echoes.
Step S102: extracting a plurality of characteristic factor data in the sample data; wherein a plurality of
The characteristic factor data specifically includes: reflectivity factor, radial velocity, spectral width, signal-to-noise ratio, linear depolarization 0 ratio, echo height. When extracting a plurality of feature factor data, it is necessary to sample data
And extracting a plurality of characteristic factors from the clear sky echo data and the non-clear sky echo data in the clear sky.
Step S103: screening optimal characteristic factor data in the plurality of characteristic factor data by adopting a statistical algorithm; specifically, the optimal characteristic factor data in the plurality of characteristic factor data obtained through statistics of the statistical algorithm specifically includes echo height, reflectivity factor, radial velocity, spectral width and signal-to-noise ratio.
5, step S104: training the neural network recognition model by adopting the normalized optimal characteristic factor data,
and obtaining a clear sky echo recognition model. Specifically, after the optimal characteristic factors are screened, the screened optimal characteristic factors are normalized for convenience of processing; then, the normalized optimal characteristic factors are respectively labeled, for example, the optimal characteristic factor corresponding to the clear sky echo is set to be 1, and the optimal characteristic factor corresponding to the non-clear sky echo is set to be 1
The characteristic factor is set to 0; and finally, training the neural network recognition model by using the optimal characteristic factors, and optimizing the model parameters by 0 so as to obtain the clear sky echo recognition model.
According to the clear sky echo identification model construction method provided by the embodiment of the invention, the sample data is obtained, the multiple characteristic factor data in the sample data are extracted, and the optimal characteristic factor data in the multiple characteristic factor data are screened by adopting a statistical algorithm; and training the neural network recognition model by using the normalized optimal characteristic factor data to obtain a clear sky echo recognition model. Compared with the existing modes of threshold value method filtering and fuzzy logic algorithm filtering, the clear sky echo identification model constructed by the construction method can directly identify clear sky echoes, so that the clear sky echoes are filtered, and the clear sky echo identification model is convenient and easy to operate and high in accuracy.
In one embodiment, obtaining sample data includes the following steps:
step S201: acquiring weather data acquired by a millimeter wave ceilometer; specifically, when weather data are collected, partitioning is performed on the basis of climate characteristics, and areas with similar climate characteristics are partitioned into the same area; and then acquiring weather data in the same region acquired by the millimeter wave ceilometer. The acquired weather data is represented in an image mode, then weather-free process data and other obvious fault data can be removed preliminarily, and sample data sets of weather targets (non-clear-sky echoes) such as clear-sky echoes, other precipitation, weak precipitation and the like are acquired.
Step S202: performing isolated noise filtering and median filtering on the weather data to obtain weather data after quality control; wherein the lone noise filtering can filter out non-meteorological noise echoes (such as insect and airplane echoes) in the radar detection process; median filtering can smooth some singular values. Therefore, through two kinds of filtering, certain singular value data are smoothed while isolated clutter is removed, and weather data after quality control are obtained
Step S203: and marking the quality-controlled weather data to obtain sample data containing clear sky echo data and non-clear sky echo data. Specifically, the echoes can be artificially classified by combining the prior knowledge of the radar echo characteristics, and are divided into 2 targets, namely clear sky echoes and non-clear sky echoes, and the point of the clear sky echoes is set to be 1 and the point of the non-clear sky echoes is set to be 0.
In one embodiment, the screening the optimal feature factor data in the feature factor data by using a statistical algorithm includes: performing histogram statistics on the distribution of each characteristic factor in clear sky echo data and non-clear sky echo data; determining the distinguishing capability of each characteristic factor on clear sky echo data and non-clear sky echo data according to the histogram statistical result; and screening the optimal characteristic factor data in the clear air echo data and the non-clear air echo data according to the distinguishing capability.
Specifically, when the distinguishing capability is determined, the distinguishing capability of each feature factor can be ranked according to the histogram statistical result, that is, for each feature factor, whether the histogram statistical result corresponding to the clear sky echo and the non-clear sky echo is greatly distinguished or not is judged, the more distinguished histogram statistical result is ranked in the front, and the less distinguished histogram statistical result is ranked in the back; and then selecting a plurality of characteristic factors ranked at the top as optimal characteristic factors.
In one embodiment, training a neural network recognition model by using normalized optimal characteristic factor data to obtain a clear sky echo recognition model, including: according to the value range of each optimal characteristic factor, the method will
Normalizing each characteristic factor to a preset interval to obtain normalized optimal characteristic factor data; dividing the normalized 5 optimal characteristic factor data into a training data set, a verification data set and a test data set; base of
Training a BP neural network model in the training data set to obtain a clear sky echo recognition model; and verifying and testing the clear sky echo recognition model by adopting the verification data set and the test data set.
In the normalization, the characteristic factor data can be normalized to the [0,1] interval by adopting an extreme value method.
Specifically, for the BP neural network model, it includes forward propagation and backward propagation; when the BP neural network 0 network model is trained, data is input into an input layer neuron and a hidden layer processes the data,
and the output layer outputs the current result, then backward propagation is carried out, the error between the predicted value and the true value is calculated, the derivation is carried out on each neuron in sequence, then the characteristic value is updated by using methods such as gradient descent and the like so as to achieve the purpose of minimizing the cost function, and the weight and the threshold value of the connecting neuron are subjected to cyclic iteration updating according to the error obtained by the neuron of the hidden layer until a preset stop condition is reached.
In an embodiment, as shown in fig. 2, the clear sky echo identification model construction method is implemented by adopting the following processes:
step 1: historical echo base data of the millimeter wave cloud measuring instrument in a certain area are collected, a base data image is drawn according to the days, weather-free process data and other obvious fault data are preliminarily removed through image display, and a sample data set of weather targets (non-clear-sky echoes) such as clear sky echoes, other rainfall, weak rainfall and the like is obtained.
0, step 2: and performing isolated noise filtering and median filtering on the historical echo base data of the millimeter wave ceilometer, removing isolated clutter and simultaneously performing smoothing processing on some singular value data to obtain a base data set after quality control.
And step 3: and (2) manually classifying the echoes by combining the prior knowledge of the radar echo characteristics, distinguishing 2 targets of clear sky echoes and non-clear sky echoes, setting the point of the clear sky echo as 1, and setting the point of the non-clear sky echo as 0.
And 4, step 4: and performing histogram statistics on the distribution conditions of the reflectivity factor, the radial velocity, the spectrum width, the signal-to-noise ratio, the linear depolarization ratio, the echo height and other factor data in the quality-controlled base data set on the clear sky echo and other meteorological echoes, and screening out an optimal characteristic factor capable of effectively distinguishing the two types of echoes.
And 5: and normalizing each selected optimal characteristic factor data by using an extreme method according to the value range of the optimal characteristic factor data, and unifying each factor data to a [0,1] interval to obtain a normalized characteristic data set.
And 6: and dividing the normalized characteristic data set into a training data set, a verification data set and a test data set, establishing a clear sky echo recognition model, and evaluating a training result.
An embodiment of the present invention further provides a clear sky echo filtering method, as shown in fig. 3, including the following steps:
step S301: acquiring weather data to be detected; specifically, the weather data to be detected includes a plurality of weather data, which may include clear sky echo data, and the clear sky echo identification model constructed by the clear sky echo identification model construction method is required to be used for identification.
Step S302: extracting optimal characteristic factor data in weather data to be detected; in order to realize effective identification of weather data, median filtering and isolated noise filtering can be firstly carried out on the obtained weather data; and then extracting the optimal characteristic factor corresponding to each weather data, and normalizing.
Step S303: inputting the normalized optimal characteristic factor data into the clear sky echo recognition model constructed by the clear sky echo recognition model construction method in the embodiment to obtain a recognition result of the weather data to be detected; through the clear sky echo recognition model, clear sky echo data in weather data to be detected can be recognized, namely normalized optimal characteristic factors corresponding to the weather data are input into the clear sky echo recognition model, and whether the weather data are the clear sky echo data or not can be recognized.
Step S304: and filtering clear sky echo data in the weather data to be detected based on the identification result. And filtering the condition echo data after the condition echo data in the weather data are identified through the clear sky echo identification model.
According to the clear sky echo filtering method provided by the embodiment of the invention, weather data to be measured is obtained; extracting optimal characteristic factor data in weather data to be detected; inputting the normalized optimal characteristic factor data into the constructed clear sky echo recognition model to obtain a recognition result of the weather data to be detected; and filtering clear sky echo data in the weather data to be detected based on the identification result. Therefore, the clear sky echo filtering method can identify and effectively remove clear sky echoes received by the boundary layer millimeter wave cloud measuring instrument in the detection process.
An embodiment of the present invention further provides a clear sky echo recognition model building apparatus, as shown in fig. 4, the apparatus includes:
the system comprises a sample acquisition module, a data acquisition module and a data processing module, wherein the sample acquisition module is used for acquiring sample data, and the sample data comprises clear sky echo data and non-clear sky echo data; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
The extraction module is used for extracting a plurality of characteristic factor data in the sample data; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
A screening model for screening the optimal characteristic factor data among the plurality of characteristic factor data by adopting a statistical algorithm; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
And the training module is used for training the neural network recognition model by adopting the normalized optimal characteristic factor data to obtain a clear sky echo recognition model. For details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
According to the clear sky echo recognition model construction device provided by the embodiment of the invention, the sample data is obtained, the multiple characteristic factor data in the sample data are extracted, and the optimal characteristic factor data in the multiple characteristic factor data are screened by adopting a statistical algorithm; and training the neural network recognition model by using the normalized optimal characteristic factor data to obtain a clear sky echo recognition model. Compared with the existing modes of filtering by adopting a threshold method and filtering by adopting a fuzzy logic algorithm, the clear sky echo recognition model constructed by the construction device can directly recognize clear sky echoes, so that the clear sky echoes are filtered, and the clear sky echo recognition model is convenient and easy to operate and high in accuracy.
The functional description of the clear sky echo recognition model construction device provided by the embodiment of the invention refers to the description of the clear sky echo recognition model construction method in the above embodiment in detail.
An embodiment of the present invention further provides a clear sky echo filtering device, as shown in fig. 5, the device includes:
the data acquisition module is used for acquiring weather data to be detected; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
The factor extraction module is used for extracting optimal characteristic factor data in the weather data to be detected; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
The identification module is used for inputting the normalized optimal characteristic factor data into the clear sky echo identification model constructed by the clear sky echo identification model construction method in the embodiment to obtain the identification result of the weather data to be detected; for details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
And the filtering module is used for filtering clear sky echo data in the weather data to be detected based on the identification result. For details, reference is made to the corresponding parts of the above method embodiments, which are not described herein again.
The clear sky echo filtering device provided by the embodiment of the invention obtains weather data to be measured; extracting optimal characteristic factor data in weather data to be detected; inputting the normalized optimal characteristic factor data into the constructed clear sky echo recognition model to obtain a recognition result of the weather data to be detected; and filtering clear sky echo data in the weather data to be detected based on the identification result. Therefore, the clear sky echo filtering device can identify and effectively remove clear sky echoes received by the boundary layer millimeter wave ceilometer in the detection process.
An embodiment of the present invention further provides a storage medium, as shown in fig. 6, where a computer program 601 is stored on the storage medium, and when the instruction is executed by a processor, the steps of the clear sky echo identification model building method and the clear sky echo filtering method in the foregoing embodiments are implemented. The storage medium is also stored with audio and video stream data, characteristic frame data, an interactive request signaling, encrypted data, preset data size and the like. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk Drive (Hard Disk Drive, abbreviated as HDD), or a Solid State Drive (SSD); the storage medium may also comprise a combination of memories of the kind described above.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
An embodiment of the present invention further provides an electronic device, as shown in fig. 7, the electronic device may include a processor 51 and a memory 52, where the processor 51 and the memory 52 may be connected by a bus or in another manner, and fig. 7 takes the connection by the bus as an example.
The processor 51 may be a Central Processing Unit (CPU). The Processor 51 may also be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, or combinations thereof.
The memory 52, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as the corresponding program instructions/modules in the embodiments of the present invention. The processor 51 executes various functional applications and data processing of the processor by running the non-transitory software program, instructions and modules stored in the memory 52, that is, the clear sky echo identification model construction method and the clear sky echo filtering method in the above method embodiments are implemented.
The memory 52 may include a storage program area and a storage data area, wherein the storage program area may store an operating device, an application program required for at least one function; the storage data area may store data created by the processor 51, and the like. Further, the memory 52 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 52 may optionally include memory located remotely from the processor 51, and these remote memories may be connected to the processor 51 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 52, when executed by the processor 51
In the embodiment shown in fig. 1-2, a clear sky echo identification model construction method and a clear sky echo filtering method in the embodiment 5 shown in fig. 3 are performed.
The details of the electronic device may be understood by referring to the corresponding descriptions and effects in the embodiments shown in fig. 1 to fig. 3, and are not described herein again.
Although the embodiments of the present invention have been described in connection with the accompanying drawings, those skilled in the art may realize the invention without departing from the scope thereof
Various modifications and variations of the present invention are possible in light of the spirit and scope of the present invention, and such modifications and variations are within the scope of 0 as defined in the appended claims.
Claims (11)
1. A clear sky echo recognition model construction method is characterized by comprising the following steps:
acquiring sample data, wherein the sample data comprises clear sky echo data and non-clear sky echo data;
extracting a plurality of characteristic factor data in the sample data;
screening optimal characteristic factor data in the plurality of characteristic factor data by adopting a statistical algorithm;
and training the neural network recognition model by using the normalized optimal characteristic factor data to obtain a clear sky echo recognition model.
2. The clear sky echo recognition model building method according to claim 1, wherein obtaining sample data includes:
acquiring weather data acquired by a millimeter wave ceilometer;
performing isolated noise filtering and median filtering on the weather data to obtain weather data after quality control;
and marking the quality-controlled weather data to obtain sample data containing clear sky echo data and non-clear sky echo data.
3. The clear sky echo identification model construction method according to claim 1, wherein screening optimal feature factor data among the plurality of feature factor data by using a statistical algorithm includes:
performing histogram statistics on the distribution of each characteristic factor in clear sky echo data and non-clear sky echo data;
determining the distinguishing capability of each characteristic factor on clear sky echo data and non-clear sky echo data according to the histogram statistical result;
and screening the optimal characteristic factor data in the clear air echo data and the non-clear air echo data according to the distinguishing capability.
4. The clear sky echo recognition model construction method according to claim 1, wherein training a neural network recognition model by using normalized optimal feature factor data to obtain a clear sky echo recognition model, includes:
normalizing each characteristic factor to a preset interval according to the value range of each optimal characteristic factor to obtain normalized optimal characteristic factor data;
dividing the normalized optimal characteristic factor data into a training data set, a verification data set and a test data set;
training a BP neural network model based on the training data set to obtain a clear sky echo recognition model;
and verifying and testing the clear sky echo recognition model by adopting the verification data set and the test data set.
5. The clear sky echo recognition model building method according to claim 2, wherein the acquiring weather data collected by the millimeter wave ceilometer comprises:
partitioning is carried out based on the climate characteristics, and areas with similar climate characteristics are divided into the same area;
and acquiring weather data in the same area acquired by the millimeter wave ceilometer.
6. The clear sky echo identification model building method according to claim 1, wherein the plurality of characteristic factor data include: reflectivity factor, radial velocity, spectral width, signal-to-noise ratio, linear depolarization ratio, echo height.
7. A clear sky echo filtering method is characterized by comprising the following steps:
acquiring weather data to be detected;
extracting optimal characteristic factor data in weather data to be detected;
inputting the normalized optimal characteristic factor data into a clear sky echo recognition model constructed by the clear sky echo recognition model construction method according to any one of claims 1 to 6 to obtain a recognition result of weather data to be detected;
and filtering clear sky echo data in the weather data to be detected based on the identification result.
8. A clear sky echo recognition model construction device is characterized by comprising the following steps:
the system comprises a sample acquisition module, a data acquisition module and a data processing module, wherein the sample acquisition module is used for acquiring sample data, and the sample data comprises clear sky echo data and non-clear sky echo data;
the extraction module is used for extracting a plurality of characteristic factor data in the sample data;
a screening model for screening the optimal characteristic factor data among the plurality of characteristic factor data by adopting a statistical algorithm;
and the training module is used for training the neural network recognition model by adopting the normalized optimal characteristic factor data to obtain a clear sky echo recognition model.
9. A clear sky echo filtering device, characterized by comprising:
the data acquisition module is used for acquiring weather data to be detected;
the factor extraction module is used for extracting optimal characteristic factor data in the weather data to be detected;
the identification module is used for inputting the normalized optimal characteristic factor data into the clear sky echo identification model constructed by the clear sky echo identification model construction method according to any one of claims 1 to 6 to obtain the identification result of the weather data to be detected;
and the filtering module is used for filtering clear sky echo data in the weather data to be detected based on the identification result.
10. A computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions for causing the computer to execute the clear sky echo identification model construction method according to any one of claims 1-6 and the clear sky echo filtering method according to claim 7.
11. An electronic device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform a clear sky echo identification model construction method according to any one of claims 1 to 6 and a clear sky echo filtering method according to claim 7.
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| CN117991198B (en) * | 2024-04-07 | 2024-06-11 | 成都远望科技有限责任公司 | Single-shot double-receiving top-sweeping cloud radar same-frequency interference identification method and device |
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