CN110954869B - Animation display method, device and system for sand-dust meteorological disaster data - Google Patents
Animation display method, device and system for sand-dust meteorological disaster data Download PDFInfo
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- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/95—Radar or analogous systems specially adapted for specific applications for meteorological use
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Abstract
The embodiment of the invention discloses a method, a device and a system for displaying animation of sand-dust meteorological disaster data. The method is applied to the server, and by applying the scheme provided by the embodiment of the invention, the sand and dust disaster data can be three-dimensionally and visually rendered based on the middle layer caching technology of the server and the WEBGL technology of the client. Moreover, when the client-side WEBGL technology is used for displaying the sand disaster data, a plug-in is not required to be independently installed on the client side, and convenience in displaying the sand disaster data can be improved. After the original DST data is obtained, corresponding IDDI data can be obtained through calculation, and corresponding dust intensity data can be obtained according to a random forest model obtained through pre-training, so that when sand disaster data is displayed, the displayed data is the dust intensity data.
Description
Technical Field
The invention relates to the technical field of sand and dust data display, in particular to an animation display method, device and system for sand and dust meteorological disaster data.
Background
The global disaster database analysis shows that the meteorological disaster is the most frequent natural disaster, the satellite remote sensing is taken as an important macroscopic observation means, the satellite remote sensing occupies an incomparable dominant position in the meteorological disaster monitoring, and the satellite remote sensing is increasingly applied to the meteorological disaster analysis. The method for quantitatively monitoring the sand and dust disasters is a typical disaster in main meteorological disasters, and how to establish a sand and dust disaster quantitative monitoring method by utilizing long-time sequence satellite remote sensing disaster-causing factor information is a key for effectively carrying out meteorological disaster early warning.
In the known method, the sand disaster data is usually expressed in the form of an electronic document or a static text picture. However, with frequent occurrence of sand and dust disasters and increasingly diversified specific scene visualization requirements of people, the original disaster data expression mode can not meet the requirements of actual production research. Therefore, in order to improve the diversity of the expression modes of the sand disaster data, a sand disaster data display method is needed.
Disclosure of Invention
The invention provides an animation display method, device and system for sand and dust meteorological disaster data, and aims to improve the diversity of sand and dust meteorological disaster data expression modes. The specific technical scheme is as follows.
In a first aspect, an embodiment of the present invention provides an animation display method for sand-dust meteorological disaster data, where the method is applied to a server of an animation display system for sand-dust meteorological disaster data, the system further includes a client, and the method includes:
acquiring a sequence high-resolution satellite base map, a sequence meteorological satellite base map acquired by a static meteorological satellite and sand monitoring DST data acquired by the static meteorological satellite; each high-resolution satellite base map, each meteorological satellite base map and each DST data respectively comprise corresponding time stamps;
calculating IDDI (intermediate digital Difference) data of the infrared difference value sand dust index according to the DST data;
inputting the IDDI data into a random forest model obtained by pre-training to obtain predicted visibility data corresponding to the IDDI data, and calculating dust intensity data according to the predicted visibility data; the random forest model is obtained by training according to each sample pair in advance, and each sample pair comprises sample IDDI data and sample ground visibility actual measurement data;
establishing a visual data model of sand and dust intensity monitoring data based on the server middle layer cache technology, storing the sand and dust intensity data into the visual data model, and extracting vector data according to the sand and dust intensity data to serve as middle cache data corresponding to the DST data;
and when a sand and dust meteorological disaster data acquisition request sent by the client is received, sending the sequence high-resolution satellite base map, the sequence meteorological satellite base map and the intermediate cache data to the client, so that the client displays the intermediate cache data corresponding to the DST data containing the same timestamp, the corresponding high-resolution satellite base map and the meteorological satellite base map in an overlapping manner according to the sequence of the timestamps from morning to evening on the basis of the WebGL technology.
Optionally, the step of calculating the infrared difference dust index IDDI data according to the DST data includes:
reading instantaneous brightness temperature values from the DST data in sequence;
calculating the difference value between the pre-obtained reference brightness temperature value and the read instantaneous brightness temperature value as the IDDI data of the infrared difference value dust index; the reference brightness temperature value is a brightness temperature value observed by the static meteorological satellite in clear sky.
Optionally, the training process of the random forest model includes:
constructing an initial model;
obtaining each sample pair, wherein each sample pair comprises sample IDDI data and sample ground visibility measured data;
and inputting each sample pair into the initial model, obtaining predicted ground visibility data by the initial model according to the IDDI data of the samples in each sample pair, and taking the current initial model as the random forest model when the error between the predicted ground visibility data and the corresponding measured data of the sample ground visibility is smaller than a preset threshold value.
Optionally, the step of establishing a visual data model of the dust intensity monitoring data based on the server middle layer caching technology includes:
constructing a json structure;
and constructing a visual data model of the sand dust intensity monitoring data based on the json structure.
In a second aspect, an embodiment of the present invention provides an animation display apparatus for sand-dust meteorological disaster data, where the apparatus is applied to a server of an animation display system for sand-dust meteorological disaster data, the system further includes a client, and the apparatus includes:
the system comprises an original data acquisition module, a data acquisition module and a data acquisition module, wherein the original data acquisition module is used for acquiring a sequence high-resolution satellite base map, a sequence meteorological satellite base map acquired by a static meteorological satellite and sand monitoring DST data acquired by the static meteorological satellite; each high-resolution satellite base map, each meteorological satellite base map and each DST data respectively comprise corresponding time stamps;
the IDDI data calculation module is used for calculating IDDI data of the infrared difference value dust index according to the DST data;
the intensity data calculation module is used for inputting the IDDI data into a random forest model obtained by pre-training to obtain predicted visibility data corresponding to the IDDI data, and calculating dust intensity data according to the predicted visibility data; the random forest model is obtained by training according to each sample pair in advance, and each sample pair comprises sample IDDI data and sample ground visibility actual measurement data;
the data model building module is used for building a visual data model of sand and dust intensity monitoring data based on the server middle layer caching technology, storing the sand and dust intensity data into the visual data model, and extracting vector data according to the sand and dust intensity data to serve as middle caching data corresponding to the DST data;
and the sand and dust data display module is used for sending the sequence high-resolution satellite base map, the sequence meteorological satellite base map and the intermediate cache data to the client when receiving a sand and dust meteorological disaster data acquisition request sent by the client, so that the client displays the intermediate cache data corresponding to the DST data containing the same timestamp in an overlapping manner with the corresponding high-resolution satellite base map and the meteorological satellite base map according to the sequence of the timestamps from morning to evening on the basis of the WebGL technology.
Optionally, the IDDI data calculation module includes:
the data reading submodule is used for sequentially reading instantaneous brightness temperature values from the DST data;
the data calculation submodule is used for calculating the difference value between the pre-acquired reference brightness temperature value and the read instantaneous brightness temperature value to be used as infrared difference value dust index IDDI data; the reference brightness temperature value is a brightness temperature value observed by the static meteorological satellite in clear sky.
Optionally, the apparatus further comprises:
the initial model building module is used for building an initial model;
the system comprises a sample pair acquisition module, a data acquisition module and a data acquisition module, wherein the sample pair acquisition module is used for acquiring each sample pair, and each sample pair comprises sample IDDI data and sample ground visibility measured data;
and the model training module is used for inputting the sample pairs into the initial model, the initial model obtains predicted ground visibility data according to the IDDI data of the samples in each sample pair, and when the error between the predicted ground visibility data and the corresponding measured ground visibility data of the samples is smaller than a preset threshold value, the current initial model is used as the random forest model.
Optionally, the data model building module includes:
the structure construction submodule is used for constructing a json structure;
and the data model construction submodule is used for constructing a visual data model of the sand dust intensity monitoring data based on the json structure.
In a third aspect, an embodiment of the present invention provides an animation display system for sand-dust meteorological disaster data, where the system includes a server and a client;
the server is used for acquiring a sequence high-resolution satellite base map, a sequence meteorological satellite base map acquired by a static meteorological satellite and sand monitoring DST data acquired by the meteorological satellite; each high-resolution satellite base map, each meteorological satellite base map and each DST data respectively comprise corresponding time stamps; calculating IDDI (intermediate digital Difference) data of the infrared difference value sand dust index according to the DST data; inputting the IDDI data into a random forest model obtained by pre-training to obtain predicted visibility data corresponding to the IDDI data, and calculating dust intensity data according to the predicted visibility data; the random forest model is obtained by training according to each sample pair in advance, and each sample pair comprises sample IDDI data and sample ground visibility actual measurement data; establishing a visual data model of sand and dust intensity monitoring data based on the server middle layer cache technology, storing the sand and dust intensity data into the visual data model, and extracting vector data according to the sand and dust intensity data to serve as middle cache data corresponding to the DST data; when a request for acquiring the data of the sand and dust meteorological disasters sent by the client is received, sending the sequence high-resolution satellite base map, the sequence meteorological satellite base map and the intermediate cache data to the client;
and the client is used for receiving the sequence high-resolution satellite base map, the sequence meteorological satellite base map and the intermediate cache data sent by the server, and sequentially displaying the intermediate cache data corresponding to the DST data containing the same timestamp, the corresponding high-resolution satellite base map and the meteorological satellite base map in an overlapping manner according to the sequence of the timestamps from morning to evening on the basis of the WebGL technology.
Optionally, the client is specifically configured to receive the intermediate cache data sent by the server, acquire a preset pixel threshold, divide the intermediate cache data into a plurality of dust and sand monomers according to the pixel threshold, and sequentially display, based on a WebGL technology, the plurality of dust and sand monomers corresponding to the DST data including the same timestamp, and the corresponding high-resolution satellite base map and the meteorological satellite base map in an overlapping manner according to the sequence of timestamps from morning to evening; each sand dust monomer comprises: sand dust intensity, sand dust influence range, sand dust moving direction and sand dust moving speed.
As can be seen from the above, the animation display method, device and system for the sand and dust meteorological disaster data provided by the embodiments of the present invention can perform three-dimensional visualization rendering on the sand and dust meteorological disaster data based on the server-side middle layer caching technology and the client-side webbl technology. Moreover, when the client-side WEBGL technology is used for displaying the sand disaster data, a plug-in is not required to be independently installed on the client side, and convenience in displaying the sand disaster data can be improved. After the original DST data is obtained, corresponding IDDI data can be obtained through calculation, and corresponding dust intensity data can be obtained according to a random forest model obtained through pre-training, so that when sand disaster data is displayed, the displayed data is the dust intensity data. Of course, not all of the advantages described above need to be achieved at the same time in the practice of any one product or method of the invention.
The innovation points of the embodiment of the invention comprise:
1. and performing three-dimensional visual rendering on the sand disaster data based on a server-side middle layer caching technology and a client-side WEBGL technology. Moreover, when the client-side WEBGL technology is used for displaying the sand disaster data, a plug-in is not required to be independently installed on the client side, and convenience in displaying the sand disaster data can be improved. After the original DST data is obtained, corresponding IDDI data can be obtained through calculation, and corresponding dust intensity data can be obtained according to a random forest model obtained through pre-training, so that when sand disaster data is displayed, the displayed data is the dust intensity data.
2. After the random forest model is obtained through training, when sand disaster data display is carried out, IDDI data obtained through calculation according to the obtained original DST data can be obtained through the random forest model, and corresponding sand intensity data can be obtained through the random forest model, so that sand monitoring data can be displayed more visually, users can analyze visually, disaster data can be better utilized, and disaster early warning and reports can be provided for decision-making departments.
Drawings
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. It is to be understood that the drawings in the following description are merely exemplary of some embodiments of the invention. For a person skilled in the art, without inventive effort, further figures can be obtained from these figures.
FIG. 1 is a schematic flow chart of a method for displaying data of a sand-dust meteorological disaster according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an animation display effect of the sand and dust meteorological disaster data according to the embodiment of the invention;
FIG. 3 is a schematic flow chart of an animation display method for meteorological disaster data of sand and dust according to an embodiment of the present invention;
FIG. 4 is a schematic structural diagram of an animation display device for displaying data of a sand-dust meteorological disaster according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an animation display system for displaying data of a sand-dust meteorological disaster according to an embodiment of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It is to be understood that the described embodiments are merely a few embodiments of the invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
It is to be noted that the terms "comprises" and "comprising" and any variations thereof in the embodiments and drawings of the present invention are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
The embodiment of the invention discloses a method, a device and a system for displaying sand and dust meteorological disaster data by animation, which can improve the diversity of sand and dust disaster data expression modes. The following provides a detailed description of embodiments of the invention.
Fig. 1 is a schematic flow chart of a method for displaying an animation of data of a sand-dust meteorological disaster according to an embodiment of the present invention. The method is applied to the server. The method specifically comprises the following steps.
S110: acquiring a sequence high-resolution satellite base map, a sequence meteorological satellite base map acquired by a static meteorological satellite and sand monitoring DST data acquired by the static meteorological satellite; and each high-score satellite base map, each meteorological satellite base map and each DST data all contain corresponding timestamps.
The high-score satellite base map can be a base map collected by a high-score first satellite, a high-score second satellite or Google Earth. In the embodiment of the invention, in order to three-dimensionally display the animation effect of the sand disaster data, a plurality of high-resolution satellite base maps which are sequentially sequenced according to the acquisition time can be obtained, and the base maps are called as sequence high-resolution satellite base maps.
The geostationary weather satellite may be, for example, a wind-cloud-four geostationary weather satellite FY4A, or may be another satellite capable of regularly acquiring a meteorological satellite base map and DST (dust monitoring) data, which is not limited in the embodiment of the present invention. The sequence meteorological satellite base map can be a plurality of meteorological satellite base maps which are sequentially sequenced according to the acquisition time.
The time stamp corresponding to each high-resolution satellite base map is the time when the satellite acquires each high-resolution satellite base map, the time stamp corresponding to each meteorological satellite base map is the time when the geostationary meteorological satellite acquires each meteorological satellite base map, and the time stamp corresponding to each DST data is the time when the geostationary meteorological satellite acquires each DST data.
S120: and calculating IDDI data of the infrared difference sand dust index according to the DST data.
In one implementation, when calculating IDDI (InfraRed Difference Dust Index) data, the instantaneous brightness temperature values may be read from DST data in sequence; and then calculating the difference value between the pre-acquired reference brightness temperature value and the read instantaneous brightness temperature value as IDDI data. The reference brightness temperature value is a brightness temperature value observed by the stationary meteorological satellite in clear sky.
Specifically, the IDDI data may be calculated according to the following formula:
IDDI＝BTref-BTbb
BTref is the brightness temperature observed by the static meteorological satellite when the weather is clear (no cloud and sand dust), and BTbb is the instantaneous brightness temperature value observed by the static meteorological satellite.
S130: inputting the IDDI data into a random forest model obtained by pre-training to obtain predicted visibility data corresponding to the IDDI data, and calculating dust intensity data according to the predicted visibility data; the random forest model is obtained by training in advance according to each sample pair, and each sample pair comprises sample IDDI data and sample ground visibility actual measurement data.
In the embodiment of the invention, in order to more intuitively display the sand monitoring data, the random forest model can be obtained by training in advance according to each sample pair. Wherein each sample pair comprises sample IDDI data and sample ground visibility measurement data. That is to say, through the random forest model, the incidence relation between the ground visibility data and the IDDI data can be obtained through statistics.
When sand disaster data are displayed, after IDDI data are obtained, the IDDI data can be input into a random forest model obtained through pre-training, predicted visibility data corresponding to the IDDI data can be obtained, and then sand intensity data can be calculated according to the predicted visibility data. The dust intensity data and the predicted visibility data in the embodiment of the invention can be in a linear relation.
Compared with traditional statistical models such as linear regression and exponential regression, the non-parametric, non-linear and multi-parametric machine learning method is considered to be capable of better expressing various relevant parameters. In the embodiment of the invention, the correlation between IDDI data and ground visibility data can be well counted by using a random forest model, so that accurate dust intensity data can be obtained. After the predicted visibility data is calculated by using the random forest model, the predicted visibility data can be compared with the actual visibility data of the ground station, the obtained correlation coefficient is 0.79, and the average error is 0.04.
S140: based on a server middle layer cache technology, a visual data model of the sand and dust intensity monitoring data is established, the sand and dust intensity data is stored in the visual data model, and vector data are extracted according to the sand and dust intensity data and serve as middle cache data corresponding to DST data.
To visually display data at a client, visualization specifications of the data in the background need to be formulated for front-end query retrieval. The client visual interface can comprehensively display the sand and dust disaster data and relevant statistics and system information in a three-dimensional scene in a disaster theme mode, and the display of the disaster data can be organized according to a disaster theme. A disaster topic is a collection of one or more different types of disaster data. By default the system shows a "global" disaster topic, which may contain sand disaster data for up to three months.
In an implementation mode, a total json structure of disaster topics can be constructed firstly, the json structure is used for storing disaster topic general data information and contains common attributes of all data, and global retrieval is facilitated. Specifically, visualization elements can be defined from three dimensions of space, time and value to meet the dynamic visualization requirements of the sand disaster data. The data model of the dust disaster data exists in the form of a JSON file, and the JSON file comprises unique identification of the data, a monitoring object entity, tag information, style definition, sequence raster data, a document report, a production unit, production time and other attribute information.
After the overall json structure is built, a visual data model of the sand dust intensity monitoring data can be built based on the json structure, and the model can also be called as a json model. And then, storing the sand and dust intensity data into a visual data model, and extracting vector data according to the sand and dust intensity data to serve as intermediate cache data corresponding to each DST data.
In practical application, the IDDI data, the sand monitoring binary map and the sand monitoring vector boundary can be stored in the visual data model. The sand and dust detection binary image can be obtained by calculation according to IDDI data, and after the sand and dust detection binary image is obtained, a sand and dust monitoring vector boundary can be obtained through a grid-to-vector algorithm.
S150: when a sand and dust meteorological disaster data acquisition request sent by a client is received, the sequence high-resolution satellite base map, the sequence meteorological satellite base map and the intermediate cache data are sent to the client, so that the client displays the intermediate cache data corresponding to the DST data containing the same timestamp with the corresponding high-resolution satellite base map and the meteorological satellite base map in an overlapping mode according to the sequence of the timestamps from morning to evening on the basis of the WebGL technology.
In the embodiment of the invention, when a user wants to check the sand-dust meteorological disaster data, the user can send a sand-dust meteorological disaster data acquisition request to the server through the client, and specifically, the user can send the sand-dust meteorological disaster data acquisition request through the browser.
After receiving a request for acquiring the data of the sand and dust meteorological disasters sent by the client, the browser can send the sequence high-resolution satellite base map, the sequence meteorological satellite base map and the intermediate cache data to the client. After the client receives the sequence high-resolution satellite base map, the sequence meteorological satellite base map and the intermediate cache data, a preset pixel threshold value can be obtained, the intermediate cache data are divided into a plurality of sand-dust monomers according to the pixel threshold value, and the plurality of sand-dust monomers corresponding to the DST data containing the same timestamp are sequentially displayed in an overlapping mode with the corresponding high-resolution satellite base map and the meteorological satellite base map according to the sequence of the timestamp from morning to evening based on the WebGL technology.
Referring to fig. 2, a schematic diagram of an effect of the data of the meteorological disaster with sand and dust presented by the client is shown. As shown in fig. 2, the client visual interface may display the sand-dust meteorological disaster data in multiple densities, where some areas have concentrated sand-dust particles and some areas have sparse sand-dust particles.
For one intermediate cache data, each pixel point data value represents the strength value of the dust, a threshold value is set, the grid points are combined into a dust monomer when the adjacent pixel value difference is smaller than the threshold value, and then each pixel point is traversed in a circulating mode, so that each intermediate cache data can be composed of a plurality of dust monomers representing different dust strengths. That is, one intermediate cache data corresponds to a plurality of dust monomers for the client to display, and each dust monomer includes the following attribute information: sand intensity, sand influence range, sand moving direction, sand moving speed and the like. Different attribute information exists among different sand dust monomers, and finally, when a three-dimensional scene is visualized, dynamic display of multi-strength sand dust particles can be performed according to sand dust meteorological disaster data of a duration.
As can be seen from the above, in this embodiment, three-dimensional visual rendering can be performed on the sand and dust disaster data based on the server-side middle layer caching technology and the client-side webbl technology. Moreover, when the client-side WEBGL technology is used for displaying the sand disaster data, a plug-in is not required to be independently installed on the client side, and convenience in displaying the sand disaster data can be improved. After the original DST data is obtained, corresponding IDDI data can be obtained through calculation, and corresponding dust intensity data can be obtained according to a random forest model obtained through pre-training, so that when sand disaster data is displayed, the displayed data is the dust intensity data.
As an implementation manner of the embodiment of the present invention, as shown in fig. 2, the training process of the random forest model may include the following steps.
S310: and constructing an initial model.
The initial model may be a neural network model. For example, a convolutional layer, a pooling layer, etc. may be included, and the structure of the initial model is not limited in the embodiments of the present invention.
S320: sample pairs are obtained, each sample pair comprising sample IDDI data and sample ground visibility measurement data.
The IDDI data of the sample can be obtained by calculation according to DST data obtained by the static meteorological satellite, and the ground visibility data of the sample can be obtained by observation of a ground station.
S330: and inputting each sample pair into an initial model, obtaining predicted ground visibility data by the initial model according to the IDDI data of the samples in each sample pair, and taking the current initial model as a random forest model when the error between the predicted ground visibility data and the corresponding measured data of the sample ground visibility is smaller than a preset threshold value.
After obtaining each sample pair, inputting each sample pair into an initial model, wherein the initial model can obtain predicted ground visibility data according to the IDDI data of the samples in each sample pair, and calculating the error between each predicted visibility data and the corresponding measured data of the ground visibility of the samples; when the error between the predicted visibility data and the corresponding sample ground visibility measured data is smaller than a preset threshold (such as 0.01, 0.02, 0.05 and the like), the model training is finished, and the current initial model can be used as a random forest model.
After the random forest model is obtained through training, when sand disaster data display is carried out, IDDI data obtained through calculation according to the obtained original DST data can be obtained through the random forest model, and corresponding sand intensity data can be obtained through the random forest model, so that sand monitoring data can be displayed more visually, users can analyze visually, disaster data can be better utilized, and disaster early warning and reports can be provided for decision-making departments.
As shown in fig. 4, an embodiment of the present invention provides an animation display apparatus for sand-dust meteorological disaster data, where the apparatus is applied to a server of an animation display system for sand-dust meteorological disaster data, the system further includes a client, and the apparatus includes:
the original data acquisition module 410 is used for acquiring a sequence high-resolution satellite base map, a sequence meteorological satellite base map acquired by a static meteorological satellite, and sand monitoring DST data acquired by the static meteorological satellite; each high-resolution satellite base map, each meteorological satellite base map and each DST data respectively comprise corresponding time stamps;
an IDDI data calculating module 420, configured to calculate, according to the DST data, infrared difference sand-dust index IDDI data;
the intensity data calculation module 430 is configured to input the IDDI data into a random forest model obtained through pre-training, obtain predicted visibility data corresponding to the IDDI data, and calculate dust intensity data according to the predicted visibility data; the random forest model is obtained by training according to each sample pair in advance, and each sample pair comprises sample IDDI data and sample ground visibility actual measurement data;
a data model building module 440, configured to build a visual data model of the sand and dust intensity monitoring data based on the server middle layer caching technology, store the sand and dust intensity data in the visual data model, and extract vector data according to the sand and dust intensity data, as middle caching data corresponding to each DST data;
and the dust data display module 450 is configured to send the sequence high-resolution satellite base map, the sequence meteorological satellite base map, and the intermediate cache data to the client when receiving a dust meteorological disaster data acquisition request sent by the client, so that the client displays, based on a WebGL technology, the intermediate cache data corresponding to the DST data including the same timestamp, and the corresponding high-resolution satellite base map and the meteorological satellite base map in an overlapping manner in sequence from morning to evening according to the timestamp.
As an implementation manner of the embodiment of the present invention, the IDDI data calculation module 420 includes:
the data reading submodule is used for sequentially reading instantaneous brightness temperature values from the DST data;
the data calculation submodule is used for calculating the difference value between the pre-acquired reference brightness temperature value and the read instantaneous brightness temperature value to be used as infrared difference value dust index IDDI data; the reference brightness temperature value is a brightness temperature value observed by the static meteorological satellite in clear sky.
As an implementation manner of the embodiment of the present invention, the apparatus further includes:
the initial model building module is used for building an initial model;
the system comprises a sample pair acquisition module, a data acquisition module and a data acquisition module, wherein the sample pair acquisition module is used for acquiring each sample pair, and each sample pair comprises sample IDDI data and sample ground visibility measured data;
and the model training module is used for inputting the sample pairs into the initial model, the initial model obtains predicted ground visibility data according to the IDDI data of the samples in each sample pair, and when the error between the predicted ground visibility data and the corresponding measured ground visibility data of the samples is smaller than a preset threshold value, the current initial model is used as the random forest model.
As an implementation manner of the embodiment of the present invention, the data model building module 440 includes:
the structure construction submodule is used for constructing a json structure;
and the data model construction submodule is used for constructing a visual data model of the sand dust intensity monitoring data based on the json structure.
As can be seen from the above, in this embodiment, three-dimensional visual rendering can be performed on the sand and dust disaster data based on the server-side middle layer caching technology and the client-side webbl technology. Moreover, when the client-side WEBGL technology is used for displaying the sand disaster data, a plug-in is not required to be independently installed on the client side, and convenience in displaying the sand disaster data can be improved. After the original DST data is obtained, corresponding IDDI data can be obtained through calculation, and corresponding dust intensity data can be obtained according to a random forest model obtained through pre-training, so that when sand disaster data is displayed, the displayed data is the dust intensity data.
The above device embodiment corresponds to the method embodiment, and has the same technical effect as the method embodiment, and for the specific description, refer to the method embodiment. The device embodiment is obtained based on the method embodiment, and for specific description, reference may be made to the method embodiment section, which is not described herein again.
As shown in fig. 5, an embodiment of the present invention provides an animation display system for data of a sand-dust meteorological disaster, where the system includes a server 510 and a client 520;
the server 510 is configured to obtain a sequence high-resolution satellite base map, a sequence meteorological satellite base map acquired by a stationary meteorological satellite, and dust monitoring DST data acquired by the meteorological satellite; each high-resolution satellite base map, each meteorological satellite base map and each DST data respectively comprise corresponding time stamps; calculating IDDI (intermediate digital Difference) data of the infrared difference value sand dust index according to the DST data; inputting the IDDI data into a random forest model obtained by pre-training to obtain predicted visibility data corresponding to the IDDI data, and calculating dust intensity data according to the predicted visibility data; the random forest model is obtained by training according to each sample pair in advance, and each sample pair comprises sample IDDI data and sample ground visibility actual measurement data; establishing a visual data model of sand and dust intensity monitoring data based on the server middle layer cache technology, storing the sand and dust intensity data into the visual data model, and extracting vector data according to the sand and dust intensity data to serve as middle cache data corresponding to the DST data; when a request for acquiring the data of the sand and dust meteorological disasters sent by the client is received, sending the sequence high-resolution satellite base map, the sequence meteorological satellite base map and the intermediate cache data to the client 520;
the client 520 is configured to receive the sequence high-resolution satellite base map, the sequence meteorological satellite base map, and the intermediate cache data sent by the server 510, and sequentially display, based on a WebGL technology, the intermediate cache data corresponding to the DST data including the same timestamp, and the corresponding high-resolution satellite base map and the meteorological satellite base map in an overlapping manner according to the sequence of the timestamps from morning to evening.
As an implementation manner of the embodiment of the present invention, the client 520 is specifically configured to receive the intermediate cache data sent by the server, obtain a preset pixel threshold, divide the intermediate cache data into a plurality of sand-dust monomers according to the pixel threshold, and based on a WebGL technology, sequentially display, in an order from morning to evening, the plurality of sand-dust monomers corresponding to the DST data including the same timestamp, and the corresponding high-resolution satellite base map and the meteorological satellite base map in an overlapping manner; each sand dust monomer comprises: sand dust intensity, sand dust influence range, sand dust moving direction and sand dust moving speed.
As can be seen from the above, in this embodiment, three-dimensional visual rendering can be performed on the sand and dust disaster data based on the server-side middle layer caching technology and the client-side webbl technology. Moreover, when the client-side WEBGL technology is used for displaying the sand disaster data, a plug-in is not required to be independently installed on the client side, and convenience in displaying the sand disaster data can be improved. After the original DST data is obtained, corresponding IDDI data can be obtained through calculation, and corresponding dust intensity data can be obtained according to a random forest model obtained through pre-training, so that when sand disaster data is displayed, the displayed data is the dust intensity data.
The embodiment of the system and the embodiment of the method shown in fig. 1 are embodiments based on the same inventive concept, and the relevant points can be referred to each other. The system embodiment corresponds to the method embodiment, and has the same technical effect as the method embodiment, and for the specific description, reference is made to the method embodiment.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
Those of ordinary skill in the art will understand that: modules in the devices in the embodiments may be distributed in the devices in the embodiments according to the description of the embodiments, or may be located in one or more devices different from the embodiments with corresponding changes. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for displaying sand-dust meteorological disaster data in an animation manner is applied to a server of an animation display system for sand-dust meteorological disaster data, the system further comprises a client, and the method comprises the following steps:
acquiring a sequence high-resolution satellite base map, a sequence meteorological satellite base map acquired by a static meteorological satellite and sand monitoring DST data acquired by the static meteorological satellite; each high-resolution satellite base map, each meteorological satellite base map and each DST data respectively comprise corresponding time stamps;
calculating IDDI (intermediate digital Difference) data of the infrared difference value sand dust index according to the DST data;
inputting the IDDI data into a random forest model obtained by pre-training to obtain predicted visibility data corresponding to the IDDI data, and calculating dust intensity data according to the predicted visibility data; the random forest model is obtained by training according to each sample pair in advance, and each sample pair comprises sample IDDI data and sample ground visibility actual measurement data;
establishing a visual data model of sand and dust intensity monitoring data based on a server middle layer cache technology, storing the sand and dust intensity data into the visual data model, and extracting vector data according to the sand and dust intensity data to serve as middle cache data corresponding to each DST data;
and when a sand and dust meteorological disaster data acquisition request sent by the client is received, sending the sequence high-resolution satellite base map, the sequence meteorological satellite base map and the intermediate cache data to the client, so that the client displays the intermediate cache data corresponding to the DST data containing the same timestamp, the corresponding high-resolution satellite base map and the meteorological satellite base map in an overlapping manner according to the sequence of the timestamps from morning to evening on the basis of the WebGL technology.
2. The method of claim 1, wherein said step of calculating Infrared Difference Dust Index (IDDI) data from said DST data comprises:
reading instantaneous brightness temperature values from the DST data in sequence;
calculating the difference value between the pre-obtained reference brightness temperature value and the read instantaneous brightness temperature value as the IDDI data of the infrared difference value dust index; the reference brightness temperature value is a brightness temperature value observed by the static meteorological satellite in clear sky.
3. The method of claim 1, wherein the training process of the random forest model comprises:
constructing an initial model;
obtaining each sample pair, wherein each sample pair comprises sample IDDI data and sample ground visibility measured data;
and inputting each sample pair into the initial model, obtaining predicted ground visibility data by the initial model according to the IDDI data of the samples in each sample pair, and taking the current initial model as the random forest model when the error between the predicted ground visibility data and the corresponding measured data of the sample ground visibility is smaller than a preset threshold value.
4. The method of claim 1, wherein the step of establishing a visual data model of the sand dust intensity monitoring data based on the server middle layer caching technology comprises:
constructing a json structure;
and constructing a visual data model of the sand dust intensity monitoring data based on the json structure.
5. The utility model provides a sand and dust meteorological disaster data's animation display device, its characterized in that, the server of the animation display system of sand and dust meteorological disaster data is applied to the device, the system still includes the client, the device includes:
the system comprises an original data acquisition module, a data acquisition module and a data acquisition module, wherein the original data acquisition module is used for acquiring a sequence high-resolution satellite base map, a sequence meteorological satellite base map acquired by a static meteorological satellite and sand monitoring DST data acquired by the static meteorological satellite; each high-resolution satellite base map, each meteorological satellite base map and each DST data respectively comprise corresponding time stamps;
the IDDI data calculation module is used for calculating IDDI data of the infrared difference value dust index according to the DST data;
the intensity data calculation module is used for inputting the IDDI data into a random forest model obtained by pre-training to obtain predicted visibility data corresponding to the IDDI data, and calculating dust intensity data according to the predicted visibility data; the random forest model is obtained by training according to each sample pair in advance, and each sample pair comprises sample IDDI data and sample ground visibility actual measurement data;
the data model building module is used for building a visual data model of sand and dust intensity monitoring data based on a server middle layer cache technology, storing the sand and dust intensity data into the visual data model, and extracting vector data according to the sand and dust intensity data to serve as middle cache data corresponding to the DST data;
and the sand and dust data display module is used for sending the sequence high-resolution satellite base map, the sequence meteorological satellite base map and the intermediate cache data to the client when receiving a sand and dust meteorological disaster data acquisition request sent by the client, so that the client displays the intermediate cache data corresponding to the DST data containing the same timestamp in an overlapping manner with the corresponding high-resolution satellite base map and the meteorological satellite base map according to the sequence of the timestamps from morning to evening on the basis of the WebGL technology.
6. The apparatus of claim 5, wherein the IDDI data calculation module comprises:
the data reading submodule is used for sequentially reading instantaneous brightness temperature values from the DST data;
the data calculation submodule is used for calculating the difference value between the pre-acquired reference brightness temperature value and the read instantaneous brightness temperature value to be used as infrared difference value dust index IDDI data; the reference brightness temperature value is a brightness temperature value observed by the static meteorological satellite in clear sky.
7. The apparatus of claim 5, further comprising:
the initial model building module is used for building an initial model;
the system comprises a sample pair acquisition module, a data acquisition module and a data acquisition module, wherein the sample pair acquisition module is used for acquiring each sample pair, and each sample pair comprises sample IDDI data and sample ground visibility measured data;
and the model training module is used for inputting the sample pairs into the initial model, the initial model obtains predicted ground visibility data according to the IDDI data of the samples in each sample pair, and when the error between the predicted ground visibility data and the corresponding measured ground visibility data of the samples is smaller than a preset threshold value, the current initial model is used as the random forest model.
8. The apparatus of claim 5, wherein the data model building module comprises:
the structure construction submodule is used for constructing a json structure;
and the data model construction submodule is used for constructing a visual data model of the sand dust intensity monitoring data based on the json structure.
9. An animation display system for sand and dust meteorological disaster data is characterized by comprising a server and a client;
the server is used for acquiring a sequence high-resolution satellite base map, a sequence meteorological satellite base map acquired by a static meteorological satellite and sand monitoring DST data acquired by the static meteorological satellite; each high-resolution satellite base map, each meteorological satellite base map and each DST data respectively comprise corresponding time stamps; calculating IDDI (intermediate digital Difference) data of the infrared difference value sand dust index according to the DST data; inputting the IDDI data into a random forest model obtained by pre-training to obtain predicted visibility data corresponding to the IDDI data, and calculating dust intensity data according to the predicted visibility data; the random forest model is obtained by training according to each sample pair in advance, and each sample pair comprises sample IDDI data and sample ground visibility actual measurement data; establishing a visual data model of sand and dust intensity monitoring data based on a server middle layer cache technology, storing the sand and dust intensity data into the visual data model, and extracting vector data according to the sand and dust intensity data to serve as middle cache data corresponding to each DST data; when a request for acquiring the data of the sand and dust meteorological disasters sent by the client is received, sending the sequence high-resolution satellite base map, the sequence meteorological satellite base map and the intermediate cache data to the client;
and the client is used for receiving the sequence high-resolution satellite base map, the sequence meteorological satellite base map and the intermediate cache data sent by the server, and sequentially displaying the intermediate cache data corresponding to the DST data containing the same timestamp, the corresponding high-resolution satellite base map and the meteorological satellite base map in an overlapping manner according to the sequence of the timestamps from morning to evening on the basis of the WebGL technology.
10. The system of claim 9,
the client is specifically configured to receive the intermediate cache data sent by the server, acquire a preset pixel threshold, divide the intermediate cache data into a plurality of sand-dust monomers according to the pixel threshold, and display, based on a WebGL technology, the plurality of sand-dust monomers corresponding to the DST data including the same timestamp, and the corresponding high-resolution satellite base map and the meteorological satellite base map in an overlapping manner according to the sequence of timestamps from morning to evening; each sand dust monomer comprises: sand dust intensity, sand dust influence range, sand dust moving direction and sand dust moving speed.
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