CN112800350A - Live-action calendar control generation method and device, electronic equipment, storage medium and live-action calendar - Google Patents
Live-action calendar control generation method and device, electronic equipment, storage medium and live-action calendar Download PDFInfo
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Abstract
The application provides a method and a device for generating a live-action calendar control, electronic equipment, a storage medium and a live-action calendar, and is characterized in that the method for generating the live-action calendar control comprises the following steps: data initialization and data request; analyzing return data, wherein the return data comprises a current city live-action picture url, air quality prediction data and air quality actual measurement data; loading a live-action picture, wherein the live-action picture is loaded with air quality prediction data and air quality actual measurement data; displaying the first pollutants, city live-action pictures and displaying the pollution levels in different colors. According to the method and the device, the air quality live-action picture and the user-defined calendar control are combined to display, the air quality live-action picture and the calendar are integrated, the change trend of the air quality live-action can be seen more easily, the air quality forecast result can be superposed on every day picture, the monthly air quality forecast result is compared, and the requirements for air quality forecast and actual measurement condition comparison are met.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a device for generating a live-action calendar control, electronic equipment, a storage medium and a live-action calendar.
Background
In the prior art, the air quality live-action map is displayed in a calendar and live-action map separated mode, the display effect is not direct, concise and clear, and the difference cannot be compared when the comparison condition of data of the whole month is checked day by day; in the prior art, the display is generally carried out in an air quality live-action picture list mode, a user has few pictures in a visual range, the comparison condition of date pictures outside the visual range is difficult to see, the trend of the changed air quality live-action cannot be intuitively felt, and air quality forecast data and air quality observation data are not superposed.
Meanwhile, the air quality prediction models at home and abroad mainly adopt a classical regression statistical model and a numerical prediction model, and because the regression statistical prediction method is simple, convenient and economic, the air quality prediction in China mainly depends on the statistical model; the artificial neural network is used as a nonlinear intelligent statistical model different from a regression model and is also applied to air quality prediction, but the conventional air quality prediction method based on the artificial neural network takes all meteorological factors and main pollutant concentration factors as network input, with the increase of the number of the input factors, training samples are greatly increased, the network training model is large, and meanwhile, the network model is easily interfered by irrelevant factors, so that the performance of the network model is reduced.
Disclosure of Invention
In order to solve the technical problem, the invention provides a method for generating a live-action calendar control, which is characterized by comprising the following steps:
data initialization and data request, wherein a current city and a current date are sent to a server to request to obtain a city live-action picture, air quality prediction data and air quality actual measurement data;
analyzing return data, wherein the return data comprises a current city live-action picture url, air quality prediction data and air quality actual measurement data;
loading a live-action picture, and loading an urban live-action picture, air quality prediction data and air quality actual measurement data into a view corresponding to the live-action calendar control;
displaying the first pollutants, city live-action pictures and displaying the pollution levels in different colors.
Further, the air quality prediction data is obtained through prediction of an air quality prediction network model, and the air quality prediction data is obtained through the following method:
acquiring historical data and current day data, wherein the historical data comprises meteorological factor historical data and main pollutant concentration factor historical data, and the meteorological factor historical data is daily average air pressure, daily average air temperature, daily average relative humidity, daily average total cloud cover, daily average low cloud cover, daily average wind speed, daily irradiation time, daily precipitation and daily average wind speed of the current city in the past five years; the historical data of the main pollutant concentration factor are the daily average fine particulate matter (PM2.5) concentration, the daily average inhalable particulate matter (PM10) concentration, the daily average sulfur dioxide (SO2) concentration, the daily average nitrogen dioxide (NO2) concentration, the daily average ozone (O3) concentration and the daily average carbon monoxide (CO) concentration of the last five years of the current city; the current day data comprises weather factor current day data and main pollutant concentration factor current day data, wherein the weather factor current day data comprises the daily average air pressure, daily average air temperature, daily average relative humidity, daily average total cloud cover, daily average low cloud cover, daily average wind speed, daily irradiation time, daily precipitation and daily average wind speed of the current city current day; the daily data of the main pollutant concentration factor is daily average fine particulate matter (PM2.5) concentration, daily average inhalable particulate matter (PM10) concentration, daily average sulfur dioxide (SO2) concentration, daily average nitrogen dioxide (NO2) concentration, daily average ozone (O3) concentration and daily average carbon monoxide (CO) concentration of the current city on the same day;
inputting historical data of the current city as a sample of a prediction network model, and training the prediction network model;
inputting the daily data of the prediction factor set into the prediction model network to predict the concentration of the main pollutants; the main pollutant concentrations include fine particulate matter (PM2.5) concentration, respirable particulate matter (PM10) concentration, sulfur dioxide (SO2) concentration, nitrogen dioxide (NO2) concentration, ozone (O3) concentration, and carbon monoxide (CO) concentration;
calculating the air quality prediction data based on the predicted concentration of the primary pollutant.
Further, the training of the prediction network model by using the historical data of the current city as a sample input of the prediction network model comprises the following steps:
preprocessing the historical data of the current city;
screening input factors of a prediction network model by adopting a genetic algorithm, taking historical data of the candidate factor set as input of the genetic algorithm, and respectively screening prediction factor sets of the prediction network model of the fine particulate matter (PM2.5) concentration, the inhalable particulate matter (PM10) concentration, the sulfur dioxide (SO2) concentration, the nitrogen dioxide (NO2) concentration, the ozone (O3) concentration and the carbon monoxide (CO) concentration; the candidate factor set comprises daily average fine particulate matter (PM2.5) concentration, daily average inhalable particulate matter (PM10) concentration, daily average sulfur dioxide (SO2) concentration, daily average nitrogen dioxide (NO2) concentration, daily average ozone (O3) concentration, daily average carbon monoxide (CO) concentration and daily average air pressure, daily average air temperature, daily average relative humidity, daily average total cloud cover, daily average low cloud cover, daily average wind speed, daily irradiation time, daily precipitation amount and daily average wind speed; the parameters of the genetic algorithm are: the population scale is 30, the cross probability is 0.8, the mutation probability is 0.03, and the maximum genetic generation number is 50;
training a BP neural network, wherein the prediction factor sets of the concentration of fine particulate matters (PM2.5), the concentration of inhalable particulate matters (PM10), the concentration of sulfur dioxide (SO2), the concentration of nitrogen dioxide (NO2), the concentration of ozone (O3) and the concentration of carbon monoxide (CO) are respectively used as the input of the BP neural network, the training output is the neural network of the concentration of the fine particulate matters (PM2.5), the concentration of the inhalable particulate matters (PM10), the concentration of sulfur dioxide (SO2), the concentration of nitrogen dioxide (NO2), the concentration of ozone (O3) and the concentration of carbon monoxide (CO), adjusting the BP neural network parameters to reach a preset training target, selecting an optimal neural network and establishing a main pollutant concentration prediction model; the BP neural network adopts a three-layer network model, the number of neurons in an input layer is respectively the factor number of the prediction factor set, the number of neurons in an implicit layer is respectively 6, 8, 7, 5, 10 and 9, and the number of neurons in an output layer is 1.
The application also provides a live-action calendar control generating device, including:
the data initialization and request module is used for sending the current city and the current date to the server and requesting to acquire a city live-action picture, air quality prediction data and air quality actual measurement data;
the data analysis module is used for analyzing return data, and the return data comprises a current city live-action picture url, air quality prediction data and air quality actual measurement data;
the data loading module is used for loading the live-action pictures, loading the urban live-action pictures, the air quality prediction data and the air quality actual measurement data into the view corresponding to the live-action calendar control, and downloading and cache management are carried out on the urban live-action pictures by using a multi-thread asynchronous concurrent request and a strategy combining a memory cache and a hard disk cache;
and the display module is used for displaying the first pollutants, and displaying the city live-action pictures and the pollution level colors in a distinguishing way.
Preferably, the display module also supports freely switching months and cities, viewing next/previous real scene picture data or specifying month data.
The present application also provides an electronic device comprising a memory, a processor, and machine-readable instructions stored on the memory and executable on the processor, wherein the processor, when executing the machine-readable instructions, performs a method as described above.
The present application also provides a storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, performs the method as described above.
The application also provides a live-action calendar, which comprises a live-action calendar control, and is characterized in that the live-action calendar control is generated by the method as described above.
Through the technical scheme, the invention has the beneficial effects that:
the method displays the air quality live-action picture and the user-defined calendar control in a combined mode, the air quality live-action picture and the calendar are integrated, the change trend of the air quality live-action is easier to see, the city can be selected, the air quality forecast result can be superposed on the daily picture, the monthly air quality forecast result is compared, and the requirements of air quality forecast and actual measurement condition comparison are met; meanwhile, the invention also provides an air quality prediction network model, which effectively solves some problems of the existing air quality prediction and is combined into a live-action calendar.
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 obvious that the drawings in the following description are only some embodiments described in the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
Fig. 1 is a schematic flowchart of a method for generating a live-action calendar control according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of an air quality prediction network model provided in an embodiment of the present application;
fig. 3 is a schematic structural diagram of a live-action calendar control generating apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
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.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. The terminology used in the description of the invention herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
A method for generating a live-action calendar control, please refer to the method for generating the live-action calendar control in fig. 1, which comprises the following steps:
s1: data initialization and data request, wherein a current city and a current date are sent to a server to request to obtain a city live-action picture, air quality prediction data and air quality actual measurement data; the air quality prediction data is obtained through prediction of an air quality prediction network model;
s2: analyzing return data of the server, wherein the return data comprises current city real scene picture url (uniform resource locator), air quality prediction data and air quality actual measurement data;
s3: loading a live-action picture, loading the city live-action picture, air quality prediction data and air quality actual measurement data into a view corresponding to the live-action calendar control to generate the live-action calendar control, wherein the city live-action picture is loaded by using a multithread asynchronous concurrent request and a strategy of combining a memory cache and a hard disk cache to download and cache and manage the city live-action picture.
S4: displaying the first pollutants, city live-action pictures and displaying the pollution levels in different colors.
Referring to fig. 2, in the prediction process of the air quality prediction network model provided by the present application, the prediction method is as follows:
s11: acquiring historical data and current day data, wherein the historical data comprises meteorological factor historical data and main pollutant concentration factor historical data, and the meteorological factor historical data is daily average air pressure, daily average air temperature, daily average relative humidity, daily average total cloud cover, daily average low cloud cover, daily average wind speed, daily time of day, daily precipitation and daily average wind speed of the current city in the past five years; the historical data of the main pollutant concentration factor are the daily average fine particulate matter (PM2.5) concentration, the daily average inhalable particulate matter (PM10) concentration, the daily average sulfur dioxide (SO2) concentration, the daily average nitrogen dioxide (NO2) concentration, the daily average ozone (O3) concentration and the daily average carbon monoxide (CO) concentration of the last five years of the current city; the current day data comprises weather factor current day data and main pollutant concentration factor current day data, wherein the weather factor current day data comprises current city current day daily average air pressure, daily average air temperature, daily average relative humidity, daily average total cloud cover, daily average low cloud cover, daily average wind speed, daily irradiation time, daily precipitation and daily average wind speed; the daily data of the main pollutant concentration factor is daily average fine particulate matter (PM2.5) concentration, daily average inhalable particulate matter (PM10) concentration, daily average sulfur dioxide (SO2) concentration, daily average nitrogen dioxide (NO2) concentration, daily average ozone (O3) concentration and daily average carbon monoxide (CO) concentration of the current city on the same day;
s12: inputting historical data of a current city as a sample of a prediction network model, training the prediction network model, and establishing a main pollutant concentration prediction model;
s13: inputting the daily data of the prediction factor set into a main pollutant concentration prediction model to predict the main pollutant concentration; the main pollutant concentrations include fine particulate matter (PM2.5) concentration, respirable particulate matter (PM10) concentration, sulfur dioxide (SO2) concentration, nitrogen dioxide (NO2) concentration, ozone (O3) concentration, carbon monoxide (CO) concentration;
s14: calculating the air quality prediction data based on the predicted concentration of the primary pollutant.
According to the environmental air quality standard (GB 3095-2012), air quality data are calculated from the concentrations of the main pollutants.
Preferably, the training process of the predictive network model is performed according to the following steps: preprocessing historical data of a current city, and normalizing original data to [ -1, 1] by Min-Max Normalization in a simple Normalization processing mode according to data characteristics and in order to improve a model processing effect;
screening input factors of a prediction network model by adopting a genetic algorithm; using the historical data of the candidate factor set as the input of a genetic algorithm, and respectively screening prediction factor sets of a prediction network model of the concentration of fine particulate matters (PM2.5), the concentration of inhalable particulate matters (PM10), the concentration of sulfur dioxide (SO2), the concentration of nitrogen dioxide (NO2), the concentration of ozone (O3) and the concentration of carbon monoxide (CO); the parameters of the genetic algorithm are set empirically as follows: the population scale is 30, the cross probability is 0.8, the mutation probability is 0.03, and the maximum genetic generation number is 50. The genetic algorithm is a probabilistic adaptive iterative optimization process, an optimal solution can be obtained by using 3 operators of selection, intersection and variation of the algorithm, the operation process of the genetic algorithm is a technology which should be known by a person skilled in the art, and details are not repeated in the application.
Training a BP neural network; respectively taking the historical data of prediction factor sets of the concentration of fine particulate matters (PM2.5), the concentration of inhalable particulate matters (PM10), the concentration of sulfur dioxide (SO2), the concentration of nitrogen dioxide (NO2), the concentration of ozone (O3) and the concentration of carbon monoxide (CO) as the input of a BP neural network, and respectively taking the training outputs of the BP neural network as the concentration of the fine particulate matters (PM2.5), the concentration of the inhalable particulate matters (PM10), the concentration of sulfur dioxide (SO2), the concentration of nitrogen dioxide (NO2), the concentration of ozone (O3) and the concentration of carbon monoxide (CO), adjusting BP neural network parameters to reach a preset training target, selecting an optimal neural network and establishing a main pollutant concentration prediction model;
the BP neural network adopts a three-layer network model, the number of neurons in an input layer is respectively the factor number of the prediction factor set corresponding to the concentration of the main pollutants, the number of neurons in an implicit layer is respectively 6, 8, 7, 5, 10 and 9, and the number of neurons in an output layer is 1.
Please refer to fig. 3, which illustrates a schematic structural diagram of a live-action calendar control generating apparatus according to an embodiment of the present application; the embodiment of the application provides a real-scene calendar control generating device, which comprises:
the data initialization and request module 310 is configured to send the current city and the current date to the server, and request to obtain a city live-action picture, air quality prediction data, and air quality actual measurement data;
the data analysis module 320 is used for analyzing return data, wherein the return data comprises a current city live-action picture url, air quality prediction data and air quality actual measurement data;
the data loading module 330 is configured to load a live-action picture, and load an urban live-action picture, air quality prediction data, and air quality actual measurement data into a view corresponding to the live-action calendar control, where the urban live-action picture loading uses a multi-thread asynchronous concurrent request, and a policy combining a memory cache and a hard disk cache to download and cache and manage the urban live-action picture;
the display module 340 is used for displaying the first pollutants, city live-action pictures and pollution level colors in a distinguishing manner.
Preferably, the presentation module 340 also supports free switching of months and cities, viewing of next/previous month live-action picture data, or specifying month data conditions.
It should be understood that the apparatus corresponds to one embodiment of the real-world calendar control generation method described above, and is capable of executing the steps related to the above method embodiment, and specific functions of the apparatus may be referred to the above description, and a detailed description is appropriately omitted here to avoid repetition. The device includes at least one software function that can be stored in memory in the form of software or firmware (firmware) or solidified in the Operating System (OS) of the device.
Please refer to fig. 4 for a schematic structural diagram of an electronic device according to an embodiment of the present application. An electronic device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, the memory 420 storing machine-readable instructions executable by the processor 410, the machine-readable instructions when executed by the processor 410 performing the method as above.
The embodiment of the present application also provides a storage medium 430, where the storage medium 430 stores a computer program, and the computer program is executed by the processor 410 to perform the method as above.
The embodiment of the application also provides a live-action calendar, which comprises a live-action calendar control, wherein the live-action calendar control is generated by the method.
In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. The meaning of "plurality" is two or more unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (8)
1. A live-action calendar control generation method is characterized by comprising the following steps:
data initialization and data request, wherein a current city and a current date are sent to a server to request to obtain a city live-action picture, air quality prediction data and air quality actual measurement data;
analyzing return data of a server, wherein the return data comprises a current city live-action picture url, air quality prediction data and air quality actual measurement data;
loading a live-action picture, and loading an urban live-action picture, air quality prediction data and air quality actual measurement data into the live-action calendar control view;
displaying the first pollutants, city live-action pictures and displaying the pollution levels in different colors.
2. A live-action calendar control generation method as claimed in claim 1, wherein the server obtains the air quality prediction data by a prediction method comprising:
acquiring historical data and current day data, wherein the historical data comprises meteorological factor historical data and main pollutant concentration factor historical data, and the meteorological factor historical data is daily average air pressure, daily average air temperature, daily average relative humidity, daily average total cloud cover, daily average low cloud cover, daily average wind speed, daily irradiation time, daily precipitation and daily average wind speed of the current city in the past five years; the historical data of the main pollutant concentration factor are the daily average fine particulate matter (PM2.5) concentration, the daily average inhalable particulate matter (PM10) concentration, the daily average sulfur dioxide (SO2) concentration, the daily average nitrogen dioxide (NO2) concentration, the daily average ozone (O3) concentration and the daily average carbon monoxide (CO) concentration of the last five years of the current city; the current day data comprises weather factor current day data and main pollutant concentration factor current day data, wherein the weather factor current day data comprises the daily average air pressure, daily average air temperature, daily average relative humidity, daily average total cloud cover, daily average low cloud cover, daily average wind speed, daily irradiation time, daily precipitation and daily average wind speed of the current city current day; the daily data of the main pollutant concentration factor is daily average fine particulate matter (PM2.5) concentration, daily average inhalable particulate matter (PM10) concentration, daily average sulfur dioxide (SO2) concentration, daily average nitrogen dioxide (NO2) concentration, daily average ozone (O3) concentration and daily average carbon monoxide (CO) concentration of the current city on the same day;
inputting historical data of the current city as a sample of a prediction network model, training the prediction network model, and establishing a main pollutant concentration prediction model;
inputting the data of the current day of the prediction factor set into the main pollutant concentration prediction model to predict the main pollutant concentration; the main pollutant concentrations include fine particulate matter (PM2.5) concentration, respirable particulate matter (PM10) concentration, sulfur dioxide (SO2) concentration, nitrogen dioxide (NO2) concentration, ozone (O3) concentration, and carbon monoxide (CO) concentration;
calculating the air quality prediction data based on the predicted concentration of the primary pollutant.
3. The method for generating a live-action calendar control as claimed in claim 2, wherein the historical data of the current city is used as a sample input of a prediction network model, the prediction network model is trained, a main pollutant concentration prediction model is established, and the training process of the prediction network model is as follows:
preprocessing the historical data of the current city;
using the historical data of the candidate factor set as the input of a genetic algorithm, and respectively screening prediction factor sets of a prediction network model of the concentration of fine particulate matters (PM2.5), the concentration of inhalable particulate matters (PM10), the concentration of sulfur dioxide (SO2), the concentration of nitrogen dioxide (NO2), the concentration of ozone (O3) and the concentration of carbon monoxide (CO); the candidate factor set comprises daily average fine particulate matter (PM2.5) concentration, daily average inhalable particulate matter (PM10) concentration, daily average sulfur dioxide (SO2) concentration, daily average nitrogen dioxide (NO2) concentration, daily average ozone (O3) concentration, daily average carbon monoxide (CO) concentration and daily average air pressure, daily average air temperature, daily average relative humidity, daily average total cloud cover, daily average low cloud cover, daily average wind speed, daily irradiation time, daily precipitation amount and daily average wind speed; the parameters of the genetic algorithm are: the population scale is 30, the cross probability is 0.8, the mutation probability is 0.03, and the maximum genetic generation number is 50;
training a BP neural network, wherein the prediction factor sets of the concentration of fine particulate matters (PM2.5), the concentration of inhalable particulate matters (PM10), the concentration of sulfur dioxide (SO2), the concentration of nitrogen dioxide (NO2), the concentration of ozone (O3) and the concentration of carbon monoxide (CO) are respectively used as the input of the BP neural network, the training output is the neural network of the concentration of the fine particulate matters (PM2.5), the concentration of the inhalable particulate matters (PM10), the concentration of sulfur dioxide (SO2), the concentration of nitrogen dioxide (NO2), the concentration of ozone (O3) and the concentration of carbon monoxide (CO), adjusting the BP neural network parameters to reach a preset training target, selecting an optimal neural network and establishing a main pollutant concentration prediction model; the BP neural network adopts a three-layer network model, the number of neurons in an input layer is respectively the factor number of the prediction factor set, the number of neurons in an implicit layer is respectively 6, 8, 7, 5, 10 and 9, and the number of neurons in an output layer is 1.
4. A live-action calendar control generating apparatus, comprising:
the data initialization and request module is used for sending the current city and the current date to the server and requesting to acquire a city live-action picture, air quality prediction data and air quality actual measurement data;
the data analysis module is used for analyzing return data, and the return data comprises a current city live-action picture url, air quality prediction data and air quality actual measurement data;
the data loading module is used for loading the live-action pictures, loading the urban live-action pictures, the air quality prediction data and the air quality actual measurement data into the view corresponding to the live-action calendar control, and downloading and cache management are carried out on the urban live-action pictures by using a multi-thread asynchronous concurrent request and a strategy combining a memory cache and a hard disk cache;
and the display module is used for displaying the first pollutants, and displaying the city live-action pictures and the pollution level colors in a distinguishing way.
5. A realistic calendar control generating apparatus as claimed in claim 4, wherein said showing module further supports free switching of months and cities, viewing of next/previous month realistic picture data or specifying of month data condition.
6. An electronic device comprising a memory, a processor, and machine-readable instructions stored on the memory and executable on the processor, wherein the processor, when executing the machine-readable instructions, implements the live-action calendar control generation method of any one of claims 1-3.
7. A storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the live-action calendar control generation method according to any one of claims 1-3.
8. A live-action calendar comprising a live-action calendar control, wherein the live-action calendar control is generated by the method of any one of claims 1-3.
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| CN107767443A (en) * | 2017-11-02 | 2018-03-06 | 宁波宝略智能科技有限公司 | A kind of three-dimensional visualization outdoor scene methods of exhibiting based on Unity3D |
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