CN112259239B - Parameter processing method and device, electronic equipment and storage medium - Google Patents

Parameter processing method and device, electronic equipment and storage medium Download PDF

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CN112259239B
CN112259239B CN202011135592.XA CN202011135592A CN112259239B CN 112259239 B CN112259239 B CN 112259239B CN 202011135592 A CN202011135592 A CN 202011135592A CN 112259239 B CN112259239 B CN 112259239B
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climate
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CN112259239A (en
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李烨
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Ping An Technology Shenzhen Co Ltd
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/80—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for detecting, monitoring or modelling epidemics or pandemics, e.g. flu
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G06F16/27—Replication, distribution or synchronisation of data between databases or within a distributed database system; Distributed database system architectures therefor
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
    • G06F21/60—Protecting data
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Abstract

The application provides a parameter processing method, a device, electronic equipment and a storage medium, which are applied to the field of medical science and technology, wherein the method comprises the following steps: acquiring a first climate parameter of a target region on a first date, wherein the first climate parameter comprises at least one of the following: a first temperature value, a first humidity value, a first air pressure value; determining a target season corresponding to the first date; calling a preset generalized additive model to predict new cases according to the first climate parameters and the target season to obtain a prediction result, wherein the prediction result comprises the number of the new cases of the target area on the first date; and outputting the prediction result through the terminal equipment. The application can be used for predicting the transmission condition of infectious diseases based on weather. The present application relates to blockchain techniques, such as writing prediction results into blockchains.

Description

Parameter processing method and device, electronic equipment and storage medium
Technical Field
The present disclosure relates to the field of computer technologies, and in particular, to a parameter processing method, a device, an electronic apparatus, and a storage medium.
Background
The outbreak and spread of infection can have a serious impact on the economies of the various places and the lives of people. In general, the transmission of microorganisms such as viruses and bacteria is a major factor responsible for the outbreak and spread of infectious diseases. Scientific experiments have shown that climate affects the transmission of certain viruses, bacteria and other microorganisms. For example, it has been found that the transmission of infectious viruses is related to the climate, which affects the transmission of infectious viruses. If the climate can be used for prediction of the transmission of infectious diseases by studying the relation between the climate and the transmission of infectious diseases, this will have a positive effect on the control and elimination of infectious diseases. However, prediction of how to use climate for the transmission of infectious diseases is a major issue.
Disclosure of Invention
The embodiment of the application provides a parameter processing method, a device, electronic equipment and a storage medium, and can be used for predicting the transmission condition of infectious diseases.
In a first aspect, an embodiment of the present application provides a parameter processing method, including:
acquiring a first climate parameter of a target region on a first date, wherein the first climate parameter comprises at least one of the following: a first temperature value, a first humidity value, a first air pressure value;
determining a target season corresponding to the first date;
calling a preset generalized additive model to predict new cases according to the first climate parameters and the target season to obtain a prediction result, wherein the prediction result comprises the number of the new cases of the target area on the first date;
and outputting the prediction result through the terminal equipment.
Optionally, the predicting the new case according to the first climate parameter and the target season includes:
determining a target weight corresponding to the target season according to the corresponding relation between the season and the weight;
substituting the first climate parameters and the target weights into a preset connection function to calculate the number of new cases of the target area on the first date.
Optionally, substituting the first climate parameter and the target weight into a preset connection function to calculate the number of new cases of the target area on the first date includes:
weighting the first climate parameters according to the target weight to obtain a weighted result;
substituting the weighted result into a smooth function included in the connection function to calculate the number of new cases of the target area on the first date.
Optionally, substituting the weighted result into a smoothing function included in the connection function to calculate a new case number of the target area on the first date, including:
substituting the weighted result into a smooth function included in the connection function, and substituting the value of the target area and the value of the first date into the connection function to calculate the number of new cases of the target area on the first date.
Optionally, before determining the target weight corresponding to the target season according to the correspondence between seasons and weights, the method further includes:
determining a propagation probability corresponding to each of the at least one seasonal combination; the propagation probability corresponding to each seasonal combination obeys Gaussian distribution;
calculating the weight corresponding to each seasonal combination according to the propagation probability corresponding to each seasonal combination by adopting a maximum likelihood estimation method;
and constructing the corresponding relation between seasons and weights according to the weights corresponding to each season combination.
Optionally, the substituting the first climate parameter and the target weight into a preset connection function to calculate the number of new cases of the target area on the first date, and the method further includes:
acquiring a second climate parameter of the target area on a second date and a new case number of the target area on the second date; the second date is prior to the first date; the second climate parameters include at least one of: a second temperature value, a second humidity value, and a second air pressure value;
and determining a smooth function included in a preset connection function according to the second climate parameters and the number of new cases of the target area on the second date.
Optionally, the acquiring the first climate parameter of the target area on the first date includes:
acquiring a climate parameter set of a target area, wherein the climate parameter set comprises climate parameters of each date in a preset date range before a first date; the climate parameters include at least one of: temperature value, humidity value, air pressure value;
and carrying out average processing on the climate parameters of each date in a preset date range before the first date to obtain a first climate parameter of the target area on the first date.
In a second aspect, an embodiment of the present application provides a parameter processing apparatus, including:
the acquisition module is used for acquiring a first climate parameter of the target area on a first date, wherein the first climate parameter comprises at least one of the following: a first temperature value, a first humidity value, a first air pressure value;
the processing module is used for determining a target season corresponding to the first date, and calling a preset generalized additive model to predict new cases according to the first climate parameters and the target season, so as to obtain a prediction result, wherein the prediction result comprises the number of the new cases of the target area on the first date;
and the output module is used for outputting the prediction result through the terminal equipment.
In a third aspect, an embodiment of the present application provides an electronic device, including a processor, an output device, and a memory, where the processor, the output device, and the memory are connected to each other, and the memory is configured to store a computer program, where the computer program includes program instructions, and where the processor is configured to invoke the program instructions to perform a method according to the first aspect.
In a fourth aspect, embodiments of the present application provide a computer readable storage medium storing a computer program for execution by a processor to implement the method of the first aspect.
In summary, the electronic device obtains the first climate parameter of the target area on the first date, and invokes the preset generalized additive model to predict the new case according to the first climate parameter and the target season, so as to obtain a prediction result, and the terminal device outputs the prediction result, so that the process of predicting the propagation condition of the infectious disease by using the climate is realized.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a parameter processing method according to an embodiment of the present application;
FIG. 2 is a schematic trip diagram of another parameter processing method according to an embodiment of the present disclosure;
FIG. 3 is a schematic diagram of a network architecture of a parameter processing system according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a parameter processing device according to an embodiment of the present application;
fig. 5 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 application will be described below with reference to the drawings in the embodiments of the present application.
Fig. 1 is a schematic flow chart of a parameter processing method according to an embodiment of the present application. The method can be applied to an electronic device. The electronic device may be a terminal device or a server, and the terminal device includes, but is not limited to, a smart terminal such as a notebook computer, a desktop computer, and the like. The server may be a server or a cluster of servers. Specifically, the method may include the steps of:
s101, acquiring a first climate parameter of a target area on a first date, wherein the first climate parameter comprises at least one of the following: a first temperature value, a first humidity value, a first air pressure value.
The target area may be any area to be predicted or a designated area. The area herein may be a community, a street (village and town), or a city, and embodiments of the present application are not limited.
Wherein the first date may be a system date. Or the first date may be any date before the system date. Or the first date may be any date after the system date. Or the first date may be any date within a specified date range (e.g., 1 month) after the system date. The electronic device can obtain climate parameters for each date within a predetermined date range (e.g., 14 days) prior to the first date. The number of dates corresponding to the specified date range may be different from or the same as the number of dates corresponding to the preset date range.
In one embodiment, the preset date range mentioned in the embodiment of the present application may be any date range preset, or may also be a date range determined according to the latency of the pathogenic factor in the target subject. Here, the causative factors include, but are not limited to, bacterial, viral, and the like causative factors. The target objects herein include, but are not limited to, plants (e.g., oryza sativa), animals (if nutria), or humans.
In one embodiment, the manner in which the electronic device obtains the first climate parameter of the target region on the first date may be: the electronic device determines a climate parameter of the target area on a first date as a first climate parameter on the first date. The climate parameters of the first date refer to the climate parameters of the day of the first date. That is, the first climate parameter of the first date herein is the climate parameter of the first date.
In one embodiment, the manner in which the electronic device obtains the first climate parameter of the target region on the first date may be: the electronic device determines a first climate parameter for the first date based on the climate parameters for each date within a specified date range after the first date. That is, the first climate parameters of the first date herein are determined from the climate parameters of the respective dates within the specified date range after the first date. By adopting the method to acquire the first climate parameters of the first date for the prediction of the newly added case, the prediction accuracy can be improved.
In one embodiment, the process of determining the first climate parameter of the first date by the electronic device according to the climate parameters of each date within the specified date range after the first date may be: and the electronic equipment carries out average processing on the climate parameters of each date in the appointed date range after the first date to obtain the first climate parameters of the target area on the first date. That is, the first climate parameters of the first date are obtained by performing the average processing on the climate parameters of the respective dates within the specified date range after the first date. By adopting the method to acquire the first climate parameters of the first date for the prediction of the newly added case, the prediction accuracy can be improved.
S102, determining a target season corresponding to the first date.
The target season may refer to a season corresponding to the first date.
In one embodiment, the process of determining, by the electronic device, the target season corresponding to the first date may determine, for the electronic device, a month included in the first date, and determine, as the target season, a season corresponding to the month.
In one embodiment, the process of determining, by the electronic device, the target season corresponding to the first date may determine, for the electronic device, a month and a day included in the first date, and determine, as the target season, a season corresponding to the month and the day.
S103, calling a preset generalized additive model to predict new cases according to the first climate parameters and the target season, and obtaining a prediction result, wherein the prediction result comprises the number of the new cases of the target area on the first date.
S104, outputting the prediction result through the terminal equipment.
Because the weather may affect the newly added cases, in order to accurately estimate the number of newly added cases according to the weather, the electronic device may call a preset generalized additive model to predict the newly added cases according to the first weather parameter and the target season, obtain a prediction result, and output the prediction result through the terminal device. The terminal device includes, but is not limited to, smart terminals such as smart phones, tablet computers, notebook computers, desktop computers, and the like, and the embodiment of the application is not limited.
In one embodiment, the electronic device performs the prediction of the new case according to the first climate parameter and the target season, and the process of obtaining the prediction result may be: the electronic equipment determines a target weight corresponding to the target season according to the corresponding relation between the season and the weight, and determines the number of new cases of the target area on the first date according to the first climate parameter and the target weight. The target weight refers to a weight corresponding to a target season. The season-weight correspondence may include, for example, a summer-autumn correspondence with a first weight and a spring-autumn correspondence with a second weight.
In one embodiment, the process of the electronic device for predicting the new case according to the first climate parameter and the target season may be: the electronic equipment determines a target weight corresponding to the target season according to the corresponding relation between the season and the weight, and substitutes the first climate parameter and the target weight into a preset connection function to calculate the number of new cases of the target area on the first date. That is, one way for the electronic device to determine the number of new cases of the target area on the first date according to the first climate parameter and the target weight may be: the electronic equipment substitutes the first climate parameters and the target weights into a preset connection function to calculate the number of new cases of the target area on the first date.
In one embodiment, the expression of the aforementioned connection function may be as follows:
log(y it )＝a+s(w s *Temp it )+s(w s *Humi it )+s(w s *Press it )+log(y i(t-j) )+ε it
equation 1.1;
wherein y is it The number of new cases of city i on day t is indicated. s () is a smooth function, which may be, for example, a non-parametric smooth function, in particular, a smooth spline function. w (w) s And the weight of the season corresponding to t is represented. Temp (Temp) it Representing the target temperature value of region i at day t, humi it Indicating the target humidity value of region i on day t, press it The target air pressure value of the region i on the t-th day is indicated.
In one embodiment, the electronic device substitutes the first climate parameter and the target weight into a predetermined connection function to calculate the target regionThe process of adding the number of cases on the first date may be: and the electronic equipment performs weighting processing on the first climate parameters according to the target weight to obtain a weighted result, and substitutes the weighted result into a smooth function included in the connection function to calculate the number of new cases of the target area on the first date. For example, the target area is i in equation 1.1 and the first time is t in equation. The first temperature value is Temp in equation 1.1 it The first humidity value is Humi in equation 1.1 it The first air pressure value is Press in equation 1.1 it The target weight is w in equation 1.1 s . The electronic device may perform weighting processing on the first temperature value, the first humidity value and the first air pressure value according to the target weight, to obtain a first weighting result, a second weighting result and a third weighting result, and substitute the first weighting result, the second weighting result and the third weighting result into the corresponding smoothing functions s () in the formula 1.1, so as to calculate the number of new cases of the target area on the first date.
In one embodiment, the expression of the aforementioned connection function may also be as follows:
log(y it )＝a+s(w s *Temp it +s(w s *Humt it +s(w s *Press it +log(y i(t-j) )+city i +day t +ε it
equation 1.2;
equation 1.2 considers the additional lead-in area and date for the generalized additive model as compared to equation 1.1. Wherein, the city is i Representing the value of region i, day t The value on day t is indicated.
In one embodiment, the electronic device may substitute the weighted result into a smoothing function included in the connection function, and substitute the value of the target area and the value of the first date into the connection function to calculate the number of new cases of the target area on the first date. For example, the target region has a value of city i The value of the first date is day t The electronic device may substitute the weighted result into the connection function to includeAnd substituting the value of the target area and the value of the first date into the connected function in formula 1.2 to calculate the number of new cases of the target area on the first date.
In one embodiment, the aforementioned correspondence between seasons and weights can be obtained by: the electronic equipment determines the propagation probability corresponding to each season combination in at least one season combination; the propagation probability corresponding to each seasonal combination obeys Gaussian distribution; the electronic equipment calculates and obtains the weight corresponding to each seasonal combination according to the propagation probability corresponding to each seasonal combination by adopting a maximum likelihood estimation method, and builds the corresponding relation between seasons and weights according to the weight corresponding to each seasonal combination.
In one embodiment, assume that the propagation probability corresponding to winter and spring is y 1 The propagation probability corresponding to summer and autumn is y 2 . The gaussian distribution can be found in the following formula:
Figure SMS_1
in one embodiment, the process of calculating the weight corresponding to each seasonal combination by the electronic device according to the propagation probability corresponding to each seasonal combination by using the maximum likelihood estimation method may be: deriving the log-likelihood function of Gaussian distribution to minimize its derivative (e.g. 0), and calculating the weight w 1 (corresponding to winter and spring) and w 2 (corresponding to summer and autumn). The formula of the log likelihood function can be as follows:
Figure SMS_2
in one embodiment, the aforementioned connection function includes a smoothing function that may be determined by: the electronic equipment acquires a second climate parameter of the target area on a second date and the number of new cases of the target area on the second date, and determines a smooth function included in a preset connection function according to the second climate parameter and the number of new cases of the target area on the second date. Wherein the second date precedes the first date. The second date may be one or more. When the second date is a plurality, the second climate parameter of the second date may refer to the second climate parameter of each of the plurality of second dates. The second climate parameters include at least one of: a second temperature value, a second humidity value, and a second air pressure value.
In one embodiment, the manner in which the electronic device obtains the second climate parameters of the target region on the second date may be: the electronic device determines a climate parameter of the target area on a second date as a second climate parameter on the second date. That is, the second climate parameter of the second date herein is the climate parameter of the second date.
In one embodiment, the manner in which the electronic device obtains the second climate parameters of the target region on the second date may be: the electronic device determines a second climate parameter for the second date based on the climate parameters for each date within a specified range of dates subsequent to the second date. That is, the second climate parameters for the second date herein are determined based on the climate parameters for each date within the specified date range after the second date. By adopting the method to acquire the second climate parameters of the second date for the prediction of the new case, the prediction accuracy can be improved.
In one embodiment, the process of determining the second climate parameter for the second date by the electronic device based on the climate parameters for each date within the specified range of dates subsequent to the second date may be: and the electronic equipment performs average processing on the climate parameters of each date in the appointed date range after the second date to obtain the second climate parameters of the target area on the second date. That is, the second climate parameters on the second date are obtained by averaging the climate parameters on each date within the specified date range after the second date. By adopting the method to acquire the second climate parameters of the second date for the prediction of the new case, the prediction accuracy can be improved.
It can be seen that, in the embodiment shown in fig. 1, the electronic device may acquire a first climate parameter of the target area on a first date; the electronic equipment can call a preset generalized additive model to predict new cases according to the first climate parameters and the target seasons corresponding to the first dates, a prediction result is obtained, the terminal equipment outputs the prediction result, and the process realizes the prediction process of using the climate for the infectious disease transmission condition.
Fig. 2 is a flow chart of another parameter processing method according to an embodiment of the present application. The method may be applied in the aforementioned electronic device. Specifically, the method may comprise the steps of:
s201, acquiring a climate parameter set of a target area, wherein the climate parameter set comprises climate parameters of each date in a preset date range before a first date.
Wherein the climate parameters may include at least one of: temperature value, humidity value, air pressure value.
In this embodiment of the present application, the electronic device may determine, according to the climate parameters of each date in the preset date range before the first date, the first climate parameter of the target area on the first date.
S202, carrying out average processing on the climate parameters of each date in a preset date range before the first date to obtain a first climate parameter of the target area on the first date.
In this embodiment of the present application, the process of determining, by the electronic device, the first weather parameter of the target area on the first date according to the weather parameters of the dates within the preset date range before the first date may be: and the electronic equipment carries out average processing on the climate parameters of each date in a preset date range before the first date to obtain a first climate parameter of the target area on the first date.
For example, the first date is a date, the preset date range is 14 days, and the electronic device may perform average processing on the climate parameters of each day of 14 days before the date a to obtain the first climate parameters of the target area on the date a. The climate parameters are assumed to comprise temperature values. The first climate parameter includes a first temperature value. The temperature values for the first 14 days are T1-T14 respectively. Accordingly, the process of the electronic device performing the mean value processing on the temperature values of the first 14 days may be expressed as (t1+ … +t14)/14, and the electronic device may use the calculation result of (t1+ … +t14)/14 as the first temperature value of the first date.
S203, determining a target season corresponding to the first date.
S204, calling a preset generalized additive model to predict new cases according to the first climate parameters and the target season, and obtaining a prediction result, wherein the prediction result comprises the number of the new cases of the target area on the first date.
S205, outputting the prediction result through the terminal equipment.
Step S203 to step S205 may refer to step S102 to step S104 in the embodiment of fig. 1, which is not described herein.
In one embodiment, the manner in which the electronic device obtains the second climate parameters of the target region on the second date may be: and the electronic equipment determines a second climate parameter of the second date according to the climate parameters of each date in a preset date range before the second date. That is, the second climate parameters for the second date herein are determined based on the climate parameters for each date within the predetermined range of dates preceding the second date. By adopting the method to acquire the second climate parameters of the second date for the prediction of the new case, the prediction accuracy can be improved.
In one embodiment, the process of determining the second climate parameter of the second date by the electronic device according to the climate parameters of each date in the preset date range before the second date may be: and the electronic equipment carries out average processing on the climate parameters of each date in a preset date range before the second date to obtain the second climate parameters of the target area on the second date. That is, the second climate parameters of the second date are obtained by performing the mean value processing on the climate parameters of each date within the preset date range before the second date. By adopting the method to acquire the second climate parameters of the second date for the prediction of the new case, the prediction accuracy can be improved.
In one embodiment, after determining the smoothing function included in the connection function, an image may be drawn according to a relationship between the target climate parameter and the function value of the corresponding smoothing function, and a correlation between the target climate parameter and the corresponding dependent variable (i.e., the number of newly added cases) may be analyzed according to the image, where the correlation may be any one of the following: positive correlation, negative correlation and no obvious effect. Or, after determining the smoothing function included in the connection function, the electronic device may analyze a correlation between the target climate parameter and the corresponding dependent variable based on the target climate parameter and a function value of the corresponding smoothing function.
In the embodiment shown in fig. 2, the electronic device obtains the first weather parameter of the target area on the first date for predicting the new case by performing the mean value processing on the weather parameters of the dates in the preset date range before the first date, so that the prediction accuracy can be improved.
The present application relates to blockchain techniques, such as writing a prediction result into a blockchain, or writing encrypted data of a prediction result into a blockchain.
The foregoing electronic device is taken as an example of a server, to describe a parameter processing system provided in an embodiment of the present application. Referring to fig. 3, the parameter processing system shown in fig. 3 includes a server 10 and a terminal device 20. Specifically:
the server 20 may perform the new case prediction according to the first climate parameter of the first date and obtain the prediction result by performing step S101-step S103. Then, the server performs a prediction process of the propagation condition of the infectious disease according to the climate by performing step S104 to display the prediction result through the terminal device 20.
Fig. 4 is a schematic structural diagram of a parameter processing apparatus according to an embodiment of the present application. The parameter processing device can be applied to the aforementioned electronic apparatus. The parameter processing apparatus may include:
an obtaining module 401, configured to obtain a first climate parameter of the target area on a first date, where the first climate parameter includes at least one of the following: a first temperature value, a first humidity value, a first air pressure value.
The processing module 402 is configured to determine a target season corresponding to the first date, and call a preset generalized additive model to predict a new case according to the first climate parameter and the target season, so as to obtain a prediction result, where the prediction result includes a number of new cases of the target area on the first date.
And the output module 403 is configured to output the prediction result through a terminal device.
In an alternative embodiment, the processing module 402 performs the prediction of the new case according to the first climate parameter and the target season, specifically determines the target weight corresponding to the target season according to the correspondence between the season and the weight; substituting the first climate parameters and the target weights into a preset connection function to calculate the number of new cases of the target area on the first date.
In an optional implementation manner, the processing module 402 substitutes the first climate parameter and the target weight into a preset connection function to calculate the number of new cases of the target area on the first date, specifically, performs weighting processing on the first climate parameter according to the target weight, so as to obtain a weighted result; substituting the weighted result into a smooth function included in the connection function to calculate the number of new cases of the target area on the first date.
In an alternative embodiment, the processing module 402 substitutes the weighted result into the smoothing function included in the connection function to calculate the number of new cases of the target area on the first date, specifically substitutes the weighted result into the smoothing function included in the connection function, and substitutes the value of the target area and the value of the first date into the connection function to calculate the number of new cases of the target area on the first date.
In an alternative embodiment, the processing module 402 is further configured to determine, before determining the target weight corresponding to the target season according to the correspondence between seasons and weights, a propagation probability corresponding to each of at least one combination of seasons; the propagation probability corresponding to each seasonal combination obeys Gaussian distribution; calculating the weight corresponding to each seasonal combination according to the propagation probability corresponding to each seasonal combination by adopting a maximum likelihood estimation method; and constructing the corresponding relation between seasons and weights according to the weights corresponding to each season combination.
In an alternative embodiment, the processing module 402 is further configured to, before substituting the first climate parameter and the target weight into a preset connection function to calculate the number of new cases of the target area on the first date, obtain, by the obtaining module 401, a second climate parameter of the target area on the second date and the number of new cases of the target area on the second date; the second date is prior to the first date; the second climate parameters include at least one of: a second temperature value, a second humidity value, and a second air pressure value; and determining a smooth function included in a preset connection function according to the second climate parameters and the number of new cases of the target area on the second date.
In an alternative embodiment, the obtaining module 401 obtains a first climate parameter of the target area on a first date, specifically obtains a climate parameter set of the target area, where the climate parameter set includes a climate parameter of each date in a preset date range before the first date; the climate parameters include at least one of: temperature value, humidity value, air pressure value; and carrying out average processing on the climate parameters of each date in a preset date range before the first date to obtain a first climate parameter of the target area on the first date.
It can be seen that in the embodiment shown in fig. 4, the parameter processing device may obtain the first climate parameter of the target area on the first date, and call the preset generalized additive model to predict the new case according to the first climate parameter and the target season, so as to obtain a prediction result, and output the prediction result through the terminal device.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application. The electronic device described in the present embodiment may include: one or more processors 1000, one or more input devices 2000, one or more output devices 3000, and memory 4000. The processor 1000, the input device 2000, the output device 3000, and the memory 4000 may be connected through a bus. Among them, the input device 2000 and the output device 3000 are optional devices, i.e., the electronic device may include only the processor 1000, the output device 3000, and the memory 4000. In one embodiment, the input device 2000, the output device 3000 may comprise a standard wired or wireless communication interface. In one embodiment, the input device 2000 may include a touch screen, a touch display screen, or a voice recorder. Output device 3000 may include a display screen, a touch display screen, speakers, etc.
The processor 1000 may be a central processing module (Central Processing Unit, CPU) which may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Memory 4000 may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as a disk memory. Memory 4000 is used to store a set of program codes, and input device 2000, output device 3000, and processor 1000 can call up the program codes stored in memory 4000. Specifically:
the processor 1000 is configured to obtain a first climate parameter of the target region on a first date, where the first climate parameter includes at least one of: a first temperature value, a first humidity value, a first air pressure value; determining a target season corresponding to the first date; calling a preset generalized additive model to predict new cases according to the first climate parameters and the target season to obtain a prediction result, wherein the prediction result comprises the number of the new cases of the target area on the first date; the prediction result is output by the output device 3000 through the terminal device.
In one embodiment, the processor 1000 performs the prediction of the new case according to the first climate parameter and the target season, specifically determines the target weight corresponding to the target season according to the correspondence between the season and the weight; substituting the first climate parameters and the target weights into a preset connection function to calculate the number of new cases of the target area on the first date.
In one embodiment, the processor substitutes the first climate parameter and the target weight into a preset connection function to calculate the number of new cases of the target area on the first date, specifically, weights the first climate parameter according to the target weight to obtain a weighted result; substituting the weighted result into a smooth function included in the connection function to calculate the number of new cases of the target area on the first date.
In one embodiment, the processor 1000 substitutes the weighted result into the smooth function included in the connection function to calculate the number of new cases of the target area on the first date, specifically substitutes the weighted result into the smooth function included in the connection function, and substitutes the value of the target area and the value of the first date into the connection function to calculate the number of new cases of the target area on the first date.
In one embodiment, the processor 1000 is further configured to determine a propagation probability corresponding to each of at least one combination of seasons before determining the target weight corresponding to the target season according to the correspondence between seasons and weights; the propagation probability corresponding to each seasonal combination obeys Gaussian distribution; calculating the weight corresponding to each seasonal combination according to the propagation probability corresponding to each seasonal combination by adopting a maximum likelihood estimation method; and constructing the corresponding relation between seasons and weights according to the weights corresponding to each season combination.
In one embodiment, the processor 1000 is further configured to obtain a second climate parameter of the target area on a second date and a new case number of the target area on the second date before substituting the first climate parameter and the target weight into a preset connection function to calculate the new case number of the target area on the first date; the second date is prior to the first date; the second climate parameters include at least one of: a second temperature value, a second humidity value, and a second air pressure value; and determining a smooth function included in a preset connection function according to the second climate parameters and the number of new cases of the target area on the second date.
In one embodiment, the processor 1000 obtains a first climate parameter of the target region on a first date, in particular obtains a set of climate parameters of the target region, the set of climate parameters including climate parameters of each date within a predetermined range of dates preceding the first date; the climate parameters include at least one of: temperature value, humidity value, air pressure value; and carrying out average processing on the climate parameters of each date in a preset date range before the first date to obtain a first climate parameter of the target area on the first date.
In specific implementation, the processor 1000, the input device 2000, and the output device 3000 described in the embodiments of the present application may perform the implementation described in the embodiments of fig. 1 and fig. 2, and may also perform the implementation described in the embodiments of the present application, which are not described herein again.
The functional modules in the embodiments of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in the form of sampling hardware or in the form of sampling software functional modules.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed may comprise the steps of the embodiments of the methods described above. Wherein the computer readable storage medium may be volatile or nonvolatile. For example, the computer storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), or the like. The computer readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created from the use of blockchain nodes, and the like.
The blockchain referred to in the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and the like. The Blockchain (Blockchain), which is essentially a decentralised database, is a string of data blocks that are generated by cryptographic means in association, each data block containing a batch of information of network transactions for verifying the validity of the information (anti-counterfeiting) and generating the next block. The blockchain may include a blockchain underlying platform, a platform product services layer, an application services layer, and the like.
The foregoing disclosure is only a preferred embodiment of the present application, and it is not intended to limit the scope of the claims, and one of ordinary skill in the art will understand that all or part of the processes for implementing the embodiments described above may be performed with equivalent changes in the claims of the present application and still fall within the scope of the present application.

Claims (8)

1. A method of processing parameters, comprising:
acquiring a first climate parameter of a target region on a first date, wherein the first climate parameter comprises at least one of the following: a first temperature value, a first humidity value, a first air pressure value;
determining a target season corresponding to the first date;
calling a preset generalized additive model to predict new cases according to the first climate parameters and the target season to obtain a prediction result, wherein the prediction result comprises the number of the new cases of the target area on the first date;
outputting the prediction result through terminal equipment;
wherein the predicting the new case according to the first climate parameter and the target season includes:
determining a target weight corresponding to the target season according to the corresponding relation between the season and the weight; the corresponding relation between the seasons and the weights is constructed according to the weights corresponding to each seasonal combination in at least one seasonal combination, the weights corresponding to each seasonal combination are calculated according to the propagation probabilities corresponding to each seasonal combination by adopting a maximum likelihood estimation method, and the propagation probabilities corresponding to each seasonal combination obey Gaussian distribution;
substituting the first climate parameters and the target weights into a preset connection function to calculate the number of new cases of the target area on the first date.
2. The method of claim 1, wherein substituting the first climate parameter and the target weight into a preset connection function to calculate a number of new cases of the target area on the first date comprises:
weighting the first climate parameters according to the target weight to obtain a weighted result;
substituting the weighted result into a smooth function included in the connection function to calculate the number of new cases of the target area on the first date.
3. The method of claim 2, wherein substituting the weighted result into the smoothing function included in the connection function to calculate the number of new cases of the target area on the first date comprises:
substituting the weighted result into a smooth function included in the connection function, and substituting the value of the target area and the value of the first date into the connection function to calculate the number of new cases of the target area on the first date.
4. The method of claim 1, wherein substituting the first climate parameter and the target weight into a preset connection function to calculate a new number of cases for the target area on the first date, the method further comprising:
acquiring a second climate parameter of the target area on a second date and a new case number of the target area on the second date; the second date is prior to the first date; the second climate parameters include at least one of: a second temperature value, a second humidity value, and a second air pressure value;
and determining a smooth function included in a preset connection function according to the second climate parameters and the number of new cases of the target area on the second date.
5. The method of claim 1, wherein the obtaining a first climate parameter of the target region on a first date comprises:
acquiring a climate parameter set of a target area, wherein the climate parameter set comprises climate parameters of each date in a preset date range before a first date; the climate parameters include at least one of: temperature value, humidity value, air pressure value;
and carrying out average processing on the climate parameters of each date in a preset date range before the first date to obtain a first climate parameter of the target area on the first date.
6. A parameter processing apparatus, comprising:
the acquisition module is used for acquiring a first climate parameter of the target area on a first date, wherein the first climate parameter comprises at least one of the following: a first temperature value, a first humidity value, a first air pressure value;
the processing module is used for determining a target season corresponding to the first date, and calling a preset generalized additive model to predict new cases according to the first climate parameters and the target season, so as to obtain a prediction result, wherein the prediction result comprises the number of the new cases of the target area on the first date;
the output module is used for outputting the prediction result through the terminal equipment;
the processing module predicts the new case according to the first climate parameter and the target season, and is specifically configured to:
determining a target weight corresponding to the target season according to the corresponding relation between the season and the weight; the corresponding relation between the seasons and the weights is constructed according to the weights corresponding to each seasonal combination in at least one seasonal combination, the weights corresponding to each seasonal combination are calculated according to the propagation probabilities corresponding to each seasonal combination by adopting a maximum likelihood estimation method, and the propagation probabilities corresponding to each seasonal combination obey Gaussian distribution;
substituting the first climate parameters and the target weights into a preset connection function to calculate the number of new cases of the target area on the first date.
7. An electronic device comprising a processor, an output device and a memory, the processor, the output device and the memory being interconnected, wherein the memory is adapted to store a computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method of any of claims 1-5.
8. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program, which is executed by a processor to implement the method of any of claims 1-5.
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