CN113764106A - Epidemic situation prevention and control effect prediction method and related products - Google Patents
Epidemic situation prevention and control effect prediction method and related products Download PDFInfo
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Abstract
The application provides an epidemic prevention and control effect prediction method and a related product, and the method comprises the steps of obtaining prevention and control data corresponding to each prevention and control scheme; inputting the prevention and control data corresponding to each prevention and control scheme into a prevention and control effect prediction model to obtain a prediction result of the prevention and control effect of each prevention and control scheme; and visually displaying the prediction result of the prevention and control effect of each prevention and control scheme to obtain a target prevention and control scheme. According to the embodiment of the application, data reference is provided for the prevention and control effect, the optimal prevention and control scheme is determined, and the prevention and control efficiency of epidemic situations is improved.
Description
Technical Field
The application relates to the technical field of data processing, in particular to an epidemic situation prevention and control effect prediction method and a related product.
Background
With the development and spread of epidemic situations, the daily life of people is seriously influenced by the novel coronavirus. In order to effectively inhibit the spread of the novel coronavirus, related personnel set various prevention and control policies. However, which kind of prevention and control policy is effective for the prevention and control of the novel coronavirus does not have a definite judgment standard, and related personnel can only count some simple data for manual judgment. Because the manual experience is limited, the subjectivity is strong, which kind of prevention and control policy has good prevention and control effect cannot be accurately judged, and the prevention and control efficiency of the novel coronavirus is low.
Disclosure of Invention
The embodiment of the application provides an epidemic situation prevention and control effect prediction method and a related product. And data reference is provided for the prevention and control effect, the optimal prevention and control scheme is determined, and the prevention and control efficiency of epidemic situations is improved.
In a first aspect, an embodiment of the present application provides a method for predicting an epidemic situation prevention and control effect, including:
acquiring prevention and control data corresponding to each prevention and control scheme;
inputting the prevention and control data corresponding to each prevention and control scheme into a prevention and control effect prediction model to obtain a prediction result of the prevention and control effect of each prevention and control scheme;
and visually displaying the prediction result of the prevention and control effect of each prevention and control scheme to obtain a target prevention and control scheme.
In a second aspect, an embodiment of the present application provides an epidemic situation prevention and control effect prediction apparatus, including:
the acquisition unit is used for acquiring prevention and control data corresponding to each prevention and control scheme;
the prediction unit is used for inputting the prevention and control data corresponding to each prevention and control scheme into a prevention and control effect prediction model to obtain a prediction result of the prevention and control effect of each prevention and control scheme;
and the display unit is used for visually displaying the prediction result of the prevention and control effect of each prevention and control scheme to obtain a target prevention and control scheme.
In a third aspect, embodiments of the present application provide an electronic device, including a processor, a memory, a communication interface, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the processor, and the program includes instructions for performing the steps in the method according to the first aspect.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, which stores a computer program, where the computer program makes a computer execute the method according to the first aspect.
In a fifth aspect, embodiments of the present application provide a computer program product comprising a non-transitory computer-readable storage medium storing a computer program, the computer being operable to cause a computer to perform the method according to the first aspect.
The embodiment of the application has the following beneficial effects:
it can be seen that, in the embodiment of the application, the prevention and control data of various prevention and control schemes are analyzed, and the prevention and control effects corresponding to the various prevention and control schemes are predicted, so that data reference is provided for the prevention and control effects of each prevention and control scheme; in addition, each prevention and control effect is visually displayed, an optimal prevention and control scheme can be quickly positioned, the prevention and control efficiency of epidemic situations is further improved, and the spreading of the epidemic situations is effectively inhibited.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a schematic flow chart of a method for predicting epidemic prevention and control effect according to an embodiment of the present application;
fig. 2 is a star field schematic diagram of an epidemic situation prevention and control effect provided by the embodiment of the present application;
fig. 3 is a schematic flowchart of a method for constructing a prediction model according to an embodiment of the present application;
fig. 4 is a schematic flowchart of another epidemic prevention and control effect prediction method according to an embodiment of the present application;
fig. 5 is a schematic flowchart of another epidemic prevention and control effect prediction method according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an epidemic situation prevention and control effect prediction apparatus provided in the embodiment of the present application;
fig. 7 is a block diagram illustrating functional units of an epidemic situation prevention and control effect prediction apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," "third," and "fourth," etc. in the description and claims of this application and in the accompanying drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, result, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The epidemic prevention and control effect prediction device in the application can comprise a smart phone (such as an Android phone, an iOS phone, and the like), a tablet computer, a palm computer, a notebook computer, a Mobile Internet device MID (Mobile Internet Devices, abbreviated as MID), a wearable device, and the like. In practical application, the epidemic situation prevention and control effect prediction device may further include: intelligent vehicle-mounted terminal, computer equipment and the like.
Referring to fig. 1, fig. 1 is a schematic flow chart of a method for predicting epidemic situation prevention and control effect according to an embodiment of the present application. The method is applied to an epidemic situation prevention and control effect prediction device. The method of the embodiment comprises the following steps:
101: and the epidemic situation prevention and control effect prediction device acquires prevention and control data corresponding to each prevention and control scheme.
The prevention and control data includes, but is not limited to, the number of people detecting, the number of people isolating, the economic investment budget, the number of medical experts, the number of nucleic acid reagents, the number of protective clothing and the population number of epidemic areas targeted by the prevention and control scheme.
102: and the epidemic situation prevention and control effect prediction device inputs the prevention and control data corresponding to each prevention and control scheme into a prevention and control effect prediction model to obtain the prediction result of the prevention and control effect of each prevention and control scheme.
And the epidemic situation prevention and control effect prediction device inputs the prevention and control data corresponding to each prevention and control scheme into a pre-trained prevention and control effect prediction model to obtain the prediction result of the prevention and control effect of each prevention and control scheme.
Specifically, the prevention and control effect prediction model comprises a first prediction model, a second prediction model and a third prediction model, wherein the first prediction model is used for predicting the number of infected persons, the second prediction model is used for predicting the detection rate of diagnosed patients, and the third prediction model is used for predicting the number of dead persons. Of course, in practical applications, a prediction model with other dimensions may be constructed, for example, a fourth prediction model for predicting the number of cured patients is constructed, and so on. In the present application, the three prediction models are mainly used as an example for explanation, and the number and the types of the prediction models are not limited.
Therefore, the prevention and control data corresponding to each prevention and control scheme are input into the first prediction model, the number of infected persons is predicted, and a first prediction result is obtained; inputting the prevention and control data corresponding to each prevention and control scheme into a second prediction model, and predicting the detection rate of the patient with confirmed diagnosis to obtain a second prediction result; and inputting the prevention and control data corresponding to each prevention and control scheme into a third prediction model, and predicting the number of dead people to obtain a third prediction result.
103: and the epidemic situation prevention and control effect prediction device visually displays the prediction result of the prevention and control effect of each prevention and control scheme to obtain the target prevention and control scheme.
The epidemic situation prevention and control effect prediction device displays a star-sky plot of the prevention and control effect of each prevention and control scheme on a visual interface. The first prediction result, the second prediction result and the third prediction result of each prevention and control scheme are displayed through the star-sky plot, and the prevention and control scheme with the optimal prevention and control effect is determined to be the target prevention and control scheme from the star-sky plot.
As shown in fig. 2, the star-sky plots of the first prediction result, the second prediction result, and the third prediction result are shown, respectively, where the prevention and control factor in fig. 2 may be any one of the M prevention and control factors, and P01, P02, and P03 are the origins of the star-sky plots, which are the average values of the prediction results, and are not 0. As can be seen from fig. 2, the scheme B has the least number of infected persons, the highest detection rate, and the least number of dead persons, and thus, the scheme B is determined to be the prevention and control scheme with the optimal prevention and control effect, i.e., the target prevention and control scheme.
It can be seen that, in the embodiment of the application, the prevention and control data of various prevention and control schemes are analyzed, and the prevention and control effects corresponding to the various prevention and control schemes are predicted, so that data reference is provided for the prevention and control effects of each prevention and control scheme; in addition, each prevention and control effect is visually displayed, an optimal prevention and control scheme can be quickly positioned, the prevention and control efficiency of epidemic situations is further improved, and the spreading of the epidemic situations is effectively inhibited.
In some possible embodiments, the method further comprises:
selecting a target area from the epidemic area, wherein the target area can be an area with the least number of infected people, an area with the least mortality rate or other areas in the epidemic area;
and prompting to implement the target prevention and control scheme in the target area, acquiring a real-time prevention and control effect of the target area, and comparing the real-time prevention and control effect with a first prediction result, a second prediction result and a third prediction result corresponding to the target prevention and control scheme to adjust the target scheme.
It can be seen that, in the embodiment, the target area (experimental area) is selected to implement the target prevention and control scheme, and the prevention and control effect is obtained in real time to adjust the target prevention and control scheme, so that epidemic spread can be effectively inhibited.
The process of constructing the prediction model according to the present application is described in detail below.
Referring to fig. 3, fig. 3 is a schematic flowchart of a method for constructing a prediction model according to an embodiment of the present application, where the method includes the following steps:
301: and acquiring N preset prevention and control factors, wherein N is an integer greater than 1.
The N preset prevention and control factors are factors having influence on the prevention and control effect of the epidemic situation. Illustratively, the prevention and control factor may be the number of people detected per day, the number of medical experts, and the like.
302: and determining M prevention and control factors which have a remarkable effect on the prediction prevention and control effect of the prediction model A in the N preset prevention and control factors through a stepwise regression algorithm and sample data, wherein M is not more than N, and the sample data comprises prevention and control data and an actual prevention and control effect.
The sample data is historical data obtained by performing epidemic situation prevention and control on the novel coronavirus during the historical epidemic prevention period. For example, the sample data may be historical data obtained by performing epidemic situation prevention and control during the sars epidemic prevention period. The sample data includes prevention and control data and an actual prevention and control effect. The simulation data can also be obtained by carrying out epidemic situation simulation, and the source of the sample data is not particularly limited in the application. Therefore, the sample data comprises K groups of sample data, wherein K is an integer greater than 1, and each group of sample data comprises corresponding prevention and control data and an actual prevention and control result. Wherein, the actual prevention and control effect comprises the actual number of infected people, the actual examination rate of confirmed patients and the actual number of dead people.
The idea of the stepwise regression algorithm is to introduce the independent variables X into the regression equation in sequence according to the significance of the independent variable X on the dependent variable Y, wherein the significance can be preset through manual experience. And when the first introduced independent variable is not more significant due to the introduction of the subsequent independent variable, removing the first introduced independent variable from the regression equation until no independent variable with significant effect can be introduced and no independent variable with insignificant effect needs to be removed, and constructing a multivariate linear equation by using the retained variables. Therefore, according to the significance degree of the N prevention and control factors on the prevention and control effect, the N prevention and control factors are gradually introduced into the regression equation, and M prevention and control factors having significant effects on the prevention and control effect predicted by the prediction model A are determined.
303: and constructing a multivariate linear equation by using the M prevention and control factors to obtain the prediction model A.
The M prevention and control factors are used for constructing a multivariate linear equation to obtain the prediction model A, wherein the prediction model is any one of a first prediction model, a second prediction model and a third prediction model. Wherein the multiple linear equation can be expressed by formula (1):
y＝b0+b1*x1+b2*x2+…+bm*xm； (1)
wherein x is1，x2，……，xmFor the M prevention and control factors, b0，b1，……，bmAre the coefficients of the multivariate linear equation.
Therefore, the process of constructing the prediction model A can be converted into the calculation of b0，b1，……，bmThe process of (2).
Specifically, each group of sample data in the sample data is substituted into the multiple linear equation, that is, the prevention and control data corresponding to the M prevention and control factors in each group of sample data is substituted into the multiple linear equation, so as to obtain the predicted prevention and control effect corresponding to each group of sample data
Then, according to the actual data corresponding to each group of sample dataPrevention and control effect and predictive prevention and control effect determination b0，b1，……，bmObtaining the prediction model A.
Illustratively, the prediction prevention and control effect corresponding to each group of sample data in the K groups of sample data
When written as a vector, the following expression can be obtained:
wherein the content of the first and second substances,
a prediction prevention and control effect, x, corresponding to the K groups of sample dataijAnd the set of sample data is the prevention and control data corresponding to the jth prevention and control factor in the ith set of sample data.
Setting the prediction prevention and control effect of K groups of sample data as
Then can obtain
And the actual prevention and control effect of the K groups of sample data is
Further, calculating a difference value between the predicted prevention and control effect and the actual prevention and control effect of each group of sample data in the K groups of sample data, and then calculating a sum of squares of the difference values Q of the K groups of sample data, namely a loss result in the iteration process; the value of the coefficient of the multiple linear equation can be determined from the loss result. Wherein the sum of squared differences Q can be expressed by equation (2):
then
Since the significant effect is shown where the multivariate linear equation takes the most value, Q is respectively paired with b0、b1，…，bpAnd calculating partial derivatives, and enabling each calculated partial derivative to be equal to 0, so that the prevention and control factor can be determined to have a remarkable effect on the prevention and control effect. The formula after partial derivation is simplified to obtain a system of equations as shown in formula (3):
finally, solving the above equation set can solve b0、b1，…，bpB is solved by0、b1，…，bpAnd (5) replacing the multivariate linear equation to obtain a prediction model A.
In addition, after the prediction model A is obtained, the prediction model A can be verified to prevent the problem of overfitting of the prediction model A. Wherein, the linear equation can be subjected to significance test to determine whether the prediction result of the prevention and control effect and the M prevention and control factors have a linear relation. The significance test may be an F test, and the significance test performed by the F test is prior art and will not be described.
Referring to fig. 4, fig. 4 is a schematic flow chart of another epidemic prevention and control effect prediction method provided in the embodiment of the present application. The same contents in this embodiment as those in the embodiment shown in fig. 1 will not be repeated here. The method is applied to an epidemic situation prevention and control effect prediction device. The method of the embodiment comprises the following steps:
401: the epidemic situation prevention and control effect prediction device obtains N preset prevention and control factors, wherein N is an integer larger than 1.
402: the epidemic situation prevention and control effect prediction device determines M prevention and control factors which have obvious effect on the prediction prevention and control effect of the prediction model A in the N preset prevention and control factors through a stepwise regression algorithm and sample data, wherein M is less than or equal to N, and the sample data comprises prevention and control data and an actual prevention and control effect.
The prediction model A comprises any one of a first prediction model, a second prediction model and a third prediction model.
403: and the epidemic situation prevention and control effect prediction device uses the M prevention and control factors to construct a multivariate linear equation to obtain the prediction model A.
404: and the epidemic situation prevention and control effect prediction device acquires prevention and control data corresponding to each prevention and control scheme.
405: and the epidemic situation prevention and control effect prediction device inputs the prevention and control data corresponding to each prevention and control scheme into the first prediction model to predict the number of infected persons to obtain a first prediction result.
406: and the epidemic situation prevention and control effect prediction device inputs the prevention and control data corresponding to each prevention and control scheme into the second prediction model, predicts the detection rate of the confirmed patient and obtains a second prediction result.
407: and the epidemic situation prevention and control effect prediction device inputs the prevention and control data corresponding to each prevention and control scheme into the third prediction model, and predicts the death number to obtain a third prediction result.
408: and the epidemic situation prevention and control effect prediction device visually displays the first prediction result, the second prediction result and the third prediction result of each prevention and control scheme to obtain a target prevention and control scheme.
It can be seen that, in the embodiment of the application, the prevention and control data of various prevention and control schemes are analyzed through the pre-trained prediction model, and the prevention and control effects corresponding to the various prevention and control schemes are predicted, so that data reference is provided for the prevention and control effects of each prevention and control scheme; in addition, each prevention and control effect is visually displayed, an optimal prevention and control scheme can be quickly positioned, the prevention and control efficiency of epidemic situations is further improved, and the spreading of the epidemic situations is effectively inhibited. Moreover, the prevention and control effect is predicted in multiple dimensions, so that the determined target prevention and control scheme is more accurate, and the spread of epidemic situations is effectively inhibited.
Referring to fig. 5, fig. 5 is a schematic flow chart of another epidemic prevention and control effect prediction method according to an embodiment of the present application. The same contents in this embodiment as those in the embodiment shown in fig. 1 and 4 will not be repeated here. The method is applied to an epidemic situation prevention and control effect prediction device. The method of the embodiment comprises the following steps:
501: the epidemic situation prevention and control effect prediction device obtains N preset prevention and control factors, wherein N is an integer larger than 1.
502: the epidemic situation prevention and control effect prediction device determines M prevention and control factors which have obvious effect on the prediction prevention and control effect of the prediction model A in the N preset prevention and control factors through a stepwise regression algorithm and sample data, wherein M is less than or equal to N, and the sample data comprises prevention and control data and an actual prevention and control effect.
The prediction model A comprises any one of a first prediction model, a second prediction model and a third prediction model.
503: and the epidemic situation prevention and control effect prediction device uses the M prevention and control factors to construct a multivariate linear equation to obtain the prediction model A.
504: and the epidemic situation prevention and control effect prediction device acquires prevention and control data corresponding to each prevention and control scheme.
505: and the epidemic situation prevention and control effect prediction device inputs the prevention and control data corresponding to each prevention and control scheme into the first prediction model to predict the number of infected persons to obtain a first prediction result.
506: and the epidemic situation prevention and control effect prediction device inputs the prevention and control data corresponding to each prevention and control scheme into the second prediction model, predicts the detection rate of the confirmed patient and obtains a second prediction result.
507: and the epidemic situation prevention and control effect prediction device inputs the prevention and control data corresponding to each prevention and control scheme into the third prediction model, and predicts the death number to obtain a third prediction result.
508: and the epidemic situation prevention and control effect prediction device visually displays the first prediction result, the second prediction result and the third prediction result of each prevention and control scheme to obtain a target prevention and control scheme.
509: the epidemic situation prevention and control effect prediction device selects a target area from the epidemic situation area.
510: and the epidemic situation prevention and control effect prediction device prompts the target prevention and control scheme to be implemented in the target area, obtains the real-time prevention and control effect of the target area, and compares the real-time prevention and control effect with a first prediction result, a second prediction result and a third prediction result corresponding to the target prevention and control scheme so as to respectively adjust the coefficient values of the multiple linear equations corresponding to the first prediction model, the second prediction model and the third prediction model.
Specifically, the real-time prevention and control effect includes the actual number of infected persons, the actual detection rate and the actual number of dead persons. Comparing the actual number of infected people with the first prediction result to obtain a first loss, and adjusting the determined coefficient value of the multivariate linear equation corresponding to the first prediction model based on the first loss and a gradient descent method; similarly, the actual detectable rate is compared with the second prediction result to obtain a second loss, and the determined coefficient value of the multivariate linear equation corresponding to the second prediction model is adjusted based on the second loss and a gradient descent method; and comparing the actual number of the dead people with the third prediction result to obtain a third loss, and adjusting the determined coefficient value of the multivariate linear equation corresponding to the third prediction model based on the third loss and a gradient descent method. The parameter values of the model (i.e. coefficient values of the multivariate linear equation in the present application) are reversely adjusted based on the loss and gradient descent method, which is the prior art, and are not described in detail.
In the embodiment of the application, the prevention and control data of various prevention and control schemes are analyzed through a pre-trained prediction model, and the prevention and control effects corresponding to the various prevention and control schemes are predicted, so that data reference is provided for the prevention and control effects of each prevention and control scheme; in addition, each prevention and control effect is visually displayed, an optimal prevention and control scheme can be quickly positioned, the prevention and control efficiency of epidemic situations is further improved, and the spreading of the epidemic situations is effectively inhibited. And moreover, the prevention and control effect is predicted in multiple dimensions, so that the determined target prevention and control scheme is more accurate, and the spread of epidemic situations is effectively inhibited.
Referring to fig. 6, fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure. As shown in fig. 6, the epidemic prevention and control effect prediction apparatus 600 includes a processor, a memory, a communication interface, and one or more programs, the one or more programs being stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps of:
acquiring prevention and control data corresponding to each prevention and control scheme;
inputting the prevention and control data corresponding to each prevention and control scheme into a prevention and control effect prediction model to obtain a prediction result of the prevention and control effect of each prevention and control scheme;
and visually displaying the prediction result of the prevention and control effect of each prevention and control scheme to obtain a target prevention and control scheme.
In some possible embodiments, the prevention and control effect prediction model includes a first prediction model, a second prediction model, and a third prediction model, and the program is specifically configured to execute the following steps in terms of inputting the prevention and control data corresponding to each prevention and control scheme into the prevention and control effect prediction model to obtain the prediction result of the prevention and control effect of each prevention and control scheme:
inputting the prevention and control data corresponding to each prevention and control scheme into the first prediction model, and predicting the number of infected persons to obtain a first prediction result;
inputting the prevention and control data corresponding to each prevention and control scheme into the second prediction model, and predicting the detection rate of the patient with confirmed diagnosis to obtain a second prediction result;
and inputting the prevention and control data corresponding to each prevention and control scheme into the third prediction model, and predicting the number of dead people to obtain a third prediction result.
In some possible embodiments, the prediction model a is constructed by a stepwise regression algorithm, the prediction model a being any one of a first prediction model, a second prediction model, and a third prediction model, and the above program is specifically for executing the following instructions in constructing the prediction model a:
acquiring N preset prevention and control factors, wherein N is an integer greater than 1;
determining M prevention and control factors which have a remarkable effect on the prediction prevention and control effect of the prediction model A in the N preset prevention and control factors through a stepwise regression algorithm and sample data, wherein M is not more than N, and the sample data comprises prevention and control data and an actual prevention and control effect;
and constructing a multivariate linear equation by using the M prevention and control factors to obtain the prediction model A.
In some possible embodiments, in constructing a multivariate linear equation using the M prevention and control factors to obtain the prediction model a, the above program is specifically configured to execute the following steps:
constructing a multivariate linear equation by using the M prevention and control factors;
obtaining a prediction prevention and control effect according to the multivariate linear equation and the sample data;
and determining the coefficient value of the multivariate linear equation according to the prediction prevention and control effect and the actual prevention and control effect to obtain the prediction model A.
In some possible embodiments, in the aspect of visually displaying the prediction result of the prevention and control effect of each prevention and control scheme to obtain the target prevention and control scheme, the above-mentioned program is specifically used to execute the following instructions:
respectively displaying a star-sky plot of the first prediction result, the second prediction result and the third prediction result of each prevention and control scheme on a visual interface;
and determining the prevention and control scheme with the optimal prevention and control effect in each prevention and control scheme as a target prevention and control scheme according to the star-sky plot of the first prediction result, the second prediction result and the third prediction result of each prevention and control scheme.
In some possible embodiments, the program is further for executing the instructions of:
selecting a target area from the epidemic situation area;
and prompting to implement the target prevention and control scheme in the target area, acquiring a real-time prevention and control effect of the target area, and comparing the real-time prevention and control effect with a first prediction result, a second prediction result and a third prediction result corresponding to the target prevention and control scheme so as to respectively adjust coefficient values of a multi-element linear equation corresponding to the first prediction model, the second prediction model and the third prediction model.
Referring to fig. 7, fig. 7 is a block diagram illustrating functional units of an electronic device according to an embodiment of the present disclosure. The epidemic situation prevention and control effect prediction apparatus 700 includes: an obtaining unit 710, a predicting unit 720 and a showing unit 730, wherein:
an obtaining unit 710, configured to obtain prevention and control data corresponding to each prevention and control scheme;
the prediction unit 720 is configured to input the prevention and control data corresponding to each prevention and control scheme into a prevention and control effect prediction model to obtain a prediction result of the prevention and control effect of each prevention and control scheme;
and the display unit 730 is configured to visually display the prediction result of the prevention and control effect of each prevention and control scheme to obtain a target prevention and control scheme.
In some possible embodiments, the prevention and control effect prediction model includes a first prediction model, a second prediction model, and a third prediction model, and in terms of inputting the prevention and control data corresponding to each prevention and control scheme into the prevention and control effect prediction model to obtain the prediction result of the prevention and control effect for each prevention and control scheme, the prediction unit 720 is specifically configured to:
inputting the prevention and control data corresponding to each prevention and control scheme into the first prediction model, and predicting the number of infected persons to obtain a first prediction result;
inputting the prevention and control data corresponding to each prevention and control scheme into the second prediction model, and predicting the detection rate of the patient with confirmed diagnosis to obtain a second prediction result;
and inputting the prevention and control data corresponding to each prevention and control scheme into the third prediction model, and predicting the number of dead people to obtain a third prediction result.
In some possible embodiments, the epidemic prevention and control effect prediction apparatus 700 further includes a construction unit 740, where the prediction model a is constructed by a stepwise regression algorithm, and the prediction model a is any one of the first prediction model, the second prediction model, and the third prediction model, and in constructing the prediction model a, the construction unit 740 is configured to:
acquiring N preset prevention and control factors, wherein N is an integer greater than 1;
determining M prevention and control factors which have a remarkable effect on the prediction prevention and control effect of the prediction model A in the N preset prevention and control factors through a stepwise regression algorithm and sample data, wherein M is not more than N, and the sample data comprises prevention and control data and an actual prevention and control effect;
and constructing a multivariate linear equation by using the M prevention and control factors to obtain the prediction model A.
In some possible embodiments, in constructing a multivariate linear equation using the M prevention and control factors to obtain the prediction model a, the construction unit 740 is specifically configured to:
constructing a multivariate linear equation by using the M prevention and control factors;
obtaining a prediction prevention and control effect according to the multivariate linear equation and the sample data;
and determining the coefficient value of the multivariate linear equation according to the prediction prevention and control effect and the actual prevention and control effect to obtain the prediction model A.
In some possible embodiments, in the aspect of visually displaying the prediction result of the prevention and control effect of each prevention and control scheme to obtain the target prevention and control scheme, the displaying unit 730 is specifically configured to:
respectively displaying a star-sky plot of the first prediction result, the second prediction result and the third prediction result of each prevention and control scheme on a visual interface;
and determining the prevention and control scheme with the optimal prevention and control effect in each prevention and control scheme as a target prevention and control scheme according to the star-sky plot of the first prediction result, the second prediction result and the third prediction result of each prevention and control scheme.
In some possible embodiments, the epidemic prevention and control effect prediction apparatus 700 further includes an adjusting unit 750, where the adjusting unit 750 is configured to:
selecting a target area from the epidemic situation area;
and prompting to implement the target prevention and control scheme in the target area, acquiring a real-time prevention and control effect of the target area, and comparing the real-time prevention and control effect with a first prediction result, a second prediction result and a third prediction result corresponding to the target prevention and control scheme so as to respectively adjust coefficient values of a multi-element linear equation corresponding to the first prediction model, the second prediction model and the third prediction model.
The embodiment of the present application further provides a computer storage medium, where the computer storage medium stores a computer program, and the computer program is executed by a processor to implement part or all of the steps of any one of the epidemic prevention and control effect prediction methods described in the above method embodiments.
Embodiments of the present application also provide a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, where the computer program is operable to make a computer execute part or all of the steps of any one of the epidemic prevention and control effect prediction methods described in the above method embodiments.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implementing, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may be implemented in the form of a software program module.
The integrated units, if implemented in the form of software program modules and sold or used as stand-alone products, may be stored in a computer readable memory. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method described in the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. An epidemic situation prevention and control effect prediction method is characterized by comprising the following steps:
acquiring prevention and control data corresponding to each prevention and control scheme;
inputting the prevention and control data corresponding to each prevention and control scheme into a prevention and control effect prediction model to obtain a prediction result of the prevention and control effect of each prevention and control scheme;
and visually displaying the prediction result of the prevention and control effect of each prevention and control scheme to obtain a target prevention and control scheme.
2. The method according to claim 1, wherein the prevention and control effect prediction model comprises a first prediction model, a second prediction model and a third prediction model, and the inputting the prevention and control data corresponding to each prevention and control scheme into the prevention and control effect prediction model to obtain the prediction result of the prevention and control effect of each prevention and control scheme comprises:
inputting the prevention and control data corresponding to each prevention and control scheme into the first prediction model, and predicting the number of infected persons to obtain a first prediction result;
inputting the prevention and control data corresponding to each prevention and control scheme into the second prediction model, and predicting the detection rate of the patient with confirmed diagnosis to obtain a second prediction result;
and inputting the prevention and control data corresponding to each prevention and control scheme into the third prediction model, and predicting the number of dead people to obtain a third prediction result.
3. The method according to claim 1 or 2, wherein the prediction model a is constructed by a stepwise regression algorithm, the prediction model a being any one of a first prediction model, a second prediction model and a third prediction model, the construction of the prediction model a comprising the steps of:
acquiring N preset prevention and control factors, wherein N is an integer greater than 1;
determining M prevention and control factors which have a remarkable effect on the prediction prevention and control effect of the prediction model A in the N preset prevention and control factors through a stepwise regression algorithm and sample data, wherein M is not more than N, and the sample data comprises prevention and control data and an actual prevention and control effect;
and constructing a multivariate linear equation by using the M prevention and control factors to obtain the prediction model A.
4. The method of claim 3, wherein said constructing a multivariate linear equation using said M prevention and control factors to obtain said predictive model A comprises:
constructing a multivariate linear equation by using the M prevention and control factors;
obtaining a prediction prevention and control effect according to the multivariate linear equation and the sample data;
and determining the coefficient value of the multivariate linear equation according to the prediction prevention and control effect and the actual prevention and control effect to obtain the prediction model A.
5. The method according to any one of claims 2 to 4, wherein the step of visually displaying the prediction result of the prevention and control effect of each prevention and control scheme to obtain a target prevention and control scheme comprises the following steps:
respectively displaying a star-sky plot of the first prediction result, the second prediction result and the third prediction result of each prevention and control scheme on a visual interface;
and determining the prevention and control scheme with the optimal prevention and control effect in each prevention and control scheme as a target prevention and control scheme according to the star-sky plot of the first prediction result, the second prediction result and the third prediction result of each prevention and control scheme.
6. The method according to any one of claims 3-5, further comprising:
selecting a target area from the epidemic situation area;
and prompting to implement the target prevention and control scheme in the target area, acquiring a real-time prevention and control effect of the target area, and comparing the real-time prevention and control effect with a first prediction result, a second prediction result and a third prediction result corresponding to the target prevention and control scheme so as to respectively adjust coefficient values of a multi-element linear equation corresponding to the first prediction model, the second prediction model and the third prediction model.
7. An epidemic situation prevention and control effect prediction device is characterized by comprising:
the acquisition unit is used for acquiring prevention and control data corresponding to each prevention and control scheme;
the prediction unit is used for inputting the prevention and control data corresponding to each prevention and control scheme into a prevention and control effect prediction model to obtain a prediction result of the prevention and control effect of each prevention and control scheme;
and the display unit is used for visually displaying the prediction result of the prevention and control effect of each prevention and control scheme to obtain a target prevention and control scheme.
8. The apparatus of claim 7,
the prevention and control effect prediction model includes a first prediction model, a second prediction model, and a third prediction model, and in terms of inputting prevention and control data corresponding to each prevention and control scheme into the prevention and control effect prediction model to obtain a prediction result of the prevention and control effect of each prevention and control scheme, the prediction unit is specifically configured to:
inputting the prevention and control data corresponding to each prevention and control scheme into the first prediction model, and predicting the number of infected persons to obtain a first prediction result;
inputting the prevention and control data corresponding to each prevention and control scheme into the second prediction model, and predicting the detection rate of the patient with confirmed diagnosis to obtain a second prediction result;
and inputting the prevention and control data corresponding to each prevention and control scheme into the third prediction model, and predicting the number of dead people to obtain a third prediction result.
9. An electronic device comprising a processor, a memory, a communication interface, and one or more programs, wherein the one or more programs are stored in the memory and configured to be executed by the processor, the one or more programs including instructions for performing the steps in the method of any of claims 1-6.
10. 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 according to any one of claims 1-6.
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