CN113762611B

CN113762611B - Prediction method for newly-increased employment number and electronic equipment

Info

Publication number: CN113762611B
Application number: CN202111006627.4A
Authority: CN
Inventors: 葛通; 尹彦辉; 李建伟; 孙永良; 陈维强
Original assignee: Hisense TransTech Co Ltd
Current assignee: Hisense TransTech Co Ltd
Priority date: 2021-08-30
Filing date: 2021-08-30
Publication date: 2024-04-16
Anticipated expiration: 2041-08-30
Also published as: CN113762611A

Abstract

The disclosure provides a prediction method for newly-increased employment people and electronic equipment. The method is used for improving the accuracy of the prediction of the number of newly-increased employment people. The method comprises the following steps: receiving employment data corresponding to the employment data type; determining employment trend values based on the received employment data, wherein the employment trend values are used for representing current employment development trends; determining the number of newly increased employment persons in a target time period by utilizing the employment trend value; obtaining employment influence rate through the impact degree of the special event and the influence degree of the special event on the number of employment people, wherein the impact degree of the special event is used for indicating the influence degree of the special event on the set data in the appointed time period; determining the actual newly-increased employment number in the target time period according to the newly-increased employment number in the target time period and the employment influence rate, and displaying the actual newly-increased employment number.

Description

Prediction method for newly-increased employment number and electronic equipment

Technical Field

The invention relates to the technical field of data processing, in particular to a prediction method of newly-increased employment people and electronic equipment.

Background

Facilitating employment is always a major problem in the new and old kinetic energy conversion process. Therefore, there is a need to study and build employment quality assessment systems that accurately assess and pre-judge labor market conditions, thereby monitoring and predicting labor market demand conditions, supply conditions, and matching conditions. The employment quality condition of the current labor force market is accurately reflected, and timely information support can be provided for adjustment of employment regulation policies. Perfecting the loss of business monitoring system is an important link for reducing loss of business risk and preventing loss of business crisis. Therefore, establishing a scientific and reasonable prediction method for newly-increased employment people is an important task.

In the prior art, the prediction method of the number of newly increased employment persons does not consider the influence of special events such as sudden disasters and the like on the number of newly increased employment persons, and the accuracy of the prediction of the number of newly increased employment persons is low.

Disclosure of Invention

The embodiment of the disclosure provides a prediction method of the number of newly increased employment persons and electronic equipment, which are used for improving the accuracy of the prediction of the number of newly increased employment persons.

A first aspect of the present disclosure provides a method for predicting a newly increased employment number, the method comprising:

receiving employment data corresponding to the employment data type;

Determining employment trend values based on the received employment data, wherein the employment trend values are used for representing current employment development trends;

determining the number of newly increased employment persons in a target time period by utilizing the employment trend value;

obtaining employment influence rate through the impact degree of the special event and the influence degree of the special event on the number of employment people, wherein the impact degree of the special event is used for indicating the influence degree of the special event on the set data in the appointed time period;

determining the actual newly-increased employment number in the target time period according to the newly-increased employment number in the target time period and the employment influence rate, and displaying the actual newly-increased employment number.

In this embodiment, a employment trend value is determined based on each piece of received employment data, then, a new number of employment persons in a target time period is determined by using the employment trend value, and a employment influence rate is obtained by the impact degree of a special event and the influence degree of the special event on the number of employment persons, and finally, the actual new number of employment persons in the target time period is determined according to the new number of employment persons in the target time period and the employment influence rate. Therefore, in the embodiment, the impact degree of the special event and the influence degree of the special event on the employment number are combined to predict the actual newly increased employment number in the target time period, so that the accuracy of the newly increased employment number is improved.

In one embodiment, the determining employment trend values based on the received employment data includes:

preprocessing each employment data in the employment data type to obtain target employment data, wherein the preprocessing comprises data forward processing and data standardization processing;

screening each employment data type according to any two employment data types and based on target employment data in the two employment data types to obtain target employment data types;

reclassifying each target employment data in each target employment data type by using a time difference correlation analysis method to obtain a sub-employment data type of each target employment data;

and determining the employment trend value through the target employment data corresponding to each sub-employment data type.

According to the embodiment, after target employment data are obtained through preprocessing of the employment data, the target employment data are screened according to any two employment data types based on the target employment data in the two employment data types, the target employment data types are obtained, the target employment data in the target employment data types are reclassified by using a time difference correlation analysis method, sub-employment data types of the target employment data are obtained, and finally the employment trend value is determined through the target employment data corresponding to the sub-employment data types. So that the obtained employment trend value is more accurate.

In one embodiment, employment data within different time periods is included in the same employment data type;

the reclassifying the target employment data in the target employment data types by using a time difference correlation analysis method to obtain sub-employment data types of the target employment data, including:

for any one target employment data type, performing correlation calculation on each target employment data in the target employment data type and the employment data in the same time period in the preset reference employment data type by using a time difference correlation analysis method to obtain the correlation degree of each target employment data and the employment data in the same time period in the reference employment data type; and is combined with the other components of the water treatment device,

and determining the sub-employment data types corresponding to the target employment data in the target employment data types based on the time period corresponding to the target employment data with the highest correlation, wherein the sub-employment data types corresponding to the target employment data in the same target employment data type are the same.

In this embodiment, by performing correlation calculation on each employment data in any employment data type and the employment data in the same time period in the preset reference employment data type by using a time difference correlation analysis method, the correlation degree of each employment data and the employment data in the same time period in the reference employment data type is obtained, and then the sub-employment data type corresponding to each target employment data in the employment data type is determined based on the time period corresponding to the employment data with the highest correlation degree, so that the accuracy of the sub-employment data type corresponding to each determined target employment data in the employment data type is improved by determining the sub-employment data type corresponding to each target employment data in the employment data type through the time period corresponding to the employment data with the highest correlation degree.

In one embodiment, the determining the employment trend value according to the target employment data corresponding to each sub-employment data type includes:

determining the symmetrical change rate of the target employment data according to the target employment data in any one sub-employment data type and the target employment data in the last time period of the target employment data; carrying out standardization processing on the symmetrical change rate to obtain the standard symmetrical change rate of the target employment data; the method comprises the steps of,

obtaining the comprehensive change rate of the sub employment data type by utilizing the standard symmetrical change rate of each target employment data in the sub employment data type; carrying out standardized processing on the comprehensive change rate to obtain a standard comprehensive change rate of the sub employment data type; and is combined with the other components of the water treatment device,

obtaining a sub employment trend value of each target employment data in the sub employment data type according to the standard comprehensive change rate of the sub employment data type;

and determining the employment trend value through the sub-employment trend value of each target employment data in each sub-employment data type.

According to the embodiment, the sub employment trend values of the target employment data in the sub employment data are determined, and then the sub employment trend values of the target employment data in the sub employment data types are utilized to determine the employment trend values, so that the determined employment trend values are more accurate.

In one embodiment, the determining the employment trend value from the sub-employment trend values of each target employment data in each sub-employment data type includes:

determining weights corresponding to the child employment data types respectively by utilizing an entropy method;

and obtaining the employment trend value based on the weight corresponding to each sub-employment data type and the sub-employment trend value of each target employment data in each sub-employment data type.

According to the embodiment, the entropy method is utilized to determine the weights corresponding to the sub employment data types, and then the employment trend value is obtained based on the weights corresponding to the sub employment data types and the sub employment trend values of the target employment data in the sub employment data types, so that the accuracy of the employment trend value is improved.

In one embodiment, the obtaining the employment impact rate through the impact degree of the special event and the impact degree of the special event on the employment number includes:

multiplying the impact degree of the special event by the influence degree of the special event on the employment number to obtain an intermediate employment influence rate;

if the impact degree of the special event is not smaller than a first designated threshold, subtracting the intermediate employment impact rate from a second designated threshold to obtain the employment impact rate; or alternatively, the first and second heat exchangers may be,

And if the impact degree of the special event is smaller than the first specified threshold, adding the intermediate employment impact rate by using the second specified threshold to obtain the employment impact rate.

According to the embodiment, the middle employment influence rate is obtained by multiplying the impact degree of the special event by the influence degree of the special event on the employment number, and then the middle employment influence rate is calculated in a corresponding mode by the comparison result of the impact degree of the special event and the first designated threshold value, so that the employment influence rate is obtained, and the accuracy of the employment influence rate is improved.

In one embodiment, the determining the actual new employment number in the target time period according to the new employment number in the target time period and the employment impact rate includes:

multiplying the newly increased employment number in the target time period by the employment influence rate to obtain the actual newly increased employment number in the target time period.

According to the embodiment, the actual newly-increased employment number in the target time period is obtained by multiplying the newly-increased employment number in the target time period by the employment influence rate, so that the accuracy of the actual newly-increased employment number is improved.

A second aspect of the present disclosure provides an electronic device comprising a processor and a display, wherein:

the processor is configured to:

receiving employment data corresponding to the employment data type;

determining the actual newly-increased employment number in the target time period according to the newly-increased employment number in the target time period and the employment influence rate;

the display is configured to display the actual newly increased employment number.

In one embodiment, the processor executes the determining employment trend values based on the received employment data, specifically configured to:

the processor executes the time difference correlation analysis method to reclassify each target employment data in each target employment data type to obtain a sub-employment data type of each target employment data, and the processor is specifically configured to:

In one embodiment, the processor executes the target employment data corresponding to each sub-employment data type to determine the employment trend value, and is specifically configured to:

In one embodiment, the processor executes the sub-employment trend value for each target employment data from each sub-employment data type, determines the employment trend value, and is specifically configured to:

In one embodiment, the processor executes the impact degree of the special event and the influence degree of the special event on the employment number to obtain the employment influence rate, and is specifically configured to:

In one embodiment, the processor executes the step of determining the actual number of newly increased employment within the target time period according to the number of newly increased employment within the target time period and the employment impact rate, and is specifically configured to:

According to a third aspect provided by embodiments of the present disclosure, there is provided a computer storage medium storing a computer program for performing the method according to the first aspect.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings that are needed in the description of the embodiments will be briefly described below, it will be apparent that the drawings in the following description are only some embodiments of the present disclosure, and that other drawings may be obtained according to these drawings without inventive effort to a person of ordinary skill in the art.

FIG. 1 is a flow chart of a method for predicting the number of new employment according to an embodiment of the present disclosure;

FIG. 2 is one of the flow diagrams for determining employment trend values, according to an embodiment of the present disclosure;

FIG. 3 is a second flow chart of determining employment trend values according to an embodiment of the present disclosure;

FIG. 4 is a second flow chart of a method for predicting the number of new employment according to an embodiment of the present disclosure;

FIG. 5 is a predictive device for increasing employment numbers according to an embodiment of the present disclosure;

fig. 6 is a schematic structural view of an electronic device according to one embodiment of the present disclosure.

Detailed Description

For the purposes of making the objects, technical solutions and advantages of the embodiments of the present disclosure more apparent, the technical solutions of the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present disclosure, and it is apparent that the described embodiments are some embodiments of the present disclosure, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without inventive effort, based on the embodiments in this disclosure are intended to be within the scope of this disclosure.

The term "and/or" in the embodiments of the present disclosure describes an association relationship of association objects, which indicates that three relationships may exist, for example, a and/or B may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.

The application scenario described in the embodiments of the present disclosure is for more clearly describing the technical solution of the embodiments of the present disclosure, and does not constitute a limitation on the technical solution provided by the embodiments of the present disclosure, and as a person of ordinary skill in the art can know that, with the appearance of a new application scenario, the technical solution provided by the embodiments of the present disclosure is equally applicable to similar technical problems. In the description of the present disclosure, unless otherwise indicated, the meaning of "a plurality" is two or more.

Therefore, the present disclosure provides a prediction method for a newly increased employment number, which includes determining employment trend values based on received employment data, determining the newly increased employment number in a target time period by using the employment trend values, obtaining employment influence rate by impact degree of special events and influence degree of the special events on the employment number, and determining actual newly increased employment number in the target time period according to the newly increased employment number in the target time period and the employment influence rate. The actual newly-increased employment number in the target time period is predicted by combining the impact degree of the special event and the influence degree of the special event on the employment number, so that the accuracy of predicting the newly-increased employment number is improved. The following describes aspects of the present disclosure in detail with reference to the accompanying drawings.

Fig. 1 is a flow chart of a method for predicting the number of newly added employment according to the present disclosure, which may include the following steps:

step 101: receiving employment data corresponding to the employment data type;

wherein, the employment types are secondary types, and each secondary type also has a corresponding primary type and a corresponding main type, wherein, the corresponding relationship between each employment data type and the employment data can be shown in table 1:

TABLE 1

Wherein, the source of each employment data can be obtained from a third party server. Moreover, the employment data corresponding to any employment type includes employment data in each time period. For example, in quarter time units, employment data for a first quarter, employment data for a second quarter, employment data for a third quarter, and so forth may be included. The time period may be quarterly, monthly, annual, etc., and may be set according to practical situations, which is not limited herein. In addition, employment data specifically includes employment data in which time periods, and may also be set according to specific practical situations, which is not limited herein.

Step 102: determining employment trend values based on the received employment data, wherein the employment trend values are used for representing current employment development trends;

Step 103: determining the number of newly increased employment persons in a target time period by utilizing the employment trend value;

if the target time period is the next quarter, the employment data is the employment data of each quarter, and if the target time period is the next year, the employment data is the employment data of each year, and the length of the time period of the employment data is the same as the length of the target time period.

Specifically, the number Y of newly increased employment within the target time period can be determined by the formula (1) _t+1 ：

Y _t+1 ＝c+βee _t +u _t …(1)；

Where c is the intercept term, β is the regression coefficient, u _t Is the residual of the equation, and c, beta, and u _t To set the values, the setting may be performed according to actual conditions. ee (ee) _-1 The employment trend value in the t-th time period is the employment trend value in the current time period.

It should be noted that c, β, and u can be obtained from historical data based on least squares _t Then for c and beta and u _t The setting may be preset by the user, and the present embodiment is not limited herein.

Step 104: obtaining employment influence rate through the impact degree of the special event and the influence degree of the special event on the number of employment people, wherein the impact degree of the special event is used for indicating the influence degree of the special event on the set data in the appointed time period;

The setting data in this embodiment may be a domestic total production value. If the domestic production total value falls by 6.8% in the specified time period under the influence of the special event, the impact degree of the special event is determined to be-6.8%.

In one embodiment, step 104 may be implemented as: multiplying the impact degree of the special event by the influence degree of the special event on the employment number to obtain an intermediate employment influence rate, and then obtaining the employment influence rate based on the intermediate employment influence rate. Wherein based on the intermediate employment impact rate, the manner in which the employment impact rate is derived may include the following two manners:

mode one: and if the impact degree of the special event is not smaller than the first specified threshold, subtracting the intermediate employment impact rate from the second specified threshold to obtain the employment impact rate. Specifically, the employment impact rate can be determined according to the formula (2):

h _t ＝M-vD _t …(2)；

wherein M is a second specified threshold, D _t The impact degree of the special event is the image degree of the special event on the employment number.

Mode two: and if the impact degree of the special event is smaller than the first specified threshold, adding the intermediate employment impact rate by using the second specified threshold to obtain the employment impact rate. Wherein the employment impact rate can be determined by formula (3):

h _t ＝M+vD _t …(3)；

In this embodiment, the first specified threshold is 0, and the second specified threshold is 1. The setting may be performed according to specific practical situations, and the present embodiment is not limited herein.

The influence degree of the special event on the employment number can be set according to specific practical conditions, and the influence degree of the special event on the employment number can be predicted according to a dynamic random general balance model, so that the influence degree of the special event on the employment number is obtained.

Step 105: determining the actual newly-increased employment number in the target time period according to the newly-increased employment number in the target time period and the employment influence rate, and displaying the actual newly-increased employment number.

In one embodiment, the new employment number in the target time period is multiplied by the employment impact rate to obtain the actual new employment number in the target time period. Wherein the actual newly added employment number can be determined by the formula (4):

wherein,to increase employment people actually, Y _t+1 H is the number of newly increased employment persons in the target time period _t And (5) the employment influence rate is the employment influence rate.

In order to make the determined employment trend value more accurate, in one embodiment, as shown in fig. 2, to determine a flow chart of employment trend, the following steps may be included:

Step 201: preprocessing each employment data in the employment data type to obtain target employment data, wherein the preprocessing comprises data forward processing and data standardization processing;

in one embodiment, the employment data is pre-processed by:

carrying out forward processing on each piece of reverse employment data to obtain forward employment data; and carrying out standardized processing on each forward employment data by using an efficacy coefficient method to obtain each target employment data.

Wherein, forward employment data can be obtained through the formula (5):

wherein x is _n Employment data in the forward direction, x _t Is the reverse employment data.

It should be noted that whether each employment data is forward data or reverse data is preset, only the reverse employment data needs to be subjected to forward processing, and the forward data does not need to be subjected to forward processing.

The target employment data can then be determined by equation (6):

wherein x is the target employment data, x _n Employment data in the forward direction, x _min For the employment data minimum value, x in each employment data of the same employment data type as the forward employment data _max And the employment data maximum value in each piece of employment data with the same employment data type as the forward employment data.

Step 202: screening each employment data type according to any two employment data types and based on target employment data in the two employment data types to obtain target employment data types;

and if the correlation between any two employment data types is larger than a specified threshold, deleting the specified employment data type and target employment data corresponding to the employment data type in the two employment data types, and determining the rest employment data types as the target employment data types.

Step 203: reclassifying each target employment data in each target employment data type by using a time difference correlation analysis method to obtain a sub-employment data type of each target employment data;

in one embodiment, step 203 may be embodied as: for any one target employment data type, performing correlation calculation on each target employment data in the target employment data type and the employment data in the same time period in the preset reference employment data type by using a time difference correlation analysis method to obtain the correlation degree of each target employment data and the employment data in the same time period in the reference employment data type; and then determining the sub-employment data types corresponding to the target employment data in the target employment data types based on the time period corresponding to the target employment data with the highest correlation degree, wherein the sub-employment data types corresponding to the target employment data in the same target employment data type are the same.

The correlation degree of any one of the target employment data and the employment data of the same time period in the reference employment data type can be determined through a formula (7):

wherein r is _t For the correlation degree of the target employment data in the t-th time period and the employment data in the t-th time period in the reference employment data type, x _t For target employment data in the T-th time period, T ₁ For the total number of target employment data in the same target employment data type,mean value, y of each target employment data in the same target employment data type _t As the target employment data of the t-th time period in the reference employment data type, +.>The average value of all the target employment data in the same target employment data type.

In an embodiment, the determining, based on the time period corresponding to the employment data with the highest relevance, the sub-employment data type corresponding to each target employment data in the target employment data types may be implemented as:

if the target employment data with the highest correlation degree is the target employment data in a first appointed time period, determining the sub-employment data type as a first appointed sub-category, wherein the first appointed time period is later than a preset reference time period;

if the target employment data with the highest correlation degree is the target employment data in a second designated time period, determining that the sub-employment data type is a second designated sub-category, wherein the second designated time period is earlier than the preset reference time period;

And if the target employment data with the highest correlation degree is the target employment data in a third appointed time period, determining that the sub-employment data type is a third appointed sub-category, wherein the third appointed time period is equal to the preset reference time period.

For example, taking the case that the time period is a quarter and taking a second quarter as a preset reference time period, aiming at the target employment data in the same target employment data type, if the target employment data with the highest correlation degree is the target employment data in the first quarter, determining that the sub-employment data type is the second designated sub-category. And if the target employment data with the highest relevance is the target employment data in the third quarter, determining that the sub-employment data type is the first designated sub-category. And if the target employment data with the highest relevance is the target employment data in the second quarter, determining that the sub-employment data type is the second designated sub-category.

Step 204: and determining the employment trend value through the target employment data corresponding to each sub-employment data type.

In one embodiment, as shown in fig. 3, for determining employment trend values according to the target employment data corresponding to each sub-employment data type, the method may include the steps of:

Step 301: determining the symmetrical change rate of the target employment data according to the target employment data in any one sub-employment data type and the target employment data in the last time period of the target employment data; carrying out standardization processing on the symmetrical change rate to obtain the standard symmetrical change rate of the target employment data;

wherein the symmetric rate of change of the target employment data can be determined by equation (8):

wherein C is _ij (t) is symmetry of the target employment data within the t-th time period in the child employment data type jRate of change, x _j(t) For target employment data for the t-th time period in child employment data type j, x _j(t-1) Is the target employment data within the t-1 time period in the pertinent sub-industry data type j. N is the designated data, in this embodiment N may be 200.

The standard symmetric rate of change of the target employment data can then be determined by equation (9):

wherein s is _j (t) is the standard symmetric rate of change of the target employment data in the child employment data type j, C _j (T) is the symmetric rate of change, T, of the target employment data in the T-th time period in the child employment data type j ₂ Is the total number of target employment data in the same sub-employment data type.

Step 302: obtaining the comprehensive change rate of the sub employment data type by utilizing the standard symmetrical change rate of each target employment data in the sub employment data type; carrying out standardized processing on the comprehensive change rate to obtain a standard comprehensive change rate of the sub employment data type;

wherein the aggregate rate of change of the child employment data types can be determined by the formula (10):

/>

wherein r is _j (t) is the comprehensive change rate of the child employment data type j, w _j And (t) is the weight of employment data corresponding to the t-th time period in the sub-employment data type j.

It should be noted that: the rights of each employment data can be preset, or can be set by adopting equal-right processing, or can be obtained by adopting other assignment methods, and the embodiment is not limited herein.

The standard integrated rate of change f of the sub-employment data type can then be determined by equation (11) _j ：

Step 303: obtaining a sub employment trend value of each target employment data in the sub employment data type according to the standard comprehensive change rate of the sub employment data type;

wherein, the sub-employment trend value of the target employment data can be determined by the formula (12):

wherein i is _j (t) is a sub-employment trend value, i, of the target employment data corresponding to the t-th time period in the employment data type j _j And (t-1) is a sub employment trend value of the target employment data corresponding to the t-1 time period in the employment data type j.

Step 304: and determining the employment trend value through the sub-employment trend value of each target employment data in each sub-employment data type.

In one embodiment, step 304 may be implemented as: determining weights corresponding to the child employment data types respectively by utilizing an entropy method; and obtaining the employment trend value based on the weight corresponding to each sub-employment data type and the sub-employment trend value of each target employment data in each sub-employment data type.

Wherein the employment trend value may be determined by equation (13):

wherein ee is _t For the employment trend value, w _j The weight of the child employment data type is j, and n is the total number of the child employment data types.

For further understanding of the technical solution of the present disclosure, the following detailed description with reference to fig. 4 may include the following steps:

step 401: receiving employment data corresponding to the employment data type;

step 402: preprocessing each employment data in the employment data type to obtain target employment data, wherein the preprocessing comprises data forward processing and data standardization processing;

Step 403: screening each employment data type according to any two employment data types and based on target employment data in the two employment data types to obtain target employment data types;

step 404: reclassifying each target employment data in each target employment data type by using a time difference correlation analysis method to obtain a sub-employment data type of each target employment data;

step 405: determining the symmetrical change rate of the target employment data according to the target employment data in any one sub-employment data type and the target employment data in the last time period of the target employment data; carrying out standardization processing on the symmetrical change rate to obtain the standard symmetrical change rate of the target employment data;

step 406: obtaining the comprehensive change rate of the sub employment data type by utilizing the standard symmetrical change rate of each target employment data in the sub employment data type; carrying out standardized processing on the comprehensive change rate to obtain a standard comprehensive change rate of the sub employment data type;

step 407: obtaining a sub employment trend value of each target employment data in the sub employment data type according to the standard comprehensive change rate of the sub employment data type;

Step 408: determining weights corresponding to the child employment data types respectively by utilizing an entropy method;

step 409: obtaining a employment trend value based on the weight corresponding to each sub-employment data type and the sub-employment trend value of each target employment data in each sub-employment data type;

step 410: determining the number of newly increased employment persons in a target time period by utilizing the employment trend value;

step 411: obtaining employment influence rate through the impact degree of the special event and the influence degree of the special event on the number of employment people, wherein the impact degree of the special event is used for indicating the influence degree of the special event on the set data in the appointed time period;

step 412: determining the actual newly-increased employment number in the target time period according to the newly-increased employment number in the target time period and the employment influence rate, and displaying the actual newly-increased employment number.

Based on the same public conception, the prediction method of the newly increased employment number can be realized by a prediction device of the newly increased employment number. The effect of the prediction device for the number of newly increased employment persons is similar to that of the method, and is not described herein.

Fig. 5 is a schematic structural diagram of a prediction apparatus for increasing employment numbers according to an embodiment of the present disclosure.

As shown in fig. 5, the apparatus 500 for predicting the number of newly increased employment of the present disclosure may include a reception determination module 510, a employment trend value determination module 520, a number of newly increased employment determination module 530, a employment impact rate determination module 540, and an actual number of newly increased employment determination module 550.

A receiving determining module 510, configured to receive employment data corresponding to the employment data type;

a employment trend value determining module 520, configured to determine a employment trend value based on each employment data received, where the employment trend value is used to represent a current employment trend;

the newly-increased employment number determining module 530 is configured to determine, using the employment trend value, a newly-increased employment number within a target time period;

the employment impact rate determining module 540 is configured to obtain a employment impact rate according to an impact degree of a special event and an impact degree of the special event on a number of employment people, where the impact degree of the special event is used to represent an impact degree of the special event on set data in a specified time period;

the actual newly-increased employment number determining module 550 is configured to determine, according to the newly-increased employment number in the target time period and the employment impact rate, an actual newly-increased employment number in the target time period, and display the actual newly-increased employment number.

In one embodiment, the employment trend value determining module 520 is specifically configured to:

the employment trend value determining module 520 performs the reclassifying the target employment data in the target employment data types by using a time difference correlation analysis method to obtain sub-employment data types of the target employment data, which is specifically configured to:

In one embodiment, the employment trend value determining module 520 executes the target employment data corresponding to each sub-employment data type to determine the employment trend value, which is specifically configured to:

In one embodiment, the employment trend value determination module 520 executes the sub-employment trend values of the respective target employment data of the respective sub-employment data types to determine the employment trend value, and is specifically configured to:

In one embodiment, the employment impact rate determining module 540 is specifically configured to:

In one embodiment, the actual new employment number determining module 550 is specifically configured to:

Having described a method and apparatus for predicting a newly increased employment number according to an exemplary embodiment of the present disclosure, an electronic device according to another exemplary embodiment of the present disclosure is next described.

Those skilled in the art will appreciate that the various aspects of the present disclosure may be implemented as a system, method, or program product. Accordingly, various aspects of the disclosure may be embodied in the following forms, namely: an entirely hardware embodiment, an entirely software embodiment (including firmware, micro-code, etc.) or an embodiment combining hardware and software aspects may be referred to herein as a "circuit," module "or" system.

In some possible implementations, an electronic device according to the present disclosure may include at least one processor, and at least one computer storage medium. Wherein the computer storage medium stores program code which, when executed by the processor, causes the processor to perform the steps in the method of predicting the number of newly increased employment according to the various exemplary embodiments of the present disclosure described hereinabove. For example, the processor may perform steps 101-105 as shown in FIG. 1.

An electronic device 600 according to such an embodiment of the present disclosure is described below with reference to fig. 6. The electronic device 600 shown in fig. 6 is merely an example and should not be construed to limit the functionality and scope of use of embodiments of the present disclosure in any way.

As shown in fig. 6, an electronic device in an embodiment of the present disclosure includes: radio Frequency (RF) circuitry 610, power supply 620, processor 630, memory 640, input unit 650, display unit 660, camera 670, communication interface 680, wireless fidelity (Wireless Fidelity, wiFi) module 690, and the like. The wireless fidelity module 690 is a wireless network module in the present disclosure.

It will be appreciated by those skilled in the art that the structure of the electronic device shown in fig. 6 does not constitute a limitation of the terminal device, and that the electronic device provided by the embodiments of the present disclosure may include more or less components than illustrated, or may combine certain components, or may be arranged in different components.

The following describes the various constituent elements of the electronic device 600 in detail with reference to fig. 6:

the RF circuitry 610 may be used for receiving and transmitting data during a communication or session. Specifically, the RF circuit 610, after receiving downlink data of the base station, sends the downlink data to the processor 630 for processing; in addition, uplink data to be transmitted is transmitted to the base station. Typically, the RF circuitry 610 includes, but is not limited to, an antenna, at least one amplifier, a transceiver, a coupler, a low noise amplifier (Low Noise Amplifier, LNA), a duplexer, and the like.

In addition, the RF circuit 610 may also communicate with networks and other terminals through wireless communication. The wireless communication may use any communication standard or protocol including, but not limited to, global system for mobile communications (Global System of Mobile communication, GSM), general packet radio service (General Packet Radio Service, GPRS), code division multiple access (Code Division Multiple Access, CDMA), wideband code division multiple access (Wideband Code Division Multiple Access, WCDMA), long term evolution (Long Term Evolution, LTE), email, short message service (Short Messaging Service, SMS), and the like.

The WiFi technology belongs to a short-distance wireless transmission technology, and the electronic device 600 may be connected to an Access Point (AP) through a WiFi module 690 (i.e., a wireless network module described in the disclosure above), so as to implement Access to a data network. The WiFi module 690 may be used for receiving and transmitting data in a communication process.

The electronic device 600 may be physically connected to other terminals through the communication interface 680. Optionally, the communication interface 680 is connected to the communication interfaces of the other terminals through a cable, so as to implement data transmission between the electronic device 600 and the other terminals.

The electronic device 600 is capable of implementing communication services, and the electronic device 600 needs to have a data transmission function, that is, a communication module needs to be included in the electronic device 600. Although fig. 6 illustrates communication modules such as the RF circuit 610, the WiFi module 690, and the communication interface 680, it is understood that at least one of the above components or other communication modules (e.g., bluetooth modules) for enabling communication are present in the electronic device 600 for data transmission.

The memory 640 may be used to store software programs and modules. The processor 630 executes various functional applications and data processing of the electronic device 600 by running software programs and modules stored in the memory 640, and when the processor 630 executes the program code in the memory 640, part or all of the processes in fig. 6 of the embodiments of the present disclosure can be implemented.

Alternatively, the memory 640 may mainly include a storage program area and a storage data area. The storage program area may store an operating system, various application programs (such as a communication application), various modules for performing WLAN connection, and the like; the storage data area may store data created according to the use of the terminal, etc.

In addition, the memory 640 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.

The input unit 650 may be used to receive numeric or character information input by a user and to generate key signal inputs related to user settings and function controls of the terminal device 600.

Alternatively, the input unit 650 may include a touch panel 651 and other input terminals 652.

Wherein the touch panel 651, also referred to as a touch screen, may collect touch operations thereon or thereabout by a user (e.g., operations of the user on the touch panel 651 or thereabout by using any suitable object or accessory such as a finger, stylus, etc.), and drive the corresponding connection device according to a preset program. Alternatively, the touch panel 651 may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch azimuth of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device and converts it into touch point coordinates, which are then sent to the processor 630, and can receive commands from the processor 630 and execute them. Further, the touch panel 651 may be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave.

Alternatively, the other input terminals 652 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, mouse, joystick, etc.

The display unit 660 may be used to display information input by a user or provided to the user and various menus of the electronic device 600. The display unit 660 is a display system of the electronic device 600, and is configured to present an interface to implement man-machine interaction.

The display unit 660 may include a display panel 661. Alternatively, the display panel 661 may be configured in the form of a liquid crystal display (Liquid Crystal Display, LCD), an Organic Light-Emitting Diode (OLED), or the like.

Further, the touch panel 651 may cover the display panel 661, and when the touch panel 651 detects a touch operation thereon or thereabout, the touch panel is transferred to the processor 630 to determine the type of touch event, and then the processor 630 provides a corresponding visual output on the display panel 661 according to the type of touch event.

Although in fig. 6, the touch panel 651 and the display panel 661 are two separate components to implement the input and output functions of the electronic device 600, in some embodiments, the touch panel 651 may be integrated with the display panel 661 to implement the input and output functions of the electronic device 600.

The processor 630 is a control center of the electronic device 600, connects respective components using various interfaces and lines, and performs various functions of the electronic device 600 and processes data by running or executing software programs and/or modules stored in the memory 640 and calling data stored in the memory 640, thereby implementing various services based on the terminal device.

Optionally, the processor 630 may include one or more processing units. Alternatively, the processor 630 may integrate an application processor that primarily processes operating systems, user interfaces, applications, etc., with a modem processor that primarily processes wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 630.

The camera 670 is configured to implement a capturing function of the electronic device 600, and capture a picture or video.

The electronic device 600 also includes a power source 620 (e.g., a battery) for powering the various components. Optionally, the power supply 620 may be logically connected to the processor 630 through a power management system, so as to implement functions of managing charging, discharging, and power consumption through the power management system.

Although not shown, the electronic device 600 may further include at least one sensor, which is not described herein.

In some possible embodiments, aspects of a method of predicting an increased employment provided by the present disclosure may also be implemented in the form of a program product comprising program code for causing a computer device to perform the steps of the method of predicting an increased employment according to the various exemplary embodiments of the present disclosure described hereinabove when the program product is run on a computer device.

The program product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium would include the following: an electrical connection having one or more wires, a portable disk, a hard disk, a random access computer storage medium (RAM), a read-only computer storage medium (ROM), an erasable programmable read-only computer storage medium (EPROM or flash memory), an optical fiber, a portable compact disc read-only computer storage medium (CD-ROM), an optical computer storage medium, a magnetic computer storage medium, or any suitable combination of the foregoing.

The program product of the prediction of the number of new employment of embodiments of the present disclosure may employ a portable compact disk read-only computer storage medium (CD-ROM) and include program code, and may be run on an electronic device. However, the program product of the present disclosure is not limited thereto, and in this document, a readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The readable signal medium may include a data signal propagated in baseband or as part of a carrier wave with readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A readable signal medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Program code embodied on a readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

Program code for carrying out operations of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the consumer electronic device, partly on the consumer electronic device, as a stand-alone software package, partly on the consumer electronic device, partly on the remote electronic device, or entirely on the remote electronic device or server. In the case of remote electronic devices, the remote electronic device may be connected to the consumer electronic device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external electronic device (e.g., connected through the internet using an internet service provider).

It should be noted that although several modules of the apparatus are mentioned in the detailed description above, this division is merely exemplary and not mandatory. Indeed, the features and functions of two or more modules described above may be embodied in one module in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module described above may be further divided into a plurality of modules to be embodied.

Furthermore, although the operations of the methods of the present disclosure are depicted in the drawings in a particular order, this is not required to or suggested that these operations must be performed in this particular order or that all of the illustrated operations must be performed in order to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step to perform, and/or one step decomposed into multiple steps to perform.

It will be apparent to those skilled in the art that embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk computer storage media, CD-ROM, optical computer storage media, and the like) having computer-usable program code embodied therein.

The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to the disclosure. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable computer storage medium that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable computer storage medium produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

It will be apparent to those skilled in the art that various modifications and variations can be made to the present disclosure without departing from the spirit or scope of the disclosure. Thus, the present disclosure is intended to include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims

1. A method for predicting the number of newly increased employment persons, the method comprising:

receiving employment data corresponding to the employment data type;

the employment influence rate is obtained through the impact degree of the special event and the influence degree of the special event on the employment number, and the method specifically comprises the following steps:

multiplying the impact degree of the special event by the impact degree of the special event on the employment number to obtain an intermediate employment impact rate, wherein the impact degree of the special event is used for representing the impact degree of the special event on the set data in a designated time period;

if the impact degree of the special event is smaller than the first specified threshold, adding the middle employment influence rate by using the second specified threshold to obtain the employment influence rate;

2. The method of claim 1, wherein the determining employment trend values based on the received employment data comprises:

3. The method of claim 2, wherein employment data within different time periods is included in the same employment data type;

4. The method of claim 2, wherein the determining the employment trend value from the target employment data corresponding to each sub-employment data type comprises:

5. The method of claim 4, wherein the determining the employment trend value from the sub-employment trend values for each target employment data in each sub-employment data type comprises:

6. The method of claim 1, wherein the determining the actual number of new employment within the target time period based on the number of new employment within the target time period and the employment impact rate comprises:

7. An electronic device comprising a processor and a display, wherein:

the processor is configured to:

receiving employment data corresponding to the employment data type;

8. The electronic device of claim 7, wherein the processor executes the determining employment trend values based on the received employment data, specifically configured to:

9. The electronic device of claim 8, wherein employment data within different time periods is included in the same employment data type;