CN113762611A

CN113762611A - Method for predicting number of newly-added employment people and electronic equipment

Info

Publication number: CN113762611A
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: 2021-12-07
Anticipated expiration: 2041-08-30
Also published as: CN113762611B

Abstract

The disclosure provides a method for predicting the number of newly added employment people and an electronic device. The method is used for improving the accuracy of the prediction of the number of newly-added 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 the current employment development trend; determining the number of newly increased employment people in a target time period by using the employment trend value; obtaining employment influence rate according to the impact degree of the special event and the influence degree of the special event on the employment number, wherein the impact degree of the special event is used for expressing the influence degree of the special event on the set data in the appointed time period; and 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

Method for predicting number of newly-added employment people and electronic equipment

Technical Field

The invention relates to the technical field of data processing, in particular to a method for predicting the number of newly-added employment people and electronic equipment.

Background

Along with the increase of the adjustment force of the economic structure, the employment pressure is increased, and the promotion of employment is always a great problem in the conversion process of new and old kinetic energy. Therefore, there is a need to develop and establish a employment quality evaluation system for accurately evaluating and predicting the labor market conditions, so as to monitor and predict the labor market demand conditions, supply conditions and matching conditions. Accurately reflects the employment quality status of the current labor market, and can provide timely information support for the adjustment of employment regulation and control policies. Improving the unemployment monitoring system is an important link for reducing the unemployment risk and preventing the unemployment crisis. Therefore, establishing a scientific and reasonable prediction method of the number of newly-increased employment people is an important task.

In the prior art, the method for predicting the number of newly-added employment people does not take the influence of special events such as sudden disasters on the number of newly-added employment people into consideration, so that the accuracy rate of predicting the number of newly-added employment people is low.

Disclosure of Invention

The exemplary embodiment of the disclosure provides a method for predicting the number of newly added employment people and an electronic device, which are used for improving the accuracy of predicting the number of newly added employment people.

A first aspect of the present disclosure provides a method for predicting the number of newly added employment people, the method including:

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 the current employment development trend;

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

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

and 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 the embodiment, the employment trend value is determined based on the received employment data, then the employment trend value is utilized to determine the newly increased number of employment people in the target time period, the employment influence rate is obtained through the impact degree of the special event and the influence degree of the special event on the number of employment people, and finally the actual newly increased number of employment people in the target time period is determined according to the newly increased number of employment people in the target time period and the employment influence rate. Therefore, the actual new 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, and therefore the accuracy of the new 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;

aiming at any two employment data types, screening each employment data type based on target employment data in the two employment data types to obtain a target employment data type;

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

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

In the embodiment, after target employment data is obtained by preprocessing each employment data, then for any two employment data types, based on the target employment data in the two employment data types, each employment data type is screened to obtain the target employment data type, each target employment data in each target employment data type is classified again by using a time difference correlation analysis method to obtain sub-employment data types of each target employment data, and finally, the employment trend value is determined through the target employment data corresponding to each sub-employment data type. So that the obtained employment trend value is more accurate.

In one embodiment, employment data for 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 the time difference correlation analysis method to obtain the sub-employment data types of the target employment data comprises the following steps:

aiming at any one target employment data type, respectively carrying out correlation calculation on each target employment data in the target employment data type and the employment data in the same time period in a preset reference employment data type by utilizing 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 the number of the first and second electrodes,

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 degree, wherein the sub-employment data types corresponding to the target employment data in the same target employment data type are the same.

In the embodiment, each employment data in the employment data type is respectively subjected to correlation calculation with employment data of the same time period in a preset reference employment data type by utilizing a time difference correlation analysis method aiming at any one employment data type to obtain the correlation degree of each employment data and the employment data of the same time period in the reference employment data type, 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, and therefore the sub-employment data type corresponding to each target employment data in the employment data type is determined according to the time period corresponding to the employment data with the highest correlation degree, and the accuracy of the sub-employment data type corresponding to each determined target employment data is improved.

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

for any target employment data in any one sub-employment data type, determining the symmetrical change rate of the target employment data based on the target employment data 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 a standard symmetrical change rate of the target employment data; and the number of the first and second groups,

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 standardization processing on the comprehensive change rate to obtain a standard comprehensive change rate of the sub-employment data type; and the number of the first and second electrodes,

obtaining the 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.

The embodiment determines the employment trend value of each target employment data in each sub-employment data, and then determines the employment trend value by using the sub-employment trend value of each target employment data in each sub-employment data type, so as to ensure that the determined employment trend value is more accurate.

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

determining weights corresponding to the types of the sub-employment data by using 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.

In the embodiment, the weights respectively corresponding to the sub employment data types are determined by utilizing an entropy method, and then the employment trend value is obtained 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, so that the accuracy of the employment trend value is improved.

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

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

if the impact degree of the special event is not smaller than a first specified threshold, subtracting the intermediate employment influence rate by using a second specified threshold to obtain the employment influence rate; or the like, or, alternatively,

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

The embodiment multiplies the impact degree of the special event and the impact degree of the special event on the employment number to obtain an intermediate employment influence rate, and then calculates the intermediate employment influence rate in a corresponding mode according to a comparison result of the impact degree of the special event and a first specified threshold value to obtain the employment influence rate, so that the accuracy of the employment influence rate is improved.

In one embodiment, the 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 includes:

and 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.

In 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 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 number of newly added employment people.

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 classify the target employment data in the target employment data types again to obtain sub-employment data types of the target employment data, and the processor is specifically configured to:

In an 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 determining the employment trend value by the sub-employment trend value of each target employment data in each sub-employment data type, and is specifically configured to:

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

In one embodiment, the processor determines the actual new employment number in the target time period according to the new employment number in the target time period and the employment influence 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 executing the method according to the first aspect.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.

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

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

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

FIG. 4 is a second flowchart illustrating a method for predicting the number of new employment people according to an embodiment of the disclosure;

FIG. 5 is a diagram of an apparatus for predicting the number of new employment opportunities, according to one embodiment of the present disclosure;

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

Detailed Description

To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.

The term "and/or" in the embodiments of the present disclosure describes an association relationship of associated objects, and means that there may be three relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.

The application scenario described in the embodiment of the present disclosure is for more clearly illustrating the technical solution of the embodiment of the present disclosure, and does not form a limitation on the technical solution provided in the embodiment of the present disclosure, and as a person having ordinary skill in the art knows, with the occurrence of a new application scenario, the technical solution provided in the embodiment of the present disclosure is also applicable to similar technical problems. In the description of the present disclosure, the term "plurality" means two or more unless otherwise specified.

Therefore, the present disclosure provides a method for predicting the newly increased employment number, which determines an employment trend value based on received employment data, then determines the newly increased employment number in a target time period by using the employment trend value, obtains an employment influence rate by a special event impact degree and an influence degree of a special event on the employment number, and finally determines an 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. Therefore, 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, and the accuracy of predicting the newly increased employment number is improved. The embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.

Fig. 1 is a schematic flow chart of a method for predicting the number of newly-added employment people 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 type here is a secondary type, each secondary type also has a corresponding primary type and a main type, wherein, the corresponding relationship between each employment data type and employment data can be shown as table 1:

TABLE 1

Wherein the source of each employment data is accessible from a third party server. Moreover, it should be noted that the employment data corresponding to any employment type includes employment data in each time period. For example, in time units of quarters, employment data for the first quarter, employment data for the second quarter, employment data for the third quarter, and the like may be included. The time period may be a quarter, a month, a year, and the like, and may be set according to an actual situation, which is not limited herein. In addition, the employment data specifically includes employment data in which time periods, and may also be set according to a specific actual situation, 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 the current employment development trend;

step 103: determining the number of newly increased employment people in a target time period by using the employment trend value;

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

Specifically, the newly increased employment number Y in the target time period can be determined through 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_tIs the residual of the equation, and c, β and u_tFor setting the numerical value, the setting can be carried out according to the actual situation。ee_-1The employment trend value of the t time period is the employment trend value in the current time period.

Incidentally, c, β, and u can be obtained based on the least square method from the history data_tThen for c and β and u_tThe setting may be preset by a user, and the embodiment is not limited herein.

Step 104: obtaining employment influence rate according to the impact degree of the special event and the influence degree of the special event on the employment number, wherein the impact degree of the special event is used for expressing 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 total domestic production value is reduced by 6.8% within a 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: and multiplying the impact degree of the special event and 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. Based on the intermediate employment influence rate, the manner of obtaining the employment influence rate can include the following two manners:

the first method is as follows: and if the impact degree of the special event is not less than a first specified threshold, subtracting the intermediate employment influence rate by using a second specified threshold to obtain the employment influence rate. Specifically, the employment influence rate can be determined according to the formula (2):

h_t＝M-vD_t…(2)；

wherein M is a second specified threshold, D_tThe impact degree of the special event is shown, and v is the image degree of the special event to the employment number.

The second method comprises the following steps: and if the impact degree of the special event is smaller than the first specified threshold, adding the second specified threshold and the intermediate employment influence rate to obtain the employment influence rate. Wherein the employment impact rate can be determined by equation (3):

h_t＝M+vD_t…(3)；

in this embodiment, the first predetermined threshold value is 0, and the second predetermined threshold value is 1. The method can be set according to specific practical situations, and the embodiment is not limited herein.

The influence degree of the special event on the employment number can be set according to specific actual conditions, and the influence degree of the special event on the employment number can be predicted according to a dynamic random general equilibrium model to obtain the influence degree of the special event on the employment number.

Step 105: and 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 newly increased employment number in the target time period is multiplied by the employment influence rate to obtain the actual newly increased employment number in the target time period. Wherein the actual number of newly added employment people can be determined by formula (4):

wherein the content of the first and second substances,

the number of newly increased employment people Y_t+1Is the newly increased employment number h in the target time period_tThe employment influence rate.

To make the determined employment trend value more accurate, in one embodiment, as shown in fig. 2, a flow chart for determining the employment trend may include the following steps:

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:

forward processing is carried out on each reverse employment data to obtain forward employment data; and (4) carrying out standardization processing on the employment data in each forward direction by utilizing an efficacy coefficient method to obtain the employment data of each target.

Wherein, the positive employment data can be obtained by the formula (5):

wherein x is_nAs positive employment data, x_tThe data of each employment are reversed.

It should be noted that whether each employment data is forward data or reverse data is preset, and it is only necessary to forward the reverse employment data, and the forward data is not essentially subjected to the forward processing.

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

wherein x is the target employment data, x_nAs positive employment data, x_minIs the minimum value, x, of employment data in each employment data having the same type of employment data as that of the forward employment data_maxThe maximum value of employment data in each employment data with the same type as the employment data of the forward direction employment data.

Step 202: aiming at any two employment data types, screening each employment data type based on target employment data in the two employment data types to obtain a target employment data type;

the method comprises the steps of calculating the correlation between various employment data types according to a Pearson correlation coefficient method, deleting the designated employment data type in the two employment data types and target employment data corresponding to the employment data type if the correlation between any two employment data types is larger than a designated threshold value, and determining the rest employment data types as the target employment data types.

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

in one embodiment, step 203 may be embodied as: aiming at any one target employment data type, respectively carrying out correlation calculation on each target employment data in the target employment data type and the employment data in the same time period in a preset reference employment data type by utilizing 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 degree of correlation, wherein the sub-employment data types corresponding to the target employment data in the same target employment data type are the same.

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

wherein r is_tIs the correlation degree, x, 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_tIs the target employment data in the T-th time period, T₁For the total amount of target employment data in the same target employment data type,

is the average value, y, of each target employment data in the same target employment data type_tThe target employment data for the t-th time period in the reference employment data type,

is the average value of each target employment data in the same target employment data type.

In one embodiment, the determining, based on the time period corresponding to the employment data with the highest degree of correlation, the sub-employment data type corresponding to each target employment data in the target employment data type may be implemented as:

if the target employment data with the highest correlation degree is the target employment data within a first specified time period, determining that the sub-employment data type is a first specified sub-category, wherein the first specified 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 specified time period, determining that the sub-employment data type is a second specified sub-category, wherein the second specified 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 specified time period, determining that the sub-employment data type is a third specified sub-category, wherein the third specified time period is equal to the preset reference time period.

For example, taking the time period as a quarter as an example, taking the second quarter as a preset reference time period, regarding the target employment data in the same target employment data type, if the target employment data with the highest degree of correlation is the target employment data within the first quarter, it is determined that the sub-employment data type is the second designated sub-category. And if the target employment data with the highest correlation degree 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 correlation degree 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, a schematic flow chart for determining employment trend values through target employment data corresponding to each sub-employment data type may include the following steps:

step 301: for any target employment data in any one sub-employment data type, determining the symmetrical change rate of the target employment data based on the target employment data 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 a 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_ij(t) is the symmetric rate of change, x, of the target employment data within the t-th time period in the child employment data type j_j(t)Target employment data, x, for the t-th time period in the child employment data type j_j(t-1)Is the target employment data in the t-1 time period in the sub-employment data type j. N is designated data, and N may be 200 in this embodiment.

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, C, of the target employment data in the child employment data type j_j(T) is the symmetric rate of change of the target employment data in the T-th time period in the child employment data type j, T₂The total number of target employment data in the same child 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 standardization processing on the comprehensive change rate to obtain a standard comprehensive change rate of the sub-employment data type;

wherein the integrated rate of change of the sub-employment data type can be determined by equation (10):

wherein r is_j(t) is the integrated rate of change, w, of the sub-employment data type j_j(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 authority of each employment data may be preset, may be set by an equal authority process, may be obtained by other value-giving methods, and the present embodiment is not limited herein.

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

Step 303: obtaining the 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 formula (12):

wherein i_j(t) is the sub-employment trend value of the target employment data corresponding to the t time period in the employment data type j, i_jAnd (t-1) is the 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 types of the sub-employment data by using 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 can be determined by equation (13):

wherein, ee_tIs the employment trend value, w_jThe child employment data type is a weight of j, and n is the total number of 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: aiming at any two employment data types, screening each employment data type based on target employment data in the two employment data types to obtain a target employment data type;

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

step 405: for any target employment data in any one sub-employment data type, determining the symmetrical change rate of the target employment data based on the target employment data 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 a 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 standardization processing on the comprehensive change rate to obtain a standard comprehensive change rate of the sub-employment data type;

step 407: obtaining the 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 types of the sub-employment data by using an entropy method;

step 409: obtaining employment trend values based on the weights corresponding to the various sub-employment data types and the sub-employment trend values of the various target employment data in the various sub-employment data types;

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

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

step 412: and 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 concept, the method for predicting the number of the newly added employment people can be realized by a device for predicting the number of the newly added employment people. The effect of the device for predicting the number of newly added employment people is similar to that of the method, and the description is omitted.

Fig. 5 is a schematic structural diagram of an apparatus for predicting the number of newly added employment people according to an embodiment of the present disclosure.

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

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

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

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

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

and the actual newly increased employment number determining module 550 is configured to determine 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 display the actual newly increased employment number.

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

the employment trend value determination module 520 performs the time difference correlation analysis to classify each target employment data in each target employment data type again, so as to obtain a sub-employment data type of each target employment data, which is specifically configured to:

In an 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 used for:

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

In an embodiment, the employment influence rate determining module 540 is specifically configured to:

In an embodiment, the actual newly added employment number determining module 550 is specifically configured to:

After a method and an apparatus for predicting the number of newly added employment people according to an exemplary embodiment of the present disclosure are introduced, an electronic device according to another exemplary embodiment of the present disclosure is introduced next.

As will be appreciated by one skilled in the art, aspects of the present disclosure may be embodied as a system, method or program product. Accordingly, various aspects of the present disclosure may be embodied in the form of: an entirely hardware embodiment, an entirely software embodiment (including firmware, microcode, etc.) or an embodiment combining hardware and software aspects that may all generally be referred to herein as a "circuit," module "or" system.

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

An electronic device 600 according to this embodiment of the disclosure is described below with reference to fig. 6. The electronic device 600 shown in fig. 6 is only an example and should not bring any limitations to the function and scope of use of the embodiments of the present disclosure.

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

Those skilled in the art will appreciate that the configuration 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 those shown, or may combine some components, or may be arranged in different components.

The following describes each component 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 conversation. Specifically, the RF circuit 610 sends downlink data of the base station to the processor 630 for processing after receiving the downlink data; and in addition, sending the uplink data to be sent to the base station. Generally, the RF circuit 610 includes, but is not limited to, an antenna, at least one Amplifier, a transceiver, a coupler, a 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 communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Messaging Service (SMS), and the like.

The WiFi technology belongs to a short-distance wireless transmission technology, and the electronic device 600 realizes Access Point (AP) through a WiFi module 690 (i.e., a wireless network module described in the foregoing of the present disclosure), thereby implementing Access to a data network. The WiFi module 690 may be used for receiving and transmitting data during communication.

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 interface of the other terminal through a cable, so as to implement data transmission between the electronic device 600 and the other terminal.

The electronic device 600 is capable of implementing a communication service, and the electronic device 600 needs to have a data transmission function, that is, the electronic device 600 needs to include a communication module inside. Although fig. 6 shows 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 (such as a bluetooth module) for implementing communication exist 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 executing the software programs and modules stored in the memory 640, and part or all of the processes in fig. 6 of the embodiments of the present disclosure can be implemented when the processor 630 executes the program codes in the memory 640.

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

Further, 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 generate key signal inputs related to user settings and function control of the terminal apparatus 600.

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

The touch panel 651, also called a touch screen, may collect touch operations of a user (for example, operations of a user on or near the touch panel 651 by using any suitable object or accessory such as a finger or a stylus pen) and drive a 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 direction 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 sensing device, converts the touch information into touch point coordinates, sends the touch point coordinates to the processor 630, and can receive and execute commands sent by the processor 630. In addition, the touch panel 651 may be implemented in various types, such as resistive, capacitive, infrared, and surface acoustic wave.

Optionally, 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, a mouse, a joystick, and the like.

The display unit 660 may be used to display information input by a user or information provided to a user and various menus of the electronic device 600. The display unit 660 is a display system of the electronic device 600, and is used for presenting an interface to implement human-computer 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 (LCD), an Organic Light-Emitting Diode (OLED), or the like.

Further, the touch panel 651 can cover the display panel 661, and when the touch panel 651 detects a touch operation thereon or nearby, the touch operation is transmitted 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 shown as two separate components to implement the input and output functions of the electronic device 600, in some embodiments, the touch panel 651 and the display panel 661 can be integrated 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 various components using various interfaces and lines, and implements various functions and processes data of the electronic device 600 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. Optionally, the processor 630 may integrate an application processor and a modem processor, wherein the application processor mainly processes an operating system, a user interface, an application program, and the like, and the modem processor mainly processes wireless communication. 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 shooting function of the electronic device 600, and shoot a picture or a 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, power consumption, and the like through the power management system.

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

In some possible embodiments, the aspects of the method for predicting the number of new employment persons provided by the present disclosure can also be realized in the form of a program product including program code for causing a computer device to perform the steps of the method for predicting the number of new employment persons according to various exemplary embodiments of the present disclosure described above in this specification when the program product is run on the 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. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable diskette, a hard disk, a random access computer storage media (RAM), a read-only computer storage media (ROM), an erasable programmable read-only computer storage media (EPROM or flash memory), an optical fiber, a portable compact disc read-only computer storage media (CD-ROM), an optical computer storage media piece, a magnetic computer storage media piece, or any suitable combination of the foregoing.

The program product for predicting the number of new employment opportunities of the embodiments of the present disclosure may employ a portable compact disc read-only computer storage medium (CD-ROM) and include program code, and may be executable 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.

A readable signal medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. 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 for 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 and partly on a remote electronic device, or entirely on the remote electronic device or server. In the case of remote electronic devices, the remote electronic devices 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 (for example, through the internet using an internet service provider).

It should be noted that although several modules of the apparatus are mentioned in the above detailed description, such division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more of the 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 embodiments by a plurality of modules.

Further, while the operations of the disclosed methods are depicted in the drawings in a particular order, this does not require or imply that these operations must be performed in this particular order, or that all of the illustrated operations must be performed, to achieve desirable results. Additionally or alternatively, certain steps may be omitted, multiple steps combined into one step execution, and/or one step broken down into multiple step executions.

As will be appreciated by one skilled in the art, 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-ROMs, 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 present disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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 changes and modifications can be made in the present disclosure without departing from the spirit and scope of the disclosure. Thus, if such modifications and variations of the present disclosure fall within the scope of the claims of the present disclosure and their equivalents, the present disclosure is intended to include such modifications and variations as well.

Claims

1. A method for predicting the number of newly added employment people is characterized by comprising the following steps:

receiving employment data corresponding to the employment data type;

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

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

4. The method of claim 2, wherein 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 determining the employment trend value from the sub-employment trend values of the target employment data in the respective sub-employment data types comprises:

6. The method of claim 1, wherein obtaining the employment influence rate by the impact degree of the special event and the influence degree of the special event on the employment number comprises:

7. The method of claim 1, wherein determining the actual new employment count for the target time period based on the new employment count for the target time period and the employment impact rate comprises:

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

the processor configured to:

receiving employment data corresponding to the employment data type;

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

10. The electronic device of claim 9, wherein employment data for different time periods are included in the same employment data type;