CN112036831A - Human management system control method and device, readable storage medium and terminal equipment - Google Patents

Human management system control method and device, readable storage medium and terminal equipment Download PDF

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CN112036831A
CN112036831A CN202010889744.9A CN202010889744A CN112036831A CN 112036831 A CN112036831 A CN 112036831A CN 202010889744 A CN202010889744 A CN 202010889744A CN 112036831 A CN112036831 A CN 112036831A
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林荣吉
张巧丽
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Ping An Life Insurance Company of China Ltd
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Abstract

The invention belongs to the technical field of artificial intelligence, and particularly relates to a manpower management system control method, a manpower management system control device, a computer readable storage medium and terminal equipment. The method comprises the steps of respectively extracting interview flow record data of each historical statistical period from a human management system; determining the prediction increment manpower number of the target statistical period according to the interview flow record data of each historical statistical period; acquiring a current cloud desktop resource configuration list from a cloud desktop server; sequentially selecting cloud desktop resource partitions with residual resource amounts meeting the predicted incremental manpower number from a cloud desktop resource configuration list, and designating cloud desktop resources corresponding to the predicted incremental manpower number in the selected cloud desktop resource partitions; and sending a resource configuration instruction to the cloud desktop server so that the cloud desktop server performs cloud desktop resource configuration in the specified cloud desktop resources according to the predicted incremental manpower number, thereby greatly improving the intelligent degree of enterprise management and improving the working efficiency.

Description

Human management system control method and device, readable storage medium and terminal equipment
Technical Field
The invention belongs to the technical field of artificial intelligence, and particularly relates to a manpower management system control method, a manpower management system control device, a computer readable storage medium and terminal equipment.
Background
With the improvement of the informatization degree of the whole society, a plurality of enterprises construct own manpower management systems, and carry out informatization management on each link from the starting of recruitment interview to the working and post-working of candidate personnel, so that the manpower management efficiency of the enterprises is improved to a certain extent. However, the existing manpower management system can only complete simple recording and query tasks generally, has low intelligent degree and is difficult to meet the actual work requirements of enterprises.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for controlling a human power management system, a computer-readable storage medium, and a terminal device, so as to solve the problem that the existing human power management system has a low intelligence degree and is difficult to meet the actual work requirement of an enterprise.
A first aspect of an embodiment of the present invention provides a method for controlling a human power management system, where the method includes:
respectively extracting interview flow record data of each historical statistical period from a preset manpower management system;
determining the prediction increment manpower number of the target statistical period according to the interview flow record data of each historical statistical period;
acquiring a current cloud desktop resource configuration list from a preset cloud desktop server, wherein the cloud desktop resource configuration list comprises the hierarchy division condition of cloud desktop resource partitions and the residual resource amount of the cloud desktop resource partitions of each hierarchy, the hierarchy of the cloud desktop resource partitions is negatively related to a preset performance index, and the performance index comprises a time delay amount and/or an error rate;
sequentially selecting a plurality of cloud desktop resource partitions of which the residual resource quantity meets the prediction increment human number from the cloud desktop resource configuration list according to the sequence of the levels from high to low, and designating the cloud desktop resources corresponding to the prediction increment human number in the selected cloud desktop resource partitions;
and sending a resource configuration instruction to the cloud desktop server so that the cloud desktop server performs cloud desktop resource configuration in the specified cloud desktop resources according to the predicted incremental manpower number.
Further, after sending the resource configuration instruction to the cloud desktop server, the method may further include:
after receiving a resource configuration completion message fed back by the cloud desktop server, sending a resource locking instruction to the cloud desktop server so that the cloud desktop server sets a first cloud desktop resource in a locking state, wherein the first cloud desktop resource is a cloud desktop resource configured by the cloud desktop server according to the prediction increment human number;
when the target counting period is finished, extracting the actual increment human number of the target counting period from the human management system;
if the predicted incremental manpower number is consistent with the actual incremental manpower number, sending a first resource allocation instruction to the cloud desktop server so that the cloud desktop server unlocks the first cloud desktop resource and allocates the first cloud desktop resource to each incremental manpower.
Further, after extracting the actual incremental manpower number of the target statistic cycle from the manpower management system, the method may further include:
if the predicted incremental manpower number is larger than the actual incremental manpower number, calculating a first number difference value between the predicted incremental manpower number and the actual incremental manpower number;
generating a resource release instruction according to the number difference, and sending the resource release instruction to the cloud desktop server, so that the cloud desktop server releases a second cloud desktop resource from the first cloud desktop resource, wherein the second cloud desktop resource is a cloud desktop resource corresponding to the number difference, and the second cloud desktop resource is a subset of the first cloud desktop resource;
sending a second resource allocation instruction to the cloud desktop server to enable the cloud desktop server to unlock third cloud desktop resources and allocate the third cloud desktop resources to each incremental manpower, wherein the third cloud desktop resources are cloud desktop resources remaining after the second cloud desktop resources are released from the first cloud desktop resources.
Further, after extracting the actual incremental manpower number of the target statistic cycle from the manpower management system, the method may further include:
if the predicted incremental labor number is less than the actual incremental labor number, calculating a second number difference between the actual incremental labor number and the predicted incremental labor number;
sending a third resource allocation instruction to the cloud desktop server to enable the cloud desktop server to unlock the first cloud desktop resources and allocate the first cloud desktop resources to each incremental manpower;
generating a resource reconfiguration instruction according to the second number difference value, and sending the resource reconfiguration instruction to the cloud desktop server so that the cloud desktop server performs cloud desktop resource configuration according to the second number difference value;
after receiving the resource reconfiguration completion message fed back by the cloud desktop server, sending a fourth resource allocation instruction to the cloud desktop server, so that the cloud desktop server allocates a fourth cloud desktop resource to each incremental manpower, where the fourth cloud desktop resource is a cloud desktop resource allocated by the cloud desktop server according to the second number difference.
Further, the determining the predicted incremental manpower number of the target statistical period according to the interview procedure record data of each historical statistical period may include:
determining the number of participants and the conversion rate of each interview link in each historical statistical period according to the interview process recorded data;
determining the number of participants in the 1 st interview link of the target statistical period according to the number of participants in the 1 st interview link of each historical statistical period;
determining the conversion rate from the kth interview link of each associated statistical period to the kth +1 interview link of the target statistical period according to the conversion rate from the kth interview link of each historical statistical period to the kth +1 interview link of each historical statistical period, wherein the associated statistical period comprises the historical statistical period and the target statistical period;
determining the number of participants of the (k + 1) th interview link in the target statistical period according to the number of participants of the kth interview link in each associated statistical period and the conversion rate from the kth interview link in each associated statistical period to the (k + 1) th interview link in the target statistical period, wherein k is more than or equal to 1 and less than or equal to KN-1, and KN is the total number of the interview links;
and determining the number of the participants of the KN interview link in the target counting period as the prediction increment manpower number.
Further, the determining the conversion rate from the kth trial link of each associated statistical period to the (k + 1) th trial link of the target statistical period may include:
constructing an initial conversion rate prediction model;
performing parameter estimation on the conversion rate prediction model according to the conversion rate from the kth interview link of each historical statistical period to the (k + 1) th interview link of each historical statistical period to obtain the optimal model parameter of the conversion rate prediction model;
and calculating the conversion rate from the kth trial link of each associated statistical period to the (k + 1) th trial link of the target statistical period according to an optimized conversion rate prediction model, wherein the optimized conversion rate prediction model is the conversion rate prediction model using the optimal model parameters.
Further, the determining the number of participating people in the (k + 1) th interview link of the target statistical period may include:
calculating the number of participants in the (k + 1) th interview link of the target statistical period according to the following formula:
Figure BDA0002656557990000041
wherein c is the serial number of the associated statistical period, c is more than or equal to 1 and less than or equal to CN, CN is the number of the associated statistical period, Numk(c) For the kth interview link of the c-th associated statistical periodNumber of participating persons, InvRatek(c) The conversion rate, Num, from the kth surface test link of the c-th associated statistical period to the (k + 1) th surface test link of the target statistical periodk+1And counting the number of the participants in the (k + 1) th interview link of the target counting period.
A second aspect of an embodiment of the present invention provides a human power management system control apparatus, which may include:
the data extraction module is used for respectively extracting interview flow recording data of each historical statistical period from a preset manpower management system;
the increment manpower number prediction module is used for determining the predicted increment manpower number of the target statistical period according to the interview flow record data of each historical statistical period;
the resource allocation list acquisition module is used for acquiring a current cloud desktop resource allocation list from a preset cloud desktop server, wherein the cloud desktop resource allocation list comprises the hierarchy division condition of cloud desktop resource partitions and the residual resource amount of the cloud desktop resource partitions of each hierarchy, the hierarchies of the cloud desktop resource partitions are negatively related to preset performance indexes, and the performance indexes comprise time delay amount and/or error rate;
the resource partition selecting module is used for sequentially selecting a plurality of cloud desktop resource partitions of which the residual resource quantity meets the forecast increment human number from the cloud desktop resource configuration list according to the sequence of the levels from high to low, and appointing the cloud desktop resources corresponding to the forecast increment human number in the selected cloud desktop resource partitions;
and the resource configuration instruction sending module is used for sending a resource configuration instruction to the cloud desktop server so that the cloud desktop server performs cloud desktop resource configuration in the specified cloud desktop resources according to the predicted incremental manpower number. Further, the human management system control apparatus may further include:
a resource locking instruction sending module, configured to send a resource locking instruction to the cloud desktop server after receiving a resource configuration completion message fed back by the cloud desktop server, so that the cloud desktop server sets a first cloud desktop resource in a locked state, where the first cloud desktop resource is a cloud desktop resource configured by the cloud desktop server according to the predicted incremental manpower number;
the actual increment manpower number extraction module is used for extracting the actual increment manpower number of the target counting period from the manpower management system when the target counting period is ended;
a first resource allocation instruction sending module, configured to send a first resource allocation instruction to the cloud desktop server if the predicted incremental manpower number is consistent with the actual incremental manpower number, so that the cloud desktop server unlocks the first cloud desktop resource, and allocates the first cloud desktop resource to each incremental manpower.
Further, the human management system control apparatus may further include:
a first number difference calculation module for calculating a first number difference between the predicted incremental labor number and the actual incremental labor number if the predicted incremental labor number is greater than the actual incremental labor number;
a resource release instruction sending module, configured to generate a resource release instruction according to the number difference, and send the resource release instruction to the cloud desktop server, so that the cloud desktop server releases a second cloud desktop resource from the first cloud desktop resource, where the second cloud desktop resource is a cloud desktop resource corresponding to the number difference, and the second cloud desktop resource is a subset of the first cloud desktop resource;
a second resource allocation instruction sending module, configured to send a second resource allocation instruction to the cloud desktop server, so that the cloud desktop server unlocks a third cloud desktop resource, and allocates the third cloud desktop resource to each incremental manpower, where the third cloud desktop resource is a cloud desktop resource remaining after the second cloud desktop resource is released from the first cloud desktop resource.
Further, the human management system control apparatus may further include:
a second number difference calculation module, configured to calculate a second number difference between the actual incremental labor number and the predicted incremental labor number if the predicted incremental labor number is smaller than the actual incremental labor number;
a third resource allocation instruction sending module, configured to send a third resource allocation instruction to the cloud desktop server, so that the cloud desktop server unlocks the first cloud desktop resource, and allocates the first cloud desktop resource to each incremental manpower;
a resource reconfiguration instruction sending module, configured to generate a resource reconfiguration instruction according to the second number difference, and send the resource reconfiguration instruction to the cloud desktop server, so that the cloud desktop server performs cloud desktop resource configuration according to the second number difference;
and the fourth resource allocation instruction sending module is configured to send a fourth resource allocation instruction to the cloud desktop server after receiving the resource reconfiguration completion message fed back by the cloud desktop server, so that the cloud desktop server allocates a fourth cloud desktop resource to each incremental manpower, where the fourth cloud desktop resource is a cloud desktop resource allocated by the cloud desktop server according to the second number difference.
Further, the incremental human number prediction module may include:
the information determination submodule is used for determining the number of participants and the conversion rate of each interview link in each historical statistical period according to the interview process recording data;
the first number-of-participating determining submodule is used for determining the number of participating people in the 1 st interview link of the target counting period according to the number of participating people in the 1 st interview link of each historical counting period;
the conversion rate determining submodule is used for determining the conversion rate from the kth interview link of each associated statistical period to the (k + 1) th interview link of the target statistical period according to the conversion rate from the kth interview link of each historical statistical period to the (k + 1) th interview link of each historical statistical period, wherein the associated statistical period comprises the historical statistical period and the target statistical period;
the second participant number determining submodule is used for determining the participant number of the (k + 1) th interview link in the target counting period according to the participant number of the kth interview link in each associated counting period and the conversion rate from the kth interview link in each associated counting period to the (k + 1) th interview link in the target counting period, wherein k is more than or equal to 1 and less than or equal to KN-1, and KN is the total number of the interview links;
and the prediction increment manpower number determining module is used for determining the number of the participants of the KN interview link in the target counting period as the prediction increment manpower number.
Further, the conversion determination submodule may include:
the model construction unit is used for constructing an initial conversion rate prediction model;
the parameter estimation unit is used for carrying out parameter estimation on the conversion rate prediction model according to the conversion rate from the kth interview link of each historical statistical period to the (k + 1) th interview link of each historical statistical period to obtain the optimal model parameter of the conversion rate prediction model;
and the conversion rate calculation unit is used for calculating the conversion rate from the kth trial link of each associated statistical period to the (k + 1) th trial link of the target statistical period according to an optimized conversion rate prediction model, and the optimized conversion rate prediction model is the conversion rate prediction model using the optimal model parameters.
Further, the second participant number determination submodule is specifically configured to calculate the participant number of the (k + 1) th interview link of the target statistics period according to the following formula:
Figure BDA0002656557990000071
wherein c is the serial number of the associated statistical period, c is more than or equal to 1 and less than or equal to CN, CN is the number of the associated statistical period, Numk(c) InvRate, the number of participants in the kth trial link for the c-th associated statistical periodk(c) Is the kth of the c-th associated statistical periodConversion rate, Num, from the (k + 1) th interview link of the interview link to the target statistical periodk+1And counting the number of the participants in the (k + 1) th interview link of the target counting period.
A third aspect of embodiments of the present invention provides a computer-readable storage medium storing computer-readable instructions, which, when executed by a processor, implement the steps of any one of the above-mentioned human management system control methods.
A fourth aspect of the embodiments of the present invention provides a terminal device, including a memory, a processor, and computer-readable instructions stored in the memory and executable on the processor, where the processor implements any one of the above steps of the human power management system control method when executing the computer-readable instructions.
Compared with the prior art, the embodiment of the invention has the following beneficial effects: the embodiment of the invention respectively extracts interview flow record data of each historical statistical period from a preset manpower management system; determining the prediction increment manpower number of the target statistical period according to the interview flow record data of each historical statistical period; acquiring a current cloud desktop resource configuration list from a preset cloud desktop server, wherein the cloud desktop resource configuration list comprises the hierarchy division condition of cloud desktop resource partitions and the residual resource amount of the cloud desktop resource partitions of each hierarchy, the hierarchy of the cloud desktop resource partitions is negatively related to a preset performance index, and the performance index comprises a time delay amount and/or an error rate; sequentially selecting a plurality of cloud desktop resource partitions of which the residual resource quantity meets the prediction increment human number from the cloud desktop resource configuration list according to the sequence of the levels from high to low, and designating the cloud desktop resources corresponding to the prediction increment human number in the selected cloud desktop resource partitions; and sending a resource configuration instruction to the cloud desktop server so that the cloud desktop server performs cloud desktop resource configuration in the specified cloud desktop resources according to the predicted incremental manpower number. According to the embodiment of the invention, based on the existing manpower management system, the number of the added manpower is intelligently predicted through deep analysis of interview flow recorded data stored in the manpower management system, so that the configuration work of cloud desktop resources can be carried out in advance, the intelligent degree of enterprise management is greatly improved, and the working efficiency is improved.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, 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 diagram of an exemplary implementation of an embodiment of the present invention;
FIG. 2 is a flowchart illustrating an exemplary embodiment of a human management system control method according to the present invention;
FIG. 3 is a schematic flow chart of determining a predicted incremental human number for a target statistical period based on interview procedure log data for each historical statistical period;
FIG. 4 is a diagram illustrating an exemplary overall prediction process;
FIG. 5 is a block diagram of an embodiment of a human management system control apparatus according to an embodiment of the present invention;
fig. 6 is a schematic block diagram of a terminal device in an embodiment of the present invention.
Detailed Description
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
FIG. 1 is a diagram illustrating an exemplary implementation of an embodiment of the invention. The terminal equipment is an execution main body of the embodiment of the invention; the manpower management system is used for storing and inquiring data in the recruitment interview process, and can be any one of the manpower management systems in the prior art; the cloud desktop server is used for managing and configuring cloud desktop resources, is also called desktop virtualization and cloud computers, and is a new mode for replacing traditional computers. After the cloud desktop is adopted, a user does not need to configure a computer host, all components such as a CPU (central processing unit), a memory, a hard disk and the like contained in the computer host are virtualized out in a server (namely a cloud desktop server) at the back end, and a single high-performance server can virtualize 1-50 different virtual computers; the main stream of the front-end equipment is that a thin client is connected with a display and a keyboard and mouse, and a user accesses a virtual machine host on a rear-end server through a special communication protocol after installing a client to realize interactive operation, so that the experience effect consistent with that of a computer is achieved.
Referring to fig. 2, an embodiment of a method for controlling a human power management system according to an embodiment of the present invention may include:
step S201, extracting interview procedure record data of each historical statistics period from a preset human management system.
In the embodiment of the present invention, the duration of each statistical period may be set according to actual conditions, and preferably, it may be set to be one week, that is, each natural week (monday to sunday) is one statistical period. Taking the current time as a base point, the statistical period before the current time is a historical statistical period, and the statistical period where the current time is located is a target statistical period.
And S202, determining the prediction increment manpower number of the target statistical period according to interview flow recorded data of each historical statistical period.
The incremental manpower is the newly added personnel who can go on duty. Step S202 may specifically include the process shown in fig. 3:
and step S2021, determining the number of participants and the conversion rate of each interview link in each historical statistical period according to the interview process recorded data.
In the human management system, a plurality of interview links are generally performed. Taking 5 interview links as an example, the method is as follows: an interview link A, an interview link B, an interview link C, an interview link D, an interview link E, wherein the interview link A can be IQ/EQ testing, the interview link B can be first round interview, the interview link C can be second round interview, …, and the interview link E can be on duty. The adjacent interview links are in a funnel-type conversion relationship and are screened and filtered step by step, for example, many people can participate in the interview link A in each statistical period, many people can also participate in the interview link B, and the interview link A needs to be completed first in the interview link B, so that participants of the interview link B in a certain statistical period can be converted from participants of the interview link A in each previous statistical period, supposing that the number of participants of the interview link A in the ith statistical period is Num1, and Num2 of the participants participate in the interview link B in the jth statistical period, the conversion rate from the interview link A in the ith statistical period to the interview link B in the jth statistical period is as follows: (Num2 ÷ Num1) × 100%, where i and j are positive integers, and i is not more than j. The situations from the interview link B to the interview link C, from the interview link C to the interview link D, and from the interview link D to the interview link E can be analogized.
In the embodiment of the invention, statistics can be carried out according to the interview flow record data, so that the number of participants and the conversion rate of each interview link in each historical statistical period can be obtained.
Step S2022, determining the number of the participants in the 1 st interview link of the target statistical period according to the number of the participants in the 1 st interview link of each historical statistical period.
Firstly, the number of participants in the 1 st interview link (namely the interview link A) in each historical statistical period is sequentially arranged into a sequence according to the morning and evening and recorded as an original data sequence.
Then, data sequence stationarity test is carried out: carrying out stationarity analysis on the original data sequence; if the original data sequence is a stable data sequence, the processing is not needed; if the original data sequence is a non-stationary data sequence, firstly, the original data sequence is subjected to stationary processing in a differential mode to obtain a stationary data sequence, and then subsequent processing is carried out, wherein the differential mode comprises D-order general difference and D-order seasonal difference.
Then, fitting the stationary data sequence by using an AutoRegressive Moving Average Model (ARMA), and determining the order of the ARMA Model, namely determining the values of (P, Q) and (P, Q); and then the D-order general difference and the D-order seasonal difference are integrated to obtain a complete structure of the product seasonal model of data sequence fitting as follows:
φp(B)ΦP(BS)(1-B)(1-BS)Dyt＝θq(B)ΘQ(BS)t
wherein, ytAs an observation of the original data sequence,tis a residual term, B is a lag operator, S represents a variation period, 1-B represents a non-seasonal difference, 1-BSIndicates the difference of seasons, phip(B) Representing non-seasonal autoregressive polynomials, ΦP(BS) Expressing the seasonal autoregressive polynomial, thetaq(B) Representing the off-season mean shift polynomial, ΘQ(BS) Representing a seasonal average mobile polynomial, P representing the maximum hysteresis order of the non-seasonal autoregressive polynomial, P representing the maximum hysteresis order of the seasonal autoregressive polynomial, Q representing the maximum hysteresis order of the non-seasonal average mobile polynomial, Q representing the maximum hysteresis order of the seasonal average mobile polynomial, D representing the number of non-seasonal differences, and D representing the number of seasonal differences.
The above model is denoted as (P, D, Q) × (P, D, Q)SAn order Seasonal time series Model, namely a Seasonal differential autoregressive Moving Average Model (SARIMA).
And performing autocorrelation inspection and heteroscedastic inspection on the residual sequence, and analyzing to find that the sequence has periodicity, seasonality and trend, the residual sequence is a white noise sequence and has no obvious fluctuation, mainly because the link A is an online IQ/EQ test and has little mutation, and the number of participants in the 1 st interview link of the target statistical period can be predicted based on a SARIMA model. Further, the number of the participating persons in the 1 st interview link in each statistical period after the target statistical period can be predicted.
Step S2023, determining the conversion rate from the kth surface test link of each associated statistical period to the (k + 1) th surface test link of the target statistical period.
Namely, according to the conversion rate from the kth interview link of each historical statistical period to the (k + 1) th interview link of each historical statistical period, the conversion rate from the kth interview link of each associated statistical period to the (k + 1) th interview link of the target statistical period is determined. The associated statistical period comprises the historical statistical period and the target statistical period.
First, an initial conversion prediction model is constructed.
The SARIMA model is established based on the conversion rate from the kth interview link of each historical statistical period to the kth +1 interview link of each historical statistical period, the self-correlation test and the variance test are carried out on the residual sequence, the analysis shows that the sequence has periodicity, seasonality and trend and has large fluctuation, and the residual sequence is the variance sequence. Dividing a residual sequence into two parts, wherein one part is a smooth residual and is used for describing smooth fluctuation of the conversion rate, and constructing a Generalized Auto Regressive Conditional heterology model (GARCH) based on the smooth residual; the other part is a jump residual, is mainly used for depicting jump variation of the conversion rate, is represented by a Poisson jump model, is innovatively added with holiday factors and service promotion period factors on a jump behavior model, is used for representing the influence of holidays and service promotion periods on sequence fluctuation, and is used for constructing a SARIMA-GARCH _ jump model shown as follows, namely an initial conversion rate prediction model:
Figure BDA0002656557990000121
wherein, f (t, r)t-1,rt-2…) is constructed from the SARIMA model, which is described in detail above and will not be described herein.
The holiday factors and the business promotion cycle factors are as follows:
festival and holiday factors:
Figure BDA0002656557990000131
service promotion cycle factors:
Figure BDA0002656557990000132
the business promotion period factor refers to that an enterprise designs an increase scheme for promoting business within a certain period of time, for example, the increase amount slips down in 6 months, the enterprise designs a 3-quarter increase scheme at the moment, the 3-quarter mode is different from the history at the moment, and at the moment, whether the different moment is a business promotion period needs to be judged.
And then, performing parameter estimation on the conversion rate prediction model according to the conversion rate from the kth interview link to the (k + 1) th interview link of each historical statistical period to obtain the optimal model parameter of the conversion rate prediction model.
Specifically, in the embodiment of the present invention, the maximum likelihood estimation method may be used to perform parameter estimation on the SARIMA-GARCH _ jump model:
let f (t, r)t-1,rt-2…) result in μtThen, there are:t＝rt-μt，
Figure BDA0002656557990000133
Nt|Θt-1～Poisson(λt)，
Figure BDA0002656557990000134
wherein, mut＝jθ-θλt，
Figure BDA0002656557990000135
The likelihood function is then:
Figure BDA0002656557990000136
the log-likelihood function is:
Figure BDA0002656557990000141
and (3) further estimating the parameters by calculating MAX (L), namely the parameter value corresponding to the maximum value of L is the estimated optimal model parameter.
Finally, the conversion rate from the kth trial link of each associated statistical period to the (k + 1) th trial link of the target statistical period can be calculated according to an optimized conversion rate prediction model, wherein the optimized conversion rate prediction model is a conversion rate prediction model using the optimal model parameters.
And step S2024, determining the number of the participating persons in the (k + 1) th interview link of the target statistical period.
Namely, the number of the participants in the (k + 1) th interview link in the target statistical period is determined according to the number of the participants in the kth interview link in each associated statistical period and the conversion rate from the kth interview link in each associated statistical period to the (k + 1) th interview link in the target statistical period. Wherein k is more than or equal to 1 and less than or equal to KN-1, and KN is the total number of the interview links.
Specifically, the number of participants in the k +1 th interview link of the target statistical period can be calculated according to the following formula:
Figure BDA0002656557990000142
wherein c is the serial number of the associated statistical period, c is more than or equal to 1 and less than or equal to CN, CN is the number of the associated statistical period, Numk(c) For participation in the kth interview link of the c-th associated statistical periodNumber of people InvRatek(c) The conversion rate, Num, from the kth surface test link of the c-th associated statistical period to the (k + 1) th surface test link of the target statistical periodk+1And counting the number of the participants in the (k + 1) th interview link of the target counting period.
Step S2025, determining the number of participating people in the KN interview link in the target statistical period as the prediction incremental manpower number.
Fig. 4 is a specific example of the whole prediction process, in which the number of participants in each interview session in 12 weeks from 1 month to 3 months is known, and the incremental human number in the first week of 4 months is predicted according to the known number. Firstly, constructing a SARIMA model according to the historical data of an interview link A, predicting the number of participants of the interview link A in the first week of 4 months, then constructing a SARIMA-GARCH _ jump model according to the historical conversion rate from the interview link A to the interview link B, predicting the conversion rate from the interview link A in each week to the interview link B in the first week of 4 months (namely A1, A2, … and A13 in the figure) respectively, and then calculating the number of participants of the interview link B in the first week of 4 months according to the following formula: the number of participants of the interview link B in the first week of 4 months is equal to the number of participants of the interview link A in the first week of 4 months A1+ the number of participants of the interview link A in the fourth week of 3 months A2+ … + the number of participants of the interview link A in the first week of 1 month A13, the interview link B, the interview link C, the interview link D, the interview link E and the like are analogized, and finally the prediction increment manpower number is obtained.
Step S203, sending a resource configuration instruction to the cloud desktop server to obtain a current cloud desktop resource configuration list from a preset cloud desktop server.
The cloud desktop resource configuration list comprises the hierarchy division condition of the cloud desktop resource partitions and the residual resource amount of the cloud desktop resource partitions of each hierarchy. The cloud desktop resource partition level is inversely related to a preset performance index, and the performance index comprises a time delay amount and/or an error rate.
In a specific implementation of the embodiment of the present invention, the cloud desktop server may manage cloud desktop resources in a plurality of cloud desktop resource partitions, and each cloud desktop resource partition is divided into different levels according to performance indexes.
If the delay amount is selected as the performance index of the hierarchical division, the lower the delay amount is, the higher the hierarchy corresponding to the cloud desktop resource partition is, and the higher the delay amount is, the lower the hierarchy corresponding to the cloud desktop resource partition is. For example, all cloud desktop resource partitions can be divided into four levels according to the delay amount, wherein the cloud desktop resource partition with the delay amount of less than or equal to 1 millisecond is divided into the highest level and is recorded as the first level; dividing the cloud desktop resource partition with the time delay amount of more than 1 millisecond and less than or equal to 10 milliseconds into a second highest level, and recording the second level as the second level; partitioning the cloud desktop resources with the time delay amount of more than 10 milliseconds and less than or equal to 100 milliseconds into a next level, and recording the next level as a third level; and dividing the cloud desktop resource partition with the time delay amount of more than 100 milliseconds into the lowest level, and recording the lowest level as a fourth level.
If the error rate is selected as a performance index of the hierarchy division, the smaller the error rate, the higher the hierarchy corresponding to the cloud desktop resource partition, and the larger the error rate, the lower the hierarchy corresponding to the cloud desktop resource partition. For example, all cloud desktop resource partitions can be divided into four levels according to the error rate, wherein the cloud desktop resource partition with the error rate less than or equal to one millionth is divided into the highest level and is recorded as the first level; partitioning the cloud desktop resources with the error rate more than one part per million and less than or equal to one ten-thousandth into a second highest level, and recording as the second level; partitioning the cloud desktop resources with the error rate more than one ten-thousandth and less than or equal to one ten-thousandth into a next level, and recording as a third level; and partitioning the cloud desktop resources with the error rate of more than one ten-thousandth into the lowest level, and recording as the fourth level.
Step S204, sequentially selecting a plurality of cloud desktop resource partitions with residual resource amounts meeting the forecast increment human number from the cloud desktop resource configuration list according to the sequence from high to low in hierarchy, and appointing the cloud desktop resources corresponding to the forecast increment human number in the selected cloud desktop resource partitions.
It is easy to understand that the higher the level of the cloud desktop resource partition is, the better the working performance is, in the embodiment of the present invention, in order to fully utilize the cloud desktop resource, the cloud desktop resource partition of the high level is preferentially configured, and only when the cloud desktop resource partition of the high level is already occupied, the cloud desktop resource partition of the low level is configured.
Specifically, whether the residual resource amount of the cloud desktop resource partition of the first level can meet the predicted incremental manpower number is judged, wherein the cloud desktop resource amount corresponding to each incremental manpower is a preset fixed value, if yes, the cloud desktop resource corresponding to the predicted incremental manpower number is directly specified in the cloud desktop resource partition of the first level, if not, whether the sum of the residual resource amounts of the cloud desktop resource partitions of the first level and the second level can meet the predicted incremental manpower number is judged, if yes, the cloud desktop resource corresponding to the predicted incremental manpower number is specified in the cloud desktop resource partitions of the first level and the second level, and if not, whether the sum of the residual resource amounts of the cloud desktop resource partitions of the first level, the second level and the third level can meet the predicted incremental manpower number is judged, … …, and so on.
And S205, sending a resource configuration instruction to the cloud desktop server.
After receiving the resource configuration instruction, the cloud desktop server performs cloud desktop resource configuration in the specified cloud desktop resources according to the predicted incremental manpower number, and configures the cloud desktop #1, the cloud desktop #2, the cloud desktop #3 and … shown in fig. 1, and so on. And after the configuration is completed, the cloud desktop server feeds back a resource configuration completion message to the terminal equipment.
And after receiving the resource configuration completion message, the terminal equipment sends a resource locking instruction to the cloud desktop server. After receiving the resource locking instruction, the cloud desktop server sets the first cloud desktop resource to be in a locked state, and in the locked state, other users cannot call the resource. The first cloud desktop resource is a cloud desktop resource configured by the cloud desktop server according to the prediction increment human number.
And when the target counting period is ended, the terminal equipment extracts the actual increment manpower number of the target counting period from the manpower management system.
If the predicted incremental manpower number is consistent with the actual incremental manpower number, the terminal device sends a first resource allocation instruction to the cloud desktop server, and the cloud desktop server unlocks the first cloud desktop resource and allocates the first cloud desktop resource to each incremental manpower after receiving the first resource allocation instruction.
If the predicted increment manpower number is larger than the actual increment manpower number, the terminal device calculates a first number difference value between the predicted increment manpower number and the actual increment manpower number, generates a resource release instruction according to the number difference value, and sends the resource release instruction to the cloud desktop server. And after receiving the resource release instruction, the cloud desktop server releases second cloud desktop resources from the first cloud desktop resources, wherein the second cloud desktop resources are cloud desktop resources corresponding to the number difference, and the second cloud desktop resources are a subset of the first cloud desktop resources. And then, the terminal equipment sends a second resource allocation instruction to the cloud desktop server. After receiving the second resource allocation instruction, the cloud desktop server unlocks a third cloud desktop resource, and allocates the third cloud desktop resource to each incremental manpower, wherein the third cloud desktop resource is a cloud desktop resource remaining after the second cloud desktop resource is released from the first cloud desktop resource.
If the predicted incremental manpower number is smaller than the actual incremental manpower number, the terminal device calculates a second number difference between the actual incremental manpower number and the predicted incremental manpower number, and sends a third resource allocation instruction to the cloud desktop server. And after receiving the third resource allocation instruction, the cloud desktop server unlocks the first cloud desktop resource and allocates the first cloud desktop resource to each incremental manpower. And then, the terminal equipment generates a resource reconfiguration instruction according to the second number difference value, and sends the resource reconfiguration instruction to the cloud desktop server. And after receiving the resource reconfiguration instruction, the cloud desktop server performs cloud desktop resource configuration according to the second number difference value, and after the configuration is completed, feeds back a resource reconfiguration completion message to the cloud desktop server. And after receiving the resource reconfiguration completion message, the terminal equipment sends a fourth resource allocation instruction to the cloud desktop server. After receiving the fourth resource allocation instruction, the cloud desktop server allocates fourth cloud desktop resources to each incremental manpower, where the fourth cloud desktop resources are cloud desktop resources configured by the cloud desktop server according to the second number difference.
By the method, the number of the added staff can be accurately predicted according to the historical record data of each interviewing link, and the cloud desktop resources can be configured in advance according to the prediction result. The resource waste caused by excessive reservation of cloud desktop resources is avoided, and sudden resource allocation is avoided after the actual number of increment personnel is determined, and compared with the resource allocation performed in advance, the sudden resource allocation has high uncertainty and can possibly cause the situations that resources are all occupied in a short time and no resources are available. Therefore, the embodiment of the invention can realize reasonable use of resources to the maximum extent.
Preferably, in all embodiments of the present invention, the interview flow record data stored in the human management system can be uploaded to a preset blockchain to ensure the safety and the fair transparency for the user. The user equipment may download the interview flow record data from the blockchain to verify whether the interview flow record data is tampered. The blockchain referred to in this example is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm, and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
In summary, in the embodiment of the present invention, interview flow record data of each historical statistical period is respectively extracted from a preset human management system; determining the prediction increment manpower number of the target statistical period according to the interview flow record data of each historical statistical period; acquiring a current cloud desktop resource configuration list from a preset cloud desktop server, wherein the cloud desktop resource configuration list comprises the hierarchy division condition of cloud desktop resource partitions and the residual resource amount of the cloud desktop resource partitions of each hierarchy, the hierarchy of the cloud desktop resource partitions is negatively related to a preset performance index, and the performance index comprises a time delay amount and/or an error rate; sequentially selecting a plurality of cloud desktop resource partitions of which the residual resource quantity meets the prediction increment human number from the cloud desktop resource configuration list according to the sequence of the levels from high to low, and designating the cloud desktop resources corresponding to the prediction increment human number in the selected cloud desktop resource partitions; and sending a resource configuration instruction to the cloud desktop server so that the cloud desktop server performs cloud desktop resource configuration in the specified cloud desktop resources according to the predicted incremental manpower number. According to the embodiment of the invention, based on the existing manpower management system, the number of the added manpower is intelligently predicted through deep analysis of interview flow recorded data stored in the manpower management system, so that the configuration work of cloud desktop resources can be carried out in advance, the intelligent degree of enterprise management is greatly improved, and the working efficiency is improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
Fig. 5 is a structural diagram of an embodiment of a human power management system control apparatus according to an embodiment of the present invention, which corresponds to the human power management system control method described in the foregoing embodiment.
In this embodiment, a human management system control apparatus may include:
the data extraction module 501 is configured to respectively extract interview flow record data of each historical statistics period from a preset human management system;
an increment manpower number prediction module 502, configured to determine a predicted increment manpower number of a target statistics period according to interview procedure record data of each historical statistics period;
a resource allocation list obtaining module 503, configured to obtain a current cloud desktop resource allocation list from a preset cloud desktop server, where the cloud desktop resource allocation list includes a hierarchy division condition of a cloud desktop resource partition and a remaining resource amount of the cloud desktop resource partition of each hierarchy, where the hierarchy of the cloud desktop resource partition is negatively related to a preset performance index, and the performance index includes a delay amount and/or a bit error rate;
a resource partition selecting module 504, configured to sequentially select, from the cloud desktop resource configuration list, a plurality of cloud desktop resource partitions whose remaining resource amounts satisfy the predicted incremental manpower number according to a sequence from high to low in hierarchy, and designate, in the selected cloud desktop resource partitions, a cloud desktop resource corresponding to the predicted incremental manpower number;
a resource configuration instruction sending module 505, configured to send a resource configuration instruction to the cloud desktop server, so that the cloud desktop server performs cloud desktop resource configuration on a specified cloud desktop resource according to the predicted incremental manpower number. Further, the human management system control apparatus may further include:
a resource locking instruction sending module, configured to send a resource locking instruction to the cloud desktop server after receiving a resource configuration completion message fed back by the cloud desktop server, so that the cloud desktop server sets a first cloud desktop resource in a locked state, where the first cloud desktop resource is a cloud desktop resource configured by the cloud desktop server according to the predicted incremental manpower number;
the actual increment manpower number extraction module is used for extracting the actual increment manpower number of the target counting period from the manpower management system when the target counting period is ended;
a first resource allocation instruction sending module, configured to send a first resource allocation instruction to the cloud desktop server if the predicted incremental manpower number is consistent with the actual incremental manpower number, so that the cloud desktop server unlocks the first cloud desktop resource, and allocates the first cloud desktop resource to each incremental manpower.
Further, the human management system control apparatus may further include:
a first number difference calculation module for calculating a first number difference between the predicted incremental labor number and the actual incremental labor number if the predicted incremental labor number is greater than the actual incremental labor number;
a resource release instruction sending module, configured to generate a resource release instruction according to the number difference, and send the resource release instruction to the cloud desktop server, so that the cloud desktop server releases a second cloud desktop resource from the first cloud desktop resource, where the second cloud desktop resource is a cloud desktop resource corresponding to the number difference, and the second cloud desktop resource is a subset of the first cloud desktop resource;
a second resource allocation instruction sending module, configured to send a second resource allocation instruction to the cloud desktop server, so that the cloud desktop server unlocks a third cloud desktop resource, and allocates the third cloud desktop resource to each incremental manpower, where the third cloud desktop resource is a cloud desktop resource remaining after the second cloud desktop resource is released from the first cloud desktop resource.
Further, the human management system control apparatus may further include:
a second number difference calculation module, configured to calculate a second number difference between the actual incremental labor number and the predicted incremental labor number if the predicted incremental labor number is smaller than the actual incremental labor number;
a third resource allocation instruction sending module, configured to send a third resource allocation instruction to the cloud desktop server, so that the cloud desktop server unlocks the first cloud desktop resource, and allocates the first cloud desktop resource to each incremental manpower;
a resource reconfiguration instruction sending module, configured to generate a resource reconfiguration instruction according to the second number difference, and send the resource reconfiguration instruction to the cloud desktop server, so that the cloud desktop server performs cloud desktop resource configuration according to the second number difference;
and the fourth resource allocation instruction sending module is configured to send a fourth resource allocation instruction to the cloud desktop server after receiving the resource reconfiguration completion message fed back by the cloud desktop server, so that the cloud desktop server allocates a fourth cloud desktop resource to each incremental manpower, where the fourth cloud desktop resource is a cloud desktop resource allocated by the cloud desktop server according to the second number difference.
Further, the incremental human number prediction module may include:
the information determination submodule is used for determining the number of participants and the conversion rate of each interview link in each historical statistical period according to the interview process recording data;
the first number-of-participating determining submodule is used for determining the number of participating people in the 1 st interview link of the target counting period according to the number of participating people in the 1 st interview link of each historical counting period;
the conversion rate determining submodule is used for determining the conversion rate from the kth interview link of each associated statistical period to the (k + 1) th interview link of the target statistical period according to the conversion rate from the kth interview link of each historical statistical period to the (k + 1) th interview link of each historical statistical period, wherein the associated statistical period comprises the historical statistical period and the target statistical period;
the second participant number determining submodule is used for determining the participant number of the (k + 1) th interview link in the target counting period according to the participant number of the kth interview link in each associated counting period and the conversion rate from the kth interview link in each associated counting period to the (k + 1) th interview link in the target counting period, wherein k is more than or equal to 1 and less than or equal to KN-1, and KN is the total number of the interview links;
and the prediction increment manpower number determining module is used for determining the number of the participants of the KN interview link in the target counting period as the prediction increment manpower number.
Further, the conversion determination submodule may include:
the model construction unit is used for constructing an initial conversion rate prediction model;
the parameter estimation unit is used for carrying out parameter estimation on the conversion rate prediction model according to the conversion rate from the kth interview link of each historical statistical period to the (k + 1) th interview link of each historical statistical period to obtain the optimal model parameter of the conversion rate prediction model;
and the conversion rate calculation unit is used for calculating the conversion rate from the kth trial link of each associated statistical period to the (k + 1) th trial link of the target statistical period according to an optimized conversion rate prediction model, and the optimized conversion rate prediction model is the conversion rate prediction model using the optimal model parameters.
Further, the second participant number determination submodule is specifically configured to calculate the participant number of the (k + 1) th interview link of the target statistics period according to the following formula:
Figure BDA0002656557990000221
wherein c is the serial number of the associated statistical period, c is more than or equal to 1 and less than or equal to CN, CN is the number of the associated statistical period, Numk(c) InvRate, the number of participants in the kth trial link for the c-th associated statistical periodk(c) The conversion rate, Num, from the kth surface test link of the c-th associated statistical period to the (k + 1) th surface test link of the target statistical periodk+1And counting the number of the participants in the (k + 1) th interview link of the target counting period.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses, modules and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Fig. 6 shows a schematic block diagram of a terminal device according to an embodiment of the present invention, and for convenience of description, only the relevant parts related to the embodiment of the present invention are shown.
In this embodiment, the terminal device 6 may be a computing device such as a desktop computer, a notebook, a palm computer, and a cloud server. The terminal device 6 may include: a processor 60, a memory 61, and computer readable instructions 62 stored in the memory 61 and executable on the processor 60, such as computer readable instructions to perform the human management system control method described above. The processor 60, when executing the computer readable instructions 62, implements the steps of the various embodiments of the human management system control method described above, such as the steps S201-S205 shown in fig. 2. Alternatively, the processor 60, when executing the computer readable instructions 62, implements the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the modules 501 to 505 shown in fig. 5.
Illustratively, the computer readable instructions 62 may be partitioned into one or more modules/units that are stored in the memory 61 and executed by the processor 60 to implement the present invention. The one or more modules/units may be a series of computer-readable instruction segments capable of performing specific functions, which are used to describe the execution process of the computer-readable instructions 62 in the terminal device 6.
The Processor 60 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 61 may be an internal storage unit of the terminal device 6, such as a hard disk or a memory of the terminal device 6. The memory 61 may also be an external storage device of the terminal device 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 6. Further, the memory 61 may also include both an internal storage unit and an external storage device of the terminal device 6. The memory 61 is used for storing the computer readable instructions and other instructions and data required by the terminal device 6. The memory 61 may also be used to temporarily store data that has been output or is to be output.
Each functional unit in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes a plurality of computer readable instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like, which can store computer readable instructions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims (10)

1. A human management system control method is characterized by comprising the following steps:
respectively extracting interview flow record data of each historical statistical period from a preset manpower management system;
determining the prediction increment manpower number of the target statistical period according to the interview flow record data of each historical statistical period;
acquiring a current cloud desktop resource configuration list from a preset cloud desktop server, wherein the cloud desktop resource configuration list comprises the hierarchy division condition of cloud desktop resource partitions and the residual resource amount of the cloud desktop resource partitions of each hierarchy, the hierarchy of the cloud desktop resource partitions is negatively related to a preset performance index, and the performance index comprises a time delay amount and/or an error rate;
sequentially selecting a plurality of cloud desktop resource partitions of which the residual resource quantity meets the prediction increment human number from the cloud desktop resource configuration list according to the sequence of the levels from high to low, and designating the cloud desktop resources corresponding to the prediction increment human number in the selected cloud desktop resource partitions;
and sending a resource configuration instruction to the cloud desktop server so that the cloud desktop server performs cloud desktop resource configuration in the specified cloud desktop resources according to the predicted incremental manpower number.
2. The human management system control method of claim 1, after sending the resource configuration instruction to the cloud desktop server, further comprising:
after receiving a resource configuration completion message fed back by the cloud desktop server, sending a resource locking instruction to the cloud desktop server so that the cloud desktop server sets a first cloud desktop resource in a locking state, wherein the first cloud desktop resource is a cloud desktop resource configured by the cloud desktop server according to the prediction increment human number;
when the target counting period is finished, extracting the actual increment human number of the target counting period from the human management system;
if the predicted incremental manpower number is consistent with the actual incremental manpower number, sending a first resource allocation instruction to the cloud desktop server so that the cloud desktop server unlocks the first cloud desktop resource and allocates the first cloud desktop resource to each incremental manpower.
3. The human management system control method of claim 2, further comprising, after extracting the actual incremental human numbers for the target statistical period from the human management system:
if the predicted incremental manpower number is larger than the actual incremental manpower number, calculating a first number difference value between the predicted incremental manpower number and the actual incremental manpower number;
generating a resource release instruction according to the number difference, and sending the resource release instruction to the cloud desktop server, so that the cloud desktop server releases a second cloud desktop resource from the first cloud desktop resource, wherein the second cloud desktop resource is a cloud desktop resource corresponding to the number difference, and the second cloud desktop resource is a subset of the first cloud desktop resource;
sending a second resource allocation instruction to the cloud desktop server to enable the cloud desktop server to unlock third cloud desktop resources and allocate the third cloud desktop resources to each incremental manpower, wherein the third cloud desktop resources are cloud desktop resources remaining after the second cloud desktop resources are released from the first cloud desktop resources.
4. The human management system control method of claim 2, further comprising, after extracting the actual incremental human numbers for the target statistical period from the human management system:
if the predicted incremental labor number is less than the actual incremental labor number, calculating a second number difference between the actual incremental labor number and the predicted incremental labor number;
sending a third resource allocation instruction to the cloud desktop server to enable the cloud desktop server to unlock the first cloud desktop resources and allocate the first cloud desktop resources to each incremental manpower;
generating a resource reconfiguration instruction according to the second number difference value, and sending the resource reconfiguration instruction to the cloud desktop server so that the cloud desktop server performs cloud desktop resource configuration according to the second number difference value;
after receiving the resource reconfiguration completion message fed back by the cloud desktop server, sending a fourth resource allocation instruction to the cloud desktop server, so that the cloud desktop server allocates a fourth cloud desktop resource to each incremental manpower, where the fourth cloud desktop resource is a cloud desktop resource allocated by the cloud desktop server according to the second number difference.
5. The human management system control method of any one of claims 1-4, wherein the determining the predicted incremental human number for the target statistical period according to the interview flow record data of each historical statistical period comprises:
determining the number of participants and the conversion rate of each interview link in each historical statistical period according to the interview process recorded data;
determining the number of participants in the 1 st interview link of the target statistical period according to the number of participants in the 1 st interview link of each historical statistical period;
determining the conversion rate from the kth interview link of each associated statistical period to the kth +1 interview link of the target statistical period according to the conversion rate from the kth interview link of each historical statistical period to the kth +1 interview link of each historical statistical period, wherein the associated statistical period comprises the historical statistical period and the target statistical period;
determining the number of participants of the (k + 1) th interview link in the target statistical period according to the number of participants of the kth interview link in each associated statistical period and the conversion rate from the kth interview link in each associated statistical period to the (k + 1) th interview link in the target statistical period, wherein k is more than or equal to 1 and less than or equal to KN-1, and KN is the total number of the interview links;
and determining the number of the participants of the KN interview link in the target counting period as the prediction increment manpower number.
6. The method as claimed in claim 5, wherein the determining the conversion rate from the k-th trial link of each associated statistical cycle to the (k + 1) -th trial link of the target statistical cycle comprises:
constructing an initial conversion rate prediction model;
performing parameter estimation on the conversion rate prediction model according to the conversion rate from the kth interview link of each historical statistical period to the (k + 1) th interview link of each historical statistical period to obtain the optimal model parameter of the conversion rate prediction model;
and calculating the conversion rate from the kth trial link of each associated statistical period to the (k + 1) th trial link of the target statistical period according to an optimized conversion rate prediction model, wherein the optimized conversion rate prediction model is the conversion rate prediction model using the optimal model parameters.
7. The human management system control method of claim 5, wherein the determining the number of participants in the k +1 th interview session of the target statistical period comprises:
calculating the number of participants in the (k + 1) th interview link of the target statistical period according to the following formula:
Figure FDA0002656557980000041
wherein c is the serial number of the associated statistical period, c is more than or equal to 1 and less than or equal to CN, CN is the number of the associated statistical period, Numk(c) InvRate, the number of participants in the kth trial link for the c-th associated statistical periodk(c) The conversion rate, Num, from the kth surface test link of the c-th associated statistical period to the (k + 1) th surface test link of the target statistical periodk+1And counting the number of the participants in the (k + 1) th interview link of the target counting period.
8. A human management system control device, comprising:
the data extraction module is used for respectively extracting interview flow recording data of each historical statistical period from a preset manpower management system;
the increment manpower number prediction module is used for determining the predicted increment manpower number of the target statistical period according to the interview flow record data of each historical statistical period;
the resource allocation list acquisition module is used for acquiring a current cloud desktop resource allocation list from a preset cloud desktop server, wherein the cloud desktop resource allocation list comprises the hierarchy division condition of cloud desktop resource partitions and the residual resource amount of the cloud desktop resource partitions of each hierarchy, the hierarchies of the cloud desktop resource partitions are negatively related to preset performance indexes, and the performance indexes comprise time delay amount and/or error rate;
the resource partition selecting module is used for sequentially selecting a plurality of cloud desktop resource partitions of which the residual resource quantity meets the forecast increment human number from the cloud desktop resource configuration list according to the sequence of the levels from high to low, and appointing the cloud desktop resources corresponding to the forecast increment human number in the selected cloud desktop resource partitions;
and the resource configuration instruction sending module is used for sending a resource configuration instruction to the cloud desktop server so that the cloud desktop server performs cloud desktop resource configuration in the specified cloud desktop resources according to the predicted incremental manpower number.
9. A computer readable storage medium storing computer readable instructions, wherein the computer readable instructions, when executed by a processor, implement the steps of the human management system control method as claimed in any one of claims 1 to 7.
10. A terminal device comprising a memory, a processor and computer readable instructions stored in said memory and executable on said processor, wherein said processor when executing said computer readable instructions implements the steps of the human management system control method as claimed in any one of claims 1 to 7.
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