CN114579247B - Recruitment information processing method and device, electronic equipment and storage medium - Google Patents

Recruitment information processing method and device, electronic equipment and storage medium Download PDF

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CN114579247B
CN114579247B CN202210484594.2A CN202210484594A CN114579247B CN 114579247 B CN114579247 B CN 114579247B CN 202210484594 A CN202210484594 A CN 202210484594A CN 114579247 B CN114579247 B CN 114579247B
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CN114579247A (en
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王强
曹征
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Puxiong Technology Shenzhen Co ltd
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Abstract

The invention provides a recruitment information processing method, a device, electronic equipment and a storage medium, wherein after fixed task parameters are determined, the task parameters in an initial task parameter list can be adjusted according to the fixed task parameters, parameter adjusting factors and parameter judgment conditions, and a recruitment object corresponding to a target project is determined according to the adjusted parameter list.

Description

Recruitment information processing method and device, electronic equipment and storage medium
Technical Field
The present invention relates to the field of information processing, and in particular, to a method and an apparatus for processing employment information, an electronic device, and a storage medium.
Background
With the progress and development of the society, flexible employment scenes are more and more common, a large number of employment platforms are derived based on the flexible employment scenes, the employment platforms are usually that enterprises register on the employment platforms by submitting registration materials, corresponding employment demands are issued on the platforms, workers register as technical personnel on the platforms by submitting personal related technical information, and after the enterprises issue the employment demands, the technical personnel can take over work through the employment platforms.
Generally, the employment of enterprises has many special requirements, such as skill level and work experience of workers, and the skilled worker has certain requirements on the industry category, work category and corresponding salary treatment that the skilled worker can engage in, so that how to allow the worker to accurately match to the appropriate skilled worker is a problem that needs to be solved by the existing online employment platform in order to meet the diversified requirements of the worker and the skilled worker.
Therefore, it is desirable to provide a method and an apparatus for processing labor information to solve the above technical problems.
Disclosure of Invention
The embodiment of the invention provides a method and a device for processing recruitment information, which not only can allow a recruitment party to accurately match with a proper skilled person, but also can improve the processing efficiency of the recruitment information.
The embodiment of the invention provides a method for processing employment information, which comprises the following steps:
receiving an employment request for a target project;
displaying an employment processing interface of the target project, wherein the employment processing interface comprises a task description area and a task parameter area;
in response to a task description operation aiming at the task description area, displaying an initial task parameter list in the task parameter area;
responding to the parameter adjustment operation aiming at the initial task parameter list, and acquiring parameter adjustment factors and parameter judgment conditions;
determining fixed task parameters in the initial task parameter list;
adjusting the task parameters in the initial task parameter list according to the fixed task parameters, the parameter adjusting factors and the parameter judgment conditions, and dynamically displaying the parameter adjusting process in the task parameter area;
acquiring candidate employment objects according to the adjusted parameter list, and determining the employment object corresponding to the target project in the candidate employment objects based on the employment information of the candidate employment objects.
In the method for processing recruitment information according to the present invention, the fixed task parameter is a fixed task duration, and the adjusting of the task parameter in the initial task parameter list according to the fixed task parameter, the parameter adjusting factor and the parameter decision condition comprises:
identifying an item type of the target item;
outputting the maximum task amount corresponding to the target project according to the project type, the fixed task duration and the parameter judgment condition;
when the number of the recruitment objects in the initial task parameter list is increased, reducing the object grade of the recruitment objects in the initial task parameter list or increasing the task amount in the initial task parameter list according to the parameter adjusting factor;
in response to the operation of reducing the number of the recruitment objects in the initial task parameter list, increasing the object grade of the recruitment objects in the initial task parameter list and increasing the task amount in the initial task parameter list according to the parameter adjusting factor, or;
in response to the money amount increasing operation aiming at the task money in the initial task parameter list, increasing the number of the recruitment objects and/or the object grade of the recruitment objects in the initial task parameter list according to the parameter adjusting factor, or;
in response to the money amount reduction operation aiming at the task money amount in the initial task parameter list, reducing the number of recruitment objects and/or the object grade of the recruitment objects in the initial task parameter list according to the parameter adjusting factor, or;
in response to the grade improvement operation aiming at the object grade of the recruitment object in the initial task parameter list, reducing the number of the recruitment objects in the initial task parameter list according to the parameter adjustment factor, or;
in response to the grade reduction operation aiming at the object grade of the recruitment object in the initial task parameter list, increasing the number of the recruitment objects in the initial task parameter list according to the parameter adjustment factor,
wherein the adjusted task amount is less than or equal to the maximum task amount.
In the recruitment information processing method of the present invention, the outputting the maximum task amount corresponding to the target project according to the project type, the fixed task duration and the parameter decision condition includes:
determining a reference item corresponding to the target item in a preset database according to the item type;
acquiring the reference task amount of the reference item in unit time;
and calculating the product of the reference task amount and the fixed task time length based on the parameter judgment condition to obtain the maximum task amount corresponding to the target project.
In the recruitment information processing method of the present invention, the fixed task parameter is a fixed task amount, and the adjusting of the task parameter in the initial task parameter list according to the fixed task parameter, the parameter adjusting factor and the parameter decision condition includes:
identifying an item type of the target item;
outputting the maximum task time corresponding to the target project according to the project type, the fixed task amount and the parameter judgment condition;
when the number of the employment objects in the initial task parameter list is increased, reducing the task duration in the initial task parameter list according to the parameter adjusting factor;
in response to the operation of reducing the number of the recruitment objects in the initial task parameter list, increasing the object grade of the recruitment objects in the initial task parameter list or increasing the task duration in the initial task parameter list according to the parameter adjusting factor, or;
responding to a time length increasing operation aiming at the task time length in the initial task parameter list, and reducing the number of the recruitment objects and/or the object grade of the recruitment objects in the initial task parameter list according to the parameter adjusting factor, or;
responding to the time length reduction operation aiming at the task money in the initial task parameter list, and increasing the number of the recruitment objects and/or the object grade of the recruitment objects in the initial task parameter list according to the parameter adjusting factor, or;
responding to grade improvement operation aiming at the object grade of the employment object in the initial task parameter list, and reducing the number of the employment objects in the initial task parameter list and/or the task duration in the initial task parameter list according to the parameter adjusting factor, or;
responding to the grade reduction operation aiming at the object grade of the recruitment object in the initial task parameter list, and improving the number of the recruitment objects in the initial task parameter list and/or the task duration in the initial task parameter list according to the parameter adjusting factor;
and the adjusted task time length is less than or equal to the maximum task time length.
In the employment information processing method according to the present invention, the determining, based on the employment information of the candidate employment objects, an employment object corresponding to the target item among the candidate employment objects includes:
determining a task time interval corresponding to the target project;
extracting the whole labor efficiency and the to-be-completed task quantity of the candidate labor object from the labor information;
based on the overall labor efficiency and the to-be-completed task amount, estimating the completion time period of the candidate labor object for completing the to-be-completed task amount;
when the intersection of the completion time interval and the task time interval is an empty set, acquiring historical recruitment efficiency of the candidate recruitment object in each historical task time interval, and estimating the recruitment efficiency of the candidate recruitment object in the task time interval according to the historical recruitment efficiency;
calculating a difference between a completion timestamp of the completion time period and a turn-on timestamp of the task time period;
when the difference is larger than or equal to a preset value, estimating the recruitment probability of the candidate recruitment object for executing the target project based on the difference and the work consumption times of the candidate recruitment object in a plurality of historical task time periods;
and determining the recruitment object corresponding to the target project in the candidate recruitment objects according to the recruitment efficiency and the recruitment probability.
In the employment information processing method of the present invention, the estimating a probability of employment of the candidate employment object for executing the target project based on the difference and the number of times of employment of the candidate employment object in a plurality of historical task periods includes:
converting the difference value into a weight coefficient;
calculating the weight coefficient and the usage of the candidate employment object in a plurality of historical task periods based on a preset formulaThe product of the number of times of work, the preset formula is
Figure 648031DEST_PATH_IMAGE001
Wherein N is the total amount of the historical task time interval, y t 、y t-1 And y t-N+1 The working times of the candidate working objects in different historical task periods,
Figure 728114DEST_PATH_IMAGE002
are weight coefficients.
In the employment information processing method of the present invention, the obtaining of the candidate employment object according to the adjusted parameter list includes:
constructing an employment parameter characteristic corresponding to the adjusted parameter list;
and acquiring candidate recruitment objects meeting preset conditions from a recruitment object library based on the recruitment parameter characteristics.
An embodiment of the present invention further provides an apparatus for processing labor information, including:
the receiving module is used for receiving an employment request aiming at a target project;
the display module is used for displaying an employment processing interface of the target project, and the employment processing interface comprises a task description area and a task parameter area;
the first display module is used for responding to task description operation aiming at the task description area and displaying an initial task parameter list in the task parameter area;
the first acquisition module is used for responding to parameter adjustment operation aiming at the initial task parameter list and acquiring parameter adjustment factors and parameter judgment conditions;
the first determining module is used for determining fixed task parameters in the initial task parameter list;
the adjusting module is used for adjusting the task parameters in the initial task parameter list according to the fixed task parameters, the parameter adjusting factors and the parameter judgment conditions;
the second display module is used for dynamically displaying the parameter adjustment process in the task parameter area;
the second acquisition module is used for acquiring the candidate recruitment object according to the adjusted parameter list;
and the second determining module is used for determining the recruitment object corresponding to the target item in the candidate recruitment objects based on the recruitment information of the candidate recruitment objects.
The embodiment of the invention also provides electronic equipment which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the recruitment information processing method when executing the program.
The embodiment of the invention also provides a storage medium, wherein processor-executable instructions are stored in the storage medium, and the instructions are loaded by one or more processors to execute the above-mentioned employment information processing method.
The recruitment information processing method and the recruitment information processing device display a recruitment processing interface of a target project after receiving a recruitment request aiming at the target project, wherein the recruitment processing interface comprises a task description area and a task parameter area, an initial task parameter list is displayed in the task parameter area in response to task description operation aiming at the task description area, a parameter adjusting factor and a parameter judging condition are obtained in response to parameter adjusting operation aiming at the initial task parameter list, then, a fixed task parameter is determined in the initial task parameter list, then, the task parameter in the initial task parameter list is adjusted according to the fixed task parameter, the parameter adjusting factor and the parameter judging condition, a parameter adjusting process is dynamically displayed in the task parameter area, and finally, a candidate recruitment object is obtained according to the adjusted parameter list, and determining the recruitment object corresponding to the target project in the candidate recruitment objects based on the recruitment information of the candidate recruitment objects, and during processing the recruitment information, after determining the fixed task parameters, adjusting the task parameters in the initial task parameter list according to the fixed task parameters, the parameter adjusting factors and the parameter judgment conditions, and determining the recruitment object corresponding to the target project according to the adjusted parameter list.
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FIG. 1 is a schematic flow chart of the present invention for processing employment information;
FIGS. 2 to 6 are schematic diagrams of interfaces in the working information processing method according to the present invention;
FIG. 7 is a diagram illustrating a scenario of an embodiment of a worker information processing apparatus according to the present invention;
FIG. 8 is a schematic structural diagram of an embodiment of a work information processing apparatus according to the present invention;
FIG. 9 is a schematic diagram of an adjusting module according to an embodiment of the present invention;
FIG. 10 is a schematic diagram of another exemplary embodiment of an adjustment module of the present invention;
fig. 11 is a schematic view of a working environment configuration of an electronic device in which the worker information processing apparatus of the present invention is incorporated.
Detailed Description
Referring to the drawings, wherein like reference numbers refer to like elements, the principles of the present invention are illustrated as being implemented in a suitable computing environment. The following description is based on illustrated embodiments of the invention and should not be taken as limiting the invention with regard to other embodiments that are not detailed herein.
In the description that follows, specific embodiments of the invention are described with reference to steps and symbols of operations performed by one or more computers, unless otherwise indicated. It will thus be appreciated that those steps and operations, which are referred to herein several times as being computer-executed, include being manipulated by a computer processing unit in the form of electronic signals representing data in a structured form. This manipulation transforms the data or maintains it at locations in the computer's memory system, which may reconfigure or otherwise alter the computer's operation in a manner well known to those skilled in the art. The data maintains a data structure that is a physical location of the memory that has particular characteristics defined by the data format. However, while the principles of the invention have been described in language specific to above, it is not intended to be limited to the specific details shown, since one skilled in the art will recognize that various steps and operations described below may be implemented in hardware.
The recruitment information processing method and the recruitment information processing device can be arranged in any electronic equipment and are used for receiving recruitment requests for target projects and displaying recruitment processing interfaces of the target projects, each recruitment processing interface comprises a task description area and a task parameter area, an initial task parameter list is displayed in the task parameter area in response to task description operation for the task description area, a parameter adjusting factor and a parameter judging condition are obtained in response to parameter adjusting operation for the initial task parameter list, fixed task parameters are determined in the initial task parameter list, task parameters in the initial task parameter list are adjusted according to the fixed task parameters, the parameter adjusting factor and the parameter judging condition, a parameter adjusting process is dynamically displayed in the task parameter area, candidate recruitment objects are obtained according to the adjusted parameter list and are based on recruitment information of the candidate recruitment objects, and determining a recruitment object corresponding to the target item in the candidate recruitment objects. Including, but not limited to, personal computers, server computers, multiprocessor systems, consumer electronics, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The employment information processing device is preferably a data processing terminal or a server for carrying out the employment information processing, the data processing terminal or the server adjusts task parameters in an initial task parameter list according to fixed task parameters, parameter adjusting factors and parameter judgment conditions, obtains candidate employment objects according to the adjusted parameter list, finally determines the employment objects corresponding to target items in the candidate employment objects based on the employment information of the candidate employment objects, does not need a large amount of manual operation when selecting the employment objects, improves the processing efficiency of the employment information, and can determine the corresponding employment objects based on the fixed task parameters set by the employment party, thereby achieving the purpose that the employment party can be accurately matched with proper technical personnel.
In the current recruitment information processing method, after a recruitment party releases a corresponding recruitment task (namely a target project) on a recruitment platform, the recruitment party selects a corresponding worker on the recruitment platform to execute the recruitment task according to the requirement of the recruitment task (the type of the worker, the number of the workers and the skill level of the worker); or, after the worker sees the employment task on the employment platform, the worker actively delivers the resume to the employment party, so that the current employment information processing method can cause that the employment party cannot accurately match to a proper skilled worker, and needs the employment party and the worker to perform bidirectional matching, thereby the processing efficiency of the employment information is low.
The invention provides a recruitment information processing scheme, which can adjust task parameters in an initial task parameter list according to fixed task parameters, parameter adjusting factors and parameter judgment conditions after the fixed task parameters are determined, and determine a recruitment object corresponding to a target project according to the adjusted parameter list.
Referring to fig. 1, fig. 1 is a flowchart illustrating a method for processing employment information according to an embodiment of the present invention. The employment information processing method of the present embodiment can be implemented using the electronic device described above, and the employment information processing method of the present embodiment includes:
step 101, receiving a recruitment request aiming at a target project;
102, displaying an employment processing interface of a target project;
step 103, responding to task description operation aiming at the task description area, and displaying an initial task parameter list in the task parameter area;
step 104, responding to the parameter adjustment operation aiming at the initial task parameter list, and acquiring parameter adjustment factors and parameter judgment conditions;
step 105, determining fixed task parameters in an initial task parameter list;
step 106, adjusting the task parameters in the initial task parameter list according to the fixed task parameters, the parameter adjusting factors and the parameter judgment conditions, and dynamically displaying the parameter adjusting process in the task parameter area;
and 107, acquiring candidate recruitment objects according to the adjusted parameter list, and determining the recruitment object corresponding to the target item in the candidate recruitment objects based on the recruitment information of the candidate recruitment objects.
The working information processing method of the present embodiment is described in detail below.
In step 101, the target project may correspond to one employment task, or may correspond to a plurality of employment tasks, where the plurality of employment tasks may be mutually coordinated or associated, or may be mutually independent. For example, the target project is a wall painting project, the corresponding labor task is a wall painting task, and workers required by the wall painting task are painting workers; for another example, the target project is a whole house cleaning project, and the corresponding labor tasks may be a furniture cleaning task, a floor cleaning task, a house outer wall cleaning task, and the like, which are related to each other, and the required workers at least include a furniture cleaner, a floor cleaner, and a house outer wall cleaner.
In step 102, the employment processing interface includes a task description area and a task parameter area, the task description area is used for displaying description information of the target project, such as the employment tasks corresponding to the target project, the number of the employment tasks, the required employment objects, and the like, and the task parameter area is used for displaying task parameters of the employment tasks, such as the task duration, the number of the employment objects, the object grades of the employment objects, the task amount, and the like.
In step 103, the employee may input corresponding description information in the task description area, and after the description information is input, the task parameter area displays corresponding task parameters according to the input description information, where the task parameters may be displayed in a form of a task parameter list.
Referring to fig. 2, the employment processing interface includes a task description area s1 and a task parameter area s2, the display content of the task parameter area s2 is associated with the display content of the task description area s1, and in response to the task description operation directed to the task description s1 by the employment party, if the employment party inputs task description information corresponding to the target project in the task description area s1, the task parameter area s2 displays the initial task parameter list according to the task description information.
And the initial task parameter list is generated according to the task description information, such as the amount input by the recruitment party, the task type and the object required for recruitment. It should be noted that, if part of information is missing in the task description information input by the worker, the initial task parameter list of the response may be output according to a preset policy, for example, the task description information carries the task type of the worker task and the task amount corresponding to the worker task, then, the same or similar worker tasks (hereinafter referred to as reference tasks) may be determined, reference data such as the reference task amount corresponding to the reference task and the reference worker object number are obtained, the ratio between the number of the worker objects and the amount is calculated based on the reference data, the number of the worker objects corresponding to the worker task is finally output, and so on for the rest of the cases, which is not described herein.
In step 104, the parameters may include addition parameters, subtraction parameters, multiplication parameters, division parameters, assignment parameters, proportion parameters, round-up parameters, and round-down parameters, so as to perform numerical operations such as addition, subtraction, multiplication, division, assignment, proportion, round-up, and round-down operations. The parameter decision condition may include an upper limit value decision of the numerical variable and a lower limit value decision of the numerical variable, so as to subsequently define the upper and lower limits of the numerical variable.
In step 105, the fixed task parameters are the task amount, the task duration, the number of the employment objects and the object grade of the employment object, and the fixed task parameters do not change with the change of the rest task parameters of the target project.
In step 106, the user may determine corresponding fixed task parameters in the initial task parameter list according to actual requirements, for example, referring to fig. 3, before determining the fixed task parameters, the task parameter area Q displays an initial parameter list, after determining the fixed task parameters, the task parameter area Q is divided into a first area Q1 and a second area Q2, the first area Q1 is used for displaying the fixed task parameters, the second area Q2 is used for displaying other task parameters except the fixed task parameters, and in response to a parameter adjustment operation for the other task parameters, the task parameters in the initial task parameter list are adjusted according to the fixed task parameters, the parameter adjustment factors, and the parameter decision conditions.
Optionally, in some embodiments, the fixed task parameter is a fixed task duration, and step 106 may specifically include:
(11) identifying an item type of the target item;
(12) outputting the maximum task amount corresponding to the target project according to the project type, the fixed task duration and the parameter judgment condition;
(13) in response to the quantity increasing operation aiming at the quantity of the employment objects in the initial task parameter list, reducing the object grade of the employment objects in the initial task parameter list or increasing the task amount in the initial task parameter list according to the parameter adjusting factor;
(14) in response to the quantity reduction operation aiming at the quantity of the recruitment objects in the initial task parameter list, increasing the object grade of the recruitment objects in the initial task parameter list and increasing the task amount in the initial task parameter list according to the parameter adjusting factor, or;
(15) in response to the money amount increasing operation aiming at the task money in the initial task parameter list, increasing the number of the recruitment objects and/or the object grade of the recruitment objects in the initial task parameter list according to the parameter adjusting factor, or;
(16) in response to the money amount reduction operation aiming at the task money amount in the initial task parameter list, reducing the number of the recruitment objects and/or the object grade of the recruitment objects in the initial task parameter list according to the parameter adjusting factor, or;
(17) in response to the level improvement operation aiming at the object level of the recruitment object in the initial task parameter list, reducing the number of the recruitment objects in the initial task parameter list according to the parameter adjustment factor, or;
(18) and responding to the grade reduction operation aiming at the object grade of the recruitment object in the initial task parameter list, and increasing the number of the recruitment objects in the initial task parameter list according to the parameter adjustment factor.
When the fixed task parameter is the fixed task duration, the recruitment party can modify parameters except the task duration according to the self requirement, such as the number of the recruitment objects, the task amount and the object grade of the recruitment objects, wherein the adjusted task amount is less than or equal to the maximum task amount.
For example, if the fixed task duration is 30 hours, and the employee expects the task duration to be unchanged, and there are more workers who perform the project, the number of the employment objects may be increased, that is, the employee performs a number increase operation on the number of the employment objects in the initial task parameter list, and in order to ensure that the duration of the employment is unchanged, in response to the number increase operation on the number of the employment objects in the initial task parameter list, the object level of the employment object in the initial task parameter list is decreased or the task amount in the initial task parameter list is increased according to the parameter adjustment factor, as shown in fig. 4. Similarly, when the worker expects the task duration to be unchanged, and fewer workers execute the project, the skill level (i.e., the object level) of the worker object is increased, that is, the worker performs the quantity reduction operation on the quantity of the worker objects in the initial task parameter list, and in order to ensure that the worker duration is unchanged, in response to the quantity reduction operation on the quantity of the worker objects in the initial task parameter list, the object level of the worker object in the initial task parameter list is increased according to the parameter adjusting factor and the task amount in the initial task parameter list is increased, as shown in fig. 5. In addition, when the recruitment party expects the task time to be unchanged, the quality of the project is improved, and at the moment, the task amount can be improved so as to improve the number of the recruitment objects and/or the object grade of the recruitment objects, as shown in fig. 6. Similarly, under the condition of ensuring that the task duration is not changed, the recruitment party expects to reduce the task amount, namely, the recruitment object number and/or the object grade of the recruitment object in the initial task parameter list are/is reduced according to the parameter adjusting factor in response to the amount reduction operation aiming at the task amount in the initial task parameter list. It can be understood that, under the condition of ensuring that the task duration is not changed, the number of the recruitment objects in the initial task parameter list is reduced according to the parameter adjustment factors in response to the level improvement operation aiming at the object level of the recruitment objects in the initial task parameter list; and responding to the grade reduction operation aiming at the object grade of the recruitment object in the initial task parameter list, and increasing the number of the recruitment objects in the initial task parameter list according to the parameter adjustment factor.
It should be noted that, when the fixed task parameter is the task duration, the object level and/or the task amount can be adjusted according to the preset strategy and the parameter adjusting factor in response to the number adjusting operation aiming at the number of the employment objects in the initial task parameter list; responding to the grade adjustment operation aiming at the object grade of the recruitment object in the initial task parameter list, and adjusting the task amount and/or the recruitment object quantity according to a preset strategy and a parameter adjustment factor; and responding to the money amount adjusting operation aiming at the task money amount in the initial task parameter list, and adjusting the object grade and/or the number of the recruitment objects according to a preset strategy and a parameter adjusting factor.
The preset strategy can be a manual adjustment strategy and an automatic adjustment strategy, and all adjustment results are displayed under the manual adjustment strategy so as to be convenient for a worker to select; under the automatic adjustment strategy, enterprise information of the recruitment party can be acquired, the enterprise information comprises registered capital, project quantity, the condition of staff and the like, the parameter adjustment weight corresponding to the recruitment party is determined based on the enterprise information, and finally, the task parameter is adjusted automatically based on the parameter adjustment weight and the parameter adjustment factor.
Optionally, in some embodiments, the step "outputting the maximum task amount corresponding to the target project according to the project type, the fixed task duration, and the parameter decision condition" may specifically include:
(21) determining a reference item corresponding to a target item in a preset database according to the item type;
(22) acquiring the reference task amount of a reference item in unit time;
(23) and calculating the product of the reference task amount and the fixed task time length based on the parameter judgment condition to obtain the maximum task amount corresponding to the target project.
Wherein, a plurality of different project sets are recorded in a preset database, each project set comprises project information of at least one reference project, the project information records task duration, task amount, number of employment objects, corresponding object grades and task starting time, and the like, firstly, the project set corresponding to the target project is determined in the preset database according to project types, then, the similarity between each reference project and the target project is calculated, the determination of the maximum similarity is determined as the reference project corresponding to the target project, optionally, the similarity between each reference project and the target project is calculated through a neural network model, of course, the similarity can also be calculated in other manners, without limitation, next, the reference task amount in unit time of the reference project is obtained, and the unit time refers to the time of one time unit, for example, if the obtained reference task amount is 1800 yuan/hour, the product between the reference task amount and the fixed task duration may be calculated, and the parameter decision condition is used to limit the upper limit value of the task amount.
Optionally, in some embodiments, the fixed task parameter is a fixed task amount, and step 106 may specifically include:
(31) identifying an item type of the target item;
(32) outputting the maximum task duration corresponding to the target project according to the project type, the fixed task amount and the parameter judgment condition;
(33) responding to the number increasing operation aiming at the number of the employment objects in the initial task parameter list, and reducing the task duration in the initial task parameter list according to the parameter adjusting factors;
(34) in response to the operation of reducing the number of the recruitment objects in the initial task parameter list, increasing the object grade of the recruitment objects in the initial task parameter list or increasing the task duration in the initial task parameter list according to the parameter adjusting factor, or;
(35) responding to the time length increasing operation aiming at the task time length in the initial task parameter list, and reducing the number of the recruitment objects and/or the object grade of the recruitment objects in the initial task parameter list according to the parameter adjusting factors, or;
(36) responding to the time length reduction operation aiming at the task amount in the initial task parameter list, and increasing the number of the recruitment objects and/or the object grade of the recruitment objects in the initial task parameter list according to the parameter adjusting factor, or;
(37) in response to the level improvement operation aiming at the object level of the recruitment object in the initial task parameter list, reducing the number of the recruitment objects in the initial task parameter list and/or the task duration in the initial task parameter list according to the parameter adjusting factor, or;
(38) and responding to the grade reduction operation aiming at the object grade of the recruitment object in the initial task parameter list, and improving the number of the recruitment objects in the initial task parameter list and/or the task duration in the initial task parameter list according to the parameter adjusting factor.
The adjusted task duration is less than or equal to the maximum task duration, and the specific adjustment manner may refer to the foregoing embodiment, which is not described herein again.
In step 106, a task time period corresponding to the target project may be determined, then, a completion time period for completing the task amount to be completed by the candidate employment object is estimated, and the employment object corresponding to the target project is determined in the candidate employment object based on the task time period and the completion time period, that is, optionally, in some embodiments, step 106 may specifically include:
(41) determining a task time interval corresponding to a target project;
(42) extracting the whole labor efficiency and the task amount to be completed of the candidate labor object from the labor information;
(43) estimating the finishing time interval of the candidate recruitment objects for finishing the to-be-finished task amount based on the overall recruitment efficiency and the to-be-finished task amount;
(44) when the intersection of the completion time interval and the task time interval is an empty set, acquiring historical recruitment efficiency of the candidate recruitment object in each historical task time interval, and estimating the corresponding recruitment efficiency of the candidate recruitment object in the task time interval according to each historical recruitment efficiency;
(45) calculating a difference value between a completion timestamp of the completion time period and a start timestamp of the task time period;
(46) when the difference is larger than or equal to a preset value, estimating the recruitment probability of the candidate recruitment object for executing the target project based on the difference and the recruitment times of the candidate recruitment object in a plurality of historical task time periods;
(47) and determining the recruitment object corresponding to the target item in the candidate recruitment objects according to the recruitment efficiency and the recruitment probability.
Wherein, project starting time and ending time of the target project can be extracted, so as to determine a task time period corresponding to the target project, the overall employment efficiency reflects the efficiency level of the candidate employment object in the historical time period, for example, the employment speed of the candidate employment object in unit time can be calculated according to the overall employment efficiency, then, the completion time period of the candidate employment object for completing the task amount to be completed is estimated based on the employment speed and the task amount to be completed, then, the intersection between the completion time period and the task time period is detected, when the intersection between the completion time period and the task time period is an empty set, namely, the completion time period is before the task time period, and the two are not intersected, the historical employment efficiency of the candidate employment object in each historical task time period is obtained, wherein, the historical task time period corresponds to the task time period, for example, the task time period is 3 o 'clock to 5 o' clock in 4 months, the historical task time interval A can be 3 points to 5 points on 4 months and 1 day, the historical task time interval B can be 3 points to 5 points on 3 months and 6 days, the historical task time interval C can be 3 points to 5 points on 2 months and 15 days, the corresponding labor efficiency of the candidate labor object in the task time interval is estimated according to the historical labor efficiency, then, the difference value between the completion time stamp of the completion time interval and the opening time stamp of the task time interval is calculated, the interval time of the completion time interval and the task time interval is reflected by the difference value, the candidate labor object executes the labor probability of the target project according to the interval time and the labor times of the candidate labor object in the estimated time intervals of a plurality of historical tasks, and finally, the labor object corresponding to the target project is determined in the candidate labor object according to the labor efficiency and the labor probability.
For example, determining the probability of employment of the candidate employment object for executing the target item in the task period of the same category according to the number of employment in a plurality of historical task periods of the same category in the past time period, where the category may include, for example, hour, week, date, month, and so on, i.e., optionally, in some embodiments, the step "estimating the probability of employment of the candidate employment object for executing the target item based on the difference value and the number of employment of the candidate employment object in the plurality of historical task periods" may specifically include:
(51) converting the difference into a weight coefficient;
(52) and calculating the product of the weight coefficient and the working times of the candidate working object in a plurality of historical task time periods based on a preset formula.
Wherein the preset formula is
Figure 458172DEST_PATH_IMAGE001
N is the total amount of the historical task period, y t 、y t-1 And y t-N+1 The working times of the candidate working objects in different historical task periods,
Figure 658210DEST_PATH_IMAGE002
are weight coefficients.
It should be noted that, the correspondence between the difference and the weight coefficient may be constructed in advance, specifically refer to table 1:
TABLE 1
Figure 182732DEST_PATH_IMAGE003
Of course, the corresponding relationship may also be a corresponding relationship between the hour and the weight coefficient, and may be specifically set according to an actual situation, which is not limited herein.
In step 107, an employment parameter feature corresponding to the adjusted parameter list may be constructed, and then, based on the employment parameter feature, a candidate employment object meeting a preset condition is obtained from the employment object library, for example, the employment parameter feature is: the cost proportion of A workers is 20 percent, and 1 worker is needed; the cost proportion of B-class workers is 70%, and 3 workers are needed; the cost proportion of the C-type workers is 10%, 1 worker is needed, and based on the cost proportion, the candidate employment objects meeting the preset conditions are obtained from the employment object library.
This completes the working information processing process of the present embodiment.
The recruitment information processing method of the embodiment receives a recruitment request for a target project, displays a recruitment processing interface of the target project, responds to task description operation for a task description area, displays an initial task parameter list in the task parameter area, responds to parameter adjustment operation for the initial task parameter list, acquires a parameter adjusting factor and a parameter judgment condition, determines a fixed task parameter in the initial task parameter list, adjusts the task parameter in the initial task parameter list according to the fixed task parameter, the parameter adjusting factor and the parameter judgment condition, dynamically displays a parameter adjusting process in the task parameter area, finally acquires a candidate recruitment object according to the adjusted parameter list, and determines a recruitment object corresponding to the target project in the candidate recruitment object based on recruitment information of the candidate recruitment object, when the recruitment information is processed, after the fixed task parameters are determined, the task parameters in the initial task parameter list can be adjusted according to the fixed task parameters, the parameter adjusting factors and the parameter judgment conditions, and the recruitment object corresponding to the target project is determined according to the adjusted parameter list.
The embodiment of the present invention further provides a method for processing recruitment information, wherein the recruitment information processing apparatus is integrated in a server, please refer to fig. 7, and the specific flow is as follows:
step 201, a server receives an employment request aiming at a target project;
step 202, the server displays an employment processing interface of the target project;
step 203, the server responds to the task description operation aiming at the task description area, and displays an initial task parameter list in the task parameter area;
and step 204, the server responds to the parameter adjustment operation aiming at the initial task parameter list, and obtains parameter adjustment factors and parameter judgment conditions.
Step 205, the server determines the fixed task parameters in the initial task parameter list.
And step 206, the server adjusts the task parameters in the initial task parameter list according to the fixed task parameters, the parameter adjusting factors and the parameter judgment conditions, and dynamically displays the parameter adjusting process in the task parameter area.
And step 207, the server acquires the candidate employment objects according to the adjusted parameter list, and determines the employment objects corresponding to the target items in the candidate employment objects based on the employment information of the candidate employment objects.
According to the technical scheme, the server can adjust the task parameters in the initial task parameter list according to the fixed task parameters, the parameter adjusting factors and the parameter judgment conditions, and determine the employment objects corresponding to the target projects according to the adjusted parameter list.
Referring to fig. 8, fig. 8 is a schematic structural diagram of an embodiment of the recruitment information processing apparatus of the present invention, and the recruitment information processing apparatus of the present embodiment can be implemented by using the recruitment information processing method. The employment information processing apparatus 30 of the present embodiment includes a receiving module 301, a displaying module 302, a first displaying module 303, a first obtaining module 304, a first determining module 305, an adjusting module 306, a second displaying module 307, a second obtaining module 308, and a second determining module 309, which are specifically as follows:
the receiving module 301 is configured to receive an employment request for a target project.
The display module 302 is configured to display an employment processing interface of the target project, where the employment processing interface includes a task description area and a task parameter area.
A first presentation module 303, configured to present an initial task parameter list in the task parameter area in response to a task description operation for the task description area.
A first obtaining module 304, configured to obtain a parameter adjustment factor and a parameter decision condition in response to a parameter adjustment operation for the initial task parameter list.
A first determining module 305 for determining the fixed task parameter from the initial task parameter list.
And the adjusting module 306 is configured to adjust the task parameters in the initial task parameter list according to the fixed task parameters, the parameter adjusting factors, and the parameter decision conditions.
A second showing module 307, configured to dynamically show the parameter adjustment process in the task parameter area.
And a second obtaining module 308, configured to obtain the candidate employment object according to the adjusted parameter list.
The second determining module 309 is configured to determine, based on the employment information of the candidate employment objects, an employment object corresponding to the target item in the candidate employment objects.
Optionally, in some embodiments, the fixed task parameter is a fixed task duration, please refer to fig. 9, and fig. 9 is a schematic structural diagram of an adjusting module according to an embodiment of the present invention, where the adjusting module 306 specifically includes:
an identification unit 3061a for identifying an item type of the target item;
the output unit 3062a is used for outputting the maximum task amount corresponding to the target project according to the project type, the fixed task duration and the parameter judgment condition;
an adjusting unit 3063a, configured to, in response to a number increasing operation for the number of the recruitment objects in the initial task parameter list, decrease the object level of the recruitment objects in the initial task parameter list or increase the task amount in the initial task parameter list according to the parameter-adjusting factor; in response to the quantity reduction operation aiming at the quantity of the recruitment objects in the initial task parameter list, increasing the object grade of the recruitment objects in the initial task parameter list and increasing the task amount in the initial task parameter list according to the parameter adjusting factor, or; in response to the money amount increasing operation aiming at the task money in the initial task parameter list, increasing the number of the recruitment objects and/or the object grade of the recruitment objects in the initial task parameter list according to the parameter adjusting factor, or; in response to the money amount reduction operation aiming at the task money amount in the initial task parameter list, reducing the number of the employment objects and/or the object grade of the employment objects in the initial task parameter list according to the parameter adjusting factor, or; in response to the level improvement operation aiming at the object level of the recruitment object in the initial task parameter list, reducing the number of the recruitment objects in the initial task parameter list according to the parameter adjustment factor, or; and responding to the grade reduction operation aiming at the object grade of the recruitment object in the initial task parameter list, and increasing the number of the recruitment objects in the initial task parameter list according to the parameter adjustment factor.
Alternatively, in some embodiments, the output unit 3062a is particularly applicable to: determining a reference item corresponding to a target item in a preset database according to the item type; acquiring the reference task amount of a reference item in unit time; and calculating the product of the reference task amount and the fixed task time length based on the parameter judgment condition to obtain the maximum task amount corresponding to the target project.
Optionally, in some embodiments, the fixed task parameter is a fixed task amount, please refer to fig. 10, where fig. 10 is a schematic structural diagram of an adjusting module of an embodiment of the recruitment information processing apparatus of the present invention, and the adjusting module 306 may specifically include:
an identification unit 3061b that identifies an item type of the target item;
the output unit 3062b is used for outputting the maximum task time length corresponding to the target project according to the project type, the fixed task amount and the parameter judgment condition;
the adjusting unit 3063b is configured to, in response to the number increasing operation for the number of the employment objects in the initial task parameter list, decrease the task duration in the initial task parameter list according to the parameter adjustment factor; in response to the operation of reducing the number of the recruitment objects in the initial task parameter list, increasing the object grade of the recruitment objects in the initial task parameter list or increasing the task duration in the initial task parameter list according to the parameter adjusting factor, or; responding to the time length increasing operation aiming at the task time length in the initial task parameter list, and reducing the number of the employment objects and/or the object grade of the employment objects in the initial task parameter list according to the parameter adjusting factor, or; responding to the time length reduction operation aiming at the task money in the initial task parameter list, and increasing the number of the employment objects and/or the object grade of the employment objects in the initial task parameter list according to the parameter adjusting factor, or; in response to the level improvement operation aiming at the object level of the recruitment object in the initial task parameter list, reducing the number of the recruitment objects in the initial task parameter list and/or the task duration in the initial task parameter list according to the parameter adjusting factor, or; and responding to the grade reduction operation aiming at the object grade of the recruitment object in the initial task parameter list, and improving the number of the recruitment objects in the initial task parameter list and/or the task duration in the initial task parameter list according to the parameter adjusting factor.
And the adjusted task time length is less than or equal to the maximum task time length.
Optionally, in some embodiments, the second determining module 309 may specifically be configured to: determining a task time interval corresponding to a target project; extracting the overall labor efficiency and the to-be-completed task amount of the candidate labor object from the labor information, and estimating the completion time period of the candidate labor object for completing the to-be-completed task amount based on the overall labor efficiency and the to-be-completed task amount; when the intersection of the completion time interval and the task time interval is an empty set, acquiring historical recruitment efficiency of the candidate recruitment object in each historical task time interval, and estimating the corresponding recruitment efficiency of the candidate recruitment object in the task time interval according to each historical recruitment efficiency; calculating a difference between a completion timestamp of the completion period and a start timestamp of the task period; when the difference is larger than or equal to a preset value, estimating the recruitment probability of the candidate recruitment object for executing the target project based on the difference and the recruitment times of the candidate recruitment object in a plurality of historical task time periods; and determining the recruitment object corresponding to the target item in the candidate recruitment objects according to the recruitment efficiency and the recruitment probability.
This completes the process of determining the employment object by the employment information processing apparatus 30 of the present embodiment.
The specific operation principle of the employment information processing apparatus of the present embodiment is the same as or similar to that described in the embodiment of the employment information processing method, and for details, refer to the detailed description in the embodiment of the employment information processing method.
After the fixed task parameters are determined, the employment information processing device of the embodiment can adjust the task parameters in the initial task parameter list according to the fixed task parameters, the parameter adjusting factors and the parameter judgment conditions, and determine the employment object corresponding to the target project according to the adjusted parameter list.
As used herein, the terms "component," "module," "system," "interface," "process," and the like are generally intended to refer to a computer-related entity: hardware, a combination of hardware and software, or software in execution. For example, a component may be, but is not limited to being, a process running on a processor, an object, an executable, a thread of execution, a program, and/or a computer. By way of illustration, both an application running on a controller and the controller can be a component. One or more components can reside within a process and/or thread of execution and a component may be localized on one computer and/or distributed between two or more computers.
FIG. 11 and the following discussion provide a brief, general description of an operating environment for an electronic device in which an employment information processing apparatus, as described herein, is implemented. The operating environment of FIG. 11 is only one example of a suitable operating environment and is not intended to suggest any limitation as to the scope of use or functionality of the operating environment. Example electronic devices 1012 include, but are not limited to, wearable devices, head-mounted devices, medical health platforms, personal computers, server computers, hand-held or laptop devices, mobile devices (such as mobile phones, Personal Digital Assistants (PDAs), media players, and the like), multiprocessor systems, consumer electronics, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.
Although not required, embodiments are described in the general context of "computer readable instructions" being executed by one or more electronic devices. Computer readable instructions may be distributed via computer readable media (discussed below). Computer readable instructions may be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), data structures, etc. that perform particular tasks or implement particular abstract data types. Typically, the functionality of the computer readable instructions may be combined or distributed as desired in various environments.
FIG. 11 illustrates an example of an electronic device 1012 incorporating one or more embodiments of the present invention in a work information processing apparatus. In one configuration, electronic device 1012 includes at least one processing unit 1016 and memory 1018. Depending on the exact configuration and type of electronic device, memory 1018 may be volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.) or some combination of the two. This configuration is illustrated in fig. 1 by dashed line 1014.
In other embodiments, electronic device 1012 may include additional features and/or functionality. For example, device 1012 may also include additional storage (e.g., removable and/or non-removable) including, but not limited to, magnetic storage, optical storage, and the like. Such additional storage is illustrated in FIG. 11 by storage 1020. In one embodiment, computer readable instructions to implement one or more embodiments provided herein may be in storage 1020. Storage 1020 may also store other computer readable instructions to implement an operating system, an application program, and the like. Computer readable instructions may be loaded in memory 1018 for execution by processing unit 1016, for example.
The term "computer readable media" as used herein includes computer storage media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions or other data. Memory 1018 and storage 1020 are examples of computer storage media. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by electronic device 1012. Any such computer storage media may be part of electronic device 1012.
Electronic device 1012 may also include communication connection(s) 1026 that allow electronic device 1012 to communicate with other devices. Communication connection(s) 1026 may include, but is not limited to, a modem, a Network Interface Card (NIC), an integrated network interface, a radio frequency transmitter/receiver, an infrared port, a USB connection, or other interfaces for connecting electronic device 1012 to other electronic devices. The communication connection 1026 may comprise a wired connection or a wireless connection. Communication connection(s) 1026 may transmit and/or receive communication media.
The term "computer readable media" may include communication media. Communication media typically embodies computer readable instructions or other data in a "modulated data signal" such as a carrier wave or other transport mechanism and includes any information delivery media. The term "modulated data signal" may include signals that: one or more of the signal characteristics may be set or changed in such a manner as to encode information in the signal.
Electronic device 1012 may include input device(s) 1024 such as keyboard, mouse, pen, voice input device, touch input device, infrared camera, video input device, and/or any other input device. Output device(s) 1022 such as one or more displays, speakers, printers, and/or any other output device may also be included in device 1012. Input device 1024 and output device 1022 may be connected to electronic device 1012 via a wired connection, wireless connection, or any combination thereof. In one embodiment, an input device or an output device from another electronic device may be used as input device 1024 or output device 1022 for electronic device 1012.
The components of electronic device 1012 may be connected by various interconnects, such as a bus. Such interconnects may include Peripheral Component Interconnect (PCI), such as PCI express, Universal Serial Bus (USB), firewire (IEEE 13104), optical bus structures, and so forth. In another embodiment, components of electronic device 1012 may be interconnected by a network. For example, memory 1018 may be comprised of multiple physical memory units located in different physical locations interconnected by a network.
Those skilled in the art will realize that storage devices utilized to store computer readable instructions may be distributed across a network. For example, electronic device 1030 accessible via network 1028 may store computer readable instructions to implement one or more embodiments of the present invention. Electronic device 1012 may access electronic device 1030 and download a part or all of the computer readable instructions for execution. Alternatively, electronic device 1012 may download pieces of the computer readable instructions, as needed, or some instructions may be executed at electronic device 1012 and some at electronic device 1030.
Various operations of embodiments are provided herein. In one embodiment, the one or more operations may constitute computer readable instructions stored on one or more computer readable media, which when executed by an electronic device, will cause the computing device to perform the operations. The order in which some or all of the operations are described should not be construed as to imply that these operations are necessarily order dependent. Those skilled in the art will appreciate alternative orderings having the benefit of this description. Moreover, it should be understood that not all operations are necessarily present in each embodiment provided herein.
Also, although the disclosure has been shown and described with respect to one or more implementations, equivalent alterations and modifications will occur to others skilled in the art based upon a reading and understanding of this specification and the annexed drawings. The present disclosure includes all such modifications and alterations, and is limited only by the scope of the appended claims. In particular regard to the various functions performed by the above described components (e.g., elements, resources, etc.), the terms used to describe such components are intended to correspond, unless otherwise indicated, to any component which performs the specified function of the described component (e.g., that is functionally equivalent), even though not structurally equivalent to the disclosed structure which performs the function in the herein illustrated exemplary implementations of the disclosure. In addition, while a particular feature of the disclosure may have been disclosed with respect to only one of several implementations, such feature may be combined with one or more other features of the other implementations as may be desired and advantageous for a given or particular application. Furthermore, to the extent that the terms "includes," has, "" contains, "or variants thereof are used in either the detailed description or the claims, such terms are intended to be inclusive in a manner similar to the term" comprising.
Each functional unit in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium. The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Each apparatus or system described above may perform the method in the corresponding method embodiment.
In summary, although the present invention has been disclosed in the foregoing embodiments, the serial numbers before the embodiments are used for convenience of description only, and the sequence of the embodiments of the present invention is not limited. The above embodiments are not intended to limit the present invention, and those skilled in the art can make various changes and modifications without departing from the spirit and scope of the present invention, therefore, the scope of the present invention is defined by the appended claims.

Claims (8)

1. A method for processing employment information, comprising:
receiving an employment request for a target project;
displaying an employment processing interface of the target project, wherein the employment processing interface comprises a task description area and a task parameter area;
in response to a task description operation aiming at the task description area, displaying an initial task parameter list in the task parameter area;
responding to the parameter adjustment operation aiming at the initial task parameter list, and acquiring parameter adjustment factors and parameter judgment conditions;
determining fixed task parameters in the initial task parameter list;
adjusting the task parameters in the initial task parameter list according to the fixed task parameters, the parameter adjusting factors and the parameter judgment conditions, and dynamically displaying the parameter adjusting process in the task parameter area; the fixed task parameters are task amount, task duration, the number of the employment objects and object grades of the employment objects, and the fixed task parameters cannot change along with the change of other task parameters of the target project;
acquiring candidate recruitment objects according to the adjusted parameter list, and determining recruitment objects corresponding to the target items in the candidate recruitment objects based on recruitment information of the candidate recruitment objects;
the determining, in the candidate employment objects, the employment object corresponding to the target item based on the employment information of the candidate employment objects includes:
determining a task time interval corresponding to the target project; determining a task time interval corresponding to the target project according to the project starting time and the project ending time of the target project;
extracting the whole labor efficiency and the to-be-completed task quantity of the candidate labor object from the labor information; the overall employment efficiency reflects the efficiency level of the candidate employment object in the historical period;
based on the overall employment efficiency and the to-be-completed task amount, estimating the completion time period of the candidate employment object for completing the to-be-completed task amount;
when the intersection of the completion time interval and the task time interval is an empty set, acquiring historical recruitment efficiency of the candidate recruitment object in each historical task time interval, and estimating the recruitment efficiency of the candidate recruitment object in the task time interval according to the historical recruitment efficiency;
calculating a difference between a completion timestamp of the completion time period and a start timestamp of the task time period; wherein the difference reflects an interval time between a completion period and a task period;
when the difference value is larger than or equal to a preset value, converting the difference value into a weight coefficient; calculating the product of the weight coefficient and the working times of the candidate working object in a plurality of historical task periods based on a preset formula, and estimating the working probability of the candidate working object for executing the target project; the preset formula is
Figure 84310DEST_PATH_IMAGE001
Wherein N is the total amount of the historical task period, y t 、y t-1 And y t-N+1 The working times of the candidate working objects in different historical task periods,
Figure 197628DEST_PATH_IMAGE002
is a weight coefficient;
and determining the recruitment object corresponding to the target project in the candidate recruitment objects according to the recruitment efficiency and the recruitment probability.
2. The method of claim 1, wherein the fixed task parameter is a fixed task duration, and wherein adjusting the task parameters in the initial task parameter list according to the fixed task parameter, the parameter adjustment factor, and a parameter decision condition comprises:
identifying an item type of the target item;
outputting the maximum task amount corresponding to the target project according to the project type, the fixed task duration and the parameter judgment condition;
responding to the quantity increasing operation aiming at the quantity of the recruitment objects in the initial task parameter list, and reducing the object grade of the recruitment objects in the initial task parameter list or increasing the task amount in the initial task parameter list according to the parameter adjusting factor;
in response to the quantity reduction operation aiming at the quantity of the recruitment objects in the initial task parameter list, increasing the object grade of the recruitment objects in the initial task parameter list and increasing the task amount in the initial task parameter list according to the parameter adjusting factor, or;
in response to the money amount increasing operation aiming at the task money in the initial task parameter list, increasing the number of the recruitment objects and/or the object grade of the recruitment objects in the initial task parameter list according to the parameter adjusting factor, or;
in response to the money amount reduction operation aiming at the task money amount in the initial task parameter list, reducing the number of recruitment objects and/or the object grade of the recruitment objects in the initial task parameter list according to the parameter adjusting factor, or;
in response to the grade improvement operation aiming at the object grade of the recruitment object in the initial task parameter list, reducing the number of the recruitment objects in the initial task parameter list according to the parameter adjustment factor, or;
responding to the grade reduction operation aiming at the object grade of the recruitment object in the initial task parameter list, and increasing the number of the recruitment objects in the initial task parameter list according to the parameter adjustment factor;
wherein the adjusted task amount is less than or equal to the maximum task amount.
3. The method according to claim 2, wherein the outputting the maximum task amount corresponding to the target project according to the project type, the fixed task duration and the parameter decision condition comprises:
determining a reference item corresponding to the target item in a preset database according to the item type;
acquiring the reference task amount of the reference item in unit time;
and calculating the product of the reference task amount and the fixed task time length based on the parameter judgment condition to obtain the maximum task amount corresponding to the target project.
4. The method of claim 1, wherein the fixed task parameter is a fixed task amount, and wherein adjusting the task parameters in the initial task parameter list according to the fixed task parameter, the parameter adjustment factor, and the parameter decision condition comprises:
identifying an item type of the target item;
outputting the maximum task time corresponding to the target project according to the project type, the fixed task amount and the parameter judgment condition;
responding to the quantity increasing operation aiming at the quantity of the employment objects in the initial task parameter list, and reducing the task duration in the initial task parameter list according to the parameter adjusting factor;
in response to the operation of reducing the number of the recruitment objects in the initial task parameter list, increasing the object grade of the recruitment objects in the initial task parameter list or increasing the task duration in the initial task parameter list according to the parameter adjusting factor, or;
responding to a time length increasing operation aiming at the task time length in the initial task parameter list, and reducing the number of the recruitment objects and/or the object grade of the recruitment objects in the initial task parameter list according to the parameter adjusting factor, or;
responding to the time length reduction operation aiming at the task money in the initial task parameter list, and increasing the number of the recruitment objects and/or the object grade of the recruitment objects in the initial task parameter list according to the parameter adjusting factor, or;
in response to the grade improvement operation aiming at the object grade of the recruitment object in the initial task parameter list, reducing the number of the recruitment objects in the initial task parameter list and/or the task duration in the initial task parameter list according to the parameter adjusting factor, or;
responding to the grade reduction operation aiming at the object grade of the recruitment object in the initial task parameter list, and improving the number of the recruitment objects in the initial task parameter list and/or the task duration in the initial task parameter list according to the parameter adjusting factor;
and the adjusted task time length is less than or equal to the maximum task time length.
5. The method according to any one of claims 1 to 4, wherein the obtaining of the candidate employment object according to the adjusted parameter list comprises:
constructing an employment parameter characteristic corresponding to the adjusted parameter list;
and acquiring candidate recruitment objects meeting preset conditions from a recruitment object library based on the recruitment parameter characteristics.
6. A worker information processing apparatus comprising:
the receiving module is used for receiving an employment request aiming at a target project;
the display module is used for displaying an employment processing interface of the target project, and the employment processing interface comprises a task description area and a task parameter area;
the first display module is used for responding to task description operation aiming at the task description area and displaying an initial task parameter list in the task parameter area;
the first acquisition module is used for responding to parameter adjustment operation aiming at the initial task parameter list and acquiring parameter adjustment factors and parameter judgment conditions;
the first determining module is used for determining fixed task parameters in the initial task parameter list;
the adjusting module is used for adjusting the task parameters in the initial task parameter list according to the fixed task parameters, the parameter adjusting factors and the parameter judgment conditions; the fixed task parameters are task amount, task duration, the number of the employment objects and object grades of the employment objects, and the fixed task parameters cannot change along with the change of other task parameters of the target project;
the second display module is used for dynamically displaying the parameter adjustment process in the task parameter area;
the second acquisition module is used for acquiring the candidate recruitment object according to the adjusted parameter list;
the second determining module is used for determining the recruitment object corresponding to the target project in the candidate recruitment objects based on the recruitment information of the candidate recruitment objects;
the second determining module is specifically configured to determine a task time period corresponding to the target project;
extracting the whole employment efficiency and the to-be-completed task amount of the candidate employment object from the employment information;
based on the overall labor efficiency and the to-be-completed task amount, estimating the completion time period of the candidate labor object for completing the to-be-completed task amount;
when the intersection of the completion time interval and the task time interval is an empty set, acquiring historical recruitment efficiency of the candidate recruitment object in each historical task time interval, and estimating the recruitment efficiency of the candidate recruitment object in the task time interval according to the historical recruitment efficiency;
calculating a difference between a completion timestamp of the completion time period and a turn-on timestamp of the task time period;
when the difference value is larger than or equal to a preset value, converting the difference value into a weight coefficient; base ofCalculating the product of the weight coefficient and the working times of the candidate working object in a plurality of historical task periods in a preset formula, and estimating the working probability of the candidate working object for executing the target project; the preset formula is
Figure 480842DEST_PATH_IMAGE003
Wherein N is the total amount of the historical task time interval, y t 、y t-1 And y t-N+1 The working times of the candidate working objects in different historical task periods,
Figure 506567DEST_PATH_IMAGE004
is a weight coefficient;
and determining an employment object corresponding to the target project in the candidate employment objects according to the employment efficiency and the employment probability.
7. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method for processing information according to any one of claims 1 to 5 are performed by the processor when the program is executed.
8. A storage medium having stored therein processor-executable instructions to be loaded by one or more processors to perform the method for processing labor information according to any one of claims 1 to 5.
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