CN110704544A

CN110704544A - Object processing method, device, equipment and storage medium based on big data

Info

Publication number: CN110704544A
Application number: CN201910770853.6A
Authority: CN
Inventors: 王妮丹
Original assignee: Ping An Property and Casualty Insurance Company of China Ltd
Current assignee: Ping An Property and Casualty Insurance Company of China Ltd
Priority date: 2019-08-20
Filing date: 2019-08-20
Publication date: 2020-01-17

Abstract

The embodiment of the application belongs to the field of big data, and relates to an object processing method based on big data, which comprises the following steps: establishing a data warehouse of the evaluation objects, wherein the data warehouse comprises evaluation databases of all the evaluation objects, and each evaluation database of each evaluation object comprises parameters of multiple dimensions; calculating the comprehensive capacity value of each evaluation object by adopting a preset function according to the parameters of multiple dimensions included in the evaluation database of each evaluation object; comparing the comprehensive capacity value of each evaluation object with a threshold value respectively to eliminate the comprehensive capacity value of the evaluation object smaller than the threshold value so as to obtain the comprehensive capacity value of the evaluation object larger than or equal to the threshold value; and sequencing the comprehensive capacity values of the evaluation objects which are larger than or equal to the threshold value in descending order, and sending the sequenced comprehensive capacity values of the evaluation objects to a display for displaying. The application also provides an evaluation object processing device based on the big data, a computer device and a storage medium.

Description

Object processing method, device, equipment and storage medium based on big data

Technical Field

The present application relates to the field of big data technologies, and in particular, to a method and an apparatus for processing an object based on big data, a computer device, and a storage medium.

Background

In work or life, it is often necessary to assess the competency of a particular subject, for example, staff in the workplace, for example, to assess whether a staff is qualified, good or excellent in combination with the subjective impression of the supervisor, the quality and quantity of the completed work, and also a possible way of assessing the staff, for example, by recording the performance of the staff during the work through a notebook or computer and then reviewing the records at the end of the year. However, in the workplace, whether the staff is a person or a supervisor or a leader, the evaluation of the working ability or the growth prospect of the staff is generally based on the usual performance of the staff or is guided by the working result, but the evaluation is different according to the evaluation and the preference of different reviewers, so that the personnel, the supervisor or the leader cannot intuitively know the real ability and the working performance of the staff, the staff can only be evaluated by a photo-image, the professional planning of the staff can only be performed by the staff's own professional planning, and no intuitive data and performance are described, for example, the staff is evaluated whether the task is completed or not by the subjective impression of the supervisor without a comprehensive evaluation on data, a scientific evaluation system or a unified evaluation method cannot be formed, the evaluation result is greatly influenced by the subjective factor, the evaluation effect is not good, and the efficiency of manual evaluation is low, the accuracy is not high.

Moreover, even if the computer is used to record data in the excel, all data needs to be recorded, but when the data volume is very large, for example, thousands of data, and the computer is used to record data in the excel, the Processing speed is slow and even the blue screen is halted due to the occupation of many memory resources and Central Processing Unit (CPU) resources.

Disclosure of Invention

The embodiment of the application aims to provide an object processing method and device based on big data, computer equipment and a storage medium, wherein the big data and a calculation model are utilized to perform data evaluation on an evaluation object, so that the evaluation is more technical and unified, partial data can be eliminated, and only partial data meeting requirements are processed, so that the load of processing equipment can be greatly reduced, and the processing speed and the performance of the processing equipment are improved.

In order to solve the above technical problem, an embodiment of the present application provides an object processing method based on big data, which adopts the following technical solutions:

establishing a data warehouse of the evaluation objects, wherein the data warehouse comprises evaluation databases of all the evaluation objects, and each evaluation database of each evaluation object comprises parameters of multiple dimensions;

calculating the comprehensive capacity value of each evaluation object by adopting a preset function according to the parameters of multiple dimensions included in the evaluation database of each evaluation object;

comparing the comprehensive capacity value of each evaluation object with a threshold value respectively to eliminate the comprehensive capacity value of the evaluation object smaller than the threshold value so as to obtain the comprehensive capacity value of the evaluation object larger than or equal to the threshold value;

sorting the comprehensive capacity values of the evaluation objects which are larger than or equal to the threshold value in the descending order;

and sending the sequenced comprehensive capacity value of the evaluation object to a display for displaying.

In order to solve the above technical problem, another embodiment of the present application further provides an evaluation object processing apparatus based on big data, including:

the storage module is used for establishing a data warehouse of the evaluation objects, the data warehouse comprises evaluation databases of all the evaluation objects, and the evaluation database of each evaluation object comprises parameters of multiple dimensions;

the calculation module is used for calculating the comprehensive capacity value of each evaluation object by adopting a preset function according to the parameters of multiple dimensions included in the evaluation database of each evaluation object;

the processing module is used for respectively comparing the comprehensive capability values of all the evaluation objects with a threshold value to eliminate the comprehensive capability values of the evaluation objects smaller than the threshold value so as to obtain the comprehensive capability values of the evaluation objects larger than or equal to the threshold value, and sequencing the comprehensive capability values of the evaluation objects larger than or equal to the threshold value from large to small;

and the sending module is used for sending the sequenced comprehensive capacity value of the evaluation object to a display for displaying.

In order to solve the above technical problem, another embodiment of the present application further provides a computer device, including: a memory in which a computer program is stored, and a processor which, when executing the computer program, implements the steps of the big-data based object processing method as described.

In order to solve the above technical problem, another embodiment of the present application further provides a computer-readable storage medium, having a computer program stored thereon, where the computer program, when executed by a processor, implements the steps of the big-data based object processing method as described above.

Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:

by utilizing the big data and the calculation model, the data evaluation can be carried out aiming at the evaluation object, so that the evaluation is more technical and unified, the data feedback can be carried out in multiple dimensions, the multivariate evaluation is realized, partial data can be eliminated, and only partial data meeting the requirements is processed, so that the load of processing equipment can be greatly reduced, and the processing speed and the performance of the processing equipment are improved.

Drawings

In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.

FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;

FIG. 2 is a flow diagram of one embodiment of a big data based object processing method according to the present application;

FIG. 3 is a schematic structural diagram of an embodiment of a big data based object processing method apparatus according to the present application;

FIG. 4 is a schematic block diagram of one embodiment of a computer device according to the present application.

Detailed Description

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.

In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.

As shown in fig. 1, the system architecture 100 may include

terminal devices

101, 102, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the

terminal devices

101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.

The user may use the

terminal devices

101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The

terminal devices

101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.

The

terminal devices

101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to notebook computers, tablet computers, smart phones, laptop portable computers, desktop computers, and the like.

The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the

terminal devices

101, 102, 103.

It should be noted that the object processing method based on big data provided in the embodiments of the present application is generally executed by a terminal device or a server, and accordingly, the object processing apparatus based on big data is generally disposed in the terminal device or the server.

It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.

With continued reference to FIG. 2, a flow diagram of one embodiment of a big data based object processing method according to the present application is shown. The object processing method based on big data comprises the following steps.

Step 201, establishing a data warehouse of the evaluation objects by using the big data, wherein the data warehouse comprises evaluation databases of all the evaluation objects, and each evaluation database of the evaluation object comprises parameters of multiple dimensions.

And inputting all parameters related to evaluation aiming at each evaluation object on the terminal equipment or the server, and establishing a data warehouse of the evaluation object by using big data, wherein the data warehouse comprises evaluation databases of all the evaluation objects, and each evaluation object corresponds to the evaluation database thereof one by one, so that the data warehouse is the big data aiming at the evaluation, wherein the evaluation object can be an employee, a group or other objects needing evaluation. In another embodiment of the present application, the evaluation database of each evaluation object is distinguished by a unique identifier, for example, when the evaluation object is an employee, the unique identifier of the evaluation database of each employee may be a name, a job number, an email address, or any other set unique identity of the employee.

The terminal device may be various electronic devices having a display screen and supporting web browsing, including but not limited to a smart phone, a tablet computer, an e-book reader, an MP3 player (Moving Picture Experts Group audio Layer III, mpeg compression standard audio Layer 3), an MP4 player (Moving Picture Experts Group audio Layer IV, mpeg compression standard audio Layer 4), a laptop portable computer, a desktop computer, or the like.

The server may be a server providing various services, such as a background server providing support for pages displayed on the terminal device.

The rating database of each rating object includes parameters of a plurality of dimensions, for example, at least two of the following parameters: the system comprises a working confidence parameter, a target feeling parameter, a picture-and-tongue parameter, a sales capability parameter, a customer operation parameter, a customer resource parameter, a habit parameter, an age parameter and a coordination capability parameter.

The working confidence parameter, the objective feeling parameter, the intention parameter, the sales capability parameter, the customer operation parameter, the customer resource parameter, the habit parameter, the age parameter, and the coordination capability parameter may be represented by assignment values, for example, the assignment value of each parameter may be a value pre-configured by an operator when establishing a data warehouse or set by an operator through a keyboard or a virtual keyboard.

For example, the working confidence parameter indicates the confidence of the evaluation target in the industry, and may be represented by a value assignment, for example, where the working confidence parameter is [1, 2, 3, …, n ], where 1 indicates the lowest working confidence and n indicates the highest working confidence, and n may be 10 or 100, for example.

For example, the target sensation parameter indicates a degree to which the evaluation target agrees with the work target, and may be indicated by, for example, an assignment, where, for example, 1 indicates the lowest degree to which the evaluation target agrees with the work target and n indicates the highest degree to which the evaluation target agrees with the work target, and n may be 10 or 100, for example.

For example, the vanishing point parameter indicates the degree of the coring or the enthusiasm of the evaluation object, and may be indicated by an assignment, for example, where 1 indicates the lowest degree of the coring or the enthusiasm and n indicates the highest degree of the coring or the enthusiasm, and n may be 10 or 100.

For example, the sales capability parameter represents the sales capability of the evaluation target, and may be represented by a value, for example, [1, 2, 3, …, n ], where 1 represents the lowest sales capability and n represents the highest sales capability, and n may be 10 or 100, for example. For example, the sales capability parameter of 1 indicates that the sales amount in the evaluation target predetermined time is lower than the lowest threshold, and the sales capability parameter of n indicates that the sales amount in the evaluation target predetermined time is higher than the highest threshold. In another embodiment of the present application, the sales capability parameter may refer to an increase rate of sales for a predetermined time, for example, the sales capability parameter of 1 indicates that the increase rate of sales for the evaluation target for the predetermined time is lower than a lowest threshold, and the sales capability parameter of n indicates that the increase rate of sales for the evaluation target for the predetermined time is higher than a highest threshold. In another embodiment of the present application, the sales amount may be a sum amount or a product amount, and the present application is not limited thereto.

For example, the customer operation parameter indicates the ability of the evaluation target to operate or manage the customer, and may be indicated by an assignment, for example, where 1 indicates the lowest ability to operate or manage the customer, and n indicates the highest ability to operate or manage the customer, and n may be 10 or 100, for example.

For example, the customer resource parameter indicates the number or amount of customers for which the evaluation target is responsible, and may be indicated by a value, for example, where 1 indicates the lowest number or amount of customers and n indicates the highest number or amount of customers, and n may be 10 or 100, for example. In another embodiment of the present application, the customer resource parameter may also be expressed by a number or a monetary amount of the customer in substantial charge, for example, the number of the customer is from 0 to n, for example, the monetary amount is from 0 to n, and the present application is not limited thereto. However, for convenience of management, the number of customers may be set with a minimum threshold and a maximum threshold, or may be set with a region range, and the amount of money of a customer may be set with a minimum threshold and a maximum threshold, or may be set with a region range. For example, when the area range is set, different intervals may be set to correspond to different parameters, and the assignment may be obtained by correspondingly matching different intervals, for example, the setting of the client resource parameter may be as shown in table 1 below.

Table 1

Customer resource parameters	Number of clients	Amount (ten thousand)
			1	0-10	0-100
2	11-20	101-500
			3	21-30	501-500
4	31-40	501-1000
			5	41-50	1001-1500
6	51-60	1501-2000
			7	61-70	2001-2500
8	71-80	2501-3000

For example, the habit parameter indicates a work habit of an evaluation object, such as late arrival, early departure, absenteeism and/or the number of leave requests within a predetermined time, and may be indicated by an assignment, for example, where the habit parameter is [1, 2, 3, …, n ], where 1 indicates the worst work habit and n indicates the best work habit, and n may be 10 or 100, for example. In another embodiment of the present application, the habit parameter may be set to a maximum value for the evaluation object, and the habit parameter of the evaluation object is decreased by 1 every time a bad work habit (e.g., late, early, spacious or leave behind) occurs. In another embodiment of the present application, the assignment of the habit parameters may also be performed by the substantial number of times that the evaluation object arrives late, leaves early, leaves work and/or asks for leave within a predetermined time, which is not limited in the present application.

For example, the age parameter represents an age dominance score of the evaluation target, and may be represented by a value assignment, for example, where 1 represents the worst age dominance score and n represents the best age dominance score, and n may be 10 or 100, for example. In another embodiment of the present application, it may be set that the age parameter is 10 when the age of the evaluation target is equal to or less than 20 years old, 9 when the age of the evaluation target is greater than 20 years old but equal to or less than 25 years old, and so on, and 1 when the age of the evaluation target is greater than 60 years old. In another embodiment of the present application, the age parameter of the evaluation subject may be set to a highest value (e.g., 10 or 100), and the age parameter of the evaluation subject is decreased by 1 every year. In another embodiment of the present application, the age of the substantial evaluation object may also be used as the assignment of the habit parameter, and the present application is not limited thereto.

For example, the coordination capability parameter represents the capability of the evaluation target to coordinate the resource, and may be represented by a value, for example, [1, 2, 3, …, n ], where 1 represents the capability of the lowest coordination resource and n represents the capability of the highest coordination resource, and n may be 10 or 100, for example. In another embodiment of the present application, the coordination ability parameter may be set to a highest value (e.g., 10 or 100) for the evaluation object, and the coordination ability parameter of the evaluation object is decreased by 1 each time a problem occurs.

In another embodiment of the present application, the assignment of the parameters of the multiple dimensions of each evaluation object is automatically modified according to a preset rule when a corresponding event is sent. For example, the assignment of each parameter may be performed when an event corresponding to each parameter occurs, or may be a threshold value, and the assignment is decreased or increased every time an event corresponding to each parameter occurs, for example, the assignment may be a maximum value and then decreased. In another embodiment of the present application, when each parameter is expressed by a number, for example, the sales capability parameter is an amount of money, the customer resource parameter is a number of customers or an amount of money, the age parameter is age, and each value of these parameters may correspond to a range.

Step 202, according to the parameters of multiple dimensions included in the evaluation database of each evaluation object, calculating the comprehensive ability value of each evaluation object by using a predetermined function.

And obtaining the assignment of the parameters of the multiple dimensions of each evaluation object, and calculating the comprehensive capacity value of each evaluation object according to the following calculation formula (1) based on the assignment of the parameters of the multiple dimensions.

For example, the calculation formula (1) of the predetermined function may be as follows:

ψ＝k₁×L+k₂×M+k₃×N+k₄×J+k₅×P+k₆×Q+k₇×H+k₈×X+k₉x Y formula (1)

Psi is the comprehensive ability value of the evaluation object; l is the assignment of a confidence parameter of the practitioner, k₁Calculating a factor for a practitioner confidence parameter; m is the assignment of the target sense parameter, k₂Calculating a factor for the target sensation parameter; n is the assignment of the penguin parameters, k₃Calculating factors for the penguin parameters; j is the assignment of sales capability parameter, k₄Calculating a factor for a sales capability parameter; p is the assignment of the customer operating parameters, k₅Calculating a factor for a customer operating parameter; q is the assignment of a client resource parameter, k₆Calculating factors for the customer resource parameters; h is the assignment of a habit parameter, k₇Calculating factors for the habit parameters; x is the assignment of an age parameter, k₈Calculating a factor for the age parameter; y is the assignment of the coordination capability parameter, k₉A factor is calculated for the coordination capability parameter.

The above-mentioned respective calculation factors k₁、k₁、…、k₉May be the same or different. The evaluation model function and the growth trajectory model function are calculated using the above formula (1), except that the selected parameter is different or the calculation factor of each parameter is different, for example, when the evaluation model function does not select an age parameter, the calculation factor of the age parameter may be 0.

In another embodiment of the present application, the predetermined function may include an evaluation model function including a part of parameters of the predetermined function in the calculation formula (1) and a growth trajectory model function including a part of parameters of the predetermined function in the calculation formula (1), wherein the evaluation model function and the growth trajectory model function have at least one parameter different.

For example, the evaluation model function may include at least one of the following parameters: the system comprises a working confidence parameter, a target feeling parameter, a picture-and-tongue parameter, a sales capability parameter, a customer operation parameter, a customer resource parameter, a habit parameter, an age parameter and a coordination capability parameter.

For example, the growth trajectory model function may be at least one of the following parameters: the system comprises a working confidence parameter, a target feeling parameter, a picture-and-tongue parameter, a sales capability parameter, a customer operation parameter, a customer resource parameter, a habit parameter, an age parameter and a coordination capability parameter.

And obtaining the assignment of the parameters of multiple dimensions of each evaluation object, and calculating the comprehensive capacity value of each evaluation object according to the predetermined function based on the assignments of the parameters of the multiple dimensions, wherein the predetermined function is an evaluation model function or a growth trajectory model function. The evaluation model function comprises partial parameters of the following calculation formula (1), and the growth trajectory model function comprises partial parameters of the following calculation formula (1), wherein at least one parameter of the evaluation model function is different from at least one parameter of the growth trajectory model function.

For example, the calculation formula of the evaluation model function may be as the following formula (2):

k is the evaluation model function₄×J+k₅×P+k₆×Q+k₇×H+k₉X Y formula (2)

J is the assignment of sales capability parameter, k₄Calculating a factor for a sales capability parameter; p is the assignment of the customer operating parameters, k₅Calculating a factor for a customer operating parameter; q is the assignment of a client resource parameter, k₆Calculating factors for the customer resource parameters; h is the assignment of a habit parameter, k₇Calculating factors for the habit parameters; y is the assignment of the coordination capability parameter, k₉A factor is calculated for the coordination capability parameter.

For example, the calculation formula of the growth trajectory model function may be as the following formula (3):

growth trajectory model function k₁×L+k₂×M+k₃×N+k₈×X+k₉X Y formula (3)

L is the assignment of a confidence parameter of the practitioner, k₁Calculating a factor for a practitioner confidence parameter; m is the assignment of the target sense parameter, k₂Calculating a factor for the target sensation parameter; n is the assignment of the penguin parameters, k₃Calculating factors for the penguin parameters; x is the assignment of an age parameter, k₈Calculating a factor for the age parameter; y is the assignment of the coordination capability parameter, k₉A factor is calculated for the coordination capability parameter.

In another embodiment of the present application, the above-mentioned respective calculation factors are expressed in percentages, the sum of all percentages being equal to a threshold value or not being limited thereto, e.g. k₁+k₁+…+k₉＝100％。

In another embodiment of the present application, the above-mentioned calculation factors are represented by numerical values, k₁＝0.1，k₂＝0.1，k₁0.2, etc., optionally the sum of the values of the individual calculation factors may be equal to a threshold or not, e.g., k₁+k₁+…+k₉＝1。

The assignment of each calculation factor is not limited in this application and may be any rule. However, for different model functions, for example, an evaluation model function and a growth trajectory model function, a greater proportion may be set for the calculation factors of some important parameters, or priorities of the parameters may be set, and the calculation factor corresponding to the parameter with the highest priority is the largest and then decreases sequentially.

For example, for the evaluation model function, the calculation factor of the sales capability parameter, the customer operation parameter, the customer resource parameter and/or the coordination capability parameter may be set to be larger than the calculation factor of other parameters, for example, the calculation factor k of the sales capability parameter₄Calculating factor k of customer operation parameter₅Calculating factor k of client resource parameter₆And a calculation factor k of the coordination capability parameter₉30%, 20%, 10% and 15%, respectively.

For example, the calculation formula of the evaluation model function may be as the following formula (4):

evaluation model function is 5% × L + 5% × M + 5% × N + 30% × J + 20% × P + 10% × Q + 5% × H + 5% × X + 15% × Y formula (4)

L is the assignment of the practitioner confidence parameters; m is the assignment of the target feeling parameters; n is the assignment of the penguin parameters; j is the assignment of sales capability parameters; p is the assignment of the customer operation parameters; q is the assignment of the client resource parameters; h is assignment of the habit parameters; x is the assignment of an age parameter; and Y is the assignment of the coordination capability parameter.

For example, for the growth trajectory model function, it may be preferred to set the calculation factor of the age parameter and/or the coordination ability parameter to be larger than the calculation factor of other parameters, for example, to set the calculation factor k of the age parameter₈And a calculation factor k of the coordination capability parameter₉30% and 20% respectively.

For example, the calculation formula of the growth trajectory model function may be as the following formula (5):

growth trajectory model function 15% × L + 15% × M + 20% × N + 30% × X + 20% × Y formula (5)

L is the assignment of the practitioner confidence parameters; m is the assignment of the target feeling parameters; n is the assignment of the penguin parameters; x is the assignment of an age parameter; and Y is the assignment of the coordination capability parameter.

Step 203, comparing the comprehensive ability values of all the evaluation objects with a threshold value respectively to eliminate the comprehensive ability values of the evaluation objects smaller than the threshold value so as to obtain the comprehensive ability values of the evaluation objects larger than or equal to the threshold value.

For example, if the threshold is 60 minutes, the comprehensive capability value of the evaluation object smaller than 60 minutes is removed, and the comprehensive capability value of the evaluation object greater than or equal to 60 minutes is screened out for subsequent processing, so that the processing device (for example, a terminal device or a server) does not need to process the comprehensive capability value of the evaluation object, and only the comprehensive capability value of the evaluation object greater than or equal to the threshold is subjected to subsequent processing, so that the load of the processing device can be greatly reduced, and the processing speed and performance of the processing device can be improved.

And 204, sorting the comprehensive capacity values of the evaluation objects which are greater than or equal to the threshold value in the descending order.

For example, if the predetermined model includes an evaluation model and a growth trajectory model, the functions used correspondingly are the evaluation model function and the growth trajectory model function, and both the evaluation model function and the growth trajectory model function may be calculated by using the above formula (1) to obtain respective corresponding values, where the evaluation model function and the growth trajectory model function are different in the selected parameter or in the calculation factor of each parameter, for example, when the evaluation model function does not select an age parameter, the calculation factor of the age parameter may be 0.

For example, there are 10 evaluation objects, and the evaluation model capability value of each evaluation object calculated according to the evaluation model function formula (4) is shown in table 2 below.

Table 2

Evaluation object	Evaluation of model capability values
		1	80.5
2	80.1
		3	70.3
4	70.8
		5	70.6
6	70.5
		7	70.2
8	90.2
		9	80.8
10	60.5

The ranking of the evaluation models of the evaluation objects 1 to 10 can be performed according to the order of the ability values of the evaluation models from large to small, for example, the ranking of the evaluation models of the evaluation objects 1 to 10 is as follows: 8,9,1,2,4,5,6,3,7, 10.

However, the ability value of each evaluation target calculated by the formula (5) of the growth trajectory model function is shown in table 3 below.

Table 3

Evaluation object	Growth trajectory model capability value
		1	80
2	94
		3	77
4	91
		5	90
6	88
		7	75
8	85
		9	82
10	70

The growth trajectory ranking of the evaluation objects 1-10 can be performed according to the order of the growth trajectory model capability values from large to small, for example, the growth trajectory ranking of the evaluation objects 1-10 is as follows: 2,4,5,6,8,9,1,3,7, 10.

In another embodiment of the present application, the value calculated by the evaluation model function and the value calculated by the growth trajectory model function may be combined and then calculated to obtain the comprehensive ability value, for example, the comprehensive ability evaluation formula for each evaluation object may be as shown in the following formula (6):

ψ' ═ evaluation model function × R1+ growth trajectory model function × R2 formula (6)

Where ψ is a comprehensive ability value of an evaluation target, and R1 is a calculation factor of an evaluation model function, for example, R1 is 60%; r2 is a calculation factor of the growth trajectory model function, for example, R2 ═ 40%.

The evaluation model function comprises partial parameters of the following calculation formula (1), and the growth trajectory model function comprises partial parameters of the following calculation formula (1), wherein at least one parameter of the evaluation model function is different from at least one parameter of the growth trajectory model function.

For example, calculating the comprehensive ability value of the evaluation object according to formula (6) may be as shown in table 4 below.

TABLE 4

Evaluation object	Evaluation of model capability values	Growth trajectory model capability value	Value of combined capacity
				1	80.5	80	80.3
2	80.1	94	85.66
				3	70.3	77	72.98
4	70.8	91	78.88
				5	70.6	90	78.36
6	70.5	88	77.5
				7	70.2	75	72.12
8	90.2	85	88.12
				9	80.8	82	81.28
10	60.5	70	64.3

Then, the ranking of the comprehensive ability values of the evaluation objects can also be performed in order from large to small, and may be: 8,2,9,1,4,5,6,3,7, 10.

And step 205, sending the sorted comprehensive capacity value of the evaluation object to a display for displaying.

For example, the ranking is displayed on a display to which the terminal device or the server is connected. For example, the terminal device or the display displays the evaluation model sequence of the evaluation objects 1-10 as: 8, 9, 1, 2, 4, 5, 6, 3, 7, 10; or the terminal equipment or the display displays the growth track sequence of the evaluation objects 1-10 as follows: 2,4,5,6,8,9,1,3,7, 10. The terminal equipment or the display displays the sequence of the comprehensive capacity values of the evaluation objects as follows: 8,2,9,1,4,5,6,3,7, 10.

And generating corresponding performance labels for each evaluation object (namely, staff) according to the sorting, classifying or sorting the performance labels, for example, intelligently evaluating the monthly performance or annual performance ranking, for example, outputting the performance ranking by using a monthly performance data result algorithm, and also performing performance prediction and deviation value warning, for example, judging whether the comprehensive capacity value of the evaluation object is lower than a threshold value, and if the comprehensive capacity value of the evaluation object is lower than the threshold value, generating warning information. For example, the comprehensive ability value of the evaluation target calculated from the growth trajectory model function can be obtained by performing accurate training, growth trajectory tracking, and future character prediction.

In another embodiment of the present application, the sorted comprehensive capability values of the evaluation objects may be compared with at least one threshold to obtain a relationship between the comprehensive capability value of each evaluation object and the at least one threshold.

For example, a first threshold value, a second threshold value, a third threshold value, a fourth threshold value, and so on are set at the terminal device or the server, where the first threshold value (e.g., 70 points) < the second threshold value (e.g., 80 points) < the third threshold value (e.g., 85 points) < the fourth threshold value (e.g., 90 points), the terminal device or the server compares the sorted comprehensive capability values of the evaluation objects with the first threshold value, the second threshold value, the third threshold value, and/or the fourth threshold value, respectively, to obtain a relationship between the comprehensive capability value of the evaluation objects and the at least one threshold value, for example, the terminal device or the server determines that the comprehensive capability values of the evaluation objects are smaller than the first threshold value, the comprehensive capability values of the evaluation objects are between the first threshold value and the second threshold value, and so on, The combined capacity value of those evaluation objects is between the second threshold and the third threshold, the combined capacity value of those evaluation objects is between the third threshold and the fourth threshold, and/or the combined capacity value of those evaluation objects is greater than or equal to the fourth threshold.

In another embodiment of the present application, the comprehensive capability values of the evaluation objects are displayed in different colors according to the magnitude relationship between the comprehensive capability value of each evaluation object and the at least one threshold and according to the correspondence relationship between the thresholds and the colors, for example, the comprehensive capability value of the evaluation object smaller than the first threshold is displayed in red, the comprehensive capability value of the evaluation object located between the first threshold and the second threshold is displayed in yellow, the comprehensive capability value of the evaluation object located between the second threshold and the third threshold is displayed in black, the comprehensive capability value of the evaluation object located between the third threshold and the fourth threshold is displayed in light green, and the comprehensive capability value of the evaluation object equal to or larger than the fourth threshold is displayed in green.

In summary, by using the big data and the calculation model, the data evaluation can be performed on the evaluation object, so that the evaluation is more technical and unified, and the data feedback can be performed in multiple dimensions, thereby realizing the multi-element evaluation. And part of data can be removed, and only part of data meeting the requirements is processed, so that the load of processing equipment can be greatly reduced, and the processing speed and the performance of the processing equipment are improved.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).

It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a big data based object processing apparatus, which corresponds to the method embodiment shown in fig. 2, and which is specifically applicable in a terminal device or a server.

As shown in fig. 3, the big-data-based object processing apparatus 300 according to the present embodiment includes: a receiving module 301, a storage module 302, a computing module 303, a processing module 304 and a sending module 305, wherein the receiving module 301, the storage module 302, the computing module 303, the processing module 304 and the sending module 305 are connected with each other, for example, by a bus.

The receiving module 301 is configured to receive all evaluation-related parameters input for an evaluation object.

The storage module 302 is configured to establish a data warehouse of the evaluation objects according to all the evaluation parameters input for the evaluation objects, where the data warehouse includes evaluation databases of all the evaluation objects, and an evaluation database of each evaluation object includes parameters of multiple dimensions.

For example, the storage module 302 is configured to establish a data warehouse of evaluation objects by using big data, where the data warehouse includes evaluation databases of all objects, and each evaluation object is in one-to-one correspondence with its evaluation database, and then the data warehouse is the big data for evaluation, where the evaluation object may be an employee, a group, or another object that needs to be evaluated. In another embodiment of the present application, the evaluation database of each evaluation object is distinguished by a unique identifier, for example, when the evaluation object is an employee, the unique identifier of the evaluation database of each employee may be a name, a job number, an email address, or any other set unique identity of the employee.

The working confidence parameter, the objective feeling parameter, the intention parameter, the sales capability parameter, the customer operation parameter, the customer resource parameter, the habit parameter, the age parameter, and the coordination capability parameter may be represented by assignment values, for example, the assignment values of the respective parameters may be pre-configured values when the storage module 302 establishes a data warehouse or different values set by an operator through a keyboard or a virtual keyboard.

The description or definition of all parameters may be the description of the foregoing method embodiments, and are not repeated herein.

The calculating module 303 is configured to calculate, according to the parameters of the multiple dimensions included in the evaluation database of each evaluation object, the comprehensive capability value of each evaluation object by using a predetermined function.

For example, the calculating module 303 is configured to calculate the comprehensive ability value of each evaluation object by using a calculation formula (1) of the predetermined function, and the definition of the calculation formula (1) may refer to the description of the foregoing method embodiment, which is not repeated herein.

For example, the calculation formula of the evaluation model function may be as in formula (2), the calculation formula of the growth trajectory model function may be as in formula (3), and the definitions of the calculation formulas (2) and (3) may refer to the description of the foregoing method embodiment, and are not repeated herein.

In another embodiment of the present application, for the evaluation model function, the calculation factor of the sales capability parameter, the customer operation parameter, the customer resource parameter and/or the coordination capability parameter may be set to be greater than the calculation factor of other parameters, for example, the calculation factor k of the sales capability parameter₄Calculating factor k of customer operation parameter₅Calculating factor k of client resource parameter₆And a calculation factor k of the coordination capability parameter₉30%, 20%, 10% and 15%, respectively.

For example, the calculation formula of the evaluation model function may be as in formula (4), and the definition of the calculation formula (4) may refer to the description of the foregoing method embodiment, which is not repeated herein.

For example, the calculation formula of the growth trajectory model function may be as in formula (5), and the definition of the calculation formula (5) may refer to the description of the foregoing method embodiment, which is not repeated herein.

The processing module 304 is configured to compare the comprehensive capability values of the evaluation objects with a threshold respectively to remove the comprehensive capability values of the evaluation objects smaller than the threshold, so as to obtain the comprehensive capability value of the evaluation object greater than or equal to the threshold.

For example, if the threshold is 60 minutes, the processing module 304 rejects the comprehensive ability value of the evaluation object smaller than 60 minutes, and screens out the comprehensive ability value of the evaluation object greater than or equal to 60 minutes for subsequent processing, so that the processing module 304 does not need to process the comprehensive ability value of the evaluation object, and only performs subsequent processing on the comprehensive ability value of the evaluation object greater than or equal to the threshold, thereby greatly reducing the load of the processing equipment and improving the processing speed and performance of the processing equipment.

The processing module 304 is further configured to sort the comprehensive capacity values of the evaluation objects that are greater than or equal to the threshold value in a descending order.

For example, if the predetermined model includes an evaluation model and a growth trajectory model, the function used by the calculating module 303 is the evaluation model function and the growth trajectory model function, and both the evaluation model function and the growth trajectory model function may be calculated by using the above formula (1) to obtain their corresponding values, where the evaluation model function and the growth trajectory model function are different in the selected parameter or in the calculation factor of each parameter, for example, when the evaluation model function does not select an age parameter, the calculation factor of the age parameter may be 0.

For example, there are 10 evaluation objects, and the evaluation model capability value of each evaluation object calculated by the calculation module 303 according to the evaluation model function formula (4) is shown in table 2.

The processing module 304 may rank the evaluation models of the evaluation objects 1 to 10 according to the order of the ability values of the evaluation models from large to small, for example, the ranking of the evaluation models of the evaluation objects 1 to 10 is as follows: 8,9,1,2,4,5,6,3,7, 10.

However, the ability value of each evaluation object calculated by the calculation module 303 according to the formula (5) of the growth trajectory model function is shown in table 3.

The processing module 304 may rank the growth trajectories of the evaluation objects 1-10 according to the order of the growth trajectory model capability values from large to small, for example, the growth trajectories of the evaluation objects 1-10 are ranked as: 2,4,5,6,8,9,1,3,7, 10.

In another embodiment of the present application, the calculating module 303 is configured to calculate a comprehensive ability value by combining the value calculated by the evaluation model function and the value calculated by the growth trajectory model function, for example, a comprehensive ability evaluation formula for each evaluation object may be shown in formula (6), and the description of the formula (6) may refer to the description of the foregoing method embodiment, and is not repeated here.

For example, the processing module 304 may calculate the comprehensive ability value of the evaluation object according to formula (6) as shown in table 4.

Then the processing module 304 may sequence the comprehensive ability values of the evaluation objects from large to small, and may: 8,2,9,1,4,5,6,3,7, 10.

The sending module 305 is configured to send the sorted comprehensive capability values of the evaluation objects to a display for displaying.

The processing module 304 is further configured to generate corresponding performance labels for the evaluation objects (i.e., employees) according to the sorting, intelligently evaluate monthly performance or annual performance, for example, output KPI ranking by using a monthly performance data result algorithm, and also perform performance prediction and deviation value warning, for example, when the comprehensive capacity value of the evaluation objects is lower than a threshold value, warning information may be generated. For example, the comprehensive ability value of the evaluation target calculated from the growth trajectory model function can be obtained by performing accurate training, growth trajectory tracking, and future character prediction.

In another embodiment of the present application, the processing module 304 is further configured to compare the sorted comprehensive ability values of the evaluation objects with at least one threshold to obtain a relationship between the comprehensive ability value of each evaluation object and the at least one threshold. For example, a first threshold, a second threshold, a third threshold, a fourth threshold, and so on are set in the storage module 302, wherein the first threshold (e.g., 70 points) < the second threshold (e.g., 80 points) < the third threshold (e.g., 85 points) < the fourth threshold (e.g., 90 points), the processing module 304 is further configured to compare the combined capability values of the ranked evaluation objects with the first threshold, the second threshold, the third threshold, and/or the fourth threshold, respectively, to obtain a relationship between the combined capability value of the evaluation objects and the at least one threshold, for example, the processing module 304 is further configured to determine that the combined capability values of the evaluation objects are smaller than the first threshold, the combined capability values of the evaluation objects are between the first threshold and the second threshold, the combined capability values of the evaluation objects are between the second threshold and the third threshold, and so on, The comprehensive capacity value of the evaluation objects is between the third threshold and the fourth threshold and/or the comprehensive capacity value of the evaluation objects is greater than or equal to the fourth threshold.

In another embodiment of the present application, when the display sorts the comprehensive ability values of the evaluation objects, according to the magnitude relation between the comprehensive capability value of each evaluation object and the at least one threshold and the corresponding relation between the threshold and the color stored in the storage module 302, the comprehensive capability value of each evaluation object is displayed in different colors, for example, the integrated capability value of the evaluation object smaller than the first threshold value is displayed in red, the integrated capability value of the evaluation object between the first threshold value and the second threshold value is displayed in yellow, the integrated capability value of the evaluation object between the second threshold value and the third threshold value is displayed in black, and displaying the comprehensive capacity value of the evaluation object between the third threshold and the fourth threshold as light green, and displaying the comprehensive capacity value of the evaluation object which is greater than or equal to the fourth threshold as green.

In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 4, fig. 4 is a block diagram of a basic structure of a computer device according to the present embodiment.

The computer device 400 comprises a memory 401, a processor 402, a network interface 403 communicatively connected to each other via a system bus. It is noted that only computer device 400 having

components

401 and 403 is shown, but it is understood that not all of the illustrated components are required and that more or fewer components may alternatively be implemented. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.

The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.

The Memory 401 includes at least one type of readable storage medium, such as a volatile Memory or a non-volatile Memory, and the readable storage medium includes a Flash Memory, a hard disk, a multimedia Card, a Card-type Memory (e.g., a Secure Digital (SD) Card, a Flash Memory Card (Flash Card), a DX Memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a read-only Memory (ROM), an erasable programmable read-only Memory (EPROM), an electrically erasable programmable read-only Memory (EEPROM), a Flash Memory, a programmable read-only Memory (PROM), an optical disc, a magnetic Memory, a magnetic disc, or the like. In some embodiments, the storage 401 may be an internal storage unit of the computer device 400, such as a hard disk or a memory of the computer device 400. In other embodiments, the memory 401 may also be an external storage device of the computer device 400, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, or a Flash memory Card (Flash Card) provided on the computer device 400. Of course, the memory 401 may also include both internal and external memory units of the computer device 400. In this embodiment, the memory 401 is generally used for storing an operating system installed in the computer device 400 and various types of application software, such as program codes of object processing methods based on big data. In addition, the memory 401 may also be used to temporarily store various types of data that have been output or are to be output.

The processor 402 may be, in some embodiments, a Central Processing Unit (CPU), a controller, a microcontroller, a microprocessor or other data Processing chip, or 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, discrete hardware components, or the like. The processor 402 is generally operative to control the overall operation of the computer device 400. In this embodiment, the processor 402 is configured to execute the program code stored in the memory 401 or process data, for example, execute the program code of the big data based object processing method.

The network interface 403 may include a wireless network interface or a wired network interface, and the network interface 403 is generally used to establish a communication connection between the computer device 400 and other electronic devices.

The bus is used to enable connection communication between these components. For example, the bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus system may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus.

FIG. 4 only shows a computer

device having components

401 and 403, but it is to be understood that not all of the shown components are required and that more or fewer components can alternatively be implemented.

Optionally, the computer device may further comprise a user interface connected to the bus, the user interface may comprise an input unit such as a Keyboard (Keyboard), a voice input device such as a microphone (microphone) or other device with voice recognition capability, a voice output device such as a sound, a headset, or other device, and optionally the user interface may also comprise a standard wired interface, a wireless interface.

Optionally, the computer device may further comprise said display, which may also be referred to as a display screen or display unit, connected to said bus. In some embodiments, the display device may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an Organic Light-Emitting Diode (OLED) touch device, or the like. The display is used for displaying information processed in the computer device and for displaying a visualized user interface.

The present application provides yet another embodiment, which provides a computer-readable storage medium storing a big data based object handling program, which is executable by at least one processor to cause the at least one processor to perform the steps of the big data based object handling method as described above.

Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.

It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.

Claims

1. An object processing method based on big data is characterized by comprising the following steps:

2. The processing method according to claim 1, wherein the parameters of the plurality of dimensions include at least two of: the system comprises a working confidence parameter, a target feeling parameter, a penguing parameter, a sales capability parameter, a customer operation parameter, a customer resource parameter, a habit parameter, an age parameter and a coordination capability parameter; the step of calculating the comprehensive ability value of each evaluation object by using a predetermined function according to the parameters of the plurality of dimensions included in the evaluation database of each evaluation object specifically includes:

obtaining the assignment of the parameters of multiple dimensions of each evaluation object;

based on the assignment of the parameters of the multiple dimensions, calculating the comprehensive capacity value of each evaluation object according to the following calculation formula:

ψ＝k₁×L+k₂×M+k₃×N+k₄×J+k₅×P+k₆×Q+k₇×H+k₈×X+k₉×Y

wherein psi is the comprehensive capacity value of the evaluation object; l is the assignment of a confidence parameter of the practitioner, k₁For letter of businessA cardiac parameter calculation factor; m is the assignment of the target sense parameter, k₂Calculating a factor for the target sensation parameter; n is the assignment of the penguin parameters, k₃Calculating factors for the penguin parameters; j is the assignment of sales capability parameter, k₄Calculating a factor for a sales capability parameter; p is the assignment of the customer operating parameters, k₅Calculating a factor for a customer operating parameter; q is the assignment of a client resource parameter, k₆Calculating factors for the customer resource parameters; h is the assignment of a habit parameter, k₇Calculating factors for the habit parameters; x is the assignment of an age parameter, k₈Calculating a factor for the age parameter; y is the assignment of the coordination capability parameter, k₉A factor is calculated for the coordination capability parameter.

3. The processing method according to claim 1, wherein the parameters of the plurality of dimensions include at least two of: the system comprises a working confidence parameter, a target feeling parameter, a penguing parameter, a sales capability parameter, a customer operation parameter, a customer resource parameter, a habit parameter, an age parameter and a coordination capability parameter; the step of calculating the comprehensive ability value of each evaluation object by using a predetermined function according to the parameters of the plurality of dimensions included in the evaluation database of each evaluation object specifically includes:

based on the assignment of the parameters of the multiple dimensions, calculating the comprehensive capacity value of each evaluation object according to the preset function;

the preset function is an evaluation model function or a growth trajectory model function, wherein the evaluation model function comprises partial parameters of a calculation formula, and the growth trajectory model function comprises partial parameters of the calculation formula, wherein at least one parameter of the evaluation model function is different from at least one parameter of the growth trajectory model function;

wherein the calculation formula is as follows:

ψ＝k₁×L+k₂×M+k₃×N+k₄×J+k₅×P+k₆×Q+k₇×H+k₈×X+k₉×Y

wherein psi is the comprehensive capacity value of the evaluation object; l is the assignment of a confidence parameter of the practitioner, k₁Calculating a factor for a practitioner confidence parameter; m is the assignment of the target sense parameter, k₂Calculating a factor for the target sensation parameter; n is the assignment of the penguin parameters, k₃Calculating factors for the penguin parameters; j is the assignment of sales capability parameter, k₄Calculating a factor for a sales capability parameter; p is the assignment of the customer operating parameters, k₅Calculating a factor for a customer operating parameter; q is the assignment of a client resource parameter, k₆Calculating factors for the customer resource parameters; h is the assignment of a habit parameter, k₇Calculating factors for the habit parameters; x is the assignment of an age parameter, k₈Calculating a factor for the age parameter; y is the assignment of the coordination capability parameter, k₉A factor is calculated for the coordination capability parameter.

4. The processing method according to claim 1, wherein the parameters of the plurality of dimensions include at least two of: the system comprises a working confidence parameter, a target feeling parameter, a penguing parameter, a sales capability parameter, a customer operation parameter, a customer resource parameter, a habit parameter, an age parameter and a coordination capability parameter; the step of calculating the comprehensive ability value of each evaluation object by using a predetermined function according to the parameters of the plurality of dimensions included in the evaluation database of each evaluation object specifically includes:

based on the assignment of the parameters of the dimensions, calculating a comprehensive value of an evaluation model function according to an evaluation model function calculation formula, and calculating a comprehensive value of a growth trajectory model function according to a growth trajectory model function calculation formula, wherein at least one parameter of the evaluation model function is different from that of the growth trajectory model function;

ψ₁＝k₄×J+k₅×P+k₆×Q+k₇×H+k₉×Y

ψ₂＝k₁×L+k₂×M+k₃×N+k₈×X+k₉×Y

wherein psi₁Is the composite value of the evaluation model function; psi₂The comprehensive value of the growth trajectory model function is obtained; l is the assignment of a confidence parameter of the practitioner, k₁Calculating a factor for a practitioner confidence parameter; m is the assignment of the target sense parameter, k₂Calculating a factor for the target sensation parameter; n is the assignment of the penguin parameters, k₃Calculating factors for the penguin parameters; j is the assignment of sales capability parameter, k₄Calculating a factor for a sales capability parameter; p is the assignment of the customer operating parameters, k₅Calculating a factor for a customer operating parameter; q is the assignment of a client resource parameter, k₆Calculating factors for the customer resource parameters; h is the assignment of a habit parameter, k₇Calculating factors for the habit parameters; x is the assignment of an age parameter, k₈Calculating a factor for the age parameter; y is the assignment of the coordination capability parameter, k₉Calculating a factor for the coordination capability parameter;

calculating the weighted sum of the comprehensive value of the evaluation model function and the comprehensive value of the growth trajectory model function according to the following formula to obtain the comprehensive capability value of each evaluation object;

ψ’＝ψ₁×R1+ψ₂×R2

where ψ' is a comprehensive ability value of an evaluation object, R1 is a calculation factor of an evaluation model function, and R2 is a calculation factor of a growth trajectory model function.

5. The processing method according to any one of claims 2 to 4, wherein the step of obtaining the assignment of the parameters of the plurality of dimensions for each evaluation object is preceded by the steps of:

presetting initial values of parameters of multiple dimensions;

associating assignments of parameters of the plurality of dimensions with corresponding events;

when the event corresponding to each parameter occurs once, the assignment is decreased or increased from the initial value of the parameter.

6. The processing method according to any one of claims 2 to 4, wherein the step of obtaining the assignment of the parameters of the plurality of dimensions for each evaluation object is preceded by the steps of:

setting the grade of the parameter based on the accumulated occurrence number of the corresponding events in the preset time;

and when the accumulated occurrence number of the corresponding events is matched with the grade of the parameter, taking the grade of the corresponding parameter as the assignment of the parameter.

7. The processing method according to claim 1, wherein after comparing the comprehensive ability values of the respective evaluation targets with the threshold values, the method further comprises:

and generating warning information for the comprehensive capability value of the evaluation object lower than the threshold value.

8. An evaluation object processing apparatus based on big data, comprising:

9. A computer device comprising a memory in which a computer program is stored and a processor that implements the steps of the big data based object processing method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when being executed by a processor, realizes the steps of the big-data based object processing method according to any one of claims 1 to 7.