CN114386808A - Information management method, equipment and storage medium - Google Patents

Information management method, equipment and storage medium Download PDF

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CN114386808A
CN114386808A CN202111652597.4A CN202111652597A CN114386808A CN 114386808 A CN114386808 A CN 114386808A CN 202111652597 A CN202111652597 A CN 202111652597A CN 114386808 A CN114386808 A CN 114386808A
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肖雪
卢道和
谢波
朱敏毅
左云霞
陈润伟
彭纪钢
王涵韬
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WeBank Co Ltd
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    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06398—Performance of employee with respect to a job function

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Abstract

The information management method disclosed by the application comprises the following steps: determining m users to be evaluated in a target application program; wherein m is a positive integer greater than or equal to 2; acquiring a target influence value corresponding to each user to be evaluated to obtain m target influence values; sequencing the m users to be evaluated according to a preset sequencing rule based on the m target influence values to obtain a user sequencing sequence; processing the corresponding user display identification information by adopting preset evaluation identification information based on the user sorting sequence to generate target display identification information; and displaying the target display identification information so as to display the corresponding preset evaluation identification information while displaying the user display identification information. The application also discloses an information management device and a storage medium.

Description

Information management method, equipment and storage medium
Technical Field
The present application relates to the field of statistical analysis technologies, and in particular, to an information management method, device, and storage medium.
Background
With the rapid development of computer technology, more and more technologies are applied in the financial field, and the traditional financial industry is gradually changing to financial technology (Fintech), but higher requirements are also put forward on the technologies due to the requirements of the financial industry on safety and real-time performance. With the rapid development of internet technology, the internet has a great influence on the life, study, entertainment and other aspects of people. Currently, enterprises employ various instant chat applications to ensure communication among employees. In order to promote the enthusiasm of the staff, the enterprise generally scores and evaluates the staff at intervals by adopting a plurality of evaluation indexes, and selects excellent staff for display.
However, at present, the election process is mainly realized at regular intervals by adopting a manual election evaluation mode, so that the accuracy of the election evaluation process is low, and the real-time performance is poor.
Content of application
In order to solve the above technical problems, embodiments of the present application desirably provide an information management method, an information management apparatus, and a storage medium, which solve the problem of low intelligence degree in the election evaluation process at present, implement an information management method that automatically implements the election evaluation process, improve accuracy and real-time of the election evaluation process, and improve intelligence degree of the election evaluation process.
The technical scheme of the application is realized as follows:
in a first aspect, an information management method, the method comprising:
determining m users to be evaluated in a target application program; wherein m is a positive integer greater than or equal to 2;
acquiring a target influence value corresponding to each user to be evaluated to obtain m target influence values;
sequencing the m users to be evaluated according to a preset sequencing rule based on the m target influence values to obtain a user sequencing sequence;
processing the corresponding user display identification information by adopting preset evaluation identification information based on the user sorting sequence to generate target display identification information;
and displaying the target display identification information so as to display the corresponding preset evaluation identification information while displaying the user display identification information.
In a second aspect, an information management apparatus, the apparatus comprising: a memory, a processor, and a communication bus; wherein:
the memory to store executable instructions;
the communication bus is used for realizing communication connection between the processor and the memory;
the processor is configured to execute the information management program stored in the memory, and implement the steps of the information management method according to any one of the above.
In a third aspect, a storage medium has stored thereon an information management program which, when executed by a processor, implements the steps of the information management method as in any one of the above.
In the embodiment of the application, m users to be evaluated in a target application program are determined, a target influence value corresponding to each user to be evaluated is obtained, m target influence values are obtained, the m users to be evaluated are sequenced according to a preset sequencing rule based on the m target influence values, a user sequencing sequence is obtained, finally, the corresponding user display identification information is processed by adopting the preset evaluation identification information based on the user sequencing sequence, target display identification information is generated, and the target display identification information is displayed. Therefore, after the target influence values of m users to be evaluated are sequenced to obtain a user sequencing sequence, the corresponding user display identification information is processed by adopting the preset evaluation identification information according to the user sequencing sequence to generate the displayed target display identification information, and the automatic election process is realized through a plurality of parameters, so that the problem of low intelligent degree in the election evaluation process at present is solved, the information management method for automatically realizing the election evaluation process is realized, the accuracy and the real-time performance of the election evaluation process are improved, and the intelligent degree of the election evaluation process is improved.
Drawings
Fig. 1 is a schematic flowchart of an information management method according to an embodiment of the present application;
fig. 2 is a schematic flowchart of another information management method according to an embodiment of the present application;
fig. 3 is a schematic flowchart of another information management method according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an information management apparatus according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of another information management apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application.
An embodiment of the present application provides an information management method, which is applied to an information management device and is shown in fig. 1, and the method includes the following steps:
step 101, m users to be evaluated in the target application program are determined.
Wherein m is a positive integer greater than or equal to 2.
In the embodiment of the present application, the information management device may be all devices with arithmetic capability, for example, a computer device, a notebook computer, an intelligent mobile terminal device, and the like. The target application may be an application for evaluating the employee within the business entity, and may be, for example, a communication application or an application for displaying the evaluation of the employee. The user to be evaluated may be a part of or all of the users in the target application.
And 102, acquiring a target influence value corresponding to each user to be evaluated to obtain m target influence values.
In the embodiment of the application, the target influence value corresponding to each user to be evaluated is determined, and m target influence values corresponding to m users to be evaluated are obtained.
And 103, sequencing the m users to be evaluated according to a preset sequencing rule based on the m target influence values to obtain a user sequencing sequence.
In this embodiment of the application, the preset ordering rule may be a rule that orders according to the influence value from large to small, or may be a rule that orders according to the influence value from small to large. And sequencing the m target influence values corresponding to the m users to be evaluated according to a preset sequencing rule, so as to realize the sequencing of the m users to be evaluated and obtain a user sequencing order.
And 104, processing the corresponding user display identification information by adopting preset evaluation identification information based on the user sorting sequence to generate target display identification information.
In the embodiment of the application, the user sorting order is analyzed, the corresponding user identification information is identified by adopting the preset evaluation identification information, and the corresponding target display identification information is generated and obtained. The user display information is information for identifying the user and available for display, and may be avatar information of the user, and the corresponding preset evaluation identification information may be evaluation identification information for evaluating different sorting orders, and may be medal information, for example.
And 105, displaying the target display identification information.
The display target displays the identification information so as to display the corresponding preset evaluation identification information while displaying the identification information displayed by the user.
In the embodiment of the application, the generated target display identification information is displayed on the information management equipment, so that the aim of displaying the employees can be fulfilled, and a certain incentive effect is achieved.
In the embodiment of the application, m users to be evaluated in a target application program are determined, a target influence value corresponding to each user to be evaluated is obtained, m target influence values are obtained, the m users to be evaluated are sequenced according to a preset sequencing rule based on the m target influence values, a user sequencing sequence is obtained, finally, the corresponding user display identification information is processed by adopting the preset evaluation identification information based on the user sequencing sequence, target display identification information is generated, and the target display identification information is displayed. Therefore, after the target influence values of m users to be evaluated are sequenced to obtain a user sequencing sequence, the corresponding user display identification information is processed by adopting the preset evaluation identification information according to the user sequencing sequence to generate the displayed target display identification information, and the automatic election process is realized through a plurality of parameters, so that the problem of low intelligent degree in the election evaluation process at present is solved, the information management method for automatically realizing the election evaluation process is realized, the accuracy and the real-time performance of the election evaluation process are improved, and the intelligent degree of the election evaluation process is improved.
Based on the foregoing embodiments, an embodiment of the present application provides an information management method, which is applied to an information management device and is shown in fig. 2, and the method includes the following steps:
step 201, m users to be evaluated in the target application program are determined.
Wherein m is a positive integer greater than or equal to 2.
In this embodiment of the present application, the m users to be evaluated may be members in a certain group in the target application program, or may be members of all groups included in the target application program.
Taking an example that a target application is an instant chat application used in an enterprise as an example, m users to be evaluated are all users included in the target application.
And step 202, determining n evaluation index parameters.
Wherein n is a positive integer greater than or equal to 1.
In the embodiment of the present application, the evaluation index parameter may be a parameter determined according to an actual situation and used for evaluating a user, and may be set according to the actual evaluation situation.
Step 203, acquiring n evaluation index values corresponding to the n evaluation index parameters of each user to be evaluated.
In the embodiment of the application, after n evaluation index parameters are determined, n evaluation index values corresponding to the n evaluation index parameters of each user to be evaluated in m users to be evaluated are obtained, and m groups of n evaluation index values are obtained.
For example, assume that 3 evaluation index parameters are determined: x1, X2 and X3, 4 users to be evaluated Y1, Y2, Y3 and Y4, so that it can be obtained that the 3 evaluation index parameters corresponding to the user Y1 sequentially correspond to X1, X2 and X3, and the evaluation index values are X11, X12 and X13, similarly, the 3 evaluation index values corresponding to the user Y2 are X21, X22 and X23, the 3 evaluation index values corresponding to the user Y3 are X31, X32 and X33, and the 3 evaluation index values corresponding to the user Y4 are X41, X42 and X43.
And 204, determining the target influence value of each user to be evaluated based on the n evaluation index values to obtain m target influence values.
In the embodiment of the application, n evaluation index values of each user to be evaluated are analyzed, and the target influence value of each user to be evaluated is determined, so that m target influence values corresponding to m users to be evaluated are obtained.
Illustratively, analyzing X11, X12 and X13 to determine a target influence value m1 corresponding to the user Y1; analyzing X21, X22 and X23, and determining to obtain a target influence value m2 corresponding to the user Y2; analyzing X31, X32 and X33, and determining to obtain a target influence value m3 corresponding to the user Y3; and analyzing the X41, the X42 and the X43, and determining to obtain a target influence value m4 corresponding to the user Y4.
And step 205, sequencing the m users to be evaluated according to a preset sequencing rule based on the m target influence values to obtain a user sequencing order.
In the embodiment of the application, a preset sorting rule is taken as an example of a rule for sorting the influence values from large to small, and the m target influence values are sorted according to the order of the influence values from large to small to obtain a user sorting order.
Illustratively, the target influence values m1, m2, m3 and m4 are sorted according to a sorting rule from large to small to obtain m3, m1, m4 and m2, so that the corresponding user sorting orders of user Y3, user Y1, user Y4 and user Y2 can be obtained.
And step 206, processing the corresponding user display identification information by adopting preset evaluation identification information based on the user sorting sequence to generate target display identification information.
In the embodiment of the application, the corresponding user display identification information is obtained according to the user sorting sequence, and the corresponding preset evaluation identification information is adopted for identifying the information to obtain the target display identification information.
For example, when the target influence value is larger, indicating that the user contribution is larger, the user Y3, the user Y1, the user Y4 and the user Y2 may adopt the corresponding preset evaluation identification information such as medal information in sequence to perform, for example, watermarking on the avatar of the corresponding user, so as to obtain the user avatar with medal watermark information, namely the target display identification information.
And step 207, displaying the target display identification information.
The display target displays the identification information so as to display the corresponding preset evaluation identification information while displaying the identification information displayed by the user.
In the embodiment of the application, the target display identification information is displayed in the corresponding display area, so that other users can see the target display identification information of the user, and the aim of exciting other users is fulfilled.
Therefore, the process of determining the target influence value can be realized by setting the evaluation index parameters, different evaluation index parameters can be set according to actual requirements to determine the target influence value, the process of carrying out identification processing on the corresponding user display identification information by adopting the preset evaluation identification information is realized, the flexibility of the realization process is realized, the subjectivity of manual evaluation is eliminated, the accuracy of the evaluation process is ensured, the possibility of spending a large amount of manpower and material resources in the evaluation process is effectively reduced, and the cost in the evaluation process is effectively reduced.
Based on the foregoing embodiments, in other embodiments of the present application, step 204 can be implemented by steps 204a to 204 b:
step 204a, determining a target weight coefficient of each evaluation index parameter based on the n evaluation index parameters to obtain n target weight coefficients.
In the embodiment of the application, n evaluation index parameters are analyzed, and a target weight coefficient of each evaluation index parameter is determined to obtain n target weight coefficients. The method for determining the target weight coefficient of each evaluation index parameter can be realized by adopting a method for constructing a discrimination matrix in an analytic hierarchy process, a sequence relation analysis method, a Delphi method and other weight coefficient determination methods.
And 204b, determining the target influence value of each user to be evaluated based on the n evaluation index values and the n target weight coefficients to obtain m target influence values.
In the embodiment of the application, after the product of each evaluation index value in the n evaluation index values and the corresponding target weight coefficient is calculated, the first cumulative sum of the products is calculated to obtain the target influence value of each user to be evaluated, so that m target influence values can be obtained.
For example, it is assumed that the weight system corresponding to the evaluation index parameter X1 is q1, the weight system corresponding to the evaluation index parameter X2 is q2, and the target weight coefficient corresponding to the evaluation index parameter X3 is q3, so that the target influence value m1 corresponding to the user Y1 may be calculated by the formula m1 ═ X11 × 1+ X12 × 2+ X13 × 3, and similarly, the target influence value m2 corresponding to the user Y2 is X21 × q1+ X22 × 2+ X23 × 23, and the target influence value m 23 corresponding to the user Y23 is X23 ═ X23 + X23 × 23 + q 23X 23.
Therefore, the target weight coefficient corresponding to each evaluation index parameter is obtained by analyzing the n evaluation index parameters, so that the mutual influence relationship between each evaluation index parameter and other evaluation index parameters is fully considered, and thus, the target weight coefficient corresponding to each determined evaluation parameter is relatively accurate, so that the finally determined target influence value is relatively more objective and higher in accuracy.
Based on the foregoing embodiments, in other embodiments of the present application, the step 204a may be implemented by the steps a 11-a 14:
step a11, determining a reference weight coefficient for each evaluation index parameter based on the n evaluation index parameters.
In this embodiment, the reference weight coefficient of each evaluation index parameter may be obtained by performing normalization processing on matrix elements after analyzing the matrix elements obtained by using a method for constructing a discrimination matrix in an analytic hierarchy process, performing product operation on the matrix obtained after the normalization processing according to rows to obtain a product, and then opening a root of power n to the product.
And a step a12, determining the average value and the standard deviation of each evaluation index parameter in a preset time period.
In the embodiment of the application, in a current statistical preset time period, a value corresponding to each evaluation index parameter of each user to be evaluated is counted, an average value corresponding to each evaluation index parameter of each user to be evaluated is calculated to obtain a corresponding average value, and a standard deviation is calculated to obtain a corresponding standard deviation. The average calculation method may be an accumulation and averaging method, or a weighted averaging method, and the standard deviation calculation method may be various standard deviation calculation methods.
For example, when the preset time period of the current statistics is one week, the values corresponding to the evaluation index parameters of the users each day in the week can be counted, then the values corresponding to the evaluation index parameters of the users each day in the week are calculated by using an average value calculation method, that is, the average value corresponding to the evaluation index parameters of the users in the week can be obtained, and the counted values corresponding to the evaluation index parameters of the users each day in the week are calculated by using a standard deviation calculation method, so that the standard deviation corresponding to the evaluation index parameters of the users in the week is obtained.
Step a13, determining a first difference between the reference weight coefficient corresponding to each evaluation index parameter and the corresponding average value.
Step a14, determining a first ratio of the first difference value corresponding to each evaluation index parameter to the corresponding standard deviation to obtain a target weight coefficient of each evaluation index parameter, and further determining to obtain n target weight coefficients.
In the embodiment of the application, the ith evaluation index parameter of each user to be evaluated
Figure BDA0003446967960000081
wiAnd u is an average value corresponding to the ith evaluation index parameter of each user to be evaluated, and delta is a standard deviation corresponding to the ith evaluation index parameter of each user to be evaluated.
Therefore, the target weight coefficient of each evaluation index parameter takes the influence of other evaluation index parameters into consideration, so that the determined target weight coefficient of each evaluation index parameter is more objective, and the accuracy of the target weight coefficient of each evaluation index parameter in the evaluation process is ensured.
Based on the foregoing embodiments, in other embodiments of the present application, step 201 may be implemented by steps 201a to 201 b:
step 201a, detecting the current time.
In the embodiment of the present application, the current time is detected.
And step 201b, if the current time is matched with the preset time period, determining m users to be evaluated in the target application program.
In the embodiment of the present application, the preset time period may be a preset time period, for example, it may be daily, weekly, monthly, quarterly, yearly, etc., and may be determined according to actual situations, which is not limited herein. The matching of the current time and the preset time period refers to the time when the current time reaches the preset time period, for example, when the preset time period is a month, the last time of analysis is 11 months and 1 days, and when the current time is 12 months and 1 days, the matching of the current time and the preset time period is determined.
Therefore, m target users to be evaluated are periodically and automatically determined through a preset time period, the generation process of the target display identification information is carried out and displayed, the user evaluation information is dynamically updated in real time, and the execution efficiency is improved.
Based on the foregoing embodiments, in other embodiments of the present application, the step 201 or the step 201b of "determining m users to be evaluated in the target application" may be implemented by the steps b11 to b 14:
and step b11, determining p users to be analyzed in the target application program.
Wherein p is greater than or equal to m.
In the embodiment of the present application, p is the number of all users in the target application program, or the number of all users in a certain group.
And b12, acquiring n evaluation index values corresponding to the n evaluation index parameters of each user to be analyzed.
And b13, determining the activity of each user to be analyzed based on the n evaluation index values to obtain p reference activity.
In the embodiment of the application, n evaluation index values of each user to be analyzed are calculated and analyzed, and the activity of each user to be analyzed is determined. The determination process of the activity h of each user to be analyzed comprises the following steps: and determining a second accumulated sum of the n index values to be evaluated of each user to be analyzed, and calculating the ratio of the second accumulated sum to a preset time period to obtain the activity h of each user to be analyzed.
And b14, determining and obtaining m users to be evaluated from the p users to be analyzed based on the p reference activity degrees.
In the embodiment of the application, p reference liveness degrees are analyzed to determine and obtain m users to be evaluated from p users to be analyzed.
Therefore, the activity of the p users to be analyzed is analyzed, and m users to be evaluated are selected from the activity to be further analyzed, so that the users with lower activity are effectively eliminated, the calculation resources required to be consumed in the subsequent analysis process are reduced, the calculation pressure is effectively reduced, and the processing efficiency is improved.
Based on the foregoing embodiments, in other embodiments of the present application, step b14 may be implemented by steps b141 to b 142:
and b141, determining the activity degree with the activity degree greater than or equal to a preset threshold value from the p reference activity degrees to obtain m target activity degrees.
In the embodiment of the present application, the preset threshold may be an empirical value obtained through a large number of experiments and used for distinguishing the activity from the inactivity of the user, or may be an empirical value set by the user according to actual needs.
And step b142, determining the users corresponding to the m target liveness degrees from the p users to be analyzed to obtain m users to be evaluated.
Therefore, the user to be analyzed corresponding to the activity degree with the activity degree larger than or equal to the preset threshold is selected from the p reference activity degrees for subsequent analysis, so that the calculated data amount is effectively reduced, the subsequent calculation and analysis time is shortened, and the subsequent processing capacity is improved.
Based on the foregoing embodiments, step 204b can be implemented by steps c 11-c 18 in other embodiments of the present application:
and c11, when the n evaluation index parameters include the issued content index parameters, sequencing the m users to be evaluated according to a descending sequence based on the m target liveness degrees to obtain a target sequencing sequence.
In the embodiment of the application, the published content index is an index parameter for representing the published content of a user within a preset time period, for example, the published content index is used for representing the content of a published article and/or course and the like within the preset time period. If the n evaluation index parameters include the distribution content index parameter, steps c11 to c18 are performed. Firstly, based on m target liveness degrees, sequencing corresponding users to be evaluated in a descending order to obtain a target sequencing order.
And c12, determining a second ratio of each sequencing serial number of each user to be evaluated to the sequencing serial numbers except for each sequencing serial number based on the target sequencing sequence, and obtaining m-1 second ratios corresponding to each user to be evaluated.
In the embodiment of the present application, assuming that the target sorting order is A, B, C, for the user a, the corresponding second ratio is a (a, B) ═ 1/2, a (a, C) ═ 1/3; for user B, the corresponding second ratio is a (B, a) ═ 2, a (B, C) ═ 2/3; for user C, the corresponding second ratio is a (C, a) ═ 3, and a (C, B) ═ 3/2.
And c13, determining a first numerical value corresponding to each user to be evaluated based on the target sorting sequence, the m-1 second ratio, n and a preset damping coefficient.
In the embodiment of the application, the value range of the preset damping coefficient is 0-1, and the target sorting sequence, the m-1 second ratios, the n and the preset damping coefficient are calculated by adopting a preset calculation method to obtain a first value corresponding to each user to be evaluated.
For example, the first value may be a formula
Figure BDA0003446967960000111
And calculating, wherein d is a preset damping coefficient, k is the number of evaluation index parameters, k takes the value of n, b (w) is m users to be evaluated corresponding to m target liveness, a (w, v) is a second ratio, ua (v) is a sorting sequence number of the user v in a target sorting sequence, and maxua (m) is an activity value of the user m with the top sorting sequence number of 1 in the target sorting sequence.
And c14, determining target release contents with target quantity corresponding to the release content indexes of each user to be evaluated in a preset time period.
In the embodiment of the application, the released contents released by each user to be evaluated within the preset time of current analysis are counted to obtain the target released contents with the target quantity.
And c15, determining the emotional polarity of each target release content of each user to be evaluated aiming at each evaluation index parameter, and obtaining the target quantity emotional polarities of each user to be evaluated.
In the embodiment of the application, the emotion polarity of each target release content of each user to be evaluated for each evaluation index parameter can be directly obtained, and can be obtained in a web crawler manner.
And c16, determining a second numerical value corresponding to each evaluation index parameter of each user to be evaluated based on the target quantity, the target quantity and the corresponding target weight coefficient of each evaluation index parameter of each user to be evaluated.
In the embodiment of the application, the target quantity, the emotion polarity and the target quantity of each evaluation index parameter of each user to be evaluated and the corresponding target weight coefficient are calculated by adopting a specific calculation method, so that a second numerical value corresponding to each evaluation index parameter of each user to be evaluated is obtained.
Illustratively, it can be represented by a formula
Figure BDA0003446967960000121
Calculating to obtain a second value corresponding to each evaluation index parameter, wherein WjiAnd (5) for the emotion polarity of the jth target release content to the ith evaluation index parameter, | G (w) | is the target quantity.
And c17, determining a reference influence value corresponding to each evaluation index parameter of each user to be evaluated based on the first value of each user to be evaluated, the second value corresponding to each evaluation index parameter of each user to be evaluated and the target weight coefficient corresponding to each evaluation index parameter of each user to be evaluated.
In the embodiment of the application, a certain analysis method is adopted to calculate and analyze the first value of each user to be evaluated, the second value corresponding to each evaluation index parameter of each user to be evaluated and the target weight coefficient corresponding to each evaluation index parameter of each user to be evaluated, and the reference influence value corresponding to each evaluation index parameter of each user to be evaluated is determined and obtained. Exemplarily, the reference influence value corresponding to the ith evaluation index parameter of the user w to be evaluated
Figure BDA0003446967960000122
。
And c18, determining the target influence value of each user to be evaluated based on the reference influence values corresponding to the n evaluation index values of each user to be evaluated, and obtaining m target influence values.
In the embodiment of the application, after the reference influence value corresponding to the n evaluation index values of each user to be evaluated is determined, the parameter influence value corresponding to the n evaluation index values of each user to be evaluated is analyzed, for example, calculation is performed by using methods such as summation and averaging, so as to obtain the target influence value of each user to be evaluated, and thus, the target influence values of m users to be evaluated corresponding to m target activities can be determined.
Therefore, in the process of determining each evaluation index parameter of each user to be evaluated, the influence of other users is also considered, so that the incidence relation exists among all users, the target influence value of each user to be evaluated is determined to be more accurate, and the final analysis result is more objective and effective.
Based on the foregoing embodiments, in other embodiments of the present application, step 206 may be implemented by steps 206a to 206 c:
step 206a, determining a preset number of users to be evaluated with the maximum target influence value from the user sorting sequence to obtain a preset number of target evaluation users.
In the embodiment of the present application, the preset number may be an empirical value obtained through a large number of experiments, or may be an empirical value set by a user according to an actual situation. When the user sorting sequence is that the target influence values are sorted according to a rule from large to small, the corresponding preset number of users to be evaluated with the maximum target influence values are the preset number of users to be evaluated which are sorted at the top in the user sorting sequence; and when the user sorting sequence is that the target influence values are sorted according to a rule from small to large, the corresponding preset number of users to be evaluated with the maximum target influence values are the last preset number of users to be evaluated sorted in the user sorting sequence. Thus, a preset number of target evaluation users are determined.
And step 206b, acquiring user display identification information of a preset number of target evaluation users.
In the embodiment of the application, the user display identification information may be avatar information of the user or other identification information that can identify the user.
And step 206c, identifying the user display identification information of each target evaluation user by adopting the preset evaluation identification information to obtain a preset number of target display identification information.
In the embodiment of the application, the preset evaluation identification information is adopted to identify the user display identification information of each target evaluation user, so that the preset evaluation identification information is arranged on the corresponding user display identification information of each target evaluation user to obtain the target display identification information.
Exemplary, when the preset evaluation identification information is excellent employee medal identification information, and the user display identification information is the head portrait information of the user, the excellent employee medal identification information is adopted to perform watermarking on the head portrait information of each target evaluation user, and the excellent employee medal identification information is set on the head portrait information of each target evaluation user, so that the target display identification information with the excellent employee medal identification information can be obtained.
However, in some application scenarios, the corresponding preset display identification information may also be sequentially obtained according to the sorting order of the preset number of target evaluation users in the user sorting order, and the user display identification information of the corresponding target evaluation user is processed to obtain target display identification information including different preset display identification information.
Therefore, a preset number of target evaluation users are determined from the user sorting sequence to perform identification processing by adopting the preset evaluation identification information, so that the process of objectively determining the target evaluation users is realized, and the evaluation accuracy is ensured.
Based on the foregoing embodiment, in another embodiment of the present application, referring to fig. 3, after the information management apparatus performs step 207, the information management apparatus is further configured to perform 208 to 209:
and step 208, if a target operation instruction corresponding to the display identification information of the target user is received, acquiring historical evaluation information corresponding to the display identification information of the target user.
Wherein the historical evaluation information comprises: historical evaluation identification information or historical evaluation identification information and meaning interpretation information corresponding to the historical evaluation identification information.
In the embodiment of the application, the target operation instruction is an instruction which is generated when a user performs an operation, such as a single click or a double click, on the display identification information of the target user and is used for viewing the historical evaluation information corresponding to the display identification information of the target user. After a target operation instruction corresponding to the target user display identification information is detected, historical evaluation information corresponding to the target user display identification information is obtained, wherein the historical evaluation information can be evaluation information in all historical time corresponding to the target user display identification information or evaluation information in a historical time before the current time.
Illustratively, the user Y1 performs a single-click operation on the user display identification information of the user Y2 to display enumeration type options, the user Y1 selects an option for viewing history evaluation information from the enumeration type options to generate a target operation instruction, and in response to the target operation instruction, history evaluation information corresponding to the user Y2, that is, the history acquired medal identification information, is acquired.
And step 209, displaying the historical evaluation information.
In the embodiment of the application, the obtained historical evaluation information is displayed, and the viewing process is realized.
It should be noted that steps 208-209 can be performed as a stand-alone embodiment. Steps 208 to 209 may also be executed according to the received target operation instruction corresponding to the display identification information for the target user between any one of the steps before step 207.
Therefore, the user can check the historical evaluation information of each user through the target operation instruction, the process of checking the historical evaluation information is simplified, and the use experience effect of the user is improved.
Based on the foregoing embodiments, an embodiment of the present application provides a schematic structural diagram of an information management apparatus, which is shown in fig. 4 and includes: medal management system module, head portrait service system module, medal head portrait module and medal hall module; wherein:
medal management system module is mainly used for: medal picture uploading management, medal staff election management, automatic medal updating management and medal validity period timing management. The medal picture uploading management concretely comprises the functions of uploading, storing, deleting, updating and the like of the medal picture. Medal deletion or update can be performed by timing management of medal validity period to synchronize the portrait by timing tasks, removing or updating medals of staff portrait. Medal staff election management major users set and define the calculation method of medal evaluation index parameters.
The head portrait service system module is mainly used for: and managing the account user head portrait information, including storing and maintaining head portrait information of all users. In addition, the module also contains the medal head portrait module and the medal head portrait timing task module.
The medal head portrait module is used for adding medal watermarks to employee head portraits.
Medal hall module for displaying the historical medal information of the user. The medal hall module display contents mainly comprise: introduction of current medal picture and medal meaning; gazang acquired by the current user is lightened, and gazang dust not acquired by the current user is put in the gazang. In this way, medal information acquired by the staff of the enterprise can be fully and in real time shown, as well as the meaning of the medal being publicized.
The user avatar information may be managed through an Application Programming Interface (API) of an Application program.
Based on the foregoing embodiments, the present application provides an application embodiment of an information management method, where an application scenario is a user who needs to determine a medal of a lecturer, where the lecturer is a user who can actively transfer knowledge, has no private sharing wisdom, and gives a light for the growth of the whole company. The corresponding implementation process can refer to the following steps:
and d11, selecting key indexes.
The key indexes can be set and changed by the administrator according to actual needs in the medal management system module. The key index is the evaluation index parameter. The key indicators set for the instructor may be the following seven parameters: the number of course entries or article collections is x1, the number of hot searches is x2, the number of prawns is x3, the number of evaluations is x4, the number of browses is x5, the number of forwarding is x6, and the number of author replies is x 7.
And d12, counting the index values corresponding to the key indexes of each user.
Wherein, the number of the course entries or the number of the articles released by the user in the statistical time period is marked as C, correspondingly, the number of the course entries or the article collections x1 is the average of the number of the entries of the courses or the articles released by the user in the statistical period, the hot search ranking x2 is the average of the hot search rankings of the courses or the articles released by the user in the statistical period, the praise number x3 is the average of the numbers approved by other users in the statistical period, the evaluation number x4 is the average of the numbers evaluated by other users in the statistical period, the browsing number x5 is the average of the numbers browsed by other users in the statistical period, the forwarding number x6 is the average of the numbers forwarded by other users in the statistical period, the author reply quantity x7 is an average value of the number of comments returned by the user to other users under the course or article released by the user in the statistical period. In the statistical period, the value corresponding to the index parameter of the course or article released by the user can be obtained by a web crawler technology. Wherein the content of the first and second substances,
Figure BDA0003446967960000161
wherein i is 1,2, … …, 7, yjThe key indexes are corresponding to each course or article issued by the user in the statistical period.
Step d13, rejecting inactive users.
And removing the inactive users in the statistical time period without participating in personal influence statistics. For the statistical time period T, the activity of the user can be calculated by using the following calculation formula:
Figure BDA0003446967960000162
and after the activity of each user is obtained through calculation, the activity of all the users, namely the m users to be analyzed, is obtained, users with the activity lower than a preset threshold value in all the users are eliminated, and p users to be evaluated are obtained.
And d14, determining the target weight coefficient of each key index.
The example of calculating the target weight coefficient of each key index by using an analytic hierarchy process is described, and the target weight coefficient of each key index is determined by constructing a discrimination matrix method in the analytic hierarchy process, where the method of constructing the discrimination matrix may specifically be a uniform matrix method, that is: all factors are not put together for comparison, but are compared with each other two by two; relative dimensions are used to minimize the difficulty of comparing different factors of different properties with each other to improve accuracy. The discrimination matrix aijThe calibration method of (a) can be as shown in table 1.
TABLE 1
Figure BDA0003446967960000171
Correspondingly, the following 7 evaluation indexes were evaluated: the decision matrix constructed by the number of course entries or article collections x1, the number of hot searches x2, the number of prawns x3, the number of evaluations x4, the number of browses x5, the number of forwarders x6, and the number of writer replies x7 can be shown in table 2.
Based on the above table, first, a formula can be adopted
Figure BDA0003446967960000172
And determining to obtain a reference weight coefficient corresponding to the ith key index, wherein pi represents the operation of taking the product or the direct product. Then, standard processing is carried out on the first coefficient by adopting an Aterman Z-Score model, and a final target weight coefficient corresponding to each key index can be obtained:
Figure BDA0003446967960000173
wherein u is the statistics of each key indexThe mean value of the data in the time period, δ, is the standard deviation of each key index in the statistical time period. It should be noted that, the reference weight coefficient of each evaluation index in the above table may be set according to actual situations, and is only illustrated here by way of example and not specifically limited. That is, in the present application, the weighting factor for each key index (evaluation index parameter) is determined by: the method comprises the steps of firstly determining a reference weight coefficient corresponding to each key index (evaluation index parameter), then subtracting the reference weight coefficient from a data mean value to obtain a difference value, and then taking the ratio of the difference value to a standard deviation as a target weight coefficient of the key index (evaluation index parameter). It should be understood that, by calculating the target weight coefficient of each key index in this way, since the reference weight coefficient of each key index is different, the target weight coefficient of the finally calculated key index is also adaptively different, and the accuracy of calculating the weight coefficient of each key index is realized.
TABLE 2
aij i＝x1 i＝x2 i＝x3 i＝x4 i＝x5 i＝x6 i＝x7
j＝x1 1 9 3 1 1/5 7 5
j＝x2 1/9 1 5 7 9 3 3
j＝x3 1/3 1/5 1 5 5 1/5 3
j＝x4 1 1/7 1/5 1 3 1/7 1/5
j＝x5 5 1/9 1/5 1/3 1 1/7 1/3
j＝x6 1/7 1/3 5 7 7 1 9
j＝x7 1/5 1/3 1/3 5 3 1/9 1
And d15, determining the target influence value of each user.
Wherein, the following formula can be used:
Figure BDA0003446967960000181
calculating the influence value of each key index of each user w, and then passing through a formula
Figure BDA0003446967960000182
And determining to obtain a target influence value of each user w. Wherein k is the number of the key indexes set in the step b 11; g (w) is within all publicationsA set of contents, which refers to a set of all courses and articles issued by the user w in the statistical period in this embodiment; q. q.siIs the target weight coefficient of the ith key indicator of the user w; d is a damping coefficient, and the value range of d is 0 to 1, and in the embodiment, d can be 0.8; | g (w) | is the number of the published content, which means the sum of the number of the published courses and the number of the published articles published by the user w in the statistical period in this embodiment; wjiThe online social network emotion polarity analysis of the published content j aiming at the key index i, referred to as emotion polarity for short, and the value is obtained through a web crawler, for example, the praise number of the user of a course published by the user w; ua (v) is the liveness of user v; b (w) is a set of users excluding inactive users who all participate in influence statistics; maxua (m) is the activity value of the user m with the most front-ranked activity sequence value; a (w, v) is the activity sequence value ratio of user w to user v.
And d16, sorting the target influence values of all the users participating in the influence statistics in the descending order, and electing the previous N users to issue medals.
Wherein N is the preset number.
And d17, adding issued medal watermark information for the portrait services of the first N elected users, and synchronizing to the corresponding portrait display areas through the corresponding API.
Wherein, the employee can check the user's head portrait at any time and check the medal obtained by the user. For example, when the user views medals obtained by the user, the list of medals acquired by the current user and the meaning introduction of medals can be viewed from the corresponding page customized by the user, e.g. "medal hall".
It should be noted that, for the descriptions of the same steps and the same contents in this embodiment as those in other embodiments, reference may be made to the descriptions in other embodiments, which are not described herein again.
In the embodiment of the application, m users to be evaluated in a target application program are determined, a target influence value corresponding to each user to be evaluated is obtained, m target influence values are obtained, the m users to be evaluated are sequenced according to a preset sequencing rule based on the m target influence values, a user sequencing sequence is obtained, finally, the corresponding user display identification information is processed by adopting the preset evaluation identification information based on the user sequencing sequence, target display identification information is generated, and the target display identification information is displayed. Therefore, after the target influence values of m users to be evaluated are sequenced to obtain a user sequencing sequence, the corresponding user display identification information is processed by adopting the preset evaluation identification information according to the user sequencing sequence to generate the displayed target display identification information, and the automatic election process is realized through a plurality of parameters, so that the problem of low intelligent degree in the election evaluation process at present is solved, the information management method for automatically realizing the election evaluation process is realized, the accuracy and the real-time performance of the election evaluation process are improved, and the intelligent degree of the election evaluation process is improved.
Based on the foregoing embodiments, an embodiment of the present application provides an information management apparatus, and as shown in fig. 5, the information management apparatus 3 may include: a processor 31, a memory 32, and a communication bus 33, wherein:
a memory 32 for storing executable instructions;
a communication bus 33 for implementing a communication connection between the processor 31 and the memory 32;
a processor 31 for executing the information management program stored in the memory 32 to implement the steps of:
determining m users to be evaluated in a target application program; wherein m is a positive integer greater than or equal to 2;
acquiring a target influence value corresponding to each user to be evaluated to obtain m target influence values;
sequencing m users to be evaluated according to a preset sequencing rule based on the m target influence values to obtain a user sequencing sequence;
processing the corresponding user display identification information by adopting preset evaluation identification information based on the user sorting sequence to generate target display identification information;
and displaying the target display identification information so as to display the corresponding preset evaluation identification information while displaying the user display identification information.
In other embodiments of the present application, the processor executes the steps to obtain the target influence value corresponding to each user to be evaluated, and when m target influence values are obtained, the following steps may be implemented:
determining n evaluation index parameters; wherein n is a positive integer greater than or equal to 1;
acquiring n evaluation index values corresponding to the n evaluation index parameters of each user to be evaluated;
and determining the target influence value of each user to be evaluated based on the n evaluation index values to obtain m target influence values.
In other embodiments of the present application, the processor executes the step of determining the target influence value of each user to be evaluated based on the n evaluation index values, and when obtaining the m target influence values, the method may be implemented by:
determining a target weight coefficient of each evaluation index parameter based on the n evaluation index parameters to obtain n target weight coefficients;
and determining the target influence value of each user to be evaluated based on the n evaluation index values and the n target weight coefficients to obtain m target influence values.
In other embodiments of the present application, the processor executes the step of determining the target weight coefficient of each evaluation index parameter based on the n evaluation index parameters, and when the n target weight coefficients are obtained, the method may be implemented by:
determining a reference weight coefficient of each evaluation index parameter based on the n evaluation index parameters;
determining the average value and the standard deviation of each evaluation index parameter in a preset time period;
determining a first difference value between the reference weight coefficient corresponding to each evaluation index parameter and the corresponding average value;
and determining a first ratio of the first difference value corresponding to each evaluation index parameter to the corresponding standard deviation to obtain a target weight coefficient of each evaluation index parameter, and further determining to obtain n target weight coefficients.
In other embodiments of the present application, when the processor executes the step of determining m users to be evaluated in the target application, the method may be implemented by:
detecting the current time;
and if the current time is matched with the preset time period, determining m users to be evaluated in the target application program.
In other embodiments of the present application, when the processor executes the step of determining m users to be evaluated in the target application, the method may be implemented by:
determining p users to be analyzed in a target application program; wherein p is greater than or equal to m;
acquiring n evaluation index values corresponding to the n evaluation index parameters of each user to be analyzed;
determining the activity of each user to be analyzed based on the n evaluation index values to obtain p reference activities;
and determining m users to be evaluated from the p users to be analyzed based on the p reference activity degrees.
In other embodiments of the present application, when the processor executes the step of determining m users to be evaluated from p users to be analyzed based on the p reference liveness, the method may be implemented by:
determining the liveness of which is greater than or equal to a preset threshold value from the p reference liveness to obtain m target liveness;
and determining the users corresponding to the m target liveness degrees from the p users to be analyzed to obtain m users to be evaluated.
In other embodiments of the present application, the processor executes the step of determining the target influence value of each user to be evaluated based on the n evaluation index values and the n target weight coefficients, and when obtaining the m target influence values, the method may be implemented by:
when the n evaluation index parameters comprise the release content index parameters, sequencing m users to be evaluated according to a descending sequence based on m target liveness degrees to obtain a target sequencing sequence;
determining a second ratio of each sequencing serial number of each user to be evaluated to the sequencing serial numbers except for each sequencing serial number based on the target sequencing sequence to obtain m-1 second ratios corresponding to each user to be evaluated;
determining a first numerical value corresponding to each user to be evaluated based on the target sorting sequence, the m-1 second ratios, the n and a preset damping coefficient;
determining target release contents with target quantity corresponding to the release content indexes of each user to be evaluated in a preset time period;
determining the emotional polarity of each target release content of each user to be evaluated aiming at each evaluation index parameter to obtain the target number of emotional polarities of each user to be evaluated;
determining a second numerical value corresponding to each evaluation index parameter of each user to be evaluated based on the target quantity, the target quantity and the corresponding target weight coefficient of each evaluation index parameter of each user to be evaluated;
determining a reference influence value corresponding to each evaluation index parameter of each user to be evaluated based on a first numerical value of each user to be evaluated, a second numerical value corresponding to each evaluation index parameter of each user to be evaluated and a target weight coefficient corresponding to each evaluation index parameter of each user to be evaluated;
and determining the target influence value of each user to be evaluated based on the reference influence values corresponding to the n evaluation index values of each user to be evaluated to obtain m target influence values.
In other embodiments of the present application, the processor executes the steps based on the user sorting order, and processes the corresponding user display identification information by using the preset evaluation identification information, and when generating the target display identification information, the method may be implemented by the following steps:
determining a preset number of users to be evaluated with the maximum target influence value from the user sorting sequence to obtain a preset number of target evaluation users;
acquiring user display identification information of a preset number of target evaluation users;
and identifying the user display identification information of each target evaluation user by adopting the preset evaluation identification information to obtain a preset number of target display identification information.
In other embodiments of the present application, the processor is further configured to perform the steps of:
if a target operation instruction corresponding to the target user display identification information is received, acquiring historical evaluation information corresponding to the target user display identification information; wherein the historical evaluation information comprises: historical evaluation identification information or historical evaluation identification information and meaning interpretation information corresponding to the historical evaluation identification information;
and displaying the historical evaluation information.
It should be noted that, in the embodiment of the present application, the multiple or multiple programs may be explained by steps of the one or multiple processors, and refer to the method implementation processes provided in the embodiments corresponding to fig. 1 to 3, which are not described herein again.
In the embodiment of the application, m users to be evaluated in a target application program are determined, a target influence value corresponding to each user to be evaluated is obtained, m target influence values are obtained, the m users to be evaluated are sequenced according to a preset sequencing rule based on the m target influence values, a user sequencing sequence is obtained, finally, the corresponding user display identification information is processed by adopting the preset evaluation identification information based on the user sequencing sequence, target display identification information is generated, and the target display identification information is displayed. Therefore, after the target influence values of m users to be evaluated are sequenced to obtain a user sequencing sequence, the corresponding user display identification information is processed by adopting the preset evaluation identification information according to the user sequencing sequence to generate the displayed target display identification information, and the automatic election process is realized through a plurality of parameters, so that the problem of low intelligent degree in the election evaluation process at present is solved, the information management method for automatically realizing the election evaluation process is realized, the accuracy and the real-time performance of the election evaluation process are improved, and the intelligent degree of the election evaluation process is improved.
Based on the foregoing embodiments, embodiments of the present application provide a computer-readable storage medium, referred to as a storage medium for short, where one or more programs are stored in the computer-readable storage medium, and the one or more programs can be executed by one or more processors to implement the implementation process of the information management method provided in the embodiments corresponding to fig. 1 to 3, and details are not described here again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable information management apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable information management apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable information management apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable information management apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present application, and is not intended to limit the scope of the present application.

Claims (12)

1. An information management method, characterized in that the method comprises:
determining m users to be evaluated in a target application program; wherein m is a positive integer greater than or equal to 2;
acquiring a target influence value corresponding to each user to be evaluated to obtain m target influence values;
sequencing the m users to be evaluated according to a preset sequencing rule based on the m target influence values to obtain a user sequencing sequence;
processing the corresponding user display identification information by adopting preset evaluation identification information based on the user sorting sequence to generate target display identification information;
and displaying the target display identification information so as to display the corresponding preset evaluation identification information while displaying the user display identification information.
2. The method according to claim 1, wherein the obtaining of the target influence value corresponding to each user to be evaluated to obtain m target influence values comprises:
determining n evaluation index parameters; wherein n is a positive integer greater than or equal to 1;
acquiring n evaluation index values corresponding to the n evaluation index parameters of each user to be evaluated;
and determining the target influence value of each user to be evaluated based on the n evaluation index values to obtain m target influence values.
3. The method according to claim 2, wherein the determining the target influence value of each user to be evaluated based on the n evaluation index values to obtain m target influence values comprises:
determining a target weight coefficient of each evaluation index parameter based on the n evaluation index parameters to obtain n target weight coefficients;
and determining the target influence value of each user to be evaluated based on the n evaluation index values and the n target weight coefficients to obtain m target influence values.
4. The method according to claim 3, wherein the determining a target weight coefficient of each of the evaluation index parameters based on the n evaluation index parameters to obtain n target weight coefficients comprises:
determining a reference weight coefficient of each of the evaluation index parameters based on the n evaluation index parameters;
determining the average value and the standard deviation of each evaluation index parameter in a preset time period;
determining a first difference value between the reference weight coefficient corresponding to each evaluation index parameter and the corresponding average value;
determining a first ratio of the first difference corresponding to each evaluation index parameter to the corresponding standard deviation to obtain the target weight coefficient of each evaluation index parameter, and further determining to obtain n target weight coefficients.
5. The method of claim 3, wherein the determining m users to be evaluated in the target application comprises:
detecting the current time;
and if the current time is matched with a preset time period, determining m users to be evaluated in the target application program.
6. The method according to claim 1 or 5, wherein the determining m users to be evaluated in the target application program comprises:
determining p users to be analyzed in the target application program; wherein p is greater than or equal to m;
acquiring n evaluation index values corresponding to the n evaluation index parameters of each user to be analyzed;
determining the activity of each user to be analyzed based on the n evaluation index values to obtain p reference activity;
and determining m users to be evaluated from the p users to be analyzed based on the p reference liveness.
7. The method according to claim 6, wherein the determining m users to be evaluated from p users to be analyzed based on the p reference liveness includes:
determining the activity degree of which the activity degree is greater than or equal to a preset threshold value from the p reference activity degrees to obtain m target activity degrees;
and determining m users corresponding to the target activity from the p users to be analyzed to obtain m users to be evaluated.
8. The method according to claim 7, wherein the determining the target influence value of each user to be evaluated based on the n evaluation index values and the n target weight coefficients to obtain m target influence values comprises:
when the n evaluation index parameters comprise release content index parameters, sequencing the m users to be evaluated according to a descending sequence based on the m target liveness degrees to obtain a target sequencing sequence;
determining a second ratio of each sequencing serial number of each user to be evaluated to the sequencing serial numbers except each sequencing serial number based on the target sequencing sequence to obtain m-1 second ratios corresponding to each user to be evaluated;
determining a first numerical value corresponding to each user to be evaluated based on the target sorting sequence, the m-1 second ratios, n and a preset damping coefficient;
determining target release contents with target quantity corresponding to the release content indexes of each user to be evaluated in the preset time period;
determining the emotional polarity of each target release content of each user to be evaluated aiming at each evaluation index parameter to obtain the emotional polarities of the target number of each user to be evaluated;
determining a second numerical value corresponding to each evaluation index parameter of each user to be evaluated based on the target quantity and the target quantity of emotional polarities of each evaluation index parameter of each user to be evaluated, and the corresponding target weight coefficient;
determining a reference influence value corresponding to each evaluation index parameter of each user to be evaluated based on the first numerical value of each user to be evaluated, the second numerical value corresponding to each evaluation index parameter of each user to be evaluated and a target weight coefficient corresponding to each evaluation index parameter of each user to be evaluated;
and determining the target influence value of each user to be evaluated based on the reference influence value corresponding to the n evaluation index values of each user to be evaluated to obtain m target influence values.
9. The method according to any one of claims 1 to 5 and 7 to 8, wherein the processing the corresponding user display identification information by using preset evaluation identification information based on the user sorting order to generate target display identification information comprises:
determining a preset number of users to be evaluated with the maximum target influence value from the user sorting sequence to obtain a preset number of target evaluation users;
acquiring user display identification information of the preset number of target evaluation users;
and identifying the user display identification information of each target evaluation user by using the preset evaluation identification information to obtain the preset number of target display identification information.
10. The method of any one of claims 1 to 5, 7 to 8, further comprising:
if a target operation instruction corresponding to target user display identification information is received, acquiring historical evaluation information corresponding to the target user display identification information; wherein the historical evaluation information includes: historical evaluation identification information or the historical evaluation identification information and meaning interpretation information corresponding to the historical evaluation identification information;
and displaying the historical evaluation information.
11. An information management apparatus, characterized in that the apparatus comprises: a memory, a processor, and a communication bus; wherein:
the memory to store executable instructions;
the communication bus is used for realizing communication connection between the processor and the memory;
the processor, configured to execute the information management program stored in the memory, and implement the steps of the information management method according to any one of claims 1 to 10.
12. A storage medium, characterized in that the storage medium has stored thereon an information management program which, when executed by a processor, implements the steps of the information management method according to any one of claims 1 to 10.
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