CN110689219A - User behavior evaluation display method, device, equipment and medium based on big data - Google Patents

Abstract

The embodiments of the present application disclose a method, device, device and medium for displaying user behavior evaluation based on big data, which relate to the technical field of big data processing. A method for displaying user behavior evaluation based on big data, comprising: receiving a scoring instruction; calling a preset index parameter template in response to the scoring instruction; calling a preset scoring formula template to calculate the numerical value of the scoring index; and counting the numerical value of the scoring index The calculation result is calculated, and the ranking is generated according to the calculation result; the user's list viewing request is received, and the corresponding list is sent for the user to view in response to the list viewing request. The method calls the preset template and uses the method of big data statistics to carry out personalized ranking of institutions and users in real time, and it is convenient for users to view various indicators, and objectively understand the corresponding work level and level through the score of each indicator. The shortcomings can improve management efficiency, save management manpower, and stimulate the enthusiasm of staff.

Description

User behavior evaluation display method, device, equipment and medium based on big data

technical field

The embodiments of the present application relate to the technical field of big data processing, and in particular, to a method, apparatus, device, and medium for displaying user behavior evaluation based on big data.

Background technique

In recent years, with the improvement of residents' living standards, industries related to livelihood security have risen rapidly. According to the different needs of different stages of life, there are diversified livelihood security products. For example, everyone always needs financial management and asset protection. Therefore, the status of industries related to people's livelihood security in the market economy and social management is gradually highlighted, and the status in the society is gradually deepened.

At present, in industries related to people's livelihood security, some companies will conduct some scoring assessments for their institutions, operators, supervisors, etc. to analyze the ability of scoring objects. The supervisors of many enterprises usually track their organizations or employees through manual collection of lists, manual statistical operations, etc., which are inefficient, and the statistical process is prone to errors and cannot be managed in real time. Therefore, it is necessary to use technologies such as big data distributed infrastructure to realize data statistics and analysis on the order of hundreds of millions, and to facilitate calculation, analysis and statistical data. However, how to generate results suitable for each user's view through statistical data is still a problem that plagues many enterprises.

SUMMARY OF THE INVENTION

The technical problem to be solved by the embodiments of the present application is to provide a method, device, equipment and medium for displaying user behavior evaluation based on big data, which can perform personalized scoring on user data by means of big data statistics, which can satisfy different scoring requirements. requirements, and facilitate users to view the scoring results.

In order to solve the above technical problems, a method for displaying user behavior evaluation based on big data described in the embodiments of the present application adopts the following technical solutions:

A method for displaying user behavior evaluation based on big data, including:

Receive a scoring instruction; the scoring instruction is provided with a time period to be obtained, an object to be scored, and at least one scoring index to be called;

In response to the scoring instruction, call the preset index parameter template, call the corresponding scoring index from the index parameter template based on the scoring index to be called, and then retrieve the index from the database according to the scoring factor included in the scoring index. Acquiring the statistical data of the object to be scored in the time period to be obtained; wherein each scoring index includes at least one scoring factor;

Calling the preset scoring formula template, matching the scoring index with the scoring formula in the scoring formula template, and calculating the numerical value of the scoring index according to the matching scoring formula in combination with the statistical data;

Count the calculation result of the numerical value of the scoring index, and perform ranking according to the calculation result to generate a list;

A list viewing request from a user is received, and a corresponding list is sent for the user to view in response to the list viewing request.

The method for displaying user behavior evaluation based on big data described in the embodiment of the present application, by calling a preset template and using the method of big data statistics, real-time personalized ranking of institutions and users, and it is convenient for users to view various indicators, And objectively understand the corresponding work level and deficiencies through the score of each indicator, which can improve management efficiency, save management manpower, and stimulate the enthusiasm of staff.

Further, in the method for displaying user behavior evaluation based on big data, before the step of receiving a scoring instruction, the method further includes:

The index parameter template is configured to set a number of scoring indexes in the index parameter template, and set a number of scoring factors for each scoring index.

By pre-configuring the index parameter template, different scoring indexes can be set for different scoring scenarios, and the flexible application of scoring indexes can be realized, so that it is convenient for different users to optimize the setting of scoring lists according to different needs.

Further, in the method for displaying user behavior evaluation based on big data, the step of configuring the indicator parameter template specifically includes:

setting at least one level for the several scoring indicators;

The settings between scoring indexes of adjacent levels are as follows: a high-level scoring index is composed of a combination of at least one lower-level scoring index.

By setting different levels for different scoring indicators, the categories of scoring indicators can be more reasonably divided, and it is convenient to distinguish the structure and importance of scoring indicators.

Further, in the method for displaying user behavior evaluation based on big data, before the step of invoking a preset scoring formula template, the method further includes: configuring the scoring formula template; the scoring formula template includes: Several scoring formulas are used to calculate the numerical value of the scoring index.

By configuring different scoring formulas in advance according to the setting needs of different users' index scoring, and recording and storing them in the scoring formula template, the scoring formula template can be updated at any time to conveniently adapt to the needs of users. In this way, each organization and job position in the enterprise will have different scoring formulas for matching calculation, and the scoring list can be customized according to the organization and position. good stats.

Further, in the method for displaying user behavior evaluation based on big data, after the step of performing ranking and generating a list according to the calculation result, the method further includes:

Set the permissions dimension of the list to set different access permissions for different users.

In this way, the data security of the list can be managed more reasonably

Further, in the method for displaying user behavior evaluation based on big data, the list includes statistical data on scoring factors, and the step of setting the authority dimension of the list further includes: setting different scoring factors for different scoring factors. access rights.

Further, in the method for displaying user behavior evaluation based on big data, the steps of receiving a user's list viewing request and sending a corresponding list for the user to view in response to the list viewing request include:

After receiving the user's list viewing request, match the access rights corresponding to the user in the rights dimension, and send the specified content in the list to the user based on the access rights for the user to view.

In order to solve the above technical problems, the embodiment of the present application also provides a user behavior evaluation display device based on big data, which adopts the following technical solutions:

A user behavior evaluation display device based on big data, comprising:

a receiving module for receiving a scoring instruction; the scoring instruction is provided with a time period to be obtained, an object to be scored, and at least one scoring index to be called;

The first response module is used to call the preset index parameter template in response to the scoring instruction, and call the corresponding scoring index from the index parameter template based on the scoring index to be called, wherein the scoring index includes at least a scoring factor, and then obtain the statistical data of the object to be scored in the time period to be obtained from the database according to the scoring factor included in the scoring index; wherein, each scoring index includes at least one scoring factor;

The second response module is configured to call a preset scoring formula template, match the scoring index with the scoring formula in the scoring formula template, and calculate the numerical value of the scoring index according to the matching scoring formula and the statistical data ;

A generation module, used to count the calculation result of the numerical value of the scoring index, and to rank and generate a list according to the calculation result;

A sending module, configured to receive a user's list viewing request, and send a corresponding list for the user to view in response to the list viewing request.

The display device for user behavior evaluation based on big data described in the embodiment of the present application, by invoking a preset template, and using the method of big data statistics, real-time personalized ranking of institutions and users, and it is convenient for users to view various indicators, And objectively understand the corresponding work level and deficiencies through the score of each indicator, which can improve management efficiency, save management manpower, and stimulate the enthusiasm of staff.

In order to solve the above-mentioned technical problems, the embodiment of the present application also provides a computer device, which adopts the following technical solutions:

A computer device, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the method for displaying user behavior evaluation based on big data as described in any one of the above technical solutions is realized A step of.

In order to solve the above technical problems, the embodiments of the present application also provide a computer-readable storage medium, which adopts the following technical solutions:

A computer-readable storage medium, storing a computer program on the computer-readable storage medium, and when the computer program is executed by a processor, realizes the method for displaying user behavior evaluation based on big data as described in any one of the above technical solutions A step of.

Compared with the prior art, the embodiments of the present application mainly have the following beneficial effects:

The embodiments of the present application disclose a method, device, equipment and medium for displaying user behavior evaluation based on big data. The method for displaying user behavior evaluation based on big data described in the embodiments of the present application includes: receiving a scoring instruction; responding to the scoring instruction , call the preset index parameter template; call the preset scoring formula template to calculate the value of the scoring index; count the calculation results of the numerical value of the scoring index, and rank and generate a list according to the calculation results; receive the user's list view request, The corresponding list is sent for the user to view in response to the list viewing request. The method calls the preset template and uses the method of big data statistics to carry out personalized ranking of institutions and users in real time, and it is convenient for users to view various indicators, and objectively understand the corresponding work level and level through the score of each indicator. The shortcomings can improve management efficiency, save management manpower, and stimulate the enthusiasm of staff.

Description of drawings

In order to illustrate the technical solutions in the embodiments of the present application more clearly, the following briefly introduces the drawings required in the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, other drawings can also be obtained from these drawings without any creative effort.

Additional drawings can be obtained from these drawings.

FIG. 1 is an exemplary system architecture diagram to which an embodiment of the present application can be applied;

FIG. 2 is a flowchart of an embodiment of the method for displaying user behavior evaluation based on big data described in the embodiment of the application;

FIG. 3 is a schematic structural diagram of an embodiment of the display device for user behavior evaluation based on big data described in the embodiment of the application;

FIG. 4 is a schematic structural diagram of an embodiment of a computer device in an embodiment of the present application.

Detailed ways

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs. The terms used herein in the specification of the application are for the purpose of describing specific embodiments only, and are not intended to limit the application.

It should be noted that the terms "comprising", "comprising" and "having" in the description and claims of the present application and the above-mentioned drawings, as well as any modifications thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units is not limited to the listed steps or units, but optionally also includes unlisted steps or units, or optionally also includes For other steps or units inherent to these processes, methods, products or devices. Terms in the claims, description, and drawings of this application, relational terms such as "first" and "second", etc., are only used to associate one entity/operation/object with another entity/operation/ Objects are distinguished without necessarily requiring or implying any such actual relationship or ordering between these entities/operations/objects.

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 present application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor a separate or alternative embodiment that is mutually exclusive of other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

In order to enable those skilled in the art to better understand the solutions of the present application, the following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the relevant drawings in the embodiments of the present application.

As shown in FIG. 1 , the system architecture 100 may include terminal devices 101 , 102 , and 103 , a network 104 and a server 105 . The network 104 is a medium used to provide a communication link between the terminal devices 101 , 102 , 103 and the server 105 . The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, among others.

The user can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages and the like. Various communication client applications may be installed on the terminal devices 101 , 102 and 103 , such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, and the like.

The terminal devices 101, 102, and 103 may be various electronic devices that have a display screen and support web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III, moving image Expert Compression Standard Audio Layer 3), MP4 (Moving PictureExperts Group Audio Layer IV, Moving Picture Experts Group Audio Layer 4) Players, Laptops and Desktops, etc.

The server 105 may be a server that provides various services, such as a background server that provides support for the pages displayed on the terminal devices 101 , 102 , and 103 .

It should be noted that the method for displaying user behavior evaluation based on big data provided by the embodiments of the present application is generally performed by a server/terminal device, and accordingly, a device for displaying user behavior evaluation based on big data is generally set in the server/terminal device.

It should be understood that the numbers of terminal devices, networks and servers in FIG. 1 are merely illustrative. There can be any number of terminal devices, networks and servers according to implementation needs.

In this application, the results of the scoring assessment are counted and displayed in the form of a list, which can be more convenient for personnel in various positions of the company to view. For the scoring list in which business operators are surveyors, this application divides it into two modules: the core index list and the vehicle cost index list.

The core index list includes: comprehensive sub-list, NPS achievement rate list, 3A list, Wanxia seven-day payment rate list, pending stock ratio list, Wanxia full case cycle list.

The list of car costs includes: one-time pass rate list for damage determination, list of customized parts for damage determination, list of user-defined percentage of work hours for damage determination, list of positive deviation of parts damage determination, list of comprehensive evaluation of damage determination quality .

Among them, the parameters in the comprehensive sub-list include: comprehensive rating, NPS achievement rate, 3A performance this month, Wanxia July payment rate, Wanxia full cycle, and the proportion of pending stocks, etc.

The following are definitions of some of these parameters:

NPS (Net Promoter Score, Net Promoter Score) achievement rate, here refers to the value obtained by the customer's score of 8 or more after returning to the customer, minus the amount below 6 points, and then divided by the total number of return visits.

3A, 3A here means 3 different indicators, and the case closing rate is one of them.

The 7-day payment rate of 10,000 yuan refers to the proportion of the total number of cases under 10,000 yuan that are completed within 7 days from reporting to payment.

The whole case cycle under 10,000 yuan refers to the average closing time of cases under 10,000 yuan.

The outstanding stock refers to the number of cases whose progress remains at the operator and has not yet been closed.

The custom proportion of damage assessment accessories. In the process of specifying damage, if there are no standard accessories, the operator will enter the custom accessories. The proportion of this part of the case volume in the overall damage assessment case volume.

The custom proportion of work hours for damage determination. In the process of specifying damage, if there are no standard accessories, the operator will enter the customized accessories. The processing time of this part of the case volume accounts for the proportion of the processing time of the overall damage determination case volume.

The positive deviation of the fixed loss of the accessories, after the specified loss, the difference between the finalized amount and the reported amount.

Continuing to refer to FIG. 2 , a flowchart of an embodiment of the method for displaying user behavior evaluation based on big data described in the embodiment of the present application is shown. The method for displaying user behavior evaluation based on big data includes the following steps:

Step 201: Receive a scoring instruction; the scoring instruction sets a time period to be acquired, an object to be scored, and at least one scoring index to be called.

The scoring instruction is used to score the user index of the object to be scored by using the scoring index to be called.

When scoring the user indicators of the object to be scored, it is generally scored by time period. For example, when scoring the technical indicators and performance indicators of business operators within an enterprise, it is necessary to select the operators in a certain month, a certain quarter or a certain period. The business data in the year and other time periods is analyzed, and the score is calculated after analyzing this part of the business data.

The range of objects to be scored can also be personalized according to the needs of the enterprise. For example, organizations can be divided according to different organizations of the enterprise, and personnel can be divided according to different groups. In this way, the function of displaying the scoring results of the scoring objects in the list in a personalized way is realized.

In the embodiment of the present application, the electronic device (for example, the server/terminal device shown in FIG. 1 ) on which the big data-based user behavior evaluation and presentation method runs may receive the data sent by the user through a wired connection or a wireless connection. Grading Instructions. It should be pointed out that the above wireless connection methods may include but are not limited to 3G/4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other wireless connection methods currently known or developed in the future .

Step 202: In response to the scoring instruction, call a preset index parameter template, call the corresponding scoring index from the index parameter template based on the scoring index to be called, and then according to the scoring factor included in the scoring index Acquire the statistical data of the object to be scored within the to-be-acquired time period from a database; wherein each scoring index includes at least one scoring factor.

In the embodiment of the present application, each of the scoring indexes in the index parameter template includes at least one scoring factor, and each of the scoring factors is one of the parameters used for evaluating the user index.

Wherein, each of the scoring indicators may be regarded as a set of scoring factors, and a set of scoring factors includes at least one scoring factor. Multiple scoring factors can not only be combined to form different scoring indicators for evaluating different user indicators, but also different scoring indicators can be formed through different combinations to evaluate a same user indicator, that is, some different scores. Metrics can be used to evaluate the same user metrics.

In the embodiment of the present application, before the step 201 of receiving a scoring instruction, the method for displaying user behavior evaluation based on big data further includes the step of: configuring the indicator parameter template, so as to set a number of parameters in the indicator parameter template There are several scoring indicators, and several scoring factors are set for each scoring indicator.

Wherein, the index parameter template includes several scoring indexes, that is, several groups of scoring factors, and the required and suitable parameters are preselected as the scoring factors of each scoring index, and the parameters and number of scoring factors in each group of scoring factors are All are preset and can be adjusted and optimized according to actual needs.

By pre-configuring the index parameter template, different scoring indexes can be set for different scoring scenarios, and the flexible application of scoring indexes can be realized, so that different users can optimize the setting of scoring lists according to different needs.

In a specific implementation of the embodiment of the present application, in the step of configuring the indicator parameter template, the method for displaying user behavior evaluation based on big data further includes:

setting at least one level for the several scoring indicators;

The settings between scoring indexes of adjacent levels are as follows: a high-level scoring index is composed of a combination of at least one lower-level scoring index.

Since a scoring indicator may be composed of one or more other scoring indicators, the levels of the scoring indicators can be distinguished. For example, the minimum constituted scoring index is set as a first-level scoring index, and a higher-level scoring index may include several low-level scoring indexes. The essence of the high-level scoring index including several low-level scoring indexes is several groups of scoring factors including several low-level scoring indexes.

For example, when the second-level scoring index includes two first-level scoring indexes. If you want to score user indicators through secondary indicators, you need to first calculate the above two first-level scoring indicators, and then calculate the second-level score by assigning weights and other certain calculation rules based on the above two first-level scoring indicators. index.

Specifically, among the parameters mentioned in the above-mentioned scoring list for surveyors in this application, comprehensive score, NPS achievement rate, 3A, Wanxia seven-day payment rate, ratio of outstanding stock, Wanxia full case cycle, The one-time pass rate of the fixed loss, the custom proportion of the parts for the loss, the custom proportion of the time for the loss, the positive deviation of the parts, and the comprehensive evaluation of the quality of the loss can be used as scoring indicators, of which the comprehensive score can be used as a secondary scoring index. The rest are used as first-level scoring indicators.

By setting different levels for different scoring indicators, the categories of scoring indicators can be more reasonably divided, and it is convenient to distinguish the structure and importance of scoring indicators.

After receiving the scoring instruction, the corresponding data is called from the database to respond to the scoring instruction through the technology of big data, so as to complete the execution of the scoring instruction, so that the required statistical data can be obtained.

Step 203: Call a preset scoring formula template, match the scoring index with the scoring formula in the scoring formula template, and calculate the value of the scoring index according to the matching scoring formula and the statistical data.

In this embodiment of the present application, before the step of calling a preset scoring formula template, the method for displaying user behavior evaluation based on big data further includes: configuring the scoring formula template; the scoring formula template includes several item is used to calculate the scoring formula of the numerical value of the scoring index.

The value of the scoring index represents the scoring of the user index. Different scoring indicators can be used to evaluate the same user indicator, but there are multiple different scoring factor combinations for the same scoring indicator. At this time, a scoring indicator corresponds to different scoring formulas, so sometimes it is necessary to After matching the scoring formula, further select the desired scoring formula for scoring. The specific selection principle can be selected according to the data acquisition difficulty of the scoring factor or the number of scoring factors, or further according to the user's needs. Users generally tend to choose a scoring formula with a lower data acquisition difficulty for the scoring factor and a smaller number of scoring factors.

By configuring different scoring formulas in advance according to the setting needs of different users' index scoring, and recording and storing them in the scoring formula template, the scoring formula template can be updated at any time to conveniently adapt to the needs of users. In this way, each organization and job position in the enterprise will have different scoring formulas for matching calculation, and the scoring list can be customized according to the organization and position. good stats.

In a specific implementation of the embodiment of the present application, taking the scoring of the operator as a surveyor as an example, the comprehensive score of the surveyor is regarded as a three-level scoring index, and the comprehensive score is in the three-level scoring index. Including: service, timeliness, operation quality, risk control and production capacity 5 secondary scoring indicators. The service scoring indicators include: NPS and complaint volume, a total of 2 first-level scoring indicators; the timeliness scoring indicators include: Wanxia 7-day payment rate, 5,001-day payment rate, case closing rate and Wanxia full case cycle, There are a total of 4 first-level scoring indicators; the work quality scoring indicators include: the two-core reduction rate, the two-core return rate, the average paint surface of the vehicle and the average vehicle accessories, a total of 4 first-level scoring indicators; the risk control scoring indicators include: : Investigate the amount of impairment and the success rate of manual promotion, with a total of 2 first-level scoring indicators; production capacity including workload, with a total of 1 first-level scoring indicator.

After assigning weights to each scoring index, the calculation formula of the three-level scoring index for setting the specific comprehensive score of the surveyor is as follows:

Surveyor's comprehensive score = (NPS*60%+complaint volume*40%)*25%+(10,000 7-day payment rate*20%+5,000 1-day payment rate*20%+case closing rate*30%+10,000 xia Whole case cycle*30%)*20%+(Two-core reduction rate*20%+Two-core return rate*20%+Average paint surface*30%+Average accessories*30%)*15%+(Investigation impairment Amount*80%+success rate of manual adjustment*20%)*15%+(workload*100%)*25%

The calculation formula based on the scoring factor corresponding to the first-level scoring index has been omitted, and this formula is a better calculation formula for the scoring index of the surveyor's comprehensive score.

Step 204: Count the calculation results of the numerical values of the scoring indicators, and perform ranking according to the calculation results to generate a list.

The sorting rules of the list may be ranked according to the order or reverse order of the numerical values calculated by the scoring indicators. If the values are the same, the order of the first letter of the object name can be ranked. When the top three and bottom three in the list need to be strengthened, they can be highlighted by means of more conspicuous colors or font sizes, so as to motivate or warn the relevant objects.

Step 205: Receive a user's list viewing request, and send a corresponding list for the user to view in response to the list viewing request.

When a user requests to view the relevant data of the list, a list view request is sent to the server side. After the server side receives the list view request, it sends the corresponding list to the display board for the user to view.

In the embodiment of the present application, after the step of generating a list by ranking according to the numerical value, the method for displaying user behavior evaluation based on big data further includes:

Set the permissions dimension of the list to set different access permissions for different users.

Employees with different positions in the enterprise have different permissions, and we need to set different access permissions for different employees, so as to control the data security of the list more reasonably.

In the embodiment of the present application, the list further includes statistical data on scoring factors, so the step of setting the authority dimension of the list may further include: setting different access permissions for different scoring factors.

The number of the generated lists is not limited, and multiple lists can be generated at the same time. And the list not only records the value of the scoring index, but also records the value of each scoring factor. For example, the list can be recorded in the form of a list for each scoring index and the value of the scoring factor under each scoring index. show later. Viewing users can choose what to view according to their needs, so as to better understand the scoring results for user metrics.

The authority dimension of the ranking list includes: generating different permissions according to different rankings, and setting different permissions for different scoring factors under the same ranking.

When generating different permissions based on different lists, you can take the size of the region as an example, divide it into different dimensions such as the country, province, and city, and set different levels to target users with different permissions.

When setting different permissions for different scoring factors in the same list, you can use the level of the user group as the division rule, such as: operator, group leader, first-level organization administrator, second-level organization administrator, general manager, etc. And according to the management needs of the enterprise, the viewing authority for different scoring factors is given in turn.

In one implementation of the embodiment of the present application, the step 205 includes: after receiving the user's list viewing request, matching the access authority corresponding to the user in the authority dimension, and sending a request to the user based on the access authority. The above-mentioned user sends the specified content in the list for the user to view.

The method for displaying user behavior evaluation based on big data described in the embodiment of the present application, by calling a preset template and using the method of big data statistics, real-time personalized ranking of institutions and users, and it is convenient for users to view various indicators, And objectively understand the corresponding work level and deficiencies through the score of each indicator, which can improve management efficiency, save management manpower, and stimulate the enthusiasm of staff.

Those of ordinary skill in the art can understand that the realization of all or part of the processes in the methods of the above embodiments can be accomplished by instructing relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium, and the program is During execution, it may include the processes of the embodiments of the above-mentioned methods. The aforementioned storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (Read-Only Memory, ROM), or a random access memory (Random Access Memory, RAM).

It should be understood that although the various steps in the flowchart of the accompanying drawings are sequentially shown in the order indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless explicitly stated herein, the execution of these steps is not strictly limited to the order and may be performed in other orders. Moreover, at least a part of the steps in the flowcharts of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but may be executed at different times, and the execution sequence is also It does not have to be performed sequentially, but may be performed alternately or alternately with other steps or at least a portion of sub-steps or stages of other steps.

Further referring to FIG. 3 , FIG. 3 shows a schematic structural diagram of an embodiment of the apparatus for displaying user behavior evaluation based on big data described in the embodiment of the present application. As an implementation of the method shown in FIG. 2 above, the present application provides an embodiment of an apparatus for displaying user behavior evaluation based on big data. The apparatus embodiment corresponds to the method embodiment shown in FIG. 2 . Can be used in various electronic devices.

As shown in FIG. 3 , the display device for user behavior evaluation based on big data described in this embodiment includes:

Receiving module 301; used for receiving scoring instructions.

A time period to be acquired, an object to be scored, and at least one scoring index to be called are set in the scoring instruction.

The first response module 302 is used for calling a preset index parameter template in response to the scoring instruction, calling a corresponding scoring index from the index parameter template based on the scoring index to be called, and then according to the scoring index The scoring factor included in obtains the statistical data of the object to be scored within the time period to be obtained from the database; wherein each scoring index includes at least one scoring factor.

The second response module 303 is used to call a preset scoring formula template, match the scoring index with the scoring formula in the scoring formula template, and calculate the score of the scoring index according to the matching scoring formula and the statistical data. numerical value.

The generating module 304 is used to count the calculation result of the numerical value of the scoring index, and perform ranking to generate a list according to the calculation result.

Sending module 305: for receiving a user's list viewing request, and sending a corresponding list for the user to view in response to the list viewing request

In the embodiment of the present application, the big data-based user behavior evaluation display device further includes: a parameter setting module. The parameter setting module is configured to configure the index parameter template to set several scoring indexes in the index parameter template, and set several scoring factors for each scoring index.

In the implementation of the embodiment of the present application, the parameter setting module is further configured to set at least one level for the several scoring indicators. The settings between scoring indexes of adjacent levels are as follows: a high-level scoring index is composed of a combination of at least one lower-level scoring index.

In the embodiment of the present application, the big data-based user behavior evaluation display device further includes: a formula setting module. The formula setting module is used to configure the scoring formula template; the scoring formula template includes several scoring formulas for calculating the numerical value of the scoring index.

In the embodiment of the present application, the big data-based user behavior evaluation display device further includes: an authority configuration module. The authority configuration module is used to set the authority dimension of the list, so as to set different access authority for different users.

In the specific implementation of the embodiment of the present application, the permission configuration module is further configured to set different access permissions for different scoring factors.

In the specific implementation of the embodiment of the present application, the sending module 305 is configured to, after receiving the user's list viewing request, match the access authority corresponding to the user in the authority dimension, and send a request to the user based on the access authority. The user sends the specified content in the list for the user to view.

The display device for user behavior evaluation based on big data described in the embodiment of the present application, by invoking a preset template, and using the method of big data statistics, real-time personalized ranking of institutions and users, and it is convenient for users to view various indicators, And objectively understand the corresponding work level and deficiencies through the score of each indicator, which can improve management efficiency, save management manpower, and stimulate the enthusiasm of staff.

To solve the above technical problems, the embodiments of the present application also provide computer equipment. Please refer to FIG. 4 for details. FIG. 4 is a block diagram of a basic structure of a computer device according to this embodiment.

The computer device 6 includes a memory 61 , a processor 62 , and a network interface 63 that communicate with each other through a system bus. It should be pointed out that only the computer device 6 with components 61-63 is shown in the figure, but it should be understood that it is not required to implement all of the shown components, and more or less components may be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculation and/or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, special-purpose Integrated circuit (ApplicationSpecific Integrated Circuit, ASIC), programmable gate array (Field-Programmable GateArray, FPGA), digital processor (Digital Signal Processor, DSP), embedded equipment, etc.

The computer equipment may be a desktop computer, a notebook computer, a palmtop computer, a cloud server and other computing equipment. The computer device can perform human-computer interaction with the user through a keyboard, a mouse, a remote control, a touch pad or a voice control device.

The memory 61 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static Random Access Memory (SRAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), Programmable Read Only Memory (PROM), Magnetic Memory, Magnetic Disk, Optical Disk, etc. In some embodiments, the memory 61 may be an internal storage unit of the computer device 6 , such as a hard disk or a memory of the computer device 6 . In other embodiments, the memory 61 may also be an external storage device of the computer device 6, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, flash memory card (FlashCard) and so on. Of course, the memory 61 may also include both the internal storage unit of the computer device 6 and its external storage device. In this embodiment, the memory 61 is generally used to store the operating system and various application software installed on the computer device 6 , such as program codes of a method for displaying user behavior evaluation based on big data. In addition, the memory 61 can also be used to temporarily store various types of data that have been output or will be output.

In some embodiments, the processor 62 may be a central processing unit (Central Processing Unit, CPU), a controller, a microcontroller, a microprocessor, or other data processing chips. This processor 62 is typically used to control the overall operation of the computer device 6 . In this embodiment, the processor 62 is configured to run the program code stored in the memory 61 or process data, for example, run the program code of the method for displaying user behavior evaluation based on big data.

The network interface 63 may include a wireless network interface or a wired network interface, and the network interface 63 is generally used to establish a communication connection between the computer device 6 and other electronic devices.

The present application also provides another embodiment, which is to provide a computer-readable storage medium, where the computer-readable storage medium stores a user behavior evaluation display program based on big data, and the big data-based user behavior evaluation display program The program may be executed by at least one processor, so that the at least one processor executes the steps of the above-mentioned method for displaying user behavior evaluation based on big data.

From the description of the above embodiments, those skilled in the art can clearly understand that the method of the above embodiment can be implemented by means of software plus a necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is better implementation. Based on this understanding, the technical solution of the present application can be embodied in the form of a software product in essence or in a part that contributes to the prior art, and the computer software product is stored in a storage medium (such as ROM/RAM, magnetic disk, CD-ROM), including several instructions to make a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) execute the methods described in the various embodiments of this application.

In the above embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the device embodiments described above are only illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there may be other division methods. For example, multiple modules or components may be combined. Either it can be integrated into another system, or some features can be omitted, or not implemented.

The modules or components may or may not be physically separated, and components shown as modules or components may or may not be physical modules, and may be located in one place or distributed over multiple network elements. Some or all of the modules or components may be selected according to actual needs to achieve the purpose of the solution in this embodiment.

The present application is not limited to the above-mentioned embodiments, the above are the preferred embodiments of the present application, and the examples are only used to illustrate the present application and not to limit the scope of the present application. , without departing from the principles of the present application, it is still possible to carry out several improvements and modifications to the technical solutions described in the foregoing specific embodiments, or to perform equivalent replacements for some of the technical features. Any equivalent structure made by using the contents of the description and drawings of this application, which is directly or indirectly used in other related technical fields, shall be deemed to be included within the protection scope of this application.

Obviously, the above-described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The accompanying drawings show the preferred embodiments of the present application, but do not limit the scope of the patent of the present application. This application may be embodied in many different forms, rather these embodiments are provided so that a thorough and complete understanding of the disclosure of this application is provided. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or perform equivalent replacements for some of the technical features. . Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative work, as well as any equivalent structures made by using the contents of the description and drawings of this application, are directly or indirectly used in Other related technical fields are all within the scope of the patent protection of the present application.

Claims (10)

1. A big data-based user behavior evaluation display method is characterized by comprising the following steps:

receiving a grading instruction; the scoring instruction is provided with a time period to be acquired, an object to be scored and at least one scoring index to be called;

responding to the scoring instruction, calling a preset index parameter template, calling a corresponding scoring index from the index parameter template based on the scoring index to be called, and further acquiring statistical data of the object to be scored in the time period to be acquired from a database according to a scoring factor included in the scoring index; wherein each scoring index comprises at least one scoring factor;

calling a preset scoring formula template, matching the scoring indexes with scoring formulas in the scoring formula template, and calculating the numerical values of the scoring indexes according to the matched scoring formulas and the statistical data;

counting the calculation result of the numerical value of the scoring index, and ranking according to the calculation result to generate a list;

receiving a list checking request of a user, and sending a corresponding list for the user to check in response to the list checking request.

2. The big data-based user behavior evaluation presentation method according to claim 1, wherein before the step of receiving a scoring instruction, the method further comprises:

and configuring the index parameter template to set a plurality of scoring indexes in the index parameter template and set a plurality of scoring factors for each scoring index.

3. The big data-based user behavior evaluation presentation method according to claim 2, wherein the step of configuring the index parameter template specifically comprises:

setting at least one grade for the plurality of grading indexes;

the settings between the scoring indexes of adjacent levels are as follows: the high-level scoring index is composed of at least one low-level scoring index combination.

4. The big data-based user behavior evaluation presentation method according to claim 1, wherein before the step of invoking a preset scoring formula template, the method further comprises:

configuring the scoring formula template; the scoring formula template comprises a plurality of scoring formulas used for calculating the numerical value of the scoring index.

5. The big data-based user behavior evaluation presentation method according to claim 1, wherein after the step of ranking and generating a list according to the calculation result, the method further comprises:

and setting authority dimensionality of the list so as to set different access authorities for different users.

6. The big data-based user behavior evaluation presentation method according to claim 5, wherein the list includes statistics of scoring factors, and the step of setting the permission dimension of the list further includes: different access rights are set for different scoring factors.

7. The big data-based user behavior evaluation presentation method according to claim 5, wherein the step of receiving a list viewing request of a user, and the step of sending a corresponding list for the user to view in response to the list viewing request comprises:

and after receiving a list viewing request of a user, matching the access authority corresponding to the user in the authority dimension, and sending specified content in the list to the user for viewing by the user based on the access authority.

8. A big data-based user behavior evaluation display device is characterized by comprising:

the receiving module is used for receiving a grading instruction; the scoring instruction is provided with a time period to be acquired, an object to be scored and at least one scoring index to be called;

the first response module is used for responding to the grading instruction, calling a preset index parameter template, calling a corresponding grading index from the index parameter template based on the grading index to be called, and further acquiring the statistical data of the object to be graded in the time period to be acquired from a database according to a grading factor included in the grading index; wherein each scoring index comprises at least one scoring factor;

the second response module is used for calling a preset scoring formula template, matching the scoring indexes with scoring formulas in the scoring formula template, and calculating the numerical values of the scoring indexes according to the matched scoring formulas and the statistical data;

the generating module is used for counting the calculation result of the numerical value of the scoring index and ranking according to the calculation result to generate a list;

the sending module is used for receiving a list checking request of a user and sending a corresponding list for the user to check in response to the list checking request.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the big data based user behavior evaluation presentation method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when executed by a processor, the computer program implements the steps of the big data-based user behavior evaluation presentation method according to any one of claims 1 to 7.

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