CN110689219A - Big data-based user behavior evaluation display method, device, equipment and medium - Google Patents
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Abstract
The embodiment of the application discloses a user behavior evaluation display method, device, equipment and medium based on big data, and relates to the technical field of big data processing. A big data-based user behavior evaluation display method comprises the following steps: receiving a grading instruction; responding to the grading instruction, and calling a preset index parameter template; calling a preset scoring formula template, and calculating the numerical value of a scoring index; counting the calculation result of the numerical value of the grading 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 in response to the list checking request for the user to check. According to the method, through calling the preset template and utilizing a big data statistics mode, personalized ranking is carried out on mechanisms and users in real time, the users can conveniently check various indexes, the corresponding working level and defects can be objectively known through the grading of the indexes, the management efficiency can be improved, the management labor is saved, and the working enthusiasm of workers is stimulated.
Description
Technical Field
The embodiment of the application relates to the technical field of big data processing, in particular to a user behavior evaluation display method, device, equipment and medium based on big data.
Background
In recent years, with the improvement of the living standard of residents, the industry related to the civil security rises rapidly, and diversified civil security products are provided according to different requirements of different stages of life, such as the requirements of each person for financing, asset security and the like. Therefore, the market economy and social management status of the industry related to the civil guarantee is gradually highlighted, and the social status is also gradually deepened.
In the related industry of the current civil guarantee, some enterprises can carry out some grading evaluations on flag-off mechanisms, operators, supervisors and other people so as to analyze the capacity of a grading object. The mechanisms or staff under the administrative assessment flags of many enterprises are generally tracked by means of manual collection of lists, manual statistics and the like, the efficiency is low, errors are easy to occur in the statistics process, and real-time management cannot be achieved. Therefore, it is necessary to realize data statistics and analysis of hundred million level order by using techniques such as a large data distributed infrastructure, and to facilitate calculation, analysis, and data statistics. How to generate results suitable for individual users to view through statistical data remains a problem that plagues many businesses.
Disclosure of Invention
The technical problem to be solved by the embodiments of the present application is to provide a method, an apparatus, a device and a medium for displaying user behavior evaluation based on big data, and the method, the apparatus, the device and the medium perform personalized scoring on the user data in a big data statistics manner, so as to meet different scoring requirements and facilitate the user to check scoring results.
In order to solve the above technical problem, the method for evaluating and displaying the user behavior based on big data according to the embodiment of the present application adopts the following technical solutions:
a big data-based user behavior evaluation display method comprises 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.
According to the user behavior evaluation display method based on the big data, the preset template is called, the big data statistics mode is utilized, personalized ranking is conducted on the mechanism and the user in real time, the user can conveniently check all indexes, the corresponding working level and the corresponding defects can be objectively known through the grading of all the indexes, the management efficiency can be improved, the management manpower is saved, and the working enthusiasm of workers is stimulated.
Further, before the step of receiving a scoring instruction, the method for displaying the user behavior evaluation based on big data further includes:
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.
Different scoring indexes can be set for different scoring scenes by pre-configuring the index parameter template, so that the scoring indexes are flexibly applied, and different users can conveniently realize the optimal setting of the scoring lists according to different requirements
Further, in the big data-based user behavior evaluation presentation method, the step of configuring the index parameter template specifically includes:
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.
Different grades are set for different scoring indexes, the categories of the scoring indexes can be divided more reasonably, and the structure and the importance of the scoring indexes are distinguished conveniently.
Further, before the step of calling a preset scoring formula template, the method for displaying the user behavior evaluation based on big data further includes: 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.
Different scoring formulas are configured in advance according to setting requirements for scoring indexes of different users and are recorded in the scoring formula template for storage, and the scoring formula template can be updated at any time so as to conveniently adapt to the requirements of the users. Therefore, different scoring formulas are adopted for each mechanism and operation post in the enterprise to carry out matching calculation, and a scoring list which is individually defined according to the mechanism and the post is realized, so that the statistical requirements of some companies can be met more closely, and a better statistical effect is obtained.
Further, after the step of ranking according to the calculation result to generate a list, the method for showing the user behavior evaluation based on the big data further includes:
and setting authority dimensionality of the list so as to set different access authorities for different users.
Thereby more reasonably managing the data security of the control list
Further, in the big data-based user behavior evaluation presentation method, the list includes statistical data of scoring factors, and the step of setting the authority dimension of the list further includes: different access rights are set for different scoring factors.
Further, the step of receiving a list viewing request of a user and sending a corresponding list in response to the list viewing request for the user to view includes:
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.
In order to solve the above technical problem, an embodiment of the present application further provides a device for evaluating and displaying user behavior based on big data, which adopts the following technical solutions:
a big data-based user behavior evaluation display device comprises:
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 scoring instruction, calling a preset index parameter template, calling corresponding scoring indexes from the index parameter template based on the scoring indexes to be called, wherein the scoring indexes all comprise at least one scoring factor, and further acquiring statistical data of the objects to be scored in the time period to be acquired from a database according to the scoring factors included in the scoring indexes; 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.
The user behavior evaluation display device based on the big data carries out personalized ranking on mechanisms and users in real time by calling the preset template and utilizing a big data statistics mode, is convenient for the users to check all indexes, objectively knows corresponding working levels and defects through the grading of all indexes, can improve management efficiency, saves management manpower, and stimulates the working enthusiasm of workers.
In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:
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 the above technical solutions when executing the computer program.
In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:
a computer-readable storage medium, wherein a computer program is stored on the computer-readable storage medium, and when being 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 the above technical solutions.
Compared with the prior art, the embodiment of the application mainly has the following beneficial effects:
the embodiment of the application discloses a method, a device, equipment and a medium for displaying user behavior evaluation based on big data, and the method for displaying user behavior evaluation based on big data comprises the following steps: receiving a grading instruction; responding to the grading instruction, and calling a preset index parameter template; calling a preset scoring formula template, and calculating the numerical value of a scoring index; counting the calculation result of the numerical value of the grading 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 in response to the list checking request for the user to check. According to the method, through calling the preset template and utilizing a big data statistics mode, personalized ranking is carried out on mechanisms and users in real time, the users can conveniently check various indexes, the corresponding working level and defects can be objectively known through the grading of the indexes, the management efficiency can be improved, the management labor is saved, and the working enthusiasm of workers is stimulated.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Other figures can be derived from these figures.
FIG. 1 is a diagram of an exemplary system architecture to which embodiments of the present application may be applied;
FIG. 2 is a flowchart of an embodiment of a big data-based user behavior evaluation presentation method according to an embodiment of the present application;
FIG. 3 is a schematic structural diagram of an embodiment of a big data-based user behavior evaluation presentation apparatus according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an embodiment of a computer device in an embodiment of the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application.
It is noted that the terms "comprises," "comprising," and "having" and any variations thereof in the description and claims of this application and the drawings described above are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus. In the claims, the description and the drawings of the specification of the present application, relational terms such as "first" and "second", and the like, may be used solely to distinguish one entity/action/object from another entity/action/object without necessarily requiring or implying any actual such relationship or order between such entities/actions/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 application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below 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, 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the terminal devices 101, 102, 103 and the server 105. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the server 105 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The server 105 may be a server providing various services, such as a background server providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the method for evaluating and presenting user behavior based on big data provided in the embodiment of the present application is generally executed by a server/terminal device, and accordingly, the apparatus for evaluating and presenting user behavior based on big data is generally disposed in the server/terminal device.
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
The scoring assessment results are counted and displayed in a list mode, and the scoring assessment results can be conveniently checked by staff of each position of an enterprise. The scoring list of the business operation personnel, which is a survey person, is divided into two modules, namely a core index list and a vehicle cost index list.
The core index list includes: the comprehensive ranking list, the NPS achievement rate list, the 3A list, the Ten thousand-day payment rate list, the pending inventory proportion list and the Ten thousand-day period list.
The car cost list includes: the method comprises the steps of determining a loss one-time passing rate list, determining a loss accessory custom ratio list, determining a loss working hour custom ratio list, determining a loss correction deviation list of accessories and determining a loss quality comprehensive evaluation list.
Wherein, the parameters in the comprehensive ranking list comprise: comprehensive rating, NPS achievement rate, 3A achievement in this month, payment rate in July under ten thousand, total press period in ten thousand, pending inventory proportion, etc.
The following are definitions of some of the parameters:
the Net Promoter Score (NPS) achievement rate is a value obtained by dividing the sum of revisits by a value obtained by subtracting a value of 6 points from a value of 8 points or more, which is assigned to the customer after the customer is revisited.
3A, 3A means 3 different indexes, and the rate of the solution is one of the indexes.
The payment rate of ten thousand days is the proportion of the number of cases which are completed within 7 days from the case report to the case payment in the number of cases less than ten thousand yuan.
The ten-thousand complete case periods refer to the average case ending duration of less than ten thousand cases.
The pending inventory refers to the amount of cases that remain at the operator and have not yet been finalized.
And (3) setting loss accessories, namely setting loss accessories, and if no standard accessories exist in the loss process, inputting the self-defined accessories by an operator, wherein the proportion of the part of case quantity in the whole loss-setting case quantity is larger.
And (3) setting loss working hours by self, and in the process of setting loss, if no standard accessory exists, inputting the self-defined accessory by an operator, wherein the processing time of the part of case quantity accounts for the processing time of the whole loss setting case quantity.
And (4) determining the positive deviation of the damage of the accessory, and finally determining the difference value between the final finalized sum and the reported sum after the damage is determined.
With continuing reference to fig. 2, a flowchart of an embodiment of the big data based user behavior evaluation presentation method in an embodiment of the present application is shown. The big data-based user behavior evaluation display method comprises the following steps:
step 201: 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.
And the scoring instruction is used for scoring the user index of the object to be scored through the scoring index to be called.
When the user index of the object to be scored is scored, generally, the scoring is performed by time intervals, for example, when an enterprise evaluates the technical index and the performance index of a business operator, the business data of the operator in a certain time interval of a month, a certain quarter or a year and the like needs to be selected, and the scoring is calculated after the business data is analyzed.
The range of the objects to be scored can be set individually according to the needs of the enterprise, for example, the organizations can be divided according to different organizations under the enterprise flag, and the personnel can be divided according to different groups. Therefore, the function of displaying the scoring result of the object to be scored in a personalized manner by the list is realized.
In the embodiment of the application, an electronic device (for example, the server/terminal device shown in fig. 1) on which the big data based user behavior evaluation presentation method operates may receive a scoring instruction sent by a user through a wired connection manner or a wireless connection manner. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.
Step 202: 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 includes at least one scoring factor.
In an 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 parameters used for evaluating a user index.
Each of the scoring indexes can be regarded as a group of scoring factors, and at least one scoring factor is included in the group of scoring factors. The multiple scoring factors can be combined to form different scoring indexes for evaluating different user indexes, and different scoring indexes can be formed through different combinations and used for evaluating a same user index respectively, namely, different scoring indexes can be used for evaluating the same user index.
In this embodiment of the application, before the step 201 of receiving a scoring instruction, the method for presenting user behavior evaluation based on big data further includes the steps of: 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.
The index parameter template comprises a plurality of scoring indexes, namely a plurality of groups of scoring factors, required and appropriate parameters are selected in advance to serve as the scoring factors of each scoring index, the parameters and the number of the scoring factors in each group of scoring factors are preset, and the parameters and the number of the scoring factors can be adjusted and optimized according to actual needs.
By configuring the index parameter template in advance, different scoring indexes can be set for different scoring scenes, and flexible application of the scoring indexes is realized, so that different users can conveniently realize optimal setting of scoring lists according to different requirements.
In a specific implementation manner of the embodiment of the present application, in the step of configuring the index parameter template, the method for displaying user behavior evaluation based on big data further includes:
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.
Since one scoring index may be composed of one or more other scoring indexes, the levels of the scoring indexes may be distinguished. If the minimum score index is a first-level score index, the higher-level score index may include several lower-level score indexes. The high level scoring index comprises a number of low level scoring indices and is essentially a number of sets of scoring factors comprising a number of low level scoring indices.
If the secondary scoring index includes two primary scoring indexes. If the user index is to be scored through the second-level index, the two first-level scoring indexes need to be calculated first, and then the second-level scoring index is calculated through a certain calculation rule such as weight distribution based on the two first-level scoring indexes.
Specifically, in the parameters mentioned in the scoring list of the surveyor, the comprehensive score, the NPS achievement rate, 3A, the seven-day payment rate, the pending inventory ratio, the complete plan period, the loss assessment one-time pass rate, the loss assessment accessory self-defined ratio, the loss assessment time self-defined ratio, the accessory loss assessment positive deviation and the loss assessment quality comprehensive evaluation can be used as scoring indexes, wherein the comprehensive score can be used as a secondary scoring index, and the rest can be used as a primary scoring index.
Different grades are set for different scoring indexes, the categories of the scoring indexes can be divided more reasonably, and the structure and the importance of the scoring indexes are distinguished conveniently.
After the scoring instruction is received, calling corresponding data from a database through a big data technology to respond to the scoring instruction so as to complete the execution of the scoring instruction, and thus the statistical data required by us can be obtained.
Step 203: and 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.
In this embodiment of the application, before the step of calling the preset scoring formula template, the method for showing user behavior evaluation based on big data further includes: 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.
The value of the scoring index represents the score of the user index. Different scoring indexes can be used for evaluating the same user index, and under the condition that the same scoring index has various scoring factor combinations, one scoring index corresponds to different scoring formulas, so that the required scoring formula needs to be further selected for scoring after the scoring formula is matched according to the scoring indexes. The specific selection principle can be selected according to the data acquisition difficulty of the scoring factors or the number of the scoring factors or further according to the requirements of the user, and the user generally tends to select a scoring formula with lower data acquisition difficulty and smaller number of the scoring factors.
Different scoring formulas are configured in advance according to setting requirements for scoring indexes of different users and are recorded in the scoring formula template for storage, and the scoring formula template can be updated at any time so as to conveniently adapt to the requirements of the users. Therefore, different scoring formulas are adopted for each mechanism and operation post in the enterprise to carry out matching calculation, and a scoring list which is individually defined according to the mechanism and the post is realized, so that the statistical requirements of some companies can be met more closely, and a better statistical effect is obtained.
In a specific implementation manner of the embodiment of the present application, taking a score of an operator as a surveyor as an example, a comprehensive score of the surveyor is regarded as 1 three-level score index, and the three-level score index includes: 5 secondary grade scoring indexes of service, timeliness, operation quality, wind control and productivity. The service scoring index includes: NPS and complaint amount, 2 first grade scoring indexes in total; the age scoring index comprises: 4 first-level scoring indexes including ten thousand 7-day payment rate, five thousand 1-day payment rate, plan settlement rate and ten thousand complete plan periods; the job quality scoring index includes: the two-core reduction rate, the two-core return rate, the vehicle-uniform paint surface and the vehicle-uniform accessories are totally 4 first-level scoring indexes; the wind control scoring indexes comprise: investigating loss sum and manual upgrading success rate, wherein the two indexes are 2 first-level scoring indexes; productivity includes workload, and 1 grade-one scoring index is provided.
After the weights are distributed to all the grading indexes, the calculation formula of the three-level grading index of the comprehensive score of a specific surveyor is set as follows:
the surveyor's integrated score ═ 25% + (NPS: + 40% complaint: + 25% + (7 days payment rate under ten thousand: + 20% payment rate five thousand 1 days: + 30% end rate + 30% full period:)%) 20% + (two nuclear core loss rate: + 20% two nuclear return rate + 30% oil-equalizing vehicle finish + 30% vehicle-equalizing vehicle fittings). +%) 15% + (loss reduction amount 80% + 20% manual extraction success rate) + 15% + (work amount 100%) + 25% + (work amount%
The calculation formula based on the scoring factor corresponding to the first-level scoring index is omitted, and the calculation formula is a better calculation formula for the comprehensive scoring index of the surveyor.
Step 204: and counting the calculation result of the numerical value of the scoring index, and ranking according to the calculation result to generate a list.
The ranking rule of the list can be ranked according to the order or the reverse order of the numerical values calculated by the scoring indexes. If the values are the same, the ranking may be in order of the initials of the object names. When the first three and the last three in the list need to enhance the prompting effect, the first three and the last three in the list can be highlighted in a more prominent mode such as color or font size, so as to play a role in exciting or warning the related objects.
Step 205: 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.
When a user requests to check the relevant data of the list, the list checking request is sent to the server side, and after the server side receives the list checking request, the corresponding list is sent to the display board for the user to check.
In this embodiment of the application, after the step of ranking and generating a list according to the numerical value, the method for showing user behavior evaluation based on big data further includes:
and setting authority dimensionality of the list so as to set different access authorities for different users.
Employees in different positions in an enterprise have different authorities, and different authorities need to be set for different employees, so that the data security of the list is managed more reasonably.
In this embodiment of the application, the list further includes statistical data of scoring factors, and therefore the step of setting the authority dimension of the list may further include: different access rights are set for different scoring factors.
The number of the generated list is not limited, and a plurality of lists can be generated simultaneously. And the list records not only the values of the scoring indexes, but also the values of the scoring factors, for example, the list can be displayed after recording the values of the scoring indexes and the scoring factors under the scoring indexes in a list form. The viewed user can select the viewed content as required, so that the scoring result of the user index can be better understood.
And the permission dimensions of the list include: and generating different authorities according to different lists, and setting different authorities according to different scoring factors under the same list.
When different rights are generated according to different lists, the region size can be taken as an example, the region size is divided into dimensions of the country, provinces, cities and the like, and different levels are set for users with different rights.
When different weights are set for different scoring factors under the same list, the user group level can be used as a division rule, for example, the division rule is as follows: an operator, a group leader, a primary organization administrator, a secondary organization administrator, a general administrator, etc. And sequentially endowing viewing permissions for different scoring factors according to the management requirements of enterprises.
In an implementation manner of the embodiment of the present application, the step 205 includes: 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.
According to the user behavior evaluation display method based on the big data, the preset template is called, the big data statistics mode is utilized, personalized ranking is conducted on the mechanism and the user in real time, the user can conveniently check all indexes, the corresponding working level and the corresponding defects can be objectively known through the grading of all the indexes, the management efficiency can be improved, the management manpower is saved, and the working enthusiasm of workers is stimulated.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
With further reference to fig. 3, fig. 3 is a schematic structural diagram illustrating an embodiment of a big data-based user behavior evaluation presentation apparatus according to an embodiment of the present application. As an implementation of the method shown in fig. 2, the present application provides an embodiment of a big data-based user behavior evaluation presentation apparatus, where the embodiment of the apparatus corresponds to the embodiment of the method shown in fig. 2, and the apparatus may be specifically applied to various electronic devices.
As shown in fig. 3, the big data-based user behavior evaluation display device according to this embodiment includes:
a receiving module 301; for receiving scoring instructions.
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.
A first response module 302; the system comprises a scoring instruction, a preset index parameter template, a corresponding scoring index and a database, wherein the scoring instruction is used for responding to the scoring instruction, calling the preset index parameter template, calling the corresponding scoring index from the index parameter template based on the scoring index to be called, and further acquiring the statistical data of the object to be scored in the time period to be acquired from the database according to the scoring factor included in the scoring index; wherein each scoring index includes at least one scoring factor.
A second response module 303; the system comprises a score formula template and a statistic data processing module, wherein the score formula template is used for calling a preset score formula template, matching the score index with a score formula in the score formula template, and calculating the value of the score index according to the matched score formula and the statistic data.
A generation module 304; and the calculation result is used for counting the numerical value of the scoring index, and ranking according to the calculation result to generate a list.
A sending module 305; the system is used for receiving a list viewing request of a user and sending a corresponding list for the user to view in response to the list viewing request
In this embodiment of the present application, the device for evaluating and displaying user behavior based on big data further includes: and a parameter setting module. The parameter setting module is used for configuring the index parameter template so as to set a plurality of grading indexes in the index parameter template and set a plurality of grading factors for each grading index.
In an implementation manner of the embodiment of the present application, the parameter setting module is further configured to set at least one level for the plurality of scoring 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.
In this embodiment of the present application, the device for evaluating and displaying user behavior based on big data further includes: and a formula setting module. The formula setting module is used for 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.
In this embodiment of the present application, the device for evaluating and displaying user behavior based on big data further includes: and a permission configuration module. The permission configuration module is used for setting permission dimensionality of the list so as to set different access permissions for different users.
In a specific implementation manner of the embodiment of the present application, the permission configuration module is further configured to set different access permissions for different scoring factors.
In a specific implementation manner of the embodiment of the application, the sending module 305 is configured to, after receiving a list viewing request of a user, match an access right corresponding to the user in the right dimension, and send specified content in the list to the user for the user to view based on the access right.
The user behavior evaluation display device based on the big data carries out personalized ranking on mechanisms and users in real time by calling the preset template and utilizing a big data statistics mode, is convenient for the users to check all indexes, objectively knows corresponding working levels and defects through the grading of all indexes, can improve management efficiency, saves management manpower, and stimulates the working enthusiasm of workers.
In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 4, fig. 4 is a block diagram of a basic structure of a computer device according to the present embodiment.
The computer device 6 comprises a memory 61, a processor 62, a network interface 63 communicatively connected to each other via a system bus. It is noted that only a computer device 6 having components 61-63 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable gate array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 61 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an 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 Media Card (SMC), a Secure Digital (SD) Card, a flash Card (FlashCard), and the like, which are provided on the computer device 6. Of course, the memory 61 may also comprise both an internal storage unit of the computer device 6 and an external storage device thereof. In this embodiment, the memory 61 is generally used for storing an operating system installed in the computer device 6 and various types of application software, such as program codes of a big data-based user behavior evaluation presentation method. Further, the memory 61 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 62 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The 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 a program code stored in the memory 61 or process data, for example, run a program code of the big data based user behavior evaluation presentation method.
The network interface 63 may comprise a wireless network interface or a wired network interface, and the network interface 63 is typically used for establishing a communication connection between the computer device 6 and other electronic devices.
The present application further provides another embodiment, which is to provide a computer-readable storage medium, where a big data-based user behavior evaluation presentation program is stored, where the big data-based user behavior evaluation presentation program is executable by at least one processor, so as to cause the at least one processor to execute the steps of the big data-based user behavior evaluation presentation method as described above.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
In the above embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is merely a logical division, and other divisions may be realized in practice, for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed.
The modules or components may or may not be physically separate, and the components shown as modules or components may or may not be physical modules, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules or components can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The present application is not limited to the above-mentioned embodiments, the above-mentioned embodiments are preferred embodiments of the present application, and the present application is only used for illustrating the present application and not for limiting the scope of the present application, it should be noted that, for a person skilled in the art, it is still possible to make several improvements and modifications to the technical solutions described in the foregoing embodiments or to make equivalent substitutions for some technical features without departing from the principle of the present application. All equivalent structures made by using the contents of the specification and the drawings of the present application can be directly or indirectly applied to other related technical fields, and the same should be considered to be included in the protection scope of the present application.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All other embodiments that can be obtained by a person skilled in the art based on the embodiments in this application without any creative effort and all equivalent structures made by using the contents of the specification and the drawings of this application can be directly or indirectly applied to other related technical fields and are within the scope of 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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