CN110348703B - Data processing method and device based on user behavior portraits and electronic equipment - Google Patents
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Abstract
The invention discloses a data processing method and device based on user behavior portraits and electronic equipment. The method comprises the following steps: identifying user identity information, target acquisition information and data to be processed contained in an evaluation report acquisition instruction; analyzing the acquired target behavior record information corresponding to the user identity information to obtain an evaluation report; selecting a target score matched with the key evaluation type from the evaluation report, and generating an evaluation report corresponding to the user identity information according to the key evaluation type and the target score; determining authority information corresponding to the data processing type according to the evaluation report; and processing the data to be processed according to the generated data processing mode matched with the authority information and the data processing type. According to the method, based on the user behavior portrayal technology in the user portrayal of data analysis, the data processing mode can be determined according to the daily behaviors of the user. In conclusion, the accuracy of data processing is improved.
Description
Technical Field
The present invention relates to the field of data analysis technologies, and in particular, to a data processing method and apparatus based on user behavior portraits, and an electronic device.
Background
Currently, when a data processing device processes data, the type of the data is usually determined, and then a corresponding data processing mode is determined according to the type of the data, and the determined data processing mode is utilized to process the data. However, in practice, it is found that when the data processing apparatus processes data of different users, the same data processing manner may be used to process the data of the same type of different users, and therefore, the data processing manner cannot be adopted according to different users, and a data processing result that is relatively matched with the user cannot be obtained, so that the accuracy of data processing is low.
Disclosure of Invention
In order to solve the technical problem of low accuracy of data processing in the related art, the invention provides a data processing method and device based on user behavior portraits and electronic equipment.
A method for processing data based on user behavior portraits, the method comprising:
when an input evaluation report acquisition instruction is detected, identifying user identity information, target acquisition information and data to be processed contained in the evaluation report acquisition instruction, wherein the target acquisition information contains key evaluation types and data processing types;
acquiring pre-stored target behavior record information corresponding to the user identity information;
analyzing the target behavior record information to obtain an evaluation report corresponding to the user identity information, wherein the evaluation report comprises scores corresponding to evaluation types;
selecting a target score matched with the key evaluation type from the evaluation report, and generating an evaluation report corresponding to the user identity information according to the key evaluation type and the target score;
determining authority information corresponding to the data processing type according to the evaluation report;
generating a data processing mode matched with the authority information and the data processing type, and processing the data to be processed according to the data processing mode.
A user behavior representation-based data processing apparatus, the apparatus comprising:
the identification unit is used for identifying user identity information, target acquisition information and data to be processed contained in the evaluation report acquisition instruction when the input evaluation report acquisition instruction is detected, wherein the target acquisition information contains key evaluation types and data processing types;
the acquisition unit is used for acquiring pre-stored target behavior record information corresponding to the user identity information;
the analysis unit is used for obtaining an evaluation report corresponding to the user identity information by analyzing the target behavior record information, wherein the evaluation report comprises scores corresponding to evaluation types;
the generation unit is used for selecting a target score matched with the key evaluation type from the evaluation report, and generating an evaluation report corresponding to the user identity information according to the key evaluation type and the target score;
the determining unit is used for determining authority information corresponding to the data processing type according to the evaluation report;
and the processing unit is used for generating a data processing mode matched with the authority information and the data processing type and processing the data to be processed according to the data processing mode.
An electronic device, the electronic device comprising:
a processor;
a memory having stored thereon computer readable instructions which, when executed by the processor, implement a method as described above.
A computer-readable storage medium, characterized in that it stores a computer program that causes a computer to perform the method as described above.
The technical scheme provided by the embodiment of the invention can comprise the following beneficial effects:
when an input evaluation report acquisition instruction is detected, identifying user identity information, target acquisition information and data to be processed contained in the evaluation report acquisition instruction, wherein the target acquisition information contains key evaluation types and data processing types; acquiring pre-stored target behavior record information corresponding to user identity information; analyzing the target behavior record information to obtain an evaluation report corresponding to the user identity information, wherein the evaluation report comprises scores corresponding to the evaluation types; selecting a target score matched with the key evaluation type from the evaluation report, and generating an evaluation report corresponding to the user identity information according to the key evaluation type and the target score; determining authority information corresponding to the data processing type according to the evaluation report; generating a data processing mode matched with the authority information and the data processing type, and processing the data to be processed according to the data processing mode.
According to the method, based on the user behavior portrayal technology in the user portrayal of data analysis, the score of each evaluation type of the user can be obtained by analyzing the behavior information related to the evaluation of the user, further, the evaluation report of the student user can be generated according to the key evaluation type required to be checked by the target user, so that the generated evaluation report is related to the daily evaluation behavior of the user, and a final data processing mode is obtained according to the generated evaluation report and the data processing type, so that the mode of carrying out data processing on the data to be processed is related to the behavior of the user. In conclusion, the accuracy of data processing is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a schematic diagram illustrating a network scenario in which a user behavior portrayal-based data processing method is applicable, in accordance with an exemplary embodiment;
FIG. 2 is a flowchart illustrating a method of data processing based on user behavior portraits, according to an exemplary embodiment;
FIG. 3 is a flowchart illustrating a method of data processing based on a user behavior representation, according to another exemplary embodiment;
FIG. 4 is a block diagram illustrating a user behavior representation-based data processing apparatus in accordance with an exemplary embodiment;
FIG. 5 is a block diagram illustrating a user behavior portrayal-based data processing apparatus according to another exemplary embodiment.
Detailed Description
Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the invention. Rather, they are merely examples of apparatus and methods consistent with aspects of the invention as detailed in the accompanying claims.
The implementation environment of the invention can be a portable mobile device, such as a smart phone, a tablet computer, a desktop computer. The image stored in the portable mobile device may be: an image downloaded from the internet; images received through a wireless connection or a wired connection; the obtained image is shot by a camera built in the camera.
Fig. 1 is a schematic diagram of an apparatus according to an example embodiment. The apparatus 100 may be the portable mobile device described above. As shown in fig. 1, the apparatus 100 may include one or more of the following components: a processing component 102, a memory 104, a power supply component 106, a multimedia component 108, an audio component 110, a sensor component 114, and a communication component 116.
The processing component 102 generally controls overall operation of the device 100, such as operations associated with display, telephone calls, data communications, camera operations, and recording operations, among others. The processing component 102 may include one or more processors 118 to execute instructions to perform all or part of the steps of the methods described below. Further, the processing component 102 can include one or more modules to facilitate interactions between the processing component 102 and other components. For example, the processing component 102 may include a multimedia module for facilitating interaction between the multimedia component 108 and the processing component 102.
The memory 104 is configured to store various types of data to support operations at the apparatus 100. Examples of such data include instructions for any application or method operating on the device 100. The Memory 104 may be implemented by any type of volatile or non-volatile Memory device or combination thereof, such as static random access Memory (Static Random Access Memory, SRAM), electrically erasable Programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM), erasable Programmable Read-Only Memory (Erasable Programmable Read Only Memory, EPROM), programmable Read-Only Memory (PROM), read-Only Memory (ROM), magnetic Memory, flash Memory, magnetic disk, or optical disk. Also stored in the memory 104 are one or more modules configured to be executed by the one or more processors 118 to perform all or part of the steps in the methods shown below.
The power supply assembly 106 provides power to the various components of the device 100. The power components 106 may include a power management system, one or more power sources, and other components associated with generating, managing, and distributing power for the device 100.
The multimedia component 108 includes a screen between the device 100 and the user that provides an output interface. In some embodiments, the screen may include a liquid crystal display (Liquid Crystal Display, LCD for short) and a touch panel. If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from a user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensor may sense not only the boundary of a touch or slide action, but also the duration and pressure associated with the touch or slide operation. The screen may also include an organic electroluminescent display (Organic Light Emitting Display, OLED for short).
The audio component 110 is configured to output and/or input audio signals. For example, the audio component 110 includes a Microphone (MIC) configured to receive external audio signals when the device 100 is in an operational mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signals may be further stored in the memory 104 or transmitted via the communication component 116. In some embodiments, the audio component 110 further comprises a speaker for outputting audio signals.
The sensor assembly 114 includes one or more sensors for providing status assessment of various aspects of the device 100. For example, the sensor assembly 114 may detect an on/off state of the device 100, a relative positioning of the assemblies, the sensor assembly 114 may also detect a change in position of the device 100 or a component of the device 100, and a change in temperature of the device 100. In some embodiments, the sensor assembly 114 may also include a magnetic sensor, a pressure sensor, or a temperature sensor.
The communication component 116 is configured to facilitate communication between the apparatus 100 and other devices in a wired or wireless manner. The device 100 may access a Wireless network based on a communication standard, such as WiFi (Wireless-Fidelity). In one exemplary embodiment, the communication component 116 receives broadcast signals or broadcast related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 116 further includes a near field communication (Near Field Communication, NFC) module for facilitating short range communications. For example, the NFC module may be implemented based on radio frequency identification (Radio Frequency Identification, RFID) technology, infrared data association (Infrared Data Association, irDA) technology, ultra Wideband (UWB) technology, bluetooth technology, and other technologies.
In an exemplary embodiment, the apparatus 100 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated ASIC), digital signal processors, digital signal processing devices, programmable logic devices, field programmable gate arrays, controllers, microcontrollers, microprocessors or other electronic components for executing the methods described below.
FIG. 2 is a flowchart illustrating a method of data processing based on user behavior portraits, according to an exemplary embodiment. As shown in fig. 2, the method includes the following steps.
Step 201, when an input evaluation report acquisition instruction is detected, identifying user identity information, target acquisition information and data to be processed contained in the evaluation report acquisition instruction, wherein the target acquisition information contains key evaluation types and data processing types.
In the embodiment of the invention, the evaluation report acquisition instruction may be a credit report acquisition instruction, the user identity information may be a target user (such as a student) corresponding to the evaluation report, the target acquisition information may be an element of another target user needing to be checked with emphasis for checking the evaluation report of the target user, the other user may be an enterprise recruiting the target user, etc., the data to be processed may be data processed according to a proper data processing mode, the data processing type may be a type of a processing mode in which the data to be processed needs to be processed, the emphasis evaluation type may be an emphasis credit effect type, the credit report acquisition instruction may be input by a recruiter or a manager of the enterprise, etc., and the recruiter or the manager of the enterprise may judge whether the student meets the recruitment requirement of the enterprise according to the acquired credit report, thereby enabling the enterprise to have more basis for deciding whether to record the target student.
In the embodiment of the invention, the credit report acquisition instruction can comprise student information and enterprise information, and the student information can comprise identity information of students needing to acquire the credit report, so that the credit report generation device based on the user behavior portrait can determine the unique student user according to the student information. The enterprise information may include a key credit type that the enterprise is focused on to view, and the key credit type may be matched with the business nature of the enterprise. The credit effect type may include a class attendance type, an asset status type, an assignment completion type, and the like of the student.
Step 202, obtaining pre-stored target behavior record information corresponding to user identity information.
In the embodiment of the invention, all the behaviors related to the credit, which occur in the school, of the student can be recorded to obtain the behavior record information matched with the student, so that all the behaviors which occur in the past of the student can be obtained according to the user identity information of the student.
As an alternative embodiment, the method for acquiring the pre-stored target behavior record information corresponding to the user identity information may include the following steps:
reading an effective time period from target acquisition information, and acquiring pre-stored behavior record information corresponding to user identity information;
and selecting target behavior record information matched with the effective time period from the behavior record information.
According to the implementation mode, the behavior record information of a specific time period which the target user wants to view can be obtained according to the requirement of the target user, and then the evaluation report for the time period is generated, so that the generated evaluation report meets the requirement of the target user.
And 203, analyzing the target behavior record information to obtain an evaluation report corresponding to the user identity information, wherein the evaluation report comprises scores corresponding to the evaluation types.
In the embodiment of the invention, the evaluation report can be a credit report, all behavior information in the target behavior record information can be analyzed to obtain the credit generation effect of each piece of behavior information on the user, and then the score corresponding to each credit effect type related to the credit is obtained through calculation according to all the behavior information, so as to generate the credit report, and the credit report at least comprises the score of each credit effect type corresponding to the target behavior record information and the total credit score of the user obtained through calculation according to the score of each credit effect type.
And 204, selecting a target score matched with the key evaluation type from the evaluation report, and generating an evaluation report corresponding to the user identity information according to the key evaluation type and the target score.
In the embodiment of the invention, the evaluation report can be a credit report, and the credit condition of the student can be obtained according to the score analysis of each credit effect type in the credit report, for example, the score of a certain credit effect type of the student is higher, the quality of the student corresponding to the credit effect type with higher score can be better, the quality of the student corresponding to the credit effect type with lower score is poorer, and the credit report of the student is further generated. In addition, the credit report can be matched with the credit condition required by the enterprise according to the credit condition of the student to obtain the reference information whether the student is suitable for the enterprise, so that recruiters or management staff of the enterprise can assist in deciding whether to record the student according to the reference information.
And step 205, determining authority information corresponding to the data processing type according to the evaluation report.
In the embodiment of the invention, the authority of the data processing type can be determined according to the information in the evaluation report, for example, the information such as the function which can be called by the user when the user processes the data of the data processing type can be determined according to the credit level of the user in the evaluation report, and then the authority information corresponding to the data processing type is generated according to the information such as the function which can be called by the user.
And 206, generating a data processing mode matched with the authority information and the data processing type, and processing the data to be processed according to the data processing mode.
In the method described in fig. 2, the manner of performing data processing on the data to be processed can be related to the behavior of the user, so that the accuracy of data processing is improved. Furthermore, implementing the method described in FIG. 2, the generated assessment report may be made to conform to the needs of the target user.
FIG. 3 is a flowchart illustrating a method of data processing based on a user behavior representation, according to another exemplary embodiment. As shown in fig. 3, the method includes the following steps.
Step 301, when the evaluation behavior triggered by the user is detected, personal information of the user is obtained, and the evaluation behavior is analyzed to obtain an evaluation type generated by the evaluation behavior.
In the embodiment of the invention, the evaluation behavior can be credit behavior, and the credit behavior can comprise card punching in class, job submitting, school fee paying, dormitory entering and exiting and the like, so the embodiment of the invention is not limited. Each credit action can generate a corresponding credit effect, such as a credit effect when a student plays a card during a lesson, and the corresponding effect type of the credit effect can be a class attendance type, so that each credit action of a student user can be analyzed to obtain a utility effect type corresponding to each credit action.
Step 302, determining a basic score corresponding to the evaluation type.
As an alternative embodiment, the manner of determining the basic score corresponding to the evaluation type may include the following steps:
obtaining a standard score corresponding to the evaluation type, and determining an effect grade corresponding to the evaluation type;
and calculating according to the standard score and the effect grade to obtain a basic score corresponding to the evaluation type.
According to the embodiment, the standard score can be adjusted according to the effect grade on the basis of the basic score corresponding to the evaluation type, so that the basic score corresponding to the current evaluation behavior of the user is obtained, and the calculation grade of the basic score accords with the actual situation.
Step 303, generating current behavior record information containing the evaluation behavior, the evaluation type and the basic score, and storing the current behavior record information in association with the personal information.
In the embodiment of the present invention, the above steps 301 to 303 are implemented, so that the behavior of the student user can be detected at any time, if the behavior is an evaluation behavior, the evaluation behavior can be evaluated, so as to generate behavior record information of the evaluation behavior, and the behavior record information is stored, so that an evaluation report can be generated according to the behavior record information at any time later.
Step 304, when an input evaluation report acquisition instruction is detected, identifying user identity information, target acquisition information and data to be processed contained in the evaluation report acquisition instruction, wherein the target acquisition information contains key evaluation types and data processing types.
Step 305, obtaining pre-stored target behavior record information corresponding to the user identity information.
And 306, analyzing the target behavior record information to obtain at least one target evaluation type, and acquiring a target basic score matched with the target evaluation type from the target behavior record information.
In the embodiment of the invention, because the target behavior record information contains the basic score of each item target evaluation type, each item target evaluation type can be identified from the target behavior record information, and then the basic score corresponding to each item target evaluation type is determined from the target behavior record information.
Step 307, determining the weight value of the target evaluation type according to the priority corresponding to the target evaluation type.
In the embodiment of the invention, the priority corresponding to each evaluation type can be preset, the evaluation type can be analyzed, the influence of each evaluation type on the credit evaluation of the user can be determined, the priority of the evaluation type with larger influence can be determined as the priority of the higher level, and the priority of the evaluation type with smaller influence can be determined as the priority of the lower level, so that the weight value of the target evaluation type can be related to the priority of the target evaluation type, the weight value of the target evaluation type with higher priority can be larger, and the weight value of the target evaluation type with lower priority can be smaller. In addition, the weight value of the target evaluation type may be irrelevant to the priority of the target evaluation type, which is not limited in the embodiment of the present invention.
As an alternative embodiment, the manner of determining the weight value of the target evaluation type according to the priority corresponding to the target evaluation type may include the following steps:
counting the first number of the priorities corresponding to the target evaluation types, and counting the second number of the target evaluation types corresponding to the priorities;
dividing the standard weight value by the first quantity to obtain sub weight values corresponding to the priorities;
dividing the sub weight values by the second number of each priority in turn to obtain weight values of the target evaluation types corresponding to each priority, wherein the weight values of the target evaluation types with the same priority are the same.
According to the implementation mode, the standard weight values can be equally divided according to the number of the existing priorities, and the target evaluation types under each priority are further equally divided to obtain the weight values corresponding to the target evaluation types, so that the determination of the weight values of the target evaluation types is more standardized.
And 308, calculating to obtain the score of the target evaluation type according to the target basic score and the weight value.
In the embodiment of the invention, the product obtained by multiplying the target basic score corresponding to the target evaluation type and the weight value corresponding to the same target evaluation type can be determined as the score of the target evaluation type.
And 309, calculating the sum of the scores of the target evaluation types to obtain a total evaluation score.
And 310, generating an evaluation report corresponding to the user identity information according to the scores of the target evaluation types and the total evaluation scores.
In the embodiment of the present invention, the steps 306 to 310 are implemented, where the weight value of the target evaluation type may be determined according to the priority corresponding to the target evaluation type, and the score of each item target evaluation type is obtained by calculating according to the weight value and the basic score, and then the total credit value corresponding to the student information is obtained by calculating, so as to generate an evaluation report corresponding to the student information, so that the generated evaluation report is more accurate.
And 311, selecting a target score matched with the key evaluation type from the evaluation report, and generating an evaluation report corresponding to the user identity information according to the key evaluation type and the target score.
And step 312, determining authority information corresponding to the data processing type according to the evaluation report.
Step 313, generating a data processing mode matched with the authority information and the data processing type, and processing the data to be processed according to the data processing mode.
And step 314, when the error correction instruction input by the user is detected, the error correction type and correct behavior record information contained in the error correction instruction are acquired.
In the embodiment of the invention, students can check own behavior record information, and if the students find that errors occur in the behavior record information of the students, the error correction instructions can be input through the data processing device based on the user behavior portraits so as to correct the error behavior record information.
And step 315, generating an error correction application according to the error correction type and the correct behavior record information, and storing the error correction application so as to enable a manager to process the error correction application.
In the embodiment of the invention, the steps 314 to 315 are implemented, so that when the student finds that the own behavior record information has a record error, the student can timely put forward an error correction application and upload correct behavior record information, so that a manager can timely process the error correction application, and the accuracy of the stored behavior record information of the student is ensured.
Alternatively, steps 314-315 may be performed before or after any of steps 301-313 without affecting embodiments of the present invention.
In the method described in fig. 3, the manner of performing data processing on the data to be processed can be related to the behavior of the user, so that the accuracy of data processing is improved. In addition, the method described in fig. 3 is implemented, so that the calculation level of the basic score can be matched with the actual situation. Furthermore, by implementing the method described in fig. 3, an evaluation report can be generated from the behavior record information at any time. Furthermore, the determination of the weight value of the target evaluation type can be made more standardized by implementing the method described in fig. 3. In addition, by implementing the method described in fig. 3, the generated evaluation report can be more accurate. In addition, the method described in fig. 3 is implemented, so that the accuracy of the stored behavior record information of the students is ensured.
The following are device embodiments of the present invention.
FIG. 4 is a block diagram illustrating a user behavior portrayal-based data processing apparatus in accordance with an exemplary embodiment. As shown in fig. 4, the apparatus includes:
the identifying unit 401 is configured to identify, when an input evaluation report acquisition instruction is detected, user identity information, target acquisition information, and data to be processed, which are included in the evaluation report acquisition instruction, the target acquisition information including a key evaluation type and a data processing type.
An obtaining unit 402, configured to obtain target behavior record information corresponding to the user identity information obtained by the identifying unit 401, where the target behavior record information is stored in advance.
As an alternative embodiment, the manner of acquiring the target behavior record information corresponding to the user identity information stored in advance by the acquiring unit 402 may specifically be:
reading an effective time period from target acquisition information, and acquiring pre-stored behavior record information corresponding to user identity information;
and selecting target behavior record information matched with the effective time period from the behavior record information.
According to the implementation mode, the behavior record information of a specific time period which the target user wants to view can be obtained according to the requirement of the target user, and then the evaluation report for the time period is generated, so that the generated evaluation report meets the requirement of the target user.
An analysis unit 403, configured to obtain an evaluation report corresponding to the user identity information obtained by the identification unit 401 by analyzing the target behavior record information obtained by the obtaining unit 402, where the evaluation report includes a score corresponding to the evaluation type.
A generating unit 404, configured to select, from the evaluation report obtained by the analyzing unit 403, a target score matching the key evaluation type obtained by the identifying unit 401, and generate an evaluation report corresponding to the user identity information according to the key evaluation type and the target score.
A determining unit 405, configured to determine rights information corresponding to the data processing type according to the evaluation report generated by the generating unit 404.
And a processing unit 406, configured to generate a data processing manner matched with the authority information and the data processing type determined by the determining unit 405, and process the data to be processed according to the data processing manner.
It can be seen that, in the apparatus described in fig. 4, the manner of performing data processing on the data to be processed can be related to the behavior of the user, so that the accuracy of data processing is improved. In addition, in the apparatus depicted in FIG. 4, the generated assessment report may be made to conform to the needs of the enterprise.
FIG. 5 is a block diagram illustrating a user behavior portrayal-based data processing apparatus according to another exemplary embodiment. The data processing device based on the user behavior portrayal shown in fig. 5 is obtained by optimizing the data processing device based on the user behavior portrayal shown in fig. 4. In comparison with the user behavior representation-based data processing apparatus shown in fig. 4, the user behavior representation-based data processing apparatus shown in fig. 5 may further include:
the behavior analysis unit 407 is configured to, when the identification unit 401 detects an input evaluation report acquisition instruction, identify user identity information, target acquisition information, and data to be processed included in the evaluation report acquisition instruction, and when an evaluation behavior triggered by a user is detected, acquire personal information of the user, and analyze the evaluation behavior to obtain an evaluation type generated by the evaluation behavior.
The score determining unit 408 is configured to determine a basic score corresponding to the evaluation type obtained by the behavior analyzing unit 407.
As an alternative embodiment, the manner in which the score determining unit 408 determines the basic score corresponding to the evaluation type may specifically be:
obtaining a standard score corresponding to the evaluation type, and determining an effect grade corresponding to the evaluation type;
and calculating according to the standard score and the effect grade to obtain a basic score corresponding to the evaluation type.
According to the embodiment, the standard score can be adjusted according to the effect grade on the basis of the basic score corresponding to the evaluation type, so that the basic score corresponding to the current evaluation behavior of the user is obtained, and the calculation grade of the basic score accords with the actual situation.
A first storage unit 409 for generating current behavior record information including the evaluation behavior obtained by the behavior analysis unit 407, the evaluation type, and the basic score determined by the score determination unit 408, and storing the current behavior record information in association with the personal information obtained by the behavior analysis unit 407.
In the embodiment of the invention, the behavior of the student user can be detected at any time, if the behavior is the evaluation behavior, the evaluation behavior can be evaluated, so that the behavior record information of the evaluation behavior is generated, and the behavior record information is stored, so that an evaluation report can be generated at any time according to the behavior record information.
As an alternative embodiment, the analysis unit 403 of the user behavior portrayal-based data processing apparatus shown in fig. 5 may include:
a first analysis subunit 4031, configured to analyze the target behavior record information to obtain at least one target evaluation type, and obtain a target basic score matched with the target evaluation type from the target behavior record information;
a first determining subunit 4032, configured to determine a weight value of the target evaluation type according to the priority corresponding to the target evaluation type obtained by the first analyzing subunit 4031;
a first calculating subunit 4033, configured to calculate a score of the target evaluation type according to the target basic score obtained by the first analyzing subunit 4031 and the weight value determined by the first determining subunit 4032;
a second calculating subunit 4034, configured to calculate a sum of the scores of the target evaluation types obtained by the first calculating subunit 4033, to obtain a total evaluation score;
a first generating subunit 4035, configured to generate an evaluation report corresponding to the user identity information obtained by the identifying unit 401 according to the score of each target evaluation type obtained by the first calculating subunit 4033 and the total evaluation score obtained by the second calculating subunit 4034.
According to the implementation mode, the weight value of the target evaluation type can be determined according to the priority corresponding to the target evaluation type, the score of each item target evaluation type is calculated according to the weight value and the basic score, and then the total evaluation value corresponding to the student information is calculated, so that an evaluation report corresponding to the student information is generated, and the generated evaluation report is more accurate.
As an optional implementation manner, the first determining subunit 4032 may specifically determine the weight value of the target evaluation type according to the priority corresponding to the target evaluation type:
counting the first number of the priorities corresponding to the target evaluation types, and counting the second number of the target evaluation types corresponding to the priorities;
dividing the standard weight value by the first quantity to obtain sub weight values corresponding to the priorities;
dividing the sub weight values by the second number of each priority in turn to obtain weight values of the target evaluation types corresponding to each priority, wherein the weight values of the target evaluation types with the same priority are the same.
According to the implementation mode, the standard weight values can be equally divided according to the number of the existing priorities, and the target evaluation types under each priority are further equally divided to obtain the weight values corresponding to the target evaluation types, so that the determination of the weight values of the target evaluation types is more standardized.
As an alternative embodiment, the user behavior portrayal-based data processing apparatus shown in FIG. 5 may further comprise:
an information obtaining unit 410, configured to obtain, when an error correction instruction input by a user is detected, an error correction type and correct behavior record information included in the error correction instruction;
the storage unit 411 is configured to generate an error correction application according to the error correction type and the correct behavior record information acquired by the information acquisition unit 410, and store the error correction application, so that a manager can process the error correction application.
By implementing the implementation mode, when the student finds that the own behavior record information has a record error, the student can timely put forward an error correction application and upload the correct behavior record information, so that a manager can timely process the error correction application, and the accuracy of the stored behavior record information of the student is ensured.
It can be seen that, in the apparatus described in fig. 5, the manner of performing data processing on the data to be processed can be related to the behavior of the user, so that the accuracy of data processing is improved. In addition, in the apparatus described in fig. 5, the calculation level of the basic score may be made to fit the actual situation. In addition, in the apparatus described in fig. 5, the evaluation report can be generated from the behavior record information at any time. Further, in the apparatus described in fig. 5, the determination of the weight value of the target evaluation type can be made more standardized. In addition, in the device described in fig. 5, the generated evaluation report can be more accurate. In addition, in the apparatus described in fig. 5, the accuracy of the stored behavior record information of the student is ensured.
The invention also provides an electronic device, comprising:
a processor;
and a memory having stored thereon computer readable instructions which, when executed by the processor, implement a user behavior representation-based data processing method as previously described.
The electronic device may be the apparatus 100 shown in fig. 1.
In an exemplary embodiment, the present invention also provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements a data processing method based on user behavior portraits as shown before.
It is to be understood that the invention is not limited to the precise arrangements and instrumentalities shown in the drawings, which have been described above, and that various modifications and changes may be effected without departing from the scope thereof. The scope of the invention is limited only by the appended claims.
Claims (8)
1. A method for processing data based on user behavior portraits, the method comprising:
when an input evaluation report acquisition instruction is detected, identifying user identity information, target acquisition information and data to be processed contained in the evaluation report acquisition instruction, wherein the target acquisition information contains key evaluation types and data processing types;
acquiring pre-stored target behavior record information corresponding to the user identity information;
analyzing the target behavior record information to obtain an evaluation report corresponding to the user identity information, wherein the evaluation report comprises scores corresponding to evaluation types;
selecting a target score matched with the key evaluation type from the evaluation report, and generating an evaluation report corresponding to the user identity information according to the key evaluation type and the target score;
determining authority information corresponding to the data processing type according to the evaluation report;
generating a data processing mode matched with the authority information and the data processing type, and processing the data to be processed according to the data processing mode;
the step of obtaining the evaluation report corresponding to the user identity information by analyzing the target behavior record information comprises the following steps: analyzing the target behavior record information to obtain at least one target evaluation type, and obtaining a target basic score matched with the target evaluation type from the target behavior record information; counting the first number of the priorities corresponding to the target evaluation types, and counting the second number of the target evaluation types corresponding to the priorities; dividing the standard weight value by the first quantity to obtain sub weight values corresponding to the priorities; dividing the sub weight values by the second number of the priorities in turn to obtain weight values of the target evaluation types corresponding to the priorities, wherein the weight values of the target evaluation types with the same priorities are the same; calculating according to the target basic score and the weight value to obtain the score of the target evaluation type; calculating the sum of the scores of the target evaluation types to obtain a total evaluation score; and generating an evaluation report corresponding to the user identity information according to the score of each target evaluation type and the total evaluation score.
2. The method according to claim 1, wherein when the input evaluation report acquisition instruction is detected, before identifying the user identity information, the target acquisition information, and the data to be processed contained in the evaluation report acquisition instruction, the method further comprises:
when the evaluation behavior triggered by the user is detected, personal information of the user is obtained, and the evaluation behavior is analyzed to obtain an evaluation type generated by the evaluation behavior;
determining a basic score corresponding to the evaluation type;
generating current behavior record information containing the evaluation behavior, the evaluation type and the basic score, and storing the current behavior record information in association with the personal information.
3. The method of claim 2, wherein the determining the base score for the type of evaluation comprises:
obtaining a standard score corresponding to the evaluation type, and determining an effect grade corresponding to the evaluation type;
and calculating to obtain a basic score corresponding to the evaluation type according to the standard score and the effect grade.
4. A method according to claim 3, wherein the obtaining pre-stored target behavior record information corresponding to the user identity information comprises:
reading an effective time period from the target acquisition information, and acquiring pre-stored behavior record information corresponding to the user identity information;
and selecting target behavior record information matched with the effective time period from the behavior record information.
5. The method according to any one of claims 1-4, further comprising:
when an error correction instruction input by a user is detected, acquiring an error correction type and correct behavior record information contained in the error correction instruction;
generating an error correction application according to the error correction type and the correct behavior record information, and storing the error correction application so that a manager can process the error correction application.
6. A user behavior representation-based data processing apparatus, the apparatus comprising:
the identification unit is used for identifying user identity information, target acquisition information and data to be processed contained in the evaluation report acquisition instruction when the input evaluation report acquisition instruction is detected, wherein the target acquisition information contains key evaluation types and data processing types;
the acquisition unit is used for acquiring pre-stored target behavior record information corresponding to the user identity information;
the analysis unit is used for obtaining an evaluation report corresponding to the user identity information by analyzing the target behavior record information, wherein the evaluation report comprises scores corresponding to evaluation types;
the generation unit is used for selecting a target score matched with the key evaluation type from the evaluation report, and generating an evaluation report corresponding to the user identity information according to the key evaluation type and the target score;
the determining unit is used for determining authority information corresponding to the data processing type according to the evaluation report;
the processing unit is used for generating a data processing mode matched with the authority information and the data processing type and processing the data to be processed according to the data processing mode;
the step of obtaining the evaluation report corresponding to the user identity information by analyzing the target behavior record information comprises the following steps: analyzing the target behavior record information to obtain at least one target evaluation type, and obtaining a target basic score matched with the target evaluation type from the target behavior record information; counting the first number of the priorities corresponding to the target evaluation types, and counting the second number of the target evaluation types corresponding to the priorities; dividing the standard weight value by the first quantity to obtain sub weight values corresponding to the priorities; dividing the sub weight values by the second number of the priorities in turn to obtain weight values of the target evaluation types corresponding to the priorities, wherein the weight values of the target evaluation types with the same priorities are the same; calculating according to the target basic score and the weight value to obtain the score of the target evaluation type; calculating the sum of the scores of the target evaluation types to obtain a total evaluation score; and generating an evaluation report corresponding to the user identity information according to the score of each target evaluation type and the total evaluation score.
7. An electronic device, the electronic device comprising:
a processor;
a memory having stored thereon computer readable instructions which, when executed by the processor, implement the method of any of claims 1 to 5.
8. A computer readable storage medium, characterized in that it stores a computer program, which causes a computer to execute the method of any one of claims 1 to 5.
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| CN111738778B (en) * | 2020-07-20 | 2020-12-01 | 平安国际智慧城市科技股份有限公司 | User portrait generation method and device, computer equipment and storage medium |
| CN112101786A (en) * | 2020-09-15 | 2020-12-18 | 广东工贸职业技术学院 | Student data analysis method and device based on big data and computer equipment |
| CN113052434A (en) * | 2021-02-26 | 2021-06-29 | 一智科技(成都)有限公司 | Construction object portrait labeling method and system |
| CN113723990B (en) * | 2021-08-10 | 2022-07-15 | 苏州众言网络科技股份有限公司 | Information processing method for determining user value |
| CN116664014A (en) * | 2023-07-25 | 2023-08-29 | 临沂大学 | Comprehensive evaluation system and method for college student management |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106127522A (en) * | 2016-06-29 | 2016-11-16 | 苏州迈科网络安全技术股份有限公司 | Dig based on network data and analyze method and system according to the enterprise management condition of technology |
| CN106779856A (en) * | 2016-12-23 | 2017-05-31 | 广州帷策智能科技有限公司 | The method and device that big data customer value is evaluated |
| CN106934412A (en) * | 2015-12-31 | 2017-07-07 | 中国科学院深圳先进技术研究院 | A kind of user behavior sorting technique and system |
| WO2018120427A1 (en) * | 2016-12-29 | 2018-07-05 | 平安科技（深圳）有限公司 | Risk assessment method, apparatus, and device based on location service, and storage medium |
| CN108520045A (en) * | 2018-04-03 | 2018-09-11 | 平安健康保险股份有限公司 | The service response method and device of data |
| CN109784707A (en) * | 2019-01-04 | 2019-05-21 | 深圳壹账通智能科技有限公司 | Rating business credit method, apparatus, computer equipment and storage medium |
-
2019
- 2019-06-21 CN CN201910544198.2A patent/CN110348703B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106934412A (en) * | 2015-12-31 | 2017-07-07 | 中国科学院深圳先进技术研究院 | A kind of user behavior sorting technique and system |
| CN106127522A (en) * | 2016-06-29 | 2016-11-16 | 苏州迈科网络安全技术股份有限公司 | Dig based on network data and analyze method and system according to the enterprise management condition of technology |
| CN106779856A (en) * | 2016-12-23 | 2017-05-31 | 广州帷策智能科技有限公司 | The method and device that big data customer value is evaluated |
| WO2018120427A1 (en) * | 2016-12-29 | 2018-07-05 | 平安科技（深圳）有限公司 | Risk assessment method, apparatus, and device based on location service, and storage medium |
| CN108520045A (en) * | 2018-04-03 | 2018-09-11 | 平安健康保险股份有限公司 | The service response method and device of data |
| CN109784707A (en) * | 2019-01-04 | 2019-05-21 | 深圳壹账通智能科技有限公司 | Rating business credit method, apparatus, computer equipment and storage medium |
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