CN116342342A

CN116342342A - Student behavior detection method, electronic device and readable storage medium

Info

Publication number: CN116342342A
Application number: CN202310596570.0A
Authority: CN
Inventors: 韩运恒; 王烁名; 周梓鑫
Original assignee: Shenzhen Jieyi Technology Co ltd
Current assignee: Shenzhen Jieyi Technology Co ltd
Priority date: 2023-05-25
Filing date: 2023-05-25
Publication date: 2023-06-27

Abstract

The application discloses a student behavior detection method, an electronic device and a readable storage medium, which are applied to an information management general platform and comprise the following steps: acquiring student behavior basic data of students to be detected stored in at least one information management sub-platform; at least one student behavior basic index converted from the student behavior basic data is counted under a student behavior index system, wherein the student behavior basic index is used for representing the student basic behavior of the student to be detected; determining a total behavior benchmark value of a total student behavior index commonly corresponding to the basic student behavior indexes under the student behavior index system according to the basic student behavior indexes, wherein the total student behavior index is used for representing the behavior state of the student to be detected; and performing behavior detection on the students to be detected according to the total behavior reference value to obtain student behavior detection results. The technical problem that the detection accuracy of student behavior detection is low is solved.

Description

Student behavior detection method, electronic device and readable storage medium

Technical Field

The present disclosure relates to the field of computer technologies, and in particular, to a student behavior detection method, an electronic device, and a readable storage medium.

Background

Along with the continuous development of science and technology, the construction of intelligent campus is also advancing steadily, and through numerous intelligent sensing devices such as wireless network, sensing equipment, wearable equipment and campus card, mass data that the student produced in the campus are gathered, are put in order and are analyzed to optimize the education mode, and simultaneously, the education decision is also gradually changed from empirical transformation to scientific, so how to keep the objectivity of education decision becomes the focus of attention.

At present, a school is generally configured to collect student behavior data in a distributed manner by using a plurality of information systems, such as an intelligent school management system, a student management system, an intelligent security system, an intelligent campus big data system and the like, and a professional teacher is configured to analyze the student behavior data in the system to determine whether the student behavior is abnormal, however, since different behavior data generated by the same student are dispersed in a plurality of information systems and the data between the systems are relatively isolated, the behavior state of the student cannot be completely reflected by analyzing the student behavior data in a certain system, and therefore, the early warning false alarm rate for the student behavior is high, so that the current detection accuracy of student behavior detection is low.

Disclosure of Invention

The main purpose of the application is to provide a student behavior detection method, electronic equipment and readable storage medium, and aims to solve the technical problem of low detection accuracy of student behavior detection in the prior art.

In order to achieve the above object, the present application provides a student behavior detection method applied to an information management platform, the student behavior detection method includes:

acquiring student behavior basic data of students to be detected stored in at least one information management sub-platform;

at least one student behavior basic index converted from the student behavior basic data is counted under a student behavior index system, wherein the student behavior basic index is used for representing the student basic behavior of the student to be detected;

determining a total behavior benchmark value of a total student behavior index commonly corresponding to the basic student behavior indexes under the student behavior index system according to the basic student behavior indexes, wherein the total student behavior index is used for representing the behavior state of the student to be detected;

and performing behavior detection on the students to be detected according to the total behavior reference value to obtain student behavior detection results.

In order to achieve the above object, the present application further provides a student behavior detection device, which is applied to an information management general platform, and the student behavior detection device includes:

the acquisition module is used for acquiring student behavior basic data of students to be detected stored in at least one information management sub-platform;

the statistics module is used for counting at least one student behavior basic index converted from the student behavior basic data under a student behavior index system, wherein the student behavior basic index is used for representing the student basic behavior of the student to be detected;

the determining module is used for determining a total behavior standard value of a student behavior total index which corresponds to the student behavior basic index in common under the student behavior index system according to the student behavior basic index, wherein the student behavior total index is used for representing the behavior state of the student to be detected;

and the detection module is used for detecting the behaviors of the students to be detected according to the total behavior reference value to obtain a student behavior detection result.

The application also provides an electronic device comprising: the system comprises a memory, a processor and a program of the student behavior detection method stored on the memory and capable of running on the processor, wherein the program of the student behavior detection method can realize the steps of the student behavior detection method when being executed by the processor.

The present application also provides a computer-readable storage medium having stored thereon a program for implementing a student behavior detection method, which when executed by a processor implements the steps of the student behavior detection method as described above.

The present application also provides a computer program product comprising a computer program which, when executed by a processor, implements the steps of a student behavior detection method as described above.

The application provides a student behavior detection method, electronic equipment and a readable storage medium, which are applied to an information management general platform, namely, student behavior basic data of students to be detected stored in at least one information management sub-platform are acquired; at least one student behavior basic index converted from the student behavior basic data is counted under a student behavior index system, wherein the student behavior basic index is used for representing the student basic behavior of the student to be detected; determining a total behavior benchmark value of a total student behavior index commonly corresponding to the basic student behavior indexes under the student behavior index system according to the basic student behavior indexes, wherein the total student behavior index is used for representing the behavior state of the student to be detected; and performing behavior detection on the students to be detected according to the total behavior reference value to obtain student behavior detection results. The student behavior basic indexes are formed by converting the student behavior basic data based on different information management sub-platforms, namely, the student behavior basic data can represent the student basic behaviors of the students to be detected, so that the on-school behaviors of the students can be completely fed back through the different student behavior basic indexes, and further, the total behavior standard value of the student behavior total indexes under the student behavior index system can be determined through the student behavior basic indexes. The student's behavior is analyzed through the behavior data of the student in the school instead of the single information system, so that the technical defect that the behavior state of the student cannot be completely reflected through the analysis of the behavior data of the student in the school due to the fact that different behavior data generated by the same student are scattered in a plurality of information systems and the inter-system data are relatively isolated is overcome, and the detection accuracy of the student behavior detection is improved.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.

FIG. 1 is a schematic flow chart of a first embodiment of a student behavior detection method according to the present application;

FIG. 2 is a schematic diagram of a student behavior index system according to the student behavior detection method of the present application;

FIG. 3 is a flowchart of a second embodiment of a student behavior detection method according to the present application;

FIG. 4 is a schematic diagram of an embodiment of a student behavior detection device according to the present application;

fig. 5 is a schematic device structure diagram of a hardware operating environment related to a student behavior detection method in an embodiment of the present application.

The implementation, functional features and advantages of the present application will be further described with reference to the accompanying drawings in conjunction with the embodiments.

Detailed Description

In order to make the above objects, features and advantages of the present invention more comprehensible, the following description of the embodiments accompanied with the accompanying drawings will be given in detail. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

In the first embodiment of the present application, it should be understood that, first, analysis of student behavior in a school is considered to be one of the important topics of attention in the current education field by means of education data mining technology and artificial intelligence technology, it is very necessary to discover abnormal students early, stop abnormal behavior in advance and intervene abnormal psychology in time by flexibly using massive student behavior data resources, at present, when performing behavior detection on students, data of a single information management system is usually relied on to analyze, for example, whether the student behavior is abnormal or not can not be completely reflected in a single dimension of the student behavior through showing of relevant data in an intelligent school management system, and due to data barriers between different information management systems, relevant analysts are difficult to comprehensively and dimensionally judge the student behavior, and even if a great amount of time is consumed to make statistics, the analysis is still only dependent on the analysis personnel to judge the student behavior in advance, so that the early warning for the student behavior is high in early warning, and the accuracy of the current detection is needed.

In a first embodiment of the student behavior detection method of the present application, referring to fig. 1, the student behavior detection method includes:

step S10, student behavior basic data of students to be detected stored in at least one information management sub-platform are obtained;

step S20, at least one student behavior basic index converted from the student behavior basic data is counted under a student behavior index system, wherein the student behavior basic index is used for representing the student basic behavior of the student to be detected;

step S30, determining a total behavior standard value of a student behavior total index which corresponds to the student behavior basic index in common under the student behavior index system according to the student behavior basic index, wherein the student behavior total index is used for representing the behavior state of the student to be detected;

and step S40, performing behavior detection on the students to be detected according to the total behavior reference value to obtain student behavior detection results.

In this embodiment, it should be noted that, the information management total platform is used for characterizing a centralized information management system of the smart campus, and may integrate student behavior basic data of the information management sub-platform and perform targeted processing on the student behavior basic data according to requirements, where the information management total platform is disposed on a student behavior detection device, and the student behavior detection device may specifically be a terminal such as a computer, a mobile phone or a personal PC, for example, in one implementation manner, an architecture of the information management total platform may be designed as a presentation layer, an application layer, a base platform layer and an infrastructure layer, where the presentation layer includes a parent end applet, a teacher end applet and a front end page, the application layer includes a smart management module, a smart school and a smart education, the base platform layer includes a data center, a development intercommunication interface, a unified management module and a basic service, and the infrastructure layer includes an interconnection intranet access and a computing center, where student behavior detection for a certain student may be triggered by inputting a student behavior detection instruction at the information management total platform by a relevant analyst, and the behavior detection instruction may be a relevant running code or a click instruction generated by a relevant analyst.

Additionally, it should be noted that, the students to be detected may be one or more students waiting for behavior detection, in an implementation manner, the student behavior detection may be performed by using a class as a unit, the student behavior basic data is used to characterize basic data generated by the students through behaviors in a campus, and may be consumption data, book borrowing data, dormitory access data, school performance data, campus track data, internet log data, and the like, where different student behavior basic data is stored in different information management sub-platforms, the information management sub-platforms are used to characterize a distributed information management system of a smart campus, and may be, for example, an intelligent school management system, a student management system, an intelligent security management system, an intelligent library management system, and the like, in an implementation manner, the student management system stores attendance data of different schools, the library management system stores book borrowing data of different students, and the information management sub-platforms may perform data transmission between the information management sub-platforms, and the information management sub-platforms may be, and the transmission may be, for example, DDS (Data Distribution Service, distributed communication protocol, or web communication protocol, etc.

Additionally, it should be noted that, when detecting the student's behavior in the school, a student behavior index system is built by means of a data warehouse and combined with methods such as expert experience method and fuzzy comprehensive evaluation method, for example, in an implementation manner, it is assumed that data fields of basic data of the student's behavior are abstracted through the data warehouse to obtain 40 indexes for characterizing the student's behavior in the school, wherein, for 40 indexes, student behavior index systems with different hierarchical structures can be built, the index system can be divided into two hierarchical structures, namely, a student basic behavior index and a student behavior total index, the student behavior total index comprises three student behavior total indexes of "behavior safety", "behavior stability" and "behavior frequency", the student basic behavior index comprises 40 indexes such as "score details", "borrowing records", "night homing condition" and "week consumption", wherein, in the implementation manner, one or more lower-level influence indexes exist in the indexes, namely, the student basic behavior index is subject to one or more than one student behavior total index, the student basic behavior index is specifically mapped by the student's behavior total index, the relevant behavior is mapped by the student's behavior, the "behavior frequency is not allowed to exist in the same level at night, and the night" the overall risk condition is not allowed to be represented by the fact that the student's behavior is mapped by the relevant behavior at night's level, and the overall index is not allowed to exist at night ' by the relevant condition at night's time ' the index's performance frequency is not allowed to stand, the student behavior of the students to be detected at different stages can be accurately detected.

Additionally, it should be noted that, the student behavior base indicator is used to represent a student base behavior of the student to be detected, the student base behavior may be borrowing, checking, asking for leave, and the like, the student behavior total indicator is used to represent a student behavior state of the student to be detected, for example, a safe state (safe or unsafe) of the behavior, a steady state (stable or unstable) of the behavior, a frequency state (high frequency or low frequency) of the behavior, and a total behavior reference value is used to represent a determination value of a behavior abnormality degree, for example, in one embodiment, the total behavior reference value may include 0 and 1, where 0 represents a behavior abnormality, 1 represents a behavior abnormality, and if the total behavior reference value of any two student behavior total indicators in the three college student behavior total indicators represents a behavior abnormality, it is determined that the student behavior detection result of the student to be detected is the student behavior abnormality to be detected.

As an example, steps S10 to S40 include: if a student behavior detection instruction input for a student to be detected is detected, pulling student behavior basic data of the student to be detected to at least one information management sub-platform; counting index data meeting construction standards of a student behavior index system in the student behavior basic data, and quantizing the index data into at least one student behavior basic index under the student index system, wherein whether the construction standards are met or not can be judged based on data identifiers carried by the student behavior basic data, for example, in one implementation mode, if the student behavior basic index related to a library in the student behavior index system is only "book borrowing time record", and the student behavior basic data comprises 14:20 of arrival time of a student A at the library, 16:00 of departure time of the student A at the library, and books a and b are borrowed, and the student behavior basic data are stored in an information management sub-platform "intelligent library management system", and the index data are the times of the student A carrying books a and b leaving the library; according to each student behavior basic index, counting the total behavior reference value of the student behavior total index under the student behavior index system, wherein the total behavior reference value can be formed by judging the index state of the student behavior basic index under the slave, for example, in one implementation mode, if the night homing time is later than 12 points at night, judging that the night homing condition is an abnormal state, further, if the student behavior basic indexes belonging to a certain student behavior total index share x1, x2, x3, x4 and x5, when the index state of any three student behavior basic indexes is abnormal, calibrating the total behavior reference value to be 0 (abnormal behavior reference value), otherwise, calibrating the total behavior reference value to be 1 (normal behavior reference value); detecting whether the total behavior reference value of each student behavior total index has a preset number of abnormal behavior reference values, if so, determining that the student behavior detection result of the student to be detected is abnormal, and if not, determining that the student behavior detection result of the student to be detected is normal, wherein the value of the preset number is larger than 1 and smaller than the total number of the student behavior total indexes under a student behavior index system.

According to the embodiment of the application, the student behavior basic data in the multiple information management sub-platforms are collected, so that the student behavior in-school behavior of a student is reflected from multiple dimensions, the student behavior basic data is quantized into the student behavior basic index, the total behavior reference value of at least one student behavior total index under a student index system is determined, and the student behavior is quantitatively detected based on the total behavior reference value.

The step of determining the total behavior reference value of the student behavior total index commonly corresponding to the student behavior basic index under the student behavior index system according to each student behavior basic index comprises the following steps:

Step A10, obtaining basic behavior membership degree of each student behavior basic index;

step A20, constructing a basic behavior membership matrix according to each basic behavior membership;

step A30, determining a behavior dimension membership matrix commonly corresponding to each student behavior dimension index according to the basic behavior membership matrix and a first index weight of at least one student behavior dimension index to which each student behavior basic index belongs under the student behavior index system;

and step A40, determining the total behavior membership degree of each student behavior total index according to the behavior dimension membership degree matrix and the second index weight of at least one student behavior total index to which each student behavior dimension index belongs under the student behavior index system.

In this embodiment, it should be noted that, since the student's behaviors in the school are many and complicated, only the student behavior index system composed of two layers of indexes is designed, the differences between the different student's behaviors in the school cannot be finely distinguished, meanwhile, the student's behaviors cannot be finely distinguished from each other by defining the abnormal or non-abnormal behaviors, and in general, the number of students registered under the information management platform is large, if the student's behaviors are not more finely and quantitatively detected, the time will be spent on the students with stronger self-adjustment capability, that is, the subsequent targeted coaching of the students is not facilitated, so the total behaviors can be introduced to be subordinate to the degree, and the behaviors of different students can be divided to the abnormal degree.

In addition, it should be noted that, based on the analytic hierarchy process, the student behavior index system is set to be a three-layer index structure, which not only can ensure fine granularity division of student behaviors, but also can balance redundancy of platform data to a certain extent, that is, the student behavior index system is divided into a three-layer index structure of student behavior basic index, student behavior dimension index and student behavior total index, wherein a mapping relationship exists between the student behavior basic index and the student behavior dimension index, a mapping relationship exists between the student behavior dimension index and the student behavior total index, the student behavior dimension index is used for characterizing the influence dimension of student behaviors to be detected, the basic behavior membership is used for characterizing the importance degree of the student behavior basic index to the corresponding student behavior dimension index, the calibration can be performed according to actual requirements, for example, assuming that the student behavior dimension index is Y, the student behavior basic index is Y1, Y2 and Y3, and if no correlation relationship exists between Y3 and Y2, only has basic behavior membership degree of Y1 and Y2 and the student behavior dimension index, the basic behavior membership degree of Y1 to Y is 0.3, the basic behavior membership degree of Y2 to Y is used for characterizing the basic behavior membership degree of the basic matrix is used for representing a specific membership degree of the basic behavior membership degree, and the basic membership degree of the matrix is implemented as follows:

Wherein,,

for the basic behavior membership matrix, +.>

、/>

、/>

……/>

All are basic behavior membership degrees, +.>

For the number of student behavior base indicators affecting the same student behavior dimension indicator, < ->

Basic behavior membership degree number for basic index of student behavior,/-degree>

And->

May be the same or different, and may be specifically 1, 2, 3, 4, 5, or the like.

Additionally, it should be noted that, the first index weight is an influence weight of the student behavior basic index on the corresponding student behavior dimension index, the second index weight is an influence weight of the student behavior dimension index on the corresponding student behavior total index, the behavior dimension membership matrix is used for representing a set of behavior dimension membership degrees, the behavior dimension membership degrees are used for representing importance degrees of the student behavior dimension index on the corresponding student behavior total index, and the total behavior membership degrees are used for representing importance degrees of the student behavior total index on the student behavior detection.

As an example, steps a10 to a40 include: obtaining basic behavior membership degree of each student behavior basic index; constructing a basic behavior membership matrix according to each basic behavior membership; determining the behavior dimension membership degree of each student behavior dimension index according to the basic behavior membership degree matrix and the first index weight of at least one student behavior dimension index to which each student behavior basic index belongs under the student behavior index system, and constructing a behavior dimension membership degree matrix which corresponds to each student behavior dimension index together according to each behavior dimension membership degree; and determining the total behavior membership degree of each student behavior total index according to the behavior dimension membership degree matrix and the second index weight of at least one student behavior total index to which each student behavior dimension index belongs under the student behavior index system.

The step of determining the behavior dimension membership degree of each student behavior dimension index specifically includes the steps of:

wherein b is the degree of membership of the behavior dimension,

for the first index weight, ++>

For the influence weight of the v-th student behavior basic index on the corresponding student behavior dimension index, after the behavior dimension membership corresponding to each student behavior dimension index is calculated, a behavior dimension membership matrix can be constructed based on the behavior dimension membership, and the method specifically comprises the following steps:

wherein, among them,

membership matrix for behavior dimension +.>

、/>

、/>

……/>

All are behavior dimension membership degrees, +.>

Index number for student behavior dimension index +.>

And->

May be the same or different, and may be specifically 1, 2, 3, 4, 5, etc., where in one embodiment, the basic behavior membership degrees of the basic indicators of student behavior in the student behavior indicator system may be set to be 7, that is->

And->

7, wherein, since one or more student behavior basic indexes exist in a certain student behavior dimension index, the student behavior basic indexes are +. >

。

The specific step of determining the total membership degree of the student behavior total index according to the behavior dimension membership degree matrix and the second index weight of at least one student behavior total index to which each student behavior dimension index belongs under the student behavior index system refers to the above, and only differences exist in weight setting, which is not described herein.

The step of detecting the behaviors of the students to be detected according to the total behavior reference value to obtain a student behavior detection result comprises the following steps:

step B10, selecting a total behavior membership extremum from the total behavior membership degrees by comparing the magnitude relation between the total behavior membership degrees;

and step B20, inquiring a student behavior abnormality grade corresponding to the total behavior membership extremum in a preset behavior abnormality mapping table, and taking the student behavior abnormality grade as the student behavior detection result.

In this embodiment, it should be noted that, in order to determine the abnormal degree of behavior of different students in a targeted manner, for example, in one implementation, the student's behavior is examinedThe measurement result may be five abnormal grades of student behaviors such as micro grade, light grade, medium grade, heavy grade and severe grade, and the total membership extremum is the maximum value of the total membership, for example, in one implementation manner, it is assumed that the total membership of the total index of four students is z1, z2, z3 and z4 respectively, and

And taking z4 as a membership extremum of the total behaviors, and storing a mapping relation between the abnormal grades of the behaviors of the students and the membership extremum of the total behaviors in a preset behavior abnormal mapping table.

As an example, steps B10 to B20 include: selecting a total behavior membership extremum from the total behavior membership degrees by comparing the magnitude relation between the total behavior membership degrees; and inquiring the corresponding abnormal grades of the student behaviors in a preset behavior abnormal mapping table by taking the maximum total behavior membership extreme value as an index, and taking the abnormal grades of the student behaviors as the student behavior detection result. The membership degree can represent the membership degree between indexes under the student behavior index system, so that the behavior abnormality degrees of different students are distinguished through the student behavior abnormality grades, and the aim of improving the pertinence of student behavior detection on the basis of ensuring the accuracy of student behavior detection from the aspect of multidimensional can be fulfilled.

The step of obtaining student behavior basic data of the student to be detected stored in at least one information management sub-platform comprises the following steps:

step C10, acquiring data query information input by a user on a data pulling page aiming at students to be detected;

Step C20, detecting whether a corresponding behavior data index exists according to the query tag carried by the data query information;

and step C30, if yes, pulling the student behavior basic data of the student to be detected from at least one information management sub-platform identified by the behavior data index.

In this embodiment, it should be noted that, because there is a data barrier between the information management main platform and the information management sub-platform, the traditional data pulling mode of pulling the information management sub-platform from the information management main platform requires a developer to write related codes, and a certain technical barrier exists, and aiming at the smart campus, the operator is independently engaged in operation and maintenance of the information management main platform, which not only increases the cost, but also can present the risk of information leakage, so that the cost is usually increased, and the information management main platform is usually used for taking account by internal cultivation of composite talents, so that in order to reduce the detection difficulty of student behavior detection by the information management main platform, the deployment of related data extraction commands can be performed in the initial development stage.

In addition, it should be noted that, the data query information is used for querying student behavior basic data of the student to be detected, specifically may be text data query information or voice data query information, etc., the data pull page is a visual interface for querying the student behavior basic data, the query tag is used for defining the data query information, specifically may be an information tag of the data query information, for example, in an implementation manner, assuming that the data query information is "check-in information of H classmates and K classmates", the query tag may be "check-in information of H classmates and" check-in information of K classmates ", the behavior data index is used for indexing the student behavior basic data, and when the behavior data index corresponding to the student behavior basic data exists, the student behavior basic data may be automatically queried through the behavior data index.

As an example, steps C10 to C30 include: acquiring data query information input by a user on a data pulling page aiming at students to be detected; extracting a carried query tag from the data query information according to a preset tag extraction rule, and detecting whether a behavior data index corresponding to the query tag exists or not; and if the existence of the behavior data index corresponding to the query tag is detected, pulling the student behavior basic data of the student to be detected at least one information management sub-platform identified by the behavior data index.

In an implementation manner, if no behavior data index corresponding to the query tag is detected, error prompt information is generated, wherein the error prompt information is used for prompting a user that the student behavior basic data pulling fails.

The user inputs data query information through simple man-machine interaction on the data pulling page, can realize the query label carried by the automatically extracted data query information, automatically indexes the student behavior basic data stored in the information management sub-platform based on the query label when detecting that the corresponding behavior data index exists, and needs to be pulled to the student behavior basic data of the information management main platform, so that the purpose of pulling the student behavior basic data to the information management main platform is overcome, the technical defect that in the process of student behavior detection, related codes are needed to be written, and the detection efficiency of the student behavior detection is low is further caused, and therefore, the acquisition efficiency of acquiring the student behavior basic data scattered in different information management sub-platforms is improved.

The step of detecting whether the corresponding behavior data index exists according to the query tag carried by the data query information comprises the following steps:

step D10, word segmentation is carried out on the data query information through a preset word segmentation device, and data query word segmentation is obtained;

step D20, screening the data query word segmentation to obtain a first word segmentation and a second word segmentation;

step D30, splicing the first word and the second word to obtain the query tag of the student to be detected;

and D40, inquiring whether a behavior data index corresponding to the inquired label exists in a preset label mapping table.

In this embodiment, it should be noted that, in the scenario of the smart campus, since there are many students and the situation that nouns are repeated easily occurs, and thus there is a situation that the query tag of single semantic information is used to query student behavior basic data of a student, and the queried student behavior basic data is not matched with the student, it may be considered that before querying the corresponding behavior data index based on the query tag, data query words capable of differentially reflecting the characteristics of the students are fused first, where the data query words are used to characterize different semantic information in the data query information, for example, in an implementation manner, it is assumed that the data query information input by the user is "borrowing record of L classmates of the second class and the third class, the first word is" borrowing record of the third class and the second word may be "borrowing record of the third class", and the query tag is different from the previous "borrowing record of the third class and the preset word may be IKAnalyzer, ansj and the ms4 j.

As an example, steps D10 to D40 include: word segmentation is carried out on the data query information through word segmentation rules set by a preset word segmentation device, so that at least one data query word segmentation is obtained; screening each data query word segment to obtain a first word segment and a second word segment; the query tag of the student to be detected is obtained by splicing the first word segmentation and the second word segmentation; and inquiring whether a behavior data index corresponding to the query tag exists in a preset tag mapping table by taking the query tag as an index, wherein the preset tag mapping table stores the mapping relation between the query tag and the behavior data index. By word segmentation of the data query information, different semantic information in the data query information is reserved as far as possible, and further the query labels of the multi-dimensional characterization of the student characteristics of the students to be detected are obtained by splicing the data query word segmentation of the different semantic information, and further the purpose of matching the student identity of the students to be detected with the student behavior basic data can be achieved through the behavior data index indexed by the query labels, so that the technical defect that the students to be detected with the same name cannot be distinguished due to the fact that the query labels of the single semantic information exist is overcome, and further the situation that the students to be detected are not matched with the student behavior basic data is caused, and the foundation is laid for improving the accuracy of student behavior detection.

Before the step of obtaining the student behavior basic data of the student to be detected stored in the at least one information management sub-platform, the student behavior detection method further comprises the following steps:

step E10, acquiring sample behavior data of at least one behavior sample student through a preset data warehouse;

and E20, establishing a student behavior index system with a hierarchical structure according to each sample behavior data, wherein the student behavior index system comprises a student behavior basic index, a student behavior dimension index and a student behavior total index.

In this embodiment, before the student behavior detection, a student behavior index system with a hierarchical structure needs to be established, and behavior sample students are randomly extracted from management students registered by the information management platform, where the sample behavior data is student behavior basic data of the behavior sample students.

As an example, steps E10 to E20 include: acquiring sample behavior data of at least one behavior sample student through a preset data warehouse; and establishing a student behavior index system with a hierarchical structure according to each sample behavior data, wherein the student behavior index system comprises a student behavior basic index, a student behavior dimension index and a student behavior total index.

In one embodiment, referring to fig. 2, fig. 2 is a schematic diagram showing a student behavior index system, in which there is a student behavior total index, M is a student behavior total index, N1, N2 and N3 are student behavior dimension indexes, and C1, C2 … … C10 are student behavior base indexes.

The embodiment of the application provides a student behavior detection method which is applied to an information management general platform, namely, student behavior basic data of students to be detected stored in at least one information management sub-platform are obtained; at least one student behavior basic index converted from the student behavior basic data is counted under a student behavior index system, wherein the student behavior basic index is used for representing the student basic behavior of the student to be detected; determining a total behavior benchmark value of a total student behavior index commonly corresponding to the basic student behavior indexes under the student behavior index system according to the basic student behavior indexes, wherein the total student behavior index is used for representing the behavior state of the student to be detected; and performing behavior detection on the students to be detected according to the total behavior reference value to obtain student behavior detection results. The student behavior basic indexes are formed by converting the student behavior basic data based on different information management sub-platforms, namely, the student behavior basic data can represent the student basic behaviors of the students to be detected, so that the on-school behaviors of the students can be completely fed back through the different student behavior basic indexes, and further, the total behavior standard value of the student behavior total indexes under the student behavior index system can be determined through the student behavior basic indexes. The student's behavior is analyzed through the behavior data of the student in the school instead of the single information system, so that the technical defect that the behavior state of the student cannot be completely reflected through the analysis of the behavior data of the student in the school due to the fact that different behavior data generated by the same student are scattered in a plurality of information systems and the inter-system data are relatively isolated is overcome, and the detection accuracy of the student behavior detection is improved.

Further, referring to fig. 3, in the second embodiment of the present application, the same or similar contents as those of the first embodiment can be referred to the description above, and the description is omitted. On the basis, the total behavior reference value comprises total behavior anomaly degree, and the step of determining the total behavior reference value of the total student behavior index corresponding to the basic student behavior indexes under the student behavior index system comprises the following steps:

step F10, obtaining a first behavior characteristic value of each student behavior basic index;

step F20, summing the behavior losing values mapped by the first behavior characteristic values to obtain second behavior characteristic values of at least one student behavior dimension index to which each student behavior basic index belongs under the student behavior index system;

step F30, converting the second behavior characteristic value into a third characteristic value of at least one student behavior total index of which each student behavior dimension index belongs to under the student behavior index system;

and F40, taking the sum of products of the third characteristic values and the index weight values of the corresponding student behavior total indexes as the total behavior anomaly degree.

In this embodiment, it should be noted that, though students to be detected can be differentiated from different abnormal degree areas through the total behavior membership, the difference in behavior between different students cannot be intuitively reflected, and for the manager of the information management total platform, the difference in behavior between different students cannot be intuitively understood, so that the total behavior anomaly is introduced to more finely characterize the behaviors of the students, wherein the total behavior anomaly is a specific score of a percentage, specifically may be 69, 85 or 92, and the behavior state of the students to be detected can be clearly quantified through the total behavior anomaly.

Additionally, it should be noted that, the first behavior feature value is used to represent the influence weight of the student behavior basic index on the student behavior dimension index with the association relationship, the second behavior feature value is used to represent the influence weight of the student behavior dimension behavior on the student behavior total index, and the third behavior feature value is used to represent the influence weight of the student behavior total index on the student behavior detection, where the value ranges of the first behavior feature value, the second behavior feature value and the third behavior feature value may be (0, 1), the behavior losing value is a losing value of the percentile, and may be specifically 5, 6 and 7, for example, in an embodiment, assuming that the first behavior feature value is 0.95, the behavior losing value mapped by the first behavior feature value is 5.

As an example, steps F10 to F40 include: acquiring a first behavior characteristic value of each student behavior basic index; summing the behavior losing values mapped by the first behavior characteristic values to obtain second behavior characteristic values of at least one student behavior dimension index of which each student behavior basic index belongs to under the student behavior index system; converting the second behavior characteristic value into a third characteristic value of at least one student behavior total index to which each student behavior dimension index belongs under the student behavior index system; taking the sum of products of the third characteristic values and the index weight values of the corresponding student behavior total indexes as the total behavior anomaly degree, for example, in one implementation manner, assuming that the student behavior total indexes have three indexes K1, K2 and K3, and the index weight values corresponding to the three indexes are 0.2, 0.3 and 0.5 respectively, the total behavior anomaly degree k=k1×0.2+k2×0.3+k3×0.5.

After the step of performing behavior detection on the student to be detected according to the total behavior reference value to obtain a student behavior detection result, the student behavior detection method further comprises the following steps:

step G10, detecting whether the total behavioral anomaly degree is smaller than a preset behavioral anomaly threshold;

Step G20, if the detected student monitoring information is greater than or equal to the detected student monitoring information, generating behavior early warning information, and pushing the behavior early warning information according to the detected student monitoring information;

and G30, if so, returning to execute the step and the subsequent steps of detecting whether the total behavioural abnormality is smaller than a preset behavioural abnormality threshold value until the behavioural monitoring period of the student to be detected is reached.

In this embodiment, it should be noted that, the associated guardian information is used to characterize a guardian associated with a student to be detected, specifically may be a guardian name, a guardian contact way, and the like, the behavior early warning information is used to characterize early warning information of abnormal behavior, specifically may be an abnormal behavior analysis report of the student J, and the preset behavior abnormal threshold may be set by a user according to actual needs.

As an example, steps G10 to G30 include: detecting whether the total behavioural abnormal degree is smaller than a preset behavioural abnormal threshold value; if the total behavior anomaly degree is detected to be greater than or equal to a preset behavior anomaly threshold value, generating behavior early warning information, inquiring an associated guardian of the student to be detected according to the associated guardian information of the student to be detected, and pushing the behavior early warning information to terminal equipment of the associated guardian; if the total behavioural anomaly degree is detected to be smaller than the preset behavioural anomaly threshold value, returning to execute the step and the subsequent steps of detecting whether the total behavioural anomaly degree is smaller than the preset behavioural anomaly threshold value or not until reaching the behavioural monitoring period of the student to be detected, wherein the behavioural monitoring period can be specifically one week, half month or one month.

The embodiment of the application provides a student behavior detection instruction judging method, namely, a first behavior characteristic value of each student behavior basic index is obtained; summing the behavior losing values mapped by the first behavior characteristic values to obtain second behavior characteristic values of at least one student behavior dimension index of which each student behavior basic index belongs to under the student behavior index system; converting the second behavior characteristic value into a third characteristic value of at least one student behavior total index to which each student behavior dimension index belongs under the student behavior index system; and taking the sum of products of the third characteristic values and the index weight values of the corresponding student behavior total indexes as the total behavior anomaly degree. That is, through the association relation among the indexes of different levels under the student behavior index system, the total behavior anomaly degree capable of quantifying whether the student behaviors are abnormal or not is finally calculated, and the difference in behaviors among different students can be intuitively reflected, so that the detection accuracy of detecting the student behaviors is improved.

The third embodiment of the present application further provides a student behavior detection device, which is applied to an information management general platform, referring to fig. 4, and includes:

An acquisition module 101, configured to acquire student behavior basic data of a student to be detected stored in at least one information management sub-platform;

a statistics module 102, configured to count at least one student behavior base indicator converted from the student behavior base data under a student behavior index system, where the student behavior base indicator is used to characterize a student base behavior of the student to be detected;

a determining module 103, configured to determine, according to each of the student behavior base indexes, a total behavior reference value of a total student behavior index that corresponds to each of the student behavior base indexes in common under the student behavior index system, where the total student behavior index is used to characterize a behavior state of the student to be detected;

and the detection module 104 is configured to perform behavior detection on the student to be detected according to the total behavior reference value, so as to obtain a student behavior detection result.

Optionally, the total behavior reference value includes at least one total behavior membership degree, and the determining module 103 is further configured to:

obtaining basic behavior membership degree of each student behavior basic index;

constructing a basic behavior membership matrix according to each basic behavior membership;

Determining a behavior dimension membership matrix commonly corresponding to each student behavior dimension index according to the basic behavior membership matrix and a first index weight of at least one student behavior dimension index to which each student behavior basic index belongs under the student behavior index system;

and determining the total behavior membership degree of each student behavior total index according to the behavior dimension membership degree matrix and the second index weight of at least one student behavior total index to which each student behavior dimension index belongs under the student behavior index system.

Optionally, the detection module 104 is further configured to:

selecting a total behavior membership extremum from the total behavior membership degrees by comparing the magnitude relation between the total behavior membership degrees;

inquiring a student behavior abnormality grade corresponding to the total behavior membership extremum in a preset behavior abnormality mapping table, and taking the student behavior abnormality grade as the student behavior detection result.

Optionally, the total behavior reference value includes a total behavior anomaly, and the determining module 103 is further configured to:

acquiring a first behavior characteristic value of each student behavior basic index;

Summing the behavior losing values mapped by the first behavior characteristic values to obtain second behavior characteristic values of at least one student behavior dimension index of which each student behavior basic index belongs to under the student behavior index system;

converting the second behavior characteristic value into a third characteristic value of at least one student behavior total index to which each student behavior dimension index belongs under the student behavior index system;

and taking the sum of products of the third characteristic values and the index weight values of the corresponding student behavior total indexes as the total behavior anomaly degree.

Optionally, the student behavior detection device is further configured to:

detecting whether the total behavioural abnormal degree is smaller than a preset behavioural abnormal threshold value;

if the information is greater than or equal to the relevant monitoring information, generating behavior early warning information, and pushing the behavior early warning information according to the relevant monitoring information of the student to be detected;

and if so, returning to execute the step and the subsequent steps of detecting whether the total behavioural abnormal degree is smaller than a preset behavioural abnormal threshold value until the behavioural monitoring period of the student to be detected is reached.

Optionally, the obtaining module 101 is further configured to:

Acquiring data query information input by a user on a data pulling page aiming at students to be detected;

detecting whether a corresponding behavior data index exists or not according to a query tag carried by the data query information;

if yes, pulling the student behavior basic data of the student to be detected from at least one information management sub-platform identified by the behavior data index.

Optionally, the obtaining module 101 is further configured to:

the data query information is segmented through a preset word segmentation device, and data query segmentation is obtained;

screening the data query word segmentation to obtain a first word segmentation and a second word segmentation;

splicing the first word segmentation and the second word segmentation to obtain a query tag of the student to be detected;

inquiring whether a behavior data index corresponding to the inquired label exists in a preset label mapping table.

Optionally, the student behavior detection device is further configured to:

acquiring sample behavior data of at least one behavior sample student through a preset data warehouse;

and establishing a student behavior index system with a hierarchical structure according to each sample behavior data, wherein the student behavior index system comprises a student behavior basic index, a student behavior dimension index and a student behavior total index.

The student behavior detection device provided by the invention solves the technical problem of low detection accuracy in student behavior detection by adopting the student behavior detection method in the embodiment. Compared with the prior art, the student behavior detection device provided by the embodiment of the invention has the same beneficial effects as those of the student behavior detection method provided by the embodiment, and other technical features in the student behavior detection device are the same as those disclosed by the method of the embodiment, so that the description is omitted herein.

A fourth embodiment of the present application provides an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the student behavior detection method in the first embodiment.

Referring now to fig. 5, a schematic diagram of an electronic device suitable for use in implementing embodiments of the present disclosure is shown. The electronic devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (e.g., in-vehicle navigation terminals), and the like, and stationary terminals such as digital TVs, desktop computers, and the like. The electronic device shown in fig. 5 is merely an example and should not be construed to limit the functionality and scope of use of the disclosed embodiments.

As shown in fig. 5, the electronic device may include a processing apparatus 1001 (e.g., a central processing unit, a graphics processor, etc.), which may perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 1002 or a program loaded from a storage apparatus 1003 into a Random Access Memory (RAM) 1004. In the RAM1004, various programs and data required for the operation of the electronic device are also stored. The processing device 1001, the ROM1002, and the RAM1004 are connected to each other by a bus 1005. An input/output (I/O) interface 1006 is also connected to the bus.

In general, the following systems may be connected to the I/O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, and the like; an output device 1008 including, for example, a Liquid Crystal Display (LCD), a speaker, a vibrator, and the like; storage device 1003 including, for example, a magnetic tape, a hard disk, and the like; and communication means 1009. The communication means may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. While electronic devices having various systems are shown in the figures, it should be understood that not all of the illustrated systems are required to be implemented or provided. More or fewer systems may alternatively be implemented or provided.

In particular, according to embodiments of the present disclosure, the processes described above with reference to flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program may be downloaded and installed from a network via the communication device 1009, or installed from the storage device 1003, or installed from the ROM 1002. The above-described functions defined in the method of the embodiment of the present disclosure are performed when the computer program is executed by the processing device 1001.

The electronic equipment provided by the invention solves the technical problem of low detection accuracy of student behavior detection by adopting the student behavior detection method in the embodiment. Compared with the prior art, the beneficial effects of the electronic device provided by the embodiment of the invention are the same as those of the student behavior detection method provided by the embodiment, and other technical features of the electronic device are the same as those disclosed by the method of the embodiment, so that the description is omitted herein.

It should be understood that portions of the present disclosure may be implemented in hardware, software, firmware, or a combination thereof. In the description of the above embodiments, particular features, structures, materials, or characteristics may be combined in any suitable manner in any one or more embodiments or examples.

The foregoing is merely illustrative of the present invention, and the present invention is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

A fifth embodiment of the present application provides a computer-readable storage medium having computer-readable program instructions stored thereon for performing the student behavior detection method in the above embodiment.

The computer readable storage medium according to the embodiments of the present invention may be, for example, a usb disk, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this embodiment, a computer-readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, fiber optic cables, RF (radio frequency), and the like, or any suitable combination of the foregoing.

The above-described computer-readable storage medium may be contained in an electronic device; or may exist alone without being assembled into an electronic device.

The computer-readable storage medium carries one or more programs that, when executed by an electronic device, cause the electronic device to: acquiring student behavior basic data of students to be detected stored in at least one information management sub-platform; at least one student behavior basic index converted from the student behavior basic data is counted under a student behavior index system, wherein the student behavior basic index is used for representing the student basic behavior of the student to be detected; determining a total behavior benchmark value of a total student behavior index commonly corresponding to the basic student behavior indexes under the student behavior index system according to the basic student behavior indexes, wherein the total student behavior index is used for representing the behavior state of the student to be detected; and performing behavior detection on the students to be detected according to the total behavior reference value to obtain student behavior detection results.

Computer program code for carrying out operations of the present disclosure may be written in one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).

The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.

The modules described in the embodiments of the present disclosure may be implemented in software or hardware. Wherein the name of the module does not constitute a limitation of the unit itself in some cases.

The computer readable storage medium provided by the invention stores the computer readable program instructions for executing the student behavior detection method, and solves the technical problem of low detection accuracy in student behavior detection. Compared with the prior art, the beneficial effects of the computer readable storage medium provided by the embodiment of the invention are the same as those of the student behavior detection method provided by the above embodiment, and are not described in detail herein.

A sixth embodiment of the present application further provides a computer program product, including a computer program, which when executed by a processor implements the steps of the student behavior detection method as described above.

The computer program product provided by the application solves the technical problem of low detection accuracy in student behavior detection. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the invention are the same as those of the student behavior detection method provided by the embodiment, and are not described in detail herein.

The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the scope of the claims, and all equivalent structures or equivalent processes using the descriptions and drawings of the present application, or direct or indirect application in other related technical fields are included in the scope of the claims.

Claims

1. A student behavior detection method, which is applied to an information management general platform, the student behavior detection method comprising:

2. The student behavior detection method as recited in claim 1, wherein the total behavior reference value comprises at least one total behavior membership degree,

the step of determining the total behavior standard value of the total student behavior index corresponding to the basic student behavior indexes under the student behavior index system according to the basic student behavior indexes comprises the following steps:

3. The student behavior detection method as claimed in claim 2, wherein the step of performing behavior detection on the student to be detected according to the total behavior reference value to obtain a student behavior detection result comprises:

4. The student behavior detection method as recited in claim 1, wherein the total behavior reference value comprises a total behavior anomaly,

5. The student behavior detection method as defined in claim 4, wherein after the step of performing behavior detection on the student to be detected based on the total behavior reference value to obtain a student behavior detection result, the student behavior detection method further comprises:

6. The student behavior detection method as recited in claim 1, wherein the step of acquiring student behavior base data of the student to be detected stored in the at least one information management sub-platform comprises:

7. The student behavior detection method as recited in claim 6, wherein the step of detecting whether a corresponding behavior data index exists according to a query tag carried by the data query information comprises:

8. The student behavior detection method as recited in claim 7, wherein prior to the step of obtaining student behavior base data of a student to be detected stored in at least one information management sub-platform, the student behavior detection method further comprises:

9. An electronic device, the electronic device comprising:

at least one processor; the method comprises the steps of,

a memory communicatively coupled to the at least one processor; wherein,,

The memory stores instructions executable by the at least one processor to enable the at least one processor to perform the steps of the student behavior detection method of any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a program for realizing the student behavior detection method, the program for realizing the student behavior detection method being executed by a processor to realize the steps of the student behavior detection method according to any one of claims 1 to 8.