CN113705985B - Student status analysis early warning method, system, terminal and medium - Google Patents
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Abstract
The application discloses a student status analysis and early warning method, a student status analysis and early warning system, a student status analysis and early warning terminal and a student status analysis and early warning medium, which relate to the technical field of information processing and have the technical scheme that: establishing a real-time track distribution diagram of a corresponding user; constructing a real-time track sequence according to the frequency information and the corresponding time information of the location mark; training historical consumption information and punching information based on a deep learning algorithm, establishing a standard track distribution diagram, and establishing a standard track sequence according to the standard track distribution diagram by adopting a real-time track sequence construction method; marking the place mark as a corresponding main mark or auxiliary mark; and calculating an abnormal overlapping value according to the difference of the location marks in the real-time track sequence and the standard track sequence, and outputting abnormal early warning information if the abnormal overlapping value exceeds a basic abnormal threshold value. The application can monitor the early abnormal condition of college students and provides data support for the status management of college students.
Description
Technical Field
The application relates to the technical field of information processing, in particular to a student status analysis and early warning method, a student status analysis and early warning system, a student status analysis and early warning terminal and a student status analysis and early warning medium.
Background
Higher education is various specialized education performed after medium education. In China, higher education includes two types, namely, full-day higher education and adult higher education, and the latter belongs to the category of adult education. Higher education is a platform for students to initially contact society, the restraint force of parents and teachers is gradually reduced, and the whole learning process mainly depends on self learning ability of learning, so that the monitoring of student status is very necessary for benign development of college students.
The traditional student status analysis method is limited to a questionnaire form, so that the data collection difficulty is high, and the information reliability is low. With the rapid development of big data, the prior art has the steps of collecting the activity track information of students in a web crawler searching and active filling mode by a big data intelligent analysis method, and analyzing the status of the students by keywords in the collected data. However, although college students have a certain free activity space, the main activity range is still in the campus range, and the data acquisition by the web crawler technology is high in input cost, and the acquired data information is high in missing degree, so that the accuracy of the big data analysis result is low. In addition, the reliability of the information is lower when the information is acquired in an active filling mode, so that the error of the analysis result of the big data is larger.
Therefore, how to study and design a student status analysis and early warning method, system, terminal and medium capable of overcoming the defects is a problem which needs to be solved at present.
Disclosure of Invention
In order to solve the defects in the prior art, the application aims to provide a student status analysis and early warning method, a student status analysis and early warning system, a student status analysis and early warning terminal and a student status analysis and early warning medium.
The technical aim of the application is realized by the following technical scheme:
in a first aspect, a student status analysis and early warning method is provided, including the following steps:
acquiring consumption information and card punching information in an authorization range according to user information, and establishing a real-time track distribution diagram of a corresponding user according to the consumption information and the card punching information;
extracting a place mark in the real-time track distribution map, and constructing a real-time track sequence according to the frequency information of the place mark and the corresponding time information;
training historical consumption information and punching information based on a deep learning algorithm, establishing a standard track distribution diagram, and establishing a standard track sequence according to the standard track distribution diagram by adopting a real-time track sequence construction method;
after classifying all the place marks through a classifier, marking the place marks as corresponding main marks or auxiliary marks;
and calculating an abnormal overlapping value according to the difference of the location marks in the real-time track sequence and the standard track sequence, and outputting abnormal early warning information if the abnormal overlapping value exceeds a basic abnormal threshold value.
Further, the consumption information is extracted from the collection information of stores in and around the campus, and the consumption information comprises consumption details, consumption time and store positioning information;
the card punching information is extracted from the lesson report information and library record information, and comprises learning point positioning information and learning time;
and establishing a track point distribution map according to the store positioning information and the learning point positioning information, and sequentially connecting each track point in the track point distribution map according to the sequence of the consumption time and the learning time to obtain a real-time track distribution map.
Further, the construction process of the real-time track sequence specifically comprises the following steps:
matching according to the time information of the location mark to obtain a dynamic priority value of the corresponding frequency;
calculating to obtain a sequencing priority value according to the dynamic priority values of all frequencies of the same place mark and the basic priority values of the corresponding place marks;
and (3) carrying out descending order arrangement on different place marks according to the size of the sorting priority value to obtain a real-time track sequence.
Further, the calculation formula of the sorting priority value of the location mark specifically includes:
wherein Y is n A ranking priority value representing the location indicator n; g n A base priority value representing a location marker n; y is n (t i ) The location mark n indicating the ith time at time t i The corresponding dynamic priority value.
Further, the calculation formula of the abnormal overlap value is as follows:
wherein M represents an abnormal superposition value of the difference between the real-time track sequence and the standard track sequence; q represents the number of main marks in the real-time track sequence; y is Y j A ranking priority value representing the jth primary flag; j (j) 0 Representing a phaseThe sequence number value of all main marks in the standard track sequence with the main mark is j if the corresponding main mark is a newly added main mark in the real-time track sequence 0 The value is q; epsilon j And the influence factor of the auxiliary mark covered by the j-th main mark is represented.
Further, the calculation formula of the influence factor specifically includes:
k is the number of auxiliary marks covered by the corresponding main mark in the real-time track sequence, namely the number of auxiliary marks between the current main mark and the next main mark; p is the number of auxiliary marks covered by the corresponding main mark in the standard track sequence; y is Y j (k) A sorting priority value of a kth auxiliary mark covered by a jth main mark in the real-time track sequence is represented; y is Y j0 (p) represents the j-th in the standard track sequence 0 The p-th secondary flag covered by the primary flag.
Further, the main sign comprises a classroom place, a laboratory place, a canteen place, a bedroom place, a library place, a playground place and a restaurant place; the auxiliary mark comprises a milky tea shop, an internet bar place, a gym place, a bathhouse place, a leisure area place, a barbecue place, a KTV place and a hotel place.
In a second aspect, a student status analysis and early warning system is provided, including:
the distribution map construction module is used for acquiring consumption information and card punching information in an authorization range according to the user information and establishing a real-time track distribution map of a corresponding user according to the consumption information and the card punching information;
the sequence construction module is used for extracting the place marks in the real-time track distribution diagram and constructing a real-time track sequence according to the frequency information of the place marks and the corresponding time information;
the data training module is used for building a standard track distribution diagram after training historical consumption information and punching information based on a deep learning algorithm, and building a standard track sequence according to the standard track distribution diagram by adopting a real-time track sequence building method;
the mark classification module is used for marking the place marks as corresponding main marks or auxiliary marks after classifying all the place marks through the classifier;
the abnormality analysis module is used for calculating an abnormality overlapping value according to the difference of the location marks in the real-time track sequence and the standard track sequence, and outputting abnormality early warning information if the abnormality overlapping value exceeds a basic abnormality threshold value.
In a third aspect, a computer terminal is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements a student status analysis and early warning method according to any one of the first aspects when the program is executed.
In a fourth aspect, there is provided a computer readable medium having stored thereon a computer program for execution by a processor to implement a student status analysis pre-warning method as described in any one of the first aspects.
Compared with the prior art, the application has the following beneficial effects:
1. after the authorization rights of the activity sites in the campus range and the campus periphery range are obtained, the data acquired in real time are compared with standard data trained according to historical data, so that the difference between corresponding students and the historical activity conditions can be accurately and reliably obtained, the early abnormal conditions of the students in the colleges and universities can be monitored by analyzing and early warning the student conditions based on the difference analysis results, and data support is provided for the condition management of the students in the colleges and universities;
2. the application forms the corresponding track sequence by mining the real-time data and the historical data, and assigns and sorts the place marks in the track sequence, so that the calculation of the differential analysis result is simple, the data processing amount is small, and the differential analysis result is more accurate and reliable by considering the relevance and the difference between the main marks and the auxiliary marks.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the principles of the application. In the drawings:
FIG. 1 is a flow chart in an embodiment of the application;
fig. 2 is a system block diagram in an embodiment of the application.
Detailed Description
For the purpose of making apparent the objects, technical solutions and advantages of the present application, the present application will be further described in detail with reference to the following examples and the accompanying drawings, wherein the exemplary embodiments of the present application and the descriptions thereof are for illustrating the present application only and are not to be construed as limiting the present application.
Example 1: a student status analysis early warning method, as shown in figure 1, comprises the following steps:
s1: acquiring consumption information and card punching information in an authorization range according to user information, and establishing a real-time track distribution diagram of a corresponding user according to the consumption information and the card punching information;
s2: extracting a place mark in the real-time track distribution map, and constructing a real-time track sequence according to the frequency information of the place mark and the corresponding time information;
s3: training historical consumption information and punching information based on a deep learning algorithm, establishing a standard track distribution diagram, and establishing a standard track sequence according to the standard track distribution diagram by adopting a real-time track sequence construction method;
s4: after classifying all the place marks through a classifier, marking the place marks as corresponding main marks or auxiliary marks;
s5: and calculating an abnormal overlapping value according to the difference of the location marks in the real-time track sequence and the standard track sequence, and outputting abnormal early warning information if the abnormal overlapping value exceeds a basic abnormal threshold value.
In this embodiment, the consumption information is extracted from the collection information of the stores inside and around the campus, and the consumption information includes consumption details, consumption time and store location information; the card punching information is extracted from the lesson report information and library record information, and comprises learning point positioning information and learning time; and establishing a track point distribution map according to the store positioning information and the learning point positioning information, and sequentially connecting each track point in the track point distribution map according to the sequence of the consumption time and the learning time to obtain a real-time track distribution map.
The construction process of the real-time track sequence specifically comprises the following steps: matching according to the time information of the location mark to obtain a dynamic priority value of the corresponding frequency; calculating to obtain a sequencing priority value according to the dynamic priority values of all frequencies of the same place mark and the basic priority values of the corresponding place marks; and (3) carrying out descending order arrangement on different place marks according to the size of the sorting priority value to obtain a real-time track sequence.
The calculation formula of the sorting priority value of the place mark is specifically as follows:
wherein Y is n A ranking priority value representing the location indicator n; g n A base priority value representing a location marker n; y is n (t i ) The location mark n indicating the ith time at time t i The corresponding dynamic priority value.
Y is the same as that of the prior art n The value of (2) is positive and negative; positive values characterize positive effects, with greater absolute values indicating a higher degree of effect; negative values characterize negative effects, with larger absolute values indicating a higher degree of effect. And are sorted in absolute magnitude at the time of sorting.
The calculation formula of the abnormal superposition value is as follows:
wherein M represents an abnormal superposition value of the difference between the real-time track sequence and the standard track sequence; q represents the number of main marks in the real-time track sequence; y is Y j A ranking priority value representing the jth primary flag; j (j) 0 Representing all main marks of the same main mark in a standard track sequenceSequencing the sequence number value, if the corresponding main mark is a newly added main mark in the real-time track sequence, j 0 The value is q; epsilon j And the influence factor of the auxiliary mark covered by the j-th main mark is represented.
The calculation formula of the influence factor is specifically as follows:
k is the number of auxiliary marks covered by the corresponding main mark in the real-time track sequence, namely the number of auxiliary marks between the current main mark and the next main mark; p is the number of auxiliary marks covered by the corresponding main mark in the standard track sequence; y is Y j (k) A sorting priority value of a kth auxiliary mark covered by a jth main mark in the real-time track sequence is represented; y is Y j0 (p) represents the j-th in the standard track sequence 0 The p-th secondary flag covered by the primary flag.
Main signs include, but are not limited to, classroom sites, laboratory sites, canteen sites, bedroom sites, library sites, playground sites, restaurant sites; auxiliary signs include, but are not limited to, milky tea shops, internet cafes, gyms, bathhouses, leisure areas, barbecue spots, KTV spots, hotel spots.
It should be noted that, after the real-time data is analyzed, the historical data is added, and the historical data is retrained.
Example 2: a student status analysis early warning system, as shown in figure 2, comprises a distribution diagram construction module, a sequence construction module, a data training module, a sign classification module and an abnormality analysis module.
The distribution map construction module is used for acquiring consumption information and card punching information in an authorization range according to user information, and establishing a real-time track distribution map of a corresponding user according to the consumption information and the card punching information. The sequence construction module is used for extracting the location mark in the real-time track distribution diagram and constructing a real-time track sequence according to the frequency information of the location mark and the corresponding time information. The data training module is used for building a standard track distribution diagram after training the historical consumption information and the punching information based on the deep learning algorithm, and building the standard track sequence according to the standard track distribution diagram by adopting a real-time track sequence building method. The mark classification module is used for marking the place marks as corresponding main marks or auxiliary marks after classifying all the place marks through the classifier. The abnormality analysis module is used for calculating an abnormality overlapping value according to the difference of the location marks in the real-time track sequence and the standard track sequence, and outputting abnormality early warning information if the abnormality overlapping value exceeds a basic abnormality threshold value.
Working principle: after the authorization rights of the activity sites in the campus range and the campus periphery range are obtained, the data acquired in real time are compared with standard data trained according to historical data, so that the difference between corresponding students and the historical activity conditions can be accurately and reliably obtained, the early abnormal conditions of the students in the colleges and universities can be monitored by analyzing and early warning the student conditions based on the difference analysis results, and data support is provided for the condition management of the students in the colleges and universities; the corresponding track sequence is formed by mining the real-time data and the historical data, and the position marks in the track sequence are assigned and sequenced, so that the differential analysis result is simple to calculate, the data processing amount is small, and the differential analysis result is more accurate and reliable by considering the relevance and the difference between the main marks and the auxiliary marks.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing detailed description of the application has been presented for purposes of illustration and description, and it should be understood that the application is not limited to the particular embodiments disclosed, but is intended to cover all modifications, equivalents, alternatives, and improvements within the spirit and principles of the application.
Claims (6)
1. A student status analysis and early warning method is characterized by comprising the following steps:
acquiring consumption information and card punching information in an authorization range according to user information, and establishing a real-time track distribution diagram of a corresponding user according to the consumption information and the card punching information;
extracting a place mark in the real-time track distribution map, and constructing a real-time track sequence according to the frequency information of the place mark and the corresponding time information;
training historical consumption information and punching information based on a deep learning algorithm, establishing a standard track distribution diagram, and establishing a standard track sequence according to the standard track distribution diagram by adopting a real-time track sequence construction method;
after classifying all the place marks through a classifier, marking the place marks as corresponding main marks or auxiliary marks;
calculating an abnormal overlapping value according to the difference of the location marks in the real-time track sequence and the standard track sequence, and outputting abnormal early warning information if the abnormal overlapping value exceeds a basic abnormal threshold value;
the construction process of the real-time track sequence specifically comprises the following steps:
matching according to the time information of the location mark to obtain a dynamic priority value of the corresponding frequency;
calculating to obtain a sequencing priority value according to the dynamic priority values of all frequencies of the same place mark and the basic priority values of the corresponding place marks;
the different place marks are arranged in a descending order according to the size of the sorting priority value, and a real-time track sequence is obtained;
the calculation formula of the sorting priority value of the place mark specifically comprises the following steps:
wherein Y is n A ranking priority value representing the location indicator n; g n A base priority value representing a location marker n; y is n (t i ) The location mark n indicating the ith time at time t i The corresponding dynamic priority value;
the calculation formula of the abnormal added value is as follows:
wherein M represents an abnormal superposition value of the difference between the real-time track sequence and the standard track sequence; q represents the number of main marks in the real-time track sequence; y is Y j A ranking priority value representing the jth primary flag; j (j) 0 Representing the sequence number value of all main marks of the same main mark in the standard track sequence, if the corresponding main mark is a newly added main mark in the real-time track sequence, j 0 The value is q; epsilon j An influence factor representing the auxiliary mark covered by the j-th main mark;
the calculation formula of the influence factor is specifically as follows:
k is the number of auxiliary marks covered by the corresponding main mark in the real-time track sequence, namely the number of auxiliary marks between the current main mark and the next main mark; p is the number of auxiliary marks covered by the corresponding main mark in the standard track sequence; y is Y j (k) A sorting priority value of a kth auxiliary mark covered by a jth main mark in the real-time track sequence is represented; y is Y j0 (p) represents the j-th in the standard track sequence 0 The p-th secondary flag covered by the primary flag.
2. The student status analysis and early warning method according to claim 1, wherein the consumption information is extracted from collection information of stores in and around the campus, and the consumption information includes consumption details, consumption time and store location information;
the card punching information is extracted from the lesson report information and library record information, and comprises learning point positioning information and learning time;
and establishing a track point distribution map according to the store positioning information and the learning point positioning information, and sequentially connecting each track point in the track point distribution map according to the sequence of the consumption time and the learning time to obtain a real-time track distribution map.
3. The student status analysis pre-warning method according to claim 1 or 2, wherein the main sign comprises a classroom location, a laboratory location, a dining room location, a bedroom location, a library location, a playground location and a restaurant location; the auxiliary signs comprise a milky tea shop, an internet bar place, a gym place, a bathhouse place, a leisure area place, a barbecue place, a KTV place and a hotel place.
4. A student status analysis early warning system is characterized by comprising:
the distribution map construction module is used for acquiring consumption information and card punching information in an authorization range according to the user information and establishing a real-time track distribution map of a corresponding user according to the consumption information and the card punching information;
the sequence construction module is used for extracting the place marks in the real-time track distribution diagram and constructing a real-time track sequence according to the frequency information of the place marks and the corresponding time information;
the data training module is used for building a standard track distribution diagram after training historical consumption information and punching information based on a deep learning algorithm, and building a standard track sequence according to the standard track distribution diagram by adopting a real-time track sequence building method;
the mark classification module is used for marking the place marks as corresponding main marks or auxiliary marks after classifying all the place marks through the classifier;
the abnormality analysis module is used for calculating an abnormality overlapping value according to the difference of the location marks in the real-time track sequence and the standard track sequence, and outputting abnormality early warning information if the abnormality overlapping value exceeds a basic abnormality threshold value;
the construction process of the real-time track sequence specifically comprises the following steps:
matching according to the time information of the location mark to obtain a dynamic priority value of the corresponding frequency;
calculating to obtain a sequencing priority value according to the dynamic priority values of all frequencies of the same place mark and the basic priority values of the corresponding place marks;
the different place marks are arranged in a descending order according to the size of the sorting priority value, and a real-time track sequence is obtained;
the calculation formula of the sorting priority value of the place mark specifically comprises the following steps:
wherein Y is n A ranking priority value representing the location indicator n; g n A base priority value representing a location marker n; y is n (t i ) The location mark n indicating the ith time at time t i The corresponding dynamic priority value;
the calculation formula of the abnormal added value is as follows:
wherein M represents an abnormal superposition value of the difference between the real-time track sequence and the standard track sequence; q represents the number of main marks in the real-time track sequence; y is Y j A ranking priority value representing the jth primary flag; j (j) 0 Representing the sequence number value of all main marks of the same main mark in the standard track sequence, if the corresponding main mark is a newly added main mark in the real-time track sequence, j 0 The value is q; epsilon j An influence factor representing the auxiliary mark covered by the j-th main mark;
the calculation formula of the influence factor is specifically as follows:
k is the number of auxiliary marks covered by the corresponding main mark in the real-time track sequence, namely the number of auxiliary marks between the current main mark and the next main mark; p is the number of auxiliary marks covered by the corresponding main mark in the standard track sequence;Y j (k) A sorting priority value of a kth auxiliary mark covered by a jth main mark in the real-time track sequence is represented; y is Y j0 (p) represents the j-th in the standard track sequence 0 The p-th secondary flag covered by the primary flag.
5. A computer terminal comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements a student status analysis and early warning method as claimed in any one of claims 1 to 3 when the program is executed by the processor.
6. A computer readable medium having a computer program stored thereon, wherein the computer program is executed by a processor to implement a student status analysis pre-warning method as claimed in any one of claims 1 to 3.
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| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
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| CN109462818A (en) * | 2018-09-27 | 2019-03-12 | 北京奇虎科技有限公司 | A kind of method, apparatus and terminal of identification user trip track |
| CN109636062A (en) * | 2018-12-25 | 2019-04-16 | 湖北工业大学 | A kind of students ' behavior analysis method and system based on big data analysis |
| CN110751164A (en) * | 2019-03-01 | 2020-02-04 | 西安电子科技大学 | Old man travel abnormity detection method based on location service |
| CN111179136A (en) * | 2019-08-01 | 2020-05-19 | 北京百分点信息科技有限公司 | Dynamic control method and device and electronic equipment |
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| JP2009140360A (en) * | 2007-12-07 | 2009-06-25 | Toshiba Corp | Behavior analysis device and method |
| CN105894119A (en) * | 2016-04-05 | 2016-08-24 | 成都寻道科技有限公司 | Student ranking prediction method based on campus data |
| CN109462818A (en) * | 2018-09-27 | 2019-03-12 | 北京奇虎科技有限公司 | A kind of method, apparatus and terminal of identification user trip track |
| CN109636062A (en) * | 2018-12-25 | 2019-04-16 | 湖北工业大学 | A kind of students ' behavior analysis method and system based on big data analysis |
| CN110751164A (en) * | 2019-03-01 | 2020-02-04 | 西安电子科技大学 | Old man travel abnormity detection method based on location service |
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