Self-study seat occupation detection management method and system based on multivariate data analysis

Abstract

The invention discloses a self-study seat occupying detection management method and system based on multivariate data analysis, and belongs to the technical field of teaching resource management, and the method comprises the steps: pasting an NFC card on the desktop of each self-study seat of a self-study room, installing a monitoring sensor on each self-study seat, building a self-study room four-dimensional model, and carrying out the detection of the self-study seat occupying detection management. Students reserve idle self-study seats in the self-study room four-dimensional model, NFC cards on the reserved self-study seats are attached and logged in, the monitoring sensor performs real-time monitoring, and the states of the self-study seats in the self-study room four-dimensional model are changed according to whether the self-study seats are reserved or not, whether the NFC cards are attached or not and monitoring data of the monitoring sensor. A classroom self-study detection model is established, and when the self-study room four-dimensional model does not have idle self-study seats in the current time period, the classroom self-study detection model is used for detecting idle classrooms, obtaining current idle classrooms and recommending the idle seats to students, so that whether the current self-study seats are reserved or not can be judged, disputes caused by the occupation phenomenon are avoided, and the efficiency is improved. And convenience is brought to students.

Description

Technical Field

[0001] The present application belongs to the technical field of teaching resource management, and specifically relates to a method and system for self-study seat occupancy detection and management based on multivariate data analysis. Background Art

[0002] The library's study room has always been the best place for college students to study because of its quiet environment and abundant reference materials. This has made study room seats a scarce resource. Due to the shortage of seat resources, the phenomenon of seat occupation has become a troublesome problem in the daily management of the study room.

[0003] At the same time, the phenomenon of seat occupancy in study rooms is obvious. The seat status of the study room is often unknown to both the demanders and the administrators. This greatly increases the return rate of seat users and the time spent looking for seats. In addition, the administrators are unable to use the most effective method to exercise their authority to allocate seat resources, which has also exacerbated the contradiction of seat resource shortage to a certain extent.

[0004] Therefore, how to reasonably manage seating resources, establish a relatively fair seating management mechanism, and effectively avoid the phenomenon of seat occupancy are issues that must be considered in the daily management of the library. Summary of the Invention

[0005] To solve the above problems and technical defects, this application adopts the following technical solution, a self-study seat occupancy detection and management method based on multivariate data analysis, including:

[0006] An NFC card is attached to the tabletop of each study seat in the study room, and a monitoring sensor is installed on each study seat to build a four-dimensional model of the study room;

[0007] Students reserve an available study seat in the four-dimensional model of the study room, attach an NFC card to the reserved seat and log in, and the monitoring sensor conducts real-time monitoring;

[0008] The status of the study seats in the four-dimensional model of the study room changes according to whether they are reserved, whether the NFC card is attached, and the monitoring data of the monitoring sensor;

[0009] A classroom self-study detection model is established. When there are no vacant study seats in the four-dimensional model of the study room in the current time period, the classroom self-study detection model is used to detect vacant classrooms, obtain the current vacant classrooms, and recommend vacant seats to students.

[0010] Preferably, the process of establishing the four-dimensional model of the study room is as follows:

[0011] A three-dimensional model of the study room is created based on the distribution of the study rooms and the arrangement of the study seats. Each study seat is associated with a seat number. Each study seat has a corresponding seat number, and each NFC card is associated with a seat number.

[0012] The reservation time or usage time is displayed on each study seat, and a four-dimensional model of the study room is established. Different color labels are displayed on the study seats according to the reservation time or usage time of the study seats.

[0013] Furthermore, the state change includes:

[0014] S1. The initial state of the study seats on the four-dimensional model of the study room is that there is no one;

[0015] S2. Students log in to the 4D model of the study room and make a reservation for an unoccupied study seat. The seat will be reserved during the specified study time and will remain unoccupied during other time periods.

[0016] S3. When the self-study time arrives, the user uses their mobile phone to attach the NFC card on the reserved self-study seat to log in, and the self-study seat on the 4D model of the study room is changed to the occupied state;

[0017] S4. When the student reaches the self-study start time, the timer starts counting. If the timer reaches the preset time threshold and the student has not logged in, the self-study seat on the four-dimensional model of the study room will exit the reservation state and change to an unoccupied state.

[0018] Furthermore, the self-study reservation includes: self-study seat selection and self-study time range filling, and the self-study seat selection includes autonomous selection and random selection;

[0019] Random selection includes: binding student information, obtaining students' historical self-study information, extracting seating habits based on students' historical self-study information, and giving priority to students' frequently used seats.

[0020] Furthermore, when students randomly select study seats, students with similar study time ranges will be selected to the same study room or adjacent study seats based on their study time ranges.

[0021] Furthermore, the monitoring sensor includes: a human infrared sensor, a temperature sensor, and a pressure sensor. The temperature sensor is installed on the seat, the pressure sensor is installed under the study table, and the human infrared sensor is installed on the side of the study seat.

[0022] When the human infrared sensor detects that there is a student in the current study seat, and the corresponding NFC card in the four-dimensional model of the study room is logged in and attached, the temperature sensor and pressure sensor start monitoring;

[0023] When the preset time period of the current study seat has not been reached and the human infrared sensor detects that the student in the current study seat has left, the temperature sensor and pressure sensor start timing monitoring;

[0024] When the preset time is reached and the student who left has not returned, calculations are performed based on the data monitored by the temperature sensor and pressure sensor. Based on the calculation results, it is determined whether the student in the current study seat has left, and the status of the study seat is changed.

[0025] Furthermore, the calculation formula based on the data monitored by the temperature sensor and the pressure sensor is as follows:

[0026] T=((μ*logα) 2 +(λ*∏β) 2 ) / θ

[0027] Among them, α is the temperature change value of the temperature sensor within the preset time period, β is the pressure change value of the pressure sensor within the preset time period, μ is the temperature calculation coefficient, λ is the pressure calculation coefficient, θ is the time difference between the student's departure time and the appointment time, and T is the calculation result judgment value. When T is greater than or equal to the preset threshold, it is judged that the student in the current study seat has left.

[0028] A self-study seat occupancy detection and management system based on multivariate data analysis, comprising:

[0029] The NFC card module is used to stick on the desk of each study seat in the study room and bind it to the seat number. Students use their mobile phones to attach it, so that the student account and seat number can be verified and logged in;

[0030] The study room four-dimensional model module is used to establish a four-dimensional model of the study room based on the distribution of the study rooms, the arrangement of the study seats and the current seat status;

[0031] The seat status change module is used to change the status of the study seat according to the reservation status of the study seat and the attachment status of the NFC card module.

[0032] An electronic device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the contents of the self-study seat occupancy detection and management system based on multivariate data analysis as described above are implemented.

[0033] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the content of the self-study seat occupancy detection and management system based on multivariate data analysis as described above.

[0034] Compared with the prior art, the present invention has the following advantages:

[0035] (1) This application establishes a four-dimensional model of the study room, and sticks NFC cards on the desks of the study seats, allowing students to make reservations. After making a reservation, students can use their mobile phones and NFC cards to log in on site and verify the reserved study seats. In this way, students who have not made a reservation can determine whether the current study seat is reserved, avoiding disputes due to seat occupancy.

[0036] (2) This application establishes a three-dimensional model of the study room based on the distribution of the study rooms and the arrangement of the study seats, binds each study seat to a seat number, displays the reservation time or usage time on each study seat, and establishes a four-dimensional model of the study room. When students make reservations, they can intuitively see the status of the study seats and thus choose the appropriate study seat for reservation, which brings convenience to students;

[0037] (3) This application will give priority to students’ frequently used seats based on their chosen study seats, filled-in study time range, and seating habits. At the same time, students with similar study time ranges will be assigned to the same study room or adjacent study seats to avoid affecting the learning efficiency of students who study for a long time due to the frequent changes of students who study for a short time around them.

[0038] (4) This application uses monitoring sensors to monitor the current status of study seats in real time. When a student leaves before the deadline of the reservation time period, the monitoring sensor monitors and calculates, and determines whether the student has left and is no longer using the current study seat based on the calculation results, thereby avoiding the situation where a vacant seat becomes idle due to the student's temporary departure and the status is not updated in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] In the attached figure:

[0040] Figure 1 A schematic diagram of the method steps of an embodiment of the present application;

[0041] Figure 2 A schematic diagram of the system structure of an embodiment of the present application;

[0042] Figure 3 This is a schematic diagram of the device structure of an embodiment of the present application. DETAILED DESCRIPTION

[0043] In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below in combination with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Generally, the components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations.

[0044] Example 1: Figure 1As shown, a self-study seat occupancy detection and management method based on multivariate data analysis includes:

[0045] An NFC card is pasted on the table of each study seat in the study room, and a monitoring sensor is installed on each study seat. Each study seat has a corresponding seat number, and each NFC card is bound to a seat number.

[0046] Establish a four-dimensional model of the study room. The establishment process is as follows:

[0047] Build a 3D model of the study room based on the distribution of the study rooms and the arrangement of the study seats, and bind each study seat to a seat number;

[0048] Display the reservation time or usage time on each study seat and build a four-dimensional model of the study room;

[0049] Different color labels are displayed on the study seats according to the reservation time or usage time of the study seats.

[0050] Students reserve an available study seat in the four-dimensional model of the study room, attach an NFC card to the reserved seat and log in, and the monitoring sensor conducts real-time monitoring;

[0051] The study seats in the 4D model of the study room change their status based on whether they are reserved or not and whether an NFC card is attached. Status changes include:

[0052] S1. The initial state of the study seats on the four-dimensional model of the study room is that there is no one;

[0053] S2. Students log in to the 4D model of the study room and make a reservation for an unoccupied study seat. The seat will be reserved during the specified study time and will remain unoccupied during other time periods.

[0054] S3. When the self-study time arrives, the user uses their mobile phone to attach the NFC card on the reserved self-study seat to log in, and the self-study seat on the 4D model of the study room is changed to the occupied state;

[0055] S4. When the student reaches the self-study start time, the timer starts counting. If the timer reaches the preset time threshold and the student has not logged in, the self-study seat on the four-dimensional model of the study room will exit the reservation state and change to an unoccupied state.

[0056] After the self-study period ends, students can choose whether to continue their self-study.

[0057] If you do not choose to extend the time, the study seat on the 4D model of the study room will be changed to an empty state;

[0058] If you choose to extend the time, determine whether there is anyone else who has made a reservation for the study seat after the end time. If not, extend the time. If there is someone else who has made a reservation, select another unoccupied study seat to make a new reservation.

[0059] If a student who has not made a reservation attaches an NFC card to a reserved seat, the student will be prompted that the current study seat has been reserved.

[0060] Self-study reservation includes: self-study seat selection and self-study time range. Self-study seat selection includes self-selection and random selection.

[0061] Random selection includes: binding student information, obtaining students' historical self-study information, extracting seating habits based on students' historical self-study information, and giving priority to students' frequently used seats.

[0062] When students randomly select study seats, students with similar study time ranges will be selected to the same study room or adjacent study seats based on their study time ranges.

[0063] The monitoring sensors include: human infrared sensor, temperature sensor, and pressure sensor. The temperature sensor is installed on the seat, the pressure sensor is installed under the study table, and the human infrared sensor is installed on the side of the study seat.

[0064] When the human infrared sensor detects that there is a student in the current study seat, and the corresponding NFC card in the four-dimensional model of the study room is logged in and attached, the temperature sensor and pressure sensor start monitoring;

[0065] When the preset time period of the current study seat has not been reached and the human infrared sensor detects that the student in the current study seat has left, the temperature sensor and pressure sensor start timing monitoring;

[0066] When the preset time is reached and the student who left has not returned, calculations are performed based on the data monitored by the temperature sensor and pressure sensor. Based on the calculation results, it is determined whether the student in the current study seat has left, and the status of the study seat is changed.

[0067] The calculation formula based on the data monitored by the temperature sensor and pressure sensor is as follows:

[0068] T=((μ*logα) 2 +(λ*∏β) 2 ) / θ

[0069] Among them, α is the temperature change value of the temperature sensor within the preset time period, β is the pressure change value of the pressure sensor within the preset time period, μ is the temperature calculation coefficient, λ is the pressure calculation coefficient, θ is the time difference between the student's departure time and the appointment time, and T is the calculation result judgment value. When T is greater than or equal to the preset threshold, it is judged that the student in the current study seat has left.

[0070] A classroom self-study detection model is established. When there are no vacant study seats in the four-dimensional model of the study room in the current time period, the classroom self-study detection model is used to detect vacant classrooms, obtain the current vacant classrooms, and recommend vacant seats to students.

[0071] When there is no vacant study seat at the self-study start time filled in by the student, an unoccupied classroom will be recommended and the student will enter the study seat waiting queue at the same time;

[0072] Recommendations for unmanned classrooms include:

[0073] Enter the course schedule of each class this semester, conduct course data statistics, and obtain idle classrooms without teaching tutorials during self-study time;

[0074] Access the classroom monitoring system, analyze the target classroom, and determine whether there is anyone in the target classroom;

[0075] If there is no one, make a recommendation for the target classroom;

[0076] If there is someone, monitor and analyze the behavior of the person in the surveillance video to determine whether the person's behavior threshold is less than the preset behavior threshold. If so, recommend the target classroom.

[0077] If there are people in the target classroom during class activities or club activities, no recommendation will be made. If the people in the classroom are also students studying, the characters' movements are small and the students studying will not interfere with each other, so the classroom will be recommended.

[0078] Example 2: Figure 2 As shown, a self-study seat occupancy detection and management system based on multivariate data analysis includes:

[0079] The NFC card module is used to stick on the desk of each study seat in the study room and bind it to the seat number. Students use their mobile phones to attach it, so that the student account and seat number can be verified and logged in;

[0080] The study room four-dimensional model module is used to establish a four-dimensional model of the study room based on the distribution of the study rooms, the arrangement of the study seats and the current seat status;

[0081] The seat status change module is used to change the status of the study seat according to the reservation status of the study seat and the attachment status of the NFC card module.

[0082] The NFC card module includes a QR code scanning module, which is used to stick a QR code on the NFC card module so that students can log in by scanning the QR code.

[0083] Example 3: Figure 3 As shown, from the hardware level, the present application provides an embodiment of an electronic device that implements all or part of the content of a self-study seat occupancy detection and management method based on multivariate data analysis. The electronic device includes a service processor and a distributed memory. The service processor is connected to the memory. The distributed memory stores a service self-management program configured to store machine-readable instructions. The service processor executes the service self-management program. When the instructions are executed by the processor, they implement a self-study seat occupancy detection and management method based on multivariate data analysis as described above.

[0084] From a hardware perspective, in order to effectively improve the flexibility, versatility, and efficiency of data collection, the present application provides an embodiment of an electronic device that implements all or part of the self-study seat occupancy detection and management method based on multivariate data analysis. The electronic device specifically includes the following:

[0085] A processor, a memory, a communications interface, and a bus; wherein the processor, the memory, and the communications interface communicate with each other via the bus; the communications interface is used to implement information transmission between the self-study seat occupancy detection and management method based on multivariate data analysis and related devices such as core business systems, user terminals, and related databases; the logic controller can be a desktop computer, a tablet computer, a mobile terminal, etc., but this embodiment is not limited thereto. In this embodiment, the logic controller can be implemented with reference to an embodiment of a self-study seat occupancy detection and management method based on multivariate data analysis in the embodiment, as well as an embodiment of a data acquisition device based on a distributed model, the contents of which are incorporated herein and repeated parts are not repeated.

[0086] It is understandable that the user terminal may include a smart phone, a tablet electronic device, a network set-top box, a portable computer, a desktop computer, a personal digital assistant (PDA), a vehicle-mounted device, a smart wearable device, etc. Among them, the smart wearable device may include smart glasses, a smart watch, a smart bracelet, etc.

[0087] In actual applications, part of the self-study seat occupancy detection and management method based on multivariate data analysis can be executed on the electronic device side as described above, or all operations can be completed on the client device. The specific selection can be based on the processing power of the client device and the limitations of the user's usage scenario, etc., and this application does not limit this. If all operations are completed on the client device, the client device may also include a processor.

[0088] The above-mentioned client device may have a communication module (i.e., a communication unit), which can communicate with a remote server to realize data transmission with the server. The server may include a server on the task scheduling center side, and other implementation scenarios may also include a server on an intermediate platform, such as a server on a third-party server platform that has a communication link with the task scheduling center server. The server may include a single computer device, or a server cluster consisting of multiple servers, or a server structure of a distributed device.

[0089] Example 4: The embodiments of the present application also provide a computer-readable storage medium that can implement the entire contents of the self-study seat occupancy detection and management method based on multivariate data analysis in the above-mentioned embodiment, in which the execution subject is a server or a client. The computer-readable storage medium stores a computer program, which, when executed by a processor, implements the entire contents of the self-study seat occupancy detection and management method based on multivariate data analysis in which the execution subject is a server or a client in the above-mentioned embodiment.

[0090] The embodiments of the present application may be provided as methods, apparatuses, or computer program products. Thus, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0091] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (apparatus), and computer program products according to the embodiments of the present application. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of the processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0092] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0093] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0094] The above-described embodiments merely represent preferred implementations of the present application. While the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present application. It should be noted that a person skilled in the art would be able to make numerous variations, improvements, and substitutions without departing from the spirit of the present application, all of which fall within the scope of protection of the present application.

Claims

1. A self-study seat occupancy detection and management method based on multivariate data analysis, characterized in that: include: An NFC card is attached to the tabletop of each study seat in the study room, and a monitoring sensor is installed on each study seat to build a four-dimensional model of the study room; Students reserve an available study seat in the four-dimensional model of the study room, attach an NFC card to the reserved seat and log in, and the monitoring sensor conducts real-time monitoring; The status of the study seats in the four-dimensional model of the study room changes according to whether they are reserved, whether the NFC card is attached, and the monitoring data of the monitoring sensor; A classroom self-study detection model is established. When there are no vacant study seats in the four-dimensional model of the study room in the current time period, the classroom self-study detection model is used to detect vacant classrooms, obtain the current vacant classrooms, and recommend vacant seats to students.

2. The method for detecting and managing self-study seat occupancy based on multivariate data analysis according to claim 1, characterized in that: The process of establishing the four-dimensional model of the study room is as follows: A three-dimensional model of the study room is created based on the distribution of the study rooms and the arrangement of the study seats. Each study seat is associated with a seat number. Each study seat has a corresponding seat number, and each NFC card is associated with a seat number. The reservation time or usage time is displayed on each study seat, and a four-dimensional model of the study room is established. Different color labels are displayed on the study seats according to the reservation time or usage time of the study seats.

3. The method for detecting and managing self-study seat occupancy based on multivariate data analysis according to claim 1, characterized in that: The status changes include: S1. The initial state of the study seats on the four-dimensional model of the study room is that there is no one; S2. Students log in to the 4D model of the study room and make a reservation for an unoccupied study seat. The seat will be reserved during the specified study time and will remain unoccupied during other time periods. S3. When the self-study time arrives, the user uses their mobile phone to attach the NFC card on the reserved self-study seat to log in, and the self-study seat on the 4D model of the study room is changed to the occupied state; S4. When the student reaches the self-study start time, the timer starts counting. If the timer reaches the preset time threshold and the student has not logged in, the self-study seat on the four-dimensional model of the study room will exit the reservation state and change to an unoccupied state.

4. The method for detecting and managing self-study seat occupancy based on multivariate data analysis according to claim 3, characterized in that: The self-study reservation includes: self-study seat selection and self-study time range filling, and self-study seat selection includes self-selection and random selection; Random selection includes: binding student information, obtaining students' historical self-study information, extracting seating habits based on students' historical self-study information, and giving priority to students' frequently used seats.

5. The method for detecting and managing self-study seat occupancy based on multivariate data analysis according to claim 4, characterized in that: When students randomly select study seats, students with similar study time ranges will be selected to the same study room or adjacent study seats based on their study time ranges.

6. The method for detecting and managing self-study seat occupancy based on multivariate data analysis according to claim 1, characterized in that: The monitoring sensors include: a human infrared sensor, a temperature sensor, and a pressure sensor. The temperature sensor is installed on the seat, the pressure sensor is installed under the study table, and the human infrared sensor is installed on the side of the study seat. When the human infrared sensor detects that there is a student in the current study seat, and the corresponding NFC card in the four-dimensional model of the study room is logged in and attached, the temperature sensor and pressure sensor start monitoring; When the preset time period of the current study seat has not been reached and the human infrared sensor detects that the student in the current study seat has left, the temperature sensor and pressure sensor start timing monitoring; When the preset time is reached and the student who left has not returned, calculations are performed based on the data monitored by the temperature sensor and pressure sensor. Based on the calculation results, it is determined whether the student in the current study seat has left, and the status of the study seat is changed.

7. The method for detecting and managing self-study seat occupancy based on multivariate data analysis according to claim 6, characterized in that: The calculation formula based on the data monitored by the temperature sensor and pressure sensor is as follows: T=((μ*logα) 2 +(λ*∏β) 2 ) / θ Among them, α is the temperature change value of the temperature sensor within the preset time period, β is the pressure change value of the pressure sensor within the preset time period, μ is the temperature calculation coefficient, λ is the pressure calculation coefficient, θ is the time difference between the student's departure time and the appointment time, and T is the calculation result judgment value. When T is greater than or equal to the preset threshold, it is judged that the student in the current study seat has left.

8. A self-study seat occupancy detection and management system based on multivariate data analysis, characterized in that: include: The NFC card module is used to stick on the desk of each study seat in the study room and bind it to the seat number. Students use their mobile phones to attach it, so that the student account and seat number can be verified and logged in; The study room four-dimensional model module is used to establish a four-dimensional model of the study room based on the distribution of the study rooms, the arrangement of the study seats and the current seat status; The seat status change module is used to change the status of the study seat according to the reservation status of the study seat and the attachment status of the NFC card module.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the content of the self-study seat occupancy detection and management system based on multivariate data analysis described in claim 1 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the content of the self-study seat occupancy detection and management system based on multivariate data analysis described in claim 1 is realized.

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