Student informatization management method and system based on Internet of Things

Abstract

The invention discloses a student informatization management method and system based on the Internet of Things. The method comprises the following steps: acquiring basic information of students; inquiring all idle classrooms and specific positions of the idle classrooms in the self-study time period, connecting the idle classrooms according to a time sequence, and connecting the idle classrooms with a departure place and a destination to form an initial transfer path; evaluating the number of times of the student for transferring the study room, and screening the initial transfer path based on the number of times; a grey wolf optimization algorithm is adopted to optimize the screened initial transfer path based on the shortest moving path, and an optimal transfer path is obtained; after the free classrooms are inquired by acquiring the self-study time periods of the students, the free classrooms can be connected into an initial transfer path, and then screening based on the number of times and optimization based on the shortest moving distance are carried out, so that the optimal transfer path meeting the number of times of the students and the shortest moving distance is obtained. Based on the optimal transfer path, not only can the classroom be quickly transferred to the idle classroom for self-study, but also the transfer path can be shortened.

Description

Technical Field

[0001] The present invention relates to the field of information processing technology, and in particular to a student information management method and system based on the Internet of Things. Background Art

[0002] In recent years, with the popularization of the Internet of Things, smart campuses have also developed rapidly. By obtaining information about students, teachers and classrooms in schools, comprehensive resource management and teaching planning can be carried out. However, most smart campuses only plan classrooms that need to be used, and students will go to vacant classrooms for self-study during normal study, especially on the eve of exams. However, due to the lack of information management of vacant classrooms, students will be transferred to other classrooms during a self-study period due to subsequent course arrangements in vacant classrooms. Due to the lack of information management of vacant classrooms, students need to spend a lot of time looking for other vacant classrooms, which affects their independent review. Summary of the invention

[0003] In view of this, the present invention proposes a student information management method and system based on the Internet of Things, which can plan available idle classrooms based on students' self-study time periods and meet students' requirements for the number of transfers and distances.

[0004] The technical solution of the present invention is achieved in this way:

[0005] A student information management method based on the Internet of Things includes the following steps:

[0006] Step S1, obtaining basic information of the student, the basic information including self-study time period, departure place and destination;

[0007] Step S2, query all free classrooms and specific locations of free classrooms during the self-study period, connect the free classrooms in chronological order, and connect them with the departure place and the destination to form an initial transfer path;

[0008] Step S3: Evaluate the number of times students need to transfer to the study room, and screen the initial transfer path based on the number of times required;

[0009] Step S4: Use the Grey Wolf Optimization Algorithm to optimize the selected initial transfer paths based on the shortest moving distance, and obtain the optimal transfer path.

[0010] Preferably, the specific steps of step S1 are:

[0011] Step S11, responding to a self-study request initiated by a student, and querying the historical self-study time period of the corresponding student;

[0012] Step S12: Feedback the historical self-study time period to the student, and let the student decide whether to change the self-study time period;

[0013] Step S13: The student actively inputs the departure place and destination before and after the self-study session.

[0014] Preferably, the specific steps of querying all free classrooms and specific locations of free classrooms within the self-study time period in step S2 are:

[0015] Step S21, obtaining the self-study time period of the students, searching for classrooms that are not included in the course schedule during the self-study time period, and outputting them as free classrooms;

[0016] Step S22: Obtain a planning distribution map of the teaching building, and find the specific location of the vacant classroom from the planning distribution map.

[0017] Preferably, the specific steps of connecting the idle classrooms in chronological order and connecting them with the departure place and the destination to form an initial transfer path in step S2 are:

[0018] Step S23: randomly select a suitable free classroom according to the initial time of the self-study time period;

[0019] Step S24: when the use time of an idle classroom is earlier than the end time of the self-study time period, the next idle classroom that meets the conditions is randomly selected starting from the use time of the previous idle classroom, until the use time of the selected idle classroom is later than the end time of the self-study time period, and the selected idle classrooms are connected into a classroom connection path in chronological order;

[0020] Step S25, repeat steps S23-S24 to obtain multiple classroom connection paths, and connect the departure point and the destination to the beginning and end of the classroom connection path respectively to form an initial transfer path.

[0021] Preferably, the specific steps of evaluating the number of times students need to transfer study rooms in step S3 are:

[0022] Step S31, collecting the self-study supplies used during the self-study period input by the students;

[0023] Step S32: using an expert scoring method to evaluate the quantity and weight of the self-study supplies and obtain a transfer difficulty value;

[0024] Step S33: When the transfer difficulty value is greater than a preset threshold, feedback is given to the student, who then determines whether there is a need to transfer to the study room a certain number of times and obtains the limited number of times.

[0025] Preferably, the specific steps of screening the initial transfer path based on the number requirement in step S3 are:

[0026] Step S34, obtaining the number of transfers to different free classrooms in each initial transfer path;

[0027] Step S35: Eliminate the initial transfer paths whose transfer times are greater than the limit times.

[0028] Preferably, the specific steps of step S4 are:

[0029] Step S41, initializing the gray wolf population and the number of iterations;

[0030] Step S42, randomly selecting an initial transfer path after screening, and calculating the fitness value of the gray wolf based on the shortest moving distance;

[0031] Step S43, selecting another screened initial transfer path, calculating the fitness value, comparing it with the fitness value calculated last time, and retaining the initial transfer path with the larger fitness value;

[0032] Step S44: perform iterative calculation to obtain the initial transfer path with the maximum fitness value, and output it as the optimal transfer path.

[0033] Preferably, the number of iterations is the number of initial transfer paths after screening.

[0034] A student information management system based on the Internet of Things, comprising:

[0035] An acquisition unit, used to acquire basic information of students, including self-study time period, departure place and destination;

[0036] A query unit, used to query all free classrooms and specific locations of free classrooms during the self-study period, connect the free classrooms in chronological order, and connect them with the departure place and the destination to form an initial transfer path;

[0037] An evaluation unit, used to evaluate the number of times students need to transfer to study rooms, and screen the initial transfer path based on the number of times required;

[0038] An optimization unit is used to optimize the selected initial transfer path based on the shortest moving distance using the gray wolf optimization algorithm and obtain the optimal transfer path;

[0039] The acquisition unit, the query unit, the evaluation unit and the optimization unit are sequentially data-connected.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] The student information management method and system based on the Internet of Things of the present invention can plan a number of initial transfer paths including idle classrooms according to the students' self-study time period. The idle classrooms in the initial transfer paths can all be used for students to study. Then, it is evaluated whether the students have a number of transfer requirements. If there is a number of transfer requirements, the initial transfer paths can be screened, and then the screened initial transfer paths can be optimized by the gray wolf optimization algorithm to obtain the shortest transfer path. Students can move from a departure point to an idle classroom according to the optimal transfer path, and transfer between idle classrooms, and go to the destination after the self-study. The students' moving distance in the optimal transfer path is the shortest, which can save time for transferring in idle classrooms, and at the same time meet the students' needs for the number of transfers, thereby ensuring that suitable idle classrooms are provided for students. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only preferred embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0043] Figure 1 A flow chart of a student information management method based on the Internet of Things of the present invention;

[0044] Figure 2 This is a flow chart of step S1 of a student information management method based on the Internet of Things of the present invention;

[0045] Figure 3 This is a flow chart of step S2 of a student information management method based on the Internet of Things of the present invention;

[0046] Figure 4 A flowchart of step S3 of a student information management method based on the Internet of Things of the present invention;

[0047] Figure 5 A flowchart of step S4 of a student information management method based on the Internet of Things of the present invention;

[0048] Figure 6 A schematic diagram of a student information management system based on the Internet of Things of the present invention;

[0049] In the figure, 1, acquisition unit; 2, query unit; 3, evaluation unit; 4, optimization unit. DETAILED DESCRIPTION

[0050] In order to better understand the technical content of the present invention, a specific embodiment is provided below, and the present invention is further described in conjunction with the accompanying drawings.

[0051] See also Figures 1 to 5 The present invention provides a student information management method based on the Internet of Things, comprising the following steps:

[0052] Step S1, obtaining basic information of the student, the basic information including self-study time period, departure place and destination;

[0053] Step S2, query all free classrooms and specific locations of free classrooms during the self-study period, connect the free classrooms in chronological order, and connect them with the departure place and the destination to form an initial transfer path;

[0054] Step S3: Evaluate the number of times students need to transfer to the study room, and screen the initial transfer path based on the number of times required;

[0055] Step S4: Use the Grey Wolf Optimization Algorithm to optimize the selected initial transfer paths based on the shortest moving distance, and obtain the optimal transfer path.

[0056] The invention discloses a student information management method based on the Internet of Things, which is used to manage the self-study schedule of students on campus. When students need to study in a self-study room, it is necessary to determine available idle classrooms, and whether the classroom is idle is determined according to its teaching arrangement. The teaching arrangement time of different classrooms is different. During the entire self-study period of students, some classrooms may change from an idle state to a state of having classes, so there will be a process of transferring to other idle classrooms. Especially when the self-study period is relatively long, the number of transfers may increase. The management method of the invention can quickly obtain idle classrooms, and the idle classrooms are combined into transfer paths. Students can transfer to different idle classrooms according to the transfer paths, eliminating the process of independently searching for idle classrooms.

[0057] In addition to obtaining vacant classrooms, the present invention also plans the paths for students to move from a departure point to an vacant classroom, between vacant classrooms, and from an vacant classroom to a destination. Before querying an vacant classroom, the self-study time period, the departure point, and the destination of the students are first obtained. At different times during the self-study time period, the number and location of vacant classrooms will change. Therefore, after all the vacant classrooms that can be used during the entire self-study time period are queried, the available vacant classrooms are connected in chronological order, and the departure point and the destination are connected at both ends of all the vacant classrooms to form an initial transfer path. The initial transfer path includes the students going from the departure point to the vacant classroom for self-study and The idle classrooms are transferred when there are classes, and finally the entire path from the idle classrooms to the destination, all initial transfer paths can meet the students' requirements for self-study, but some paths may have too many transfers and too long moving distances. Therefore, we first evaluate the number of times students need to transfer to the study room. If there is a number requirement, the initial transfer paths are screened based on the number requirement, and the initial transfer paths with more transfers are screened out. Then, the gray wolf optimization algorithm is used to search for the shortest moving distance to obtain the optimal transfer path, where the optimal transfer path can meet the students' needs for the number of transfers, and at the same time shorten the moving path, avoiding students moving for a long time and wasting self-study time.

[0058] Preferably, the specific steps of step S1 are:

[0059] Step S11, responding to a self-study request initiated by a student, and querying the historical self-study time period of the corresponding student;

[0060] Step S12: Feedback the historical self-study time period to the student, and let the student decide whether to change the self-study time period;

[0061] Step S13: The student actively inputs the departure place and destination before and after the self-study session.

[0062] Most students have a habit of studying at school. Therefore, when obtaining students' basic information, students take the initiative to initiate a request for planning a transfer route. After responding to the self-study request initiated by the students, the historical self-study time periods of the students are queried through big data. The historical self-study time periods can be fed back to the students, and the students can judge whether the current self-study time period is the same as the historical time period. If they are different, students will take the initiative to change the self-study time period, thereby shortening the time for data acquisition. After determining the self-study time period, students are also required to enter the departure place before the start of this self-study and the destination after the end of the self-study, so as to facilitate route planning.

[0063] Preferably, the specific steps of step S2 are:

[0064] Step S21, obtaining the self-study time period of the students, searching for classrooms that are not included in the course schedule during the self-study time period, and outputting them as free classrooms;

[0065] Step S22: Obtain a planning distribution map of the teaching building, and find the specific location of the vacant classroom from the planning distribution map.

[0066] Step S23: randomly select a suitable free classroom according to the initial time of the self-study time period;

[0067] Step S24: when the use time of an idle classroom is earlier than the end time of the self-study time period, the next idle classroom that meets the conditions is randomly selected starting from the use time of the previous idle classroom, until the use time of the selected idle classroom is later than the end time of the self-study time period, and the selected idle classrooms are connected into a classroom connection path in chronological order;

[0068] Step S25, repeat steps S23-S24 to obtain multiple classroom connection paths, and connect the departure point and the destination to the beginning and end of the classroom connection path respectively to form an initial transfer path.

[0069] During the entire self-study period, there may be changes in the vacant classrooms. Therefore, the selection of vacant classrooms needs to be strictly determined according to the time sequence of the self-study period. First, all available vacant classrooms are obtained according to the time of the self-study period. The specific acquisition method is: when the idle period of the classroom overlaps with the self-study period, the classroom can be output as an idle classroom, and then the specific location of each vacant classroom is obtained according to the planning distribution map of the teaching building, so as to facilitate the subsequent judgment of the distance between each classroom.

[0070] After obtaining all the free classrooms, take the initial time of the self-study time period as the starting point, query the free classrooms that are free at the initial time, and randomly select them. Then use the use time of the selected free classroom as the selection time of the next free classroom for search, and so on, until the use time of the last free classroom is later than the end time of the self-study time period. Then connect the departure place and the destination at the two ends of the classroom connection path composed of the free classrooms to form an initial transfer path. Each time an free classroom is selected, it is randomly selected, so several initial transfer paths can be formed. Each initial transfer path can meet the entire process of students starting from the departure place to the free classroom, transferring in the free classroom, and moving from the free classroom to the destination, ensuring that students can quickly move to the free classroom for self-study.

[0071] Preferably, the specific steps of step S3 are:

[0072] Step S31, collecting the self-study supplies used during the self-study period input by the students;

[0073] Step S32: using an expert scoring method to evaluate the quantity and weight of the self-study supplies and obtain a transfer difficulty value;

[0074] Step S33: When the transfer difficulty value is greater than a preset threshold, feedback is given to the student, who then determines whether there is a need to transfer to the study room a certain number of times and obtains the limited number of times.

[0075] Step S34, obtaining the number of transfers to different free classrooms in each initial transfer path;

[0076] Step S35: Eliminate the initial transfer paths whose transfer times are greater than the limit times.

[0077] After obtaining the initial transfer path, it is necessary to screen the initial transfer path based on the number of requirements. For some students, due to their learning efficiency or the problem of carrying items, they expect to transfer classrooms as few times as possible during self-study, that is, there is a demand for the number of transfers to the study room. First, the self-study supplies input by the students are obtained. After evaluating and scoring the quantity and weight of similar self-study supplies used by the public, the transfer difficulty value can be obtained. The larger the transfer difficulty value, the fewer the number of transfers needs to be. After comparing the transfer difficulty value with the preset threshold, if the transfer difficulty value is greater than the preset threshold, it can be fed back to the students, and the students can independently determine whether there is a demand for the number of transfers to the study room, and can manually enter the limit number, and then the number of transfers to different idle classrooms in each initial transfer path is obtained. If the number of transfers is greater than the limit number, it means that the initial transfer path has too many transfers and does not meet the student's demand for the number of transfers to the study room. At this time, the corresponding initial transfer path can be eliminated to reduce the number of initial transfer paths, reduce the amount of calculation, and improve the efficiency of obtaining the optimal transfer path.

[0078] Preferably, the specific steps of step S4 are:

[0079] Step S41, initializing the gray wolf population and the number of iterations, where the number of iterations is the number of initial transfer paths after screening;

[0080] Step S42, randomly selecting an initial transfer path after screening, and calculating the fitness value of the gray wolf based on the shortest moving distance;

[0081] Step S43, selecting another screened initial transfer path, calculating the fitness value, comparing it with the fitness value calculated last time, and retaining the initial transfer path with the larger fitness value;

[0082] Step S44: perform iterative calculation to obtain the initial transfer path with the maximum fitness value, and output it as the optimal transfer path.

[0083] The initial transfer path includes the distances of students moving from the starting point to the vacant classroom, the transfer between vacant classrooms, and the distances from the vacant classroom to the destination. The overall moving distances of different initial transfer paths are different. Therefore, after preliminary screening of the initial transfer paths, the present invention adopts the gray wolf optimization algorithm to find the best solution. The criterion for finding the best solution is to minimize the moving distance. The fitness value of the gray wolf of each initial transfer path is calculated and then compared. The initial transfer path with the largest fitness value is output as the optimal transfer path. When students move according to the optimal transfer path, the moving distance is the shortest, and the transfer of vacant classrooms also meets the students' own needs.

[0084] Reference Figure 6 The student information management system based on the Internet of Things shown includes:

[0085] An acquisition unit 1 is used to acquire basic information of the student, the basic information including self-study time period, departure place and destination;

[0086] Query unit 2, used to query all free classrooms and specific locations of free classrooms during the self-study period, connect the free classrooms in chronological order, and connect them with the departure place and the destination to form an initial transfer path;

[0087] Evaluation unit 3, used to evaluate the number of times students need to transfer to the study room, and screen the initial transfer path based on the number of times required;

[0088] The optimization unit 4 is used to optimize the selected initial transfer path based on the shortest moving distance by using the gray wolf optimization algorithm, and obtain the optimal transfer path;

[0089] The acquisition unit 1, the query unit 2, the evaluation unit 3 and the optimization unit 4 are sequentially connected in data.

[0090] The acquisition unit 1 is used to obtain the basic information of the students, and the basic information will be transmitted to the query unit 2. The query unit 2 can find the idle classrooms according to the self-study time period in the basic information, and connect the idle classrooms into several classroom connection paths as time goes by. At the same time, the query unit 2 will connect the departure point and the destination at both ends of the classroom connection path to form an initial transfer path. Then the evaluation unit 3 and the optimization unit 4 can screen and optimize the initial transfer path based on the number of requirements and the moving distance, and finally obtain the optimal transfer path. When students move according to the optimal transfer path, the moving distance is the shortest, and the number of times students need to transfer to the self-study room can be met.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A student information management method based on the Internet of Things, characterized in that: The following steps are involved: Step S1, obtaining basic information of the student, the basic information including self-study time period, departure place and destination; Step S2, query all free classrooms and specific locations of free classrooms during the self-study period, connect the free classrooms in chronological order, and connect them with the departure place and the destination to form an initial transfer path; Step S3: Evaluate the number of times students need to transfer to the study room, and screen the initial transfer path based on the number of times required; Step S4: Use the Grey Wolf Optimization Algorithm to optimize the selected initial transfer paths based on the shortest moving distance, and obtain the optimal transfer path.

2. According to the method for student information management based on the Internet of Things in claim 1, it is characterized in that: The specific steps of step S1 are: Step S11, responding to a self-study request initiated by a student, and querying the historical self-study time period of the corresponding student; Step S12: Feedback the historical self-study time period to the student, and let the student decide whether to change the self-study time period; Step S13: The student actively inputs the departure place and destination before and after the self-study session.

3. According to the method of student information management based on Internet of Things in claim 1, it is characterized in that: The specific steps of querying all free classrooms and the specific locations of free classrooms during the self-study time period in step S2 are: Step S21, obtaining the self-study time period of the students, searching for classrooms that are not included in the course schedule during the self-study time period, and outputting them as free classrooms; Step S22: Obtain a planning distribution map of the teaching building, and find the specific location of the vacant classroom from the planning distribution map.

4. According to the method of student information management based on the Internet of Things in claim 1, it is characterized in that: The specific steps of connecting the idle classrooms in chronological order and connecting them with the departure place and the destination to form an initial transfer path in step S2 are: Step S23: randomly select a suitable free classroom according to the initial time of the self-study time period; Step S24: when the use time of an idle classroom is earlier than the end time of the self-study time period, the next idle classroom that meets the conditions is randomly selected starting from the use time of the previous idle classroom, until the use time of the selected idle classroom is later than the end time of the self-study time period, and the selected idle classrooms are connected into a classroom connection path in chronological order; Step S25, repeat steps S23-S24 to obtain multiple classroom connection paths, and connect the departure point and the destination to the beginning and end of the classroom connection path respectively to form an initial transfer path.

5. According to the method of student information management based on Internet of Things in claim 1, it is characterized in that: The specific steps of evaluating the student's demand for the number of times to transfer the study room in step S3 are: Step S31, collecting the self-study supplies used during the self-study period input by the students; Step S32: using an expert scoring method to evaluate the quantity and weight of the self-study supplies and obtain a transfer difficulty value; Step S33: When the transfer difficulty value is greater than a preset threshold, feedback is given to the student, who then determines whether there is a need to transfer to the study room a certain number of times and obtains the limited number of times.

6. The method for student information management based on the Internet of Things according to claim 5 is characterized in that: The specific steps of screening the initial transfer path based on the number requirement in step S3 are: Step S34, obtaining the number of transfers to different free classrooms in each initial transfer path; Step S35: Eliminate the initial transfer paths whose transfer times are greater than the limit times.

7. The method for student information management based on the Internet of Things according to claim 1 is characterized in that: The specific steps of step S4 are: Step S41, initializing the gray wolf population and the number of iterations; Step S42, randomly selecting an initial transfer path after screening, and calculating the fitness value of the gray wolf based on the shortest moving distance; Step S43, selecting another screened initial transfer path, calculating the fitness value, comparing it with the fitness value calculated last time, and retaining the initial transfer path with the larger fitness value; Step S44: perform iterative calculation to obtain the initial transfer path with the maximum fitness value, and output it as the optimal transfer path.

8. The method for student information management based on the Internet of Things according to claim 7 is characterized in that: The number of iterations is the number of initial transfer paths after screening.

9. A system using the student information management method based on the Internet of Things according to any one of claims 1 to 8, characterized in that: include: An acquisition unit, used to acquire basic information of the student, the basic information including self-study time period, departure place and destination; A query unit, used to query all free classrooms and specific locations of free classrooms during the self-study period, connect the free classrooms in chronological order, and connect them with the departure place and the destination to form an initial transfer path; An evaluation unit, used to evaluate the number of times students need to transfer to study rooms, and screen the initial transfer path based on the number of times required; An optimization unit is used to optimize the selected initial transfer path based on the shortest moving distance using the gray wolf optimization algorithm and obtain the optimal transfer path; The acquisition unit, the query unit, the evaluation unit and the optimization unit are sequentially data-connected.

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