Iot-based intelligent campus management system

Abstract

The application relates to the technical field of smart campus, in particular to a smart campus management system based on the Internet of Things. The system comprises a server and a personnel label. The server comprises an identification acquisition module, which is used for acquiring identification information generated when a front-end identification device identifies the personnel label. The identification information comprises personnel information associated with the personnel label, an identification event generated according to the position and type of the front-end identification device and the moving track of the personnel label, and an identification time. An event identification module is used for judging whether the identification information meets a preset judgment rule of one or more identification events when the identification information of the identification event with the judgment rule is acquired. When the judgment rule is not met, an abnormal record is generated. A record generation module is used for generating in-school records of the identification information and the abnormal record of the students according to the personnel information.

Description

Technical Field

[0001] This invention relates to the field of smart campus technology, and more specifically to a smart campus management system based on the Internet of Things. Background Technology

[0002] Smart campuses represent the top-level design of national education informatization. With the rapid development and marketization of advanced technologies such as big data, cloud computing, and the Internet of Things, the concept of smart campuses has been placed on the education system's construction agenda, following the popularity of concepts like "smart earth" and "smart city." The concept of a smart campus aims to achieve rapid information exchange and management across various campus applications, including personnel, finances, and materials, thereby improving the efficiency and orderliness of daily teaching, learning, research, and management activities. Campus security, library management, and asset management can be achieved through internet technology using smart cards or identity tags. The core of a smart campus is a highly centralized big data center that efficiently integrates and merges business data generated by various campus application service systems. Currently, most smart campuses only collect and centralize data generated within the campus, with limited data application. Therefore, how to centrally apply campus-generated data, uncover its value, and facilitate campus management is a pressing issue that needs to be addressed. Summary of the Invention

[0003] The technical problem solved by this invention is to provide a smart campus management system based on the Internet of Things, which can make campus management more convenient.

[0004] The basic solution provided by this invention is an IoT-based smart campus management system, which includes a server, personnel tags, a front-end identification device for identifying personnel tags, and a client. The personnel tags are associated with unique personnel information of students. The server includes an identification acquisition module, an event identification module, a record generation module, and an information sending module.

[0005] The identification and acquisition module is used to acquire the identification information generated when the front-end identification device identifies a person tag. The identification information includes the person information associated with the person tag, the identification event determined based on the location and type of the front-end identification device and the movement trajectory of the person tag, and the identification time.

[0006] The event recognition module is used to determine whether the recognition information meets the judgment rules when the recognition information of the recognition event with the judgment rules is obtained, based on one or more preset judgment rules for recognition events. If the judgment rules are not met, an exception is recorded.

[0007] The record generation module is used to generate on-campus records based on personnel information, student identification information, and abnormal records.

[0008] The information sending module is used to send school records to a client bound to a student's unique personal information.

[0009] The principle and advantages of this invention are as follows: Students wear personnel tags, which identify students and the events that occur. When the front-end identification device recognizes the tag, it can obtain the student's identification information based on the type and location of the front-end identification device and the movement trajectory of the personnel tag, while simultaneously recording the identification time to determine the student's movement within the school. It also determines whether the identified event conforms to preset judgment rules, thereby judging whether the student has committed any violations. The identified information and abnormal records are used to generate an attendance record, which is sent to a client bound to the student's identity information. This allows the student's guardian to understand the student's behavior at school and their whereabouts.

[0010] Furthermore, the server also includes an image acquisition module;

[0011] The image acquisition module is used to acquire images of the corresponding students when they generate recognition information, based on personnel information.

[0012] The information sending module is also used to send images to the client.

[0013] The system collects images of students when they generate identification information and sends these images to the client, allowing guardians to verify the authenticity of students' school attendance records or absences.

[0014] Furthermore, the event recognition module includes a campus access control module, the campus access control module includes a campus gate recognition module, the front-end recognition device includes a campus gate recognition device, and the event recognition module includes a campus event recognition module;

[0015] The school gate recognition module is used to control the school gate gate to open when the school gate recognition device recognizes a person's tag, and to determine whether it is an exit event or an entry event based on the movement trajectory of the recognized person's tag, and to record the recognition time and personnel information of the exit event or entry event.

[0016] The campus event recognition module is used to determine whether the exit time or entry time meets the preset exit rules when an exit event or entry time is detected. If it does not meet the rules, an exception record is generated.

[0017] The school gate recognition device is installed at the school gate to identify entry and exit events. Based on the movement trajectory of personnel tags, it can determine whether a student is entering or leaving the school. It also records the time of entry and exit. Furthermore, according to preset entry and exit rules, it determines whether the entry and exit times comply with the rules. If not, it is considered lateness or early departure, thus generating an exception record. Subsequently, the information sending module sends the time and video of the student's entry and exit to the client, allowing guardians to know when the student arrived at school, when the student left school, whether the student arrived at school, and whether the student left school. This facilitates school gate management and allows guardians to understand the student's situation.

[0018] Furthermore, the server also includes a visitor management module, which includes a visitor information acquisition module and a visitor information verification module;

[0019] The visitor information acquisition module is used to acquire visitor identity information and visitor relationships;

[0020] The visitor information verification module is used to send visitor information to the administrator's client, obtain the verification result, and temporarily label the visitor information after successful verification.

[0021] The school gate recognition module is also used to identify visitor information. When the visitor information has a temporary tag, it controls the school gate gate to open and generates a visitor record, which includes visitor information and the time of entry and exit from the school gate.

[0022] When visitors arrive, they must undergo visitor authentication. This involves obtaining the visitor's identity information and the visitor-visitor relationship, which is then submitted to administrators for verification. The school gate identification system will only open the gate after the visitor's information has been verified. Visitors must obtain administrator approval before entering, thus enhancing campus security.

[0023] Furthermore, the event recognition module also includes a dormitory management module and a dormitory event recognition module. The dormitory management module includes a dormitory recognition module, and the front-end recognition device includes a dormitory equipment recognition module.

[0024] The dormitory identification module is preset with exit time and return time. It is used to determine whether a student has left or returned to the dormitory based on the movement trajectory of the last person tag identified by the dormitory identification module during the exit time or return time, and records the exit time and return time.

[0025] The dormitory event recognition module is used to identify those who have not left the dormitory and those who have not returned to the dormitory after the dormitory exit and return times have ended.

[0026] Dormitory management differs from campus gate management in that dormitory students may enter and exit the campus multiple times within a given timeframe. Therefore, based on the movement trajectory of the last student's tag within a set time period, it's determined whether a student left or returned to their dormitory. The return and departure times are recorded. Additionally, a list of students who did not return to their dormitory or did not leave their dormitory is compiled.

[0027] Furthermore, the server also includes a site recognition module and a behavior analysis module, and the front-end recognition device also includes a site recognition device. The site recognition device is installed at the entrance and exit of the campus site and is used to identify personnel tags. The behavior analysis module includes a behavior recording module, a portrait recognition module and an anomaly judgment module.

[0028] The site recognition module is used to generate entry and exit records for each student on campus based on the recognition information generated when the site recognition device recognizes personnel tags.

[0029] The behavior recording module is used to identify and record students' daily behaviors based on their access records to campus areas associated with their information.

[0030] The profile recognition module is used to generate a profile of each student's daily behavior based on historical records and identified events.

[0031] The anomaly detection module is used to analyze whether there is any abnormal behavior when new student daily behavior and identified events are acquired, based on the student's daily behavior profile. When abnormal behavior is found, the personnel information and abnormal behavior are sent to the administrator's client.

[0032] In addition to rule-based judgment of events such as entering and leaving the campus and returning to and from dormitories, the system can also manage student access records for various locations on campus. For example, it can install site recognition devices at the entrances and exits of facilities such as libraries, gymnasiums, and laboratories. When students enter or leave these facilities, their personal information is obtained by identifying their tags, thus identifying the time of their entry and exit from each location on campus, and obtaining access records. By collecting student access records and recognition events over a long period, a daily behavior profile for each student can be generated based on these events and access records. Different students will frequent different locations on campus due to different interests and hobbies, and their usage time will also vary depending on their individual schedules. Therefore, student daily behavior can be identified based on their access records for each location. A daily behavior profile is then generated based on the daily behavior records stored over a historical period. Subsequently, new daily behaviors of students are analyzed using these profiles to identify any anomalies. When anomalies are found, the administrator is notified. Attached Figure Description

[0033] Figure 1 This is a logical block diagram of an embodiment of the Internet of Things-based smart campus management system of the present invention. Detailed Implementation

[0034] The following detailed description illustrates the specific implementation method:

[0035] The basic implementation examples are as follows: Figure 1 As shown:

[0036] The IoT-based smart campus management system includes a server, personnel tags, a front-end identification device for identifying personnel tags, and a client. Each personnel tag is associated with a student's unique information. The server includes an identification acquisition module, an event recognition module, a record generation module, an information sending module, an image acquisition module, and a visitor management module. Specifically, in this embodiment, the personnel tag is an RFID tag, and the front-end identification device is an RFID reader / writer.

[0037] The identification acquisition module is used to acquire the identification information generated when the front-end identification device identifies a person tag. The identification information includes the person information associated with the person tag, the identification event determined based on the location and type of the front-end identification device and the movement trajectory of the person tag, and the identification time.

[0038] The event recognition module is used to determine whether the recognition information meets the judgment rules when the recognition information of the recognition event with the judgment rules is obtained, based on one or more preset judgment rules for recognition events. If the judgment rules are not met, an exception is recorded.

[0039] The record generation module is used to generate on-campus records based on personnel information, student identification information, and abnormal records.

[0040] The information sending module is used to send school records to a client bound to a student's unique personal information.

[0041] The image acquisition module is used to acquire images of the corresponding student when recognition information is generated, based on personnel information. In this embodiment, the image acquisition module is specifically a camera.

[0042] The information sending module is also used to send images to the client.

[0043] The event recognition module specifically includes a campus access control module, which includes a campus gate recognition module, and a front-end recognition device including a campus gate recognition device. The event recognition module includes a campus event recognition module.

[0044] The school gate recognition module is used to control the school gate gate to open when the school gate recognition device recognizes a person's tag, and to determine whether it is an exit event or an entry event based on the movement trajectory of the recognized person's tag, and to record the recognition time and personnel information of the exit event or entry event.

[0045] The campus event recognition module is used to determine whether the exit time or entry time meets the preset exit rules when an exit event or entry time is detected. If it does not meet the rules, an exception record is generated.

[0046] Specifically, by pre-setting school entry and exit times, such as entry before 9:00 AM and exit after 5:30 PM, the system uses an RFID reader to read the pre-written personnel information in the RFID tags. Simultaneously, based on the student's movement trajectory, it determines whether the student is moving into or out of the school, thus confirming entry or exit. Then, based on the entry and exit times, it checks if the time is earlier than 9:00 AM or later than 5:30 PM; otherwise, an exception record is generated. This determines whether a student is late or leaves early. The image capture module then sends the captured images of the student entering or leaving the school, along with the entry and exit times, to the client device linked to the student's personnel information—that is, to the guardian.

[0047] The school gate recognition device is installed at the school gate to identify entry and exit events. Based on the movement trajectory of personnel tags, it can determine whether a student is entering or leaving the school. It also records the time of entry and exit. Furthermore, according to preset entry and exit rules, it determines whether the entry and exit times comply with the rules. If not, it is considered lateness or early departure, thus generating an exception record. Subsequently, the information sending module sends the time and video of the student's entry and exit to the client, allowing guardians to know when the student arrived at school, when the student left school, whether the student arrived at school, and whether the student left school. This facilitates school gate management and allows guardians to understand the student's situation.

[0048] The visitor management module includes a visitor information acquisition module and a visitor information verification module.

[0049] The visitor information acquisition module is used to obtain visitor identity information and visitor relationships.

[0050] The visitor information verification module is used to send visitor information to the administrator's client, obtain the verification result, and temporarily label the visitor information after successful verification.

[0051] The school gate recognition module is also used to identify visitor information. When the visitor information has a temporary tag, it controls the school gate gate to open and generates a visitor record, which includes visitor information and the time of entry and exit from the school gate.

[0052] When visitors arrive, they must undergo visitor authentication. This involves obtaining the visitor's identity information and the visitor-visitor relationship, which is then submitted to administrators for verification. The school gate identification system will only open the gate after the visitor's information has been verified. Visitors must obtain administrator approval before entering, thus enhancing campus security.

[0053] The event recognition module also includes a dormitory management module, a dormitory event recognition module, a dormitory recognition module, and a front-end recognition device that includes dormitory equipment recognition.

[0054] The dormitory identification module is preset with exit time and return time. It is used to determine whether a student has left or returned to the dormitory based on the movement trajectory of the last person tag identified by the dormitory identification module during the exit time or return time, and records the exit time and return time.

[0055] The dormitory event recognition module is used to identify those who have not left the dormitory and those who have not returned to the dormitory after the dormitory exit and return times have ended.

[0056] Dormitory management differs from campus gate management in that boarding students may enter and exit the campus multiple times within a given timeframe. Therefore, based on the movement trajectory of the last person's tag within a set time period, it determines whether a student has left or returned to their dormitory. The return and departure times are recorded. Simultaneously, a list of students who have not returned or left their dormitories is compiled. Specifically, during the departure time, all boarding student departure events are identified; those who did not leave their dormitories during this time are considered as not having left. The same logic applies to returning.

[0057] The technical solution of this application involves students wearing personnel tags. These tags identify students and the events that occur within them. When the front-end identification device recognizes the tag, it can obtain the student's identification information based on the type and location of the front-end identification device and the movement trajectory of the personnel tag. Simultaneously, it records the identification time to determine the student's movement within the school. It also determines whether the identified event conforms to preset judgment rules to identify whether the student has committed any violations. The identified information and abnormal records are used to generate an attendance record, which is sent to a client bound to the student's identity information. This allows the student's guardian to understand the student's behavior at school and their whereabouts.

[0058] Example 2

[0059] The difference between this embodiment and Embodiment 1 is that in this embodiment, the server further includes a site recognition module and a behavior analysis module. The front-end recognition device also includes a site recognition device, which is installed at the entrances and exits of the school sites to identify personnel tags. The behavior analysis module includes a behavior recording module, a profile recognition module, and an anomaly detection module. In this embodiment, the school sites include a library, a sports hall, laboratories, and a canteen. The site recognition device is installed at the entrances and exits of the school sites, enabling identification when students enter or exit.

[0060] The site recognition module is used to generate entry and exit records for each student on campus based on the recognition information generated when the site recognition device identifies personnel tags. Once the student's identification information is obtained, the entry and exit records for each student in each site are generated, such as the time they entered and the time they left.

[0061] The behavior recording module is used to identify and record students' daily behaviors based on their access records to campus locations linked to their information. For example, a student might have spent an hour in the library on Monday and half an hour in the gymnasium on Tuesday.

[0062] The profile recognition module is used to generate daily behavior profiles for each student based on historical records of their daily behavior and identified events. Based on each student's daily behavior and identified events, a student's daily behavior profile is obtained, including, for example, the time periods when the student usually leaves the dormitory, their frequently visited campus locations (library, sports hall, or laboratory), the time periods they typically travel to these locations, and the amount of time they spend in those locations.

[0063] The anomaly detection module is used to analyze newly acquired student daily behaviors and identified events against the student's daily behavior profile to determine if any abnormal behavior exists. If abnormal behavior is found, the module sends the student's information and the abnormal behavior to the administrator's client. For example, if a student's daily behavior profile shows they visit the library every other day, but newly acquired daily behaviors indicate they haven't visited the library for several consecutive days, this is considered an anomaly. It's possible that some influencing factors have altered the student's daily routine, and this information is sent to the administrator's client so they can monitor the student's situation.

[0064] By collecting student entry and exit records and identifying events over a long period, a daily behavior profile is generated for each student based on these events and entry / exit records. Different students frequent different campus locations due to varying interests and schedules, and the time they spend using these locations also differs. Therefore, student daily behavior is identified based on their entry and exit records. A daily behavior profile is then generated based on historically stored student daily behavior over a specific period. Subsequently, new student daily behaviors are analyzed using these profiles to identify any anomalies. When anomalies are detected, the administrator is notified.

[0065] Example 3

[0066] The difference between this embodiment and embodiment two is that in this embodiment, the server also includes a diet management module, and the front-end identification device also includes a leftover food identification device. The leftover food identification device is located in the canteen's food disposal area and is used to identify the leftover food information in the leftover food bin. The diet management module includes a diet acquisition module, a leftover food identification module, an intake identification module, an exercise identification module, and a health judgment module.

[0067] The food acquisition module is used to obtain the food information of each student's meal on campus based on the payment information generated when making on-campus payments according to the personnel tags.

[0068] The leftover food recognition module is used to analyze the amount of leftover food from students based on the information about the leftover food in the leftover food bin identified by the leftover food recognition module.

[0069] The intake recognition module is used to identify the intake of students at each meal based on their dietary information and the amount of leftover food.

[0070] The exercise volume recognition module is used to identify the student's daily exercise volume based on the student's daily behavior profile;

[0071] The health assessment module is used to determine whether a student's intake and exercise volume within a cycle meet the preset ratio. If the preset ratio is not met, the student's dietary information and daily behavior profile are sent to the administrator.

[0072] The leftover food identification device includes a weight detection device and a label recognition device. The weight detection device is used to identify weight changes in the leftover food bin, and the label recognition device is used to identify personnel labels and bind the weight changes in the leftover food bin with the personnel information on the personnel labels and upload it to the server. The leftover food identification module is used to analyze and obtain the amount of leftover food of students based on the weight changes bound to the personnel information and dietary information.

[0073] Specifically, in this embodiment, students can make payments on campus using personnel tags. Each personnel tag is linked to a student account, which is then topped up. During consumption, the personnel tag is identified, and the consumption information is recorded to determine the student's dietary information in the cafeteria, i.e., what food the student purchased, such as the quantity of rice, meat dishes, and vegetable dishes. A leftover food identification module obtains the amount of food the student discards after eating. Based on the purchased and discarded food, the student's intake for each meal is determined. Simultaneously, the student's daily behavior profile is used to determine their activity level. Specifically, in this embodiment, the intake calculation is performed by calculating the total calories of the purchased food based on the student's dietary information, and then calculating the calories of the discarded food, thus determining the student's total calorie intake. To calculate exercise volume, the system first identifies whether students have physical education classes or other physical activity classes based on their daily schedule, and whether they visit on-campus locations such as the gymnasium and swimming pool. The duration of these visits determines the calories burned. Then, based on students' arrival and departure times, dormitory arrival and departure times, and the times they reach various on-campus locations, the system determines the distance students travel on campus and calculates the calories burned during walking, thus obtaining the student's exercise volume. Next, the system tracks students' intake and exercise volume over a period of time. In this plan, intake and exercise volume are tracked for one week. Then, based on student information, such as gender and weight, the system checks whether the pre-defined ratio of exercise volume to intake for that gender and weight is met. If the ratio is not met, the administrator is notified.

[0074] The key to this solution lies in identifying student intake. Traditional intake and exercise management systems primarily serve individuals, requiring individual input of intake statistics, thus ensuring accurate individual intake tracking. This solution, however, tracks the intake of all students within the school. Students outside the nutrition and health department cannot accurately track their individual intake, and not all purchased meals are consumed. Therefore, this solution proposes calculating the calories of uneaten food by measuring the weight of discarded food, thereby tracking each student's intake at each meal. Combined with student behavior profiles, this allows for the identification of daily exercise levels. While the technical solution in this application may introduce some errors in calculating intake at each meal and daily exercise, such as errors in the weight of leftover food containers or distance traveled within the school, these errors are offset by the intake and exercise data collected over the statistical period, thus reducing overall error. This allows for the determination of each student's weekly intake and exercise levels.

[0075] The leftover food recognition module is also used to determine whether to count the amount of leftover food based on weight changes. When the weight change is lower than the preset weight, the amount of leftover food is not counted.

[0076] If the weight change is small, only some leftover soup is poured out. Therefore, when the weight change is small, the amount of leftover soup is not counted.

[0077] The weight detection equipment is also used to count the total weight of leftover food in the leftover food bin after each meal service.

[0078] The leftover food identification module is also used to calculate the total weight change of the meal and the information of each person, determine whether the total weight is less than the total weight change, and whether the difference is within the preset error range. When it is not within the error range, it identifies whether there are students with the same weight change at the same time point and marks them as error groups. It identifies all error groups and, based on the number of people in each error group and the dietary information of each student in the error group, proportionally distributes the weight change of the error group to the amount of leftover food for each student.

[0079] Specifically, when there are many people in the cafeteria, multiple people may dump leftover food simultaneously or continuously. That is, after one person dumps their food, the weight detection device starts measuring and identifies the person's tag. However, before that person finishes dumping and the weight detection device stops detecting the weight change, another person starts dumping their food. The device then identifies this new person, and after they finish, the weight detection stops, resulting in two weight changes. However, since two separate tags are identified, the weight changes for each person are associated with their tags, thus increasing the total weight change. By finally calculating the total weight in the leftover food bins and the total weight change for each person, if this situation occurs, the total weight change for each person will be greater than the total weight, but outside the error range. Because this happens when two people dump food simultaneously or continuously, the recorded weight change times are the same, and the food dumped by each person is associated with their respective tags, resulting in identical weight changes. Therefore, by identifying whether there are students with identical weight changes linked to their personal information at the same time point, the above situation can be identified, and these two individuals are marked as the error group. Subsequently, all error groups are identified, and based on the weight changes linked to each error group and each individual's dietary information, the leftover food is proportionally distributed to the students in the error groups, thereby reducing the errors caused by multiple people simultaneously or continuously discarding food.

[0080] The above are merely embodiments of the present invention. Commonly known structures and characteristics are not described in detail here. Those skilled in the art are aware of all common technical knowledge in the field prior to the application date or priority date, are aware of all existing technologies in that field, and have the ability to apply conventional experimental methods prior to that date. Those skilled in the art can, under the guidance of this application, improve and implement this solution in combination with their own capabilities. Some typical known structures or methods should not be obstacles for those skilled in the art to implement this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the structure of the present invention. These should also be considered within the scope of protection of the present invention, and will not affect the effectiveness of the implementation of the present invention or the practicality of the patent. The scope of protection claimed in this application should be determined by the content of its claims, and the specific embodiments described in the specification can be used to interpret the content of the claims.

Claims

1. A smart campus management system based on the Internet of Things, characterized in that: It includes a server, personnel tags, a front-end recognition device for identifying personnel tags, and a client. The personnel tags are associated with unique personnel information of students. The server includes an identification acquisition module, an event recognition module, a record generation module, and an information sending module. The identification and acquisition module is used to acquire the identification information generated when the front-end identification device identifies a person tag. The identification information includes the person information associated with the person tag, the identification event determined based on the location and type of the front-end identification device and the movement trajectory of the person tag, and the identification time. The event recognition module is used to determine whether the recognition information meets the judgment rules when the recognition information of the recognition event with the judgment rules is obtained, based on one or more preset judgment rules for recognition events. If the judgment rules are not met, an exception is recorded. The record generation module is used to generate on-campus records based on personnel information, student identification information, and abnormal records. The information sending module is used to send school records to a client bound to a student's unique personal information. The server also includes a site recognition module and a behavior analysis module. The front-end recognition device also includes a site recognition device, which is installed at the entrances and exits of the campus sites to identify personnel tags. The behavior analysis module includes a behavior recording module, a profile recognition module, and an anomaly judgment module. The site recognition module is used to generate entry and exit records for each student on campus based on the recognition information generated when the site recognition device recognizes personnel tags. The behavior recording module is used to identify and record students' daily behaviors based on their access records to campus areas associated with their information. The profile recognition module is used to generate a profile of each student's daily behavior based on historical records and identified events. The anomaly detection module is used to analyze whether there is any abnormal behavior when new student daily behavior and identified events are acquired, based on the student's daily behavior profile. When abnormal behavior is found, the personnel information and abnormal behavior are sent to the administrator's client. The server also includes a diet management module, and the front-end identification device also includes a leftover food identification device. The leftover food identification device is located in the canteen's food disposal area and is used to identify the leftover food information in the leftover food bin. The diet management module includes a diet acquisition module, a leftover food identification module, an intake identification module, an exercise identification module, and a health assessment module. The food acquisition module is used to obtain the food information of each student's meal on campus based on the payment information generated when making on-campus payments according to the personnel tags. The leftover food recognition module is used to analyze the amount of leftover food from students based on the information about the leftover food in the leftover food bin identified by the leftover food recognition module. The intake recognition module is used to identify the intake of students at each meal based on their dietary information and the amount of leftover food. The exercise volume recognition module is used to identify the student's daily exercise volume based on the student's daily behavior profile; The health assessment module is used to determine whether the student's intake and exercise volume within a cycle meet the preset ratio. If the preset ratio is not met, the student's dietary information and daily behavior profile will be sent to the administrator. The leftover food identification device includes a weight detection device and a label recognition device. The weight detection device is used to identify weight changes in the leftover food bin, and the label recognition device is used to identify personnel labels and bind the weight changes in the leftover food bin with the personnel information on the personnel labels and upload it to the server. The leftover food identification module is used to analyze and obtain the amount of leftover food of students based on the weight changes bound to the personnel information and dietary information. The weight detection equipment is also used to count the total weight of leftover food in the leftover food bin after each meal service. The leftover food identification module is also used to calculate the total weight change of the meal and the information of each person, determine whether the total weight is less than the total weight change, and whether the difference is within the preset error range. When it is not within the error range, it identifies whether there are students with the same weight change at the same time point and marks them as error groups. It identifies all error groups and, based on the number of people in each error group and the dietary information of each student in the error group, proportionally distributes the weight change of the error group to the amount of leftover food for each student.

2. The smart campus management system based on the Internet of Things according to claim 1, characterized in that: The server also includes an image acquisition module; The image acquisition module is used to acquire images of the corresponding students when they generate recognition information, based on personnel information. The information sending module is also used to send images to the client.

3. The smart campus management system based on the Internet of Things according to claim 2, characterized in that: The event recognition module includes a school gate recognition module, the front-end recognition device includes a school gate recognition device, and the event recognition module includes a campus event recognition module; The school gate recognition module is used to control the school gate gate to open when the school gate recognition device recognizes a person's tag, and to determine whether it is an exit event or an entry event based on the movement trajectory of the recognized person's tag, and to record the recognition time and personnel information of the exit event or entry event. The campus event recognition module is used to determine whether the exit time or entry time meets the preset exit rules when an exit event or entry time is detected. If it does not meet the rules, an exception record is generated.

4. The smart campus management system based on the Internet of Things according to claim 3, characterized in that: The server also includes a visitor management module, which includes a visitor information acquisition module and a visitor information verification module. The visitor information acquisition module is used to acquire visitor identity information and visitor relationships; The visitor information verification module is used to send visitor information to the administrator's client, obtain the verification result, and temporarily label the visitor information after successful verification. The school gate recognition module is also used to identify visitor information. When the visitor information has a temporary tag, it controls the school gate gate to open and generates a visitor record, which includes visitor information and the time of entry and exit from the school gate.

5. The smart campus management system based on the Internet of Things according to claim 2, characterized in that: The event recognition module also includes a dormitory management module and a dormitory event recognition module. The dormitory management module includes a dormitory recognition module, and the front-end recognition device includes dormitory equipment recognition. The dormitory identification module is preset with exit time and return time. It is used to determine whether a student has left or returned to the dormitory based on the movement trajectory of the last person tag identified by the dormitory identification module during the exit time or return time, and records the exit time and return time. The dormitory event recognition module is used to identify those who have not left the dormitory and those who have not returned to the dormitory after the dormitory exit and return times have ended.

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