Intelligent campus teaching space overall management system

Abstract

The invention belongs to the technical field of intelligent management, and discloses an intelligent campus teaching space overall management system, which comprises a data acquisition module used for acquiring teaching space data, teaching plan data, resource use data and reservation request data; the data processing module is used for preprocessing the acquired data to obtain teaching space parameter data, teaching plan parameter data, resource use parameter data and reservation parameter data; performing weighted fusion on the teaching space parameter data and the resource use parameter data to obtain space feature data; the resource management module is used for constructing a teaching space resource library according to the spatial feature data; the intelligent scheduling module is used for carrying out primary distribution on the teaching space according to the teaching plan parameter data and obtaining free space data; the appointment management module is used for carrying out secondary distribution on the teaching space; the communication module is used for sending the reservation message to a user; and an efficient and flexible solution is provided for management of the smart campus.

Description

Technical Field

[0001] The present invention relates to the field of intelligent management technology, and more specifically, to a smart campus teaching space overall management system. Background Art

[0002] In the context of the rapid development of information technology, the education industry is also constantly seeking change and innovation. The construction of smart campuses has become an important part of modern education management. It integrates and optimizes various information and resources on campus to provide convenient and efficient education services and create a new learning and living environment for teachers and students. As an important part of the construction of smart campuses, the smart campus teaching space coordination management system has gradually been developed and improved, playing an important role in improving teaching efficiency and teaching quality, and promoting the fairness and sustainable development of education.

[0003] The patent with application publication number CN117216928A discloses a full-process intelligent teaching space construction management system, which involves the technical field of intelligent education, including a first target subsystem, a second target subsystem and a third target subsystem, and the first target subsystem, the second target subsystem and the third target subsystem are respectively related to each other through interfaces; the first target subsystem is used to store, search, consult and download standard files related to the teaching space construction model; the second target subsystem is used to present the teaching space construction model and guide the teaching space construction model; the third target subsystem is used to store excellent case space construction models and provide a panoramic display of the excellent case space construction models. This application has the effect of facilitating the management of teaching space construction.

[0004] The traditional campus teaching space management system mainly relies on manual operation and simple electronic management, and has the following main problems: It is unable to integrate seamlessly with other systems to achieve data sharing and business collaboration, and its data collection and system integration capabilities are weak. Not only is the time and labor costs high, but it is also unable to meet the ever-changing teaching space management needs in a timely manner. It is unable to automatically adjust the allocation strategy according to different teaching plans, and its ability to handle complex allocation problems is poor, and it cannot guarantee the flexibility and accuracy of allocation. The allocation process is complex and cumbersome, not flexible and efficient enough, and cannot effectively improve the utilization efficiency of teaching space. It relies too much on historical experience and neglects to explore new and potentially more effective allocation methods. The lack of effective resource optimization methods may lead to some teaching spaces not being fully utilized, resulting in waste of resources. When the number of teaching spaces is large or the needs are complex, traditional methods are difficult to effectively manage on a large scale.

[0005] In view of this, the present invention proposes a smart campus teaching space coordinated management system to solve the above problems. Summary of the invention

[0006] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solutions: a smart campus teaching space overall management system, comprising: Data acquisition module, used to acquire teaching space data, teaching plan data, resource usage data and reservation request data; The data processing module is used to pre-process the acquired teaching space data, teaching plan data, resource usage data and reservation request data to obtain teaching space parameter data, teaching plan parameter data, resource usage parameter data and reservation parameter data; and to perform weighted fusion on the teaching space parameter data and resource usage parameter data to obtain space feature data; Resource management module, used to build a teaching space resource library based on spatial feature data; The intelligent scheduling module makes the initial allocation of teaching space according to the teaching plan parameter data; based on the initial allocation results, the teaching space status is updated in real time in the teaching space resource library, and the free space data is obtained; The reservation management module re-allocates the teaching space according to the reservation parameter data and free space data; The communication module sends the reservation message to the user according to the result of the secondary allocation; each module is connected by wired and / or wireless means.

[0007] Furthermore, the teaching space data includes basic space information, space equipment information and opening time period information; the basic space information includes the type, capacity, number, location and status data of the teaching space; the space equipment information includes the type, number and status of the equipment in the teaching space; Teaching plan data includes course data and teaching resource demand data; course data includes course name, course category, lecturer, teaching time and participating student information; teaching resource demand data includes equipment requirements and space requirements; resource usage data includes space usage frequency and space utilization rate; reservation request data includes reservation user information, reservation time, reservation purpose and required equipment; Teaching space data and teaching plan data are collected through the teaching affairs system; resource usage data are collected through the visualization terminal; and reservation request data are collected through the user terminal.

[0008] Furthermore, the method of preprocessing the acquired teaching space data, teaching plan data, resource usage data and reservation request data to obtain teaching space parameter data, teaching plan parameter data, resource usage parameter data and reservation parameter data includes: Use the K-means clustering algorithm to identify outliers in the teaching space data, teaching plan data, resource usage data, and reservation request data, and remove the identified outliers to obtain processed teaching space parameter data, teaching plan parameter data, resource usage parameter data, and reservation parameter data; The processed teaching space parameter data, teaching plan parameter data, resource usage parameter data and reservation parameter data are normalized by standard deviation and converted into standard normal distribution to obtain normalized teaching space parameter data, teaching plan parameter data, resource usage parameter data and reservation parameter data.

[0009] Furthermore, the method of weighted fusion of teaching space parameter data and resource usage parameter data to obtain space feature data includes: The normalized teaching space parameter data and resource usage parameter data are fused through a weighted formula to obtain spatial feature data, which is recorded as , resource usage parameter data is recorded as ; The weighted formula is: ;in, The weight coefficients of the resource usage parameter data; The weight factor for using parameter data for the resource.

[0010] Furthermore, the method for constructing a teaching space resource library includes: The teaching space resource library is a relational database MySQL. The database table structure is defined according to the acquired spatial feature data, the database is created using the SQL language, and the API interface is written to implement the data addition, deletion, modification and query functions to obtain the built teaching space resource library. The specific method is: S41. Define a database table structure according to the acquired spatial feature data; the database table structure consists of a teaching space table, a device table and a timetable; the teaching space table is used to store the basic spatial information of each teaching space; the device table is used to store the spatial device information of each teaching space; the timetable is used to store the available time period information of each teaching space; S42. Use SQL language to create a teaching space table, equipment table and schedule in a relational database; define the field type, primary key and foreign key of each table; the teaching space table uses SpaceID as the primary key; the equipment table uses EquipmentID as the primary key and SpaceID as the foreign key; the schedule uses ScheduleID as the primary key and SpaceID as the foreign key; the teaching space table and the equipment table establish a one-to-many relationship through the unique identifier of the SpaceID space; the teaching space table and the schedule establish a one-to-many relationship through SpaceID; S43. Use Flask to create an API framework and set up a server; write a processing function at each API endpoint to implement data addition, deletion, modification and query functions; the API framework is connected to a relational database through a database connection library and performs database operations required by the API endpoint; use an API testing tool to verify the functionality of the API framework; After the above steps, a well-constructed teaching space resource library is finally obtained.

[0011] Furthermore, the method for initially allocating teaching space according to teaching plan parameter data includes: The allocation of teaching space is modeled as a Markov decision process, and the Q-learning reinforcement learning algorithm is used to learn the Q value function. When the Q value reaches convergence, the optimal allocation strategy is obtained. The specific steps are: S61. Build a reinforcement learning framework and define , , and ;in, For teaching space The usage status at the moment; For The action taken at every moment is to allocate teaching space for a certain teaching plan; To select a strategy, that is, according to the current state Choose the best action ; is the index of the moment; For Always perform actions Immediate rewards after It is obtained through the spatial adaptability function. The specific mathematical formula is: ;in, The difference between teaching resource demand and teaching space. The smaller the difference, the higher the adaptability. S62, use Q-learning reinforcement learning algorithm, initialize Q value function, set learning rate and discount factor ; S63. Obtain the current status according to the teaching space resource library ; at every moment , according to the current Choose the best action ; Execute the best action , get the new state and instant rewards ; Optimal Action use The greedy strategy is selected; S64. According to the new state obtained and instant rewards Update the Q value function, the specific mathematical formula is: ;in, For the current state Next action Q value; In the new state All possible actions In the example above, we select the action that maximizes the Q value and obtains the maximum expected reward. In the new state All possible actions; S65, Set it as the new current state, repeat steps S53-S54 until the Q value reaches convergence, and obtain the optimal allocation strategy; perform the initial allocation of the teaching space according to the optimal allocation strategy, and obtain the initial allocation result.

[0012] Furthermore, the setting learning rate and discount factor The methods include: Using the learning rate adjustment formula To limit, the specific mathematical formula is: ;in, is the initial learning rate; is the updated learning rate; is the update step size, the value range is (0,1]; is the time step; The factor adjustment formula is used to To limit, the specific mathematical formula is: ;in, is the initial discount factor; is the updated discount factor; To reward importance; Furthermore, the method for secondary allocation of teaching space using the reservation parameter data and the free space data includes: The multi-armed bandit model is used to re-allocate the idle teaching space. The specific steps are as follows: S81. Assume that Different arms, each arm corresponds to a different free teaching space; set the initial number of times each arm is pulled The initial average reward value of each arm is ;in, is the index of the arm, ; Set parameters , controls the width of the confidence interval; S82. For each reservation request, calculate the upper confidence limit of each arm, specifically: ;in, is the current total number of pulls; select the arm with the largest upper confidence limit and get the reward ; According to the reward , determine the appointment result; S83, updating the average reward value and the number of pulls of the selected arm; S84, repeating steps S82-S83 until a predetermined number of iterations is reached; Furthermore, the reward , the methods for judging the appointment results include: Design a reward mechanism, taking successful appointments and conflict failures as feedback results; When it is a positive number, the reservation is considered successful; when the reward If it is 0 or a negative number, the reservation is considered failed; When the reservation is successful, the teaching space represented by the selected arm is the result of secondary allocation of the teaching space based on the reservation request and the free space data.

[0013] Furthermore, the method of sending the reservation message to the user according to the result of the secondary allocation includes: The user who sent the reservation request will receive the reservation result through the user terminal; if the reservation is successful, a reservation success notification will be sent; if the reservation fails, a reservation failure notification and the reason for the failure will be sent.

[0014] The present invention is based on the technical effects and advantages of a smart campus teaching space overall management system: The teaching space resource library based on MySQL database can be seamlessly integrated with other systems, such as teaching system, campus card system, etc., to achieve data sharing and business collaboration, and has powerful data collection and system integration capabilities; the database table structure can be easily expanded according to actual needs to meet the ever-changing teaching space management needs; Through the reinforcement learning algorithm, not only can the allocation strategy be automatically adjusted according to different teaching plans to adapt to the ever-changing teaching needs, but it can also handle complex allocation problems and quickly respond to changes in teaching plans; through the learning rate adjustment formula, the learning rate can be dynamically adjusted as the time step increases, so that the algorithm can converge quickly in the initial learning stage, and adjust the strategy more finely in the later stage; through the discount factor adjustment formula, the immediate reward and long-term reward can be balanced according to the importance of the reward, so that the algorithm can consider the current interests while focusing on future benefits; The use of the multi-armed bandit model for secondary allocation of teaching space can effectively improve the utilization efficiency of teaching space, enhance the system's adaptability and response speed, while simplifying the complexity of the allocation process, providing an efficient and flexible solution for the management of smart campuses. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a schematic diagram of the structure of a smart campus teaching space overall management system of the present invention; Figure 2 It is a schematic flow chart of a method for overall management of a smart campus teaching space according to the present invention. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] Example 1 See also Figure 1 As shown, this embodiment provides a smart campus teaching space coordination management system, including: Data acquisition module, used to acquire teaching space data, teaching plan data, resource usage data and reservation request data; The data processing module is used to pre-process the acquired teaching space data, teaching plan data, resource usage data and reservation request data to obtain teaching space parameter data, teaching plan parameter data, resource usage parameter data and reservation parameter data; and to perform weighted fusion on the teaching space parameter data and resource usage parameter data to obtain space feature data; Resource management module, used to build a teaching space resource library based on spatial feature data; The intelligent scheduling module makes the initial allocation of teaching space according to the teaching plan parameter data; based on the initial allocation results, the teaching space status is updated in real time in the teaching space resource library, and the free space data is obtained; The reservation management module re-allocates the teaching space according to the reservation parameter data and free space data; The communication module sends the reservation message to the user according to the result of the secondary allocation; each module is connected by wired and / or wireless means.

[0018] The teaching space data includes basic space information, space equipment information and opening period information; basic space information includes the type, capacity, number, location and status data of the teaching space; space equipment information includes the type, number and status of equipment in the teaching space; Teaching plan data includes course data and teaching resource demand data; course data includes course name, course category, lecturer, teaching time and participating student information; teaching resource demand data includes equipment requirements and space requirements; resource usage data includes space usage frequency and space utilization rate; reservation request data includes reservation user information, reservation time, reservation purpose and required equipment; Teaching space data and teaching plan data are collected through the teaching affairs system; resource usage data are collected through the visualization terminal; and reservation request data are collected through the user terminal.

[0019] The method of preprocessing the acquired teaching space data, teaching plan data, resource usage data and reservation request data to obtain teaching space parameter data, teaching plan parameter data, resource usage parameter data and reservation parameter data includes: Use the K-means clustering algorithm to identify outliers in the teaching space data, teaching plan data, resource usage data, and reservation request data, and remove the identified outliers to obtain processed teaching space parameter data, teaching plan parameter data, resource usage parameter data, and reservation parameter data; The processed teaching space parameter data, teaching plan parameter data, resource usage parameter data and reservation parameter data are normalized by standard deviation and converted into standard normal distribution to obtain normalized teaching space parameter data, teaching plan parameter data, resource usage parameter data and reservation parameter data.

[0020] The method of weighted fusion of teaching space parameter data and resource usage parameter data to obtain space feature data includes: The normalized teaching space parameter data and resource usage parameter data are fused through a weighted formula to obtain spatial feature data, which is recorded as , resource usage parameter data is recorded as ; The weighted formula is: ;in, The weight coefficients of the resource usage parameter data; The weight factor for using parameter data for the resource.

[0021] The method for constructing a teaching space resource library comprises: The teaching space resource library is a relational database MySQL. The database table structure is defined according to the acquired spatial feature data, the database is created using the SQL language, and the API interface is written to implement the data addition, deletion, modification and query functions to obtain the built teaching space resource library. The specific method is: S41. Define a database table structure according to the acquired spatial feature data; the database table structure consists of a teaching space table, a device table and a timetable; the teaching space table is used to store the basic spatial information of each teaching space; the device table is used to store the spatial device information of each teaching space; the timetable is used to store the available time period information of each teaching space; S42. Use SQL language to create a teaching space table, equipment table and schedule in a relational database; define the field type, primary key and foreign key of each table; the teaching space table uses SpaceID as the primary key; the equipment table uses EquipmentID as the primary key and SpaceID as the foreign key; the schedule uses ScheduleID as the primary key and SpaceID as the foreign key; the teaching space table and the equipment table establish a one-to-many relationship through the unique identifier of the SpaceID space; the teaching space table and the schedule establish a one-to-many relationship through SpaceID; S43. Use Flask to create an API framework and set up a server; write a processing function at each API endpoint to implement data addition, deletion, modification and query functions; the API framework is connected to a relational database through a database connection library and performs database operations required by the API endpoint; use an API testing tool to verify the functionality of the API framework; After the above steps, a well-constructed teaching space resource library is finally obtained.

[0022] The method for initially allocating teaching space according to teaching plan parameter data includes: The allocation of teaching space is modeled as a Markov decision process, and the Q-learning reinforcement learning algorithm is used to learn the Q value function. When the Q value reaches convergence, the optimal allocation strategy is obtained. The specific steps are: S61. Build a reinforcement learning framework and define , , and ;in, For teaching space The usage status at the moment; For The action taken at every moment is to allocate teaching space for a certain teaching plan; To select a strategy, that is, according to the current state Choose the best action ; is the index of the moment; For Always perform actions Immediate rewards after It is obtained through the spatial adaptability function. The specific mathematical formula is: ;in, The difference between teaching resource demand and teaching space. The smaller the difference, the higher the adaptability. S62, use Q-learning reinforcement learning algorithm, initialize Q value function, set learning rate and discount factor ; S63. Obtain the current status according to the teaching space resource library ; at every moment , according to the current Choose the best action ; Execute the best action , get the new state and instant rewards ; Optimal Action use The greedy strategy is selected; For example: Assume that in a certain state, there are three optional actions b1, b2, and b3, and their Q values ​​are: Q(b1)=10; Q(b2)=15; Q(b3)=8; According to the greedy strategy, the optimal action we choose is ; Next, execute action b2 and update the Q value; S64. According to the new state obtained and instant rewards Update the Q value function, the specific mathematical formula is: ;in, For the current state Next action Q value; In the new state All possible actions In the example above, we select the action that maximizes the Q value and obtains the maximum expected reward. In the new state All possible actions; S65, Set it as the new current state, repeat steps S53-S54 until the Q value reaches convergence, and obtain the optimal allocation strategy; perform the initial allocation of the teaching space according to the optimal allocation strategy, and obtain the initial allocation result.

[0023] The learning rate is set and discount factor The methods include: Using the learning rate adjustment formula To limit, the specific mathematical formula is: ;in, is the initial learning rate; is the updated learning rate; is the update step size, the value range is (0,1]; is the time step; The factor adjustment formula is used to To limit, the specific mathematical formula is: ;in, is the initial discount factor; is the updated discount factor; To reward importance, ; For example: Assuming the initial learning rate is 0.2, the update step is 0.5, and the time step is 0.2, according to the learning rate adjustment formula, the updated learning rate is ; Assuming the initial discount factor is 0.9, the reward importance is 0.7, and the update step is 0.2, according to the factor adjustment formula, the updated discount factor is ; The method for secondary allocation of teaching space by using reservation parameter data and free space data includes: The multi-armed bandit model is used to re-allocate the idle teaching space. The specific steps are as follows: S81. Assume that Different arms, each arm corresponds to a different free teaching space; set the initial number of times each arm is pulled The initial average reward value of each arm is ;in, is the index of the arm, ; Set parameters , controls the width of the confidence interval; S82. For each reservation request, calculate the upper confidence limit of each arm, specifically: ;in, is the current total number of pulls; select the arm with the largest upper confidence limit and get the reward ; According to the reward , determine the appointment result; S83, updating the average reward value and the number of pulls of the selected arm; S84, repeating steps S82-S83 until a predetermined number of iterations is reached; For example, suppose there are four arms, representing free teaching spaces f1, f2, f3, and f4; initially, the number of times each space is selected and the average reward value are both 0, and the parameter is 0.2; after multiple iterations, the current state is: Space f1: average reward value = 3.8, number of choices = 30; Space f2: average reward value = 3.2, number of choices = 25; Space f3: average reward value = 3.0, number of choices = 20; Space f4: average reward value = 2.5, number of choices = 15; For an appointment request, compute the upper confidence bound for each arm: ; ; ; ; According to the UCB value, space f1 has the highest UCB. Space f1 is selected as the teaching space for secondary distribution. The reward for selecting space f1 is calculated. If the reward If it is a positive number, the reservation is considered successful. If it is a negative number, the reservation is considered failed; Assuming the reward for the selection space f1 Is a positive number, update the average reward value of space f1: ;The number of times it was pulled is 31; The award is based on , the methods for judging the appointment results include: Design a reward mechanism, taking successful appointments and conflict failures as feedback results; When it is a positive number, the reservation is considered successful; when the reward If it is 0 or a negative number, the reservation is considered failed; When the reservation is successful, the teaching space represented by the selected arm is the result of secondary allocation of the teaching space based on the reservation request and the free space data.

[0024] The method of sending the reservation message to the user according to the result of the secondary allocation includes: Send the reservation result to the user who sent the reservation request through the user terminal; if the reservation is successful, send a reservation success notification; if the reservation fails, send a reservation failure notification and the reason for failure; In this embodiment, the teaching space resource library based on MySQL database can be seamlessly integrated with other systems, such as the teaching affairs system, the campus card system, etc., to achieve data sharing and business collaboration, and has powerful data collection and system integration capabilities; the database table structure can be easily expanded according to actual needs to meet the ever-changing teaching space management needs; Through the reinforcement learning algorithm, not only can the allocation strategy be automatically adjusted according to different teaching plans to adapt to the ever-changing teaching needs, but it can also handle complex allocation problems and quickly respond to changes in teaching plans; through the learning rate adjustment formula, the learning rate can be dynamically adjusted as the time step increases, so that the algorithm can converge quickly in the initial learning stage, and adjust the strategy more finely in the later stage; through the discount factor adjustment formula, the immediate reward and long-term reward can be balanced according to the importance of the reward, so that the algorithm can consider the current interests while focusing on future benefits; The use of the multi-armed bandit model for secondary allocation of teaching space can effectively improve the utilization efficiency of teaching space, enhance the system's adaptability and response speed, while simplifying the complexity of the allocation process, providing an efficient and flexible solution for the management of smart campuses.

[0025] Example 2 See also Figure 2 As shown, this embodiment provides a method for overall management of a smart campus teaching space, including: S1. Obtain teaching space data, teaching plan data, resource usage data and reservation request data; S2. Preprocess the acquired teaching space data, teaching plan data, resource usage data and reservation request data to obtain teaching space parameter data, teaching plan parameter data, resource usage parameter data and reservation parameter data; perform weighted fusion on the teaching space parameter data and resource usage parameter data to obtain spatial feature data; S3. Construct a teaching space resource library based on spatial feature data; S4. Perform the initial allocation of teaching space according to the teaching plan parameter data; based on the initial allocation results, update the teaching space status in the teaching space resource library in real time and obtain the free space data; S5. Perform secondary allocation of teaching space according to reservation parameter data and free space data; S6. According to the result of the secondary allocation, the reservation message is sent to the user; each module is connected via wired and / or wireless means.

[0026] Since the electronic device introduced in this embodiment is an electronic device used to implement a smart campus teaching space coordinated management system in the embodiment of this application, based on the smart campus teaching space coordinated management system introduced in the embodiment of this application, the technical personnel of this field can understand the specific implementation of the electronic device of this embodiment and its various variations, so how the electronic device implements the method in the embodiment of this application is not described in detail here. As long as the technical personnel of this field implement the electronic device used in the smart campus teaching space coordinated management system in the embodiment of this application, it belongs to the scope of protection of this application.

[0027] The above formulas are all dimensionless and numerical calculations. The formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters and thresholds in the formula are set by technicians in this field according to actual conditions.

[0028] The above is only a preferred embodiment of the present invention, and the protection scope of the present invention is not limited to the above embodiments. All technical solutions under the concept of the present invention belong to the protection scope of the present invention. It should be pointed out that for ordinary technical users in this technical field, some improvements and modifications without departing from the principle of the present invention should also be regarded as the protection scope of the present invention.

Claims

1. A smart campus teaching space overall management system, characterized in that: include: Data acquisition module, used to acquire teaching space data, teaching plan data, resource usage data and reservation request data; A data processing module is used to pre-process the acquired teaching space data, teaching plan data, resource usage data and reservation request data to obtain teaching space parameter data, teaching plan parameter data, resource usage parameter data and reservation parameter data; Perform weighted fusion on the teaching space parameter data and resource usage parameter data to obtain the space feature data; Resource management module, used to build a teaching space resource library based on spatial feature data; The intelligent scheduling module is used to make the initial allocation of teaching space according to the teaching plan parameter data; based on the initial allocation results, the teaching space status is updated in real time in the teaching space resource library, and the free space data is obtained; A reservation management module is used to re-allocate teaching space according to reservation parameter data and free space data; The communication module is used to send the reservation message to the user according to the result of the secondary allocation; each module is connected by wire and / or wireless means.

2. According to claim 1, the smart campus teaching space overall management system is characterized in that: The teaching space data includes basic space information, space equipment information and opening period information; basic space information includes the type, capacity, number, location and status data of the teaching space; space equipment information includes the type, number and status of equipment in the teaching space; Teaching plan data includes course data and teaching resource demand data; course data includes course name, course category, lecturer, teaching time and participating student information; teaching resource demand data includes equipment requirements and space requirements; resource usage data includes space usage frequency and space utilization rate; reservation request data includes reservation user information, reservation time, reservation purpose and required equipment; Teaching space data and teaching plan data are collected through the teaching system; resource usage data are collected through visual terminals; The reservation request data is collected through the user terminal.

3. The smart campus teaching space overall management system according to claim 2 is characterized in that: The method of preprocessing the acquired teaching space data, teaching plan data, resource usage data and reservation request data to obtain teaching space parameter data, teaching plan parameter data, resource usage parameter data and reservation parameter data includes: Use the K-means clustering algorithm to identify outliers in the teaching space data, teaching plan data, resource usage data, and reservation request data, and remove the identified outliers to obtain processed teaching space parameter data, teaching plan parameter data, resource usage parameter data, and reservation parameter data; The processed teaching space parameter data, teaching plan parameter data, resource usage parameter data and reservation parameter data are normalized by standard deviation and converted into standard normal distribution to obtain normalized teaching space parameter data, teaching plan parameter data, resource usage parameter data and reservation parameter data.

4. According to claim 3, the smart campus teaching space overall management system is characterized in that: The method of weighted fusion of teaching space parameter data and resource usage parameter data to obtain space feature data includes: The normalized teaching space parameter data and resource usage parameter data are fused through a weighted formula to obtain spatial feature data, which is recorded as , resource usage parameter data is recorded as ; The weighted formula is: ;in, The weight coefficients of the resource usage parameter data; The weight factor for using parameter data for the resource.

5. The smart campus teaching space overall management system according to claim 4 is characterized in that: The method for constructing a teaching space resource library comprises: The teaching space resource library is a relational database MySQL. The database table structure is defined according to the acquired spatial feature data, the database is created using the SQL language, and an API interface is written to implement the data addition, deletion, modification and query functions to obtain a well-built teaching space resource library.

6. The smart campus teaching space overall management system according to claim 5 is characterized in that: The method for initially allocating teaching space according to teaching plan parameter data includes: The allocation of teaching space is modeled as a Markov decision process, and the Q-learning reinforcement learning algorithm is used to learn the Q value function. When the Q value reaches convergence, the optimal allocation strategy is obtained. The specific steps are: S61. Build a reinforcement learning framework and define , , and ;in, For teaching space The usage status at the moment; For The actions taken at every moment; To select strategies; is the index of the moment; For Always perform actions Immediate rewards after It is obtained through the spatial adaptability function. The specific mathematical formula is: ;in, The difference between teaching resource demand and teaching space. The smaller the difference, the higher the adaptability. S62, use Q-learning reinforcement learning algorithm, initialize Q value function, set learning rate and discount factor ; S63. Obtain the current status according to the teaching space resource library ; at every moment , according to the current Choose the best action ; Execute the best action , get the new state and instant rewards ; Optimal Action use The greedy strategy is used for selection; S64. According to the new state obtained and instant rewards Update the Q value function; S65, Set it as the new current state, repeat steps S63-S64 until the Q value reaches convergence, and obtain the optimal allocation strategy; perform the initial allocation of the teaching space according to the optimal allocation strategy, and obtain the initial allocation result.

7. The smart campus teaching space overall management system according to claim 6 is characterized in that: The learning rate is set and discount factor The methods include: Using the learning rate adjustment formula To limit, the specific mathematical formula is: ;in, is the initial learning rate; is the updated learning rate; is the update step size; is the time step; The factor adjustment formula is used to To limit, the specific mathematical formula is: ;in, is the initial discount factor; is the updated discount factor; To reward importance.

8. The smart campus teaching space overall management system according to claim 7 is characterized in that: The method for secondary allocation of teaching space according to reservation parameter data and free space data comprises: The multi-armed bandit model is used to re-allocate the idle teaching space. The specific steps are as follows: S81. Assume that Different arms, each arm corresponds to a different free teaching space; set the initial number of times each arm is pulled , the initial average reward value of each arm is ;in, is the index of the arm, ; Set parameters , controls the width of the confidence interval; S82. For each reservation request, calculate the upper confidence limit of each arm, specifically: ;in, is the current total number of pulls; select the arm with the largest upper confidence limit and get the reward ; According to the reward , determine the appointment result; S83, updating the average reward value and the number of pulls of the selected arm; S84. Repeat steps S82-S83 until a predetermined number of iterations is reached.

9. The smart campus teaching space overall management system according to claim 8 is characterized in that: The award is based on , the methods for judging the appointment results include: Design a reward mechanism, taking successful appointments and conflict failures as feedback results; When it is a positive number, the reservation is considered successful; when the reward If it is 0 or a negative number, the reservation is considered failed; When the reservation is successful, the teaching space represented by the selected arm is the result of secondary allocation of the teaching space based on the reservation request and the free space data.

10. The smart campus teaching space overall management system according to claim 9 is characterized in that: The method of sending the reservation message to the user according to the result of the secondary allocation includes: The user who sent the reservation request will receive the reservation result through the user terminal; if the reservation is successful, a reservation success notification will be sent; if the reservation fails, a reservation failure notification and the reason for the failure will be sent.

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