Intelligent campus integrated management platform

Abstract

The invention discloses a smart campus integrated management platform, which comprises a facility access layer, a data storage layer, a communication layer, a service layer and a business display layer, and is characterized in that the facility access layer is used for performing data connection with access control equipment, channel equipment, online patrol equipment, teaching and scientific research equipment, video monitoring equipment, noise acquisition equipment and signal source acquisition equipment; collecting information about entrance guard, security channel, campus inspection, teaching and research equipment occupation and video monitoring; the data storage layer has data processing and storage functions, a personnel information base is pre-stored in the data storage layer, and the personnel information base at least comprises relevant information about students, faculty and employees, visitors and logistics personnel. The platform provided by the invention has multiple values, and can bring benefits in multiple aspects such as improvement of management efficiency, improvement of teaching quality, enhancement of campus safety, improvement of comprehensive quality of students and reduction of operation cost for schools.

Description

Technical Field

[0001] The present invention relates to the field of smart campus, and in particular to a smart campus comprehensive management platform. Background Art

[0002] With the country's emphasis on educational informatization, the construction of smart campuses has become an important part of educational informatization reform. A series of policies and standards issued by the Ministry of Education provide clear directions and guidance for the development of smart campuses. A smart campus refers to a campus that uses information technology to carry out intelligent transformation of the campus and provide intelligent and convenient educational services and management methods. The construction of a smart campus is based on in-depth research on educational development and a deep understanding of school management needs. Through the use of modern information technology, the school's education and teaching level and management efficiency are comprehensively improved to create a good educational environment and learning atmosphere.

[0003] The subsystems of traditional smart campuses are maintained by different companies, and the data between the subsystems cannot be shared and communicated. At the same time, it is also impossible to achieve visual perception in the fields of security, identity authentication, teaching and research management, etc., resulting in the dispersion of the overall system control and the inability to control the campus as a whole. Based on this, the present invention proposes a smart campus comprehensive management platform, which has many values ​​and can bring many benefits to the school, such as improved management efficiency, improved teaching quality, enhanced campus safety, improved comprehensive quality of students and reduced operating costs. Specifically including:

[0004] 1. Improve management efficiency: Through the smart campus comprehensive management platform, schools can realize digital and intelligent management of various businesses and improve management efficiency. For example, through face recognition technology, functions such as attendance management and access control management can be automated; through data analysis technology, data from various aspects such as teaching, scientific research, and management can be deeply mined and analyzed to provide a scientific basis for school decision-making.

[0005] 2. Improve teaching quality: The smart campus comprehensive management platform can integrate various teaching resources to realize the sharing and optimal allocation of teaching resources; at the same time, through information technology, it can innovate teaching methods and means to improve teaching effectiveness and students' learning interest.

[0006] 3. Enhance campus safety: The smart campus comprehensive management platform can realize the linkage and intelligent management of campus safety monitoring, alarm, crowd flow prediction monitoring, emergency response and other functions, and improve the campus safety prevention and emergency response capabilities. Summary of the invention

[0007] The purpose of the present invention is to provide a smart campus comprehensive management platform to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solutions: a smart campus comprehensive management platform, comprising:

[0009] The facility access layer is used to connect data with access control equipment, channel equipment, online inspection equipment, teaching and scientific research equipment, video surveillance equipment, noise collection equipment, and signal source collection equipment, and collect information about access control, safe channels, campus inspections, teaching and research equipment occupancy, and video surveillance;

[0010] The data storage layer has data processing and storage functions. A personnel information database is pre-stored in the data storage layer. The personnel information database includes at least relevant information about students, faculty and staff, visitors and logistics personnel. The data storage layer is also used to obtain the information about access control, security passages, campus inspections, teaching and research equipment occupancy, and video surveillance collected by the facility access layer, and process the information to obtain the processed facility data, and store the facility data in the management information database, video information database, and alarm information database respectively;

[0011] The communication layer is used to provide firewall services for computers and provide campus intranet for teachers and students to use;

[0012] The service layer provides the business functions required for comprehensive campus management and provides information management services for users, including user management module, teaching and research equipment management module, authority management module, security warning module, data storage management module, streaming media management module, personnel and equipment management module and log management module;

[0013] The business display layer provides users with an application operating system. The internal data of the smart campus is collected, organized, and statistically analyzed to provide a visual display for campus managers to make management decisions, track, and implement them.

[0014] Preferably, the business display layer also provides scientific research management and teaching management displays for teaching and research personnel, including scientific research project establishment, progress, achievements, teaching plans, course arrangements, student course selection and grade management displays; provides students with personal information, academic information, reward and punishment information displays; provides security inspection and logistics personnel with monitoring, recording playback, crowd density alarm and safety warning information displays.

[0015] Preferably, the specific process of the business display layer providing crowd density alarm and safety warning information display for security inspection and logistics personnel includes:

[0016] a: sampling the crowd flow data of each monitoring point in the campus at a preset time interval, wherein the crowd flow data includes a historical crowd flow density image set, a noise data set and a signal source data set of each monitoring point in the campus;

[0017] b: For each monitoring point, the above historical crowd density image set, noise data set and signal source data set are preprocessed and time-aligned, and stored in a ternary array data structure to obtain the data sequence of each monitoring point;

[0018] c: Perform time alignment on the data sequences formed by all monitoring points on campus to obtain aligned time series;

[0019] d: inputting the aligned time series into an initial crowd density prediction model, training the initial crowd density prediction model, and obtaining a target crowd density prediction model;

[0020] e: obtaining real-time crowd flow data of each monitoring point and preprocessing the real-time crowd flow data;

[0021] f: Input the preprocessed real-time crowd flow data into the target crowd flow density prediction model to obtain the crowd flow density prediction results of each monitoring point;

[0022] g: Input the crowd density prediction results of each monitoring point into the GIS engine to obtain the predicted crowd distribution map on campus, and display the predicted crowd distribution map.

[0023] Preferably, the preprocessing process of the crowd density image set in step b includes: background modeling, foreground extraction, image binarization and statistics for the crowd density image; the preprocessing process for the noise data includes: filtering, inverse Fourier transform and overlap-addition processing; the preprocessing process for the signal source includes: determining the number of signal sources and the radiation area.

[0024] Preferably, the specific process of background modeling for the crowd density image includes:

[0025] The mixed Gaussian background model is used, that is, at any time t, the observation value x of the pixel point (x0, y0) t , its probability density function can be expressed by M three-dimensional Gaussian functions:

[0026]

[0027] In the above formula: M is the number of mixed Gaussian models, ω t,i represents the weight of the i-th Gaussian mixture model at time Represents the probability density function of the i-th Gaussian distribution at time t, whose mean is μ t,i

[0028] The covariance matrix is ​​∑ tj ,Right now:

[0029]

[0030] Wherein i=1,2,3……M。

[0031] Preferably, the specific process of determining the number of signal sources and the radiation area includes: determining the number of signal sources emitting the wireless network signals in the target scene according to the radiation area of ​​the acquisition device and the number of wireless network signals captured in the target scene, wherein the signal sources are mobile devices carried by users; determining the regression coefficient of a preset linear regression algorithm, and determining the error value based on the maximum number of people received by the target scene, and calculating the ratio of the site area to the signal capture area, which ratio is the data required for the preprocessing process.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] The platform of the present invention has many values ​​and can bring benefits to schools, such as improved management efficiency, improved teaching quality, enhanced campus safety, improved comprehensive quality of students, and reduced operating costs. Through the smart campus comprehensive management platform, schools can realize digital and intelligent management of various businesses and improve management efficiency. At the same time, it can improve teaching quality, realize the linkage and intelligent management of campus security monitoring, alarm, crowd flow prediction monitoring, emergency response and other functions, and improve campus security prevention and emergency response capabilities. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a system deployment diagram of the present invention;

[0035] Figure 2 It is a system level architecture diagram of the present invention. DETAILED DESCRIPTION

[0036] 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.

[0037] See also Figure 1-2 The present invention provides a technical solution: a smart campus comprehensive management platform, comprising:

[0038] The facility access layer is used to connect data with access control equipment, channel equipment, online inspection equipment, teaching and scientific research equipment, video surveillance equipment, noise collection equipment, and signal source collection equipment, and collect information about access control, safe channels, campus inspections, teaching and research equipment occupancy, and video surveillance;

[0039] The data storage layer has data processing and storage functions. A personnel information database is pre-stored in the data storage layer. The personnel information database includes at least relevant information about students, faculty and staff, visitors and logistics personnel. The data storage layer is also used to obtain the information about access control, security passages, campus inspections, teaching and research equipment occupancy, and video surveillance collected by the facility access layer, and process the information to obtain the processed facility data, and store the facility data in the management information database, video information database, and alarm information database respectively;

[0040] The communication layer is used to provide firewall services for computers and provide campus intranet for teachers and students to use;

[0041] The service layer provides the business functions required for comprehensive campus management and provides information management services for users, including user management module, teaching and research equipment management module, authority management module, security warning module, data storage management module, streaming media management module, personnel and equipment management module and log management module;

[0042] The business display layer provides users with an application operating system. The internal data of the smart campus is collected, organized, and statistically analyzed to provide a visual display for campus managers to make management decisions, track, and implement them.

[0043] In this embodiment, the business display layer also provides scientific research management and teaching management displays for teaching and research personnel, including scientific research project establishment, progress, achievements, teaching plans, course arrangements, student course selection and grade management displays; provides students with personal information, academic information, reward and punishment information displays; provides security inspection and logistics personnel with monitoring, recording playback, crowd density alarm and safety warning information displays.

[0044] In this embodiment, the specific process of the business display layer providing crowd density alarm and safety warning information display for security inspection and logistics personnel includes:

[0045] a: sampling the crowd flow data of each monitoring point in the campus at a preset time interval, wherein the crowd flow data includes a historical crowd flow density image set, a noise data set and a signal source data set of each monitoring point in the campus;

[0046] b: For each monitoring point, the above historical crowd density image set, noise data set and signal source data set are preprocessed and time-aligned, and stored in a ternary array data structure to obtain the data sequence of each monitoring point;

[0047] c: Perform time alignment on the data sequences formed by all monitoring points on campus to obtain aligned time series;

[0048] d: inputting the aligned time series into an initial crowd density prediction model, training the initial crowd density prediction model, and obtaining a target crowd density prediction model;

[0049] e: obtaining real-time crowd flow data of each monitoring point and preprocessing the real-time crowd flow data;

[0050] f: Input the preprocessed real-time crowd flow data into the target crowd flow density prediction model to obtain the crowd flow density prediction results of each monitoring point;

[0051] g: Input the crowd density prediction results of each monitoring point into the GIS engine to obtain the predicted crowd distribution map on campus, and display the predicted crowd distribution map.

[0052] In this embodiment, the preprocessing process of the crowd density image set in step b includes: background modeling, foreground extraction, image binarization and statistics for the crowd density image; the preprocessing process for the noise data includes: filtering, inverse Fourier transform and overlap-addition processing; the preprocessing process for the signal source includes: determining the number of signal sources and the radiation area.

[0053] In this embodiment, the specific process of background modeling for the crowd density image includes:

[0054] The mixed Gaussian background model is used, that is, at any time t, the observation value x of the pixel point (x0, y0) t , its probability density function can be expressed by M three-dimensional Gaussian functions:

[0055]

[0056] In the above formula: M is the number of mixed Gaussian models, ω t,i represents the weight of the i-th Gaussian mixture model at time Represents the probability density function of the i-th Gaussian distribution at time t, whose mean is μ t,i

[0057] The covariance matrix is ​​∑ tj ,Right now:

[0058]

[0059] Wherein i=1,2,3……M。

[0060] In this embodiment, the specific process of determining the number of signal sources and the radiation area includes: determining the number of signal sources emitting the wireless network signals in the target scene according to the radiation area of ​​the acquisition device and the number of wireless network signals captured in the target scene, wherein the signal source is a mobile device carried by the user; determining the regression coefficient of a preset linear regression algorithm, and determining an error value based on the maximum number of people received by the target scene, and calculating the ratio of the site area to the signal capture area, which is the data required for the preprocessing process.

[0061] In this embodiment, the above platform has many values ​​and can bring benefits to the school, such as improved management efficiency, improved teaching quality, enhanced campus safety, improved comprehensive quality of students, and reduced operating costs. Through the smart campus comprehensive management platform, the school can realize the digital and intelligent management of various businesses and improve management efficiency. At the same time, it can improve the teaching quality, realize the linkage and intelligent management of campus security monitoring, alarm, crowd flow prediction monitoring, emergency response and other functions, and improve the campus security prevention and emergency response capabilities.

[0062] The above embodiments can be implemented in whole or in part by software, hardware, firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website site, computer, server or data center to another website site, computer, server or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0063] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0064] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0065] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only one, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0066] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0067] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0068] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0069] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0070] Finally: 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 principles of the present invention should be included in the protection scope of the present invention.

[0071] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A smart campus comprehensive management platform, characterized in that: It includes the facility access layer, data storage layer, communication layer, service layer and display business layer, among which: The facility access layer is used to connect data with access control equipment, channel equipment, online inspection equipment, teaching and scientific research equipment, video surveillance equipment, noise collection equipment, and signal source collection equipment, and collect information about access control, safe channels, campus inspections, teaching and research equipment occupancy, and video surveillance; The data storage layer has data processing and storage functions. A personnel information database is pre-stored in the data storage layer. The personnel information database includes at least relevant information about students, faculty and staff, visitors and logistics personnel. The data storage layer is also used to obtain the information about access control, security passages, campus inspections, teaching and research equipment occupancy, and video surveillance collected by the facility access layer, and process the information to obtain the processed facility data, and store the facility data in the management information database, video information database, and alarm information database respectively; The communication layer is used to provide firewall services for computers and provide campus intranet for teachers and students to use; The service layer provides the business functions required for comprehensive campus management and provides information management services for users, including user management module, teaching and research equipment management module, authority management module, security warning module, data storage management module, streaming media management module, personnel and equipment management module and log management module; The business display layer provides users with an application operating system. The internal data of the smart campus is collected, organized, and statistically analyzed to provide a visual display for campus managers to make management decisions, track, and implement them.

2. According to claim 1, a smart campus integrated management platform is characterized by: The business display layer also provides scientific research management and teaching management displays for teaching and research personnel, including scientific research project establishment, progress, achievements, teaching plans, course arrangements, student course selection and grade management displays; provides students with personal information, academic information, reward and punishment information displays; provides security patrol and logistics personnel with monitoring, recording playback, crowd density alarm and safety warning information displays.

3. According to claim 1, a smart campus integrated management platform is characterized by: The specific process of the business display layer providing crowd density alarm and safety warning information display for security inspection and logistics personnel includes: a: sampling the crowd flow data of each monitoring point in the campus at a preset time interval, wherein the crowd flow data includes a historical crowd flow density image set, a noise data set and a signal source data set of each monitoring point in the campus; b: For each monitoring point, the above historical crowd density image set, noise data set and signal source data set are preprocessed and time-aligned, and stored in a ternary array data structure to obtain the data sequence of each monitoring point; c: Perform time alignment on the data sequences formed by all monitoring points on campus to obtain aligned time series; d: inputting the aligned time series into an initial crowd density prediction model, training the initial crowd density prediction model, and obtaining a target crowd density prediction model; e: obtaining real-time crowd flow data of each monitoring point and preprocessing the real-time crowd flow data; f: Input the preprocessed real-time crowd flow data into the target crowd flow density prediction model to obtain the crowd flow density prediction results of each monitoring point; g: Input the crowd density prediction results of each monitoring point into the GIS engine to obtain the predicted crowd distribution map on campus, and display the predicted crowd distribution map.

4. According to claim 1, a smart campus integrated management platform is characterized by: Regarding: the preprocessing process of the crowd density image set in step b includes: background modeling, foreground extraction, image binarization and statistics for the crowd density image; the preprocessing process for noise data includes: filtering, inverse Fourier transform and overlap-addition processing; the preprocessing process for the signal source includes: determining the number of signal sources and the radiation area.

5. According to claim 4, a smart campus integrated management platform is characterized in that: About: The specific process of background modeling for the crowd density image includes: The mixed Gaussian background model is used, that is, at any time t, the observation value x of the pixel point (x0, y0) t , its probability density function can be expressed by M three-dimensional Gaussian functions: In the above formula: M is the number of mixed Gaussian models, ω t,i represents the weight of the i-th Gaussian mixture model at time Represents the probability density function of the i-th Gaussian distribution at time t, whose mean is μ t,i The covariance matrix is ​​∑ tj ,Right now: In the formula, i=1, 2, 3...M.

6. According to claim 4, a smart campus integrated management platform is characterized by: The specific process of determining the number of signal sources and the radiation area includes: determining the number of signal sources emitting the wireless network signals in the target scene according to the radiation area of ​​the acquisition device and the number of wireless network signals captured in the target scene, wherein the signal source is a mobile device carried by the user; determining the regression coefficient of a preset linear regression algorithm, and determining the error value based on the maximum number of people received by the target scene, and calculating the ratio of the site area to the signal capture area, which is the data required for the preprocessing process.

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