Campus information management system based on Internet of Things technology
By integrating Internet of Things technology in the campus information management system and using access control data for space-time mining and mode reorganization, the problem that traditional systems are difficult to predict personnel behavior patterns is solved, more accurate resource allocation and activity organization is achieved, and campus management and service quality is improved.
Patent Information
- Application Number
- CN202510061477.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-15
- Publication Date
- 2025-03-21
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional campus information management systems are difficult to comprehensively and accurately predict the behavioral patterns of people in school, resulting in waste of resources and poor organization of activities. At the same time, they are unable to detect safety hazards in a timely manner and provide personalized information push.
Design a campus information management system based on Internet of Things technology, including access control management module, mode mining module, information restructuring module and information push module. By mining the access control data in time and space, it generates spatio-temporal information restructuring rules and mode information restructuring rules, and then predicts the participation of personnel in and out of the places and activities, and pushes relevant information based on the time series.
It has achieved effective integration and in-depth analysis of access control data on campus, and can comprehensively and accurately predict the entry and exit places and activities of on-campus personnel, helping to optimize and improve campus management and services.
Smart Images
Figure CN119671807A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of information management technology, and specifically to a campus information management system based on Internet of Things technology. Background Art
[0002] In today's campus management, with the continuous expansion of campus scale and the increasingly refined management requirements, traditional campus information management methods face many challenges. Existing campus information management often relies on decentralized systems, lacking effective integration and in-depth analysis between systems. Among them, the access control system only plays a basic role in controlling the entry and exit of personnel, and the large amount of access control data generated by it has not been fully mined and utilized. The problems are mainly reflected in the following aspects:
[0003] 1. Traditional campus management makes it difficult to make comprehensive and accurate predictions of the behavioral patterns of on-campus personnel; when arranging campus activities and allocating resources, there is a lack of data support based on the patterns of personnel flow and the tendency to participate in activities, resulting in waste of resources or poor organization of activities. For example, when holding large-scale lectures or club activities, it is impossible to accurately estimate the number of participants and the source areas of personnel in advance, which may lead to unreasonable venue arrangements and insufficient material preparation.
[0004] 2. Existing technologies are also insufficient in terms of security management and service optimization on campus. They cannot promptly detect abnormal patterns of personnel flow, such as abnormal gatherings of people in specific areas during non-teaching hours, making it difficult to provide forward-looking warnings for campus security. In terms of services, they cannot provide personalized information push based on students' daily behavior habits, such as learning resource recommendations and reminders for the use of campus facilities, which affects the efficiency of campus management and the experience of teachers and students.
[0005] In summary, a new technical solution for campus information management based on Internet of Things technology is urgently needed to solve the above problems and improve the intelligence level of campus management and the comprehensive service quality. Summary of the invention
[0006] The purpose of this application is to provide a campus information management system based on Internet of Things technology to solve the technical problems raised in the above background technology.
[0007] To achieve the above-mentioned purpose, the present application discloses the following technical solutions: A campus information management system based on Internet of Things technology, comprising an access control management module, a pattern mining module, an information reorganization module and an information push module which are sequentially connected in communication;
[0008] The access control management module is configured to collect access control data generated by access control devices on campus; wherein the access control devices are access control devices for various places on campus, and the access control data are used to characterize the access of people in the places, and the access includes time information for characterizing the time of access and exit of the places and spatial information for characterizing the type of access and exit of the places;
[0009] The pattern mining module is configured to: perform time mining and space mining on the access control data to generate a spatiotemporal information reorganization rule, perform pattern mining on the spatiotemporal information reorganization rule based on a preset pattern mining rule to generate a pattern information reorganization rule; wherein the spatiotemporal information reorganization rule is used to reorganize information on campus places, the pattern mining rule is used to generate the pattern information reorganization rule, and the pattern information reorganization rule is used to reorganize information on campus activities;
[0010] The information reorganization module is configured to: reorganize the spatiotemporal information based on the spatiotemporal information reorganization rule and generate reorganized spatiotemporal information, and reorganize the pattern information based on the pattern information reorganization rule and generate reorganized pattern information; wherein the reorganized spatiotemporal information is used to characterize the predicted situation of the entry and exit of the school personnel, and the reorganized pattern information is used to characterize the predicted situation of the participation of the school personnel in the activities;
[0011] The information pushing module is configured to push the recombinant spatiotemporal information and / or the recombinant pattern information based on a time series.
[0012] Preferably, the time mining and the space mining are specifically:
[0013] Extracting features from the time information to obtain a time feature chain, which is used to characterize the time distribution probability of the time when the school personnel enter and leave the venue;
[0014] Feature extraction is performed on the spatial information to obtain a spatial feature chain, which is used to characterize the spatial distribution probability of the types of places that school personnel enter and exit.
[0015] Preferably, the generation process of the spatiotemporal information reorganization rule includes:
[0016] Performing correlation analysis on the time feature chain and the space feature chain;
[0017] Based on preset statistical analysis rules, the correlation between different entry and exit times and different entry and exit types of the venue is determined, and based on the correlation, a spatiotemporal information reorganization rule is obtained.
[0018] Preferably, the pattern mining rules are specifically:
[0019] Obtaining historical data of campus activities, which at least includes course schedule data, community activity data, and campus facility usage pattern data;
[0020] An association analysis is performed on the historical data, the access control data and the spatiotemporal information reorganization rules, the association analysis is to associate the places and activities where there is temporal overlap and spatial overlap, statistically analyze the association, set an overlap threshold based on the association, and when the pattern obtained by pattern mining meets the overlap threshold, determine that the pattern is allowed to be used for the generation of the pattern information reorganization rules.
[0021] Preferably, the pattern mining is specifically:
[0022] The spatiotemporal information recombination rules are input into a mining engine constructed based on the pattern mining rules to mine the association between the entry and exit patterns of on-campus personnel in different places and campus activities; wherein the mining engine is used to mine the association between the entry and exit patterns of on-campus personnel in different places and campus activities based on the spatiotemporal information recombination rules and the pattern mining rules, and generate pattern information recombination rules.
[0023] Preferably, the process of generating the pattern information reorganization rule includes:
[0024] Extracting features from the association between the entry and exit patterns of on-campus personnel at different places and campus activities mined by the mining engine to obtain pattern information features; wherein the pattern information features at least include the activity type, activity time range, characteristics of personnel participating in the activity, and the corresponding entry and exit patterns of the places;
[0025] Regularize the pattern information features and generate pattern information reorganization rules.
[0026] Preferably, the spatiotemporal information reorganization is specifically as follows:
[0027] Based on the spatiotemporal information reorganization rule, the time information and the spatial information in the access control data are integrated, and missing data are filled in, where the missing data refers to the situation where school personnel do not use access control when entering and exiting the venue.
[0028] Preferably, the generation process of the reorganized spatiotemporal information includes:
[0029] Using the access control data after reorganization of the spatiotemporal information, the future entry and exit conditions of school personnel are predicted through a preset spatiotemporal prediction model, and the generated prediction result is defined as the reorganized spatiotemporal information, which at least includes the prediction content of the future flow of people in different places and the time distribution of entry and exit of people; wherein, the spatiotemporal prediction model is used to predict the future entry and exit conditions of the venue based on the access control data after reorganization of the spatiotemporal information of the school personnel.
[0030] Preferably, the mode information is reorganized as follows:
[0031] Based on the pattern information reorganization rules, the access control data, on-campus personnel, and places related to campus activities are reorganized.
[0032] Preferably, the process of generating the recombination pattern information includes:
[0033] Based on the results of the reorganization of the pattern information, the possibility and participation pattern of school personnel in different activities are predicted through a preset activity participation prediction model, and the generated prediction results are defined as reorganized pattern information, which at least includes activity organization planning and resource allocation; wherein, the activity participation prediction model is used based on the results of the reorganization of the pattern information.
[0034] Beneficial effects: The campus information management system based on the Internet of Things technology of this application utilizes a campus information management system composed of an access control management module, a pattern mining module, an information reorganization module, and an information push module that are connected in sequence, to achieve effective integration and in-depth analysis of access control data including time information and space information generated by access control equipment on campus. Through the collaborative work of each module, based on the collected access control data, the spatiotemporal information reorganization rules and pattern information reorganization rules are mined, and then the reorganized spatiotemporal information and reorganized pattern information are generated, and finally the relevant information is pushed based on the time series, so that the entry and exit of the school personnel and the participation in activities can be comprehensively and accurately predicted, which helps to optimize and improve campus management and services. BRIEF DESCRIPTION OF THE DRAWINGS
[0035] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.
[0036] Figure 1 This is a structural block diagram of a campus information management system based on Internet of Things technology provided in an embodiment of the present application. DETAILED DESCRIPTION
[0037] The technical solutions in the embodiments of the present application are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.
[0038] In this article, the term "comprising" is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, the elements defined by the sentence "comprising..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.
[0039] This embodiment discloses Figure 1 A campus information management system based on the Internet of Things technology shown includes an access control management module, a pattern mining module, an information reorganization module and an information push module that are sequentially connected in communication;
[0040] The access control management module is configured to collect access control data generated by access control devices on campus; wherein the access control devices are access control devices for various places on campus, and the access control data is used to characterize the access of people in the places, and the access includes time information for characterizing the time of access and exit of the places and spatial information for characterizing the type of access and exit of the places;
[0041] The pattern mining module is configured to: perform time mining and space mining on the access control data to generate time-space information reorganization rules, perform pattern mining on the time-space information reorganization rules based on the preset pattern mining rules to generate pattern information reorganization rules; wherein the time-space information reorganization rules are used to reorganize the information of the campus places, the pattern mining rules are used to generate pattern information reorganization rules, and the pattern information reorganization rules are used to reorganize the information of the campus activities;
[0042] The information reorganization module is configured to: reorganize the spatiotemporal information based on the spatiotemporal information reorganization rules and generate reorganized spatiotemporal information, and reorganize the pattern information based on the pattern information reorganization rules and generate reorganized pattern information; wherein the reorganized spatiotemporal information is used to characterize the predicted situation of the entry and exit of the school personnel, and the reorganized pattern information is used to characterize the predicted situation of the participation of the school personnel in the activities;
[0043] The information push module is configured to push the reorganization spatiotemporal information and / or the reorganization pattern information based on the time series.
[0044] Based on the above, this embodiment utilizes a campus information management system composed of an access control management module, a pattern mining module, an information reorganization module, and an information push module that are connected in sequence, to achieve effective integration and in-depth analysis of access control data including time information and space information generated by access control equipment on campus. Through the collaborative work of each module, based on the collected access control data, the spatiotemporal information reorganization rules and pattern information reorganization rules are mined, and then the reorganized spatiotemporal information and reorganized pattern information are generated, and finally the relevant information is pushed based on the time series, so that the entry and exit of the school personnel and the participation in activities can be comprehensively and accurately predicted, which helps to optimize and improve campus management and services.
[0045] Specifically, time mining and space mining are as follows:
[0046] Extract features from time information to obtain a time feature chain, which is used to characterize the time distribution probability of the time when the school personnel enter and leave the venue;
[0047] The spatial information is feature extracted to obtain a spatial feature chain, which is used to characterize the spatial distribution probability of the types of places that students enter and exit.
[0048] It should be noted that this embodiment uses existing feature extraction technology to realize feature extraction of time information and space information, and realizes statistics of time distribution probability and space distribution probability based on existing statistical technology. In actual campus management, it can be understood that the types of people in school (including teachers and students) appearing in related places and the corresponding time of appearance are relatively fixed based on their identities. Therefore, this embodiment designs time mining and space mining to provide a data basis for the generation of spatiotemporal information reorganization rules and pattern information reorganization rules.
[0049] Based on the above, this embodiment utilizes the method of extracting features from time information and space information respectively to obtain time feature chains and space feature chains, thereby achieving a clear representation of the time distribution probability of the time of entry and exit of school personnel and the spatial distribution probability of the types of entry and exit of venues, providing basic data support for subsequent more accurate analysis of access control data, mining of correlation relationships, and generation of various reorganization rules, further improving the accuracy and effectiveness of the entire campus information management system in analyzing personnel behavior patterns.
[0050] Specifically, the generation process of spatiotemporal information reorganization rules includes:
[0051] Conduct correlation analysis between the temporal feature chain and the spatial feature chain;
[0052] Based on preset statistical analysis rules, the correlation between different entry and exit times and different entry and exit types of the venue is determined, and based on the correlation, a spatiotemporal information reorganization rule is obtained.
[0053] It should be noted that the statistical analysis rules of this embodiment are set based on common knowledge known to those skilled in the art, and are used to define the minimum repetition rate of the association relationship between different entry and exit times and different entry and exit types.
[0054] Based on the above, this embodiment uses a method of associating and analyzing the time feature chain and the space feature chain, and determining the correlation between the time and type of different entry and exit places based on preset statistical analysis rules, to achieve accurate generation of spatiotemporal information reorganization rules. This rule can effectively integrate campus venue-related information, improve the relevance of access control data in the spatiotemporal dimension, and enable subsequent spatiotemporal information reorganization work based on this rule to be carried out smoothly, thereby better providing a reliable basis for predicting the entry and exit of campus personnel, etc., and assisting campus management decision-making.
[0055] Specifically, the pattern mining rules are as follows:
[0056] Obtaining historical data of campus activities, which at least includes course schedule data, community activity data, and campus facility usage pattern data;
[0057] An association analysis is performed on historical data, access control data and spatiotemporal information reorganization rules. The association analysis is to associate places and activities where there is temporal overlap and spatial overlap, statistically analyze the association, set an overlap threshold based on the association, and when the pattern obtained by pattern mining meets the overlap threshold, determine that the pattern is allowed to be used for the generation of pattern information reorganization rules.
[0058] It should be noted that the overlapping threshold of this embodiment is set based on statistical analysis known to technical personnel in this field and in combination with actual campus management. In a practical application, the actual campus management may be identity verification of school personnel. There is a corresponding mode when the person is a teacher and there is a corresponding mode when the person is a student.
[0059] Based on the above, this embodiment uses the method of obtaining multi-source historical data of campus activities, and performing correlation analysis with access control data and spatiotemporal information reorganization rules and setting overlapping thresholds to achieve scientific and reasonable determination of pattern mining rules. This rule can effectively screen out the association between the entry and exit patterns of qualified on-campus personnel in different places and campus activities, ensuring that the generated pattern information reorganization rules have practical reference value, thereby helping to accurately grasp the relevant laws of campus activities and provide strong guidance for campus activity organization, resource allocation and other work.
[0060] Specifically, pattern mining is as follows:
[0061] The spatiotemporal information reorganization rules are input into a mining engine constructed based on pattern mining rules to mine the association between the entry and exit patterns of on-campus personnel in different places and campus activities; wherein the mining engine is used to mine the association between the entry and exit patterns of on-campus personnel in different places and campus activities based on the spatiotemporal information reorganization rules and pattern mining rules, and generate pattern information reorganization rules.
[0062] It should be noted that the mining engine of this embodiment is constructed by using existing information mining technology.
[0063] Based on the above, this embodiment uses the method of inputting the spatiotemporal information reorganization rules into the mining engine built based on the pattern mining rules to achieve in-depth mining of the association between the entry and exit patterns of people in different places and campus activities, and generate pattern information reorganization rules. Through the efficient operation of the mining engine, it is possible to more accurately and comprehensively find the activity association patterns hidden in the access control data and related information, laying the foundation for further reorganization and analysis of campus activity information in the future, and improving the scientificity and accuracy of activity-related decisions in campus management.
[0064] Specifically, the process of generating pattern information reorganization rules includes:
[0065] Extract features from the association between the entry and exit patterns of on-campus personnel at different places and campus activities discovered by the mining engine to obtain pattern information features; wherein the pattern information features at least include the activity type, activity time range, characteristics of personnel participating in the activity, and the corresponding entry and exit patterns of the places;
[0066] Regularize the pattern information features and generate pattern information reorganization rules.
[0067] It should be noted that the regularized pattern information features of this embodiment are achieved by reorganizing the pattern information features using the existing data architecture, thereby obtaining pattern information reorganization rules.
[0068] Based on the above, this embodiment uses the feature extraction and regularization operations of the mined associations to realize the conversion of pattern information features into pattern information reorganization rules. This process can sort out complex association information into a clear and directly applicable rule system, covering key elements of activities and corresponding venue entry and exit modes, etc., so as to facilitate the subsequent accurate reorganization of campus activity related information based on these rules, and provide effective support for accurately predicting the participation of on-campus personnel in activities and optimizing the arrangement of campus management activities.
[0069] Specifically, the space-time information is reorganized as follows:
[0070] Based on the rules for the reorganization of spatiotemporal information, the time and space information in the access control data are integrated, and the missing data are filled in, which refers to the situation where school personnel do not use access control when entering and exiting the venue.
[0071] Based on the above, this embodiment uses the time and space information in the access control data to integrate and fill in the missing data based on the time and space information reorganization rules, so as to make the access control data more complete in the time and space dimensions. It makes up for the data missing problem caused by some school personnel not using the access control, optimizes the data quality, and makes the subsequent time and space prediction based on complete data more reliable, so as to more accurately predict the future entry and exit of school personnel, and provide a more practical basis for campus resource allocation, safety management and other aspects.
[0072] Specifically, the generation process of reorganized spatiotemporal information includes:
[0073] Using the access control data after spatiotemporal information reorganization, the future entry and exit conditions of on-campus personnel are predicted through a preset spatiotemporal prediction model, and the generated prediction result is defined as the reorganized spatiotemporal information, which at least includes the prediction content of the future flow of people in different places and the time distribution of entry and exit of people; wherein the spatiotemporal prediction model is used to predict the future entry and exit conditions of the venue based on the access control data after the spatiotemporal information of the on-campus personnel is reorganized.
[0074] Based on the above, this embodiment uses the access control data after the spatiotemporal information reorganization combined with the preset spatiotemporal prediction model to achieve accurate prediction of the future entry and exit of school personnel, and generate reorganized spatiotemporal information including key contents such as the future flow of people in different places and the time distribution of people entering and leaving. This enables campus managers to grasp the trend of personnel flow in advance, make corresponding preparations such as venue arrangement and security precautions in advance, effectively avoid waste of resources and potential management risks, and improve the foresight and scientific nature of the overall campus management.
[0075] Specifically, the mode information is reorganized as follows:
[0076] Based on the pattern information reorganization rules, the access control data, on-campus personnel, and venues related to campus activities are reorganized.
[0077] Based on the above, this embodiment uses the mode information reorganization rules to reorganize the access control data, on-campus personnel, and venues related to campus activities, thereby realizing the construction of an orderly information structure with campus activities as the core. Through this reorganization, the intrinsic connection between activities and personnel and venues can be presented more clearly, which facilitates the subsequent in-depth analysis of activity participation, targeted resource allocation, and optimization of activity organization and planning, and helps to improve the efficiency and quality of campus activities.
[0078] Specifically, the process of generating the recombination pattern information includes:
[0079] Based on the results of the reorganization of the pattern information, the possibility and participation pattern of school personnel in different activities are predicted through a preset activity participation prediction model, and the generated prediction results are defined as the reorganized pattern information, which at least includes activity organization planning and resource allocation; wherein, the activity participation prediction model is used based on the results of the reorganization of the pattern information.
[0080] Based on the above, this embodiment uses the method of predicting the participation rate of different activities of school personnel by using the results of the reorganization of the pattern information through the preset activity participation prediction model, which effectively predicts the possibility and participation mode of different activities of school personnel, and generates reorganization pattern information including key contents such as activity organization planning and resource allocation. This helps campus managers to reasonably arrange activity resources and optimize the organization form of activities in advance according to the prediction results, better meet the needs of school personnel, and improve the overall satisfaction and management efficiency of campus activities.
[0081] In summary, the campus information management system based on the Internet of Things technology of this embodiment utilizes a campus information management system composed of an access control management module, a pattern mining module, an information reorganization module, and an information push module that are connected in sequence, to achieve effective integration and in-depth analysis of access control data including time information and space information generated by access control equipment on campus. Through the collaborative work of each module, based on the collected access control data, the spatiotemporal information reorganization rules and pattern information reorganization rules are mined, and then the reorganized spatiotemporal information and reorganized pattern information are generated, and finally the relevant information is pushed based on the time series, so that the entry and exit of the school personnel and the participation in activities can be comprehensively and accurately predicted, which helps to optimize and improve campus management and services.
[0082] In the embodiments provided in the present application, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code or any appropriate combination thereof. For hardware implementation, the processor can be implemented in one or more of the following units: application specific integrated circuit (ASIC), digital signal processor (DSP), digital signal processing device (DSPD), programmable logic device (PLD), field programmable gate array (FPGA), processor, controller, microcontroller, microprocessor, other electronic units designed to implement the functions described herein or their combination. For software implementation, part or all of the flow of the embodiment can be completed by instructing the relevant hardware through a computer program. When implemented, the above program can be stored in a computer-readable storage medium or transmitted as one or more instructions or codes on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein the communication medium includes any medium that is convenient for transmitting a computer program from one place to another. The storage medium can be any available medium that a computer can access. The computer-readable storage medium can include but is not limited to RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer.
[0083] Finally, it should be noted that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.
Claims
1. A campus information management system based on Internet of Things technology, characterized in that: It includes an access control management module, a pattern mining module, an information reorganization module and an information push module which are sequentially connected in communication; The access control management module is configured to collect access control data generated by access control devices on campus; wherein the access control devices are access control devices for various places on campus, and the access control data are used to characterize the access of people in the places, and the access includes time information for characterizing the time of access and exit of the places and spatial information for characterizing the type of access and exit of the places; The pattern mining module is configured to: perform time mining and space mining on the access control data to generate a spatiotemporal information reorganization rule, perform pattern mining on the spatiotemporal information reorganization rule based on a preset pattern mining rule to generate a pattern information reorganization rule; wherein the spatiotemporal information reorganization rule is used to reorganize information on campus places, the pattern mining rule is used to generate the pattern information reorganization rule, and the pattern information reorganization rule is used to reorganize information on campus activities; The information reorganization module is configured to: reorganize the spatiotemporal information based on the spatiotemporal information reorganization rule and generate reorganized spatiotemporal information, and reorganize the pattern information based on the pattern information reorganization rule and generate reorganized pattern information; wherein the reorganized spatiotemporal information is used to characterize the predicted situation of the entry and exit of the school personnel, and the reorganized pattern information is used to characterize the predicted situation of the participation of the school personnel in the activities; The information pushing module is configured to push the recombinant spatiotemporal information and / or the recombinant pattern information based on a time series.
2. The campus information management system based on Internet of Things technology according to claim 1 is characterized in that: The time mining and the space mining are specifically: Extracting features from the time information to obtain a time feature chain, which is used to characterize the time distribution probability of the time when the school personnel enter and leave the venue; Feature extraction is performed on the spatial information to obtain a spatial feature chain, which is used to characterize the spatial distribution probability of the types of places that school personnel enter and exit.
3. The campus information management system based on Internet of Things technology according to claim 2 is characterized in that: The generation process of the spatiotemporal information reorganization rule includes: Performing correlation analysis on the time feature chain and the space feature chain; Based on preset statistical analysis rules, the correlation between different entry and exit times and different entry and exit types of the venue is determined, and based on the correlation, a spatiotemporal information reorganization rule is obtained.
4. The campus information management system based on Internet of Things technology according to claim 1 is characterized in that: The pattern mining rules are specifically: Obtaining historical data of campus activities, which at least includes course schedule data, community activity data, and campus facility usage pattern data; An association analysis is performed on the historical data, the access control data and the spatiotemporal information reorganization rules, the association analysis is to associate the places and activities where there is temporal overlap and spatial overlap, statistically analyze the association, set an overlap threshold based on the association, and when the pattern obtained by pattern mining meets the overlap threshold, determine that the pattern is allowed to be used for the generation of the pattern information reorganization rules.
5. The campus information management system based on Internet of Things technology according to claim 4 is characterized in that: The pattern mining is specifically: The spatiotemporal information recombination rules are input into a mining engine constructed based on the pattern mining rules to mine the association between the entry and exit patterns of on-campus personnel in different places and campus activities; wherein the mining engine is used to mine the association between the entry and exit patterns of on-campus personnel in different places and campus activities based on the spatiotemporal information recombination rules and the pattern mining rules, and generate pattern information recombination rules.
6. The campus information management system based on Internet of Things technology according to claim 5 is characterized in that: The generation process of the pattern information reorganization rule includes: Extracting features from the association between the entry and exit patterns of on-campus personnel at different places and campus activities mined by the mining engine to obtain pattern information features; wherein the pattern information features at least include the activity type, activity time range, characteristics of personnel participating in the activity, and the corresponding entry and exit patterns of the places; Regularize the pattern information features and generate pattern information reorganization rules.
7. The campus information management system based on Internet of Things technology according to claim 3 is characterized in that: The spatiotemporal information reorganization is specifically as follows: Based on the spatiotemporal information reorganization rule, the time information and the spatial information in the access control data are integrated, and missing data are filled in, where the missing data refers to the situation where school personnel do not use access control when entering and exiting the venue.
8. The campus information management system based on Internet of Things technology according to claim 7 is characterized in that: The generation process of the reorganized spatiotemporal information includes: Using the access control data reorganized by the spatiotemporal information, the future entry and exit conditions of school personnel are predicted through a preset spatiotemporal prediction model, and the generated prediction result is defined as the reorganized spatiotemporal information, which at least includes the prediction content of the future flow of people in different places and the time distribution of entry and exit of people; wherein, the spatiotemporal prediction model is used to predict the future entry and exit conditions of the venue based on the access control data reorganized by the spatiotemporal information of the school personnel.
9. The campus information management system based on Internet of Things technology according to claim 6 is characterized in that: The mode information reorganization is specifically as follows: Based on the pattern information reorganization rules, the access control data, on-campus personnel, and places related to campus activities are reorganized.
10. The campus information management system based on Internet of Things technology according to claim 9 is characterized in that: The generation process of the recombination pattern information includes: Based on the results of the reorganization of the pattern information, the possibility and participation pattern of school personnel in different activities are predicted through a preset activity participation prediction model, and the generated prediction results are defined as reorganized pattern information, which at least includes activity organization planning and resource allocation; wherein, the activity participation prediction model is used based on the results of the reorganization of the pattern information.