27 results about "Smart campus" patented technology

The application discloses a kind of intelligent campus-oriented electromechanical installation management method and system, it is related to computer platform load balancing technical field, including constructing electromechanical installation object basis set and object level installation constraint set.Further generate electromechanical installation task, task branch regulation is executed in combination with construction state data, form three kinds of tasks of executable, to be made up and collaborative adjustment.For executable task, installation process data is collected, object level installation process record is constructed, object level installation verification and abnormal disposal path regulation are executed, and object level installation closed loop result is formed.Based on installation closed loop result, systematized debugging, acceptance collection, file construction and constraint update processing are implemented, and constraint optimization result is formed.The method described in the application realizes the fine flow of intelligent campus electromechanical installation task, abnormal closed loop disposal and result feedback optimization, significantly improves the accuracy, cooperativity and traceability of installation management.

This invention belongs to the field of smart campus security technology and discloses an artificial intelligence-based real-time monitoring and early warning system for abnormal behavior on university campuses. The system includes: a perception layer: deploying AI cameras, environmental sensors, audio equipment, and a positioning system to cover key areas such as campus gates, walls, dormitories, and laboratories, collecting multimodal data including video, environmental, audio, and personnel positioning; an edge computing layer: using edge computing devices to process the real-time data collected by the perception layer locally, including video analysis, behavior recognition, and data preprocessing, achieving low-latency data processing; a cloud analysis layer: storing historical data, deploying and continuously training and optimizing deep learning models, supporting cross-campus risk model sharing and big data analysis; and an application layer: integrating a security command center screen, a mobile APP, and an emergency response system to achieve event visualization, real-time early warning push, and cross-departmental collaborative handling. This invention achieves dual monitoring of physical safety and mental health, covering all campus security needs; multimodal data fusion and deep learning algorithms improve recognition accuracy, reduce false alarm rates, and ensure the reliability of early warnings.

This invention discloses a campus emergency command and rescue vehicle, relating to the field of campus emergency management and rescue command equipment technology. This invention is adaptable to all scenarios including campus fires, stampedes, sudden illnesses, earthquakes, security incidents, epidemics, and off-campus coordinated rescue, achieving integrated campus emergency duty, on-site handling of emergencies, student injury and sick transfer, fire hazard investigation, daily emergency drills, and campus security patrols. This invention integrates seven major functional systems: a full-domain communication command system, an on-site emergency rescue and disposal system, an emergency power supply guarantee system, an environmental safety monitoring system, an on-site command and office and emergency analysis system, a logistics support and security protection system, and a smart campus linkage system. It possesses comprehensive capabilities such as multi-network converged communication, satellite emergency communication, fire fighting and rescue, campus medical emergency care, personnel evacuation and security, emergency power supply guarantee, environmental safety monitoring, command and office analysis, logistics support and protection, and smart campus linkage, meeting the emergency management needs of various types of schools, including primary and secondary schools, vocational schools, and universities.

This utility model relates to an easy-to-install wall-mounted smart campus POS terminal, belonging to the technical field of smart campus POS terminals. It includes an input unit and a payment terminal, both equipped with a quick-installation assembly. The quick-installation assembly includes a mounting frame slidably mounted on the surface of the input unit and payment terminal. Four mounting feet are fixedly mounted on the side of the mounting frame, and a mounting shell is fixedly mounted on one side of each mounting foot. A suction cup is slidably mounted inside the mounting shell. A groove is formed on the inner side wall of the mounting frame, and a slot is formed on the side of the input unit and payment terminal. This easy-to-install wall-mounted smart campus POS terminal, by using the quick-installation assembly on the input unit and payment terminal, allows the suction cup to adhere the input unit and payment terminal to a wall or glass surface. When the height of the input unit and payment terminal needs to be adjusted, the locking bolts are loosened, and after adjustment, the locking bolts are tightened again.

The present application relates to the technical field of data security encryption, in particular to a data security encryption method based on a smart campus cloud platform, which comprises the following steps: receiving a data processing request carrying an original data file and preset metadata tags thereof; matching a target encryption security level and encryption control parameters based on a tag query security policy library; starting a multi-channel encryption key generation engine to generate a core working key, a data integrity check key and an access session key after verifying that a user identity attribute meets an access condition; using the core working key to block and obfuscate encode the original file to form a timestamped intermediate encrypted data block; using the integrity check key to calculate an overall hash value and generate a to-be-encapsulated ciphertext package; and finally using the access session key to perform secondary encapsulation to form a final ciphertext stream. The present application realizes dynamic adaptation of encryption strength according to data attributes, and completes integrated security processing of encryption, integrity protection and session control through multi-key cooperation.

The application discloses an Internet-based intelligent campus platform intelligent operation and maintenance system, comprising a global data acquisition layer, an intelligent analysis and decision-making layer, an automatic execution layer and a zero-trust security layer; the global data acquisition layer is used for collecting multi-modal operation and maintenance data in real time through probes and Internet of Things gateways arranged at each node of the campus platform; the multi-modal operation and maintenance data at least comprises timing performance indexes of servers, unstructured log data, network traffic metadata and user behavior data; and the global data acquisition layer supports multiple protocols and edge pre-aggregation, and ensures data real-time performance and integrity. The application relates to the technical field of technical operation and maintenance management, and provides a campus dining hall fund whole-process closed-loop supervision system based on the Internet of Things; the system solves the technical problems of lagging fault response, difficult root cause positioning, low operation and maintenance automation, static security defense and unbalanced resource utilization in the prior art.

This invention relates to the field of smart campus management technology, specifically a smart campus resource allocation and management method and system based on big data. The method includes: collecting and integrating real-time signals from various locations within the campus to generate standardized resource status description information; analyzing future teaching and activity plans to generate a spatiotemporal distribution prediction map of future resource demands; inputting the real-time status and future demand map into a collaborative scheduling decision model for optimization analysis to generate a globally optimal resource allocation and operation instruction set; and generating and issuing control codes to drive energy, facilities, security, and information dissemination systems based on the instruction set. This method, by integrating real-time status and future demands for joint decision-making, achieves forward-looking and collaborative resource scheduling, effectively optimizing the spatiotemporal allocation and operational efficiency of campus resources.

The invention discloses a smart campus authority management method and related equipment thereof, and relates to the technical field of Internet of Things. When an authority verification request is triggered, identity information and multi-dimensional context features of a user are synchronously obtained, and a static identity tag and a dynamic relation graph of the user are obtained through analysis according to the identity information; on the premise that the static identity meets a basic access condition, extracting a target associated entity having a preset association relationship with the controlled resource type according to the controlled resource type, and further executing campus situation consistency verification in combination with the current campus situation mode, the target associated entity and a link between a user and the entity; the access behavior is more comprehensively and dynamically judged safely by fusing the multi-dimensional context features, illegal or unsuitable access caused by authorization management only according to the identity attribute of the user is prevented, and the technical problem of low safety of controlled resources in the smart campus can be solved.

The present application relates to the technical field of data transmission, in particular to a big data sharing method in a wisdom campus middle station, which collects facial images of students and parents in real time, screens relevant data according to historical facial data in a storage unit, marks suspicious frame images in the relevant data to train generated simulation data, and restores facial expressions according to simulation data and displays images, which can avoid limitations and dependence of big data in propagation due to bandwidth and network problems, retain more key facial information, ensure convergence speed when training a big data model, and improve communication efficiency.

This application relates to the field of smart campus technology and provides a campus security monitoring method and device. The campus security monitoring method includes: collecting facial images of personnel when they enter or exit the campus through a campus access control gate; acquiring historical combinations of people who have been identified by the personnel when they pass through the access control gate, and the corresponding sets of people for these historical combinations; searching for the personnel's identity information from the set of people corresponding to the historical combinations based on the facial images; if the personnel's identity information cannot be found from the set of people corresponding to the historical combinations, searching for the personnel's identity information based on the entire campus personnel set; and if the personnel's identity information is found, controlling the campus access control gate to switch to a preset open state and adjusting the historical combinations of people to monitor personnel entering and exiting the campus. The campus security monitoring method and device of this application can improve the efficiency of identity verification when entering and exiting the campus.

This utility model relates to the field of smart campus assistant technology, and discloses a smart campus assistant with intelligent interactive functions, including a lifting platform and a base. The lifting platform is movably connected inside the base, and a turntable is rotatably connected to the top of the lifting platform. A robot is fixedly connected to the top of the turntable. A camera is installed on the top of the front wall of the robot, and below the camera are indicator lights, a touch screen, a button area, a voice input module, a voice output module, and an ID card IC recognition module. A storage slot is provided on the front wall of the robot. This utility model, by incorporating a storage slot, a storage board, an infrared detector, and an infrared receiver, enables the storage board to automatically catch and the infrared detector to detect foreign objects when students forget to take their ID cards or other small items. The robot then promptly broadcasts a reminder through the voice output module, thereby reducing the occurrence of lost items and improving the security and reliability of document and item management in the smart campus.

The application provides a kind of wisdom campus non-motor vehicle management method and system based on Internet of Things, it is related to Internet of Things technical field, first, obtain the real-time position information of campus non-motor vehicle basic information and Internet of Things perception, identity association information set is generated in association, determine target mobile area based on campus area function, user use purpose and vehicle type, then combine target area, path traffic state and real-time position, generate path guide instruction and send to user terminal, after vehicle arrives, collection parking area occupancy state, combine vehicle size and allocate parking area and guide parking, when user applies for leaving school, generate leaving school check information according to identity association information, parking allocation and vehicle current state, after being verified by the verification equipment of campus entrance Internet of Things, generate leaving school state record and store in management database, thus realize all-around intelligent management of campus non-motor vehicle, improve management efficiency and campus safety level.

This invention relates to the field of intelligent laundry equipment, specifically disclosing a method and system for preventing infection during shared laundry services in smart campuses. The invention installs microbial sensors at the drain outlet or inside the washing machine to detect the microbial content in the wash water during the rinsing stage. When the detected value exceeds a preset threshold, a secondary disinfection process is triggered, and disinfection parameters are dynamically adjusted according to the degree of exceedance. The disinfection process execution and microbial detection results are uploaded to the cloud to generate a health report that users can query. Simultaneously, the complete process information of each laundry order is generated into an immutable digital certificate and stored in the blockchain, achieving transparent traceability of hygiene data. During non-use periods, the system automatically initiates drying maintenance, blowing and cleaning the inside of the washing machine to prevent moisture and mold growth. This invention achieves real-time monitoring and precise disinfection of the washing machine's hygiene, solving the trust and traceability problems in shared laundry services.

The application discloses a kind of based on big data's wisdom campus safety early warning platform, including following steps: data preprocessing module, for collecting campus multi-source heterogeneous security data and pre-processing, form early warning analysis dataset;Candidate pair generation module, for extracting record link identification and generating candidate record pair set;Candidate pair screening module, for carrying out gate screening based on campus reachable domain constraint;Matching weight formation module, for executing the matching weight formation based on identity-event double-track comparison;Associated record link module, for executing Fellegi-Sunter probability record link algorithm to generate uniform association index;Matching determination result updating module, for executing the three-value determination boundary self-updating of grey zone upgrade backflow;Hierarchical early warning module, for output hierarchical early warning result.The application improves the association merging capability and hierarchical early warning accuracy of wisdom campus multi-source heterogeneous security data.

This invention relates to the field of smart campus information management technology, specifically a personalized information management system based on a smart campus. The system includes: simultaneously acquiring student behavior trajectory data, academic interaction data, and environmental perception data through multimodal data acquisition terminals deployed throughout the campus. Based on the multi-type acquired data, the system constructs an initial individual state vector for each student. This vector is input into a spatiotemporal attention network, which intelligently analyzes and outputs the personalized information demand weight distribution corresponding to students at different times and locations. The system dynamically filters and adaptively sorts the original information entries in the campus information resource database based on the demand weight distribution, generating a dedicated personalized information push queue. Finally, the customized information push queue is accurately distributed to the corresponding student's mobile terminal via the campus wireless network. This system achieves fusion modeling and spatiotemporal correlation analysis of multimodal campus data, enabling precise mining of information needs tailored to changes in student scenarios.

This invention relates to the field of campus safety management technology, and discloses a campus violence prevention model and its construction method. The construction method includes the following steps: Step 1, acquiring environmental parameters, physiological indicators, and behavioral characteristics in real time as basic data through a multimodal data acquisition device. The environmental parameters include concentration; the physiological indicators include body temperature fluctuations; and the behavioral characteristics include limb movements and voiceprint data. Step 2, preprocessing the basic data and generating a fused feature vector. Step 3, training a bullying behavior prediction model based on a federated learning platform using a support vector machine and neural network fusion algorithm; and outputting a behavioral risk value from the bullying behavior prediction model. This invention can achieve timely and accurate detection and early warning of risky behaviors, with strong objectivity and real-time performance, contributing to the construction of a zero-violence, high-efficiency smart campus.

The application relates to the technical field of campus safety management, and discloses a campus overactive behavior prevention system and method, which comprises an Internet of Things sensing system, an intelligent analysis database and an intelligent pushing system; the Internet of Things sensing system is used for collecting multi-source data; the intelligent analysis database is used for completing multi-source data fusion and privacy protection, and comprises a time series database used for storing environment data, a graph database used for constructing a behavior correlation graph, a data processing layer based on federal learning and a privacy protection layer based on differential privacy and a block chain; a multi-modal fusion model is arranged in the intelligent pushing system; the multi-modal fusion model calculates and outputs an overactive behavior risk index based on the multi-source data, and triggers a hierarchical alarm. The application can consider privacy protection, real-time early warning and accurate identification, can effectively solve the problems of concealment, delay and privacy disputes in campus overactive behavior prevention and control, and provides a standardized solution for constructing a zero overactive behavior, high-energy-efficiency intelligent campus.

This invention provides a cloud-edge collaborative data collection and management method and system for smart campuses. It collects multi-source heterogeneous data from various scenarios within a smart campus, performs adaptive preprocessing and hierarchical transmission of the collected data at the edge, dynamically schedules cloud-edge collaborative tasks based on a digital twin virtual campus model, and employs a federated learning mechanism to perform global intelligent analysis while protecting data privacy. This generates early warning signals for the smart campus's operational status and drives the digital twin model for visualization. This invention effectively solves the problems of high bandwidth pressure, high latency, high privacy and security risks, and low resource utilization inherent in traditional centralized data processing models for smart campuses, achieving efficient, secure, and intelligent data collection and management for smart campuses.

The application discloses a kind of smart campus-oriented internet of things equipment access authentication method and system, collect terminal identity characteristics and communication behavior data to construct fingerprint drive atlas, identify fingerprint drift characteristics to generate imitated risk identification, and combine different trust decay analysis to delimit effective access coverage domain;The token request behavior of terminal in coverage domain is analyzed and frequency characteristic is extracted, and confidence score is generated by joint evaluation, and authentication task allocation strategy is generated according to confidence identification and token time window constraint;Authentication task is disassembled into core authentication sequence and auxiliary authentication sequence, hot standby authentication node cluster is configured to auxiliary sequence, and distortion signal is extracted by monitoring unauthorized access behavior to core sequence;According to distortion signal extraction permission correction parameter determines takeover authentication link, and core authentication sequence is seamlessly connected to output access authentication instruction, to realize the dynamic deployment of authentication resource and the closed-loop response of abnormal behavior.

The application relates to the technical field of Internet of Things data processing and artificial intelligence application, in particular to a smart campus AIOT big data visual analysis method, which comprises the following steps: collecting heterogeneous behavior data; performing Z-Score standardization processing on the heterogeneous behavior data to generate dimensionless standard scores; constructing a behavior state matrix; generating compliance entropy; calculating an AI intervention confidence score; comparing the AI intervention confidence score with a preset dynamic decision threshold; if the AI intervention confidence score is higher than the dynamic decision threshold, performing AI precise induction intervention; otherwise, performing space mapping calculation on the compliance entropy to generate an administrative intervention resource allocation amount; calculating the difference between the number of false positives and the system expected false positives; using the updated data entropy penalty factor for AI intervention confidence score calculation at the next time step; and the application solves the problem of high false positive rate caused by the lack of logical verification in the prior art, and ensures the robustness and reliability of early warning decision.

1. Name of the product in this design: Graphical User Interface for Smart Campus Platform (Smart Life) for Electronic Devices. 2. Purpose of this design: An electronic device. 3. The key design features of this product are its graphical user interface. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. Design 1 is designated as the basic design. 6. Use of a graphical user interface: an electronic device. Design 1's main view is the initial interface of the campus management system. Clicking the "Smart Life" button in the main view of Design 1 will take you to the interface state diagram of Design 1. Clicking the "Smart Life" button in the main view of Design 2 will take you to the interface state diagram of Design 2. Clicking the "Smart Life" button in the main view of Design 3 will take you to the interface state diagram of Design 3.

This invention provides a smart campus task collaboration method and system for multi-group service optimization, belonging to the field of campus service management technology. The method retrieves historical service logs, allocates storage nodes according to service processes to generate pre- and post-collaboration datasets; determines the group collaboration status based on the datasets, and constructs a dedicated task measurement set and group behavior feature set; quantifies cross-group task collaboration priorities through dual-evaluation modality labeling; and generates a group-task collaboration set through iterative computation, using segmentation coefficients (cohesion + separation) for selection and optimization. The system includes modules for data collection and storage, task and feature construction, collaboration priority quantification, and collaboration adaptation analysis and optimization. This invention solves the problems of insufficient precise adaptation and ambiguous priority quantification in existing multi-group collaboration technologies, achieving targeted optimization of service resources, improving task collaboration efficiency and group satisfaction, and providing an efficient solution for differentiated multi-group services in smart campuses.

This invention discloses a student trajectory prediction method based on campus road networks and adaptable to multiple travel modes, belonging to the field of smart campus technology. The method comprises the following modules: S1, Target Student Image Information Input Module: Inputs the image information of the target student; S2, Target Student Image Information Capture Module: Acquires student image information within a target time period through campus monitoring equipment and outputs capture records; S3, Target Student Auxiliary Feature Extraction Module: Extracts facial, clothing color, and height range features from the captured images to obtain valid records; S4, Multi-Feature Fusion Similarity Matching Analysis Module: Uses multi-feature fusion similarity matching to filter out valid capture sequences of the target student; S5, Target Student Trajectory Analysis Module: Generates a complete trajectory through dynamic road network weight modeling and a trajectory completion algorithm with spatiotemporal constraints. This invention possesses the technical advantages of adapting to multiple travel modes on campus, high identity recognition accuracy, accurate trajectory prediction, and close alignment with the campus scenario.

This application discloses a computer vision-assisted intelligent perception method and system for smart campus security situation, relating to the field of image data processing technology. The method includes: collecting temporal visual data from multiple monitoring areas within the campus, identifying abnormal behavior patterns, and generating a behavioral semantic graph; constructing a dynamic security influence factor graph network based on the behavioral semantic graph and the topology of the monitoring areas; dividing security communities and analyzing key nodes according to the dynamic security influence factor graph network, identifying core influence nodes and corresponding security situation indicators in multiple security communities; integrating the security situation indicators of the core influence nodes with a pre-set campus security strategy library, and using a multi-objective optimization algorithm to make adaptive scheduling decisions for security resources, outputting a real-time security early warning scheme. This solves the technical problems of limited monitoring range, slow response speed, and insufficient data analysis capabilities in existing campus security management models.

This invention discloses a smart campus security device based on the Internet of Things (IoT), belonging to the field of IoT positioning technology. It includes: an electronic school badge with a UWB positioning chip and an IoT module; multiple UWB base stations deployed within the school; electronic class sign screens installed at classroom entrances; an automatic tracking and monitoring device deployed according to the video surveillance range; and a school security backend hardware system and interactive terminal. The electronic school badge interacts with the UWB base stations for high-precision positioning; the electronic school badge works with the electronic class sign to achieve student location-based attendance; and the electronic school badge works with the automatic tracking and monitoring device to achieve location tracking. This invention solves the technical problems of fragmented existing campus security hardware, poor emergency response coordination, and low scenario adaptability; it achieves functions such as high-precision positioning, automatic location-based attendance, audio and video acquisition, anomaly recognition, local early warning, seat area monitoring, and abnormal acceleration monitoring, providing basic data support for subsequent management and control; and it is suitable for the security management needs of various primary and secondary schools and vocational schools.

The application provides a smart campus payment processing method and system, and relates to the technical field of education informatization. Through the financial end, the charging rules are configured to generate bills and publish events, the message queue synchronizes data and pushes reminders, the parent end initiates payment, the payment is completed through unified gateway scheduling and is verified by the core module, the state is updated after verification and automatic reconciliation, the problems of manual operation inefficiency, high error rate, data synchronization delay and security risk in campus payment management are solved, the campus payment process is automatically processed, the operation efficiency is significantly improved, human errors are reduced, the data real-time synchronization and payment security are ensured.