9 results about "University campus" patented technology

The invention discloses an intelligent college visitor reservation system which comprises a visitor registration module, a process approval module, a pass verification module, a data management module and a confidential pass module. The mobile terminal encrypts the confidential information of the confidential identity personnel by adopting a dynamic multi-factor encryption algorithm, and then generates a dynamic two-dimensional code from the encrypted confidential information, and an authorized applicant scans the dynamic two-dimensional code of the mobile terminal of the confidential identity personnel through the authorized mobile terminal to obtain the confidential information, automatically generates authorized access and sends the authorized access to the auditing terminal; and the auditor analyzes the confidential information through the auditing terminal, performs access auditing and then issues the passing permission. According to the invention, the campus traffic management efficiency is improved, and the method has a positive influence on guaranteeing the safety of the university campus.

The invention provides a college entrepreneurship simulation traffic control method and system, and relates to the technical field of college entrepreneurship simulation traffic control. And efficient control of a complex traffic scene is realized through multi-dimensional data fusion and dynamic strategy optimization. The system generates a three-dimensional state tensor based on a traffic perception base, extracts a cross-scene element strategy kernel, and generates a real-time control strategy in combination with a dynamic weight matrix driven by a traffic flow state difference; the double-ring feedback module generates a disturbance gradient through virtual adversarial training of a first ring, and a second ring matches and calibrates a virtual-real strategy based on optimal transmission, so that generalization is improved; in an unforeseen scene, the perception crack repair module deconstructs a strategy unit, and rapid adaptation is realized through traffic flow conservation verification and dynamic topology recombination. In a complex scene, the phase conflict rate of the system is reduced, the traffic efficiency is improved, the strategy iteration period is shortened, and an efficient intelligent control strategy is provided for complex scenes such as college gardens.

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.

The techniques presented herein are directed to a system for automated navigation assistance in indoor environments. In general, navigating an unfamiliar indoor environment can be a daunting task. This is especially true for large environments such as corporate offices, university campuses, shopping centers, and hospitals. Accordingly, the present system utilizes a reference database containing information associated with the indoor environment such as images depicting various locations, annotations describing the images, and floorplans illustrating the layout of the indoor environment. The reference database is deployed in conjunction with a multimodal language model that utilizes the collected knowledge of the reference database to provide location identification and intuitive navigation instructions. Often referred to as retrieval-augmented generation (RAG), the reference database enables the multimodal language model to respond to user requests using domain-specific information and ensure accurate outputs that are relevant to an end user's context (e.g., the indoor environment).

The invention provides a university campus heating corridor energy supply regulation and control method based on space-time people flow monitoring and prediction, and belongs to the technical field of intelligent prediction. Image data and environment data of a heating corridor are collected through various sensors; training an image recognition model by using a semantic segmentation algorithm and a deep learning algorithm; processing the picture data to obtain data of the number of people in the warm corridor sections at different time and in different spaces; processing the data acquired by the monitoring equipment, and analyzing the warm corridor space through a thermal comfort evaluation index; according to the number of monitored people, classifying the heating corridors in different sections, and establishing a time sequence database of the use frequency and the thermal comfort degree of the heating corridors in different sections; constructing and training a prediction model through a deep learning algorithm based on the time sequence database; and based on the successfully trained prediction model, carrying out data analysis to form the space-time difference heating corridor adaptive regulation and control method. The energy utilization efficiency can be remarkably improved, and the system has great significance in promoting green campus construction.

1. The name of the design product: outdoor sweeping robot. 2. The use of the design product: the design product is an outdoor sweeping robot, which is used for autonomous movement and ground cleaning in outdoor areas such as industrial parks, parks, university campuses, residential areas, etc. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The present invention discloses an optimization method for the outdoor public space of university campuses based on multi-modal spatio-temporal data, which relates to the field of public space optimization. In the field of school public space design, a morphological and vitality information database for the outdoor public space of university campuses is established through systematic empirical research and quantitative research. Quantitative research and analysis are carried out on the outdoor public space of university campuses according to the respective key index systems of morphology and vitality. Through the actual measurement and analysis of human perception data based on multi-modal spatio-temporal data, a dynamic model between spatial morphology - human perception - spatial vitality is established, and the correlation mechanism between spatial morphology and spatial vitality is studied. Furthermore, an optimization method for the outdoor public space of university campuses is refined and proposed. This method helps to improve the accuracy, scientificity and predictability of the optimization of the outdoor public space of university campuses, and also has positive reference value for the refined design of urban public spaces.

The invention relates to the field of environment, in particular to a cold-region university campus functional area carbon emission prediction method based on deep learning and image recognition. The objective of the invention is to solve the problem of low carbon emission prediction accuracy of the existing cold-region university campus functional area. According to the cold region university campus functional area carbon emission prediction method based on deep learning and image recognition, a performance simulation method and a deep learning method based on image recognition are combined, and carbon emission data of campus functional areas are obtained through performance simulation. The spatial distribution information of the spatial form and the function of the functional area is translated through the functional layout feature map, and the mapping relation among the spatial form, the functional layout and the carbon emission of the campus functional area is established through a deep learning algorithm, so that the carbon emission performance of the complex campus functional area can be quickly predicted in a short time. The method solves the problem that the existing carbon emission prediction accuracy of the functional area of the college campus in the cold region is low.

The invention belongs to the field of entrance guard of halls, particularly relates to an intelligent entrance guard device for a dormitory hall of a large campus and a use method thereof, and aims to solve the problem that the personnel evacuation efficiency and safety are affected due to the fact that a gate cannot be opened in time when a disaster occurs as the gate cannot be opened in time when the conventional device cannot enable the gate to be in a normally open state under emergency conditions such as a fire or an earthquake. According to the scheme, the anti-theft door comprises a door frame, and an electromagnetic lock is fixedly arranged at the position, located in the center, of the rear side of the door frame; an on-off mechanism is arranged on the electromagnetic lock and used for controlling on-off of power of the electromagnetic lock. Through the arrangement of the on-off mechanism, when a fire disaster or an earthquake comes, the electromagnetic lock can be powered off, and the gate is in a normally open state, so that the electromagnetic lock power supply is automatically cut off in an emergency situation to ensure that the gate is opened in time, and the personnel evacuation efficiency and safety are greatly improved; the casualty risk caused by the fact that the gate cannot be opened in time when disasters occur is effectively reduced.