290 results about "Smart campus" patented technology

The invention discloses an intelligent campus operation and maintenance management system based on digital twinning, which relates to the technical field of intelligent campus operation and maintenance management and comprises a digital twinning monitoring module, an intelligent task scheduling and resource optimization module and a space optimization and path planning module. According to the system, the visual and intelligent level of equipment management is improved by monitoring the equipment state in real time and constructing an accurate equipment virtual model, the task execution efficiency is improved by optimizing task scheduling and resource allocation, meanwhile, the energy consumption of the equipment is reduced, the service life of the equipment is prolonged, the space conflict between the equipment is solved, and the equipment management efficiency is improved. The cooperative work capability of the equipment is improved, the path planning and task execution sequence is optimized, and the safety and stability of the intelligent campus operation and maintenance system are improved.

The invention provides a smart campus safety early warning method and system based on edge computing and big data. The method comprises the following steps: collecting multiple paths of video data streams in real time through an edge computing node, performing frame sequence segmentation and spatial-temporal feature extraction, generating an initial behavior feature set, performing multi-dimensional correlation analysis on the initial behavior feature set based on a preset behavior semantic tag, extracting a spatial-temporal behavior feature vector corresponding to a target monitoring scene, and obtaining a spatial-temporal behavior feature vector; and transmitting the time-space behavior feature vector to a central server, inputting the time-space behavior feature vector into a target behavior recognition model, generating a behavior semantic description sequence corresponding to the video data stream, determining a real-time behavior monitoring result according to a matching result of the behavior semantic description sequence and a preset abnormal behavior rule base, receiving the real-time behavior monitoring result through an edge computing node, and sending the real-time behavior monitoring result to the central server. And adaptive adjustment is carried out on acquisition parameters of the video data stream based on a dynamic priority strategy. According to the invention, the real-time performance, the accuracy and the system sustainability of campus behavior monitoring can be improved.

The invention provides a smart campus-oriented multi-hyper fusion platform collaborative scheduling system and method, and is applied to the technical field of data processing. Resource sensing and dynamic modeling processing is performed on multi-hyper fusion platform resource pool data to generate target resource model data, and the target resource model data is composed of a resource real-time monitoring index, load prediction model output, a resource isomerism adaptation result and a cross-platform protocol conversion adaptation parameter; the target resource model data is processed, platform collaborative scheduling strategy parameters are generated based on reinforcement learning, and a campus business scene reward and punishment mechanism is introduced in the reinforcement learning process; processing the platform collaborative scheduling strategy parameters to generate a dynamic resource allocation scheme; processing the dynamic resource allocation scheme and the campus service demand data, and generating a service and resource matching agent model based on an intelligent optimization algorithm; and processing the target campus information based on the service and resource matching agent model to generate campus resource scheduling information.

The invention provides a smart park Internet of Things distributed energy intelligent analysis and control method and device, and relates to the technical field of smart park energy management, and the technical scheme is characterized in that the method comprises the steps: obtaining energy consumption data, meteorological data, distributed energy power generation data and user behavior data of each region of a smart campus; generating a basic load prediction value and an event-driven load prediction value according to the acquired data; making a day-ahead energy scheduling plan; when a deviation exists between the actual energy consumption condition and the predicted value, adjusting the energy scheduling plan by adopting a rolling scheduling strategy; and executing the adjusted energy scheduling plan, and controlling the distributed energy system and the energy consumption equipment to optimize campus energy utilization. The smart park Internet of Things distributed energy intelligent analysis and management and control method and device provided by the invention have the advantages of accurately predicting complex and changeable energy consumption demands and utilizing the distributed energy to the maximum extent.

The invention relates to the technical field of campus management, and particularly discloses a smart campus management system based on big data, an event-driven data management architecture is used for dynamically collecting, integrating and associating multi-source heterogeneous data in a campus, and generating a standardized event stream; the dynamic resource scheduling engine is in communication connection with the event-driven data governance architecture, generates a resource allocation instruction based on event types and priorities in event streams, and dynamically deploys campus resources; the closed-loop evaluation optimization module receives a resource scheduling result of the dynamic resource scheduling engine and generates a multi-dimensional evaluation index, and the multi-dimensional evaluation index is fed back to the data governance architecture through root cause analysis so as to optimize a subsequent decision; the privacy enhancement processing unit integrates a federated learning framework and a differential privacy algorithm, performs collaborative analysis on cross-system data and ensures the anonymity of individual data; through three core technologies of dynamic data management, intelligent resource scheduling and closed-loop evaluation optimization, intelligent upgrading of the whole campus management process is realized.

The invention relates to the technical field of fire safety, and provides a smart campus multi-dimensional fire safety monitoring method and system. The method comprises the following steps: collecting multi-source security data of a campus building; extracting features based on a preset risk dimension, and generating a fire risk feature vector; constructing a risk model according to historical fire cases, and scoring the feature vectors to obtain a real-time risk score; when the score exceeds a dynamic threshold value, triggering a grading early warning instruction; an emergency scheme is generated according to the early warning, and fire fighting equipment and a personnel evacuation system are linked to execute operation. The technical problem that an existing campus fire safety monitoring system lacks a multi-dimensional, real-time and comprehensive risk assessment means and is difficult to accurately judge different fire risks and deal with the fire risks in real time is solved, and accurate fire risk early warning and efficient emergency response are achieved through multi-source data fusion and intelligent analysis. And the campus fire safety management level is improved.

The invention relates to a smart campus data management method and system. Comprising the following steps: modeling by adopting semantic consistency-oriented data behaviors; abstracting all system data as behavior event units; uniformly converting modeling results into semantic vectors, and writing the semantic vectors into a campus semantic behavior database; establishing an event standardization rule base; constructing a dynamic data flow scheduling engine by adopting a behavior link diagram; constructing a behavior path map according to the behavior event nodes, and extracting a high-frequency behavior link; a graph search algorithm is introduced to calculate a data scheduling path; a polymorphic perception and difference recognition mechanism of heterogeneous behavior states is adopted; capturing the deviation between the actual behavior event and the expected behavior link; constructing a behavior state comparison model, and identifying a potential abnormal state or a strategy failure risk; adopting a self-adaptive optimization and feedback updating mechanism of a strategy evolution graph; introducing a reinforcement learning algorithm, and carrying out strategy scoring and value return calculation on the strategy map; and adjusting and optimizing link parameters and resource scheduling logic, and notifying rhythm.

The invention relates to the technical field of smart campuses, in particular to a Cesium-based three-dimensional GIS smart campus visualization platform system, which comprises a three-dimensional visualization engine module, a 1: 1 campus digital twin model constructed based on Cesium, and a GLB format model dynamic loading and 360-degree free view angle browsing support, the data fusion module is used for integrating security and protection monitoring and energy consumption monitoring multi-source data and realizing multi-dimensional data linkage through ECharts; the performance optimization module is used for optimizing the model loading efficiency by adopting view cone cutting and LOD technologies; according to the method, 1: 1 campus digital twin modeling is realized by adopting a Cesium engine, the reality sense and the spatial analysis capability of a three-dimensional scene are improved by combining a precise positioning algorithm of a WGS84-to-Cartesian coordinate system and a GLB format model dynamic loading technology, and meanwhile, real-time coupling of dynamic data such as energy consumption and security and protection and the three-dimensional scene is realized by adopting a CZML protocol.

The invention belongs to the technical field of smart campus, and relates to a smart campus safety management early warning method and system based on big data. The method comprises the following steps: collecting registration information, real-time behavior data and state information of an environment of a foreign vehicle; constructing a dynamic space-time diagram, and generating an edge attribute reflecting a relationship between deployment nodes in the dynamic space-time diagram; calculating a reasonable path set of the foreign vehicle, and analyzing trajectory deviation data of the foreign vehicle; abnormal scoring is carried out on the external vehicle; generating a corresponding intervention control strategy; performing causal influence analysis and generating a decision basis; and training the local graph neural network model, and carrying out centralized aggregation and distribution updating on shared model parameters in a training result of the local graph neural network model. According to the method, a dynamic behavior modeling strategy based on a heterogeneous graph neural network is introduced, structural modeling and time sequence behavior evolution expression of campus moving target multi-source data are achieved, the system can capture tiny anomalies in individual trajectory evolution, and the abnormal behavior recognition sensitivity is improved.

The invention discloses an intelligent campus security monitoring method based on image processing, and relates to the technical field of intelligent security protection, and the method comprises the steps: collecting multi-modal monitoring data, synchronizing the timestamp of the multi-modal monitoring data, and generating a synchronous multi-modal monitoring data stream; the method comprises the following steps: preprocessing a multi-modal monitoring data stream, extracting multi-modal features, and generating a multi-modal feature vector set; an abnormal event detection model is constructed based on the multi-modal feature vector set, dynamic weight distribution and abnormal event detection are performed on the multi-modal feature vectors through the abnormal event detection model, and abnormal event space-time coordinates are generated; and performing abnormal target path prediction based on the abnormal event space-time coordinates, performing hazard quantification in combination with abnormal target path prediction, and generating a risk assessment result. According to the method, the multi-modal monitoring data are collected and the timestamps are synchronized, so that effective fusion of various types of data is realized, and the consistency and accuracy of the monitoring data are remarkably improved.

The invention discloses an intelligent campus management system, and particularly relates to the field of campus management, and the system comprises a behavior data acquisition module, a behavior scene recognition module, a behavior intention reasoning module, a campus digital twinborn simulation module, a self-adaptive behavior intervention module, and a self-evolution learning module. The behavior data acquisition module is used for collecting original behavior data, sequentially executing anomaly detection, data cleaning, data standardization and feature extraction, and outputting a complete behavior data set. Management of multi-source behavior data dynamic acquisition, behavior scene recognition, behavior intention reasoning, campus digital twinborn simulation, self-adaptive behavior intervention and self-evolution learning optimization is executed through the system, so that the problems that in the prior art, a single data source is relied on, a static rule is adopted, dynamic response ability and self-adaptive optimization ability are lacked, and the reliability of campus digital twinborn simulation is poor are solved. The campus management problem that student behavior diversity and campus environment dynamic change cannot be effectively handled is solved.

The invention provides a smart campus security early warning management method and system, and relates to the technical field of smart campus management, and the method comprises the steps: extracting the security demand information of students in a campus from campus security data; determining interaction early warning attributes in a target campus security supervision period through security deployment paths and track association levels in historical security event libraries of different supervision areas of the target campus; determining a security offset node of each supervision area in the target campus according to the security risk index, and determining a correction behavior tag of the student in the target campus according to the security offset node and the interactive early warning attribute; and managing and controlling the safety behavior characteristics of the students in the target campus according to the corrected behavior labels, and generating early warning feedback information of the activities of the students in the target campus. According to the application, fusion deduction can be carried out on the association relationship between the student behavior track and the security resource dynamic demand, so that the response accuracy of the campus security system is improved.

The invention relates to the technical field of image processing, and discloses a smart campus security monitoring method and system based on visual understanding. The method comprises the following steps: carrying out adaptive semantic segmentation on a campus scene image through priori knowledge of a campus functional region to obtain a campus functional region segmentation map; performing multi-dimensional identity feature extraction on the campus personnel according to the segmentation map to obtain a personnel identity detection result; the identity detection result is input into a campus behavior semantic perception network for abnormal behavior recognition, and an abnormal behavior recognition result is obtained; and according to the segmentation map, the identity detection result and the abnormal behavior identification result, performing comprehensive evaluation through a campus scene context sensing fusion algorithm to obtain a campus security monitoring decision result. The abnormal behavior detection method and device solve the technical problem that abnormal behavior detection in an existing campus security monitoring technology lacks spatial semantic correction and time sequence correlation analysis.

The invention provides a smart campus management system and method based on the Internet of Things, and the method comprises the steps: predicting the traffic pressure of a target access control gate through the traffic flow of the target access control gate and the people flow density in a monitoring region of the target access control gate, dividing all to-be-passed people into a plurality of people groups according to the access record of each to-be-passed person, determining an access feature corresponding to each personnel group, and setting a credibility label corresponding to each personnel group in the monitoring area according to a feature difference between the access features; based on the authentication deviation of the target to-be-passed person under different authentication dimensions and in combination with the credibility label of the person group where the target to-be-passed person is located, determining the authentication confidence of the access control gate machine for passing authentication of the target to-be-passed person; and controlling the opening of the access control gate based on the authentication confidence. According to the scheme, on the basis of the authentication confidence, the access control gate can accurately authenticate and identify the passing people in a high people flow scene.

The invention discloses a smart campus converged communication system and an information interaction method thereof, and relates to the technical field of campus communication. The system comprises a terminal access module, a protocol conversion module, a hierarchical transmission module and a cross-modal interaction module. The terminal access module acquires a terminal data stream in real time by using a multi-protocol adapter; the protocol conversion module performs protocol decoupling and standardized packaging on the data flow by using a QoS bandwidth prediction model, and generates a bandwidth allocation scheme according to a dynamic bandwidth allocation algorithm in combination with a network topology state matrix; the hierarchical transmission module extracts service information data by using a data semantic analysis model, calculates Shannon entropy and constructs a transmission queue mapping table; and the cross-modal interaction module generates an interaction protocol stack adaptive to the service by using a multi-modal feature collaborative extraction mechanism and an attention weight fusion mechanism based on the transmission queue mapping table.

The invention relates to a smart campus comprehensive energy-saving regulation and control method and system based on AI decision making. The method comprises the following steps: constructing a spatial-temporal feature tensor based on campus multi-source heterogeneous data; according to the spatio-temporal feature tensor, performing prediction processing through a dynamic spatio-temporal diagram neural network, and generating a global state tensor including an energy consumption prediction baseline and an environment change gradient; inputting the global state tensor into a hierarchical reinforcement learning model for processing, and generating an equipment-level control instruction set; and inputting the equipment-level control instruction set into the digital twin environment model for simulation safety verification to obtain a verification result, and generating an equipment control instruction and a strategy interpretable report which pass verification according to the verification result. According to the method, by fusing multi-source heterogeneous data and combining a dynamic space-time diagram neural network and a hierarchical reinforcement learning model, the energy consumption prediction precision and the scientificity of regulation and control decision are improved, and by means of simulation verification of a digital twin environment model, the safety of an equipment control instruction and the interpretability of a strategy are enhanced.

The invention discloses an intelligent campus access control system based on artificial intelligence, and the system comprises an intelligent sensing layer which is used for deploying an AI camera with a skeleton key point extraction function, an integrated RFID / NFC card reader, a two-dimensional code scanning module and an environment sensor, and extracting human skeleton key points in real time by adopting a posture estimation algorithm, multi-mode identity information and environment metadata are synchronously collected; the behavior understanding layer is used for constructing a space-time diagram model based on a space-time diagram neural network engine, taking skeleton key points as nodes, taking joint connection as space edges, connecting continuous frame space diagrams in series according to a time sequence to form a space-time diagram, learning a space relationship and a time evolution rule among the nodes by adopting a graph convolutional network, and completing behavior recognition, trailing detection and abnormal behavior distinguishing; the graph analysis layer is used for constructing a campus behavior knowledge graph based on the metadata tag library, and performing sub-graph mode mining by applying a graph neural network and a community discovery algorithm; and the decision and execution layer is used for configuring a grading early warning strategy.

The invention discloses a smart campus Internet of Things multi-service collaborative scheduling control system based on an all-optical network, and relates to the technical field of Internet of Things, and the system comprises an Internet of Things digital platform which is in communication connection with the following modules: an all-optical network infrastructure module which is used for constructing an optical fiber backbone network in a campus, and each subsystem in the campus is accessed. The reliability and stability of the campus Internet of Things are enhanced by constructing all-optical network infrastructure, the subsystems are directly connected through optical fibers by adopting the passive optical network technology, and the use of active equipment is reduced, so that the equipment failure rate and the maintenance cost are reduced, optical wavelength resources are dynamically allocated by utilizing a software defined network controller, and the reliability and the stability of the campus Internet of Things are improved. The transmission path is adjusted according to the service priority, efficient operation of key services is ensured, network stability is improved, high speed and safety of data transmission are guaranteed, and solid network support is provided for various intelligent applications in the campus.

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

The invention is suitable for the field of smart campuses, and provides a multi-source fusion data-based campus subject trajectory analysis method and system, and the method comprises the following steps: synchronously collecting the original data of a vehicle through an intelligent riding terminal, the original data comprising three-axis acceleration data, tire pressure data and high-precision positioning trajectory data; preprocessing the original data and extracting an acceleration sudden change event, a tire pressure sudden drop event and track deceleration characteristics; performing spatial superposition on the event data obtained through preprocessing and a campus road network GIS layer, and generating a pit damage thermodynamic diagram based on a preset damage judgment rule; and extracting a high-risk road section based on the pit damage thermodynamic diagram, and pushing a high-risk road section warning to the user. According to the invention, a scientific basis is provided for campus road maintenance planning, preventive maintenance is realized, and pavement damage is reduced.

The invention discloses an intelligent campus student activity trajectory tracking method and system based on front and rear end fusion learning, and relates to the technical field of intelligent campus security. An embedded front end collects videos of a plurality of monitoring cameras of a campus, carries out target detection and extracts primary features of student targets; uploading the extracted primary features and the video to a back-end server; and the back-end server extracts advanced features of human body postures and gaits from the video by using a human body posture estimation algorithm and a gait recognition algorithm, fuses the advanced features and the primary features to obtain fused features, and clusters all detected student targets by using the fused features, thereby realizing cross-camera identity unification of the same student under different cameras, and improving the identification accuracy of the student. Face detection and recognition are carried out in each clustering group, a student information base is matched to determine student IDs, and the student IDs, video timestamps and monitoring camera position information are stored; and the client retrieves target information according to the input student ID or photo, and automatically clips and generates a time-space continuous complete cross-camera trajectory tracking video. According to the invention, efficient and intelligent analysis of campus monitoring data is realized, and targets can be accurately associated and tracked in a cross-camera scene.

The invention discloses an intelligent campus all-purpose card early warning method based on video monitoring. The method comprises the following steps: obtaining continuous frame images of a gate card swiping person in a preset time window, wherein the continuous frame images are collected by campus monitoring equipment; generating an image quality change curve and a behavior trajectory change curve by using a multi-modal time sequence analysis mechanism, constructing a collaborative risk change curve, determining abnormal points by using the behavior trajectory change curve and a preset standard trajectory change curve, and segmenting the collaborative risk change curve to obtain a plurality of segmentation curves corresponding to collaborative risk characteristic values; extracting a segmentation curve with the minimum collaborative risk characteristic value as a target curve, and performing illumination direction analysis on the corresponding continuous frame images to determine a face recognition image; and performing adaptive correction on the face recognition image to obtain a target image, performing face recognition on the target image to obtain a matching result of the card swiping person, and generating and executing an early warning instruction of the gate. Therefore, the accuracy and safety of campus all-purpose card control are improved.

The invention provides a campus safety analysis and early warning method and system fused with a multi-modal reasoning capability, and belongs to the technical field of smart campus safety. Comprising the steps of collecting multi-modal data in a campus; sequentially carrying out data processing, lightweight processing and privacy processing on the multi-modal data; a cloud DeepSeek engine is utilized to analyze the multi-modal data subjected to the lightweight processing and the privacy processing, and a quantitative risk assessment level is output; and triggering a third-level alarm according to the obtained risk assessment level, and pushing differentiated disposal suggestions for campus security. According to the invention, the causal reasoning ability can be considered while the privacy and security analysis efficiency of people in the campus are ensured; meanwhile, the problems of modal data splitting and mental health early warning lag are avoided, and better support and guarantee can be provided for campus safety.

The invention relates to the technical field of education management, in particular to a smart campus integrated management system and method, and the system comprises a course arrangement module, a teacher matching module, a resource scheduling module, a behavior monitoring module and an interaction guiding module. Through intelligent processing, course arrangement and teacher matching optimization, the teaching resource utilization efficiency is improved, errors and deviations in manual processing are avoided, the matching degree of courses and students is improved by means of intelligent screening and conflict elimination, course conflicts are reduced, matching is carried out according to the qualification and preference of teachers, teacher resource configuration is optimized, and the teaching efficiency is improved. The accurate scheduling of classroom and equipment resources avoids resource waste and insufficiency, the smooth campus management is ensured, the behavior monitoring technology and the safety perception means track the behaviors in the teaching process in real time, the interactive guidance provides immediate feedback and guidance in the case of abnormality, the potential safety hazard is reduced, and the teaching efficiency is improved. And the intelligent safety guarantee level of the campus is improved.

The invention relates to the technical field of smart campus and Internet of Things security, and discloses a campus security intelligent management method and system based on edge computing, and the method comprises the steps: accessing and driving monitoring equipment and an Internet of Things sensor to collect multi-modal original data; carrying out local preprocessing and real-time analysis on the multi-modal original data based on a built scene risk weight, data credibility, risk evolution potential energy and time-space correlation intensity four-dimensional grading model, and generating multi-source intermediate data containing a dynamic risk level and a local alarm signal; receiving and fusing different multi-source intermediate data, operating a multi-algorithm collaborative recognition engine to carry out deep analysis and cross validation, and generating an early warning decision by combining a risk evolution prediction result based on a time sequence rule and a machine learning model; executing a dynamic scheduling process based on the dynamic risk level and the early warning decision, and matching or dynamically generating a control instruction; and receiving and executing the control instruction. According to the invention, the accuracy of composite risk identification is improved, and the false alarm rate is reduced.

The invention discloses an intelligent park energy management system and an energy efficiency optimization method, and the system comprises a data collection module which is disposed in a multi-mode sensor network in a park, and collects the power, gas, photovoltaic, and environment temperature and humidity data; the edge calculation module is connected with the data acquisition module and is used for locally preprocessing the original data; and the cloud platform optimization module is used for receiving the output data of the edge calculation module, executing a multi-objective optimization algorithm based on deep reinforcement learning and generating a dynamic scheduling instruction. During implementation, the method at least has the following effects: 1, the energy efficiency is remarkably improved, and the comprehensive energy efficiency is improved by 18%-25% compared with that of a traditional system through a multi-objective optimization algorithm; 2, the response speed is optimized, the edge-cloud collaborative architecture enables the delay of a control instruction to be less than or equal to 50ms, and the high-dynamic scene requirement is met; 3, compatibility and expansibility: multi-protocol equipment access is supported, and the system can be expanded to novel energy sources such as wind power, energy storage and the like;

The invention relates to the technical field of intelligent monitoring, in particular to a campus safety monitoring method and system for an intelligent campus based on the Internet of Things, and the method comprises the steps: collecting video monitoring data of each region of a school, calculating a people flow dynamic change entropy value of the data of each video monitoring region in a collection process, and carrying out the calculation of the entropy value; and dividing a key people flow area and a non-key people flow area in the school according to the entropy value, setting an acquisition frequency for the key people flow area, and acquiring the image frames when people movement is detected in the non-key people flow area. According to the invention, the system can intelligently distinguish key people flow areas and non-key people flow areas in the campus by dynamically collecting video monitoring data and calculating the people flow dynamic change entropy. For these areas, the system can dynamically adjust the video monitoring acquisition frequency, performs higher-frequency acquisition in a key area, and only acquires data when people move in a non-key area, thereby optimizing the use of monitoring resources and data storage.

The invention discloses a millimeter wave radar dormitory personnel activity monitoring method and system for a smart campus. Intelligent management of dormitory personnel activities is realized through a non-contact monitoring technology. The system obtains high-precision real-time point cloud data through a 60G millimeter wave radar, effectively filters environmental noise in combination with coordinate conversion and a dynamic point cloud segmentation algorithm, realizes stable and reliable personnel detection in a complex dormitory environment, and accurately distinguishes seat and bed body area activities through accurate area division. According to the method, effective and intelligent multi-target tracking is realized by adopting sliding window statistics and a self-adaptive threshold triggering mechanism. The system only processes point cloud data, does not collect biological characteristic information, fully guarantees the privacy of students while guaranteeing the monitoring effect, can achieve 24-hour automatic monitoring, greatly reduces the workload of manual inspection, supports the extended functions of attendance statistics, safety early warning, behavior analysis and the like, provides reliable technical support for constructing a safe and intelligent campus environment, and has a wide application prospect. And the method has remarkable social benefits and application values.

The present application belongs to the technical field of resource allocation management, and provides a kind of intelligent campus teaching resource management method and system based on artificial intelligence, comprising: obtaining the learning resource search record in the present stage learning process of student, and the next stage teaching resource allocation situation of school, screening out main learning resource, according to the search record to carry out search effective frequency analysis, obtain search effective value, carry out search conversion analysis of main learning resource, obtain search conversion value, and determine search dominant platform and non-search dominant platform in combination with the reserve proportion of main learning resource in each resource platform, integrate to obtain search conversion platform group, carry out search jam risk analysis to search channel, judge whether conversion channel allocation is needed, if needed, according to search conversion record and conversion nearby analysis, in combination with search conversion value, conversion channel is distributed, which is beneficial to the rational allocation of teaching resources and improves resource utilization.

An intelligent campus logistics cooperation robot based on vehicle-vehicle-arm integration comprises an autonomous navigation and obstacle avoidance system which uses a laser radar and a pan-tilt camera to carry out non-graph path planning and obstacle avoidance on the robot and transmits data to a control and communication system. The control and communication system controls the unmanned aerial vehicle to take off and starts the unmanned aerial vehicle auxiliary scanning system, and the unmanned aerial vehicle auxiliary scanning system performs laser and image scanning and transmits unmanned aerial vehicle auxiliary scanning data to the control and communication system; the control and communication system calculates specific places where garbage cleaning, foreign matter checking and emergencies need to be carried out according to scanning data in combination with the autonomous navigation and obstacle avoidance system, so that the robot arrives at a designated position in an autonomous obstacle avoidance mode. And the mechanical arm grabbing and carrying system is controlled, and a camera and a laser radar arranged on the mechanical arm are used for grabbing and cleaning obstacles. The problem that an existing image obstacle avoidance technology is insufficient in stability and reliability in a campus environment is solved.