154 results about "Smart campus" patented technology

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 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 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 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 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.

The invention relates to the technical field of electric digital data processing, and discloses an intelligent campus network security protection system. A network data acquisition module of the system continuously monitors data transmission flow of a smart campus network environment to obtain an original data packet; the chaotic encryption engine encrypts an original data packet by using a key stream based on dynamic chaotic mapping, wherein the key stream is generated by multi-chaotic system switching and a random parameter distribution mechanism; the security protocol processing module converts the encrypted data packet into a format conforming to a network security protocol standard and transmits the encrypted data packet; the data decryption unit decrypts the received encrypted data packet; the security threat analysis module identifies and estimates potential network attack behaviors according to the decrypted plaintext data; and the protection strategy controller generates and executes a network security protection instruction according to the threat analysis result. The system can effectively protect the network security of the smart campus, resist various network attacks, and ensure the security of data transmission and storage.

The invention relates to the technical field of data processing, and particularly discloses a campus safety management method and system based on a smart campus. The system comprises a data perception fusion layer, a block chain evidence storage layer, an intelligent analysis decision-making layer and a co-processing execution layer, and through multi-source data acquisition, block chain credible evidence storage, intelligent event analysis and responsibility initial judgment, and cross-department task collaboration and closed-loop processing, full-process digital security management is realized. And the tracing efficiency, the evidence credibility and the management cooperation capability of the campus security events are improved.

The invention discloses a dynamically changing campus environment safety monitoring system and method, and relates to the technical field of intelligent campus monitoring, and the method comprises the steps: fusing a campus function map and a real-time data flow of a multi-region sensor through a graph neural network, and generating a dynamic weight instruction driven by a scene adaptation degree; constructing a structural causal model based on the dynamic weight instruction to determine causal association strength between the environmental parameters and the equipment state; the causal association strength is used as a condition to be input into the generation model, and an anti-fact risk trajectory conforming to the campus physical law is output; and fusing the anti-fact risk trajectory and the real-time data stream, and constructing a virtual task set to drive the cross-cycle migration optimization of the security monitoring model.

The invention discloses a security and protection video monitoring management method and system for a smart campus, and relates to the technical field of smart security and protection, and the method comprises the steps: deploying a multi-mode sensing device, carrying out the data collection and preprocessing, generating a data set, generating a feature set through the data set, calculating an abnormal correlation score based on the feature set, generating an abnormal feature subset, and carrying out the recognition of the abnormal feature subset. Using the abnormal feature subset to output a fusion feature vector through a GAT model, and calculating a risk value based on the fusion feature vector; calculating a risk field change rate based on the risk value, calculating a risk probability by using the change rate, updating the risk value through a graph convolutional network, and performing equipment priority division based on the risk probability and the updated risk value; z-score anomaly detection is generated based on the risk probability, and an anomaly region is obtained based on a detection result; and performing hierarchical intervention on the detected abnormal region and the divided device priority. The real-time performance and the self-adaptive capability of the security and protection system in the aspects of risk assessment and intelligent operation and maintenance are improved.

The invention relates to the technical field of safety management, in particular to a smart campus safety management method and system based on big data analysis. The system comprises a data processing and extraction module, a key parameter optimization module, a dynamic threshold generation module and a campus risk prediction module. The method comprises the following steps: firstly, collecting campus multi-source data and carrying out space-time alignment, and then carrying out data preprocessing and multi-source data feature extraction; secondly, acquiring historical campus scene data, performing campus core scene classification, setting dynamic threshold key parameters, and performing parameter optimization by using an improved dung beetle optimization algorithm; a compensation factor is calculated for dynamic offset correction, a campus scene dynamic threshold value is obtained, risk marking is carried out, and a risk index is output; and finally, establishing a campus risk prediction model to predict a campus risk, outputting a campus safety early warning value, and generating a campus safety management strategy. By analyzing and processing the campus multi-source data, the purpose of intelligent campus safety management is achieved, and the method is accurate and objective.

The invention relates to the technical field of campus management systems, in particular to an intelligent campus comprehensive management system based on geo-fence and digital twinning, and the system comprises a fence monitoring module, a scene modeling module, a trend analysis module, a resource scheduling module and a management optimization module. According to the method, regional personnel and equipment data are collected through a fence and a sensor, retention and power abnormity are identified through density clustering, parameter set driving digital twin modeling is performed, a scene is dynamically reconstructed through Kalman filtering, people flow temperature change and a deviation region are identified through trend analysis, and the security personnel position and the people flow density are combined. The support vector machine classifies scheduling priorities, intelligently screens an optimal allocation scheme, forms a closed-loop response mechanism, improves the real-time performance and accuracy of risk identification and response, enhances the synchronization capability of virtual modeling to a real scene, enhances the early warning precision and range judgment of trend analysis, improves the efficiency and rationality of scheduling response, and improves the reliability of risk identification and response. And the overall resource management and service capability of the campus is optimized.

The invention provides a smart campus task collaboration method and system oriented to multi-group service optimization, and relates to the technical field of campus service management. According to the method, historical service logs are called, and storage nodes are distributed according to a service process to generate data sets before and after collaboration; determining a group cooperation state based on the data set, and constructing an exclusive task measurement set and a group behavior feature set; quantifying a cross-group task cooperation priority through a double-evaluation mode mark; generating a group-task collaborative set in combination with iterative operation, and screening and optimizing by using a segmentation coefficient (cohesion + separation degree); the system correspondingly comprises a data acquisition and storage module, a task and feature construction module, a collaborative priority quantification module and a collaborative adaptation analysis and optimization module. According to the method, the problems of lack of precise adaptation, fuzzy priority quantization and the like in multi-group coordination in the prior art are solved, directional optimal configuration of service resources is realized, task coordination efficiency and group satisfaction are improved, and an efficient solution is provided for multi-group differentiated services of the smart campus.

The application discloses a kind of big data-based wisdom campus traffic management system and method, and the application relates to wisdom campus management technical field, the present application is by collecting RFID card reader data, monitoring video data, timetable data, and the calibration number is generated by the monitoring data calibration algorithm driven by timetable, based on decision tree and the split strategy of compound rank is constructed risk grading model, by input real-time calibration number, timetable number, time period type, RFID coverage, historical congestion frequency, weather condition, output corresponding risk level, and according to real-time risk level, corresponding measures are taken, the specific reason of generating this risk can also be analyzed.The present application can dynamically adjust risk level, significantly reduce misjudgment and omission, realize fine risk control.

The invention provides an intelligent campus application data analysis method and system based on a three-dimensional GIS, and the method comprises the steps: constructing a three-dimensional GIS campus scene substrate which carries the whole-space topological correlation and physical morphological characteristics of a campus, injecting intelligent campus application data to form a campus scene data body with a space anchor point, and achieving the binding of the data and a three-dimensional space position. And then starting the scene perception artificial intelligence model, and performing space time sequence linkage analysis on the data volume by using a three-dimensional space feature extraction unit and a time sequence association mining unit built in the scene perception artificial intelligence model. And then generating a campus scene data association map based on the output space association features and the time sequence evolution features. And finally, embedding the association map into a three-dimensional GIS campus scene substrate, and outputting a campus application data analysis result of the anchoring space position. According to the method, the correlation characteristics of the campus data in space and time dimensions can be accurately mined, and powerful support is provided for intelligent campus management decision making.

The invention discloses an intelligent campus three-dimensional visualization system based on digital twinning, and the system is characterized in that the system comprises a data collection module which is used for obtaining campus multi-angle image data through an unmanned plane oblique photography technology; the three-dimensional modeling module is used for performing three-dimensional reconstruction and model finishing on the acquired image data to generate a campus three-dimensional model; the Web end browsing module is constructed based on a Cesium library and a Vue framework and is used for displaying a campus three-dimensional model and bus route information; the information management module is developed on the basis of a Unity engine and is used for realizing visual operation of campus data circulation and teaching building state management; and the database module is used for storing and managing campus building information, classroom states and course data. According to the invention, high-trueness three-dimensional modeling, multi-terminal adaptive display and real-time data interaction of the campus are realized, and the differentiated requirements of teachers and students in the campus, visitors outside the campus and campus managers are met.

The invention discloses a smart campus operation and maintenance data management system based on the Internet, and relates to the technical field of campus operation and maintenance management, and the system comprises a topology modeling unit which is used for recognizing each target object in a campus and constructing a service dependence graph, the service dependency graph is constructed based on a calling dependency relationship between target objects and is persistently stored in a graph database; according to the method, fault positioning is accurate and rapid, the system can identify all upstream and downstream services and complete service links which are directly and intermittently affected when the services are abnormal by constructing the dynamic service dependency map, and the time of fault root cause positioning and influence range analysis is greatly shortened.

The invention discloses an intelligent campus certificate credit data management system based on artificial intelligence, which relates to the technical field of artificial intelligence and comprises a multi-source data acquisition unit, a certificate verification unit, a credit progress monitoring unit and a credit supervision unit. The method comprises the following steps: acquiring a personal certificate data set, verifying the authenticity of an electronic seal and anti-counterfeiting watermark characteristics, marking the verified personal certificate data set as a target data set, extracting an issuing mechanism in a certificate, acquiring time and skill levels, carrying out assignment representation, and integrating into a certificate value data set; the credit conversion coefficient is calculated according to the authority, skill scarcity and industry acceptance degree of a certificate issuing mechanism, the certificate, the course score and the practice record of the same student are associated, and the student credit acquisition progress is predicted through time sequence analysis. And automatically generating a personalized repair scheme and sending the personalized repair scheme to the student terminal and the monitoring party associated with the student terminal.

The application provides a kind of based on edge computing and big data's wisdom campus safety early warning method and system. Through edge computing node, real-time collection is carried out to multiple video data streams, frame sequence segmentation and space-time feature extraction are carried out, initial behavior feature set is generated, initial behavior feature set is carried out multidimensional correlation analysis based on preset behavior semantic label, space-time behavior feature vector corresponding to target monitoring scene is extracted, and is transmitted to central server, space-time behavior feature vector is input into target behavior identification model, behavior semantic description sequence corresponding to video data stream is generated, and according to its matching result with preset abnormal behavior rule library, real-time behavior monitoring result is determined, real-time behavior monitoring result is received by edge computing node, and based on dynamic priority strategy, the collection parameters of video data stream are adaptively adjusted.The application can improve the real-time performance, accuracy and system sustainability of campus behavior monitoring.

This invention provides a data compatibility processing method and IoT cloud platform based on smart campuses, relating to the fields of smart campus and data processing technology. In this invention, multiple campus IoT surveillance videos collected by multiple campus IoT terminal devices are subjected to data compatibility conversion operations to form multiple corresponding target campus IoT surveillance videos; based on the corresponding video frame timestamp information, video frame stitching processing is performed on the target campus IoT surveillance video frames included in the multiple target campus IoT surveillance videos to form a corresponding stitched campus IoT surveillance video; using a video recognition neural network formed through network optimization, video recognition processing is performed on the stitched campus IoT surveillance video to output the target video recognition result corresponding to the stitched campus IoT surveillance video. Based on the above, the problem of low efficiency in data compatibility processing in existing technologies can be improved.

The invention discloses an intelligent campus electronic class board system and method based on multi-source information perception, and belongs to the technical field of intelligent campus multi-source data fusion. The system comprises a multi-scene electronic class board terminal, an environment sensing module, a power supply control module and a server platform, wherein the electronic class board terminal integrates three core units of face recognition, expression analysis and Internet of Things communication; through cross-scene deployment of a terminal, student identity data, expression data, campus environment data and dormitory power consumption data are synchronously collected and uploaded to a server platform for multi-source data fusion analysis, a comprehensive management report is generated, and personalized pushing and safety linkage response are triggered. According to the invention, an electronic class board is upgraded from a passive information display to an active smart sensing hub, full-cycle sensing of learning, life and psychological states of students and closed-loop management and control of campus safety are realized, the defects of data splitting and safety management and control deficiency in the prior art are overcome, and the system can be widely applied to smart campus construction of middle and primary schools and institutions of higher learning.

The application relates to the field of smart campus management technology, in particular to a smart campus management system and method based on big data. The method comprises the following steps: if login information input by a user is received, acquiring function operation information of the user; judging whether the number of the function operation information of the user corresponds to a preset number; if yes, generating auxiliary information and feeding back the auxiliary information to a terminal for display, wherein the auxiliary information represents prompt information for providing the user with a query whether help is needed; if demand information input by the user based on the auxiliary information is received, analyzing the demand information, determining guide information based on an analysis result, and feeding back the demand information to a terminal corresponding to the guide information. The application has the effect of improving the efficiency of the user in searching for a required function.

The application relates to the technical field of information security and access control, and particularly discloses a smart campus multi-agent zero-trust dynamic access control method and system, which comprises the following steps: receiving an access request, collecting identity, terminal, network, resource and behavior information, reconstructing a dynamic access context, generating an identity trust result, a terminal trust result, a behavior deviation state and a resource sensitivity state by multiple agents, forming a comprehensive access risk state by a policy decision agent and outputting an access control result, executing and feeding back execution feedback data by a policy execution node, correcting the risk state according to behavior changes or inconsistent feedback, and triggering a fallback control when an agent outputs an exception. Through context reconstruction, cooperative judgment, feedback correction and fallback control, the application can adjust the access permission according to the changes of the subject, the terminal, the behavior, the resource and the execution state, and reduce the risk of misauthorization of sensitive resources and continuous access of abnormal sessions.

The application discloses a kind of based on video monitoring intelligent campus one-card early warning method, the method includes: obtaining the continuous frame image of gate machine card swiping personnel in preset time window collected by campus monitoring equipment;Using multimodal time series analysis mechanism, generate image quality change curve and behavior trajectory change curve, construct collaborative risk change curve, determine abnormal point using behavior trajectory change curve and preset standard trajectory change curve, segment the collaborative risk change curve, obtain multiple segmentation curves corresponding to collaborative risk characteristic values;Extract the segmentation curve with minimum collaborative risk characteristic value as target curve, determine face recognition image by analyzing the light direction of corresponding continuous frame image;Adaptive correction is carried out to face recognition image to obtain target image, face recognition is carried out to target image to obtain the matching result of card swiping personnel, generate the early warning instruction of gate machine and execute.

The invention discloses an energy consumption detection system based on a smart campus, and relates to the technical field of energy consumption monitoring and adjustment, and the system comprises a data collection module which is used for obtaining curriculum schedule data from an educational administration system, obtaining a work and rest time table from a dormitory management system, and collecting the water and electricity energy consumption data of each region in real time; the dynamic reference generation module is used for associating reference energy consumption according to a course type and dynamically correcting a reference value based on an inter-class rest period and a light-off period in a work and rest time table; the anomaly detection module is used for calculating the deviation between the actual energy consumption and the corrected reference value, and generating a grading alarm signal when the deviation exceeds a threshold value; and the control execution module responds to the alarm signal to execute equipment linkage control. Therefore, the monitoring real-time performance can be improved, the prediction precision can be improved, and the operation and maintenance cost can be reduced.

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.

1. Name of the product in this design: Electronic device with graphical user interface for deploying smart campus map beacons. 2. Intended use of this design: for use in an electronic device. 3. The key design features of this product are its graphical user interface. 4. The image or photograph that best illustrates the design's key points: the front view. 5. Electronic devices are designed in a conventional way, so the rear view, left and right views and the top and bottom views are omitted. 6. Purpose of the graphical user interface: It is used by park managers to manage maps and bind beacons via mobile devices. 7. Human-computer interaction method of graphical user interface: The main view is the initial login interface. After selecting any item in the list in the main view, it jumps to the interface change state diagram 1; after selecting any building in the list in the interface change state diagram 1, it jumps to the interface change state diagram 2, which displays the floor map of that building. Different floor maps can be switched. The map can be zoomed in and out using gestures. The device icon is displayed on the map; after clicking the device icon (i.e., the teardrop icon) on the map in the interface change state diagram 2, it jumps to the interface change state diagram 3, where beacon information can be popped up and modified or deleted; after clicking the add beacon button "⊕", clicking on a map area will show a gray icon, as shown in the interface change state diagram 4. You can click on other map areas again, and the gray icon will follow until you confirm and the beacon position will be fixed, completing the addition; clicking the search icon in the lower right corner is shown in the interface change state diagram 5.