195 results about "Aging care" patented technology

The invention belongs to the technical field of information technology and intelligent health service, and discloses a community old-age care service resource optimal configuration method based on edge computing, which comprises the following steps: collecting physiological data, environmental data and activity data of old people to form original heterogeneous data, uploading the original heterogeneous data to a data warehouse through an urban and rural community network adaptation mechanism, generating a space-time tagging multi-modal data set; constructing a dynamic digital twinborn model, generating a space thermodynamic diagram, identifying different level priority requirements, and generating a time period allocation scheme through a conflict priority scoring mechanism; an MILP model is constructed to dynamically optimize edge node computing power and functional area space resources, and a resource optimization report and a block chain transaction list are generated; desensitizing the original data of the old people, creating a digital identity card, generating an encrypted service log, and synchronously recording migration behaviors of the old people; and triggering network strategy switching and priority threshold self-adaption, and carrying out resource scheduling and strategy closed-loop optimization.

The invention relates to the technical field of old-age care robots, in particular to a medical monitoring type old-age care robot which comprises a physiological monitoring unit, an air particle regulation and health feedback unit, an emotion and health fusion unit and a nursing auxiliary unit. The physiological monitoring unit is used for collecting physiological data of the old people in real time; the air particle regulation and control unit is used for detecting pollutants and biological particles in the air, analyzing the air quality in real time and automatically regulating air purification equipment or an external ventilation device in the robot body according to the particle concentration in the air and physiological data of the old people; the emotion and health fusion unit analyzes the mental health of the elderly; the nursing auxiliary unit comprises a mechanical arm and a voice control system and assists the old in daily activities. According to the invention, psychological support and emotional care are provided for the elderly in time, the life quality of the elderly is improved, and psychological health problems are effectively prevented.

The invention discloses a pension service institution supplementary site selection method based on multi-source data and machine learning, and relates to the technical field of pension service institution site selection. According to the method, site selection is performed on the multi-source data of the pension service through machine learning, so that the labor cost and the dependence on manual analysis are reduced, and the automation degree of site selection is improved; according to the method, multiple machine learning models are optimized, the optimal machine learning model is selected for site selection, the application range is wide, changing requirements can be dynamically responded, it is ensured that service coverage of the old-age care facility is matched with actual requirements, and the method adapts to dynamic changes of city and population structures.

The invention discloses an emotional interactive intelligent nursing system and method based on multi-modal perception, and belongs to the technical field of intelligent medical treatment and intelligent old-age care crossing. The matrix type pressure sensor array is integrated in the nursing mattress, multi-point pressure distribution of the body of the old people on the bed surface is obtained in real time, and high-resolution body pressure mapping is achieved. Compared with an existing single-point type or linear pressure monitoring structure, the system can accurately recognize the spatial position of a local pressed area, and early recognition and quantitative evaluation of a bedsore risk area are achieved. By fusing the pressure matrix, the heart rate, the blood oxygen, the environment temperature and humidity, the urine wetting and other multi-mode sensing data, the monitoring dimension is remarkably expanded, the health condition can be comprehensively evaluated from multiple physiological indexes, compared with a single physiological parameter monitoring mode, the more comprehensive information obtaining capacity is achieved, and the monitoring precision and the nursing efficiency are effectively improved.

The invention relates to the technical field of old-age care monitoring, and discloses an intelligent old-age care monitoring method and system based on wearable equipment. According to the method, original physiological data such as heart rate signals, oxyhemoglobin saturation and body movement frequency are collected through a plurality of wearable devices, multi-modal feature extraction is conducted on the original physiological data, and a physiological feature vector set is generated. A to-be-analyzed health event sequence containing a periodic abnormal mode and a non-periodic burst mode is screened out, and a health knowledge base is called to retrieve a matched candidate health event entry set. And inputting the two health events into a health event aggregator for cross-modal semantic fusion analysis, generating a health event decision coding matrix, obtaining an intervention measure instruction set for each health event to be analyzed through hierarchical decoding, and finally generating a response operation queue executable by the pension monitoring terminal. According to the method, the health condition of the old people can be comprehensively and accurately monitored, intervention measures can be timely and effectively provided, and the quality and efficiency of old-age care monitoring are greatly improved.

The invention discloses an old people fall monitoring and multi-terminal linkage emergency response system, and relates to the technical field of medical treatment, and the system comprises the steps: carrying out the non-inductive collection of human body dynamic information in a weak light visual blind area environment; the voice interaction verification judgment module comprises a far-field voice excitation unit; and setting a priority queue management mechanism, and confirming a behavior perception output tumble instruction. According to the invention, closed-loop management of fall monitoring and emergency treatment of the old people is realized, and the system has remarkable beneficial effects; the privacy of the user is effectively protected, and the false alarm rate and the invalid scheduling of nursing resources are obviously reduced; the event discrimination accuracy and the individual suitability are improved, and it is ensured that high-risk events are not missed; the timeliness and accuracy of emergency response are improved, and safe, intelligent and humanized technical support is provided for intelligent old-age care and home health monitoring.

The invention relates to an intelligent old-age care remote state monitoring method and device combined with a user portrait, and relates to the technical field of data processing, and the method comprises the steps: communicating a positioning patch array of a first old-age care user, and receiving a positioning time sequence matrix of a first time zone; processing a first pension user portrait and the positioning time sequence matrix through a health sample N-level adjacency searcher to obtain an adjacency sample state; aggregating the adjacent sample states to generate state baseline information; the communication state monitor is used for collecting state monitoring information of the first time zone; and when the state monitoring information does not belong to the state baseline information, performing abnormal state identification on the state monitoring information. The technical problem that in the prior art, due to the fact that flexible change analysis of the health value interval cannot be achieved, the monitoring accuracy of abnormal states of old people is insufficient is solved.

The invention discloses a nursing scene data enhancement and generation method for intention recognition model training, and belongs to the technical field of artificial intelligence and smart old-age care. The method aims at solving the problems that in the prior art, high-quality nursing scene training data is deficient, and the obtaining cost is high. According to the core technical scheme, the method comprises the steps that firstly, a staring sequence and other context information (such as time, place and physiological signals) of a user are processed through a multi-modal information fusion model, and a structured initial situation vector is generated; secondly, inputting the vector into a scene generation model combined with a nursing knowledge base, and automatically generating a batch of basic nursing scene data with intention labels; key points are that a data enhancement module is introduced, and a plurality of innovative strategies such as situation element disturbance, virtual physiological data synthesis, gaze path variation and virtual scene deduction are adopted to deeply process basic data, so that the diversity and complexity of a data set are greatly enriched; and finally, combining the basic data with the enhanced data to construct a final comprehensive training data set. According to the method, large-scale and high-fidelity training data can be generated in a low-cost and high-efficiency manner, and the accuracy and robustness of the intention recognition model in a real nursing environment are remarkably improved.

The invention belongs to the technical field of smart old-age care product behavior debugging, and particularly discloses a smart old-age care product behavior dynamic adjustment method based on interactive data perception. According to the method, a sole mechanical fragment, a lower limb movement fragment and a heart rate fragment are extracted based on gait cycle alignment, gait stability, gait coordination and physiological conformity indexes are calculated, and coupling sensing of user movement behaviors and physiological states in the dynamic walking assisting process is achieved; the gait stability and the physiological conformity are fused to construct a gait physiological synchronism criterion, a hierarchical adaptive control strategy is implemented accordingly, and the limitation that a traditional supporting robot only depends on kinematics feedback control is broken through, so that on the premise that the user safety is guaranteed, response type physical and mental collaborative intelligent assistance is achieved, and the user experience is improved. And the risk prevention and control capability of rehabilitation training is effectively improved.

The invention provides a big-data-based Alzheimer's disease old-age care platform management method and system, and relates to the technical field of old-age care platform management.The method comprises the steps that patient behavior data information is received, missing data points are deduced according to pre-stored patient behavior file historical behavior pattern data and patient behavior data information, and the missing data points are sent to a server; by evaluating the reliability degree of the event chain and comparing the event chain with a dynamically generated reliability evaluation threshold value, when a comparison result reaches an early warning standard, early warning information with a reliability quantitative index is generated, and the problem that an existing platform is poor in reliability under a non-ideal operation condition is effectively solved. The problems of reliable transmission and accurate interpretation of key information are solved, and early warning failure caused by data deviation or missing is avoided, so that the nursing quality and safety of the Alzheimer's disease patient are remarkably improved, and the optimal time for early intervention and intervention is provided for nursing personnel.

The invention relates to the technical field of pension monitoring systems, and provides a work management system for smart pension services, which comprises a sensing data acquisition terminal, a data analysis terminal, a task scheduling terminal and a service quality evaluation terminal, the sensing data acquisition terminal is used for acquiring physiological index data, environmental data and behavior data of old people in real time; the data analysis terminal is used for performing anomaly detection and health assessment according to the physiological index data, the environment data and the behavior data to generate data analysis information; the task scheduling terminal is used for generating task scheduling information according to the data analysis information and service requirements input by an administrator; and the service quality evaluation terminal is used for generating service quality evaluation information according to the data analysis information and the task scheduling information. The method has the effect of improving the old-age care monitoring quality.

The invention provides a smart medical care monitoring system based on the Internet of Things. Comprising a data acquisition module, a real-time monitoring module, a communication module, a data processing module, a cloud platform, an intelligent analysis module, a remote medical module, an emergency call module, a health file management module, a user management module, an intelligent home control module, a safety monitoring module, an emotional communication module and a system maintenance module. According to the Internet of Things intelligent medical care monitoring system provided by the invention, all-around monitoring and intelligent management of life, health and safety of old people are realized by integrating a plurality of modules; meanwhile, the health data is deeply analyzed and mined by using an artificial intelligence technology to provide personalized health suggestions and intervention measures; the life safety and health of the old people are guaranteed through the functions of remote medical treatment, emergency call and the like, the development of the intelligent old-age care industry is powerfully promoted, the old-age care service quality and efficiency are improved, and a safer, more convenient and more comfortable living environment is created for the old people.

The invention discloses an intelligent old-age care service construction method based on a Fabric block chain and big data AI, and the method comprises the following steps: S1, collecting multi-source heterogeneous data of old people, and carrying out the standardization processing of the data; s2, performing classified storage on the standardized data processed in the step S1 on the basis of a Hyperledger Fabric block chain and an IPFS (Internet Protocol File System) distributed network; s3, constructing a multi-modal AI health early warning and decision model architecture, analyzing the standardized data stored in the step S2, and generating a health risk early warning and personalized nursing scheme; s4, cross-institution data authorization sharing is carried out through the block chain smart contract, an automatic process triggering rule of the smart contract is preset, and cross-institution real-time data sharing and business collaboration are carried out; and S5, configuring an aging-suitable interaction terminal, receiving health risk early warning, a personalized nursing scheme and a business cooperation event process in real time, capturing user interaction operation, and returning operation data to the step S3 to perform AI model optimization on the multi-mode AI health early warning and decision model architecture.

The invention discloses an old people accident prevention comprehensive alarm system based on a multi-modal artificial intelligence model. The system comprises a data acquisition module, a multi-modal fusion module, an artificial intelligence analysis module, an alarm module and a background management module. The data acquisition module is used for acquiring multi-modal data such as video image data, wearable equipment data and environment data of old people; the multi-modal fusion module carries out fusion processing on the data to generate a multi-modal feature vector; the artificial intelligence analysis module analyzes the feature vectors based on a multi-modal artificial intelligence model, and outputs the probability and confidence of occurrence of various accident risks; when the alarm condition is met, the alarm module is started and sends alarm information to related personnel; the background management module carries out all-around management on the system. Various modal data are fused, the artificial intelligence technology is utilized to realize early warning and timely alarm of various accident risks of the elderly, related personnel can be prompted to take measures in time, the occurrence probability of accidents is reduced, the life safety of the elderly is guaranteed, and the system is suitable for various old-age care institutions and old-age living places. The method has important safety protection value.

The invention relates to a home-based care technology for the aged, is used for solving the problems of difficulty in shopping, damage to the body caused by large activity and difficulty in distinguishing food quality in home-based care for the aged, and particularly relates to a home-based care smart cloud system based on the Internet of Things, which comprises a shopping auxiliary module, a health management module and a service evaluation module, according to the invention, the shopping auxiliary module is combined with the health management module to judge the activity amount of the elderly, so that the situation of shopping difficulty caused by non-support of the activity amount of the elderly is avoided; the health management module compares the activity data of the elderly with the optimal activity to judge whether the elderly is suitable for self-shopping, so as to prevent the physical health of the elderly from being damaged due to excessive activity; whether an article is fresh or not is judged through the service evaluation module according to quality assurance information of an article package and color difference comparison, the situation that old people eat deteriorated food to affect body health is avoided, and service scoring evaluation is conducted on stores in a set range in the aspects of pre-stocking and expenditure amount through the service evaluation module.

The invention relates to the technical field of artificial intelligence, and discloses a man-machine cooperation-based old-age care practical training evaluation system, which comprises a sensor, data preprocessing, feature extraction and fusion, clustering analysis and practical training evaluation. According to the system, capability group division is realized under double constraints of an interactive structure and attribute similarity by using a double-graph diffusion-contour weighted K-Means clustering method; in combination with a GNN-Transformer model, through joint asymmetric loss function optimization formed by cross entropy, focus loss and passive asymmetric q-norm loss, cross-modal and time-sequence-dependent deep modeling is realized, and multi-dimensional evaluation indexes such as standardization, proficiency and humanistic care degree are output; finally, the evaluation result is visualized and fed back through a human-computer interaction interface, a perception-analysis-feedback-improvement collaborative closed loop is formed, and the scientificity and guidance of nursing training of the aged are improved.

The invention relates to the technical field of big data analysis and community old-age service management, in particular to a community old-age service management method based on big data, which comprises the following steps: step 1, collecting basic information of old people; step 2, a data storage and integration module; step 3, a data analysis module; step 4, a service resource configuration module; step 5, a service supervision and feedback module; by collecting and analyzing big data information such as living habits, physical conditions and medical service requirements of the elderly, personalized elderly care service scheme customization can be realized, the pertinence and effectiveness of the service are effectively improved, the health risk degree of the elderly is evaluated, the influence degree of different factors on the health risk is determined through big data analysis, and the health risk management efficiency is improved. The health problem of the old people can be found and processed in time, the life quality and happiness of the old people are improved, meanwhile, the nursing burden of families and society is relieved, and the community nursing service is more user-friendly and intelligent.

The invention provides a pension service supervision method and system based on an image recognition AI model algorithm. The method comprises the following steps: aiming at respiratory arrest, pressure sores and limb stiffness risks in a bedridden old-age care scene, firstly, establishing a physiological abnormality library containing parameters such as a respiratory rate threshold value and a body pressure critical value; a physical sign monitoring device is deployed to collect body pressure hot area distribution data of the bedridden person in real time, breathing interval abnormity, heartbeat attenuation and body pressure exceeding areas are analyzed through an AI model, and a retention risk index is generated. And when the monitoring data exceeds an abnormal library threshold value, triggering multi-modal early warning information, fusing risk area positioning, abnormal duration and temperature gradient change, outputting a multi-modal early warning report combining images and texts, and guiding the nursing personnel to perform targeted intervention. According to the technical scheme provided by the invention, real-time monitoring and multi-mode risk early warning of health hidden dangers in bed are realized, the occurrence rate of complications such as respiratory arrest and pressure sores is reduced, and the precision and response efficiency of nursing for the aged are improved.

The invention discloses an intelligent community health care monitoring system based on cloud computing, relates to the technical field of intelligent community health monitoring, and aims to solve the problems of difficulty in multi-source heterogeneous data fusion, insufficient real-time performance and remarkable service response delay. The system comprises a multi-modal data acquisition terminal, a data fusion and preprocessing gateway, a core intelligent analysis engine and an interactive response terminal, and efficient and accurate health monitoring and early warning are realized through standardized fusion of multi-source data and behavior pattern recognition and hierarchical risk assessment based on deep learning. And the pension service response efficiency and reliability are obviously improved.

The invention provides an intelligent old-age care system based on big data integration analysis, and relates to the technical field of computer information processing, multi-modal old-age care data is fused based on a distributed architecture, and an old-age care resource data set is obtained; performing adaptive partitioning on the pension resource data set to obtain a pension service demand vector and a service resource vector, and constructing an embedded model library of pension semantics through semantic matching and space-time semantic annotation operation; according to the embedded model library, carrying out data stream response based on the pension service information to obtain pension response data, and carrying out resource scheduling reasoning on the pension response data based on the pension service knowledge graph to obtain a scheduling optimization matrix; performing service efficiency modeling on the pension demand data to obtain a service evaluation factor set; and performing multi-dimensional constraint optimization on the service evaluation factor set based on the scheduling optimization matrix to obtain a matching optimization result, so that intelligent matching and optimal scheduling of the old-age service requirements can be realized, and the accuracy of service response is improved.

The invention relates to the technical field of old-age service management, in particular to an integrated old-age service management system based on end-cloud collaboration, and aims to solve the problems that in the prior art, multi-source data of physical signs, medication and living habits cannot be integrated to predict the health change trend, abnormal fluctuation of four high cannot be perceived in advance, short-term risks cannot be pre-judged, and the service quality is poor. And a personalized intervention scheme cannot be formulated in combination with reinforcement learning. Multi-source data of physical signs, medication and living habits are integrated through the chronic disease management module, LSTM is used for predicting the health change trend, four-high abnormal fluctuation is perceived in advance, short-term risks are pre-judged, a personalized intervention scheme is formulated in combination with reinforcement learning, diet and exercise suggestions and medication reminding are covered, and the system has the advantages of being simple in structure and convenient to use. User compliance is improved by outputting custom prompts through natural languages, the system forms a complete closed loop, the system is suitable for long-term management of diabetes and hypertension diseases, the risk of acute events is reduced, and the self-management ability of patients is enhanced.

The invention relates to the technical field of smart pension, and discloses a pension community management method, system and device and a storage medium. The method comprises the steps of obtaining platform information of each old-age care group management platform, obtaining community information of each old-age care community operation platform, determining a target old-age care community operation platform connected with each old-age care group management platform according to the platform information and the community information, and sending the target old-age care community operation platform to the old-age care group management platform. And sending the target push information of the old-age care group management platform to a target old-age care community operation platform corresponding to the old-age care group management platform, and issuing the target push information to a target user corresponding to the target old-age care community operation platform. The management efficiency and the service quality of the pension community can be improved.

The invention discloses an intelligent old-age care digital management system and an intelligent old-age care digital management method based on a cloud platform. The acquisition module is used for acquiring basic health data information of the old and sending the information to the cloud platform; the path comparison module is used for acquiring real-time positions of the old people, generating real-time paths of the old people according to the real-time positions, and performing similarity calculation on the real-time paths and historical paths to obtain path similarity; the emotion recognition module is used for recognizing the emotional state of the elderly and giving an emotional state label; the health state recognition module obtains real-time health data information of the elderly, constructs a health state recognition model, trains the health state recognition model to obtain a trained health state recognition model, and recognizes the health state of the elderly to obtain a recognition result; the cloud platform judges the behavior of the old person according to the identification result, and performs early warning if the behavior is abnormal; and under the unattended condition, management personnel or family members can quickly find the abnormal state of the old people and process the abnormal state in time.

The invention discloses a home-based care intelligent dynamic early warning system based on distributed digital identity and artificial intelligence. The system generates a verifiable digital identity (DID) to realize credible authentication of the old people and the Internet of Things equipment. Various terminals are fused to collect health, physiology and environment data, space-time correlation analysis is carried out based on an AI algorithm, and an individualized risk model is constructed. Local processing is realized by adopting edge computing, and data privacy and integrity are guaranteed in combination with a block chain. The system dynamically corrects a health threshold value according to historical behaviors and equipment data, improves the anomaly detection accuracy, automatically triggers alarm and rescue response when risks occur, and constructs a safe and intelligent old-age care monitoring system.

The invention relates to the technical field of sojourn and old-age care management, and discloses an intelligent analysis system based on sojourn and old-age care residence space data. The system comprises a space multi-source sensing module, a space efficiency dynamic analysis module, a space resource configuration module and a space risk early warning module. The spatial multi-source sensing module collects physical environment, facility use state and resident behavior characteristic parameters in real time; after receiving the data, the space efficiency dynamic analysis module extracts space occupancy mode characteristics according to an evaluation period, and generates a space use frequency, a facility adaptation degree and a behavior safety evaluation value; the space resource configuration module calculates a space optimization demand index and outputs a space layout and facility configuration adjustment instruction; the space risk early warning module continuously monitors space structure stability, environment safety and abnormal behavior parameters and generates multi-level safety early warning signals. According to the system, intelligent management of the sojourn and old-age care residence space is realized.

The invention discloses a functional capability and nursing risk-based elderly care method and related equipment, and relates to the field of medical nursing, and the method comprises the steps: obtaining multi-dimensional functional capability information and nursing risk indexes of an elderly patient, carrying out the structural integration through a weighted scoring model, and generating a joint evaluation file; and outputting the function baseline value, the risk category and the comprehensive risk index. The system calls a nursing intervention rule base according to the file, and a personalized intervention list is automatically generated to serve as an execution baseline for trend monitoring. Continuously updating function and risk data in the nursing process, and generating a time sequence trend chart and a risk level change curve; when a drop in function or an increase in risk is detected, the system triggers reevaluation and automatically adjusts the intervention list. According to the method, fusion of function evaluation and risk identification and automatic generation and dynamic adjustment of intervention suggestions are realized, and the intelligent and individualized levels of elderly care are improved.

The invention relates to the technical field of intelligent old-age care monitoring, and discloses a method for identifying abnormal behaviors of home elderly based on millimeter-wave radar and vision fusion, which comprises the following steps: S1, multi-modal data acquisition and privacy protection configuration: deploying a configurable sensing suite in a home environment of the elderly, comprising a millimeter wave radar, a visual sensor (infrared and visible light), environment sensors (doors and windows, water, illumination and ground vibration) and wearable equipment (such as a smart bracelet / chest card). According to the method and the system for identifying the abnormal behaviors of the home elderly based on millimeter-wave radar and vision fusion, all-around behavior monitoring is realized on the premise of guaranteeing the privacy of the elderly through multi-modal sensor fusion and a hierarchical privacy protection mechanism, and the millimeter-wave radar and the vision sensor are complementarily fused, so that the privacy worry of a pure vision technology is overcome, and the safety of the home elderly is improved. And the limitation of a single radar on complex behavior identification is solved, and meanwhile, a configurable sensing suite and a hardware-level privacy switch are adopted.

The application relates to the field of old-age care equipment, and discloses a state recognition navigation recommendation method based on artificial intelligence and an old-age terminal device. The method comprises the following steps: acquiring terrain features, speed features, gait features and physiological features of a user in multiple walking trips when the user uses an intelligent walking stick, and acquiring map information and a walking destination of the user; wherein the terrain features, the speed features and the gait features are detected by a motion detection component of the intelligent walking stick; through a state recognition-navigation recommendation model, a navigation route and a walking guidance suggestion of the user in the next walking trip are determined according to the terrain features, the speed features, the gait features, the physiological features, the map information and the walking destination in the multiple walking trips; and the walking guidance suggestion comprises matters needing attention information when the user walks. The application can identify and detect the physical state of the old people and improve the nursing effect on the old people.

The embodiment of the invention discloses an intelligent old-age care monitoring method and device, electronic equipment and a storage medium, and relates to the technical field of intelligent old-age care guardian.The method comprises the steps that physiological indexes are collected in real time through physiological monitoring equipment, environment data are monitored through an environment sensor, and behavior data are captured through a behavior capturing terminal; analyzing the environment data by using a time sequence clustering algorithm to obtain an environment abnormal value, analyzing the physiological indexes and the behavior data based on a Bi-LSTM model and an Attention model to obtain a behavior sequence, and calculating a physiological deviation value; analyzing the behavior data through MFCC feature extraction and a Transform model to obtain an emotion fluctuation value, and generating a comprehensive risk score; and carrying out risk grade division, carrying out data recording and monitoring when the risk is low, carrying out family member early warning when the risk is medium, and triggering an emergency response mechanism to carry out multi-device linkage when the risk is high. According to the invention, the problems of high false alarm rate, response lagging, lack of psychological monitoring and personalized service and weak privacy and security mechanism in the prior art are solved.

The utility model belongs to the technical field of building design, and particularly relates to a research tool for old-age care facility building design, which comprises a packaging mechanism, the packaging mechanism comprises a bottom plate, an outer frame is arranged on the bottom plate, a turning plate is arranged at one end of the outer frame, and a limiting bottom frame is arranged in the outer frame. When in use, the assembled research tool is brought to the site of the building design of the pension facilities, the tool is used for carrying out field measurement and evaluation, recording the position and combination condition of each component, adjusting the turning plate and the limiting bottom frame to adapt to different measurement requirements, and recording data and observation results of each measurement point in detail; and finally, after the investigation is completed, the buckle mold, the buckle column plate and the limiting bottom frame are sequentially disassembled, classified and stored, the turning plate is folded, the outer frame is folded and placed on the bottom plate, and a complete packaging mechanism is formed and is properly stored for use next time.