158results about How to "Realize intelligent management" patented technology

The invention discloses a rotor surface magnetic field characteristic real-time monitoring and damage assessment system and method, and relates to the technical field of damage assessment. According to the method, a rotor surface magnetic field is monitored in real time under different rotating speed conditions, detailed magnetic field change data information is collected, the monitoring means can dynamically capture the magnetic field fluctuation condition, and the damage assessment accuracy is improved. According to the method, the damage prediction model is constructed by using the deep learning technology, the relevance between the damage prediction model and the rotating speed of the rotor is analyzed, early detection and classified evaluation of the unbalance condition are ensured, accurate data support is provided for further damage analysis, and accurate evaluation of the surface damage of the rotor is realized by using the deep learning technology to construct the damage prediction model and combining the unbalance type and the magnetic field change data. Meanwhile, the damage evaluation index is subjected to linear normalization processing, the influence between different data dimensions can be eliminated, and the evaluation result is more stable and reliable. And whether the damage state of the rotor surface is abnormal or not is judged in time by comparing and analyzing the damage evaluation index and a threshold value.

The invention relates to the technical field of electric power system safety early warning, and discloses a fault risk sensing early warning method for a high-low voltage switchgear assembly, and the method carries out the data preprocessing and feature extraction through collecting the state monitoring data, environmental parameters and basic attribute information of a device in historical operation. And based on the feature data sets, constructing a fault risk prediction model to predict the fault risk probability of the equipment in a period of time in the future, obtaining the operation state data and environmental parameters of the equipment in real time, and inputting the prediction model to obtain the fault risk probability. Meanwhile, the equipment is monitored in real time, original monitoring data is cleaned, abnormal values are removed, real-time monitoring data is compared with prediction data, whether the difference exceeds a threshold value or not is judged according to a preset risk threshold value, and risk points are marked. And evaluating the equipment fault risk according to the risk point and the evaluation strategy, and sending a result to related personnel to take prevention measures.

The invention discloses an energy consumption optimization control method and system for heating and ventilation equipment, and the method comprises the steps: carrying out the comprehensive evaluation of the energy consumption condition of the heating and ventilation equipment, and recognizing the current energy consumption condition and energy-saving potential of the equipment; the system is connected with heating and ventilation equipment through the Internet of Things technology, and collects parameters of the heating and ventilation equipment in real time. Based on the preprocessed data, establishing energy consumption models of the water chilling unit, the cooling tower, the freezing pump and the cooling pump; building an energy consumption prediction regression model by adopting a decision tree algorithm, and predicting the total energy consumption of the heating and ventilation system in real time; according to the predicted energy consumption data, an optimization control strategy of the heating and ventilation equipment is formulated in combination with a set energy consumption control objective function; and applying the formulated optimization control strategy to the heating and ventilation equipment, carrying out real-time monitoring and feedback, and carrying out iterative optimization on the control strategy according to an actual effect. According to the method, comprehensive evaluation, real-time monitoring and optimal control of the energy consumption of the heating and ventilation equipment can be realized, and a powerful guarantee is provided for energy conservation and emission reduction of a building.

The invention discloses a building energy consumption control system based on deep learning. The building energy consumption control system comprises a data acquisition module, a data processing module, a deep learning module, an optimization control module and a real-time control module, the invention further discloses a control method of the building energy consumption control system based on deep learning. The energy consumption prediction model is constructed through the long-short-term memory network, and the user behavior model is constructed through the convolutional neural network, so that the energy consumption optimization control strategy is generated through the reinforcement learning algorithm based on the output results of the energy consumption prediction model and the user behavior model, and the related data of the building energy consumption system is effectively utilized. Intelligent control over the building energy consumption system is achieved, changes of environmental parameters and user requirements are dynamically adapted by optimizing an energy consumption control strategy, and the comfort level of a user is improved while energy saving and consumption reduction are achieved.

The invention provides a sewage treatment progress monitoring method and system, and the method comprises the steps: collecting and processing a first water quality parameter value of a key node of a sewage treatment facility in a historical time period, constructing a dynamic model of a sewage treatment process through a deep neural network, and introducing a genetic algorithm to optimize the structure and parameters of the dynamic model. An optimized dynamic model is obtained, the optimized dynamic model is used for predicting the sewage treatment effect, and a prediction result and operation data of the sewage treatment facility are obtained; using a fuzzy logic control algorithm to adjust the operation parameter setting value of the sewage treatment system, and using a support vector machine algorithm to classify and identify the fault mode of the sewage treatment system to obtain a fault classification result; based on the prediction result, the operation data, the optimized operation parameter setting value and the fault classification result, a comprehensive monitoring management system based on the cloud platform is constructed; according to the invention, comprehensive monitoring and fault identification of the sewage treatment process are realized, and efficient, stable and reliable operation of sewage treatment is ensured.

The invention provides a surveying and mapping result management method and system based on big data, and the method comprises the steps: obtaining geographic space surveying and mapping data through employing an unmanned plane, a vehicle-mounted laser radar and a sensor, and recording the time, position and equipment information of the surveying and mapping data in real time; a regularizer based on online linear optimization classifies the surveying and mapping data according to geographic positions, collection time and data types, and uploads the classified surveying and mapping data to a distributed storage system; performing data cleaning, format unification and quality verification on the stored surveying and mapping data to generate standardized surveying and mapping data; constructing a knowledge base based on a large language model (LLM), performing semantic analysis and feature labeling on the standardized surveying and mapping data by using geographic space operators in the knowledge base, and establishing a spatial index of the standardized surveying and mapping data; and receiving a user retrieval request, and performing privacy protection retrieval on the standardized surveying and mapping data based on the spatial index.

The invention relates to the technical field of network advertisements, in particular to an advertisement data management system based on market trend analysis, which comprises a data acquisition module, an analysis module, a prediction module, a conversion rate calculation module and an adjustment module. The advertisement effect is reflected by acquiring click and purchase data after advertisement putting, the change trend of user behaviors is accurately recognized through the analysis module, and the user change rate is calculated, so that data support is provided for an advertisement putting strategy, and the advertisement putting strategy is more accurate by analyzing historical data, predicting the future purchase trend and comparing with predicted data in real time. The conversion rate change of the advertisement is accurately evaluated, a basis is provided for optimizing the advertisement effect, whether the advertisement putting duration needs to be adjusted or not is judged according to the comprehensive analysis of the user change rate and the conversion rate change rate, efficient utilization of advertisement resources is ensured, the accuracy and flexibility of advertisement putting are remarkably improved, the conversion rate and the return on investment of the advertisement are facilitated, and the user experience is improved. And continuous optimization and improvement of the advertisement effect are realized.

The invention, which relates to the technical field of digital information transmission, discloses an AI technology-based digital information transmission sharing operation and maintenance platform comprising a data acquisition module, an AI intelligent analysis module, a frequency spectrum dynamic allocation module, a data classification and transmission optimization module, and a storage adaptation module. According to the method, the spectrum resource utilization efficiency and the transmission stability are optimized, the platform can accurately master the spectrum use condition in real time through the AI real-time spectrum sensing and dynamic frequency allocation technology, the platform can quickly identify idle spectrums and dynamically allocate optimal frequencies for different services in the face of a complex and changeable wireless environment, spectrum congestion and interference are avoided, and the service quality of the system is improved. The fluency of bandwidth and time delay sensitive services is guaranteed, and the transmission interruption risk is reduced.

The embodiment of the invention discloses a transformer fault detection method based on acoustic emission and related equipment, and the method comprises the steps: training a machine learning model through employing an acoustic signal and a corresponding fault type, and enabling the trained machine learning model to automatically extract features from the acoustic signal obtained in an equipment operation process, and the fault type of the equipment is accurately judged based on the characteristics, so that the fault detection precision and efficiency are effectively improved, the downtime and maintenance cost of the equipment are reduced, and intelligent management and maintenance of the mechanical equipment are realized.

The utility model is suitable for the technical field of mine side slope monitoring equipment, and provides mine side slope monitoring equipment based on radar and GNSS, comprising a bottom plate, the top of which is provided with a vertical plate and two side plates; the rotating shaft is rotationally mounted on the sides, close to each other, of the two side plates and is provided with a rotating seat; the supporting column is fixedly mounted on the rotating seat; the shell is fixedly mounted at the top end of the supporting column; the leveling mechanism is arranged on the bottom plate and used for adjusting the angle of a shell of the supporting column; the monitoring alarm mechanism is arranged on the shell and is used for monitoring the landslide displacement of the mine slope; and the power supply mechanism is arranged on the shell and is used for supplying power to the equipment. The mine side slope monitoring equipment based on the radar and the GNSS can monitor and alarm landslide displacement of the mine side slope, is suitable for mine side slopes with different gradients, and has a function of cleaning the photovoltaic panel, so that black dust attached to the photovoltaic panel is prevented from influencing the power generation efficiency of the photovoltaic panel.

The invention relates to a lithium ion battery grouping method. The method comprises the following steps: performing low-current formation charging on battery cells, and monitoring and removing abnormal battery cells; then carrying out aging treatment and screening abnormal battery cells; based on the relationship between the voltage and the state of charge, adjusting the electric quantity of the battery cell and evaluating the capacity of the single battery cell through a series of tests; finally, the evaluated battery cells form a battery pack, the overall performance of the battery pack is detected by calculating the voltage difference and large-current pulse discharge impedance, grouping is completed, and effective evaluation and guarantee of the battery performance are achieved. Compared with the prior art, the method has the advantages that the electric quantity is directly adjusted to the shipment electric quantity requirement by adopting a simple process step after the battery cell formation is finished, the traditional full-full discharge capacity test process step is replaced, and control means such as voltage difference and DCR are introduced in the process are replaced, and on the basis of ensuring the battery consistency and capacity, the battery cell production and manufacturing time is shortened, and the cost is greatly reduced.

The invention relates to the technical field of big data, in particular to an intelligent service association method based on data consanguinity, which comprises the following steps of: 1, acquiring and forming metadata; tracking and collecting a service activity link to obtain a data consanguinity and a metadata consanguinity; 2, generating a business log; step 3, collecting a business log, and establishing a business atlas based on metadata consanguinity; step 4, defining a service triggering rule, a service decision point and a response rule based on the service atlas; and 5, monitoring the data change in real time, mapping the data change into the associated service map according to a service triggering rule, and carrying out service early warning prompt or associated information change display. According to the invention, automatic tracking and intelligent management of the project business process can be realized, and the utilization rate of enterprise data and the enterprise management efficiency are improved.

The invention provides a virtual power plant distributed data anomaly diagnosis method based on a deep neural network, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining the multi-modal data of each distributed energy of a virtual power plant, and obtaining the processed distributed data; modeling each node of the virtual power plant and the relationship thereof into a graph structure to obtain a multi-time-scale spatio-temporal feature vector; pre-training is carried out by constructing a self-supervised learning task, and effective features between different time scales and modal data are extracted; generating a normal data distribution model by using a generative adversarial network, and summarizing abnormal data samples into a comprehensive data set; obtaining a multi-task optimization model through sharing characteristics among tasks; a final optimized anomaly response system is obtained, and then the task of virtual power plant distributed data anomaly diagnosis is completed through the anomaly response system. According to the method, efficient anomaly diagnosis and response processing of the distributed data of the virtual power plant are realized, and the method has remarkable technical advantages and practical application value.

The present invention discloses a posture recognition management system and method for factory operators, and relates to the technical field of factory operation safety management. The system comprises a video image acquisition module, a target detection module, a behavior recognition module, a violation detection module, an alarm module, a recording module and a QT5 integration module which are operated in sequence. The application scenario of the whole management system is to identify and manage operators and automation equipment in the factory. The technical key points are as follows: in view of the problem that the behavior recognition in the factory scenario in the prior art is prone to false alarms, the present invention proposes a system for detecting violation behaviors of human-object interaction in the factory. The system integrates target detection and behavior recognition algorithms, can monitor the interaction between factory operators and equipment in real time, accurately identify possible violation behaviors, and send out alarm signals in time to improve the safety management level of the factory.

The invention discloses a material identification and automatic loading and unloading system and method suitable for a steel coil storage yard. The system comprises a first one-dimensional holder, a second one-dimensional holder, a first laser scanner, a second laser scanner, a first industrial personal computer and a second industrial personal computer. The industrial personal computer comprises an OPEN3D point cloud processing algorithm module, a holder and a laser scanning control module; the first one-dimensional holder and the first laser scanner are responsible for collecting point cloud information of a steel coil warehouse of a storage yard, and the first industrial personal computer processes the point cloud information to obtain the diameter and center coordinates of a steel coil. The second one-dimensional holder and the second scanner are responsible for collecting point cloud information of a storage yard truck parking area, and the second industrial personal computer processes the point cloud information to obtain the vehicle type, the diameter, the center coordinate and the placement position of the steel coil on the vehicle, so that automatic warehousing, loading and unloading of the steel coil are completed. The method can be applied to grabbing and positioning calculation of the steel coils on the vehicle during automatic warehousing of the steel coil storage yard and placement position calculation during delivery, and automatic warehousing, loading and unloading of the steel coils are achieved.

The invention relates to the field of smart campuses, and discloses a smart campus big data management platform, which comprises an intelligent teaching management system established based on a Spring framework, a campus intelligent security system and a campus life service system, and is characterized in that the intelligent teaching management system comprises a course management module, a teaching interaction module and a learning analysis module; the campus intelligent security and protection system is divided into a video monitoring module, an access control management module and an alarm early warning module, and the campus life service system is divided into an intelligent catering module, an intelligent dormitory management module and a campus navigation module. Through intelligent teaching management, campus intelligent security and protection and a campus life service system, teaching resource distribution is optimized, teaching interactivity is enhanced, teaching effects and student learning experience are improved, campus safety conditions are monitored in real time, abnormal conditions are quickly responded, safety risks are reduced, safety of teachers and students is guaranteed, and convenient catering, dormitory management and campus navigation services are provided. The convenience and efficiency of campus life are improved, and the satisfaction degree of teachers and students is improved.

The invention relates to the technical field of production line management, in particular to an automatic production line management method and system based on precise control, and the method comprises the steps: carrying out the searching of product processing procedure data of collected target product text feature data and established different product processing procedure data, and generating target product processing procedure data; acquiring image data corresponding to the processing procedure data of the target product in real time through an image acquisition device, and then performing data preprocessing on the image data to generate real-time image feature data of the target product; and carrying out product image feature matching on the real-time image feature data of the target product and the established qualified image feature data of the target product, judging the operation state of the automatic production line according to a product image feature matching result, and constructing operation management data of the automatic production line and pushing the operation management data to an automatic production line management platform. And intelligent management of the automatic production line is realized.

The invention belongs to the technical field of power distribution cabinets, and particularly relates to an intelligent control loop control method and system for a power distribution cabinet, and the system comprises a sensor module, a control unit module, a wireless communication module, and a remote monitoring center. The sensor module collects the voltage, current, temperature and smoke concentration of each loop in the power distribution cabinet, the sensor module transmits the collected data to the control unit module for processing and analysis, and the control unit module receives the data transmitted by the sensor module through a network and sends the data to the power distribution cabinet. The control unit module is used for processing and analyzing the data according to a preset algorithm and judging whether the power supply state is abnormal or not, and when the power supply state is abnormal, the control unit module immediately sends emergency alarm information to a remote monitoring center through the wireless communication module, so that quick response and processing are facilitated. According to the invention, a microprocessor is integrated as a core control unit, and a sensor network is combined to realize real-time monitoring and intelligent management of the power supply state of each loop in the power distribution cabinet.

The invention provides a quantitative blending device for pesticide preparation production, and relates to the technical field of pesticide blending, the quantitative blending device comprises a fixed outer cylinder, the bottom of the inner side of the fixed outer cylinder is provided with a driving motor, the output end of the driving motor is provided with a gear, and the inner side of the fixed outer cylinder is provided with two guide rails; a storage cover is mounted on the outer side of the fixed outer cylinder; a driving cylinder is mounted at the top of the outer side of the storage cover, the output end of the driving cylinder penetrates through the outer side of the storage cover, a servo motor is mounted on one side of the storage cover, and two driving wheels are mounted on the inner side of the storage cover; and a detachable barrel cover is mounted at the top of the fixed outer barrel in a closed manner. Toxic peculiar smell generated in the fixed outer barrel in the pesticide feeding and mixing process is effectively absorbed through the air suction ring, the poisoning risk caused by pesticide smell leakage to workers is avoided, and the problems that when the blending device is started in the subsequent stage, the gas injection risk is caused by internal high pressure, and toxic gas escapes are solved.

The invention relates to a regulation and control method and system for mine fireproof doors, a terminal and a medium, and belongs to the technical field of mine fireproofing. The regulation and control method comprises the steps that sensing data sent by sensors around each fireproof door are received; according to the multiple sets of sensing data, whether a fire occurs in the mine or not is judged; if yes, determining a target fireproof door according to the fire spreading area; acquiring personnel position information of workers around the target fireproof door; based on the personnel position information, whether a worker is on a fire spreading path and is located between the target fireproof door and a fire spreading boundary is judged; if yes, the closing degree of the target fireproof door is controlled according to the spreading speed of the fire; if not, the closing degree of the target fireproof door is controlled according to the fire behavior of the fire. The intelligent monitoring management system has the beneficial effect that intelligent monitoring management of the fireproof door can be conveniently realized according to the field environment.

The invention discloses an air conditioner air outlet partition flow guide control system in the field of smart home, which comprises an air conditioner indoor unit, the bottom of the air outlet end of the air conditioner indoor unit separately communicates with a plurality of independent air outlets and corresponding temperature adjusting cavities, and the inner side wall of each air outlet is rotatably connected with a plurality of up-down swing plates and left-right swing plates. An image sensor, a pickup microphone and a control unit are integrated in the air conditioner indoor unit, the motor, the temperature sensor and the air speed sensor are all connected with the control unit, and the control unit is used for automatically adjusting the air supply temperature and the air speed of an air outlet close to a user according to identity information, position information, environment preference and the like of the user. The swinging angles of the up-down swinging plate and the left-right swinging plate are controlled. The intelligent air conditioner is scientific and reasonable, the multiple air outlets, the temperature adjusting cavity, the image sensor, the pickup microphone and the control unit are integrated, identification and tracking of identity information, user positions and environment preferences are achieved, and accurate regulation and control of air supply parameters are conducted.

The invention discloses a book and archive management method, system and device and a medium, and the method comprises the steps: obtaining the image data of the serial number of a photographed book and archive, and obtaining the photographing position information; performing text detection on the picture data to obtain a text region in the picture data; performing text recognition on the text area to obtain file number information of the book; and binding the file number information of the book with the position information, and sending the bound information to a cloud end or a database for storage. Compared with the prior art, the method has the following advantages that the machine vision mode is adopted, the book and archive numbers are automatically recognized, the book and archive numbers and the book position relation are bound, compared with manual recording, the working efficiency is improved, meanwhile, wrong recording caused by human factors can be avoided, and the user experience is improved. Therefore, the accuracy of the book and archive information is ensured.

The invention provides a power plant real-time dynamic control and operation management system based on big data twinning, and belongs to the technical field of data processing, and the system comprises a construction module which obtains spatial structure data and full-working-condition full-physical process data of a power plant, and constructs a big data twinning three-dimensional simulation model; the analysis module is used for acquiring historical operation data and historical environment data of the power plant and determining an operation strategy expectation matrix of the power plant; the operation module is used for collecting real-time operation data and real-time environment data of the power plant in real time and determining a real-time operation vector of the power plant; the control module is used for determining a real-time dynamic control strategy of the power plant; and the management module is used for generating a synchronous operator operation guidance dynamic report and an index optimization report. Global monitoring and intelligent management of power plant operation can be realized, a control strategy can be accurately and timely adjusted, the small-index operation efficiency of the power plant under different working conditions is optimized, the energy utilization rate is improved, the equipment fault risk is reduced, and the flexibility and adaptability of system adjustment are ensured.

The invention discloses a correction type technique supervision and analysis system, and the key points of the technical scheme are that the system comprises the following steps: data collection and preprocessing, mode recognition and modeling, anomaly detection and evaluation, correction and feedback mechanism, personalized recommendation and service optimization, and visualization and reporting. Comprehensive optimization and fine management of the whole massage service process can be realized through the modified form manipulation supervision and analysis system, and the system identifies and predicts customer demands and potential abnormal conditions by utilizing an intelligent algorithm through accurately collecting and processing physiological and psychological data and massage manipulation information of customers. Improper techniques or risk behaviors are found and corrected in time, a personalized recommendation mechanism automatically adjusts a massage scheme according to health conditions and preferences of customers, and through a clear visual interface and a real-time report, the service quality and customer satisfaction are improved, and intelligentization, personalization and high efficiency of massage services are achieved.

The present invention relates to the field of vacuum monitoring technology, and in particular to a vacuum preloading soil vacuum degree monitoring system and method, the monitoring system comprising: a plurality of acquisition modules, a plurality of vacuum degree sensors, a first comparison module generation module, a second comparison module, a marking module, a division module, a third comparison module and an adjustment module; obtaining the preloading data of each monitoring point in the target area, and determining whether the preloading conditions of each monitoring point meet the preset standards; analyzing the preloading data of each monitoring point, and classifying and marking each monitoring point according to the analysis results; dividing the target area into regions according to the classification and marking results, and determining the actual operation mode of each vacuum pump according to the regional division results. The present invention can effectively identify problems existing in the preloading process, timely adjust the operation mode of the vacuum pump, ensure the optimization of the preloading effect, improve the utilization rate of equipment such as vacuum pumps, and reduce resource waste.

The utility model discloses a total moisture testing system which comprises a transfer module, a division module, a drying oven weighing module, a rejected sample recycling module, a sample bottle receiving, sending and storing module and a control assembly, the division module is used for dividing a sample in a sample bottle into a test sample and a prepared sample, loading the test sample into a tray, and loading the prepared sample into a bottle; the oven weighing module is used for temporarily storing a sample tray and drying and weighing the sample tray filled with a test sample, and the test sample subjected to constant weight treatment is changed into a rejected sample; the discarded sample recovery module is used for recovering discarded samples and cleaning sample plates; the sample bottle receiving, sending and storing module is used for receiving, sending and storing sample bottles filled with original samples and prepared samples; the transfer module is used for transferring the sample bottles and the sample trays among the stations; the control assembly is used for controlling all the modules to work. The total moisture testing system disclosed by the utility model has the advantages of high automation degree, high testing efficiency, convenience in sample preparation and high testing accuracy.

The present application belongs to the technical field of cleaning management, and specifically provides a combined cleaning monitoring device, which aims to solve the problem of poor management of mop and wiping material cleaning and use. The present application comprises a cleaning host, a hanging device and an RFID reader. The mop comprises a mop cloth, and the mop cloth and the wiping material are provided with RFID tags. The RFID reader identifies and reads the RFID tags. The cleaning host comprises a mop cleaning device, a wiping material cleaning device, a first RFID reader and a control module. The mop cleaning device is used for cleaning the mop cloth, and the wiping material cleaning device is used for cleaning the wiping material. The hanging device comprises a hanging mechanism, a second RFID reader and a display. The hanging device is used for monitoring the use of the mop and the wiping material. The present application can intelligently identify, clean and disinfect the mop and the wiping material, and monitor and display the cleaning use. It is beneficial for managers and the public to jointly supervise the cleaning and use of the mop cloth and the wiping material, and realizes intelligent management of the cleaning and use of the mop and the wiping material.

A power plant cable whole life cycle monitoring method based on whole-process identification and mobile terminal code scanning belongs to the technical field of cable monitoring. The method constructs a four-layer architecture of "perception layer-network layer-platform layer-application layer", uses Ecode128 coding of GB / T31866-2023 standard as the unique identification of the cable, and realizes whole-process identification binding by combining two-dimensional code and digital character combination identification carrier. The mobile terminal code scanning is used to realize paperless collection of field data, which is uploaded to the management platform based on the SpringBoot framework through 4G / 5G / Wi-Fi / data line and other transmission modes. The platform adopts front-end and back-end separation design, the back-end integrates SpringMVC and MyBatis-Plus, and the front-end realizes visual display through Vue.js. The function modules of design, construction, operation and maintenance, state monitoring and retirement are developed and designed, and the whole life cycle digital closed-loop management of the cable from design to retirement is realized. The present application effectively solves the technical problems of information dispersion, low efficiency, single monitoring and extensive management in the traditional cable management mode, and significantly improves the fine and intelligent level of cable management.

The present invention relates to the field of medical health and information technology, and specifically to a children's oral health management system based on big data, including: a data acquisition module, a data processing module, a data prediction module and a control module, wherein the data acquisition module includes an acquisition component and a collection device; when the predicted oral health condition is that the teeth cleanliness is not up to standard, the control module determines whether the vibration frequency matches according to the existing vibration frequency data of the electric toothbrush in the data acquisition module; when the predicted oral health condition is that the gums are red and swollen, the control module sends an instruction to the user's wearable health monitoring device to increase the number of oral cleaning times; when the predicted oral health condition is that bleeding occurs, the control module determines whether the cause of bleeding is caused by the electric toothbrush in the data acquisition module. The present invention is used to establish an accurate prediction model by integrating big data and artificial intelligence, so as to achieve comprehensive, accurate and personalized management of children's oral health.

The present application belongs to the technical field of cleaning, and specifically provides an integrated intelligent cleaning device, which aims to solve the problems of time-consuming and labor-consuming, poor user experience and poor cleaning management of cleaning tools. The present application comprises a mop cleaning device, a wiping material cleaning device, a tool disinfecting device, an RFID reader and a control module. The mop and the wiping material are provided with RFID tags, and the RFID reader identifies and reads the RFID tags. The mop cleaning device is used for cleaning the mop, the wiping material cleaning device is used for cleaning the wiping material, and the tool disinfecting device is used for storing and disinfecting other cleaning tools such as brooms. The present application integrates the three devices into one, intelligently identifies, cleans and disinfects and monitors the cleaning and disinfection of various cleaning tools, which not only saves time and labor, saves space and provides good user experience, but also realizes intelligent management of cleaning and disinfection of cleaning tools.