1060 results about "Intelligent equipment" patented technology

The invention discloses an intelligent glasses image adjusting system based on eye movement tracking and gesture fusion, and relates to the technical field of intelligent equipment. A multi-modal sensing module is arranged to construct a multi-modal sensing layer to capture eyeball movement tracks and gesture actions; a fixation point prediction module is set to process a dynamic scene through a space-time attention mechanism to obtain a fixation point prediction area, a gesture semantic understanding module is set to process gesture actions based on a Transform architecture, and the gesture actions of a user are converted into image adjustment instructions. An image enhancement strategy setting module designs a multi-stage image enhancement strategy according to the fixation point prediction area and the image adjustment instruction, and sets a dynamic adjustment intensity control module to perform adaptive adjustment to obtain a dynamic adjustment intensity control result; an eye movement-gesture cooperative control module is arranged to provide an eye movement-gesture cooperative control mechanism to realize image area selection and parameter adjustment, and accurate image area selection and parameter adjustment are realized.

Systems and Methods are disclosed for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand / supply related cardiac ischemia. The system may have both implanted and external components that communicate with a Physicians's programmer, and smart-devices for monitoring and alerting to detected medically relevant events or states. At least one processor provides event detection using statistical threshold criteria calculated upon at least a portion of a patient's data / distributions and set for a patient or based upon what a doctor determines as abnormal for a patient. Cardiovascular condition is tracked using histogram, trend, and summary information related to heart rate and / or cardiac features such as S-T segment measures of heartbeats. Heartbeats with elevated rates, and below a “high” range, provide medically relevant detections including medication non-compliance. Novel methods of power management and patient monitoring are disclosed.

The invention relates to the technical field of data processing and analysis, in particular to an intelligent large health system and a data processing method thereof, and the method comprises the steps: collecting the data of a multi-source heterogeneous data source in real time, and comprehensively capturing the multi-dimensional health data of a user; a multi-modal health knowledge graph is dynamically constructed and optimized through an efficient data preprocessing and feature alignment technology, and the integration value and the utilization efficiency of data are improved; a graph neural network and a time sequence analysis model are utilized to realize accurate evaluation of the health state of the user and timely prediction of future risks, and predictability and pertinence of health management are effectively improved; based on a reinforcement learning algorithm, a highly personalized intervention sequence can be generated according to the individual condition of a user, the accuracy of health intervention is enhanced, and the positive change of the health behavior of the user is promoted; an intervention instruction is executed through intelligent equipment, user feedback is monitored in real time, an intervention strategy is dynamically adjusted, and the flexibility and adaptability of intervention measures are ensured.

The invention relates to the technical field of intelligent glasses production equipment, and discloses intelligent glasses leg metal shielding cover frame automatic rubber ring pasting equipment which comprises a feeding sliding table, a glasses positioning table, an outer supporting attaching assembly used for pressing the inner side face of a rubber ring, and an opposite extrusion attaching assembly used for pressing the upper face and the lower face of the rubber ring. And the release paper adsorption positioning assembly is used for accurately positioning release paper to which the rubber rings to be assembled are adhered on the periphery of the frame of the metal shielding cover to which the rubber rings are to be adhered on the inner sides of the left glasses leg and the right glasses leg, and the equipment utilizes the glasses positioning table to position and limit the glasses frame and the glasses legs to which the rubber rings are to be adhered. Then the release paper adsorbing and positioning assembly is used for manually positioning and adsorbing the release paper pasted with the rubber ring firstly and then automatically positioning and adsorbing the release paper pasted with the rubber ring, then the outer supporting and attaching assembly and the opposite extruding and attaching assembly are used for automatically assisting in pasting the inner side face, the upper face and the lower face of the rubber ring respectively, the precise automatic pasting action is completed in a narrow space, the pasting position precision is high, and the production efficiency is high.

The invention relates to the crossing field of agricultural intelligent algorithms and intelligent equipment technologies, in particular to an environment regulation and control model algorithm and algorithm model platform fusing crop physiology. According to the model algorithm, through four-step calculation of spatio-temporal heterogeneity adaptation, multi-source data dynamic coupling, stress self-adaptive adjustment and self-learning iterative optimization, a partition precise regulation and control decision value is generated; the algorithm model platform comprises a data acquisition layer (a partition sensor and a multi-source interface), a feature processing layer (a three-dimensional feature library and data preprocessing), a model algorithm layer (deploying the model algorithm) and a platform application layer (partition regulation and control and closed loop feedback), and full-link cooperation of algorithm calculation, platform execution, effect feedback and algorithm iteration is achieved. The crop yield is increased by 15-20%, water resources are saved by 25-30%, and the method is suitable for multi-crop facility agriculture scenes.

The invention belongs to the technical field of robot and intelligent equipment driving, and discloses an integrated servo push rod and a control method.A push rod module is integrally designed, and a high-density motor, a ball push rod and a band-type brake are arranged in the push rod module. The invention has the characteristics of high power density, small volume, high precision, high response speed, low noise, long service life, high reliability and the like. The inside of the push rod is contacted with the deep groove ball bearing at the tail end of the screw rod through the ball retainer, and the outside is contacted with the oil-free bushing, so that the lateral strength of the push rod is improved, friction is reduced, and operation efficiency is improved. A linear encoder is arranged at the end of the screw rod, accurate control of the tail end can be achieved, a magnetic encoder is arranged at the tail end of the motor, input and output end double-closed-loop can be achieved, precision can be improved, and errors of band-type brake in power-off response time can be compensated; the lead screw is connected with the motor through the diaphragm coupling in the motor, so that the axial space is saved, and the service life of the motor is prolonged.

The invention relates to the technical field of earthquake monitoring and early warning, in particular to an earthquake early warning analysis system and method based on intelligent equipment. The system comprises a terminal sensing and initial judgment module which collects data through a sensor array, identifies P-wave features by using a dynamic weight adjustment model and generates an encrypted initial alarm; the adaptive network transmission module is used for establishing a dynamic self-organizing network based on a D2D protocol and realizing multi-hop relay transmission of alarms by electing relay nodes; the edge aggregation and verification module is used for executing space-time matching and cross verification on a plurality of alarms in a specific area, judging the authenticity of an event and generating graded early warning information; and the cloud control fusion and actuarial module is used for fusing the regional event data and the professional network data, executing high-precision positioning and earthquake magnitude calculation, and issuing a final early warning instruction. According to the invention, a high-density sensing network is constructed by using massive intelligent devices, the early warning blind area is effectively eliminated, and faster, more accurate and more robust earthquake early warning is realized.

The invention discloses a green plum fruit picking robot based on multi-source data fusion and a picking method thereof, and relates to the technical field of agricultural intelligent equipment. The robot comprises a crawler-type self-adaptive chassis, a multi-degree-of-freedom mechanical arm, a multi-source sensing module, a bionic flexible end effector, an intelligent control unit and a fruit collecting box, and all the parts are electrically connected with the intelligent control unit. The multi-source sensing module synchronously collects vision, spectrum, touch, acoustics and environment data, and accurate recognition and positioning of mature fruits are achieved through hierarchical weighted attention fusion algorithm processing. And fruit damage is reduced in combination with a path planning algorithm introducing a flexible obstacle avoidance coefficient and a flexible picking execution mechanism. The self-adaptive chassis can adapt to complex terrains, and parameters are dynamically optimized through reinforcement learning in the picking process. The green plum picking machine solves the problems that traditional picking is low in efficiency and high in damage rate, and existing equipment is poor in adaptability, and the intelligent level and operation quality of green plum picking are improved.

The invention discloses an intelligent equipment life cycle information fusion operation and maintenance optimization method, and relates to the technical field of intelligent equipment fusion operation and maintenance, and the method comprises the following steps: based on a judged nested task execution state, extracting state jump features from state data of load, current, vibration and the like collected in an equipment operation process, calculating state change amplitude, duration and frequency by using a time sequence window comparison algorithm, and obtaining a state hopping feature set associated with task switching; and based on the task level mapping table and the state jump feature set, constructing a graph convolutional neural network model, taking a nesting relationship between tasks as a topological structure, inputting each task node and the corresponding state jump feature into the model, and identifying each stage of the life cycle of the intelligent equipment. According to the method, the problem that life cycle division is affected by nested tasks is solved, and accurate state recognition of the intelligent equipment and dynamic optimization of operation and maintenance parameters are realized.

The invention discloses an intelligent collaborative supporting and recycling method for residual mining of special-shaped pillars in a limited space, and belongs to the technical field of mining engineering. According to the method, an intelligent closed-loop system of sensing, decision-making, execution and feedback is constructed aiming at the problems of narrow space, complicated stress, high safety risk and the like faced by special-shaped ore pillar recovery in a limited space. The method comprises the following steps: firstly, constructing a dynamic digital twinborn model by using LiDAR and GPR fusion detection; secondly, performing stoping-supporting collaborative optimization design based on numerical simulation and an AI algorithm; then, low-disturbance blasting and micro intelligent equipment are adopted for precise subsection stoping; the mining-supporting-filling cycle operation is synchronously implemented, and an artificial key layer is quickly constructed by using a quick-setting expansion material; and finally, multi-stage intelligent early warning and dynamic regulation and control are realized through a multi-element sensor network. According to the method, the recovery rate of residual ore resources can be remarkably increased to 90% or above, ground pressure disasters are effectively controlled, and green, safe and efficient recovery of mine resources is achieved.

The invention provides a video coding and compression transmission method for intelligent equipment, which belongs to the technical field of video coding and compression transmission, realizes advanced channel quality estimation by establishing a channel prediction model enhanced by a spatial transformation network, configures layered coding parameters according to a prediction result and optimizes motion search in combination with inertial measurement unit data. An optimal coding mode is selected by an application rate distortion optimization algorithm, enhancement layer sending is adjusted according to real-time feedback by adopting a dynamic transmission scheduling strategy, and differential forward error correction protection and a time-space domain error concealment mechanism are matched; the technical problem that video transmission is unstable due to violent fluctuation of channel quality when intelligent equipment moves and works in complex electromagnetic environments such as a transformer substation is solved.

A real-time target detection method and system based on RTSP flow and NPU collaborative optimization belong to the technical field of computer vision and artificial intelligence, and are characterized by comprising the following steps: adopting dynamic memory optimization management of a hybrid pipeline architecture, and performing single-time continuous copying through a CPU (Central Processing Unit); through deep integration of innovative technologies such as DMA direct transmission NPU continuous memory pool management, dynamic batch processing scheduling, hybrid assembly line processing, intelligent equipment load balancing, parallel preprocessing optimization and vectorization post-processing, RTSP flow collaborative optimization and the like, the NPU utilization rate is improved to 85% or above, the overall average FPS is improved by 200% or above, the assembly line parallelism degree achieves three times of performance gain, and the production efficiency is greatly improved. The data transmission delay is reduced by 80%, the system stability is remarkably improved, performance degradation is avoided after long-time operation, and the method is suitable for various scenes such as edge calculation and cloud reasoning.

The invention discloses a new energy automobile logistics transfer robot based on visual navigation and an implementation method, relates to the field of logistics automation and intelligent equipment, and solves the problems that traditional equipment is low in navigation precision, difficult in data fusion and weak in obstacle avoidance capability. The transferring robot body comprises Mecanum wheels, supporting legs, an intelligent temperature control battery bin, a visual navigation system, a core control center, an interactive touch screen and a mechanical arm. The core control center comprises an image acquisition module, a data processing module, a path planning module and a transfer control module; all the modules work cooperatively, the automation and intelligence level of logistics transfer is remarkably improved, and powerful support is provided for the new energy automobile logistics industry.

The invention provides a task processing method and device based on an industrial large model, a medium and a product, and relates to the technical field of industrial automation. The method comprises the following steps: constructing a multi-source data set based on an industrial scene, and training a plurality of adapters corresponding to the multi-source data set by adopting an adaptive low-rank adaptation algorithm; according to the learnable weight, fusing the plurality of adapters to obtain a fine-tuned industrial large model; and inputting an operation and maintenance task of the intelligent equipment in the industrial scene into the fine-tuned industrial large model, outputting an operation and maintenance strategy of the intelligent equipment, and controlling the intelligent equipment in the industrial scene to execute the operation and maintenance strategy. According to the method, efficient fine tuning and capability integration of an industrial large model in a multi-source heterogeneous data scene are achieved by introducing a self-adaptive low-rank adaptation algorithm and a multi-task adapter fusion framework, the fusion conflict problem of multi-source heterogeneous data is solved, efficient reasoning of full-process intelligent interaction of the air compressor is achieved, and the intelligent interaction of the air compressor is achieved. And the stability and the precision of the large industrial model in a complex industrial task are improved.

The invention discloses an automatic calculation and compensation method and system for dynamic strike delay of a laser weeding robot, and belongs to the technical field of agricultural intelligent equipment.The method is achieved through the following steps that in the static calibration stage, a laser galvanometer, a camera and collaborative parameters of the laser galvanometer and the camera of a robot system are corrected; laser scanning nonlinear errors, camera imaging distortion errors and pose and mapping errors between a galvanometer coordinate system and a camera coordinate system are eliminated; in the dynamic testing stage, the robot advances at a constant speed, laser striking is conducted on the wood chips with preset marks, and the position deviation between an actual striking point and a theoretical target point is obtained through the image recognition technology; reversely deducing system delay time based on the deviation and the speed of the robot, and iteratively updating compensation parameters; finally, after the iterative optimization strike deviation is smaller than the set precision, the compensation parameters are solidified to a control system; the method is suitable for a laser weeding robot and other dynamic visual striking systems.

The application discloses a mold control equipment operation state monitoring method and device and a medium, relates to the technical field of intelligent equipment monitoring, and comprises the following steps: carrying out denoising, normalization and outlier rejection processing on a mold operation data set to obtain clean operation data; performing correlation analysis and feature extraction on the clean operation data to generate a sound-vibration coupling mode vector set, and performing feature index calculation and mode deviation evaluation to obtain a sound-vibration state feature set; performing state coding on the sound-vibration state feature set to generate a mold operation state table, and performing health degree calculation and state evaluation to output a mold operation feature set; performing operation level judgment and trend analysis on the mold operation feature set to output an operation state result; and performing information arrangement and instruction generation on the operation state result to generate an operation state report. The mold operation state is quantitatively monitored with high precision.

The invention discloses a mold control equipment operation state monitoring method, equipment and a medium, and relates to the technical field of intelligent equipment monitoring, and the method comprises the steps: carrying out the denoising, normalization and abnormal value elimination processing of a mold operation data set, and obtaining clean operation data; performing correlation analysis and feature extraction on the clean operation data to generate a sound-vibration coupling mode vector set, and obtaining a sound-vibration state feature set through feature index calculation and mode deviation evaluation; state coding is carried out on the sound vibration state feature set, a mold operation state table is generated, health degree calculation and state evaluation are carried out, and a mold operation feature set is output; performing operation grade judgment and trend analysis on the mold operation feature set, and outputting an operation state result; and performing information arrangement and instruction generation on the operation state result to generate an operation state report. And high-precision quantitative monitoring of the operation state of the mold is realized.

The invention discloses a rice seedling leaf age intelligent identification system based on a depth camera, and belongs to the technical field of agricultural intelligent equipment and machine vision. Comprising a monitoring point gridding path planning module, a mechanical arm cooperative positioning data acquisition module, an RGB-D data preprocessing module, a dynamic foreground seedling accurate extraction module, a leaf age recognition confidence evaluation module, a leaf age data visualization presentation module and an integrated control strategy feedback module. RGB color features and connected domain analysis are combined to optimize a mask, a front single seedling is accurately segmented, background interference is eliminated, the confidence coefficient is dynamically calibrated through a deep learning model and image quality features, texture definition and edge continuity features are combined to generate calibrated confidence coefficient, confidence coefficient evaluation is directly associated with an agricultural decision, and the accuracy of the agricultural decision is improved. The leaf age result is generated only based on reliable data, the personnel rechecking demand is reduced, and the decision-making efficiency is improved.

The invention discloses a dynamic distribution method and device for network resources. The method comprises the following steps: acquiring device data, user behavior data and environment data of each intelligent device in a home network; processing the equipment data, the user behavior data and the environment data through a scene recognition model to obtain a home collaborative scene type of the home network; the scene priority of the family cooperation scene type is calculated according to the family cooperation scene type, the user identity and the time sensitivity; generating a network resource allocation strategy according to the scene priority and the equipment demand information; and distributing network resources to each intelligent device according to the network resource distribution strategy. According to the method and the device, the technical problem that resource allocation and actual requirements are disjointed due to the fact that a resource allocation mode based on static QoS configuration or simple flow classification cannot adapt to the requirements of multi-device collaboration and dynamic change in a home network in the prior art is solved.

The invention discloses a distributed photovoltaic grid-connected and off-grid coordination control method. The method comprises the following steps: a photovoltaic data acquisition terminal acquires an operation state data set of a photovoltaic array, an inverter and the like through each node sensor and intelligent equipment; the photovoltaic data preprocessing terminal performs denoising, normalization and the like on the original data to form a standardized data set; the data analysis terminal calculates a power balance equal coefficient, inputs the power balance equal coefficient into the decision model and outputs a grid-connected and off-grid coordination index; the coordination control terminal judges a grid-connected operation mode or an off-grid operation mode according to the index; the energy management terminal coordinates the power output of each unit during grid connection, and optimizes an energy storage charging and discharging strategy during off-grid; the state monitoring terminal performs real-time monitoring, discovers abnormal early warning and triggers fault processing; according to the method, the operation stability and the energy utilization efficiency of the photovoltaic system are improved.

A system is provided.SOLUTION: A system including means for acquiring image data of a plant or soil using a camera mounted on a smart device, means for communicating with a server including artificial intelligence for analyzing the acquired image data and receiving an analysis result, means for feeding back a growth state of the plant or presence or absence of a pest to a user based on the received analysis result, and software means for providing a growth prediction of the plant and an optimal coping method.SELECTED DRAWING: Figure 1

The invention relates to the technical field of agricultural intelligent equipment, in particular to intelligent monitoring and weeding equipment for gramineous weeds in a winter wheat field, and aims to solve the technical problem of high missing detection rate and false detection rate of a traditional visual identification method caused by similar forms of winter wheat and gramineous weeds in a seedling stage. The equipment comprises an autonomous mobile platform, a multi-mode sensing assembly, a data processing and decision-making unit and a targeted clearing execution mechanism. The multi-mode sensing assembly integrates a hyperspectral imaging module, a three-dimensional shape measurement module and a polarization characteristic analysis module, and pixel-level data registration is achieved in combination with the cooperative illumination system. The data processing unit performs high-confidence classification decision on the multi-source features through a hierarchical Bayesian fusion model; and the targeted clearing mechanism adopts a laser galvanometer system to carry out non-contact precise clearing on the identified weed growing points. Intelligent identification and efficient physical weeding of weeds in the winter wheat field are achieved, and the method has the advantages of being free of chemical residues and high in operation continuity.

The utility model discloses a control host of a surgical field shooting device and the surgical field shooting device, the control host of the surgical field shooting device comprises a housing, the housing is provided with a mounting cavity, the housing is provided with a first housing wall, the first housing wall is provided with a mounting port, the mounting port is communicated with the mounting cavity, and the mounting cavity is provided with an accommodating area corresponding to the mounting port; the main control board is mounted in the mounting cavity; the communication module is accommodated in the accommodating area through the mounting port, and the communication module is electrically connected with the main control board; the display screen is installed on the outer surface of the first shell wall and electrically connected with the main control board, the display screen is provided with a display area and a non-display area, the display area is used for displaying pictures, and the non-display area covers the installation opening. The communication signal of the communication module can penetrate into or out of the shell through the display screen, and the display screen can reduce the interference effect on the communication signal of the communication module, so that the signal strength of the communication module can be improved, and the communication effect between the control host and the external intelligent equipment is improved.

The utility model discloses a hydraulic support articulated shaft assembling device, and relates to the technical field of hydraulic support assembling, the hydraulic support articulated shaft assembling device comprises a load AGV, a vertical frame is installed on the load AGV, one side of the vertical frame is provided with an intelligent cantilever crane installed on the load AGV, and the other side of the vertical frame is provided with a scissor lifting platform installed on the load AGV; an electric hoist used for hoisting is mounted on the intelligent cantilever crane, and a magnetic adsorption device used for grabbing a hinge shaft is mounted at the moving end of the electric hoist; a hinge shaft supporting frame and a hinge lifting mechanism are arranged on the vertical frame; an electric cylinder push rod is mounted in the hinge shaft support frame; the device has the beneficial effects that the intelligent cantilever lifting device is adopted, an electric hoist is used for replacing a worker arm to grab a hinge shaft, an existing intelligent lifting appliance is used, the labor intensity of workers is relieved, and the working efficiency is improved; and meanwhile, through intelligent equipment, all-directional hoisting of the parts is achieved, the whole cantilever crane is loaded on the movable AGV, and the working range of grabbing and assembling of the hinge shafts is widened.

The invention discloses a nine-axis linkage intelligent welding process optimization method based on analogue simulation and digital twinning. An existing welding process mostly depends on artificial experience and single equipment control, and an integrated processing system of simulation prediction, equipment cooperation, virtual-real linkage and iterative optimization is lacked. According to the nine-axis linkage intelligent welding process optimization method, a platform is built based on simulation software, after a three-dimensional model of a to-be-welded component is built, the three-dimensional model of the to-be-welded component is subjected to multi-physics field coupling simulation processing to obtain welding data, the welding data is input into an AI multi-target optimization algorithm to form a simulation data training model, and the simulation data training model is used for training the to-be-welded component. Screening according to the simulation data training model to obtain a processing process of an optimal welding sequence and process parameters; through deep integration of intelligent equipment integration, technological process simulation optimization and digital twin control, the problems of low efficiency, poor quality and large deformation in a traditional manufacturing mode are systematically solved.

The invention provides a power grid autonomous operation and maintenance question-answering system based on a large model and an RAG open source framework, and belongs to the technical field of natural language processing. Comprising a knowledge document processing module which is used for segmenting a knowledge document in a specific field into a plurality of text blocks with independent semantics and performing semantic enhancement processing on the plurality of text blocks in combination with knowledge characteristics of the specific field; the system further comprises a vector generation and storage module, and the vector generation and storage module generates high-dimensional vector representation for each text block through an embedded model. By setting a dynamic retrieval and generation mechanism, the operation and maintenance problem of the intelligent equipment of the power grid can be quickly and accurately solved, and the system not only improves the operation and maintenance efficiency and accuracy, but also reduces the development cost, and enhances the expansibility and flexibility of the system.

The invention relates to the field of intelligent equipment control and industrial automation, in particular to an intelligent equipment butt-assembling cooperative control system with multi-axis linkage compensation. The data acquisition module is used for acquiring current, force / torque, temperature, position and speed data under a unified time reference; the benchmark reconstruction module is used for generating an ideal position benchmark and an ideal force / torque benchmark on the basis of an ideal rigid body model under a preset reference coordinate system; the parameter injection module is used for injecting thermoelastic deformation, rigidity attenuation and hysteresis friction parameters to generate a theoretical simulation state; constructing a double-track difference module of a real residual error and a theoretical residual error; the coupling judgment module is used for carrying out time sequence similarity verification and generating a state judgment result; the feedback control module is used for outputting a multi-axis linkage compensation instruction or a compliant return instruction; according to the invention, inherent drift and real assembly interference of equipment can be distinguished, and stable compensation and safe concession control are realized.

The embodiment of the invention provides a battery cell wrinkling detection method and related equipment, and belongs to the technical field of lithium battery intelligent equipment. The method comprises the following steps: acquiring position preparation information of positive and negative pole pieces and positive and negative diaphragms reaching preset positions; starting winding according to the position preparation information, and shooting to obtain an image of each circle of winding battery cell; carrying out algorithm processing on the image of the wound battery cell, and extracting to obtain a wrinkled pattern; and analyzing the wrinkles, and judging to obtain a wrinkling detection result. According to the embodiment of the invention, accurate detection of wrinkles can be realized.

The invention discloses an AI-based multi-device cooperative control method in an Internet of Things environment. By constructing a'perception-decision-control-feedback-self-adaption 'full-closed-loop architecture, terminal components such as an intelligent equipment group and a sensor group of an equipment layer, a multi-source data acquisition and enhancement preprocessing module of a perception layer, a dynamic equipment capability graph of an AI decision layer, an incremental AI decision model and other core units are integrated; and a cross-protocol instruction distribution and closed-loop feedback module of the control layer realizes deep processing of multi-source heterogeneous data, real-time perception of equipment capability, dynamic adjustment of task priority and autonomous optimization of a coordination strategy, thoroughly solves the technical problems of rigid coordination logic, response lag and unreasonable resource allocation of a traditional method, and improves the reliability of the system. And the intelligent level and the operation efficiency of the Internet of Things system are greatly improved.