22 results about "Iot architecture" patented technology

The invention belongs to the technical field of intelligent engineering management, and discloses an automatic monitoring and control method of an engineering management system based on the Internet of Things. According to the method, a four-layer Internet of Things architecture comprising a sensing layer, a network layer, a platform layer and an application layer is constructed, and the four-layer Internet of Things architecture is uploaded to a cloud platform through a 5G / narrowband Internet of Things dual-mode transmission channel. A digital twinborn technology is innovatively adopted to construct a three-dimensional visual engineering model, intelligent identification and prediction of abnormal working conditions are realized through a machine learning algorithm, and a multi-stage linkage control mechanism is established. And when construction deviation or equipment failure is detected, the system automatically generates an optimization control strategy and issues the optimization control strategy to the execution terminal, so that accurate regulation and control of the construction machinery and intelligent pushing of early warning information are realized. According to the method, a manual inspection mode of traditional engineering management is broken through, full-process automatic supervision is realized through data fusion analysis and closed-loop control, the construction quality supervision precision is effectively improved by more than 30%, the safety accident rate is reduced by 50%, and the engineering management efficiency is remarkably improved.

The invention discloses a paint spraying room temperature and humidity control system based on the Internet of Things, and relates to the technical field of industrial coating environment control. Accurate perception and disturbance pre-judgment of gate opening and closing behaviors are realized by means of cooperation of a gate state detection module and an environment monitoring module; the predictive sub-module dynamically evaluates the disturbance risk level by analyzing the door opening and closing frequency, the duration time and the environment difference, so that the control action is converted into beforehand intervention from post-event compensation; humiture sudden change caused by opening of the gate is effectively inhibited, and the overshoot phenomenon caused by response delay is reduced; meanwhile, an Internet of Things architecture is introduced, and the decision dimension of the system is expanded; the cloud collaborative management module optimizes control parameter distribution by summarizing operation data of a plurality of spray booths and utilizing a consistency algorithm, so that mutual interference caused by coupling of ventilation systems of adjacent spray booths is avoided; and the local intelligent control unit can be automatically degraded to a cache strategy when the cloud is unavailable, so that the control continuity is ensured.

The invention relates to an intelligent optimization method for sludge deep dehydration process parameters. Key parameters are collected in real time through an Internet of Things architecture, a multi-dimensional dynamic working condition data set is constructed by using a sliding time window and standardization processing, data quality is improved through abnormal value filtering and noise smoothing, a CNN-GRU hybrid model is input, and precise prediction of a working condition evolution trend is realized. On the basis of a prediction result, a trend guide feedback channel, a preference evolution feedback channel and a stability feedback channel are constructed, a multi-objective optimization weight is adjusted in a self-adaptive mode through normalization and weighted fusion, and a high-correlation optimal process parameter combination and closed-loop feedback control are obtained by combining NSGA-II algorithm multi-objective optimization and trend alignment degree screening. According to the scheme, the data perception and multi-target adaptive optimization capability of the sludge deep dehydration system is effectively enhanced, and the operation efficiency, the energy consumption utilization and the system stability are improved.

The invention discloses a stamping equipment remote fault diagnosis method and system based on the Internet of Things, and relates to the technical field of stamping equipment fault diagnosis, and the method comprises the steps: S1, deploying multiple types of sensing units based on an Internet of Things architecture, synchronously collecting the equipment operation data, production process data and external environment data of stamping equipment, and forming three-dimensional coupling data; according to the invention, by constructing an equipment-process-environment three-dimensional data acquisition framework, synchronously acquiring multi-dimensional coupling data, and combining with an edge calculation real-time association algorithm, accurately identifying and rejecting false fault data, the problem of high false alarm rate caused by single data in the existing diagnosis technology is effectively solved, and the diagnosis accuracy is improved. By embedding a working condition and equipment individual difference compensation factor in an adaptive feature migration algorithm, cross-working-condition and cross-equipment feature dynamic fusion is realized, the generalization ability of a diagnosis model is improved, and relying on an edge-cloud collaborative architecture, the real-time performance of data processing is ensured, and the efficient fusion of global features is also realized.

The application discloses a two-purpose wrench production full-process intelligent management method and system under an industrial internet of things architecture, and relates to the technical field of industrial internet of things and intelligent manufacturing. The method comprises the following steps: taking the use characteristics of a target two-purpose wrench as a constraint, obtaining single-function production sample data and full-process historical production data; constructing and training a plurality of function performance evaluation bodies based on the data to form an evaluation body set; constructing a bidirectional master-slave optimization channel comprising a first master-slave optimization channel, a second master-slave optimization channel and an information exchange channel according to the evaluation body set; performing bidirectional parallel optimization iteration in combination with the full-process historical production data and the bidirectional master-slave optimization channel, and outputting an optimal process scheme; and finally applying the optimal scheme to a full production environment to realize intelligent management. The application effectively solves the coupling conflict problem of dual-function end production process parameters through a bidirectional optimization architecture, realizes global collaborative optimization, improves product quality and production efficiency, and reduces production cost and the waste product rate.

The invention discloses a flexible intelligent manufacturing system based on AI intelligent control, and belongs to the technical field of industrial intelligent manufacturing. The system realizes sensing, transmission, analysis and control closed loop of the whole manufacturing process through an industrial Internet of Things architecture, and breaks through the bottleneck of traditional flexible manufacturing in combination with an AI algorithm; the system covers an industrial Internet of Things sensing layer, an edge transmission layer, an AI intelligent control layer, a flexible execution layer and a cloud collaboration layer. The sensing layer uses multi-type sensors to collect data precisely in a global manner. The edge transmission layer realizes low-delay and high-reliability transmission and preprocessing based on a specific protocol and a 5G gateway; the AI intelligent control layer analyzes data in real time and performs fault prediction and the like; the flexible execution layer responds to the instruction to realize flexible cooperation; and the cloud collaboration layer gathers data and iterates the model. According to the method, multiple technical problems of traditional flexible manufacturing are solved, the system flexibility, the intelligent level and the production efficiency are improved, the cost is reduced, and the method is suitable for multi-field flexible production scenes.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, an ambient Internet of Things (AIoT) device may perform an authentication and key agreement procedure with a network function to generate a root key. The AIoT device may receive, from an AIoT controller, a key confirmation message. The AIoT device may generate, using the key confirmation message and the root key, a protection key. The AIoT device may transmit, to the AIoT controller, a key confirmation acknowledgement using the protection key and in response to the key confirmation message. Numerous other aspects are described.

The invention discloses an electric vehicle battery replacement cabinet battery remote monitoring method and system fused with the Internet of Things, and relates to the technical field of battery monitoring. The method comprises the following steps: performing Internet of Things architecture deployment based on structure distribution data of a target electric vehicle battery replacement cabinet, and establishing a three-level Internet of Things remote monitoring architecture; a plurality of battery compartment state sensing data streams are collected through a battery compartment sensing module; performing model matching and anomaly identification on the plurality of battery compartment state sensing data streams to obtain N battery compartment anomaly feature sets; and uploading the N battery compartment abnormal feature sets to a cloud monitoring center through a communication transmission module to perform global fault detection, determining a battery fault root cause of the target battery replacement cabinet, and performing operation and maintenance scheduling control. The technical problems of lagging of battery fault diagnosis and low operation and maintenance efficiency of the battery replacement cabinet in the prior art are solved, remote real-time monitoring and intelligent abnormity identification of the battery replacement cabinet battery are realized through a multi-stage Internet of Things architecture, and the technical effect of improving the real-time performance of battery fault diagnosis and the operation and maintenance efficiency is achieved.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, an ambient Internet of Things (AIoT) device may perform an authentication and key agreement procedure with a network function to generate a root key. The AIoT device may receive, from an AIoT controller, a key confirmation message. The AIoT device may generate, using the key confirmation message and the root key, a protection key. The AIoT device may transmit, to the AIoT controller, a key confirmation acknowledgement using the protection key and in response to the key confirmation message. Numerous other aspects are described.

This invention discloses a time-frequency energy resource allocation system based on an intelligent reflective surface for an underground Internet of Things (IoT) architecture. Belonging to the field of resource allocation, this invention designs a frequency-division duplex IoT architecture for underground spaces by jointly using distributed energy access points and a Resource Allocation System (RIS). To achieve efficient utilization of channel and energy resources, based on this IoT architecture, this invention models the resource allocation problem as a joint allocation of time, spectrum, and energy to meet QoS requirements, along with RIS phase shift configuration. This problem is a mixed-integer nonlinear programming problem, difficult to solve in polynomial time. To reduce the complexity of the solution, this invention constructs a multi-agent collaborative resource allocation algorithm based on near-end policy optimization. Overall, the purpose of this invention is to build an efficient monitoring IoT in underground spaces, achieving an energy-saving and highly efficient resource allocation scheme.

The application relates to the technical field of building monitoring, in particular to a video monitoring system for multi-source data fusion of a building site under an Internet of Things architecture, which solves the problems that, in the process of video monitoring of a building construction site, the construction site environment is complex, the construction scene is various, multi-source data of different building scenes cannot be fused and processed, the building monitoring and control effect is affected, and accurate identification and abnormal processing according to scene data are inconvenient; the video monitoring system comprises a data acquisition module, an acquisition scene identification module, a data fusion module and an application management module; the data acquisition module is used for acquiring real-time scene data of multiple building monitoring scenes from various sensors and video devices; the scene data is composed of video data, environment data and personnel positioning data; the multi-source data of different building scenes are fused and identified, scene abnormalities can be effectively identified, and the monitoring and control effect of the building scene is improved.

The invention discloses a coal mine gas early warning and monitoring management system, which comprises an Internet of Things architecture formed by a sensing layer, a transmission layer, a platform layer and an application layer, and is characterized in that the sensing layer performs full-time and multi-dimensional coal mine state data acquisition on each monitoring area in a coal mine through a sensing subsystem, and transmits the data to the platform layer through the transmission layer; the transmission layer is used for transmitting the coal mine state data acquired by the sensing layer in real time to the platform layer, and the platform layer carries out message queue, parallel computing, protocol processing, operation and maintenance management and real-time alarm according to the coal mine state data transmitted by the transmission layer and serves as sensing data of the sensing layer and service connection of the application layer; the application layer is used for processing and analyzing the coal mine state data of the sensing layer according to the platform layer and acquiring a prediction decision; and basis and data support are provided for making scientific decisions through rich internet-of-things sensing means, and the intelligent decision level of gas management is improved.

The present application relates to the field of power distribution system equipment state monitoring, in particular to a high-voltage fuse array operation monitoring method and system based on an Internet of Things architecture, which pre-constructs an array topology graph containing thermal neighbor relationships and heat dissipation area division in the commissioning phase; in the operation phase, the current environmental reference temperature is estimated based on the low-load fuse set in the array in each sampling period, and the expected temperature of each fuse is calculated by combining the current-carrying self-heating temperature rise and the heat dissipation condition offset, then the group environmental disturbance and isolated point degradation are distinguished by neighbor deviation synchronization rate, and finally the alarm is output after multi-cycle continuity confirmation. The present application can absorb the group temperature rise caused by the change of cabinet ventilation and environmental drift, significantly reduce the false alarm rate, and at the same time improve the recognition sensitivity to early degradation of the melt. The present application uses the array collective behavior as a self-referencing benchmark instead of a fixed threshold, which improves the early degradation detection capability and suppresses false alarms.

An intelligent, standalone, and modular secure parcel management platform integrates Internet of Things (IoT) architecture with artificial intelligence (AI) and machine learning (ML) to autonomously receive, authenticate, monitor, and secure delivery, return, and exchange of parcels. The system includes a parcel receptacle equipped with a removable and upgradeable IoT enclosure housing cameras, sensors, processors, communication modules, and power components, wherein AI and ML models analyze images, video, sensor data, contextual signals, and external data sources to identify delivery events, authenticate delivery personnel, detect anomalies, predict delivery windows, and dynamically control access mechanisms. The platform supports edge and cloud-based intelligence, authenticated integrations with carrier and e-commerce systems, location awareness, and real-time user interaction via networked devices. By combining modular hardware design with adaptive AI-driven decision making, the invention provides a scalable, future-proof secure delivery infrastructure that improves reliability, automation, and security of unattended parcel handling across residential, commercial, and multi-unit environments.

The device is a DNARNAAvionics (DRAC) ClamSat platform modeled on Biomimetic, Object Oriented (OO) and Internet of Things (IOT) structures. This platform is for controlling the operational aspects of a nano Satellite termed a ClamSat.A ClamSat bi-valve shell uses nano hardware and OO software structures to process solar energy in a biomimetic system characterized by energy storage means. It is operationally managed by OO programs in a DNA / RNA messaging format. The Object-oriented programming (OOP) is a method of structuring a program by grouping related property-es and behaviors into individual objects. The ClamSat OO programs are structured in a class, object, and attribute model of OO DNA / RNA strands within modules will provide interfaces within and across components and subsystems and allow for adaptability and reusability over time. The device communicates wirelessly with a Command-and-Control host infrastructure.The DNA / RNA strand will have Internet of Things (IOT) touchpoints to provide information back to the ClamSat system for Command, Control and Data Handling within the platform.

This application relates to the field of intelligent commodity management technology, and in particular to a commodity management method and system based on electronic tag technology and Internet of Things architecture. The method includes: establishing an intermodal correlation matrix for the tag data in the initial tag dataset of each commodity acquired through a reader, and obtaining the correlation coefficient between each tag data; the tag data is a received signal strength tag, a Doppler frequency shift tag, or a time difference of arrival tag; for each tag data, when the detected outlier value of the tag data is greater than a preset threshold, calculating a joint anomaly score for the tag data based on the outlier value of the tag, the correlation coefficient corresponding to the tag data, and the outlier values ​​of other tag data; when the joint anomaly score is greater than the preset threshold, deleting the tag data; and sequentially performing time-domain filtering, frequency-domain filtering, low-pass filtering, and standardization processing on the initial tag dataset after deletion to obtain a target dataset.

This invention provides a gateway housing splicing device for manufacturing IoT gateway architectures, relating to the field of IoT gateway manufacturing. It includes a base, a workbench fixedly connected to the top of the base, two partitions fixedly connected to the top of the workbench, and a slide block slidably fitted between the two partitions. A lower housing is disposed on the top of the slide block, and a first fixing groove matching the lower housing is formed on the top of the slide block. A housing splicing mechanism is disposed on one side of the workbench. When splicing and assembly are required, this gateway housing splicing device first activates a first motor, which drives a winding wheel to rotate, causing the winding wheel to wind up a first pull rope while simultaneously extending a second pull rope until the slide block moves to a position away from the housing splicing mechanism. Then, a worker places the lower housing into the first fixing groove on the slide block and the upper housing into the second fixing groove on the rotating plate. The first motor is then activated again.

This invention provides a method and system for constructing an Internet of Things (IoT) for collaborative operations of fishery equipment in high sea state environments. The method includes: constructing a hierarchical heterogeneous collaborative IoT architecture; deploying a software-defined converged communication gateway at the converged communication layer to obtain multi-source data; constructing a network state digital twin model based on the environmental field coupling of the target sea area, mapping the physical network topology and predicting link interruption risks; deploying lightweight neural networks on autonomous underwater vehicles and sensor terminals to generate semantic features, and reconstructing a real-time collaborative state diagram at fog-end nodes; establishing a cloud-edge-device hierarchical collaborative decision-making mechanism based on the real-time collaborative state diagram, generating collaborative control commands, and triggering an active network reconstruction strategy based on the link interruption risks predicted by the network state digital twin model. This invention achieves millisecond-level equipment collaboration and active wave compensation control in complex operational scenarios such as harvesting, grading, and transfer, significantly improving the intelligence level and operational safety of deep-sea fishery production.

The invention provides a fire rescue path searching method, device and system based on an Internet of Things architecture. The method comprises the following steps: acquiring fire scene information in real time through an Internet of Things architecture; and operating a pre-established hybrid particle swarm optimization-whale optimization model, and generating a global optimal rescue path based on the fire scene information. According to the method, the particle swarm optimization algorithm and the whale optimization algorithm are combined, and the optimal escape route is found for escape personnel through PSO in the initial stage of search, so that the particle convergence speed is accelerated, and rapid optimization is realized; wOA is introduced in the middle stage of searching to manage related dependence in a fire event and the learning phenomenon of escape personnel, and the ability of particles to jump out of local optimum is enhanced. Meanwhile, the hybrid particle swarm optimization-whale optimization model is combined with an Internet of Things architecture, fire scene information can be obtained in real time, a rescue path can be dynamically adjusted, the limitation of a traditional path planning method in a complex fire environment is overcome, and accurate path planning support is provided for fire rescue.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, an intermediate node may determine a transmit power for a message to at least one ambient Internet of Things (A-IoT) device using a maximum allowed transmit power. The intermediate node may transmit the message to the at least one A-IoT device using the transmit power. Numerous other aspects are described.

Various aspects of the present disclosure generally relate to wireless communication. In some aspects, an ambient Internet of Things (A-IoT) device may receive, from an A-IoT reader, a paging message triggering a random access channel (RACH) procedure and including an indication that the RACH procedure is associated with device-originated autonomous (DOA) traffic. The A-IoT device may transmit, to the A-IoT reader during the RACH procedure, a DOA message based on the indication and a determination that the A-IoT device has DOA data to transmit. Numerous other aspects are described.

The application discloses a partial discharge monitoring and positioning system and method based on an internet of things architecture, relates to the technical field of power monitoring, collects ideal operation parameters of power equipment, establishes an optimal operation contrast array, selects a plurality of ultrasonic wave emitting points and ultrasonic wave collecting points based on the power equipment to be detected, acquires a plurality of power equipment sound signal arrays, analyzes sound signals in the plurality of power equipment sound signal arrays based on the plurality of power equipment sound signal arrays, determines characteristic data in the sound signals, establishes a power equipment characteristic matrix, evaluates abnormal characteristics in the power equipment characteristic matrix based on the power equipment characteristic matrix and the optimal operation contrast array, determines an abnormal state of the power equipment, and marks an abnormal position of the abnormal state power equipment based on the abnormal state of the power equipment. The application has the advantages of accurately identifying abnormal discharge of the power equipment, reducing maintenance cost, and improving the stability of the power equipment.