ABSTRACT
Abstract
Flexible scheduling, self-healing, high-utilization strong stability for cellular network communications are discussed. Cellular network communication settings and strategies are developed with communication interval, transmit power data rate, channel delay, spectrum occupancy, source coding, receiving sensitivity, channel coding, signal-to-noise ratio, packet loss rate, channel occupancy rate, reception frequencies frequency band size of data package transmission frequencies, data package structure, modulation scheme, information coding scheme, antenna configuration.
Description
CROSS REFERENCES TO RELATED APPLICATIONS
This application claims priority to the following: the U.S. application 63/240,965 submitted on Sep. 5, 2021, with the title of âA wireless systemâ, and the application U.S. 63/325,613 submitted on Mar. 31, 2022, with the title of âIoT Networksâ, the application U.S. 63/353,816 filed on Jun. 20, 2022, with the title of âAn IoT Systemâ: the application CN202210571576.8 filed on May 24, 2022, titled âInternet of Things Data Utilization and Deep Learning Methodâ, all of which are incorporated herein by reference in their entirety. This application is PCT/CN2022/116928 (WO2023030513A1) national phase entry in USA This application is a continuation-in-part of application Ser. No. 16/605,191, with a PCT (PCT/US2019/042729) filed on Jul. 22, 2019, which claims priority of 62/701,837 filed on Jul. 22, 2018, all of which are incorporated herein by reference in their entirety.
This application is a continuation-in-part of US Application Ser. No. US17/902.825 filed on Sep. 3, 2022, all of which are incorporated herein by reference in their entirety.
This application is a continuation-in-part of U.S. Pat. No. 10,469,898 issued on Nov. 5, 2019 with an application number of Ser. No. 16/132,079, all of which are incorporated herein by reference in their entirety.
TECHNICAL FIELD
This disclosure involves the Internet of Things system and its multiple layers, including terminal layer, transmission layer, support layer, artificial intelligence business platform layer and city operation comprehensive IOC layer, also includes: security management platform, unified operation and maintenance management platform and IT resource service. It specifically involves technologies such as industry terminals, edge computing, intelligent data fusion, artificial intelligence, streaming media, blockchain security management, digital twins, integrated communications, intelligent inspection, unified operation and maintenance, and cloud management.
BACKGROUND TECHNIQUE
At present, the Internet of Things technology has not yet achieved the interconnection of all things, there is no effective integration of data, and no data warehouse that can be used in the entire industry has been formed. In addition, the Internet of Things system in related technologies still has high latency, high power consumption, incomplete network coverage, and insecure data. Problems such as low load capacity, insecure data, and failure to effectively allocate communication resources on application terminals are obstacles to the development of the Internet of Things technology. The integration of the Internet of Things and vertical industries will be a comprehensive network and scene with multiple devices, multiple networks, multiple applications, interconnection, and mutual integration. The standardization of device interface standards, communication protocols, and management protocols is a systematic technological innovation. Only by solving the above problems can the Internet of Things technology be popularized and applied.
At present, the âdata islandsâ and âindustry chimneysâ in this field, technically speaking, mainly have the following problems to be solved urgently. 1. In industrial applications, there are unreliable network connections (especially in remote areas). Due to problems such as high operation and maintenance costs, inconsistent access technologies, and insecure industry data, there is a lack of a ubiquitous, dynamic, and real-time network, and the problem of âInternet of Everythingâ needs to be solved; 2. Different communication protocols, access authentication methods. Different manufacturers and different types of terminals with network bandwidth requirements and application protocols lack a secure and unified access method to access the network, and the problem of âubiquitous access and unified managementâ needs to be solved, 3 Lack of a unified security protection system, including various access security issues of various types of IoT edge devices, multi-mode transmission channel diversification security protection issues, and security risks brought about by multi-scenario business coupling, data sharing, and data interaction need to be resolved. Problem, 4. Lack of core functions such as data fusion platform, unified collection, aggregation, data specification, storage management, analysis and mining, fusion algorithm, and on-demand services of multi-source data, which need to solve the âinformation islands and application chimneysâ among smart applications Problem; 5. The âdevice-cloudâ technical solution based on cloud computing can effectively utilize the powerful computing resources and storage resources of the cloud, but it is difficult to meet the low-latency requirements of many real-time applications, and new technical solutions are urgently needed, 6 Intelligent AI technology needs to be able to quickly meet the comprehensive management of intelligent application classification, clustering, prediction, and association analysis artificial intelligence models.
Based on one or more technical problems including but not limited to the foregoing, the present disclosure proposes an Internet of Things system.
Contents of the Invention.
The Internet of Things system or industrial Internet system provided by this disclosure is built for the smart twin/smart empowerment of various industries, covering multiple levels. The whole can be divided into five horizontal and three vertical, and the five horizontal from bottom to top are terminal layer, transmission layer, support layer, artificial intelligence business platform layer, and urban operation comprehensive IOC layer. The three verticals are security, operation and maintenance, and IT resource services, in which security and operation and maintenance vertically run through all horizontal levels, providing full-chain, end-to-end services: IT resource services are support layer, artificial intelligence business platform layer and urban operation integration. The IOC layer provides services (For example: FIG. 1 B ).
The first aspect is to introduce the structure and relationship of the âfive horizontal layersâ proposed in this disclosure.
(1) Terminal Layer
The terminal layer includes thousands of terminals in different industries and types of sensing, linkage, mobile, video, etc.
Sensing terminals can detect the multi-dimensional state of the city ubiquitously, in real time, and dynamically, such as water, gas, electricity, soil, sound, fire, etc. and the sensing data is uploaded to center platform.
Linkage terminals can realize edge-side sensing linkage based on the communication network that dynamically adjusts any communication parameters according to industry requirements or/and physical location, such as linkage alarms, linkage calls, linkage control valves/doors, linkage SMS/email notifications, etc.
Converged communication terminals provide those sensors that do not have communication transmission capabilities to dynamically adjust transmission and interconnection according to industry requirements or/and physical locations. Support composite sensing technology, multi-sensor data fusion, and support unified access of sensing devices from different manufacturers. Perception/detection technology combined with edge computing technology realizes edge correction and self-correction of sensor data, and an optimized sampling strategy is derived from it, such as dynamically changing the sampling interval, sampling accuracy and sending frequency, etc. in connection with response time, power consumption of the whole machine, and network bandwidth occupation can be taken into account at the same time.
Mobile terminals include handhelds, walkie-talkies, vehicle-mounted devices, positioning terminals, wearable terminals, etc. which detects and applied in the mobile state, and realize wide, medium and narrow through a communication network that dynamically adjusts any communication parameters based on industry requirements or/and physical locations. Combined, voice/video/text fusion communication applications.
Video-type terminals include cameras, thermal imaging, hyperspectral and other diversified video-aware terminals, which are uploaded to the central platform through a communication network that dynamically adjusts any communication parameters according to industry requirements or/and physical location.
(2) Communication Layer
The communication layer can be understood as the root and stem of the tree, which is the bridge connecting the tentacles and the trunk of the tree. The communication layer uploads the perception/detection, control, status and other information of the tentacles to the support layer (trunk of the big tree) through wireless/wired means.
The communication layer is an intelligent Internet of Things composed of base stations and gateways. It dynamically adjusts any communication parameters according to industry requirements or/and physical locations to establish a network. In addition to mainstream communication modes, it also includes advanced components such as Mesh, relay, and SDN. Network mode, providing network support for fixed-mobile convergence, combination of broadband, medium and narrowband, and voice/video/text communication for the terminal layer.
The base station covers various communication networks such as satellite, private network, WLAN, bridge, public network, etc. and dynamically adjusts any communication parameters according to industry requirements or/and physical location to establish a network. For example, it supports data splitting and aggregation for multi-path transmission. Different strategies are adopted according to needs during multipath transmission. For example, when the equipment in the blind area cannot be directly connected to the base station, a mesh network can be established with other equipment, and uplink communication can be realized with the help of equipment that can be connected to the base station. The device can be switched between the star network and the mesh network; when working in the mesh network mode, the terminal can be used as a routing node or a normal node. It supports point-to-point intercommunication between devices, reducing the bandwidth occupation of the base station.
The core network and the base station can collect the link information of the base station, routing node, and terminal, including: communication standard, communication path, signal-to-noise ratio, packet loss rate, delay, channel occupancy rate and other information, and it is better to do link prediction and deduction through deep learning solution, adaptive adjustment of device connection mode (direct connection to base station, mesh network, point-to-point), transmission path (single path, multi-path), radio frequency parameters on demand (bandwidth, response time, reliability, connection distance, etc.) (Modulation mode, rate, spectrum occupancy, receiving bandwidth). Gateways include different types of edge AI, security, positioning, video, mid-range communication, CPE, RFID, technical detection, etc. which can realize network interconnection with different high-level protocols, including wired and wireless networks, and dynamically adjust any communication parameters according to industry requirements or/and physical locations.
(3) Support Layer
The support layer can be understood as the trunk of a big tree, and all the data and services required by the upper-level business are provided by the support layer. The sensing/detection, control and other data at the root of the big tree will enter the crown and each branch through the support layer. The supporting layer mainly includes IoT sensing platform, data intelligent fusion platform, digital twin middle platform, artificial intelligence industry algorithm middle platform, integrated communication middle platform and streaming media platform.
1. The sensing platform of the Internet of Things, which aggregates the data of the terminal layer and the communication layer, supports the device management of the terminal layer and the communication layer, and provides communication network services and edge computing services that dynamically adjust any communication parameters according to industry requirements or/and physical locations.
Communication network services not only provide separate access and management services for existing satellite links, cellular network links, RFID network management, LTE core network, WLAN network management, LoRa core network and other network communications; but also provide core network-based wireless access services, supporting integrated access and unified management of wireless networks. Communication network services provide network services that dynamically adjust any communication parameters according to industry requirements or/and physical locations, such as adjustable physical communication parameters such as source coding, channel coding, modulation model, signal time slot, and transmission power; flexible scheduling, flexible and expanded wireless link access and management technology Communication network services can perform functions such as remote control, upgrade, parameter reading/modification, and management of equipment, support link self-healing, and provide high-utilization, strong stability, and easy-to-restore professional wireless network hosting services.
Edge computing service, for the connected communication network, provides dynamic and adaptive network allocation with edge computing capabilities of the converged network, and provides different delays, different bandwidths. Networks with different time slots can dynamically, automatically and rationally allocate network resources. For example, the environmental protection industry requires thousands of sites to report data at the same time, which not only requires low latency, but also high concurrency at the same time, but the time interval between two reports may be as long as 1 hour or 4 hours, which requires our Edge computing services provide support and dynamically and reasonably allocate network resources.
2. The intelligent data fusion platform provides cross-departmental and cross-industry services including structured, semi-structured and unstructured multi-source heterogeneous data collection, data cleaning, data fusion, resource catalog and data sharing and exchange services.
Data aggregation can be connected to the sensor data uploaded by the IoT sensing platform, and the data shared by other third-party platforms or upper-lower-level platforms, and unified aggregation forms a data lake. At the same time, it gathers business data/control data/algorithm early warning data/required data of different industries/physical location data, etc. that are generated or need to interact with other sections (business section, other support sections).
Data cleaning, fusion, and resource catalogs mainly manage and classify the aggregated data to form various theme libraries and topic libraries, etc. to facilitate the extraction of different business data. Support platforms such as platforms and streaming media platforms and artificial intelligence business platforms provide the data they need.
Data sharing and exchange, providing data sharing and exchange with third-party platforms and upper and lower platforms.
3. Streaming Media Platform.
The streaming media platform provides services such as video recording, PTZ control, streaming media, SDK. ONVIF, and national standard p
CROSS REFERENCES TO RELATED APPLICATIONS
This application claims priority to the following: the U.S. application 63/240,965 submitted on Sep. 5, 2021, with the title of âA wireless systemâ, and the application U.S. 63/325,613 submitted on Mar. 31, 2022, with the title of âIoT Networksâ, the application U.S. 63/353,816 filed on Jun. 20, 2022, with the title of âAn IoT Systemâ: the application CN202210571576.8 filed on May 24, 2022, titled âInternet of Things Data Utilization and Deep Learning Methodâ, all of which are incorporated herein by reference in their entirety. This application is PCT/CN2022/116928 (WO2023030513A1) national phase entry in USA This application is a continuation-in-part of application Ser. No. 16/605,191, with a PCT (PCT/US2019/042729) filed on Jul. 22, 2019, which claims priority of 62/701,837 filed on Jul. 22, 2018, all of which are incorporated herein by reference in their entirety.
This application is a continuation-in-part of US Application Ser. No. US17/902.825 filed on Sep. 3, 2022, all of which are incorporated herein by reference in their entirety.
This application is a continuation-in-part of U.S. Pat. No. 10,469,898 issued on Nov. 5, 2019 with an application number of Ser. No. 16/132,079, all of which are incorporated herein by reference in their entirety.
TECHNICAL FIELD
This disclosure involves the Internet of Things system and its multiple layers, including terminal layer, transmission layer, support layer, artificial intelligence business platform layer and city operation comprehensive IOC layer, also includes: security management platform, unified operation and maintenance management platform and IT resource service. It specifically involves technologies such as industry terminals, edge computing, intelligent data fusion, artificial intelligence, streaming media, blockchain security management, digital twins, integrated communications, intelligent inspection, unified operation and maintenance, and cloud management.
BACKGROUND TECHNIQUE
At present, the Internet of Things technology has not yet achieved the interconnection of all things, there is no effective integration of data, and no data warehouse that can be used in the entire industry has been formed. In addition, the Internet of Things system in related technologies still has high latency, high power consumption, incomplete network coverage, and insecure data. Problems such as low load capacity, insecure data, and failure to effectively allocate communication resources on application terminals are obstacles to the development of the Internet of Things technology. The integration of the Internet of Things and vertical industries will be a comprehensive network and scene with multiple devices, multiple networks, multiple applications, interconnection, and mutual integration. The standardization of device interface standards, communication protocols, and management protocols is a systematic technological innovation. Only by solving the above problems can the Internet of Things technology be popularized and applied.
At present, the âdata islandsâ and âindustry chimneysâ in this field, technically speaking, mainly have the following problems to be solved urgently. 1. In industrial applications, there are unreliable network connections (especially in remote areas). Due to problems such as high operation and maintenance costs, inconsistent access technologies, and insecure industry data, there is a lack of a ubiquitous, dynamic, and real-time network, and the problem of âInternet of Everythingâ needs to be solved; 2. Different communication protocols, access authentication methods. Different manufacturers and different types of terminals with network bandwidth requirements and application protocols lack a secure and unified access method to access the network, and the problem of âubiquitous access and unified managementâ needs to be solved, 3 Lack of a unified security protection system, including various access security issues of various types of IoT edge devices, multi-mode transmission channel diversification security protection issues, and security risks brought about by multi-scenario business coupling, data sharing, and data interaction need to be resolved. Problem, 4. Lack of core functions such as data fusion platform, unified collection, aggregation, data specification, storage management, analysis and mining, fusion algorithm, and on-demand services of multi-source data, which need to solve the âinformation islands and application chimneysâ among smart applications Problem; 5. The âdevice-cloudâ technical solution based on cloud computing can effectively utilize the powerful computing resources and storage resources of the cloud, but it is difficult to meet the low-latency requirements of many real-time applications, and new technical solutions are urgently needed, 6 Intelligent AI technology needs to be able to quickly meet the comprehensive management of intelligent application classification, clustering, prediction, and association analysis artificial intelligence models.
Based on one or more technical problems including but not limited to the foregoing, the present disclosure proposes an Internet of Things system.
Contents of the Invention.
The Internet of Things system or industrial Internet system provided by this disclosure is built for the smart twin/smart empowerment of various industries, covering multiple levels. The whole can be divided into five horizontal and three vertical, and the five horizontal from bottom to top are terminal layer, transmission layer, support layer, artificial intelligence business platform layer, and urban operation comprehensive IOC layer. The three verticals are security, operation and maintenance, and IT resource services, in which security and operation and maintenance vertically run through all horizontal levels, providing full-chain, end-to-end services: IT resource services are support layer, artificial intelligence business platform layer and urban operation integration. The IOC layer provides services (For example: FIG. 1 B ).
The first aspect is to introduce the structure and relationship of the âfive horizontal layersâ proposed in this disclosure.
(1) Terminal Layer
The terminal layer includes thousands of terminals in different industries and types of sensing, linkage, mobile, video, etc.
Sensing terminals can detect the multi-dimensional state of the city ubiquitously, in real time, and dynamically, such as water, gas, electricity, soil, sound, fire, etc. and the sensing data is uploaded to center platform.
Linkage terminals can realize edge-side sensing linkage based on the communication network that dynamically adjusts any communication parameters according to industry requirements or/and physical location, such as linkage alarms, linkage calls, linkage control valves/doors, linkage SMS/email notifications, etc.
Converged communication terminals provide those sensors that do not have communication transmission capabilities to dynamically adjust transmission and interconnection according to industry requirements or/and physical locations. Support composite sensing technology, multi-sensor data fusion, and support unified access of sensing devices from different manufacturers. Perception/detection technology combined with edge computing technology realizes edge correction and self-correction of sensor data, and an optimized sampling strategy is derived from it, such as dynamically changing the sampling interval, sampling accuracy and sending frequency, etc. in connection with response time, power consumption of the whole machine, and network bandwidth occupation can be taken into account at the same time.
Mobile terminals include handhelds, walkie-talkies, vehicle-mounted devices, positioning terminals, wearable terminals, etc. which detects and applied in the mobile state, and realize wide, medium and narrow through a communication network that dynamically adjusts any communication parameters based on industry requirements or/and physical locations. Combined, voice/video/text fusion communication applications.
Video-type terminals include cameras, thermal imaging, hyperspectral and other diversified video-aware terminals, which are uploaded to the central platform through a communication network that dynamically adjusts any communication parameters according to industry requirements or/and physical location.
(2) Communication Layer
The communication layer can be understood as the root and stem of the tree, which is the bridge connecting the tentacles and the trunk of the tree. The communication layer uploads the perception/detection, control, status and other information of the tentacles to the support layer (trunk of the big tree) through wireless/wired means.
The communication layer is an intelligent Internet of Things composed of base stations and gateways. It dynamically adjusts any communication parameters according to industry requirements or/and physical locations to establish a network. In addition to mainstream communication modes, it also includes advanced components such as Mesh, relay, and SDN. Network mode, providing network support for fixed-mobile convergence, combination of broadband, medium and narrowband, and voice/video/text communication for the terminal layer.
The base station covers various communication networks such as satellite, private network, WLAN, bridge, public network, etc. and dynamically adjusts any communication parameters according to industry requirements or/and physical location to establish a network. For example, it supports data splitting and aggregation for multi-path transmission. Different strategies are adopted according to needs during multipath transmission. For example, when the equipment in the blind area cannot be directly connected to the base station, a mesh network can be established with other equipment, and uplink communication can be realized with the help of equipment that can be connected to the base station. The device can be switched between the star network and the mesh network; when working in the mesh network mode, the terminal can be used as a routing node or a normal node. It supports point-to-point intercommunication between devices, reducing the bandwidth occupation of the base station.
The core network and the base station can collect the link information of the base station, routing node, and terminal, including: communication standard, communication path, signal-to-noise ratio, packet loss rate, delay, channel occupancy rate and other information, and it is better to do link prediction and deduction through deep learning solution, adaptive adjustment of device connection mode (direct connection to base station, mesh network, point-to-point), transmission path (single path, multi-path), radio frequency parameters on demand (bandwidth, response time, reliability, connection distance, etc.) (Modulation mode, rate, spectrum occupancy, receiving bandwidth). Gateways include different types of edge AI, security, positioning, video, mid-range communication, CPE, RFID, technical detection, etc. which can realize network interconnection with different high-level protocols, including wired and wireless networks, and dynamically adjust any communication parameters according to industry requirements or/and physical locations.
(3) Support Layer
The support layer can be understood as the trunk of a big tree, and all the data and services required by the upper-level business are provided by the support layer. The sensing/detection, control and other data at the root of the big tree will enter the crown and each branch through the support layer. The supporting layer mainly includes IoT sensing platform, data intelligent fusion platform, digital twin middle platform, artificial intelligence industry algorithm middle platform, integrated communication middle platform and streaming media platform.
1. The sensing platform of the Internet of Things, which aggregates the data of the terminal layer and the communication layer, supports the device management of the terminal layer and the communication layer, and provides communication network services and edge computing services that dynamically adjust any communication parameters according to industry requirements or/and physical locations.
Communication network services not only provide separate access and management services for existing satellite links, cellular network links, RFID network management, LTE core network, WLAN network management, LoRa core network and other network communications; but also provide core network-based wireless access services, supporting integrated access and unified management of wireless networks. Communication network services provide network services that dynamically adjust any communication parameters according to industry requirements or/and physical locations, such as adjustable physical communication parameters such as source coding, channel coding, modulation model, signal time slot, and transmission power; flexible scheduling, flexible and expanded wireless link access and management technology Communication network services can perform functions such as remote control, upgrade, parameter reading/modification, and management of equipment, support link self-healing, and provide high-utilization, strong stability, and easy-to-restore professional wireless network hosting services.
Edge computing service, for the connected communication network, provides dynamic and adaptive network allocation with edge computing capabilities of the converged network, and provides different delays, different bandwidths. Networks with different time slots can dynamically, automatically and rationally allocate network resources. For example, the environmental protection industry requires thousands of sites to report data at the same time, which not only requires low latency, but also high concurrency at the same time, but the time interval between two reports may be as long as 1 hour or 4 hours, which requires our Edge computing services provide support and dynamically and reasonably allocate network resources.
2. The intelligent data fusion platform provides cross-departmental and cross-industry services including structured, semi-structured and unstructured multi-source heterogeneous data collection, data cleaning, data fusion, resource catalog and data sharing and exchange services.
Data aggregation can be connected to the sensor data uploaded by the IoT sensing platform, and the data shared by other third-party platforms or upper-lower-level platforms, and unified aggregation forms a data lake. At the same time, it gathers business data/control data/algorithm early warning data/required data of different industries/physical location data, etc. that are generated or need to interact with other sections (business section, other support sections).
Data cleaning, fusion, and resource catalogs mainly manage and classify the aggregated data to form various theme libraries and topic libraries, etc. to facilitate the extraction of different business data. Support platforms such as platforms and streaming media platforms and artificial intelligence business platforms provide the data they need.
Data sharing and exchange, providing data sharing and exchange with third-party platforms and upper and lower platforms.
3. Streaming Media Platform.
The streaming media platform provides services such as video recording, PTZ control, streaming media, SDK. ONVIF, and national standard protocols for video data uploaded from different industries and locations based on the communication network, and supports the artificial intelligence business platform.
The interaction with the intelligent data fusion platform includes receiving information such as video, pictures, and streaming media access from the intelligent data fusion platform, feeding back control information, screenshot information, etc. to the intelligent data fusion platform and storing them in the corresponding theme/special library. At the same time, it is sent to terminals corresponding to industries and corresponding physical locations through the communication network to realize control.
4. Converged communication center, based on the communication network that dynamically adjusts any communication parameters according to industry requirements or/and physical location, realizes the converged communication services of different types of data or files such as text, voice, picture, video, location, attachment, etc. Converged communication services include data uplink and downlink Uplink includes uploading of different types of data and files, and downlink includes downlinking of different types of data and files to terminals in corresponding industries and/or physical locations.
The communication center platform of the present disclosure can provide integrated communication services of different types of data or files such as text, voice, picture, video, location, attachment, etc. to support the artificial intelligence business platform. For example, WeChat chat supports sending and receiving different types of data and files; for example, event reporting supports filling in text when reporting, adding information such as voice, video, picture, location or attachment, etc. It can access the text, voice, picture, video, location, files, etc. provided by the intelligent data fusion platform. The data of the intelligent data fusion platform comes from the communication network of the terminal and the communication layer. It supports feeding back the data generated by the fusion communication to the intelligent data fusion platform and storing the data in the corresponding theme/theme library. For the converged communication of video, the streaming media platform provides camera control and streaming media services for the converged communication center. Some control information can be downlinked to terminals corresponding to industries and corresponding physical locations through the communication network.
5. The artificial intelligence industry algorithm center provides artificial intelligence algorithms with management services such as algorithm deployment, algorithm configuration, algorithm training, and algorithm viewing/importing/deleting/upgrading. The inputs or video sources of the platform in the artificial intelligence industry algorithm are aggregated and uploaded from communication networks that are dynamically deployed according to industry requirements or/and physical locations, including various sensor data, alarms, and video data. At the same time, data such as linkage control, linkage shouting, linkage alarm, linkage SMS/email notification generated in the algorithm of the artificial intelligence industry are dynamically downloaded to the corresponding terminal according to industry requirements or/and physical location through the multi-mode heterogeneous communication network.
The artificial intelligence industry algorithm platform can access the input parameters and video data required by different algorithms uploaded by the data intelligent fusion platform, and can output alarms/characteristic values to the artificial intelligence business platform to realize early warning based on artificial intelligence and algorithms check.
The alarms/characteristic values generated by the platform in the artificial intelligence industry algorithm will also be fed back to the data intelligent fusion platform and stored in the corresponding theme/special library.
For video algorithms, the artificial intelligence industry algorithm center can retrieve the required video/picture through the streaming media center.
For prediction algorithms, such as fire spread prediction, gas diffusion prediction, etc. it is necessary to display the predicted diffusion range after a period of time (such as one hour) in a three-dimensional form. In such cases, the artificial intelligence industry algorithm center will provide data such as eigenvalues and predictive simulations to the digital twin center.
6. The digital twin middle platform, based on the dynamic sensor data of different industries and locations uploaded by the communication network, provides urban 3D twin services for the artificial intelligence business platform. The CIM, AR, VR, BIM, GIS, etc. required by the artificial intelligence business platform all require the support of the digital twin platform.
At the same time, the data generated by the modification and definition of maps, layers, key points, etc. in the digital twin platform will also be fed back to the data intelligent fusion platform and stored in the corresponding theme/theme library.
(4) AI Business Platform Layer.
Display, analyze, predict, forecast, rehearse, etc. the data uploaded by the communication network in different industries and different physical locations, provide artificial intelligence-based unified module component management and smart applications in different industries, receive data from various supporting platforms, and integrate business Terminal operation information is fed back to each support platform. At the same time, some operational data can be dynamically adjusted according to industry requirements or/and physical location, and sent to the terminal through the communication layer to realize linkage.
(5) Comprehensive IOC Layer of Urban Operation.
Integrating the data of various industries can realize the overview of the overall situation of the city, monitoring and early warning, command and dispatch, event handling, operation decision-making, etc. The bridge/support of various convergence and downlink data of the city operation comprehensive IOC layer relies on the communication network established by dynamically adjusting any communication parameters according to industry requirements or/and physical location.
The second aspect is to introduce the structure and relationship of the âthree vertical layersâ proposed in this disclosure.
The three vertical verticals are security, operation and maintenance, and IT resource services, in which security and operation and maintenance vertically run through all horizontal levels, providing full-chain, end-to-end unified security and unified operation and maintenance services. IT resource service provides unified monitoring and dynamic allocation services including computing resources, storage resources and network resources for the support layer, artificial intelligence business platform layer and urban operation comprehensive IOC layer according to different needs such as business volume and time.
The security management platform starts with the terminal, runs through the transport layer and the multi-mode heterogeneous core network, reaches the support layer, and finally reaches the application layer, and dynamically controls security from the root, instead of ensuring security only at the platform layer.
The unified operation and maintenance management platform dynamically controls the status of all devices based on the dynamically adjusted communication network. At the same time, it can also be sent to each terminal according to the demand through the dynamic communication network to realize functions such as alarm, work order, and inspection.
The following describes a sensor terminal device in the general inventive concept. For the inventive concept of the Internet of Things or Industrial Internet system, a sensor calibration method and system thereof are provided.
In order to extend the service life of sensors, reduce maintenance costs, and improve sensor data accuracy and sensitivity analysis, this disclosure uses deep learning calibration algorithms to perform historical data reported by sensors and at least part of the corresponding historical data collected by standard sensors. The original model is obtained through training, and combined with the computing power characteristics of sensor terminal equipment, base stations, and cloud servers, deep learning pruning or knowledge distillation is performed on the trained original model to achieve a balance between accuracy and response speed.
The calibration algorithm based on deep learning in the present disclosure adopts a deformer model (Transformer model) based on a multi-head attention mechanism (Multi-Head Attention). The Transformer model is an Encoder-Decoder model based entirely on the attention mechanism. Further deep learning pruning or knowledge distillation is carried out on the obtained original model, so that it can achieve model compression and optimization on the basis of no obvious decrease in accuracy, so that it has the ability to be deployed separately in sensor terminal equipment, base stations and cloud servers.
The technical problem solved by the present disclosure is to provide a sensor calibration method and system thereof, which realize hierarchical, efficient, intelligent calibration and multi-level collaborative calibration of sensors.
In this context, the embodiments of the present disclosure expect to provide a method and system for calibrating sensors based on deep learning.
The Present Disclosure Provides a Method for Calibrating a Sensor Based on Deep Learning, and the Calibration Method Includes the Following Steps:
The sensor collects historical data in chronological order;
Accurate values of historical data collected at least in part by standard sensors, providing said historical data and said accurate values to a deformer model;
The deformer model trains the historical data and the accurate value to obtain an original model; performing multi-level compression optimization on the original model through deep learning pruning or knowledge distillation to obtain a multi-level compression optimized model:
The raw data collected by the sensor is then calibrated according to the original model or the multi-stage compression optimized model.
The present disclosure also provides a calibration system for sensors based on deep learning, the calibration system includes: sensors, which are used to collect historical data in chronological order; standard sensors, which are used to collect accurate values of at least part of the corresponding historical data. A training device, which is used to receive the historical data and the accurate value, and train the historical data and the accurate value to obtain the original model; a compression optimization device, which is used for pruning or knowledge distillation through deep learning performing multi-level compression optimization on the original model to obtain a multi-level compression optimized model; a calibration device for calibrating the original data collected by the sensors according to the original model or the multi-level compression optimized model. The present disclosure also provides a deep learning processing method, and the processing method includes the following steps: collecting historical data in chronological order; collecting accurate values of at least part of the corresponding historical data; providing the historical data and the accurate values to the deformer model; the deformer model trains the historical data and the accurate value to obtain the original model; performs multi-level compression optimization on the original model through deep learning pruning or knowledge distillation to obtain multi-level compression optimization. For the final model, the first-level compressed and optimized model obtained through knowledge distillation is deployed on the terminal device, the second-level compressed and optimized model obtained through deep learning pruning is deployed on the base station, and the original model is deployed on the cloud server, the processing accuracy of the model after the two-stage compression optimization is higher than that of the model after the one-stage compression optimization, and lower than the processing accuracy of the original model, and the response of the model after the two-stage compression optimization is lower than the response speed of the model after the first-level compression optimization, and higher than the response speed of the original model, and the data calculation amount of the model after the second-level compression optimization is higher than that of the model after the first-level compression optimization. The data calculation amount of the model is lower than the data calculation amount of the original model; according to the processing accuracy requirements, response speed and/or data calculation amount, determine the terminal device, base station or cloud server uses the deployed model for processing (raw) data collected.
The present disclosure also provides an application of a sensor calibration method based on deep learning, the application comprising the following steps; the sensor collects raw data in real time; the standard sensor collects an accurate value corresponding to the raw data in real time; Take a certain amount of the original data and the corresponding accurate value according to the sampling rate, and upload the original data and the accurate value to the base station; the base station compares the certain amount of the original data with the corresponding accurate value; If the difference between the two is greater than a certain accuracy threshold and the proportion is less than the ratio threshold, the sensor is marked as a sensor in a normal state, all raw data is accepted and uploaded to the cloud server.
According to the method and system for calibrating sensors based on deep learning provided in this disclosure, the following are achieved. First, the Transformer model based on the multi-head attention mechanism is adopted. This deep learning model can not only effectively learn and imitate the characteristics of time series data, but also it can use the multi-head attention mechanism to help the Transformer model capture more abundant sensor features and information, and further comprehensively process the captured sensor features and information. Inter-data correlation, alarm for abnormal values, and has a strong filtering ability, which realizes the application in multiple types of sensor equipment; second, hierarchical calibration, deep learning pruning or knowledge of the original model trained distillation enables it to achieve model compression and optimization on the basis of no significant decrease in accuracy, so that it has the ability to be deployed separately on sensor terminal equipment, base stations, and cloud servers. Combining the computing power characteristics of sensor terminal equipment, base stations, and cloud servers, intelligent match the calibration position to achieve a balance between the ratio of accuracy and response speed, perform quick calibration with low accuracy on sensor terminal devices with weak computing power, and perform high-precision calibration on cloud servers with strong computing power, realize the application in multiple scenarios; third, multi-level collaborative calibration, upload the low-level calibration results and original data to the high-level device or environment, and perform advanced calibration on at least part of the original data in the high-level device or environment, such as performing primary calibration on the raw data in the sensor terminal equipment, uploading the obtained primary calibrated data and original data to the base station, and performing secondary calibration on at least part of the original data at the base station, and obtaining at least part of the secondary calibration, comparing the at least part of the data after secondary calibration with the corresponding data after primary calibration, if the difference between the two is less than a certain error threshold, then accept all the data after primary calibration, otherwise, the received raw data is subjected to secondary calibration by using the model optimized by secondary compression to obtain all secondary calibrated data Multi-level collaborative calibration can use multi-level calibration models of different precision to calibrate part of the original data, (spot) check whether the calibration results reported by the low-level calibration models are qualified, and realize the simple and efficient inspection of the received calibration results.
DESCRIPTION OF DRAWINGS
In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following will briefly introduce the drawings that need to be used in the embodiments or related technical descriptions. It should be noted that the drawings in the following description are only the present disclosure. For some embodiments of the invention, those skilled in the art can also obtain other drawings according to these drawings without paying creative efforts.
FIG. 1 is a general architecture diagram of the Internet of Things provided by the present disclosure;
FIG. 1 A is a composition diagram of the Internet of Things provided by the present disclosure;
FIG. 1 B is a global relationship diagram of the Internet of Things provided by the present disclosure;
FIG. 1 C is a communication network flow and system relationship diagram provided by the present disclosure;
FIG. 1 D is an example diagram of an Internet of Things service flow provided by the present disclosure;
FIG. 1 E is a schematic design diagram of a communication network in the Internet of Things provided by the present disclosure;
FIG. 1 F is a schematic diagram of a communication link in the Internet of Things provided by the present disclosure;
FIG. 1 - 1 is a flow chart of low-power wide-area wireless IoT edge computing and fog computing technology provided by the present disclosure;
FIG. 1 - 2 is a schematic diagram of an application scenario of smart fire fight edge computing provided by the present disclosure;
1 - 3 are data flow charts of the edge computing gateway platform provided by the present disclosure;
FIGS. 1 - 4 are flow charts of edge computing data provided by the present disclosure;
FIG. 2 - 1 is a schematic diagram of communication between terminals provided by the present disclosure;
FIG. 2 - 2 is a schematic diagram of timing communication between terminals using different channels provided by the present disclosure;
FIG. 2 - 3 is a schematic diagram of wake-up communication of the terminal listening to the long data packet header provided by the present disclosure;
2 - 4 are schematic diagrams of the control of transmit power and reception sensitivity between terminals provided by the present disclosure;
FIG. 3 - 1 and FIG. 3 - 2 are related schematic diagrams ( 1 to 2 ) of the invention points of the Internet of Things terminal power consumption control provided by the present disclosure;
FIG. 4 - 1 is a schematic diagram of a highly configurable edge computing framework provided by the present disclosure:
FIG. 4 - 2 is a schematic diagram of an edge computing decision-making loop provided by the present disclosure;
FIG. 5 - 1 is a flow chart of an embodiment of a method for calibrating a sensor based on deep learning according to the present disclosure provided by the present disclosure;
FIG. 5 - 2 is a schematic diagram of hierarchical deployment of the original model provided by the present disclosure after different processing:
FIG. 5 - 3 is a sensor network topology diagram provided by the present disclosure;
FIG. 5 - 4 is a flow chart of training the original model from the Transformer model provided by the present disclosure:
FIG. 5 - 5 is the flow chart of the multi-level cooperative calibration of the calibration method of the sensor based on deep learning according to the present disclosure provided by the present disclosure;
5 - 6 are Flow Charts of Retraining the Updated Original Model Provided by the Present Disclosure; 5 - 7 are Schematic Diagrams of the Relationship Between Temperature and Humidity Learned by the Transformer Model Provided by the Present Disclosure;
5 - 8 are structural block diagrams of a sensor calibration system based on deep learning according to the present disclosure provided by the present disclosure, 5 - 9 are structural block diagrams of a compression optimization device 940 provided by the present disclosure;
5 - 10 are structural block diagrams of a calibration device 950 provided by the present disclosure; 5 - 11 are flowcharts of an exemplary application of the deep learning-based calibration method provided by the present disclosure to the same type of sensor calibration;
FIG. 5 - 12 is the flow chart of using the adaptive network topology numerical calibration of the LSTM neural network provided by the present disclosure;
FIG. 6 - 1 to FIG. 6 - 4 are schematic diagrams ( 1 to 4 ) related to the invention points of the composite gas leakage sensor terminal provided by the present disclosure, FIG. 7 - 1 to FIG. 7 - 4 are related diagrams ( 1 to 4 ) of the multi-mode ad hoc network mutual identification intelligent positioning badge and system provided by the present disclosure;
FIG. 8 - 1 to FIG. 8 - 3 are chassis structure diagrams ( 1 to 3 ) provided by the present disclosure, FIG. 9 - 1 to FIG. 9 - 3 are schematic diagrams of tree multi-dimensional monitoring terminals provided by the present disclosure ( 1 to 3 );
FIG. 10 - 1 and FIG. 10 - 2 are related schematic diagrams ( 1 to 2 ) of the emergency crashable and non-destructive barrier gate system provided by the present disclosure;
FIG. 11 - 1 is a schematic diagram of the AI-based drowning recognition and automatic rescue system provided by the present disclosure:
FIG. 12 - 1 and FIG. 12 - 2 are related schematic diagrams ( 1 to 2 ) of the weak blocking type road gate system for accumulated water provided by the present disclosure;
FIG. 13 - 1 and FIG. 13 - 2 are schematic diagrams of the global water quality detection system provided by the present disclosure ( 1 to 2 );
FIG. 14 - 1 is a schematic diagram of the support technology of the water level bucket provided by the present disclosure;
FIG. 15 - 1 is a schematic diagram related to the intelligent multi-mode LPWA gateway provided by the present disclosure,
FIG. 16 - 1 is a schematic diagram of a communication network provided by the present disclosure:
FIG. 16 - 2 is a schematic diagram of communication resource coordination provided by the present disclosure;
FIG. 16 - 3 is a schematic diagram of the Internet of Things provided by the present disclosure;
FIG. 17 - 1 is a schematic diagram of the overall architecture of the IoT sensing platform system provided by the present disclosure:
FIG. 18 - 1 is a schematic diagram of node state switching provided by the present disclosure;
FIG. 18 - 2 is a flow chart of node network access provided by this disclosure;
FIG. 18 - 3 is a flow chart of node sending and receiving after network access provided by the present disclosure,
FIG. 18 - 4 is a flow chart of the data transmission request and response provided by the present disclosure;
FIG. 18 - 5 is a flow chart of data transmission provided by this disclosure;
FIG. 18 - 6 is a schematic diagram of summaries of different data packets sent for a negotiation channel based on an embodiment of the present disclosure;
FIG. 18 - 7 is a schematic diagram of summaries of different data packets sent by the data channel provided by the present disclosure;
FIG. 19 - 1 is a schematic diagram related to the communication technology of the hybrid connection network provided by the present disclosure;
FIG. 20 - 1 is a flow chart of OTA dedicated protocol firmware upgrade provided by the present disclosure;
FIG. 20 - 2 is a composition diagram of an adaptive coordinated multi-point system provided by the present disclosure under the condition that multiple gateways and multiple terminals coexist;
FIG. 21 - 1 is an overall architecture diagram of the device management system provided by the present disclosure;
FIG. 21 - 2 is a flow chart of equipment network and communication monitoring data access processing provided by the present disclosure;
FIG. 21 - 3 is a flowchart of crawler service processing provided by the present disclosure;
FIG. 21 - 4 is a flow chart of LoRa communication parameter access service processing provided by the present disclosure;
FIG. 22 - 1 is a schematic diagram of the signaling real-time tracking interface provided by the present disclosure;
FIG. 22 - 2 is a schematic diagram of the signaling real-time tracking details interface provided by the present disclosure;
FIG. 23 - 1 is a technical flow chart of the signaling tracking packet capture service provided by the present disclosure;
FIG. 23 - 2 is a flow chart of the signaling tracking signaling packet capture control service provided by the present disclosure,
FIG. 24 - 1 is a flow chart of network thermal analysis provided by the present disclosure;
FIG. 25 - 1 is a flow chart of the voice interaction technology of the human-computer interaction terminal and gateway provided by the present disclosure;
FIG. 25 - 2 is a flow chart of the video interaction technology of the human-computer interaction terminal and the gateway provided by the present disclosure;
FIG. 25 - 3 is a flow chart of the terminal data calibration technology based on the edge computing mode provided by the present disclosure;
FIG. 25 - 4 is a technical flow chart of dynamically adjusting sensor coefficients based on the edge computing mode provided by the present disclosure;
FIG. 26 - 1 is an overall architecture diagram of the intelligent data fusion platform provided by the present disclosure;
FIG. 26 - 2 is a flow chart of data collection provided by the present disclosure;
FIG. 26 - 3 is a flow chart of receiving the Internet of Things network protocol provided by the present disclosure,
<para-num num
CLAIMS
Claims ( 21 )
1 - 34 . (canceled)
35 . A wireless device for communicating information via a base station comprising:
memory, firmware, computing module, and a wireless communication interface for communicating with a mesh network to achieve an uplink communication with the base station; wherein the wireless device is configured to receive data from a sensor and transmit sensor data via the base station; wherein a communication path for transmission of the sensor data is determined with one or more of the following associated with the transmission of the sensor data: modulation mode, transmission rate, power, distance, spectrum occupation, and/or bandwidth.
36 . The wireless device of claim 35 , wherein the sensor data is encrypted with network communication path information associated with the sensor data.
37 . The wireless device of claim 35 , wherein the sensor data is encrypted with one or more of: geographic location information of the gateway through which the sensor data passes; location of route point; intermediate node information; and/or latitude and longitude of coordinate data.
38 . The wireless device of claim 34 , wherein the sensor data is encrypted with time information associated with transmission of the sensor data; and wherein the sensor data is transmitted from an IoT node.
39 . The wireless device of claim 34 , wherein the sensor data is encrypted with one of more of the following information: communication path, frequency, bandwidth, transmission speed, and/or communication protocol information associated with transmission of the sensor data; and wherein the sensor data is transmitted from an IoT node.
40 . The wireless device of claim 34 , wherein the communication path for transmission of the sensor data is associated with at least two of the following:
communication interval, transmit power, data rate, channel delay, spectrum occupancy, source coding, receiving sensitivity, channel coding, signal-to-noise ratio, packet loss rate, channel occupancy rate, reception frequencies, frequency band, size of data package, transmission frequencies, data package structure, modulation scheme, information coding scheme, antenna configuration; and wherein the determination of the communication path is made to optimize spectral efficiency, network resource utilization rate, and/or to meet the requirement associated with transmission of the sensor data; and wherein sampling interval, sampling accuracy, and/or transmission rate for the sensor data are adjusted based on change of the sensor data and/or relationship among sensor data.
41 . The wireless device of claim 34 , wherein sampling interval, sampling accuracy, and/or transmission rate associated with the sensor data are adjusted based on change of the sensor data and/or relationship among multiple sensor data.
42 . The wireless device of claim 34 , wherein the sensor data is split into distinct data streams for transmission along different communication paths.
43 . The wireless device of claim 34 , wherein the wireless device is configured to change encoding of the sensor data and transmit the sensor data in a newly encoded format.
44 . The wireless device of claim 34 , wherein the wireless device is configured to prioritize transmission of the sensor data.
45 . A method for communicating information via a base station comprising:
receiving data from a sensor; communicating with a mesh network to achieve an uplink communication with the base station; and transmitting the sensor data via the base station; wherein communication path for transmission of the sensor data is determined with one or more of following associated with the transmission of the sensor data: modulation mode, transmission rate, power, distance, spectrum occupation, bandwidth.
46 . The method of claim 45 , wherein the sensor data is encrypted with network communication path information associated with the sensor data.
47 . The method of claim 46 , wherein the sensor data is encrypted with one or more of: geographic location information of the gateway through which the sensor data passes; location of route point; intermediate node information; and/or latitude and longitude of coordinate data.
48 . The method of claim 45 , wherein the sensor data is encrypted with time information associated with transmission of the sensor data.
49 . The method of claim 45 , wherein the sensor data is encrypted with one of more of the following information: communication serial number, communication path, frequency, bandwidth, transmission speed, and/or communication protocol information associated with transmission of the sensor data.
50 . The method of claim 45 , wherein decryption key for the sensor data is associated with network transmission path; wherein the wireless device is an IoT node, and the sensor data is received by another IoT node; wherein the receiving IoT node uses location information of the previous IoT node for decryption of the received sensor data in sequence, thereby implementing layer by layer encryption and layer by layer decryption.
51 . The method of claim 45 , wherein the sensor data sampling and/or the sensor data transmission linkage is optimized based on network condition for transmission of the sensor data.
52 . The method of claim 45 , wherein a stream of the sensor data is split into distinct streams for transmission along different communication paths.
53 . The method of claim 45 , wherein the sensor data is encoded into a new format for transmission.
54 . The method of claim 45 , wherein the sensor data is sent from an IoT node; and
wherein communication path for transmission of the sensor data is determined with at least two of the following: communication interval, transmit power, data rate, channel delay, spectrum occupancy, source coding, receiving sensitivity, channel coding, signal-to-noise ratio, packet loss rate, channel occupancy rate, reception frequencies, frequency band, size of data package, transmission frequencies, data package structure, modulation scheme, information coding scheme, antenna configuration; wherein the determination of the network settings is made to optimize spectral efficiency, network resource utilization rate, and/or to meet the requirement associated with transmission of the sensor data; and wherein sampling interval, sampling accuracy, and/or transmission rate for the sensor data are adjusted based on change of the sensor data and/or relationship among sensor data.
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