211 results about "Data cloud" patented technology

The invention discloses a distributed RID receiving system based on edge multi-source fusion, which relates to the technical field of low-altitude supervision, and is characterized in that a distributed RID receiving node cluster adopts a fixed node and maneuvering node collaborative grid deployment scheme to capture and preliminarily analyze low-altitude aircraft RID signals in real time, and node data is output after the low-altitude aircraft RID signals are processed by a primary fusion algorithm; the edge multi-source fusion processing unit is deployed in each management and control partition, converges node data in the area, completes calibration, de-duplication and noise reduction through a secondary fusion algorithm, and outputs structured RID data; the cloud management and control platform realizes global data overall planning, situation visualization, intelligent decision making and instruction issuing through a three-level fusion algorithm, and is linked with the urban low-altitude scheduling system to form closed-loop management and control; the clock synchronization module provides nanosecond time reference for the whole system. According to the method, the problems of incomplete coverage and insufficient data processing precision of a traditional RID receiving scheme are solved, and global sensing, accurate data processing and closed-loop management and control of the low-altitude aircraft are realized.

The invention relates to the field of power transmission line operation and maintenance, in particular to a power transmission line tree obstacle intelligent supervision system and method based on the Internet of Things, and the system comprises an intelligent sensing module, a data cloud transmission module, a fusion prediction module, an analysis decision module and a linkage execution module. According to the coupling prediction, historical data are processed through an algorithm model, future tree growth and conductor wind deviation are predicted, and the scientificity and accuracy of prediction are ensured through cooperation of a state space model and a Kalman filtering algorithm, so that the prediction accuracy is improved through a sensor deployed at the front end and an intelligent algorithm at the cloud end. The device can continuously and automatically monitor for 7 * 24 hours, not only measures the current static distance, but also predicts the future growth trend of the tree based on historical data, and calculates the dynamic windage yaw of the lead by combining the real-time wind speed and wind direction, thereby being convenient to give out early warning before the tree actually approaches the safe distance, realizing the process from post-event disposal to pre-event prediction, and improving the working efficiency. And the preventive safety level of the power grid is improved.

The invention provides a cooperative emergency disposal method, system and device for a new energy vehicle and a storage medium, and a vehicle end collects and uploads vehicle state data. The cloud server extracts the position of the vehicle, obtains associated external environment data and fire-fighting, traffic and other collaborative party data, then calculates the air pressure and temperature risk values of each monomer, and integrates the voltage data to identify the fault monomer with the highest risk. And based on the fault monomer risk value, the overall state of the battery pack, the environment temperature, the fire-fighting station distance and the traffic state, the cloud determines a vehicle dynamic risk index and divides risk levels, and generates a co-processing instruction in combination with fault monomer information and emergency resources. And the vehicle end and the collaborative party respectively respond to the instruction, the vehicle executes control operation adaptive to the risk level and the fault monomer, and the collaborative party carries out corresponding emergency operation. By the adoption of the method, timeliness and accuracy of new energy automobile safety risk early warning and pertinence and efficiency of disposal operation can be improved.

A method of displaying an operational parameter of a surgical system is disclosed. The method includes receiving, by a cloud computing system of the surgical system, first usage data, from a first subset of surgical hubs of the surgical system; receiving, by the cloud computing system, second usage data, from a second subset of surgical hubs of the surgical system; analyzing, by the cloud computing system, the first and the second usage data to correlate the first and the second usage data with surgical outcome data; determining, by the cloud computing system, based on the correlation, a recommended medical resource usage configuration; and displaying, on respective displays on the first and the second subset of surgical hubs, indications of the recommended medical resource usage configuration.

The invention provides a photovoltaic module management system based on wireless communication, and the system comprises a plurality of photovoltaic modules, the interior of each photovoltaic module is integrated with an Internet of Things communication module, and the operation data of the corresponding photovoltaic module are collected through the Internet of Things communication module; the access point is in wireless connection with the Internet of Things communication module of each photovoltaic module so as to manage data acquired by each Internet of Things communication module; and the cloud server is connected with the access point so as to receive the operation data of the photovoltaic module or send a control instruction to the photovoltaic module. Wired communication of a cable is replaced by wireless communication, so that the cost is saved. And meanwhile, the power station cloud platform control center controls photovoltaic module controllers such as a turn-off device and an optimizer, the working mode of the photovoltaic module is optimized, and the working efficiency of solar photovoltaic is improved.

The invention belongs to the technical field of intelligent household appliances, and particularly relates to a control method and equipment of water purification equipment and a storage medium. The method comprises the steps that human physiological data of a target user is obtained through cloud equipment, the cloud equipment classifies the target user based on the human physiological data, and the cloud equipment determines a water purification scheme for the target user based on the classification of the target user and the human physiological data of the target user, the cloud equipment sends the water purification scheme to water purification equipment, and the water purification equipment adjusts the water purification equipment based on the water purification scheme and a raw water detection result; according to the method, the water quality of the water purification equipment is adjusted according to the intake requirements of different types of user groups on trace elements, the intelligence of the water purification equipment is improved, and the user experience is improved.

The invention provides an oil depot state low-cost monitoring method and system based on the Internet of Things, and relates to the technical field of oil depot detection, and the method comprises the steps: deploying a sensor group at the position of an oil depot storage tank, and collecting original monitoring data; preprocessing the collected original monitoring data, and then uploading the data to a cloud server through an Internet of Things gateway; based on the received data, the cloud server performs inversion calculation on the real-time liquid level height of the storage tank through an intelligent algorithm, and performs calibration calculation on the volume of the special-shaped storage tank; state fusion analysis and abnormity diagnosis are carried out based on the real-time liquid level height, the volume data and the multi-source environment data; and triggering graded early warning according to a preset rule based on results of analysis and abnormality diagnosis. According to the method provided by the invention, hardware deployment cost is revolutionarily reduced, and all-around efficiency leap from manual regular inspection to unmanned real-time monitoring, from single parameter measurement to multi-source fusion diagnosis, and from passive reading to active early warning is realized.

The invention provides a low-carbon strategy generation method based on carbon emission factor prediction. The method belongs to the technical field of power system low-carbon strategies. The method comprises the steps that a terminal layer collects power generation side output data, user side load data and environmental meteorological data of a power system; the cloud layer inputs power generation side output data, user side load data and environmental meteorological data into a dynamic carbon emission factor prediction model to generate a carbon emission factor prediction curve; the carbon emission factor prediction curve is input into a low-carbon regulation and control optimization model with the goal of carbon emission minimization, and a long-term low-carbon control strategy is obtained; and based on the peak valley characteristics of the carbon emission factor prediction curve, the long-term low-carbon control strategy and the user load characteristic spectrum, determining a low-carbon strategy meeting the power grid carbon emission reduction target and the user energy consumption demand. According to the method, the dynamic prediction of the carbon emission factor can be realized, the low-carbon energy consumption strategy is generated in combination with the user side demand, and the accuracy of carbon emission metering and the effectiveness of participation in emission reduction by the user are improved.

The invention relates to a vehicle data processing system and a vehicle, the system comprises a vehicle end data module and a cloud service module which are in communication connection, subunits of the vehicle end data module comprise a gateway unit, a cabin domain controller unit, an intelligent driving domain controller unit and a vehicle-mounted communication unit, the cabin domain controller unit is in communication connection with the gateway unit, the intelligent driving domain controller unit and the vehicle-mounted communication unit. The vehicle end data module is used for collecting various initial vehicle data and processing and transmitting the initial vehicle data to obtain corresponding target vehicle data; the cloud service module is used for receiving and processing the target vehicle data transmitted by the vehicle end data module, generating a corresponding remote control instruction and sending the remote control instruction to the vehicle end data module; at least one subunit in the vehicle end data module is used for executing the received remote control instruction; the effectiveness and richness of cloud data can be improved, so that the recognition and solving efficiency of abnormal conditions of vehicles is improved.

The invention relates to the technical field of intelligent medical care, and discloses a drainage port nursing recommendation method and system for a patient with bile leakage. The system comprises a data acquisition module used for acquiring bile drainage data and state data of skin around a drainage port in real time; the cloud decision module is internally provided with a nursing scheme recommendation model associated with a bile leakage nursing knowledge graph, and is used for carrying out fusion analysis on the multi-modal data and generating a personalized nursing plan; the local execution terminal is used for receiving and displaying the nursing plan and guiding nursing personnel to execute and record actual nursing operation data; and the closed-loop feedback module is used for returning the actual execution data and the post-nursing state data so as to optimize the nursing scheme recommendation model and the bile leakage nursing knowledge graph. The technical problems that in traditional nursing, evaluation is subjective, decision-making depends on experience, execution is not standard, and knowledge is difficult to accumulate are solved, and standardization, individuation and continuous self-optimization of bile leakage drainage opening nursing are achieved.

The invention discloses an airplane icing early warning method, and relates to the technical field of icing early warning. According to the airplane icing early warning method, cloud picture data and cloud product file data of a set airplane are obtained, a cloud field operation state feature set of the set airplane is extracted based on the cloud product data of the set airplane, and a cloud liquid water path value of the set airplane is analyzed; based on a pre-trained cloud picture error mapping model and in combination with cloud picture data of the set airplane, analyzing a cloud type temperature correction factor of the set airplane, and in combination with the cloud field operation state feature set, analyzing an optimized cloud top temperature value of the set airplane; based on the cloud liquid water path value of the set airplane, the cloud top temperature value is optimized, and the ice accretion index of the set airplane is analyzed, ice accretion early warning processing is performed on the set airplane based on the ice accretion index, so that the finally generated ice accretion index result has higher environmental adaptability, dynamic correction and high-precision characterization of the ice accretion risk are realized, and the method is suitable for large-scale popularization and application. And thus, the accuracy of ice accumulation early warning is improved.

The invention discloses an air conditioner adjustable potential prediction method based on a cloud-side collaborative architecture, electronic equipment, a storage medium and a product, comprising a cloud end deployed on a server and a side end deployed on user side computing equipment, the cloud end shares side end data, and the cloud end performs prediction on the adjustable potential of an air conditioner based on historical air conditioner load time sequence data. Obtaining a bidirectional LSTM prediction model capable of predicting an air conditioner load value at a specific time in the future, and carrying out iterative optimization on the bidirectional LSTM prediction model based on air conditioner load time sequence data collected by an edge end in real time; and the side end carries out lightweight processing on the bidirectional LSTM prediction model after iterative optimization, deploys the bidirectional LSTM prediction model on user side computing equipment, and carries out air conditioner load value prediction based on air conditioner load time sequence data collected in real time to further obtain an air conditioner adjustable potential prediction result. According to the method, for the problems that an existing prediction model is insufficient in generalization ability and difficult in edge deployment, the cross-scene adaptability is improved through MMD transfer learning, ONNX quantization and Docker containerization are combined, and edge lightweight and standardized efficient deployment is achieved.

Techniques are described through which groups of individuals and / or other entities may interface with a data cloud blockchain network and / or cloud-based platform to collectively share data in a secure, controlled manner. Decentralized groups that are connected to the data cloud network may be registered and listed in a searchable directory. Entities that are interested in accessing data associated with a group may browse the directory, execute smart contracts within a blockchain, and track online content interactions of a group in a manner that does not compromise the anonymity of individual group members. Data usage and performance metrics may be tracked on the blockchain network using data cloud services, and the metrics may be written to distributed ledgers within the blockchain network. Smart contracts and chaincode within the network may initiate blockchain transactions based on performance metrics and / or other aspects associated with accessing information about a group.

The invention relates to the technical field of vehicle intelligent management, in particular to a fault data driving analysis method and system for a new energy commercial vehicle, and the method comprises the steps: collecting vehicle operation data, cloud environment data and external working condition data to form multi-source time series data; preprocessing the data and generating a sliding time window data set; basic features, time-frequency domain features and environment coupling features are extracted from the data set; splicing the features into a sequence, inputting the sequence into a pre-trained anomaly detection model, outputting an anomaly score, and triggering early warning according to a preset rule; and analyzing the contribution degree of each feature to the exception score, and performing rule matching with a single-vehicle fault mode library in which a fault evolution path is stored to obtain a predicted exception type and generate a diagnosis report. According to the invention, accurate detection and real-time early warning of low-frequency gradual change faults under complex working conditions can be realized, an interpretable diagnosis report is generated, and the maintenance decision-making efficiency is improved.

The invention provides an electric power system monitoring data cloud unified access and resource intelligent prediction system and method based on cloud edge collaboration. The system is provided with a unified access module, an alarm rule configuration module, a real-time alarm module, a real-time monitoring display module and an intelligent trend prediction and health degree evaluation and maintenance module. According to the system and method, Java language development is adopted, the system and method serve as a central platform to receive and manage monitoring data from a plurality of lower-level power platforms in a unified mode, and standardized data unified access, real-time multi-dimensional monitoring, intelligent trend prediction, dynamic alarm management and system health degree evaluation and maintenance of a CPU, a memory, a hard disk, network resources and middleware services are achieved; the method is very suitable for a power system, can also be widely applied to other industries needing high reliability and real-time performance, and effectively solves the technical problems that monitoring data of multiple servers cannot be managed in a unified mode, the resource prediction precision is low, and system fault response lags behind.

The invention discloses a power distribution network equipment fault root cause analysis data modeling method, relates to the technical field of power distribution network fault analysis, and aims to solve the technical problems of low fault root cause analysis accuracy and slow response under high-frequency topology dramatic change and edge terminal computing power fluctuation of an existing power distribution network. Comprising the following steps: S1, acquiring equipment operation data and real-time topology information by a heterogeneous edge terminal of a power distribution network, constructing a dynamic lightweight time sequence diagram model with online self-adaptive capability according to an edge terminal computing power high-frequency dynamic topology dramatic change scene, and outputting a fault root cause coarse positioning result; s2, the heterogeneous edge terminal uploads topology increment information and a coarse positioning result in a grading manner based on topology change intensity, wherein the topology increment information is structured data of local topology change of the power distribution network; and S3, the cloud platform updates the global time sequence diagram according to the topology change intensity in a grading manner. The method has the advantages of improving the fault root cause analysis accuracy and the response speed.

The invention discloses a continuous optical zoom slit-lamp microscope system and an inspection method and application thereof, and belongs to the technical field of ophthalmology medical equipment. The system is composed of a continuous zooming module, a light source module, a data processing module, a 3D imaging module and an AI identification module. The inspection method comprises the steps of equipment initialization and head position fixation of an inspected person, AI low-magnification (5 *) preliminary screening, adaptive lens high-magnification (40 *) fine observation, dynamic light source adjustment, three-dimensional data acquisition and cloud intelligent diagnosis. The method is applied to examination and intelligent diagnosis of eye diseases such as cataract. The problems that a traditional slit-lamp microscope is discontinuous in zooming, inconvenient to operate, lack of intelligent diagnosis and free of 3D stereoscopic display are solved, and the objective of the invention is to realize simple and convenient equipment operation, continuous examination process, intelligent diagnosis and stereoscopic display, facilitate collective teaching, significantly improve the diagnosis efficiency and accuracy of eye diseases such as cataract and the like, and improve the diagnosis efficiency and accuracy of eye diseases such as cataract and the like. And DICOM standard data cloud sharing and remote consultation are supported.

The invention relates to the technical field of smart park management and cloud computing, in particular to a smart park safety management method based on cloud edge cooperative computing. According to the method, the multi-source data is acquired through the terminal device which is calibrated periodically, and the lightweight AI model of the edge node performs preliminary analysis according to the screening threshold and uploads the suspected hidden danger data. And the cloud platform performs deep analysis by using the prediction model, evaluates the security situation level and triggers early warning and emergency response. Meanwhile, the cloud model recognition accuracy, the edge model false alarm rate and the actual hidden danger proportion in the uploaded data are comprehensively calculated, the safety management characterization value is generated, the system state is judged based on the safety management characterization value, then the edge screening threshold value and the data uploading strategy are adaptively and dynamically adjusted, and a continuously-optimized management closed loop is formed. The safety management efficiency of the smart park is improved.

This application provides a method and system for monitoring and alarming the tilt of overhead ground wire suspension insulator strings. The method includes: synchronously acquiring insulator string operating status and environmental data through a multi-sensor acquisition module, including micro-meteorological, tilt angle, infrared image sensors, and a right-angle gate module; performing Kalman filtering, noise reduction, normalization, and LZ77 compression on the data to form a unified feature dataset; real-time anomaly detection and data upload at the edge using a lightweight CNN-LSTM fusion model; and fault type identification and severity assessment via ResNet in the cloud, including accurate displacement calculation; generating early warning information and maintenance suggestions by combining historical data and maintenance strategies, and pushing them through a multi-channel method including tower-side audible and visual alarms and mobile terminal push notifications. This application achieves all-weather, high-precision monitoring, improves the accuracy of anomaly identification and alarm timeliness, ensures the safe operation of transmission lines, and reduces power outage losses.

The application relates to the technical field of substation operation and maintenance, and is a substation secondary test equipment on-site operation and maintenance system and method, which comprises the following steps: a control platform outputs an operation and maintenance command to secondary online detection equipment in a substation in combination with detection items, and receives and analyzes test result data; a cloud server transmits and stores data between the control platform and the secondary online detection equipment; the secondary online detection equipment performs online detection on a measured device of the substation in combination with the operation and maintenance command; a transmission network is connected with the secondary online detection equipment and the measured device respectively, and transmits related data information; and the measured device transmits detection information and autonomous digital message information back to the secondary online detection equipment in combination with information transmitted by the secondary online detection equipment. The application realizes remote full-automatic test on secondary equipment, greatly reduces labor cost, realizes intelligent, digital and remote operation of substation secondary equipment detection, and realizes 24-hour intelligent operation and maintenance of electric power.

This invention provides an agentless intelligent monitoring and alarm method and apparatus, relating to the field of computer technology. The method includes: a cloud center platform acquiring monitoring task configuration information and synchronizing it to an edge collector; the edge collector pulling monitoring data through a multi-protocol adapter corresponding to the monitoring task configuration information to obtain monitoring data to be processed; the cloud center platform processing the monitoring data to be processed sent by the edge collector to obtain target monitoring data; the cloud center platform using an anomaly detection model to perform anomaly detection on the target monitoring data, using a time series prediction model to predict the trend of the target monitoring data, adjusting the alarm threshold through an adaptive alarm threshold engine, and comparing the anomaly detection results, trend prediction results, target monitoring data, and alarm threshold to generate alarm information. This invention solves the problems of high deployment and maintenance costs, high security risks, low alarm accuracy, and limited coverage in large-scale complex environments.

The invention discloses a high-precision truth value discovery method for privacy protection in vehicle crowd sensing, and the method comprises the steps: enabling a vehicle to determine a privacy budget, obtaining noise through a smart contract, encrypting the noise and original data, and submitting the encrypted noise and original data to a block chain; the vehicle generates a re-encryption key according to the private key of the vehicle and the public key of the cloud server, and sends the re-encryption key to the block chain; the smart contract performs homomorphic aggregation on the noise ciphertext and the data ciphertext to generate disturbance data encrypted by a vehicle public key; the intelligent contract uses the re-encryption key to convert the vehicle encrypted disturbance data into cloud server encrypted disturbance data, and sends the disturbance data to the cloud server to restore the disturbance data; and the cloud server screens the data sources, fuses the disturbance data based on the weight and the credibility of the data sources to obtain a disturbance truth phase, and carries out noise asymptotic elimination on all the data sources to obtain a truth phase aggregated by all the data sources. According to the method, the accuracy of true value discovery can be remarkably improved on the premise of protecting vehicle data privacy, and hostile attack resistance is achieved.

Embodiments described herein provide a data cloud platform that facilitates an end-to-end workflow for scientific and biomedical research, such that scientists and researchers may have easy access to scientific and biomedical data.

The invention provides a vehicle mileage data backup method and a vehicle mileage data backup system, and relates to the technical field of vehicle data storage. The method comprises the steps that in the driving process of a target vehicle, instrument equipment periodically uploads current driving data of the target vehicle to a cloud server through a communication terminal according to a preset time interval; the current driving data comprises first current mileage data; the cloud server performs real-time cloud backup on the first current mileage data; when the target vehicle is powered off, the instrument equipment sends second current mileage data when the target vehicle is powered off to the vehicle end controller; and the vehicle end controller performs vehicle end local backup on the second current mileage data. According to the method, cloud periodic backup can be carried out in the running process of the target vehicle, vehicle end local backup can be carried out when the target vehicle is powered off, multi-end storage of the mileage data is realized through combination of the cloud periodic backup and the vehicle end local backup, and mileage data loss caused by instrument equipment failure or replacement is avoided.

Level is detected of contents of a container or drum. Systems may wirelessly report when contents of the container are nearly depleted. Components include a plug, a sensor, a structural member, a terminal connector in the plug, and internal and external cables. The plug may screw into a vent hole in the container and a signal connector on the external cable connects to the terminal connector. The connectors may be configured or have magnets to prevent reverse connection. Various embodiments have a second sensor, a wireless gateway, GPS location data, cloud data processing, or a recess to receive a tool to rotate the plug and remove the system from the container. The structural member may bend to maintain verticality.

The invention relates to a system for determining rock properties from a 3-dimensional data cloud with machine learning.

The invention discloses an intelligent equipment safety production management system and method. The system is used for generating an activation request according to an identifier of an edge gateway through a cloud platform under the condition that the edge gateway accesses a production environment for the first time, and sending the activation request to the edge gateway according to the activation request; the authentication with the edge gateway is completed, if the authentication succeeds, an encrypted communication link is established with the edge gateway, and the activation state of the edge gateway is kept; the cloud platform is also used for configuring the IP for the edge gateway through the IP white list according to the application scene under the condition that the IP is configured for the edge gateway; the cloud platform is also used for receiving the IP data uploaded by the edge gateway according to a preset period, and storing the IP data to a database, so that the IP data is queried under the condition that a query instruction is received; and the cloud platform is also used for verifying the IP in the access request according to the IP white list under the condition that the production tool sends the access request. The technical effect of improving the agility and reliability of safety management of the production environment can be achieved.

The application provides a non-intrusive intelligent device log tracking system, which comprises a device end integration module, a cloud server module and a cloud database module.The device end integration module is in communication connection with a device to be tracked during use, is configured to obtain log data of the device to be tracked, and uploads the log data confidentially; the cloud server module is in communication connection with the device end integration module, is configured to generate a log tracking strategy of the device to be tracked and receive the log data; the log tracking strategy is used to instruct the device end integration module to obtain the log data according to corresponding operation and maintenance requirements; and the cloud database module is in communication connection with the cloud server module, is configured to store the log data, the log tracking strategy and device data of the device to be tracked.The application solves the problems of inflexible log collection, strong invasiveness and low operation and maintenance efficiency of the existing device tracking technology through the above system.

The application relates to the field of construction scheduling control, in particular to a water conservancy engineering construction mechanical group collaborative operation intelligent scheduling system, which comprises the following steps: a geological mechanical coupling twin platform constructs a bidirectional coupling closed loop of a geological field and construction mechanical behavior according to real-time working condition data of a region to be constructed, obtains a dynamic geological attribute field quantity, and fits a geological disturbance sensitive coefficient of the region to be constructed; a cloud edge end collaborative network collects real-time working condition data through an intelligent agent node; a cloud computing center solves a global construction task scheduling scheme by adopting a multi-objective optimization algorithm; an edge computing node adopts a multi-agent deep reinforcement learning algorithm to perform real-time rolling optimization on the collaborative operation of construction machines in a local jurisdiction region according to a screened optimal local construction task distribution scheme, generates a local collaborative scheduling instruction, and schedules each construction machine. The scheduling system can realize real-time sensing of geological changes, reduce the probability of engineering accidents, and dynamically optimize the collaborative operation of the mechanical group.

The invention belongs to the technical field of mine intelligent mining and industrial Internet of Things crossing, particularly relates to an IIOT-remote mobile cooperative monitoring and intelligent management and control system for a fully mechanized coal mining face of a coal mine, and solves the problems of poor mobility, data fragmentation, slow emergency response and difficulty in cross-platform adaptation of a traditional monitoring system for the fully mechanized coal mining face of the coal mine. A'cloud-end 'framework is adopted, an equipment layer, a network layer, a cloud data layer and a mobile application layer are integrated, the equipment layer collects operation data of fully mechanized mining equipment, the cloud data layer realizes data convergence, storage and rule engine operation based on an industrial Internet of Things platform, and the mobile application layer develops a cross-platform terminal based on a Flutter framework. A multi-equipment monitoring module, a data analysis module, a fault alarm module and the like are integrated, alarm millisecond-level pushing is achieved through long connection communication and a rule engine, and real-time mobile monitoring, unified data research and judgment, rapid fault response and cross-platform intelligent management and control of fully mechanized coal mining face equipment are achieved.