112 results about "Edge analytics" patented technology

The invention discloses a wind power plant equipment fault intelligent diagnosis method and system. The method comprises the following steps: acquiring multi-dimensional sensor data of wind power plant equipment; carrying out preliminary screening on the data by utilizing an edge analysis algorithm, and determining data needing to be further diagnosed; the screened data are input into a pre-constructed fault diagnosis model, the data are fused through a multi-modal deep learning model in the pre-constructed fault diagnosis model, and then a fault occurrence area is determined through transfer learning, domain adaptation and a multi-agent reinforcement learning framework; meanwhile, the causal inference engine and the knowledge graph enhanced neural symbol inference module are responsible for deep processing and fault diagnosis. And finally, inputting the determined fault area and the diagnosis result into a dynamic system health index calculation module to generate a final fault diagnosis result. According to the method, the diagnosis efficiency and accuracy of the equipment fault of the wind power plant are remarkably improved, and a powerful guarantee is provided for stable operation of the wind power plant.

The invention discloses an underground mine operation state analysis system and method based on video monitoring data, and the system comprises a data collection module which is used for collecting mine video and environment parameter data through a distributed sensor network, and generating a multi-dimensional data fusion set based on a space-time label technology; the edge analysis module is used for extracting feature parameters through a convolutional neural network algorithm based on the multi-dimensional data fusion set and generating a mine operation state recognition result; the fence construction module is used for constructing a three-dimensional digital model and a dynamic safety boundary based on the mine operation state recognition result to form a real-time monitoring reference framework; and the decision execution module is used for performing hierarchical risk assessment on the monitoring data in the security boundary based on the real-time monitoring reference framework, and generating a security early warning and disposal scheme with a tracing identifier. Each piece of early warning and disposal information is attached with a unique tracing identification code, so that follow-up event backtracking analysis is facilitated, and the risk management and control capability is continuously improved.

The invention discloses an intelligent pipe network leakage detection method based on edge calculation, and relates to the technical field of pipe network monitoring and leakage detection, and the method comprises the steps: carrying out the feature extraction of a preprocessing data set through an edge analysis terminal, and obtaining a pressure stability coefficient, a sound wave correlation coefficient and a flow mutation coefficient; inputting the pressure stability coefficient, the sound wave correlation coefficient and the flow abrupt change coefficient into a leakage probability evaluation model, and outputting a leakage risk index; and the edge decision terminal generates a leakage level signal according to the leakage risk index and triggers a positioning instruction. The pressure stability coefficient, the sound wave correlation coefficient and the flow abrupt change coefficient are extracted through the edge analysis terminal and input into the leakage probability evaluation model, the leakage risk index is output, accurate quantitative evaluation of the leakage risk is achieved, the model can adapt to changes of different working conditions through introduction of the dynamic weight, and the reliability of the model is improved. And the adaptability and accuracy of evaluation are improved.

Systems and methods for real-time monitoring and control of well site operations employ well site edge analytics to detect abnormal operations. The systems and methods use machine learning (ML) based analytics on an edge device directly at the well site to detect possible occurrence of abnormal events and automatically respond to such events. The event detection may be based on trends identified in the data acquired from the well site operations in real time. The trends may be identified by correlation and by fitting line segments to the data and analyzing the slopes of the line segments. Upon detecting an unusual event, the edge device can issue alerts regarding the event and take predefined steps to reduce potential damage resulting from such event. This can help decrease downtime and minimize lost productivity and cost as well as reduce health and safety risks for field personnel.

The invention discloses a detection method and device for intelligent visual detection of parts and a storage medium, belongs to the technical field of industrial quality detection, and aims to solve the problems that surface and internal defects of complex parts are difficult to identify synchronously and the identification precision is low. The method comprises the following steps: acquiring multi-modal image data obtained through structured light three-dimensional imaging and laser ultrasonic scanning; performing geometric registration and scale normalization on the image to generate a fused image; carrying out image preprocessing and edge analysis, and extracting a region of interest; dividing the candidate region of interest into a plurality of image blocks, and inputting the image blocks into an anomaly detection network; calculating a reconstruction error between the original image block and the reconstructed image block, and generating an abnormal scoring graph; and finally, extracting a defect area through image post-processing, and outputting information such as a defect type, a spatial position, a geometric dimension and a severity level. According to the method, the robustness and accuracy of multi-modal defect identification are improved, and the method is suitable for an online visual inspection task of an industrial production line.

The invention discloses a water conservancy data acquisition device, and relates to the technical field of Internet of Things, and the device comprises the following steps: dividing a target water area into a plurality of monitoring water areas; performing characteristic factor analysis on each monitored water area to obtain a characteristic factor analysis result; adjusting the installation number and positions of the sensors in the monitored water area according to the characteristic factor analysis result, and obtaining hydrological data monitored in real time; performing edge calculation analysis on the hydrological data monitored in real time to obtain an edge analysis result; performing data synchronization on the edge analysis result and uploading the edge analysis result to a cloud platform to obtain cloud data; performing intelligent analysis on the cloud data to obtain a water area dynamic report; performing risk early warning according to the water area dynamic report; the acquisition device collects and analyzes hydrological data of different water areas by combining real-time data acquisition of a sensor and an edge calculation and analysis method. According to the method, loss and errors of hydrological data are reduced, and consistency, high efficiency, accuracy and timeliness of the hydrological data are enhanced.

The invention discloses an electric power centralized control system transformer state acquisition system and method based on edge analysis, and belongs to the field of enterprise substation operation and maintenance technology management.The system comprises an intelligent gateway machine, an edge analysis device, a camera and a transformer winding temperature measuring device; the edge analysis device is connected with the intelligent gateway machine, the camera and the transformer winding temperature measurement device, and the camera and the transformer winding temperature measurement device are arranged on a transformer site and used for collecting transformer oil liquid level video information and transformer winding temperature information respectively and transmitting the information to the edge analysis device. The intelligent gateway machine is deployed in a substation secondary room and is used for acquiring power load information of a transformer and transmitting the power load information to the edge analysis device; and the edge analysis device is deployed in a transformer substation secondary room, analyzes the transformer oil liquid level video information, the transformer winding temperature information and the transformer power load information through an edge analysis algorithm to obtain a transformer health state value, and transmits the transformer health state value to the electric power centralized control system after conversion.

Disclosed are methods, systems, and one or more computer-readable mediums for receiving, at an edge device from an external server, a rules database comprising a set of rules; receiving, by the edge device, first data from at least one device; processing, by the edge device, the received first data by comparing the received data to each rule among the set of rules; identifying a first triggering event in response to detecting a match between the received data and a rule of the rules database; and outputting an alert corresponding to the first triggering event.

The invention is suitable for the technical field of distribution network detection, and provides a distribution network overhead line partial discharge integrated live detection system based on a ubiquitous Internet of Things architecture, which comprises a pulse signal detection end, an edge gateway and a cloud monitoring platform, the pulse signal detection end is used for detecting a partial discharge pulse signal of the overhead line and transmitting the partial discharge pulse signal to the edge gateway; the edge gateway comprises a synchronization pulse trigger module, a time calibration module and an edge analysis module. The generation module is used for generating a positioning distribution diagram of partial discharge points; the cloud monitoring platform comprises a multi-terminal data fusion module, a risk assessment module and a result feedback module; the server is used for fusing the positioning distribution map and the pulse characteristics, outputting a global discharge point correction distribution map, identifying a discharge type based on a discharge characteristic spectrogram, generating an insulation state evaluation result and feeding back the evaluation result to a terminal user; and a high-reliability, high-efficiency and low-cost solution is provided for the management and control of the insulation state of the distribution network.

The invention discloses a traffic incident detector system based on a domestic intelligent DSP and a detection method. The system comprises a video acquisition unit, a data exchange and storage unit, an intelligent processing core and a communication interaction unit. The intelligent processing core adopts a heterogeneous SoC chip integrating a multi-core DSP, an NPU and an FFT hardware acceleration module, the FFT module is responsible for video frame frequency domain enhancement, the NPU runs a deep learning model to carry out target detection, and the multi-core DSP executes multi-target parallel tracking and event judgment. And the data exchange and storage unit supports direct carrying of data among the modules through a unified physical address space and a DMA mechanism. The method comprises the following steps: carrying out NPU target detection on a video subjected to environment self-adaptive preprocessing; a multi-core DSP is used for tracking a target in parallel and judging an event; and finally, alarm information is generated and reported. Through heterogeneous hardware division and software and hardware cooperation, high-real-time and high-precision edge analysis is realized, and the detection reliability is improved.

The invention discloses an intelligent home control system, which relates to the technical field of intelligent home and comprises an illumination sensor, an infrared human body sensor, an auxiliary sensor, an edge analysis module, an illumination controller and an illumination strategy optimization module. The multi-source sensing data is subjected to fusion analysis through an edge analysis module, so that accurate light control based on multi-condition judgment is realized; and the lighting strategy optimization module dynamically adjusts a logic weight or a threshold value based on the abnormal behavior characteristics of the user, so that the control strategy has an adaptive optimization capability, and the response accuracy, stability and individuation level of the system are remarkably improved.

An anthropomorphic AI control system including a plurality of sensors configured to collect industrial input data. The control system also includes an artificial intelligence-enabled edge-deployed computing device configured to analyze and fuse multimodal input data and generate an output through anthropomorphic computing. The computing device includes a cognitive module performing real-time decision-making at the sensor edge and a controller to manage industrial process actuators across operational domains. In management of industrial fluid flow, the control system may include AI-enabled fluid characterization modules to calculate the Reynolds number and incorporate compliance with AGA3 and AGA8 standards.

The invention relates to a distribution line power factor real-time monitoring and collaborative optimization system and method, and belongs to the technical field of distribution line energy efficiency management systems and methods. According to the technical scheme, the system comprises a multi-source data acquisition and preprocessing module, an edge node calculation and edge analysis module, a power factor real-time analysis and state evaluation module, a multi-subject collaborative optimization decision module and a dynamic reactive compensation control execution module. An edge and cloud data interaction and full life cycle management module; and an abnormity early warning and fault diagnosis module. The method has the beneficial effects that the line network loss and the operation and maintenance cost are reduced at the same time, the fault-tolerant capability is achieved, the modular design supports multi-scene expansion, the method not only adapts to the process requirements of vanadium-titanium steel rolling, smelting and the like, but also can be flexibly applied to other high-load industrial 11kV lines, and the power factor regulation and control effectiveness and the system full-life-cycle benefits are improved.

The invention relates to the technical field of atmospheric environment monitoring, and discloses a sand and dust path monitoring system, which is characterized by comprising a data acquisition module, an image processing module, a feature extraction module, a path fitting module, a path fusion module and a geographic mapping module which are sequentially cascaded, the data acquisition module is used for receiving remote sensing data of a stationary meteorological satellite and generating a multi-time sand and dust monitoring binary image; the image processing module performs edge detection and morphological operation on the sand and dust monitoring binary image to realize accurate segmentation of a sand and dust region; the feature extraction module comprises a minimum enclosing rectangle calculation unit and an edge analysis unit and is used for extracting four types of feature points of the sand and dust area, and the four types of feature points comprise the lower right corner, the upper right corner, the right side line and the lower side line. According to the invention, by fusing edge detection, morphological operation and multi-feature endpoint identification, precise segmentation of a complex dust region and stable capture of motion features are realized, and the problem that feature points are easy to drift in a traditional method is effectively solved.

The invention relates to the technical field of risk early warning, and particularly discloses a substation risk early warning method and system based on an edge AI analyzer, and the method comprises the steps: determining the position of the edge AI analyzer and the application range of the edge AI analyzer according to a determined gunlock position; the method comprises the following steps: receiving a video acquired by a gun camera regularly based on an edge AI analyzer, performing target positioning on the video, determining a target frame, identifying contents in the target frame based on a preset convolution feature table, and outputting a risk type; and when the output of identifying the content in the target frame based on the preset convolution feature table is null, identifying the content in the target frame based on an AI identification model in the edge AI analyzer, and outputting a risk type. The local identification module based on the convolution kernel table and the AI identification module based on the AI model are arranged in the edge AI analyzer, the transformer substation risk is identified in a gradient mode, the identification speed is high, and the identification range is large.

The invention discloses an intelligent bus duct and cable online monitoring system for wireless dual-mode collaborative networking, and the system comprises a data collection and preprocessing module which is used for collecting and preprocessing the monitoring data of a bus duct and a cable; the feature extraction module is used for performing normalization and feature cascade processing on the preprocessed monitoring data to generate monitoring feature data; the data encapsulation and communication module is used for encapsulating the monitoring characteristic data into a standardized data frame and transmitting the standardized data frame through a main communication link; the gateway convergence module is used for generating gateway convergence data; the edge analysis module is used for receiving gateway aggregation data, inputting the gateway aggregation data into an improved MFDFA-XGBoost fusion model for state judgment and outputting a state result; the cloud diagnosis module is used for receiving the state result and the monitoring feature data and generating and storing event records; and the alarm and operation and maintenance module is used for generating alarm information and realizing real-time monitoring and active early warning of the bus duct and the cable.

The invention relates to the field of intelligent inspection of substation equipment, discloses a substation equipment inspection path planning system based on an unmanned aerial vehicle, and aims to solve the problems that high-definition images are difficult to return in real time in low-frequency transmission under strong electromagnetic interference of a substation, and path planning is single. The system comprises five core modules, an equipment three-dimensional digital reference and feature library is constructed, new equipment is adapted in combination with operator similarity migration, and the observation pose is accurately reproduced to guarantee the image comparison precision; after airborne edge analysis, selectively returning data, and adapting to a low-frequency transmission link; and according to equipment similarity in batches, planning a global optimal path by fusing the path length and the flight risk. The system realizes efficient transmission of inspection data, is accurate and reliable in detection, balances the inspection efficiency and the flight safety, and meets the core requirements of safe, efficient and accurate inspection of the transformer substation.

The invention relates to the technical field of power system equipment monitoring, in particular to a substation equipment state monitoring embedded edge analysis system, which comprises a monitoring front-end module for acquiring and transmitting substation equipment operation state data; the edge processing module comprises a multi-scale wavelet transform unit, a topology continuity analysis unit and a manifold learning unit, wherein the multi-scale wavelet transform unit decomposes data and separates signals and noise through a self-adaptive threshold mechanism; the topological persistence analysis unit maps the de-noised signal to a high-dimensional feature space, calculates a persistent coherence group, and extracts a topological feature vector; and the manifold learning unit constructs a nonlinear manifold structure, calculates a geodesic distance, and executes discharge type identification and anomaly evaluation, and the FPGA and ARM heterogeneous calculation architecture is adopted, and through the synergistic effect of multi-scale wavelet transform, topology persistence analysis and manifold learning, high-precision extraction of weak discharge signals and accurate identification of fault modes are realized.

The invention specifically relates to the technical field of real-time fault diagnosis, and discloses an intelligent ship equipment state monitoring and fault diagnosis system, which comprises a data acquisition processing module, an edge analysis reasoning module, a local decision execution module, a cloud edge comprehensive analysis module, an optimization strategy making module and a man-machine interaction feedback module, the data acquisition and processing module is used for constructing a ship equipment state feature set; the edge analysis reasoning module is used for outputting a ship equipment state analysis result; the local decision execution module is used for generating a preliminary decision instruction; the cloud edge comprehensive analysis module is used for judging the ship equipment health level; the optimization strategy making module is used for making a global equipment optimization strategy; the man-machine interaction feedback module is used for providing an equipment state panel; by constructing a cloud edge collaborative model optimization mechanism, a global ship equipment health state evaluation model is continuously trained and optimized based on real-time feature data and historical data, and the real-time performance of fault response and the dynamic adaptability of state real-time perception are improved.

The invention discloses an intelligent garden irrigation control method, and relates to the technical field of garden irrigation, and the technical scheme is characterized in that the method comprises the following steps: collecting soil humidity, temperature and net radiation through a soil parameter detection module; acquiring wind speed, illumination intensity and future rainfall prediction through a meteorological data acquisition module; calculating and outputting a dynamic irrigation demand index based on an edge analysis module; the pressure compensation module generates a valve control instruction based on pipe network position analysis. According to the system, the dynamic irrigation demand index is calculated and output through diversified collection and the edge analysis module, so that precise irrigation can be carried out on the garden according to the actual demand; by arranging the pressure compensation module, the actual flow of each position of the pipe network can be dynamically calculated, and the flow of the pipe network is adjusted, so that the flow difference of each position of the pipe network can be reduced, and irrigation of each position of the garden is more comprehensive and accurate.

The invention discloses a tea fermentation degree identification method, system and device and a storage medium, and the method comprises the steps: inputting a to-be-identified tea fermentation image into a tea fermentation identification network, carrying out the feature extraction of the to-be-identified tea fermentation image through a first processing layer, and obtaining a first semantic feature map; performing multi-scale processing and edge enhancement processing on the to-be-identified tea fermentation image through an edge analysis and enhancement module layer to obtain a detail feature map; performing global dependency modeling processing through a second processing layer according to the first semantic feature map and the detail feature map to obtain a second semantic feature map; and performing classification identification on the second semantic feature map through a classification decision layer to obtain a fermentation degree identification result of the to-be-identified tea leaf fermentation image, thereby enhancing identification of image edge and texture detail features and establishing a cross-regional global context dependency relationship. Cooperative perception of local microscopic change and overall uniformity in the fermentation process is achieved, and then the identification accuracy of the tea fermentation degree is improved.

The invention belongs to the technical field of power system intelligent operation and maintenance, and discloses a power distribution operation and maintenance intelligent inspection system fused with edge calculation. A terminal data preprocessing unit; distributed edge analysis nodes; the nodes are interconnected with a communication link; an inspection task scheduling platform; a defect alarm generator; and a work order distribution and maintenance guide center. The surface temperature and partial discharge pulse signals of the power distribution equipment are obtained through the field inspection sensing device, time sequence alignment of multi-source data is achieved through time stamps in a unified format, normalization processing and trend modeling analysis are carried out in the terminal data preprocessing unit, and the system adopts a temperature change rate formula and a data normalization formula, so that the accuracy of the system is improved. Standardized conversion and dynamic index extraction are carried out on continuous time domain data, so that the recognition capability of early-stage heating hidden dangers and abnormal discharge symptoms is remarkably improved, and the misjudgment risk caused by data scale inconsistency or time dislocation is reduced.

The invention discloses a traffic video edge analysis system based on Yolov5-Edge and Prewitt algorithms, and the system achieves the real-time collection, intelligent recognition and encrypted transmission of a vehicle-mounted video through the construction of a layered architecture of an object-side equipment layer, an edge calculation layer and a cloud service layer. According to the system, a lightweight Yolov5-Edge model and a Prewitt edge detection algorithm are fused in edge equipment, multi-channel video parallel processing is realized under finite computing power, and the video inspection efficiency is effectively improved; the frame rate and the number of processing paths are dynamically adjusted through the resource monitoring and load control module, and the real-time performance and stability of AI reasoning are guaranteed; a data management and encryption mechanism is adopted, only abnormal data are stored at an edge end and clouded through RSA and AES encryption modes, and security and compliance of sensitive behavior information in the transmission and storage process are ensured. The system can identify and classify abnormal behaviors of drivers and passengers without depending on a high-performance server.

The invention relates to an edge analysis system supporting remote operation and maintenance of a charging station. The system comprises a core main process control module, a communication module, a database module, an electrical internet of things data alarm event analysis module, a video AI image analysis module and a camera agent registration module. The core main process control module is used for connecting the working logic of each module in series; the control module is also used for controlling sensor data, charging pile electrical Internet of Things state data, video stream data and state operation data to be stored in the database module, uploading an alarm event, monitoring a monitoring retrieval action of a superior master station and pushing the corresponding video stream data; and the controller is also used for monitoring a remote control instruction issued by the superior master station and controlling the external data acquisition device according to the remote control instruction. Data of various sensors for remote operation and maintenance of an external charging station are received and gathered, hidden danger information is found, and an alarm is given, so that full-coverage intelligent monitoring of remote operation and maintenance equipment can be realized, the cost is reduced, and the loss is reduced.

The invention relates to the technical field of geographic information management, in particular to a land surveying and mapping data intelligent management system, which comprises an information acquisition module for acquiring image information, environment parameter information and soil parameters of a surveying and mapping area, an edge analysis module for analyzing an edge curve of a planting land, an area division module and a data processing module, the system comprises an area division module used for carrying out area division on a planting land to obtain a plurality of fluctuation analysis areas, a terrain fluctuation analysis module used for analyzing the terrain fluctuation state of the planting land and adjusting the analysis process of the fluctuation state of the planting land, and a quality analysis module used for analyzing the land fertility state of the planting land and analyzing the quality of the planting land. And the data storage module is used for storing a land quality grading result and an edge curve analysis result of the planting land. The accuracy of land quality analysis is effectively improved.

The invention provides a train pantograph detection system and method based on artificial intelligence, and the system comprises a plurality of camera devices which are installed in a train detection room in a dispersed manner, and are used for collecting the image data of a train identification part and a pantograph part on a train entering the train detection room; the edge analysis device is connected with the camera device and is used for processing the image data based on a pre-trained image analysis model, identifying a train identifier, obtaining a pantograph analysis result and sending the pantograph analysis result to the cloud platform; and the cloud platform is connected with the edge analysis device, and is used for storing the pantograph analysis results in a classified manner according to the train identifiers, and generating alarm information when the pantograph analysis results have pantograph abnormities. The detection efficiency is improved, the subjectivity of manual detection is reduced, the accuracy and reliability of a detection result are improved, the standardization of a detection process is realized, and the situations of manual misjudgment, missing detection and the like are reduced.

A method for analyzing device test data includes accessing a core analytics rule that is based on manufacturing data of a plurality of devices. Each of the plurality of devices are produced in one of a plurality of manufacturing facilities and are of a same type as a first device being tested on a tester. The method also includes receiving initial test results of a plurality of other devices of a same type tested at a testing facility, generating, based on the initial test results, an edge analytics rule, modifying the core analytics rule based on the edge analytics rule, wherein the modified core analytics rule including modified binning limits, applying the modified core analytics rule to testing data obtained by testing the first device, and determining, based on applying the modified core analytics rule, that the first device is an outlier with respect to the modified binning limits.

Systems and methods described herein provide an application function and interface for a Network Data Analytics Function (NWDAF) to receive, from User Equipment (UE) devices, user data for edge applications. An NWDAF receives an analytics request for user analytics for an edge application and sends, to an Edge Analytics Engine (EAE) associated with an edge location, a data request based on the analytics request. The EAE receives user data from an Edge Enabler Client (EEC) on the UE device that is connected to the edge location. The EAE forwards the user data to the network device. The NWDAF receives the user data responsive to the analytics request and generates an analytics report based on the user data.

The invention provides an edge position determination method, an edge position determination device and an edge analysis method. In the edge position determination method, an average edge position is determined by summing up intensity values in the direction of extension of lines and lines in spaced SEM images, and a first intensity distribution is fitted using a weighting function in which the weight at the average edge position is maximum and a fitting function determined from the first intensity distribution, and a second intensity distribution is fitted using a fitting function determined from the second intensity distribution. The first intensity distribution represents a distribution of intensity values along a direction perpendicular to the extension direction at coordinates representing each position in the extension direction of the SEM image, and the edge position at the coordinates is determined on the basis of a second intensity distribution obtained by fitting.

The invention discloses a multi-situation photographing edge analysis method. According to the method, an environment map is constructed in real time through an improved SLAM algorithm, the position of the robot is positioned, and all-around environment perception is achieved through multiple types of sensors. After the robot arrives at an inspection point, the angle and the height of a camera are adjusted through a mechanical arm to collect images, and preprocessing operations such as denoising and contrast enhancement are carried out on the images. And then instrument data in the image is identified by using a deep learning model and an OCR technology, and preliminary data analysis is carried out on edge computing equipment. Important data are uploaded to a cloud end through a wireless network for further processing. According to the invention, the inspection efficiency and safety are improved, the data accuracy and reliability are improved, and the data management and real-time performance are optimized.