19 results about "Edge function" patented technology

Enhanced techniques applicable to a ray tracing hardware accelerator for traversing a hierarchical acceleration structure and its underlying primitives are disclosed. For example, traversal speed is improved by grouping processing of primitives sharing at least one figure (e.g., a vertex or an edge) during ray-primitive intersection testing. Grouping the primitives for ray intersection testing can reduce processing (e.g., projections and transformations of primitive vertices and / or determining edge function values) because at least a portion of the processing results related to the shared feature in one primitive can be used to determine whether the ray intersects another primitive(s). Processing triangles sharing an edge can double the culling rate of the triangles in the ray / triangle intersection test without replicating the hardware.

Enhanced techniques applicable to a ray tracing hardware accelerator for traversing a hierarchical acceleration structure and its underlying primitives are disclosed. For example, traversal speed is improved by grouping processing of primitives sharing at least one figure (e.g., a vertex or an edge) during ray-primitive intersection testing. Grouping the primitives for ray intersection testing can reduce processing (e.g., projections and transformations of primitive vertices and / or determining edge function values) because at least a portion of the processing results related to the shared feature in one primitive can be used to determine whether the ray intersects another primitive(s). Processing triangles sharing an edge can double the culling rate of the triangles in the ray / triangle intersection test without replicating the hardware.

The invention discloses an intraocular light field simulation method and system based on physiological constraint and pathology traceability, the system takes a Physics Informed Kolmogorov-Arnold Net (PI-KAN) network as a core, namely physical information KAN, the method comprises the following steps: obtaining personalized parameters of eyeballs; 5-dimensional light field parameters including space, wavelength and time are input into a pre-trained PI-KAN model for light field solving, a three-layer network architecture including an input layer, a hidden layer and an output layer is established, a physical information edge function is constructed, and training is performed through fusion of a Helmholtz equation and a loss function of boundary conditions; generating an OCT image based on the light field solved by simulation; by analyzing side function mapping, visualization and pathological traceability of a light field propagation physical mechanism are realized. According to the method, the sparsity and interpretability of PI-KAN are utilized, the problems that a traditional method is low in calculation efficiency, difficult in high-dimensional modeling, weak in physical constraint and poor in interpretability are solved, millisecond-level, high-precision and interpretable simulation of the eye light field is achieved, and the method is suitable for ophthalmic clinical auxiliary diagnosis, surgical planning and equipment optimization.

Edge functions at an edge location of a content delivery network (CDN) may use APIs of a datastore engine in order to read / write or create / delete local tables at the edge location. Data may be accumulated in the local tables and the new data may be used to enhance decision at the edge. Some of the local tables may be initially populated from a back-end database. This allows the functions to modify the data from the back-end database, without affecting the actual source data at the back-end database (modifications to local tables remain local to the edge location).

The invention discloses a server-free edge function reliability deployment method and system based on reinforcement learning, and the method is oriented to server-free edge calculation, takes the minimization of long-term network cost as a target, and systematically solves the problems of high cold start cost, limited resources, difficult reliability guarantee and the like. A system model including user movement, channel fading, function deployment and a backup mechanism is constructed, four types of costs of cold start, maintenance, transmission and punishment are accurately described and converted into a Markov decision process, and a state-action-award framework is utilized to realize dynamic decision. The PPO algorithm fusing the greedy strategy to assist the action selection is further provided, and a function deployment scheme meeting the reliability constraint is efficiently generated under the condition that resources are limited. According to the method, through fusion of reinforcement learning and a greedy strategy, the problems of feasibility, reliability and cost of function deployment in server-free edge computing are solved, and the method has the capabilities of efficient deployment, adaptive scheduling and long-term performance optimization.

Edge functions at an edge location of a content delivery network (CDN) may use APIs of a datastore engine in order to read / write or create / delete local tables at the edge location. Data may be accumulated in the local tables and the new data may be used to enhance decision at the edge. Some of the local tables may be initially populated from a back-end database. This allows the functions to modify the data from the back-end database, without affecting the actual source data at the back-end database (modifications to local tables remain local to the edge location).

The present application relates to the technical field of edge function computing, and to a method for implementing an edge function of a clocked flip-flop type, aiming to solve the problem that traditional methods for triggering an edge function by http cannot meet the data processing requirements of some Internet of Things devices. The key points of the technical solution of the method comprise S1: setting an edge function, causing an edge function of a flip-flop type to rely on a CDN, setting a corresponding script and trigger frequency, and deploying the script to an edge environment; S2, triggering and updating a timed task; S3, when the script is not bound with a domain name, responding to the function of a clocked flip-flop type; S4, responding to the timed task during operation and executing access authentication; and S5, data acquisition: acquiring and monitoring logs in a domain-name-free state to acquire data. More efficient and reliable task scheduling and data processing effects are achieved in edge computing environments.

The application discloses an image denoising method and device, and the method comprises the following steps: constructing a hybrid partial differential image denoising model based on an adaptive edge detection algorithm, wherein the model combines ROF and LLT two denoising models in a manner of weighting by a fractional order edge detection function; determining an optimal order of the image fractional order edge function by a pollination algorithm according to feature information of the image; and solving the denoising model to output a denoised image. The application integrates the advantages of total variation and fourth-order partial differential equations, and compared with other advanced denoising algorithms, the application can maintain image edges and texture details while avoiding the "step effect" of smooth areas.

The present invention relates to the fields of advanced driver assistance systems (ADAS), computer vision and machine learning (ML). The present invention provides an ML-based method for training a neural network based on a synthetic image (generated from a real image or a virtual image) to improve the process of determining a depth map, relative pose or semantic segmentation. Therefore, the present invention provides a device (100) for determining a depth map (101), a relative pose (102) or a semantic segmentation (103). The device (100) comprises: a neural network (104) for determining the depth map (101), the relative pose (102) or the semantic segmentation (103) based on an input image (105) during an inference phase; a generator (106) for generating a synthetic image (107) based on a real image (108) or a virtual image (109) and according to a loss function (110) during a training phase; the neural network (104) is trained based on the synthetic image (107); wherein the loss function (110) includes a semantic edge function (111).

The invention discloses an optimal power flow calculation method, device, equipment and medium based on a Kolmogov-Arnode graph neural network, and relates to the technical field of power systems, and the method comprises the steps: obtaining the current operation parameters of a power system, and constructing a corresponding graph data structure based on the current operation parameters; inputting the graph data structure into a preset optimal power flow analysis model, and performing aggregation processing on the voltage state characteristics of the neighborhood nodes based on the injected active power, the injected reactive power and a node admittance matrix through the preset optimal power flow analysis model to obtain aggregated node characteristics; and carrying out nonlinear transformation on the aggregated node features by utilizing a learnable edge function of a Kolmogov-Arnod network in a preset optimal power flow analysis model to obtain target node feature representation so as to generate and output a generator set optimization output scheme of the power system. According to the real-time change of the power grid, a faster decision-making speed is provided for suppressing frequency fluctuation, optimizing voltage distribution and preventing cascading failures.

This invention discloses an iterative optimization method for internal gear turning processes and tool parameters. The method includes: establishing a cutting edge function and a cutting edge sweep surface function in the turning space, and constructing a three-dimensional chip model; calculating micro-element chip parameters based on the chip model and establishing a cutting force calculation model; establishing a finite element simulation model for turning; setting initial values ​​for tool parameters, and optimizing process parameters with cutting force, maximum chip cross-sectional area, and material removal rate as objectives; optimizing tool structure parameters using finite element simulation based on the optimized process parameters; iteratively optimizing until convergence conditions are met, and outputting the optimal collaborative parameters. This invention achieves systematic collaborative optimization of process parameters and tool structure parameters, effectively reducing cutting forces, improving machining quality and tool life, and is suitable for efficient machining of high-precision internal gears.

Systems and methods for locating microserver nodes in proximity to edge devices using georouting. Microservers automatically form a global peer-to-peer network to serve edge functions and content to edge devices. Edge devices use HyperText Transfer Protocol (HTTP) to execute serverless functions or otherwise retrieve data from edge nodes and / or microservers located in proximity to the HTTP client. The cloud platform locates the nearest edge node and / or microserver. Edge devices georoute HTTP requests to the nearest edge node and / or microserver. Serverless functions are implemented in secure, isolated environments using a blockchain.

The present invention discloses a method for implementing a timed trigger-type edge function, which relates to the field of edge function computing technology and aims to solve the problem that the traditional method of triggering edge functions by HTTP can no longer meet the data processing needs of some IoT devices. The key points of the technical solution are S1: setting the edge function, relying on the CDN for the trigger-type edge function, setting the corresponding script and trigger frequency, and deploying and sending the script to the edge environment; S2: triggering and updating the scheduled task; S3: implementing the response of the timed trigger-type function when the script is not bound to a domain name; S4: responding to the scheduled task at runtime and access authentication; S5: data collection, collecting and monitoring logs in the absence of a domain name to implement data collection. This achieves the effect of making task scheduling and data processing in the edge computing environment more efficient and reliable.

The embodiment of the invention provides a picture encryption method and related equipment / products, and relates to the field of information security. The method is used in the edge function service of the CDN node. The method comprises the following steps: receiving a picture request sent by a client, wherein the picture request comprises encryption indication information and a first public key matched with a first private key in the client; generating a second private key and a second public key which are matched with each other; encrypting a target picture corresponding to the picture request according to the first public key, the second private key and the encryption indication information to obtain an encrypted picture corresponding to the target picture; generating a picture ciphertext according to the second public key, the encrypted picture and the encryption indication information; and returning the picture ciphertext to the client, so that the client decrypts the encrypted picture according to the second public key, the first private key and the encryption indication information to obtain the target picture. According to the technical scheme of the embodiment of the invention, the security and efficiency of picture encryption can be improved, and the delay of picture acquisition by the client and the load of the server are reduced.

Systems, industrial devices, and methods for controlling communication within an industrial system. One system includes a PLC of the industrial system. The PLC may execute a control application to control performance of an industrial process of the industrial system. The control application may be locally stored at the PLC. The PLC may retrieve tag data related to performance of the industrial process, where the tag data may be specific to a tag related to performance of the industrial process. The PLC may route an electronic message including a portion of the tag data to an edge application subscribed to the tag. The edge application may be locally stored at the PLC. The PLC may execute the edge application based on the portion of the tag data such that the edge application performs an edge function related to the industrial system using the portion of the tag data.

The invention provides a method for analyzing data in the coal mine industry, belongs to the technical field of data processing, and aims to innovatively integrate all-around and multi-type data sources in the coal mine industry, cover traditional coal mine business entity data, and introduce Internet to collect data, deeply mine and associate data values of the data sources. Therefore, leading-edge functions of construction query, multi-dimensional association configuration of data, data analysis presentation, data collection, data sharing and the like of a data source in the coal mine field are achieved, the whole process from collection, real-time monitoring to dynamic analysis of the data in the coal mine field is completed in a one-stop mode, and a data working party is enabled with unique convenience; and service is provided for a data lookup party by using distinctive intuition.

The application provides an edge function triggering method and device, electronic equipment and a storage medium, comprising: obtaining attribute information of full edge functions from a configuration center, determining whether there is attribute information of a new edge function in the attribute information of the full edge functions according to the attribute information of the full edge functions and attribute information of edge functions sent by the configuration center, if there is attribute information of a new edge function in the attribute information of the full edge functions, listening to an event queue according to event queue meta information, if the event queue is listened to, determining a target edge function according to the event queue and a corresponding relationship between the edge function and the event queue, and triggering the target edge function. In the case that the event trigger listens to the event queue, the execution of the edge function is triggered, which can immediately respond and trigger the edge function when the event occurs, solving the problem of low real-time performance of edge data processing caused by large delay in triggering the edge function.

The application discloses a data warehouse layer-by-layer carrying control method based on segmented construction, and relates to the technical field of data processing.The method comprises the following steps: calling a theme domain ETL log, taking a dependency priority as a constraint, and mining a hierarchical dependency topology; extracting an adjacent business domain to combine a segmented allocation function to construct an edge function node; configuring a data channel according to the hierarchical dependency to form a processing topology; and K layers receiving source data, progressively performing mutual verification and upward carrying, and outputting a result.The method solves the technical problems that the dependency relationship between business theme domains is complex and lacks hierarchical constraints in the existing data warehouse ETL processing process, leading to chaotic data processing links, easy propagation of errors, and difficulty in guaranteeing data consistency, and achieves the technical effects of automatically mining business dependency priorities through historical processing logs, constructing hierarchical dependency topologies and allocating edge function nodes, realizing mutual verification of intra-layer data and layer-by-layer carrying of inter-layer data in the processing process, and improving the accuracy, consistency and automation level of data processing.

The invention relates to an electroencephalogram signal fine-grained emotion recognition method which comprises the following steps: selecting a tested electroencephalogram signal from an FACED data set, and performing frequency division segmentation on the preprocessed tested electroencephalogram signal; constructing a co-fluctuation edge time sequence ETS and a co-fluctuation edge function connection matrix EFC for all the electroencephalogram signals subjected to frequency division and segmentation by using a co-fluctuation-based edge center analysis method; according to the high-order space-time characteristics of the common fluctuation edge time sequence ETS and the common fluctuation edge function connection matrix EFC, a CoEdge-STNet model is constructed, and the CoEdge-STNet model is a fine-grained emotion classification model; in different frequency bands in a subject, performing fine-grained emotion recognition by using a CoEdge-STNet model; and under different frequency bands crossing a subject, carrying out fine-grained emotion recognition by using a CoEdge-STNet model. The invention further relates to an electroencephalogram signal fine-grained emotion recognition system. According to the invention, fine-grained emotions can be effectively recognized, and the accuracy of emotion recognition is improved.