10 results about "Multilayered architecture" patented technology

The invention provides a PLC code generation method. The method comprises the following steps of obtaining a process formula file defined by a user; the process formula file comprises a multi-layer structure, and the multi-layer structure comprises a variable definition layer, a step execution layer, a process jump layer and a process arrangement layer; extracting data from each layer structure of the process formula file to generate universal structured data; and obtaining a target PLC template, and filling the structured data into a corresponding position of the target PLC template to generate a target PLC code. The invention further provides a corresponding PLC code generation device. According to the PLC code automatic generation method based on the structured process formula file, standardized batch input of process logic and cross-platform code adaptation are achieved, the development efficiency is improved, the system flexibility is enhanced, and the maintenance cost is reduced.

The invention relates to a source network load storage collaborative planning optimization method and system. The method comprises the steps of abstracting physical entities of a source side, a power grid side, a load side and an energy storage side into virtual energy units with energy attributes, dynamic response functions and coupling weight parameters, establishing a cross-layer energy interaction relationship based on an energy flow and information flow mapping rule, and constructing a coupling graph reflecting a source-grid-load-storage multilayer structure. And performing time sequence correlation analysis on the coupling graph to obtain the coupling strength among the virtual energy units, and dividing a local optimization unit and a collaborative optimization unit according to the coupling strength to form a global collaborative planning scheme. And collecting an operation result in a scheme execution process, carrying out online correction on virtual energy unit attributes and coupling weight parameters, iteratively updating a coupling graph and a collaborative planning scheme until a preset stop condition is met, and outputting a source side capacity configuration scheme, a power grid side topology adjustment scheme, a load side adjustable resource configuration scheme and an energy storage capacity planning scheme. And the precision and the adaptability of the source-network load-storage collaborative planning can be improved.

The invention belongs to the technical field of equipment digital modeling, and particularly relates to an equipment digital main line construction method based on a product decomposition structure. The method comprises the steps that an aircraft forward research and development global data architecture based on an aircraft product decomposition structure is constructed, the aircraft product decomposition structure is decomposed into a multi-layer structure according to aircraft products, and each layer comprises a demand transmission data chain, a product research and development data chain and a demand verification data chain; aiming at a demand transfer process, determining a demand transfer relationship of each level, and running through a demand transfer data link of each level from top to bottom; for a product research and development process, determining an interaction relationship among a task domain, a scheme domain, a product domain, a manufacturing domain and a guarantee domain of each level, and establishing a product research and development data chain penetrating through the whole process; and for the demand verification process, establishing a demand verification data link communicating the demand verification method, the demand verification task, the demand verification scene and the demand verification result. According to the method, global association analysis of aircraft research and development full-process model data can be realized.

The utility model relates to the technical field of vehicle domain controllers, in particular to a central domain controller with a three-chip architecture, which comprises a first chip used for controlling a whole vehicle electronic unit, a second chip used for assisting intelligent driving and a third chip of an intelligent drive-by-wire chassis, the three kinds of chips communicate through an SPI to CANFD circuit module or directly through a whole vehicle CAN line. The central domain controller uses a three-chip architecture, uses three main chips to complete each function and can be flexibly configured, the integrated architecture solves the problem of fragmentation of computing power of a traditional architecture, and the central domain controller is not provided with a multi-layer structure and a necessary heat dissipation device structurally, is higher in software and hardware integration level, stronger in compatibility and stronger in expandability, and can be applied to the field of communication. And the reliability is higher, a space is reserved for subsequent product upgrading iteration, and the product expansibility is improved.

The present invention discloses an apparatus and method for generating a dataset for artificial intelligence learning at the interface driver stage. Specifically, the present invention standardizes physical address-based data collected in a heterogeneous PLC environment into a multi-layered structure and integrates the physical address-based data standardized into the multi-layered structure with the state variable identifier system of the SECS / GEM standard to directly generate a dataset for artificial intelligence learning at the interface driver stage. This allows for the generation of a semantic-based AI learning structure at the data generation point (Driver layer) without undergoing a separate data lake and / or ETL (Extract-Transform-Load) reprocessing stage after the collection of physical address-based data.

Method for manufacturing a uniform deflection element (10) comprising at least one reinforcing element (14) and a plurality of regularly spaced projections (18) extending from said reinforcing element (14), wherein the reinforcing element (14) comprises MD-oriented elements and CD-oriented elements in a multi-layered configuration, wherein at least two of the projections (18) are similar in size and shape, each projection (18) comprising a transition section (24) with a transition section width (TW) and a forming section (26) with a forming section width (FW), and wherein the transition section width (TW) is less than the forming section width (FW), wherein the projections (18) are uniform with the reinforcing element (14) such that, in cross-section, the projections (18) and the reinforcing element (14) are formed as a single unit and not from separate parts.the procedure comprises the following steps: , a. Providing a manufacturing device for additive manufacturing; b. Providing at least one material for the uniform deflection element (10), wherein the material to be used is compatible with the additive manufacturing device; c. Generating a three-dimensional digital image of objects in a repeating element of the uniform deflection element (10), wherein the objects include the at least one reinforcement element (14) and the projections (18) of the uniform deflection element (10), and wherein the objects further include the transition sections (24) and the forming sections (26) on the projections (18); d. Compiling the objects into a digitized file containing the projections (18) located on the reinforcement element (14); e. Importing the digitized file to generate a numerical control file for the additive manufacturing device; and f. Applying the material using the additive manufacturing device to produce the uniform deflection element.

The application belongs to the technical field of equipment digital modeling, and particularly relates to an equipment digital main line construction method based on product decomposition structure. The method comprises the following steps: constructing an aircraft forward research and development global data architecture based on an aircraft product decomposition structure, decomposing the aircraft product into a multi-layer structure, each layer comprising a requirement transmission data chain, a product research and development data chain and a requirement verification data chain; determining the requirement transmission relationship of each layer for the requirement transmission process, and penetrating the requirement transmission data chain of each layer from top to bottom; determining the interaction relationship between the task domain, scheme domain, product domain, manufacturing domain and support domain of each layer for the product research and development process, and establishing a product research and development data chain penetrating the whole process; and establishing a requirement verification data chain connecting the requirement verification method, requirement verification task, requirement verification scene and requirement verification result for the requirement verification process. The application can realize global correlation analysis of the aircraft research and development whole-process model data.

The invention belongs to the field of network flow measurement and network data processing, and relates to a graph flow data compression and recovery method and system based on a compressed sensing theory. The method comprises the following steps: establishing a multi-layer structure for storing and compressing graph stream data; when the graph stream data arrives, executing a layer-by-layer insertion process by utilizing a multi-layer structure to realize compression storage of the graph stream data; performing query operation on the compressed and stored graph stream data, wherein the query operation comprises edge query, subsequent query and predecessor query; and decompressing the compressed and stored graph stream data to realize recovery of the graph stream data. Layered architecture design is adopted, efficient compression and accurate recovery of graph stream data are achieved through an ingenious coding mode and optimization solution, and efficient precursor and subsequent weight and query under constant complexity are supported.

The invention discloses an AI model training method and an echo cancellation method. The training method comprises the steps that first-stage training is carried out on a basic model, the basic model comprises an encoder, a decoder and an RNN model, a GCN-Complex model is adopted by the encoder, the GCN-Complex model is adopted by the decoder, and a DPRNN model is adopted by the RNN model; after the basic model is connected with an optimization model, second-stage training is carried out, parameter changes of the basic model are inhibited in the second-stage training process, and the optimization model comprises a multi-layer LSTM structure and a full connection layer; after the second-stage training is completed, an echo cancellation model is obtained; the echo cancellation model is used for performing streaming processing on the audio file needing echo cancellation. According to the method and the device, a good echo cancellation effect is achieved by streaming processing while the parameter quantity is reduced.

The invention discloses a federal traffic flow prediction method based on trajectory-driven directed relation modeling. The method comprises the following steps: 1) generating a basic hidden representation; (2) the directivity of traffic flow is enhanced; 4) mapping inflow and outflow representations to a shared potential space, and realizing directional semantic alignment by comparing loss; (5) constructing a directed relation graph subjected to minimum and maximum normalization; 6) generating time sequence features and uploading the time sequence features to a server; 7) dynamically updating the directed relation graph; 8) the server performs multilayer structure propagation on the time sequence features of the client by using the updated relational graph to obtain spatial enhancement features and returns the spatial enhancement features to the client; and 9) the server propagates client model parameters along the directed relational graph, acquires a personalized aggregation model and issues the personalized aggregation model to the client, so that the relational graph structure and the prediction model keep joint optimization in the federal training process. According to the method, a directed relation structure conforming to traffic direction dependence can be constructed, and fair, stable and accurate federal traffic flow prediction is realized.