7 results about "Process scaling" patented technology

The invention relates to an AI-driven intelligent design and preparation method of an inorganic hydrated salt phase change material, and solves the problem that the traditional technology is mainly based on experience trial and error and single performance optimization and cannot give consideration to multi-performance balance and multi-scene efficient adaptation development requirements of the inorganic hydrated salt phase change material. The method comprises the following steps: acquiring multi-dimensional performance requirements (including phase change temperature, latent heat value and the like) of a material, generating a candidate formula and a prediction result by using a trained Gaussian process regression model, and performing multi-objective optimization to screen out a Pareto optimal formula; and carrying out experimental verification and calculating deviation, retraining the model by complementary data exceeding a threshold value, and determining a final formula after reaching the standard so as to be matched with continuous process large-scale preparation. The method has the advantages that the AI replaces experience trial and error, multi-performance cooperation of materials is achieved, the research and development period is greatly shortened, the cost is reduced, and the method is suitable for multiple energy storage scenes.

Aspects related to reducing application carbon footprints using predictive load balancing and adaptive process scaling are provided. An adaptive scaling platform may train a delegation model to output event pools based on input of event processing traffic information. The platform may receive event processing traffic information. The platform may generate an event pool using the delegation model. The event pool may comprise tasks required to fulfill event processing requests corresponding to the event processing traffic information and indicators of applications corresponding to the plurality of tasks. The platform may receive event processing information from a server. The platform may cause, based on the event processing information, an application to identify thread requirements. The computing platform may allocate resources based on the thread requirements. The computing platform may delegate tasks to the threads based on the allocating. The platform may update the delegation model based on monitoring the threads.

This invention belongs to the field of rail vehicle technology. It proposes a method and system for adjusting the alignment of workpieces during the intermediate stage of bogie welding. Based on the design 3D model of the bogie workpiece and the process scaling requirements, key dimensions are selected and adjusted to construct a virtual intermediate model. Feature points are collected from the key feature surfaces of the virtual intermediate model to obtain reference values ​​for each feature point. A 3D scan of the actual bogie workpiece during the intermediate welding stage is performed to acquire point cloud data. Feature points are collected and processed from the corresponding key feature surfaces of the point cloud data to obtain the actual values ​​of each feature point. By comparing the reference values ​​and actual values ​​of each feature point, the deviation of each feature point is obtained. For the key dimensions of the bogie workpiece, a scribing weight mechanism is introduced to assign weight priorities to each key dimension. Based on the deviation of each feature point and the weight priorities, the position of the baseline of the key feature surface is adjusted. This invention achieves a closed-loop process from model adaptation and data comparison to baseline adjustment.

Embodiments of the present disclosure relate to the technical field of chemical engineering process scale-up, and specifically provide a process parameter optimization method and device for large-scale production of ternary positive electrode materials. The method comprises: obtaining reference data corresponding to a target process section of large-scale production of ternary positive electrode materials, the reference data including a set of pilot process parameter values, a set of pilot quality detection values, and a set of large-scale production quality target values; retrieving matching historical data from a pre-set historical database, the historical data including historical process parameters and quality detection data of pilot and large-scale production; using a process parameter optimization model corresponding to the target process section, obtaining a set of predicted values of large-scale production process parameters based on the above data; carrying out large-scale process verification according to the predicted values, if the deviation between the actual value of the verified product quality and the quality target value meets a pre-set condition, determining the set of optimized process parameters, otherwise adjusting the parameters and iteratively verifying until the pre-set condition is met.

The application relates to an AI-driven inorganic hydrated salt phase change material intelligent design and preparation method, solves the problem that traditional technologies mainly rely on experience trial and error and single performance optimization and cannot balance the multi-performance balance and multi-scene efficient adaptation development requirements of inorganic hydrated salt phase change materials, and the method comprises the following steps: obtaining material multi-dimensional performance requirements (containing phase change temperature, latent heat value and the like), generating a candidate formula and a prediction result by using a trained Gaussian process regression model, screening out a Pareto optimal formula through multi-objective optimization, verifying through experiments, calculating a deviation, supplementing data above a threshold value to retrain the model, determining a final formula after reaching the standard, and matching a continuous process large-scale preparation. The application has the following effects: AI replaces experience trial and error, realizes material multi-performance cooperation, greatly shortens a research and development cycle, reduces cost, and adapts to multiple energy storage scenes.

The embodiment of the invention relates to the technical field of chemical process amplification, and particularly provides a process parameter optimization method and device for large-scale production of a ternary positive electrode material. The method comprises the following steps: acquiring reference data corresponding to a target process section for large-scale production of the ternary positive electrode material, wherein the reference data comprises a pilot plant test process parameter value set, a pilot plant test quality detection value set and a large-scale production quality target value set; retrieving matched historical data from a preset historical database, wherein the historical data comprises historical process parameters and quality detection data of pilot plant test and large-scale production; utilizing a process parameter optimization model corresponding to the target process section to obtain a large-scale production process parameter prediction value set based on the data; and carrying out large-scale process verification according to the predicted value, if the deviation between the actual product quality value and the target quality value is verified to meet a preset condition, determining an optimized process parameter set, otherwise, adjusting the parameters and carrying out iterative verification until the preset condition is met.

The invention provides a coupled communication topology controlled multi-solver dynamic process scheduling method and system, and relates to the technical field of multi-solver coupling calculation. According to the method, a coupling calculation form of multiple solvers is embedded in a single executable program (a main function), under the constraint of a fixed total process number, role recombination (dynamic quota) of a process set of the multiple solvers is achieved through operation period scheduling, self-adaptive configuration and load balancing are carried out on the scale of processes participating in coupling exchange in all the solvers, and the process size is optimized. Explicit constraints are applied to the communication topology complexity in the coupling exchange stage, and communication topology explosion caused by endpoint scale expansion is avoided; and meanwhile, by combining a security point mechanism, cache multiplexing and coupling exchange overlapping execution, synchronous waiting and repeated overhead in a coupling stage is reduced, so that the overall parallel efficiency and expandability are improved.