10 results about "Continuous evolution" patented technology

The invention discloses a climate prediction method and device based on hot start and background constraint and a medium. The method comprises the following steps: training an auto-encoder by using actual climate data in a first historical annual interval, and taking the auto-encoder as an initial climate prediction model decoder; obtaining first climate data of a current year and a second historical year interval predicted by a specified climate prediction model; for each month in the second historical annual interval, generating a sample pair of the month by taking the first climate data of the month and a preset number of months as an input feature and taking the actual climate data of the month as a label; the training sample library is used for training a climate prediction model trained in the last year with the minimum joint loss as the target, and the joint loss comprises a precision constraint representing model prediction performance, a climate background constraint and a prediction score constraint; the trained model is used for predicting the climate of the current year. The continuous evolution and stable prediction of the prediction model are realized, and the convergence speed of the prediction model is improved.

The invention discloses a next interest point recommendation method based on multi-aspect continuous dynamic preference modeling of hypergraph ODE. According to the method, modeling can be efficiently carried out on multi-aspect dynamic preferences of the user from a continuous angle, and more personalized recommendation is provided for the user. Multi-aspect user preferences are decoupled by constructing a multi-view hypergraph from collaborative, geographic, and transformational aspects. By expanding discrete hypergraph convolution operation into a continuous form based on an ordinary differential equation (ODE), continuous evolution of multi-aspect user dynamic preferences is modeled. User preferences under different views are integrated by adopting a self-adaptive fusion mechanism, and a dynamic relationship between the user preferences is captured. In addition, an inter-view and in-view comparative learning normal form is designed to enhance the expressivity and robustness of interest point representation. A large number of experiments carried out on four real spatio-temporal data sets with different scales verify that the method has remarkable advantages and efficiency.

This invention discloses a physical world model-driven multi-stage computational lithography evolution reasoning method, which can unfold the lithography state in the potential state space of each stage and, combined with process intervention strategies, achieve high-precision modeling and optimization of the lithography pattern evolution process. The method includes the following steps: Step 1, constructing a complete dataset by collecting lithography data under different layout and process parameter combinations; Step 2, mapping the physical state of each stage of the lithography process to the potential space, and modeling the potential state of each stage using a spatial embedding method; Step 3, establishing a process intervention strategy model in the potential space, and planning process adjustment schemes for different stages; Step 4, establishing a state transition model to simulate the continuous evolution of the potential state between stages, predicting the potential state of the next stage through the current stage potential state and intervention strategy, and achieving continuous deduction of the lithography state by iteratively updating the intervention scheme.

The application discloses an agent continuous evolution method and device, equipment and storage medium. The method comprises the following steps: constructing an empty skill library to store skill assets, generating a structured conversation record based on a structured record of a target task whole process, performing multi-time scale progressive growth processing based on the conversation record to obtain an intra-conversation temporary strategy and an optimized execution result, performing attribution analysis to generate an attribution result after the conversation ends, extracting a candidate skill definition based on multiple rounds of records and the attribution result, performing upgrade gate verification and verification evaluation on the candidate skill, writing the candidate skill passing the evaluation into the skill library, continuously monitoring the skill library and downgrading or archiving invalid skills, realizing automatic precipitation of reusable skills from conversation experience of the agent, controllable and verifiable autonomous continuous evolution, improving task execution efficiency and experience reuse rate in long-term operation of the agent, reducing memory pollution and system error rate, reducing maintenance cost, and improving behavior stability and long-term performance.

The invention discloses a self-evolution computing system and a self-evolution computing method, which realize millisecond-level online optimization and minute-level continuous evolution through a sensing-diagnosis-decision-execution-control five-layer closed-loop architecture (as shown in attached drawings of the abstract). The system has causal diagnosis, strategy self-learning and microcode level reconfiguration capabilities, the actually measured energy consumption is reduced by more than or equal to 18%, the delay is reduced by more than or equal to 25%, the decision process is completely explained, the maintenance cost of the system is remarkably reduced, and the comprehensive performance is improved.

The application discloses a welding quality online monitoring method based on sound-light combination advanced prediction, models a welding process as a classification task with time dependence, avoids removing state transition stage data, and is more suitable for continuous evolution characteristics of actual welding states. In view of the problems of molten pool image blurring and unstable characteristics in TIG welding, a sound signal collected synchronously is introduced as an auxiliary mode, and a STFT spectrum enhancement model is combined to enhance the perception ability of energy transmission. The application adopts a CNN to extract molten pool image features and acoustic spectrum features, effectively fuses information in space and time dimensions, realizes deep interaction through a cross-modal attention mechanism and a gate fusion structure, and then utilizes a Transformer to model time dependence, so that the future molten pool state can be predicted in advance.

The present application relates to a kind of continuous evolution system based on T7 RNA polymerase mutant and its application, belong to the technical field of evolutionary engineering.The present application is based on the T7 RNA polymerase mutant with the ability of synthesizing single-stranded DNA, constructs the continuous evolution system comprising single-stranded DNA synthesis template and T7 RNA polymerase mutant, the continuous evolution system is introduced into host, T7 RNA polymerase mutant is recombined with target gene by synthesizing single-stranded DNA, so that host is constructed to obtain mutant library in the process of continuous passage, host is subjected to screening pressure, and host realizes continuous evolution.The system is applied to complete the evolution of tetracycline efflux pump protein in 7 days, and the tryptophan deficiency of saccharomyces cerevisiae is repaired in 24 hours.The tetracycline efflux pump protein mutant screened can tolerate tetracycline and tigecycline, and the tolerance to tigecycline is increased by 8 times, and the tolerance to tetracycline is increased by 2 times, which is of great significance to improve the performance of cell factory.

The application discloses a climate prediction method and device based on a hot start and background constraint, and a medium. The method comprises the following steps: training a self-encoder by using actual climate data of a first historical annual interval, and then decoding the self-encoder as an initial climate prediction model decoder; obtaining first climate data of a current annual interval and a second historical annual interval predicted by a specified climate prediction model; for each month in the second historical annual interval: taking the first climate data of the month and a set number of months before the month as input features, and taking actual climate data of the month as a label, to generate a sample pair of the month; training the climate prediction model trained in the last year by using a training sample library, with the joint loss being minimized as the target, wherein the joint loss comprises an accuracy constraint representing the prediction performance of the model, a climate background constraint and a prediction score constraint; and the trained model is used for predicting the climate of the current year. The application realizes continuous evolution and stable prediction of the prediction model, and improves the convergence speed of the prediction model.

The present application relates to the technical field of prediction analysis, and particularly relates to a multi-parameter data joint prediction method and system. The method comprises the following steps: obtaining multi-source heterogeneous data and performing joint driving variable decoupling to separate external driving factors from state representation; performing dynamic evolution processing based on the data, extracting state derivatives, and constructing a nonlinear driving operator to represent the driving and state change relationship; using the driving operator to reconstruct the transition field of the state space, forming a continuous evolution field, and performing state boundary analysis based on the same to obtain state partition; performing topological structure analysis on the state partition, extracting critical topological features, and identifying system critical turning points to obtain risk mutation data; performing future evolution deduction based on the risk mutation data to output joint prediction data. The present application improves the accuracy of system evolution prediction by constructing a nonlinear driving operator and a state transition field, and combining topological structure analysis to realize critical turning point identification.

In certain aspects of the disclosure, a computer-implemented method includes receiving a selection of a model from a plurality of models running on an ecosystem. The method includes receiving annotated datasets with correct examples and incorrect examples. The method includes training, responsive to receiving the annotated datasets, the model based on the annotated datasets. The method includes running the model based on the training. The method includes receiving a feedback score of results from running the model based on the annotated datasets. The method includes iteratively running, until the feedback score is above a predetermined threshold, the model responsive to user evaluation of the results from running the model based on the annotated datasets. The method includes publishing the model responsive to the feedback score being above the predetermined threshold.