6results about How to "Realize accumulation" patented technology

The invention relates to the technical field of biology, in particular to construction and application of a recombinant strain for synthesizing veratrazine and steroid precursors of the veratrazine, and the steroid precursors comprise squalene, ergosterol, lanosterol, 22R-hydroxy cholesterol, 22R, 26-dihydroxy cholesterol and 22-hydroxy-26-amino-cholesterol. The recombinant strain provided by the invention can efficiently express steroid precursors, for example, the content of lanosterol reaches 172mg / L and is increased by about 170 times compared with that of an original strain, the yield of 22R-hydroxycholesterol reaches 3482.83 mu g / L, the yield of 22R, 26-dihydroxycholesterol reaches 1003.05 mu g / L, veratrazine can be efficiently synthesized, and the yield of veratrazine reaches 265.38 mu g / L.

The application discloses a kind of establishment methods of fuel cell fault embedding model, including fault function embedding, electrochemical model calculation, membrane state water content solution, liquid water content solution gas component concentration solution and temperature equation solution sub-item calculation.The model is embedded by fault function, realizes the accurate simulation under the working condition of pressure fault, humidity fault and flow fault.Under the condition of meeting the operation requirement of fuel cell, the complex heat and mass transfer process inside fuel cell under different fault working conditions and the dynamic response of multiple transient conditions can be understood.The complex physical field change inside fuel cell under fault working condition is solved by the mutual coupling of electrochemistry, fluid and heat.The problem of unbalanced fault data type and poor data quality in current fault diagnosis research is solved, the accumulation of various fault data is realized, the model support is provided for fuel cell fault diagnosis algorithm development, and the product development cycle and bench test cost are greatly shortened.

The present application relates to the recombinant microorganism for producing 3-amino propanol by fermentation method and its construction method and application. Specifically, the present application provides a construction method of the recombinant microorganism for producing 3-amino propanol, which is introducing the coding gene of omega-transaminase into the microorganism producing 3-hydroxy propionaldehyde, wherein the omega-transaminase can catalyze the conversion of 3-hydroxy propionaldehyde into 3-amino propanol in the presence of amino donor. The present application introduces the omega-transaminase capable of catalyzing the synthesis of 3-amino propanol with 3-hydroxy propionaldehyde as the substrate into the microorganism producing 3-hydroxy propionaldehyde based on gene editing, molecular cloning and other technologies, which first creates a brand new full-path biosynthesis process route of 3-amino propanol.

The invention discloses an intelligent optimization method for an annealing temperature curve of a high-silica glass fiber, and relates to the technical field of high-silica glass fiber processing, which thoroughly gets rid of the dependence on artificial experience, can calculate a theoretical optimal annealing curve under a specific working condition by constructing a data-driven prediction model and utilizing an intelligent algorithm for optimization, and improves the processing accuracy of the high-silica glass fiber. Accurate temperature control is achieved, changes of working conditions such as the wire drawing speed and the fiber diameter can be sensed in real time, an annealing temperature curve is dynamically adjusted when the working conditions fluctuate, the uniformity and stability of product quality under different working conditions are ensured, and meanwhile through collaborative optimization of multiple targets such as residual stress and mechanical properties, the product quality is improved. According to the method, the residual stress of the high-silica glass fiber can be remarkably reduced, the strength and flexibility of the fiber can be improved, so that the yield is improved, the energy consumption is reduced, the method has a model self-adaptive updating capability, and through a production-detection-feedback-learning closed loop, the system can adapt to long-term working condition changes, and accumulation and iterative optimization of process knowledge are realized.

The invention discloses an equipment password automatic updating system based on protocol playback and artificial intelligence, which relates to the technical field of network security and equipment management and comprises a protocol agent recording module, an AI strategy generation module, a strategy and version management module and an automatic execution engine module. According to the method, full-process automation from strategy generation to verification execution is achieved through proxy connection between the user and the target equipment, the method is used for combining captured interaction time sequence logs with predefined strategy templates and extracting semantic prompt information of contexts, executable strategies comprise state matching rules and instruction sequences, and therefore the interaction time sequence logs can be automatically generated. Accumulation and reuse of strategy assets are realized, real cross-protocol and cross-manufacturer intelligent adaptation is realized, a corresponding strategy is automatically loaded from a strategy knowledge base according to a device connection parameter, an authentication credential and a password update period preset by a user, result verification and audit log recording are carried out after execution is finished, and the security and compliance of an update system are enhanced.

This invention discloses a multi-stage simulation training method, system, device, and medium for a large language model. The method includes: extracting multi-dimensional difficulty features from training samples, dividing them into difficulty levels to construct a sample difficulty label system; setting multiple progressively advancing training stages, configuring a matching preset sample difficulty distribution for each stage, and sampling to construct training datasets for each stage; monitoring the model's accuracy on validation sets at each difficulty level in real time during training; dynamically adjusting sampling weights based on accuracy: reducing the weight of levels whose accuracy consistently exceeds a first threshold and assigning them to higher levels, increasing the weight of levels whose accuracy consistently falls below a second threshold, and then normalizing; after each stage, extracting low-difficulty samples to add to the next stage's dataset, and performing parameter freezing and fine-tuning; and adjusting the difficulty feature weights, stage division thresholds, and control judgment thresholds using training process data. This invention achieves adaptive control of training difficulty and multi-stage progressive learning.