72 results about "Adaptive evolution" patented technology

The invention belongs to the technical field of intelligent control, particularly relates to a deep learning-driven intelligent home scene dynamic adaptation method, and aims to solve the problem that an existing intelligent home system is difficult to realize high-precision personalized scene adaptation in a multi-user and multi-device environment due to dependence on a static rule. The method comprises the steps of collecting multi-source heterogeneous user behavior data and performing semantic enhancement preprocessing, constructing a hierarchical time sequence behavior coding model to extract local time sequence dependence and cross-equipment long-range association features, clustering to generate a dynamic scene prototype and mapping the dynamic scene prototype into an executable condition-action rule, after the rules are deployed, a closed-loop optimization mechanism is constructed through explicit and implicit user feedback, and online incremental updating and self-adaptive evolution of the behavior model and the scene rules are achieved. According to the technical scheme, the user complex behavior mode can be deeply understood, the scene adaptation precision is continuously optimized, the individuation level, logic consistency and system robustness of intelligent services are improved, and meanwhile privacy safety and real-time response are guaranteed through edge calculation.

The invention relates to the technical field of network security and data processing, and discloses a violation short message recognition method and system based on deep semantic understanding, and the method comprises the steps: firstly cleaning an original short message, generating a mixed embedding vector through characters, sub-words and pinyin, and carrying out the recognition of the violation short message; then processing through a double-layer detection engine, wherein the first layer utilizes rules and a lightweight model for rapid preliminary screening; in the second layer, for suspected samples, a double-tower fusion neural network architecture is adopted, local and global features are combined, fusion is carried out through a gating unit, and a large language model is input to carry out deep semantic reasoning. The system executes strategies such as interception or flow limiting according to the risk score, and realizes model iteration through a dynamic knowledge base and incremental learning. According to the method, the resource consumption and the detection precision are balanced through the layered architecture, the antagonistic variants are effectively identified by utilizing multi-dimensional feature fusion, and the method has the adaptive evolution capability for a novel violation mode.

The invention discloses kluyveromyces marxianus and application of the kluyveromyces marxianus in production of single-cell protein by taking straw as a raw material. The Kluyveromyces marxianus is a ZH-2 bacterial strain which is CGMCC (China General Microbiological Culture Collection Center) No.32295 The strain is obtained from a wild strain in a laboratory adaptive evolution mode, wheat straw and an inorganic nitrogen source can be used as raw materials for efficiently producing single-cell protein, compared with a parent strain, the utilization speed and the utilization rate of biomass, glucose and xylose are remarkably increased, the yield of ZH-2 SCP can reach 0.047 g.g <-1 >, and the yield of ZH-2 SCP can reach 0.045 g.g <-1 >. And the essential amino acid mode basically accords with adult and infant amino acid modes, and can be used as high-quality protein nutrition supply. The Kluyveromyces marxianus ZH-2 strain capable of obtaining single-cell protein by fermenting wheat straw as a raw material is provided from the perspective of utilizing cheap waste resources, so that the production cost of SCP is greatly reduced, and the problem of protein resource shortage is effectively solved.

The invention discloses a probiotic strain Bifidobacterium animalis subsp. Lactiss S8 of a high-yield biological membrane, the preservation number is GDMCC NO.66518, the characteristics of the stable high-yield biological membrane are obtained through directional screening and adaptive evolution, and the yield of the biological membrane is three times higher than that of a positive control. The extracellular polymeric substance (EPS) of the strain can significantly enhance the tolerance of the strain to severe environments such as gastric acid, cholate and the like, and improve the intestinal colonization ability. Experiments prove that the strain remarkably reduces two liver function indexes, namely ALT and AST, in an animal model, and shows that the strain has a certain effect of improving liver injury.

The application discloses a molecular conformation generation method and device based on an evolutionary clustering algorithm. The method optimizes an initial molecular structure input through a local optimization strategy, obtains an optimized initial molecular structure, and generates an initial population based on the optimized initial molecular structure. Clustering analysis is performed through a hierarchical clustering strategy, and a plurality of structure clusters are obtained. Abnormal structure clusters and normal structure clusters are obtained by judging the maximum distance in the clusters. For the abnormal structure clusters, the abnormal structures in the clusters are taken as parents in a set proportion to perform population iteration. For the normal structure clusters, a search strategy is optimized through an adaptive evolution strategy to perform population iteration. An algorithm termination condition is generated based on energy convergence criteria and structure convergence criteria. When the population iteration meets the algorithm termination condition or reaches a maximum iteration number, the iteration is stopped. A molecular conformation meeting energy criteria in the iteration process is output, and a final molecular conformation is obtained. The application can improve the efficiency and intelligent level of structure search.

This invention discloses a method and system for generating self-evolving world models based on the synergy of rule-based and neural network dual engines, belonging to the field of artificial intelligence technology. The invention's overall concept is based on the collaborative autonomous evolution of dual-engine intelligent agents. Rule-based intelligent agents ensure decision-making stability, while neural network intelligent agents enable adaptive evolution in complex environments. The global state of the model is synchronously input into both engines. After the intelligent agent orchestrator fuses the decisions, the autonomous evolution engine automatically adjusts the model parameters and training strategies, achieving closed-loop autonomous evolution without human intervention. The system synchronously integrates a multimodal unified encoder, a differentiable physics engine, and a 3D Gaussian decoder. Each module is tightly coupled under a unified data flow, fully complying with the patent single-use requirement. This invention achieves integrated protection of the core architecture and auxiliary functions through a single patent, providing broad protection scope and strong decomposability. It is applicable to the fields of artificial intelligence world models, digital twins, AGI autonomous evolution, and 3D virtual scene generation.

The application discloses a fruit tree load intelligent decision-making method based on pollen tube behavior and carbon accumulation balance, and belongs to the field of fruit tree growth management. The method collects physiological data such as fruit tree pollen tube growth and leaf photosynthesis in real time through multiple sensors, combines environmental factors and carbon balance parameters, constructs a multi-source data fusion model, and realizes fine perception and dynamic optimization and regulation and control of fruit load under different growth states of fruit trees. The system introduces an abnormal data detection and self-adaptive evolution mechanism to improve the adaptability and robustness of the model to complex field environments and various varieties. The application can effectively improve the yield and quality of fruit trees, optimize the utilization efficiency of carbon resources, and promote the intelligent and fine management of orchards.

The application relates to the technical field of automation operation and maintenance, and discloses an intention recognition method and system based on dynamic ontology evolution and multi-agent, which comprises the following steps: receiving a natural language instruction, performing entity extraction and probabilistic linking by using a dynamic ontology knowledge base, and generating an initial intention based on predicate analysis; automatically completing missing key slots by using a probabilistic graph model, and generating a standardized intention; decomposing the standardized intention into an atomic subtask sequence, dynamically matching an execution agent based on an agent capability-demand matrix, generating a collaborative workflow, controlling the execution agent to call an atomic tool to execute a task, performing causal correlation analysis on multi-source results according to logical relations between ontology instances, generating a structured reasoning chain, and feeding back; and extracting a new treatment script based on execution feedback by using an evolution engine, and updating an ontology knowledge base and an agent confidence degree. The application can realize accurate understanding, automatic execution and adaptive evolution of a knowledge base of a fuzzy operation and maintenance intention.

The application discloses a gene prdR for regulating myxobacteria predation and a protein coded by the gene, and belongs to the technical field of biotechnology. The mutant DK10 with significantly improved predation capacity is obtained by using the strategy of adaptive evolution; genetic variation sites of the mutant DK10 are analyzed by using the bacterial genome resequencing technology, and functions of the genetic variation sites are researched by using the gene knockout technology, and it is found that the gene deletion of MXAN_2902 (prdR, the nucleotide sequence is shown as SEQ ID NO. 1) significantly enhances the predation capacity of Myxococcus xanthus. The nucleic acid sequence and the amino acid sequence of the transcriptional activator PrdR for regulating the predation of Myxococcus xanthus are disclosed, and the functional strain of Myxococcus xanthus capable of efficiently predating Salmonella enteritidis can be developed by using the prdR gene, and a new strategy is provided for the prevention and control of drug-resistant HVPG pathogenic bacteria.

The invention discloses a method for improving the beta-alanine production capacity of escherichia coli by using adaptive evolution and escherichia coli engineering bacteria, which comprises the following steps: carrying out first round of test tube subculture on escherichia coli 630B serving as an original strain on the basis of an LB liquid culture medium containing 15g / L beta-alanine; the concentration of the beta-alanine is increased with the amplitude of 15-30 g / L for gradient culture, each beta-alanine is subjected to passage for 8-10 times, and escherichia coli engineering bacteria with high yield of the beta-alanine are obtained through screening; the engineering bacterium has higher beta-alanine production capacity, the yield of beta-alanine is obviously increased, and the engineering bacterium has important significance for subsequent screening of high-yield beta-alanine engineering bacteria and industrial application of production of beta-alanine.

A generalized demodulation kernel-based evolutionary optimization single hydrophone multi-source signal separation method comprises the following steps: preprocessing a plurality of low and medium frequency non-stationary sound source mixed signals collected by a single hydrophone, generating a complex signal, and performing generalized demodulation to obtain a complex modulation signal; in a high-dimensional parameter space, an adaptive evolutionary optimization strategy is utilized to iteratively search demodulation parameters, a corresponding signal component is extracted in combination with time-frequency filtering, and a residual signal is used as a next round of separation input to carry out iterative processing, so that high-precision separation and reconstruction of multi-source signals are realized. Under the condition of no target time-frequency priori knowledge, parameter optimization is carried out through a differential evolution algorithm, and time-domain waveforms of different underwater sound sources are obtained through separation.

The application discloses a structural damage identification method based on a proxy model and an improved covariance matrix adaptive evolution strategy, and relates to the technical field of structural damage identification.The application comprises the following steps: obtaining a structure to be identified, constructing a finite element model of the structure to be identified, and respectively applying excitation to the structure to be identified and the finite element model to obtain a data set; a hybrid network model based on a convolutional neural network and a long short-term memory network is constructed, and the data set is used for training to obtain a proxy model; the proxy model is improved by using a covariance matrix adaptive evolution strategy based on a model gradient to obtain an optimal solution; a variational Bayesian model correction method is used to correct the optimal solution to obtain the most possible value of a structural damage geometric size parameter, so that damage geometric size and boundary information of the structure to be identified are determined.The application can efficiently and accurately identify structural damage geometric size information.

The invention relates to the technical field of microorganisms, and particularly discloses bacillus coagulans 18 and application thereof in fermentation production of L-lactic acid. According to the invention, heavy ion beam mutagenesis is combined with high-temperature adaptive evolution breeding to obtain the high-temperature-resistant bacillus coagulans, specifically bacillus coagulans 18, and the preservation number of the bacillus coagulans 18 is CGMCC (China General Microbiological Culture Collection Center) No. 33049. The high-temperature resistance of the strain is remarkably improved, efficient production of lactic acid can be achieved at high temperature, particularly, a two-stage heating method process control strategy developed for the strain can remarkably improve the raw material conversion rate of the strain, and the strain is suitable for industrial production of lactic acid, reduces the economic cost of lactic acid biological manufacturing and is suitable for application and popularization.

The present invention relates to novel strains of Lactobacillus genus having extended shelf life properties, i.e., capable of extending the shelf life of food products (e.g., fermented milk products) at room temperature without severely impairing the antimicrobial, antifungal and / or anti-yeast effect ability of said strains when added to such products. The invention also relates to methods of producing such novel strains, in particular to methods of producing such novel strains via adaptive evolution.

The application discloses a kind of low permeability reservoir underground gas storage multi-cycle injection-production condition under the prediction method of effective reservoir capacity boundary dynamic evolution, and the application is based on injection-production intensity driving seepage parameter self-adaptive evolution under the condition of low permeability reservoir underground gas storage multi-cycle strong injection and strong production, and the effective reservoir capacity boundary is dynamically predicted, to solve the systematic overestimation problem caused by parameter static, threshold effect neglect and connectivity loss in the effective reservoir capacity boundary prediction of low permeability reservoir underground gas storage under the condition of multi-cycle strong injection and strong production. Realize: the effective reservoir capacity boundary of injection stage and production stage is predicted respectively;Seepage parameter self-adaptive evolution with cycle and injection-production system intensity, not dependent on field testing / monitoring data;Through threshold-conductivity-connectivity accessibility triple criterion, the effective reservoir capacity boundary meeting engineering definition is obtained.

The invention discloses molecular markers for identifying five caragana plants and application of the molecular markers, and relates to the technical field of molecular identification. The five caragana plants are caragana arborescens, caragana medii, caragana intermediary, rubus corchorifolius and caragana glandulifera; the molecular marker is shown as SEQ ID No.1, the molecular marker is located at the 4-582nd site of a caragana arborescens plastid genome, the total length of the molecular marker is 579 bp, and the insertion and deletion variation of the molecular marker occurs between the 196-198 site and the 442-443 site; the invention provides an important basis for the classification and identification of caragana plants, and lays a theoretical foundation for the subsequent development of adaptive evolution, germplasm resource protection and medicinal sustainable utilization research.

The application discloses a self-evolution multi-agent event semantic understanding system and method based on a dynamic scalable model, which comprises a multi-agent cooperation framework modeling module, a demand understanding and task planning module, a capability verification module, a model dynamic expansion module, a task execution module and a completion module connected in sequence, wherein the multi-agent cooperation framework modeling module comprises a central control type multi-agent, a task execution type multi-agent and a model expansion type multi-agent. The method focuses on a news event semantic understanding scene, constructs a dynamic scalable system architecture through a multi-agent cooperation mechanism, introduces a capability verification link to optimize a task execution process, and realizes on-demand iteration of model capability by using an active learning framework, so that adaptive evolution of agent capability is realized, thereby solving the problems of model rigidity and poor adaptability of a traditional event semantic understanding system in a complex news event scene, and significantly improving the dynamic adaptability and accuracy of news event semantic understanding.

The invention relates to the technical field of genetic engineering, in particular to a recombinant bacterium for high-yield serine and application of the recombinant bacterium in fermentation production of serine. The ptsI gene of escherichia coli is knocked out, a glucose PTS system is blocked, glucose transport and phosphorylation and PEP-pyruvic acid associated reaction are relieved, and pyruvic acid is synthesized by reducing serine synthesis precursor 3-phosphoglyceric acid branches. Growth adaptive evolution is further carried out on escherichia coli with the ptsI gene knocked out, an obtained mutant strain is sequenced, the mutation condition is shown in a table 2, and finally the growth rate accelerating chassis bacterium is obtained. Afterwards, by enhancing the expression serine key genes serA, serB and serC of the mutant strain and knocking out serine degradation pathway genes sdaA, sdaB and tdcG, the engineering bacterium for fermentation production of serine is constructed, and the serine yield of the engineering bacterium is remarkably improved.

The invention discloses a software test case self-adaptive evolutionary generation method, which comprises the following steps of: structurally analyzing demand information through natural language processing and a scene extraction model, automatically distributing scene labels, mapping service functions, generating versioned test scene metadata, and then utilizing clustering and multi-index weight analysis to obtain a test case self-adaptive evolutionary generation result. And carrying out association mapping, importance scoring and grading archiving on the rules and the scenes. Through rule cold storage, unfreezing, activation and elimination mechanisms, similarity matching and fault detection are combined, intelligent compensation and safe elimination of cross-version scene knowledge are realized, automatic and manual intervention parameter configuration is supported, dynamic optimization and whole-process traceability of the knowledge base are ensured, and the method and the device are suitable for large-scale popularization and application. According to the method, the scene coverage rate and the defect detection capability of test case generation are improved, and efficient evolution and safety management of test knowledge are realized.

The invention provides a yarrowia lipolytica adaptive evolution vector as well as a construction method and application thereof. The vector comprises a fusion expression unit containing a yarrowia lipolytica source dnaB helicase gene and a cytidine deaminase gene human source rAPOBEC1, an escherichia coli resistance gene segment AmpR, a URA selection marker, a yarrowia lipolytica autonomous replication sequence ARS / CEN, a promoter UAS1B8-tef and a plasmid replication origin ori. The vector can efficiently and quickly mutate cytosine at any site on a yarrowia lipolytica genome into thymine, and amino acid mutation or gene inactivation can be realized. The carrier is used for domesticating the yarrowia lipolytica under various conditions, so that the growth performance of the yarrowia lipolytica under corresponding culture conditions can be quickly and remarkably improved, and the evolution cycle is shortened.

Described herein are TIGIT binding peptides, nanoparticle systems such as dendrimer systems including the TIGIT binding peptides, pharmaceutical formulations, and methods of use. Also described are methods of identifying peptides that bind a target receptor, the method including phage display, chemical synthesis, determination of binding affinities, and computational modeling. Advantageously, adaptive evolution modeling can be used to optimize the peptides identified in the methods for improved binding affinities.

The invention discloses a self-evolution multi-agent event semantic understanding system and method based on a dynamic extensible model, and the system comprises a multi-agent cooperation framework construction module, a demand understanding and task planning module, a capability verification module, a model dynamic extension module, a task execution module, and a completion module which are connected in sequence. The multi-agent collaboration framework construction module comprises a central control type multi-agent, a task execution type multi-agent and a model expansion type multi-agent, the method focuses on a news event semantic understanding scene, a dynamic extensible system architecture is constructed through a multi-agent collaboration mechanism, a capability verification link is introduced to optimize a task execution process, and the task execution efficiency is improved. And on-demand iteration of the model capability is realized by using an active learning framework, and adaptive evolution of the agent capability is realized, so that the problems of model stiffness, poor adaptability and the like of a traditional event semantic understanding system in a complex news event scene are solved, and the dynamic adaptability and accuracy of news event semantic understanding are remarkably improved.

The invention relates to an adaptive evolution culture method of Halomonas and application of the Halomonas in preparation of PHB, and belongs to the technical field of biology. According to the method, the red algae are pretreated with the acid with the specific concentration, the hydrolysis rate of the red algae can be increased, and more raw materials are provided for PHB preparation. According to the invention, the halomonas is subjected to 35 times of adaptive evolution culture in the red algae pre-hydrolysate, so that the tolerance and transformation capability of the strain to an inhibitor are improved, the prepared culture solution containing the halomonas evolved strain is used for preparing PHB (poly-3-hydroxybutyrate), and the yield of the PHB can be obviously improved. According to the method, the PHB is prepared by fermentation of the Halomonas evolved strain, the red algae are taken as the raw material, the hydrolysate obtained by pre-hydrolysis does not need solid-liquid separation, and fermentation is performed after enzymolysis of cellulase, so that more raw materials can be released, the content of the PHB is further increased, the utilization rate of the red algae is increased, and meanwhile, the preparation process steps are simplified.

The present application relates to the technical field of data analysis, and is a load-bearing beam performance digital model adaptive evolution method and system, specifically comprising: collecting virtual state parameters of the load-bearing beam in real time and generating a residual vector flow, screening incoherent fluctuation abnormal data representing physical burst of the load-bearing beam and establishing a simulation drift fingerprint; retrieving from a historical memory, defining sample coordinates with high matching degree as evolution drift source pre-judgment points; performing risk level division of the pre-judgment points through a stiffness reduction proxy model; calculating the change of the damage state index before and after disturbance, quantifying the damage sensitivity index of each pre-judgment point of the twin body, and locating the mapping coordinate point corresponding to the real physical burst loss according to the damage sensitivity index; dividing the evolution distortion level of the mapping coordinate point, and driving the digital twin body to complete adaptive update; the present application solves the problem of residual bearing capacity prediction and safety early warning failure of the system at key response nodes in the prior art.

The application discloses a novel flexible manufacturing system dynamic scheduling method based on multi-agent reinforcement learning, relates to the technical field of intelligent manufacturing, and comprises the following steps: constructing a cross-domain knowledge graph driven state space, fusing process knowledge and equipment capacity to perform semantic embedding; establishing an adaptive evolution intelligent agent collaborative network of a space-time heterogeneous graph, and dynamically adjusting a topological structure according to task dependence and equipment coupling; adopting a progressive collaborative value decomposition network to optimize a strategy and accurately realize credit distribution; and constructing a multi-scale closed-loop compensation decision mechanism to form deep coupling and closed-loop feedback at the equipment layer, the production line layer and the system layer, so that the scheduling efficiency and robustness of the flexible manufacturing system are significantly improved.

The invention relates to the technical field of intelligent agriculture and artificial intelligence crossing, and discloses a greenhouse vegetable disease and pest prediction system based on multi-parameter fusion and XGBoost, and the system comprises a multi-source environment perception and standardization module which is used for processing multi-dimensional sensor data; the self-adaptive weighted covariance tensor construction module is used for weighting the interaction effect of the environmental factors by using the evolvable biological influence matrix and constructing a covariance tensor representing coupling characteristics; the physical and data double-flow coupling prediction module runs the SIR mechanism reference flow and the XGBoost residual error correction flow in parallel, and outputs the SIR mechanism reference flow and the XGBoost residual error correction flow through linear superposition to obtain a final prediction result; and the residual feedback and kernel evolution module is used for extracting feature importance gains of the XGBoost. According to the method, the feature extraction is deepened by constructing the weighted covariance tensor, the interpretability and accuracy of the result are ensured in combination with double-flow prediction, and the adaptive evolution of the model is realized through a feedback mechanism.

The invention discloses a propionibacterium mutant strain with high propionic acid tolerance as well as a mutation breeding method and application of the propionibacterium mutant strain, and belongs to the technical field of industrial microorganism breeding. The mutant strain is named as propionibacterium CCTCC M20252200D5-6, the propionic acid tolerance upper limit of the mutant strain reaches 85 g / L, and the propionic acid yield of the mutant strain in a 50 L fermentation tank is 76 g / L and is increased by 25.25% compared with that of a wild strain. The breeding method comprises the following steps: 1) carrying out ARTP mutagenesis (treatment is carried out for 80s, and the fatality rate is 92.2%); 2) adaptive evolution of gradient propionic acid pressure (30-80g / L); (3) carrying out primary screening on a 100g / L propionic acid flat plate; 4) carrying out targeted screening on bacterial colonies with high content of trehalose by virtue of a Raman spectrum (532nm excitation, 850cm <-1 > characteristic peak); 5) fermentation verification; the method solves the problems of low screening efficiency and poor positive mutation rate of traditional breeding, the screening flux reaches 1000 strains per day, the period is shortened to 5 days, and the method can be directly applied to industrial production of propionic acid through a microbial fermentation method and has remarkable economic and environmental benefits.

The application discloses an engineering building structure evolution method, relates to the field of civil engineering and building informatization technology, and realizes dynamic mapping with the entity by constructing a full-element digital twin of the building structure, completes multi-dimensional evolution demand quantitative evaluation, generates a hierarchical evolution strategy and completes intelligent comparison and selection of multiple schemes, verifies the full performance through digital simulation, accurately controls the construction in a digital manner, and finally realizes closed-loop verification of the evolution effect and iteration update of the twin. The application solves the problems that the existing static design of the building structure cannot adapt to changes in the whole life cycle demand, is fragmented in reconstruction, has information faults, has high carbon emission, and lacks hierarchical adaptive evolution strategies, can realize sustainable evolution of the building structure in multiple rounds of the whole life cycle with low disturbance and low carbon emission, greatly reduces the reconstruction carbon emission and the whole life cycle cost, and can be widely applied to sustainable reconstruction of various buildings, municipal and water conservancy structures.

The application relates to the technical field of industrial visual detection, and discloses a neural computing model adaptive evolution method and system under non-equilibrium sample distribution. The method comprises the following steps: obtaining an industrial surface image to perform multi-frequency texture capture and significant enhancement, and obtaining an initial saliency map; performing spatial weighting and feature dimension reduction on the initial saliency map according to a preset gradient threshold, and obtaining a weighted feature matrix; performing multi-scale fusion and direction feature extraction on the weighted feature matrix, and obtaining a comprehensive texture matrix; calculating a contrast difference value of the comprehensive texture matrix to update an initial probability distribution, and obtaining a calibrated probability distribution; performing clustering resampling on the calibrated probability distribution, and obtaining refined defect coordinates; calculating a relative deviation rate to generate a penalty loss function to fine-tune a neural model, and obtaining micro crack positioning information. The method can solve the problem that weak features are easily submerged, and realizes high-precision defect positioning and dynamic adaptive evolution of a model.

The invention relates to the field of microbial technology and genetic breeding, and discloses a mutagen-assisted adaptive laboratory evolution method and application, the mutagen-assisted adaptive laboratory evolution method comprises the following steps: A1, mutagenesis; a2, carrying out passage; a3, continuous mutagenesis and passage are carried out, and during each repetition, preset adaptive conditions are gradually improved according to a preset amplification until a target culture is screened out, according to the method, an efficient mutagenesis mechanism and a continuous directed evolution process are subjected to in-situ and deep integration, so that high-frequency and directed adaptive evolution of microbial genotypes under strong selection pressure is realized, and the efficiency is improved.