8results about How to "Build reliable" patented technology

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data pipeline construction method, device and equipment and a medium, and the method comprises the steps: receiving a target data set and a task processing target, and generating a data general situation report through a data perception intelligent agent; generating and updating a solution strategy by utilizing a strategy planning agent based on the data general situation report, constructing an executable code script by utilizing a code generation and execution agent, and running in a sandbox environment to obtain a processing result; and executing grammar and logic verification by using a quality verification agent, submitting staged review after the verification is passed, and promoting the next stage to complete assembly line construction. According to the method, a closed-loop process of data understanding, strategy planning, code generation, execution and verification is realized through cooperation of multiple agents, and the automation degree and the processing reliability of data pipeline construction are improved.

The invention provides a state space model identification method and device, electronic equipment and a storage medium, and is applied to the technical field of computers, and the method comprises the steps: obtaining input time sequence data and output time sequence data of a target industrial system, generating a training data set based on the input time sequence data and the output time sequence data, and carrying out the recognition of a state space model; based on an initial state space model created by a neural network, initial parameters of the initial state space model are optimized by using a particle swarm optimization algorithm and based on the training data set to obtain target initial parameters, and then the target state space model is obtained based on the target initial parameters and the training data set. And performing network training on the initial state space model by taking minimization of the total loss function as a target to obtain a state space model used for representing the target industrial system, the state space model identification method provided by the invention can efficiently and reliably construct the state space model suitable for the complex industrial environment, and the identification accuracy of the state space model is improved. And a basis is provided for system analysis prediction and advanced control of an industrial system.

The invention discloses a multi-coal-source coal blending data processing method based on a WSMOTE algorithm, and belongs to the technical field of coal processing data processing, and the method comprises the steps: data collection and classification: collecting multi-dimensional data of a raw coal floating and sinking test, a coal blending test and the like, carrying out the preprocessing, dividing the data into a missing data set and a complete data set, carrying out the interpolation optimization of a missing value, and carrying out the calculation of the complete data set. And carrying out adaptive processing according to data distribution types, or carrying out weighted combination normalization according to feature importance, analyzing the number and distribution characteristics of minority class samples, dynamically adjusting the neighbor sample size and the new sample synthesis amount, and completing data enhancement and standardization. According to the method, standardized parameters are dynamically adjusted to adapt to data distribution changes, abnormal values are accurately processed, the combined interpolation model is subjected to weight optimization, errors are reduced, and data integrity is improved. WSMOTE parameters are adaptively combined with data distribution to synthesize samples, data are balanced, and the over-fitting risk is reduced. In practical application, the quality of coal blending model training data can be improved, and optimization of a multi-coal-source coal blending scheme is assisted.

The present disclosure provides a knowledge graph construction method, a knowledge graph construction device, a computer readable storage medium and an electronic device, and relates to the technical field of artificial intelligence. The knowledge graph construction method comprises: acquiring a plurality of visual tags, and determining the relationship between the visual tags; generating nodes based on the visual tags, generating edges between the nodes based on the relationship between the visual tags, to construct a visual tag knowledge graph; adding node attributes to the nodes according to the characteristics of the visual tags, and adding edge attributes to the edges according to the relationship types between the visual tags. The present disclosure can generate an accurate and reliable visual tag knowledge graph, so as to facilitate the wide application of the knowledge graph in scenes related to visual tags.

The application belongs to the technical field of animal experiment models, and discloses a method for constructing a mouse model of autism spectrum disorder and application. Pregnant mice are orally exposed to different doses of 1-NP every day during the whole gestation period, the pregnant mice naturally give birth, and the offspring mice are evaluated for autism-like behaviors at PND28 after birth by using a three-chamber social experiment. The offspring mice are evaluated for autism-like behaviors at PND70 after birth by using a three-chamber social experiment. Maternal 1-NP exposure during pregnancy delays the migration of intermediate neurons in the forebrain of fetal mice, and the autism-like behaviors. 1-NP exposure during pregnancy down-regulates the expression of intermediate neuron migration-related genes by reducing DNA hydroxymethylation of the intermediate neuron migration-related genes. 1-NP exposure during pregnancy damages the synthesis of mitochondrial tricarboxylic acid cycle alpha-ketoglutarate (alpha-KG). Maternal alpha-KG supplementation during pregnancy improves the migration of intermediate neurons in 1-NP exposed fetal mice and the autism-like behaviors of offspring mice.

This invention relates to the field of water treatment technology for recirculating aquaculture systems (RAS), and discloses a method for rapidly cultivating stable nitrifying bacteria in a land-based recirculating aquaculture system. The method includes the following steps: Step S1, system preparation and enrichment of ammonia-oxidizing bacteria; Step S2, oxygen deprivation and regulation to inhibit ammonia-oxidizing bacteria and create a growth window for nitrite-oxidizing bacteria; Step S3, restarting oxygenation to promote the establishment of nitrite-oxidizing bacteria dominance; Step S4, stable system operation. This method for rapidly cultivating stable nitrifying bacteria in a land-based recirculating aquaculture system achieves rapid startup of the biofilter and synchronization with aquaculture production, greatly improving facility utilization. This invention completely changes the traditional two-stage model of "cultivating bacteria first, then releasing fish." By simultaneously releasing low-density aquaculture organisms after system water injection, the bacteria cultivation process is fully integrated into the early stage of aquaculture. This allows the biofilter to reach a stable operating state within 25-30 days after the start of aquaculture, significantly shortening the system idle period.

The invention discloses a desktop-level air-sea environment simulation test cabin, which relates to the technical field of environment test and observation, and comprises an outer cabin for accommodating a salt wet simulation device and an inner cabin for accommodating seawater and placing a sample. According to the desktop-level air-sea environment simulation test cabin provided by the invention, on the basis of miniaturization of the device, structural isolation of the outer cabin and the inner cabin is realized, optical, mechanical and electrical systems are arranged between the dry outer cabin and the equipment room, and seawater and salt mist only exist in the closed inner cabin, so that effective isolation is realized, and erosion and damage of the salt mist and moisture are avoided.

The invention discloses a low-voltage distribution network power failure area studying and judging method based on graph neural network and fuzzy feature fusion, and the method comprises the following steps: collecting multi-source measurement data, carrying out the normalization, time alignment and feature construction, and forming node features; modeling the abnormal degree of the measured data based on a fuzzy membership function, and fusing multi-dimensional observation information by using soft union operation to generate a user side-superior side multi-source fuzzy evidence; introducing a time decay adjacency matrix, a node type embedding matrix and a power balance consistency constraint, and establishing a power distribution network dynamic representation model based on a space-time diagram neural network; and the node features are input into a double-task output structure, so that intelligent identification and user-level research and judgment of the power failure area are realized. According to the method, a fuzzy evidence fusion mechanism and a physical consistency constraint mechanism are introduced into a space-time diagram neural network framework, a dynamic multi-layer research and judgment system for the low-voltage power distribution network is constructed, and collaborative optimization of a data layer, a model layer and a reasoning layer is achieved.