4results about How to "Configuration high" patented technology

The present application relates to the technical field of software performance monitoring and artificial intelligence, in particular to a low-code platform performance diagnosis method and system based on multi-AI provider cooperation, the method of the present application receives batch page performance audit results, extracts page quantity, average performance score, common performance problems, performance budget and page distribution data; reads low-code platform component library description, visual building rules and configuration anti-pattern knowledge, constructs diagnostic prompt words containing low-code domain context and structured output constraints; in the automatic provider mode, cascade calling is performed in the order of Azure OpenAI, Anthropic and OpenAI, and different structured output constraint strategies are adopted for the interface capabilities of different AI providers; after field integrity verification and format standardization processing of the returned results, a performance diagnosis result file is generated persistently. The present application improves the availability, output stability and executability of low-code configuration level optimization suggestions of AI performance diagnosis service.

This invention discloses a SOH estimation method based on Bayesian optimization. The method includes: acquiring a battery dataset, constructing a battery health feature matrix, initializing Bayesian optimization, multiple rounds of Bayesian optimization iteration, determining convergence conditions, and training the optimal configuration model on a full dataset. This invention belongs to the field of data processing technology, specifically referring to an SOH estimation method based on Bayesian optimization. This scheme uses a battery health feature matrix to construct a CNN+LSTM model, defines a precise objective function, and employs multiple rounds of Bayesian optimization iteration to achieve intelligent parameter search and reduce computational costs. It uses a CNN+LSTM model for dual extraction of local and long-term temporal features, covering all dimensions of battery aging features. A multi-LSTM sub-model fusion structure is used to calculate the final SOH estimation result. Bayesian optimization is used to improve the model's generalization ability, and the parameter configuration most likely to improve performance is intelligently selected based on the expected improvement value, thereby improving optimization efficiency.

The invention discloses a 5G network slice pre-arrangement method and device, equipment, a medium and a product, and the method comprises the steps: determining a predicted access base station track and a target service request type of each user terminal in a pre-arrangement time period based on the historical access base station track information of each user terminal; and performing network slice arrangement based on the predicted access base station trajectory and the target service request type to obtain at least one pre-arrangement network slice in the pre-arrangement time period. According to the invention, the preparation work of the 5G network slice can be completed in advance, the configuration efficiency of the 5G network slice and the adaptability under high load or burst flow are improved, the 5G network slice can be rapidly activated when the user demand changes, and different network resource configurations can be rapidly switched.

The application discloses a credit vehicle anti-disassembly control method and device, electronic equipment and storage medium, comprising: performing initial anti-disassembly type configuration through a platform end and issuing to a TBOX, the TBOX synchronizes the initial anti-disassembly type to a VIU, and the VIU stores the initial anti-disassembly type; after the vehicle is powered on, the TBOX queries the latest anti-disassembly type from the platform end, and the VIU performs corresponding operations based on the obtained latest anti-disassembly type or the locally stored initial anti-disassembly type; when the VIU performs anti-disassembly verification and determines an exception, the vehicle enters a limp state through a PDCU, and an anti-disassembly prompt is displayed through an IVI, so that the anti-disassembly type is flexibly adapted to the needs of different user groups and financial institutions, the financial control precision and user vehicle experience are considered, and the configuration and control reliability are improved.