10results about How to "Short build cycle" patented technology

The invention provides a UE5-based automatic construction and release method, and relates to the technical field of software development, the UE5-based automatic construction and release method comprises a construction module and a release module, and the construction module comprises the basic steps of construction triggering, pre-construction task execution, client and server packaging, log recording and automatic alarm. The release module comprises the basic steps of differential packet generation, version detection and automatic release, the intelligent prediction trigger carries out deep learning analysis on project development historical data, the peak period of code submission and the code conflict risk can be accurately predicted, construction is automatically triggered in the low-risk and resource idle period, and the construction efficiency is improved. The problem of low resource utilization efficiency caused by lack of multi-dimensional comprehensive judgment in a traditional construction triggering mode is effectively avoided.

The embodiment of the invention provides a graph construction method and device, electronic equipment and a storage medium, and belongs to the technical field of resource big data. The method comprises the steps of obtaining text data, and splitting the text data to obtain sentence data; performing semantic analysis on the sentence data to obtain a resource data model; obtaining a target label, and associating a field in the resource data model with the target label to form a genetic map; and detecting and merging repeated nodes of the gene map, and fusing the nodes to obtain a resource gene pool map. According to the embodiment of the invention, the construction period of the gene pool can be shortened, and the data accuracy is improved.

The invention belongs to the technical field of multimedia content pushing, and discloses a multimedia content accurate prediction and pushing method based on a user portrait. Comprising the following steps: collecting data information of a user on a login content platform, and constructing a user portrait; obtaining content data of the multimedia content, and constructing a multimedia content portrait; matching the user portrait with the multimedia content portrait, and determining to-be-pushed content; based on feedback data analysis of the user, judging whether the pushed content to be pushed is accurate or not; and when the to-be-pushed content is inaccurate, correspondingly adjusting the to-be-pushed content. According to the method, the hidden core content preference characteristics of the user are mined according to the dominant core content preference characteristics of the user, the potential preference requirements of the user can be mined, the subsequently recommended multimedia content fits the real interest of the user and covers the preference of the user which is not directly exposed, recommendation can accurately reach the deep preference of the user, and the user experience is improved. Therefore, the user experience and the stickiness are improved.

The application discloses a medical inquiry logic chain generation and verification method and system based on a large language model, relates to the technical field of artificial intelligence, and comprises the following steps: S1, generating a logic chain framework, constructing a medical inquiry logic chain generation model, and embedding a structured logic template and professional knowledge constraints in a large language model as a core engine; S2, designing a two-dimension multi-level verification framework to automatically check the consistency and medical rationality of the generated corpus logic chain; and S3, designing an evaluation index system and a dynamic optimization strategy, grading and marking the problem corpus identified in the verification, and performing corresponding correction, so that the correction result is fed back to the model training link to optimize the logic chain generation rule. Through NLP technology, a language model generation mechanism, medical knowledge structure analysis and an intelligent verification algorithm, the quality, the degree of structuring and the application value of the medical inquiry data set in an intelligent inquiry system are improved, and high-quality and controllable professional corpus support is provided.

The invention discloses a compliance text graph training set construction method and device, equipment and a storage medium, and the method can extract compliance corpus data from an open source training dataset according to compliance evaluation information after the open source training dataset is obtained, and then carries out the reverse deduction of cue words based on compliance pictures in the compliance corpus data. And inputting the cue word into the target text graph model to generate an expanded image through the target text graph model, and constructing a compliance text graph training set according to the cue word, the expanded image and the compliance corpus data. According to the method, the compliance training set adaptive to the specified business scene can be constructed based on the open source training data set, and the construction period of the data set is shortened. And the data volume of the training data set is increased by expanding the image, so that the text graph model can output a more accurate image generation result after the constructed training data set is applied.

This invention belongs to the field of cross-industry information model construction technology, and relates to a method and system for semantic construction and automatic generation of multi-industry information models. The method includes: generating standard format requirement parameters for cross-industry models based on cross-industry model construction requests; decomposing the standard format requirement parameters into industry requirement parameter sets; generating customized meta-models by combining the industry requirement parameter sets, a meta-model base library, and a cross-industry feature library; semantically aligning the customized meta-models based on an industry terminology list and a cross-industry semantic rule library to obtain a cross-industry model element semantic alignment table; constructing lightweight model structures based on the cross-industry model element semantic alignment table; performing syntax validation on each lightweight model structure; and generating standardized model files corresponding to each target industry. This invention achieves automated construction of cross-industry models from scratch, improves model adaptation success rate and semantic accuracy, and avoids the problem of cross-industry models being unable to communicate with each other.

The invention provides a real-time collaborative Web test capturing method and system based on multi-feature scoring, and relates to the technical field of Web automatic testing. According to the method, the interactive coordinates clicked by the user are obtained, cyclic penetration detection is carried out, and a candidate element sequence is output; then five-dimensional feature quantitative scoring is executed, a scoring result is output to select a target element, a corresponding unique identifier is generated, and a capture event is packaged; a change observer interface is used for monitoring change, and an anti-shake mechanism is combined to perceive the page steady state of the browser, so that when the page is structurally changed or redrawn, a related monitor is awakened, computing power is distributed, a target element is reselected, and a capture event is packaged, and when the page is stable, the capture event is sent to a back-end server. And broadcasting to other cooperation terminals in the same room is facilitated. According to the method, the problems of misalignment of element positioning and low cooperation efficiency can be effectively solved, and efficient real-time cooperation and financial-level data security protection of distributed crowdsourcing testing are realized while system resource loss is reduced.

The invention discloses a method for enhancing the synthetic ability of yeast total protein and dipeptide tripeptide. Relates to the technical field of synthetic biology and metabolic engineering. According to the invention, the saccharomyces cerevisiae engineering strain with high protein synthesis capability and high endogenous dipeptide and tripeptide accumulation level is successfully constructed. The metabonomics analysis reveals the unique peptide fragment accumulation spectrum, especially the generation of a large amount of lysine-rich tripeptide at the C terminal, and proposes a potential mechanism of metabolic pathway'bottleneck 'caused by peptidase activity down-regulation in combination with proteome data. The recombinant strain has important application potential in the fields of single-cell protein, functional peptide products and the like.

The invention relates to an amyotrophic lateral sclerosis zebrafish model based on beta-methylamino-L-alanine induction as well as a construction method and application of the amyotrophic lateral sclerosis zebrafish model. According to the method for constructing the amyotrophic lateral sclerosis model by inducing the zebrafish through the BMAA, the non-specific fatality rate in the early stage of modeling is effectively reduced by limiting the initial development node and culture conditions of toxin exposure, and the living animal model with typical motor nerve injury phenotype and good repeatability is obtained. The method is short in construction period and relatively low in experiment cost, can objectively reflect sporadic ALS pathological characteristics induced by environmental factors, and is suitable for being used as an in-vivo biological carrier for high-throughput preliminary screening of anti-ALS drugs and related neurotoxicological researches.

The invention discloses a communication fault intelligent research and judgment and artificial intelligence auxiliary emergency disposal method, relates to the technical field of communication fault disposal, and aims to solve the technical problem that a secondary fault is easily caused due to lack of a plan verification mechanism in current traditional communication fault disposal, and the method comprises the following steps: S300, AI auxiliary decision making and plan generation; s301, combining a historical fault case library; s302, establishing a high-fidelity network digital twin body; and S303, carrying out scientific sorting by using a TOPSIS multi-attribute decision algorithm, and generating a recommended disposal plan. According to the method, the network real-time state in the current fault scene can be completely duplicated by establishing the high-fidelity network digital twinborn body, the network state change after each candidate plan is executed can be simulated in combination with the network state equation, it is ensured that the finally executed scheme has both effectiveness and safety, and the method is suitable for large-scale popularization and application. The problem that a secondary fault is easily caused due to lack of a plan verification mechanism in current traditional communication fault handling is solved.