16 results about "Project based" patented technology

This invention relates to the field of machine learning technology, specifically disclosing a machine learning method based on engineering project contract data, comprising the following steps: Step S1: Collect historical engineering project contract data, including factor values ​​and project progress at each time point; calculate the impact sensitivity and sensitive impact value of each factor, distinguishing between high-sensitivity factors and low-sensitivity factors; Step S2: Set a monitoring interval, collect factor values ​​and project progress data in real time; dynamically correct the sensitive impact value of high-sensitivity factors based on real-time data, generate correction parameters, and ensure that the model adapts to the latest project status; Step S3: Combine the correction parameters of high-sensitivity factors and the fixed sensitivity values ​​of low-sensitivity factors to predict the project completion time interval; calculate the probability value of a reasonable long payment period for the project based on the comparison between the predicted time and the actual planned time; This method improves the accuracy of engineering project progress prediction by dynamically adjusting key factor parameters.

This application discloses a page template configuration method, apparatus, medium, and device based on engineering management, relating to the field of engineering management technology. This application uses a hierarchical page template structure for configuration. In the three-layer page template structure, adjacent template layers have a parent-child relationship. The project instance template layer can generate personalized instances based on specific project needs, while the industry template layer allows for personalized adjustments for different types of projects. During configuration, child templates inherit the configuration of the parent template while allowing for project-based personalized settings. The reuse of the standard template layer reduces redundant configuration, lowers the management system load, and improves configuration efficiency, while also meeting the personalized needs of different projects. By effectively separating information configuration operations using the industry template layer and the project instance template layer, flexible expansion is achieved, improving the effectiveness of page template configuration in multi-project applications.

The application provides a project knowledge base-based work order service tracking method, platform and medium, relates to the technical field of data processing, and the method comprises the following steps: constructing a project knowledge base in a project operation process; reading work order information, performing semantic analysis processing, and mapping the analysis result to the project knowledge base to generate a knowledge mapping result; extracting a historical processing path and a standard implementation path associated with the work order and meeting a preset condition, and constructing a service processing baseline track corresponding to the work order; in the work order execution process, real-time collection of work order processing behavior information is performed, continuous mapping to the project knowledge base is performed, and a real-time knowledge evolution track is generated; and work order service tracking analysis is performed, and a service tracking result is established. The application solves the technical problem that work order management in the prior art mostly depends on manual input and processing, lacks an automatic knowledge mining and application mechanism, and work order processing efficiency is low, error rate is high, and project progress and quality are affected.

This invention provides a project design and optimization method, system, and storage medium. The method includes: acquiring initial project information; cleaning, normalizing, and performing correlation processing on the initial project information to obtain processed project information, wherein the processed project information includes processed historical project information and processed current project information; constructing a knowledge graph by processing the processed historical project information using a natural language processing algorithm and a fusion model; constructing a project performance prediction model using machine learning methods based on the processed historical project information and the knowledge graph; acquiring project requirements; determining constraints based on the project requirements; obtaining candidate projects based on the constraints, the project performance prediction model, and the processed current project information; and determining the final project based on the candidate projects. This application can improve design efficiency and reduce resource consumption.

The application provides a project recommendation method and system based on causal popularity debiasing, and the method comprises the following steps: obtaining fixed project popularity corresponding to each project based on the project popularity of each exposed project in each historical time period; obtaining a predicted score between a user and an exposed project based on a pre-stored recommendation model, the fixed project popularity, an optimized user embedding vector set and an optimized project embedding vector set; and recommending the exposed project to the user based on the predicted score. The application can alleviate the influence of popularity bias on the recommendation result by fusing the project popularity and the user interest to quantify the user consistency.

The present invention relates to a technology for optimizing human, software (SW), and hardware (HW) resources generated in IT service development projects through dynamic automation. Specifically, the invention provides an integrated process in which (1) a machine learning prediction module (100) calculates future resource demand based on project management tools, server / network logs, SW license usage history, etc., (2) a decision engine (200) determines resource allocation logic, such as personnel reallocation, license increase / decrease, and server expansion / reduction, by referring to rule tables and priorities, (3) a resource control module (300) executes allocation / release commands for actual resources (personnel, licenses, servers), and (4) a monitoring module (400) collects and analyzes the above results and real-time project status to continuously correct prediction errors and decision logic. This enables the resolution of recurring issues during IT service development projects, such as manpower bottlenecks, license overloads or shortages, and server resource overuse or insufficient resources, effectively mitigating project schedule delays and cost increases. Furthermore, by continuously correcting discrepancies between the machine learning model and actual data during project execution through online or adaptive learning methods, it improves long-term accuracy and efficiently performs dynamic resource optimization. Consequently, various benefits are achieved, including shortened project schedules, cost reductions, and improved resource utilization. At the same time, managers can reduce their workload through automated resource allocation processes and further enhance project quality and stability.

The embodiment of the application provides a method and device for automatically generating an Internet of Things project end to end based on a large language model. The method comprises: obtaining a project requirement text; calling a large language model to analyze the project requirement text to obtain structured requirement information of the Internet of Things project; in a preset skill warehouse, finding a skill module that is semantically matched with the structured requirement information as a target skill module; analyzing the compatibility and dependency relationship between each target skill module to obtain the calling sequence of each target skill module and the data flow direction between each target skill module; calling the large language model to generate a target project code based on the structured requirement information, the calling sequence, the data flow direction, and the description information of each target skill module; and in the case that a confirmation instruction for the target project code is received, generating a version baseline of the Internet of Things project based on the target project code. The project development efficiency can be improved.

Embodiments of the present application provide a project code generation method and device, electronic equipment and storage medium. The method comprises: in response to a selection instruction input to a project prototype library browsing interface, determining a template project indicated by the selection instruction; the project prototype library stores a project document set and a project code package of each developed and verified project; in response to input of functional requirement information of a target project, inputting the functional requirement information of the target project and the project document set of the template project to a large language model to obtain functional difference information between the target project and the template project output by the large language model; based on the functional difference information and code structure information described by the project document set, generating a project code package of the target project based on a project code package of the template project by using the large language model. By applying the embodiments of the present application, the project development efficiency can be improved.

The present disclosure provides a method for learning a project code base and locating error codes, comprising: constructing a knowledge graph of a project code base based on a code framework of the project code base; generating an exploration task based on the knowledge graph; formulating an action plan comprising specific exploration steps and retrieving associated code snippets based on the exploration task; and learning the retrieved code snippets by executing the action plan to update the knowledge graph. The present disclosure can automatically learn the knowledge of the project code base.

This invention discloses an intelligent science and technology project innovation management system and method. It assists manual review in rapidly managing the establishment of science and technology innovation projects through intelligent screening. The system includes: inputting and parsing submitted science and technology innovation projects; constructing a project database; creating and training a model using random forest, and deploying the trained model as an API service; outputting the success probability P and confidence level of each project, and generating an evaluation report; and performing a three-level decision-making process based on the success probability P. This method automates and structures the input of project information using natural language processing and feature extraction technologies, and performs quantitative evaluation and risk classification of projects based on machine learning models, achieving preliminary screening of a large number of submitted projects. It also automatically processes high-potential and high-risk projects, thereby significantly shortening the review cycle and reducing subjective bias.

This application relates to a project-based bill of materials (BOM) generation and management method and system, belonging to the field of manufacturing information management technology. The method includes: responding to a project creation instruction for a target project, establishing a master project plan and at least one detailed plan associated with the master project plan, the detailed plan describing project-level product configuration requirements; generating a project BOM uniquely corresponding to the target project based on the master project plan and the detailed plan, the project BOM existing independently of the standard product BOM; and generating and outputting production instructions to guide production execution based on the project BOM. This application effectively meets the need for rapid and accurate generation of BOM closely linked to project requirements in project-based delivery scenarios for non-standardized products.

The application provides a branch management method and device; the method comprises the following steps: in response to a branch creation instruction, creating a sub-branch corresponding to a target business belonging to a project based on a master branch of the project; in response to a code submission instruction, receiving a code input based on the sub-branch; compiling the received code to obtain branch code corresponding to the sub-branch; when it is determined that there is no file directory corresponding to the sub-branch, generating a file directory corresponding to the sub-branch; and storing the branch code corresponding to the sub-branch under the file directory corresponding to the sub-branch. Through the application, the sub-branch can be conveniently added, and the efficiency and safety of project development are improved.

This invention relates to the field of machine learning technology, specifically to a configuration method, apparatus, electronic device, and storage medium for machine learning projects. The method includes, in response to a configuration request for a target machine learning project, displaying a configuration interface for the machine learning project. The configuration interface includes a computing resource selection area for selecting computing resources, which are obtained by managing the computing power of multiple computing resources. In response to a selection operation of computing resources in the computing resource selection area, determining the target computing resources corresponding to the target machine learning project. In response to a configuration operation of an operator core for the target machine learning project, determining the target operator core, and determining the target machine learning project based on the correspondence between the target computing resources and the target operator core. The operator core includes data input, functional operators, and data output. This method improves the processing efficiency of the target machine learning project.

The application discloses a code snippet generation method and system based on a project private component library semantic index, belongs to the technical field of software development tools, and comprises the following steps: scanning a current project, identifying a private component library relied on by the project, performing syntax analysis on the private component to extract metadata, and constructing a semantic feature index based on the metadata; in response to a coding input event in an integrated development environment, analyzing a semantic intention to generate an intention vector; matching the intention vector with the semantic feature index, combining project context sorting to determine a target private component; and automatically generating a calling code snippet according to the metadata of the target private component and outputting the calling code snippet to the integrated development environment. The application realizes intelligent recommendation of a private component and automatic generation of code based on natural language semantics, can reduce the cognitive burden of a developer, improve development efficiency, guarantee code specification, promote component reuse, and completes a processing process locally, thereby guaranteeing code safety.

The application provides a college student double-creativity service platform based on project whole-cycle incubation, a resource matching sub-module in the project incubation module can intelligently recommend incubation resources according to project requirements, and the problem of difficult resource acquisition in the initial stage of project start is solved; the project management module realizes dynamic perception and risk warning of the project process through the health degree evaluation algorithm built in the progress tracking sub-module, avoids progress lag and problem accumulation, meanwhile, the examination and review sub-module adopts the analytic hierarchy process to construct a scientific performance evaluation model, can quantitatively evaluate the project results and automatically generate a review report, provides valuable experience reference for subsequent projects; in addition, the intelligent analysis engine uses an improved text clustering algorithm to classify the problems in each stage of the project in a fine-grained manner, enhances the ability of knowledge reuse and experience inheritance.

An apparatus for extracting technical parameters and functional characteristics from project information includes processing circuitry configured to receive project information related to a requested project for a building access hardware, the project information including technical parameters and functional characteristics of the requested project, process the received project information using a machine learning model to extract the technical parameters and functional characteristics from natural language in the received project information, and generate a project recommendation based on the extracted technical parameters and functional characteristics, the project recommendation including one or more candidate projects related to the building access hardware, the project recommendations based on the technical parameters and functional characteristics to the building access hardware included in the project information.