51 results about "Project based" patented technology

The invention discloses a hydro-junction gate group intelligent cooperative control method based on digital twinning, and relates to the technical field of water conservancy control, and the method comprises the steps: taking a water conservancy dynamic digital twinning model as a reinforcement learning environment, setting gate group control parameters and performance indexes, carrying out the training under diversified hydrological conditions, and generating a preliminary control strategy; inputting the preliminary control strategy into a water conservancy dynamic digital twinborn model, executing rollback simulation covering multiple working conditions, and obtaining simulation responses of the corresponding working conditions; scheduling performance indexes of simulation response are analyzed, high-quality control strategies are screened, scheduling conflict identification and conflict resolution among gate groups are carried out, and an optimal control strategy set is output; according to the method, by executing rollback simulation covering multiple working conditions and automatically identifying and eliminating scheduling conflicts among gate groups, the adaptability and accuracy of a scheduling strategy are improved, conflicts and risks are reduced, and efficient and safe operation of the hydro-junction is ensured.

A multi-agent and human project management and collaboration tool can provide the ability to create and manage projects and software agents in a cohesive framework. The collaboration tool can provide a multi-agent, multi-player channel where agents and humans work together to achieve an outcome. The collaboration tool can initiate a new project, where the objective is defined, and a team is dynamically built to achieve this objective. The team roles are parsed, and an agent is created for each role. Each agent is equipped with a personalized skillset and a working memory, allowing them to effectively collaborate with humans and each other to progress autonomously through the project stages. The agents can complete tasks based on the project plan and the results are stored in the agent's working memory. The project status is updated, and a report can be generated after each stage, providing a record of the project's progress.

The invention provides a project design and optimization method and system and a storage medium, and the method comprises the steps: obtaining initial project information, and carrying out the cleaning, normalization and correlation processing of the initial project information, so as to obtain processed project information, which comprises processed historical project information and processed current project information; processing the processed historical project information based on a natural semantic algorithm and a fusion model to construct a knowledge graph; constructing a project performance prediction model by adopting a machine learning method based on the processed historical project information and the knowledge graph; obtaining a project demand, determining a constraint condition based on the project demand, and obtaining a candidate project based on the constraint condition, the project performance prediction model and the processed project information; a final item is determined based on the candidate items. The design efficiency can be improved, and the resource consumption can be reduced.

The invention discloses a manufacturing method of an installation package and a related device, and relates to the technical field of software engineering. Comprising the steps that a target MakeFile is created, and other instruction items, except for an install instruction item and a unstall instruction item, in the target MakeFile are all empty; obtaining a target compressed package corresponding to the target software; based on the target MakeFile and the target compressed package, constructing an initial project; adding a management script of the target package in the initial project to obtain a target project; and based on the target project, generating a target installation package corresponding to the Kylin Linux operating system. The manufacturing method of the installation package does not depend on source code reconstruction, a large amount of workload for compiling and maintaining complete Makefile in a traditional scheme is omitted, the packaging period is greatly shortened, and meanwhile the compatibility and integrity of the installation package are guaranteed.

The embodiment of the specification provides a model verification, a credit evaluation model verification method and device and a computing device, wherein the model verification method comprises: obtaining a verification data set from a target project, wherein the verification data set comprises user data of multiple dimensions in the target project and a project result corresponding to the user data; performing feature analysis on the user data to extract a target dimension from the multiple dimensions; generating at least one project scenario of the target project based on the target dimension; inputting the user data under the at least one project scenario into a machine learning model applied to the target project respectively to obtain a predicted project result corresponding to the user data under the at least one project scenario; and performing project scenario matching based on the predicted project result corresponding to the user data and the project result to determine the matching degree of the machine learning model and the target project, thereby improving the accuracy and efficiency of the model verification and reducing the cost of the model verification.

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 embodiment of the invention provides a project deviation degree evaluation method and device, computer equipment, a medium and a product, and the method comprises the steps: determining a current progress stage of a target project according to progress feature data of the target project; based on the current progress stage, determining an association degree between the target project and other projects and a qualitative evaluation level of the target project; according to the correlation degree and the qualitative evaluation level, adjusting an evaluation index coefficient of the target project; and determining the deviation degree of the target project according to the operation data of the target project and the adjusted evaluation index coefficient. In the project proceeding process, the progress of the project is recognized in real time, the corresponding evaluation index weight is matched, it can be ensured that the evaluation result is matched with the actual state of the project, and evaluation deviation caused by stage mismatching is avoided. In addition, by adding a collaborative index, collaborative recommendation among multiple projects is concerned, and the comprehensiveness of project evaluation is improved.

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.

Abstract of the application: The invention relates to a central digital platform in the field of educational technology that links various subject areas and promotes project-based learning approaches. The technical problem is that traditional education systems often teach isolated disciplines, which makes the practical application of knowledge in real-world contexts difficult. The invention's solution comprises a web-based platform that integrates interactive tools such as simulations, virtual laboratories, and data analysis software to enable the practical development of projects. The platform supports student teamwork and collaboration through WebRTC-based communication tools and provides teachers with a dashboard to monitor student progress. The platform enables the creation of interactive presentations and reports and ensures data security through access controls and encryption technologies.The main uses of the invention are in schools and educational institutions to promote interdisciplinary learning and the development of teamwork, critical thinking and creative problem-solving skills.

The application discloses a large-scale agile software project scheduling method based on deep reinforcement learning, and comprises the following steps: initializing state information of agile software projects and human resources in a large-scale agile software project scheduling environment; establishing a large-scale agile software project scheduling decision model based on deep reinforcement learning to generate a scheduling scheme for scheduling; collecting trajectory information generated in the scheduling process, storing the trajectory information into an experience replay pool and updating priorities of all trajectories; when the number of trajectories in the experience replay pool reaches a requirement, training parameters of the scheduling decision model by batch sampling trajectories according to the priorities; generating a better scheduling scheme by using the trained scheduling decision model for scheduling; and outputting an optimal scheduling scheme if the total value of developed user stories is stable. The application can generate an optimal scheduling scheme in time according to changes in the environment state in response to random dynamic events or uncertain factors in the agile software development process.

The invention provides a work order service tracking method and platform based on a project knowledge base and a medium, and relates to the technical field of data processing, and the method comprises the steps: constructing the project knowledge base in a project operation and maintenance process; reading the work order information, performing semantic analysis processing, and mapping an analysis result into a project knowledge base to generate a knowledge mapping result; extracting a historical processing path and a standard implementation path of which the correlation degree with the work order meets a preset condition, and constructing a service processing baseline track corresponding to the work order; in the execution process of the work order, processing behavior information of the work order is collected in real time and continuously mapped to the project knowledge base, and a real-time knowledge evolution track is generated; and executing work order service tracking analysis, and establishing a service tracking result. The technical problem that the project progress and quality are affected due to low work order processing efficiency and high error rate caused by the fact that work order management mostly depends on manual input and processing and lacks an automatic knowledge mining and application mechanism in the prior art is solved.

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 invention discloses a competitive product information analysis method and device, electronic equipment and a storage medium. The method comprises the steps that positioning information of a to-be-analyzed target item is acquired; obtaining multi-dimensional target feature data of the target item and the plurality of candidate items from an item database based on the positioning information of the target item in real time; wherein the project database comprises multi-dimensional target feature data, obtained and updated from a plurality of data sources, of each project through a distributed data acquisition cluster deployed in real time; inputting the target feature data of each candidate item and the target item into a double-tower deep learning model, performing feature processing on the target feature data of each candidate item and the target feature data of the target item, and matching the processed feature data to obtain the overall similarity of the plurality of candidate items and the target item; and based on the overall similarity between each candidate item and the target item, screening out the current competitive item of the plurality of target items.

The invention relates to the technical field of artificial intelligence, and particularly provides an engineering project risk supervision method, system and device and a storage medium, and the method comprises the steps: constructing a corresponding monitoring agent for each risk type needing to be supervised; obtaining project progress information from a project management system, and calculating a cost hyperbranched rate based on project budget and actual expenditure; combining the project progress information, the cost hyperbranched rate information and the risk coefficient output by each monitoring agent to form an input state for a reinforcement learning model; and obtaining a regulation and control instruction which is generated by the reinforcement learning model based on the input state and is used for dynamically adjusting engineering resource allocation or a progress plan to cope with the risk. According to the method, the sudden risk response speed is increased, the compound risk misjudgment rate and the project hyperbranched probability are reduced, and the economic toughness of major engineering is remarkably improved.

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 application discloses a learning resource recommendation method and system based on project field knowledge and user comments, and the method comprises the following steps: collecting learner information, learning resource characteristic information and teacher information, wherein the learner information comprises learner description information and learner interaction information with the learning resource, and the learning resource characteristic information comprises learning resource description information and learning resource characteristic information; finding a teacher with the highest similarity to the learner according to the learner information, and obtaining a matching score of a target learning resource according to the teacher characteristics through a convolutional neural network; establishing a learner short-term preference model and a long-term preference model, and fusing the two models to obtain a learner personal preference model; establishing a learner group preference model, and fusing the learner personal and group models to obtain a learner preference model; and establishing a learning resource characteristic information model and a field knowledge model by using various information characteristics of the learning resource according to the learning resource characteristic information, so that the accuracy of learning resource recommendation is improved.

A system including one or more processors and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform certain operations. The operations can include training a teacher machine-learning model to determine a level of relevance between a query and an item. The teacher machine-learning model can include a cross-encoder model comprising a large language model (LLM) component and a multilayer perceptron (MLP) component. The operations also can include training a student machine-learning model based on the teacher machine-learning model. The operations additionally can include receiving an input query from a user. The operations further can include determining relevance scores for a set of items based on item embeddings for the set of items and a query embedding for the input query. The operations additionally can include ranking the set of items based at least in part on the relevance scores. Other embodiments are described.

The invention relates to a scientific research project accurate matching recommendation method and device, equipment and a medium. The method comprises the following steps: generating a project demand feature group and a resource dynamic feature group through feature extraction processing; performing dual-channel decoupling on the resource dynamic feature group, fusing the decoupled features according to the project stage identifier, and generating a key technology anchor point set based on the project technology direction classification number set and the resource technology direction classification number set; and generating a matching degree score according to the technology anchor coverage rate, the project demand feature group and the fusion resource feature vector, generating a missing early warning instruction based on the key technology anchor set and the resource technology direction classification number set, generating a resource recommendation sequence according to the score, and generating an alternative scheme suggestion according to the instruction. According to the method, the matching accuracy of the scientific research project and the resource party of the scientific research worker is improved by comprehensively considering the multi-dimensional characteristics of the project demand and the resource party capability and dynamically fusing the long-term and short-term attributes of the resource.

The invention provides a student practice effect feedback method and system based on multi-dimensional data driving, and relates to the technical field of training feedback systems. According to the method, the expert standard action library is constructed, the joint point activity frequency is dynamically analyzed based on project types, core and secondary capture points are intelligently divided, and the collected data are subjected to grading processing in combination with a weighting mechanism, so that the system efficiency is improved while the key action information precision is guaranteed; a difference value is calculated by performing weighted comparison on an action track of a student and a standard track, layered feedback is generated according to a preset threshold value, differentiated tactile prompts are output through wearable equipment, and closed-loop control from data acquisition and intelligent evaluation to real-time feedback is realized; according to the whole scheme, the accuracy and scientificity of action evaluation are improved, students are stimulated and helped to quickly correct errors and consolidate correct actions in a non-intrusive real-time tactile interaction mode, and the training efficiency, concentration degree and autonomy are remarkably improved.

The present application relates to the technical field of resource management, in particular to an intelligent task decomposition and optimization method and system based on deep learning, comprising the following steps: based on the project task decomposition list and the collaboration record, the dependency relationship of each task is identified and the node connection is established, and then according to the human resource collaboration communication record, the flow direction of problem seeking help and solving is extracted. The present application accurately depicts the knowledge dependency degree between tasks in the coupling mode of tasks and expert resources by establishing the close association between the task node dependency relationship and the problem seeking help flow direction of human resource collaboration, improves the matching accuracy between task decomposition and resource allocation, reduces the risk of idle or overload of expert resources during task implementation; through the deep traversal of the network path dependence connection, the transmission influence of task load fluctuation on the execution time of downstream tasks is quantified, the real propagation law of load disturbance between task nodes is revealed, and the resource allocation is more in line with the actual demand of tasks.

Systems, methods, and computer-readable media are provided for generating natural language project summaries via large language models including deterministically derived data value narratives. A computer-implemented method includes processing a first input configuring data stored in association with a plurality of fields, generating a narrative for a project, and causing display of the narrative in a report for the project. The narrative is generated by applying one or more deterministic operations to derive one or more values for the project based at least in part on at least one field of the plurality of fields, based at least in part on the configured data, generating a prompt, prompting a large language model with the prompt to generate a result, and storing the result as the narrative for the project. The prompt includes the one or more derived values and a context comprising the project for which a narrative is being generated.

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.

The invention provides an automatic task planning method and system for project management, and the method comprises the steps: obtaining the project feature description of a to-be-processed project, carrying out the processing through a neural network model based on the project feature description, and generating a project portrait vector corresponding to the to-be-processed project; based on the project portrait vector, screening a preset number of historical cases through a nearest neighbor search technology; specific tasks in the screened historical cases are extracted in sequence, the specific tasks are clustered, an abstract relation chain describing the task execution sequence is generated, the abstract relation chain is added into cue words of the large language model, the dependency relationship is reasoned through the large language model, and a task plan corresponding to the to-be-processed project is generated; and executing the task plan and adjusting the task plan when the project change is received. The abstract relation chain capable of reflecting project task execution essential logic is generated, the situation that a large language model needs to consume computing resources to learn disordered historical cases is avoided, and the reasoning ability of the large language model is effectively improved.

Systems, methods, and computer-readable media are provided for generating natural language project summaries via large language models including deterministically derived data value narratives. A computer-implemented method includes processing a first input configuring data stored in association with a plurality of fields, generating a narrative for a project, and causing display of the narrative in a report for the project. The narrative is generated by applying one or more deterministic operations to derive one or more values for the project based at least in part on at least one field of the plurality of fields, based at least in part on the configured data, generating a prompt, prompting a large language model with the prompt to generate a result, and storing the result as the narrative for the project. The prompt includes the one or more derived values and a context comprising the project for which a narrative is being generated.

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.

The invention discloses an animation multi-person cloud creation management method and system, a terminal and a storage medium, and the method comprises the steps: building a development task project of an animation or animation model according to an expected creation target, and decomposing the development task project into a plurality of creation modules and a plurality of subtasks; receiving creation applications of a plurality of users participating in respective design modules, and obtaining creation capability and efficiency of each user; performing matching and algorithm analysis on each user and a plurality of sub-tasks of each creation module, and finding out a maximum completion time minimum index of a development task project; and obtaining a subtask specifically allocated to each user based on the maximum completion time minimum index, and providing a corresponding creation tool, a resource library and a corresponding access permission to project data and functions for each user. According to the method, the completion user of each sub-task can be determined, and then the corresponding resources are provided, so that the overall completion time of the project is as minimum as possible, and the improvement of the slicing efficiency is facilitated.

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.