437 results about "Management tool" patented technology

An AI-powered agile project management system for automatic risk assessment and mitigation, including: a risk identification module configured to extract project data from agile project management tools, including but not limited to Jira, Trello, Asana, and Monday.com, and identify potential risks using machine learning and NLP; a risk analysis module configured to assign risk scores based on probability, impact, and severity using predictive analytics, where the module uses historical data, real-time project status, and external dependencies to determine the severity and impact of risks; a real-time monitoring module configured to continuously track project conditions, detect anomalies, and generate immediate notifications via email, mobile apps, or messaging platforms when high-risk events are identified; a risk mitigation module configured to dynamically recommend and execute proactive corrective actions such as task reassignment, resource optimization, schedule adjustments, and implementation of alternative strategies using AI-driven decision-making, where the actions can be automated through predefined workflows or manually adjusted based on AI recommendations; an AI-driven task prioritization module configured to dynamically prioritize tasks based on risk factors, project deadlines, and team workload distribution; an integration module configured to ensure seamless compatibility with existing Agile project management frameworks through API-based synchronization; a learning and continuous improvement module configured to refine risk prediction models over time using machine learning and historical risk mitigation results, with AI-driven models continuously updating risk assessment mechanisms based on evolving project conditions, industry-specific risk factors, and historical performance metrics; a compliance and audit reporting module configured to automatically generate risk reports, audit logs, and compliance checklists; a customizable risk assessment configuration module configured to allow project managers to define custom risk parameters, probability thresholds, and mitigation preferences; an interactive dashboard module configured to visualize data in real time, including risk trends, effectiveness of remedial actions, and analysis of project progress; and a scalability feature configured to be used in industries such as IT, healthcare, finance, construction, and manufacturing.

The invention relates to the technical field of power systems, in particular to a power grid operation risk level visualization intelligent management tool system. The system obtains power grid operation data through an Internet of Things sensor network, wherein the power grid operation data comprises current, voltage, equipment operation state and other multi-dimensional information; performing real-time analysis on the acquired data by adopting multi-level clustering analysis, a predictive maintenance algorithm and a deep learning model to realize abnormal behavior identification and risk prediction; a dynamic risk map is constructed based on a GIS technology, and accurate positioning and real-time display of risk levels are realized in combination with regional feature recognition and personalized threshold setting. According to the invention, multi-source data acquisition, intelligent analysis and visual presentation are creatively and organically integrated to form a closed-loop risk management system, so that the system has adaptive learning ability. Practical application shows that the system can early warn potential risks 30 minutes in advance, the operation and maintenance response time is shortened to 3 minutes, and the power grid operation risk monitoring and management efficiency is remarkably improved.

The invention discloses a BIM technology-based comprehensive pipeline construction management system, which relates to the technical field of construction management and comprises a BIM model creation module, a pipeline construction planning module, a real-time progress monitoring module, a pipeline conflict detection and optimization module and a quality and safety management module. According to the comprehensive pipeline construction management system based on the BIM technology, multiple problems existing in traditional construction management can be effectively solved, through the functions of visual display, conflict detection and optimization, real-time progress monitoring, potential safety hazard inspection and the like of the BIM model, the system provides a comprehensive management tool for the construction process, and the construction efficiency is improved. The construction efficiency is improved, resource allocation is optimized, the construction quality and safety are greatly improved, finally, by means of the system, the overall management level of a project is remarkably improved, and the construction progress, quality and cost are more accurately and effectively controlled.

An AI-based training recommendation system (100) for personalized employee development, which includes the following: a module for creating user profiles that is configured to collect and analyze employee data, including job role, skills, learning history, performance indicators, and career aspirations; a module for real-time skills gap analysis that uses artificial intelligence to assess employee skills and identify areas for improvement; a training recommendation engine that uses machine learning algorithms to create personalized training suggestions based on employee data, company goals, and industry trends; a learning path optimization module configured to adaptively modify training recommendations based on employee progress, feedback, and evolving qualification requirements; an integration module for connecting to existing enterprise systems, including Learning Management Systems (LMS), HR platforms and performance management tools; a module for monitoring engagement and progress, designed to track employee participation in training, assess knowledge retention, and provide managers with insights; and an automated training planner configured to assign and prioritize training sessions based on employee availability and business requirements.

The present disclosure pertains to systems, computer program products, and methods for an automated mesh service-based deployment intelligence system. This system is designed to streamline the deployment of software by integrating a processing device and a non-transitory storage device. The storage device contains instructions that, when executed by the processing device, enable the ingestion of data from various sources such as monitoring systems, databases, and application performance management tools. Once ingested, the data is stored and processed to discern deployment patterns and detect any anomalies. Utilizing a machine learning model, the system anticipates potential deployment issues by analyzing this data. It then orchestrates the deployment of software artifacts accordingly, taking into account the insights gained from the machine learning model. Furthermore, the system is capable of real-time optimization of the deployment process to preemptively resolve any predicted issues, thereby enhancing the efficiency and reliability of software deployment operations.

The invention discloses a large model intelligent agent dynamic tool arrangement method and system, a medium and equipment, and relates to the technical field of large models. The method comprises the following steps: acquiring interface metadata of an open platform, generating a standardized large model tool description based on the interface metadata, and registering the standardized large model tool description to a tool library; receiving a query request of a user, and determining a target tool based on the query request and the semantic similarity of each tool description in the tool library; mapping service parameters in the query request into interface calling parameters of the target tool; executing interface calling corresponding to the target tool based on the interface calling parameter, and preprocessing a calling result; and displaying the pre-processed calling result in a visual form. By implementing the technical scheme provided by the invention, the dynamic arrangement of the management tool can be realized, the adaptability of the system to the business scene change is improved, and the management efficiency of an enterprise is further improved.

Disclosed herein are system, method, and device embodiments for providing a mobile interface powered by artificial intelligence. A user remains on a single user-interface page, conducting interactions with a customer relationship management tool using natural language. The technique leverages a large language model as an intermediary middle-layer, allowing a user to engage core functions. The technique builds an appropriate prompt including the natural language and uses the large language model to build an execution plan that references tools and tasks performable in the customer relationship management tool. By chaining prompts, the technique incorporates prior interactions into subsequent prompts. Mobile-specific information such as location, images, and scanned barcodes may be included in a prompts. Running the large language model on the client device allows the user to perform CRM functions while operating in an offline mode, a mode that secures user data and enhances privacy.

The invention relates to the technical field of intelligent teaching, in particular to a teacher console task scheduling and monitoring method based on data analysis, and the method comprises the steps: collecting task execution data, including participation degree, task completion progress and response time, of students, and combining machine learning models such as an adaptive enhancement algorithm, a long-short-term memory network and a random forest; according to the method and the system, personalized task scheduling of students is realized, a real-time data monitoring and dynamic adjustment strategy is adopted, optimal configuration of task difficulty and time length is generated according to student performance data, task risks are identified through a support vector machine model, and multi-level early warning and intervention suggestions are provided. Performing long-term trend analysis and grouping on historical data by using a K-Means clustering algorithm, and supporting a personalized optimization scheme of task allocation; according to the invention, the problem that the task allocation is not matched with the actual learning state of the student in the prior art is effectively solved, the flexibility of task scheduling and the learning effect of the student are improved, and a more accurate and efficient teaching management tool is provided for a teacher.

The invention relates to the technical field of simulation regression systems, and provides a parallel simulation regression method and system based on cluster servers. The method comprises the following steps: classifying simulation tasks according to task types, and determining a dependency relationship among a plurality of simulation tasks; generating a parallel simulation task list through a cluster server management tool according to the dependency relationship among the plurality of simulation tasks; and delivering the parallel simulation task list to a cluster server, and running simulation tasks by the cluster server to generate a simulation result. The parallel simulation regression system is conveniently constructed by utilizing the scheduling function of the cluster server, the dependency relationship of various types of tasks can be processed, the degree of parallelism of the simulation regression system is increased to the maximum extent, and the method has universality and can be widely applied to different scenes.

The embodiment of the invention provides a simulation processing method and device based on a large language model agent, and relates to the technical field of industrial simulation, and the method comprises the steps: obtaining the demand information inputted by a user, calling a large language model connected to a simulation process arrangement system through a model interface to carry out the intention recognition of the demand information, and obtaining the intention recognition result; obtaining an intention type corresponding to the demand information; if the intention type belongs to a simulation operation class, calling a simulation process management tool to perform simulation parameter setting according to demand information stored in a long and short memory module to obtain a corresponding simulation task, and executing the simulation task to generate a corresponding simulation result; according to the method, the file type which is input by the user and aims at the simulation result is obtained, the display template corresponding to the file type is determined, the simulation result is input into the display template, and the data file corresponding to the simulation result is obtained, so that the efficiency and accuracy of simulation process arrangement are improved, and the error rate is reduced.

The invention provides an intelligent code review method and system, and relates to the technical field of program testing, and the method comprises the steps: binding a triggering mechanism based on an item needing code review in a code management tool by a developer; submitted local codes are obtained, a background interface is automatically called according to a triggering mechanism, and the local codes comprise incremental codes or full-quantity codes submitted by a developer; if it is determined that the type of the local code is an incremental code, the local code is filtered, and submission information corresponding to a target code meeting a filtering condition is written into a cache to wait for a timed task to execute a review task; when the timed task is executed, the submission information is taken out from the cache, and the target code is split into single submission codes according to the submission information; scoring each submitted code to obtain code scores, and giving review suggestions for the submitted codes; and counting an average score of the plurality of code scores to obtain a comprehensive score corresponding to the target code. The code review efficiency can be improved.

The invention relates to the technical field of computers, can be applied to the field of science and technology finance / digital medical treatment, and discloses a back-end system creativity transformation method and device, electronic equipment and a storage medium. The method comprises the following steps: deploying a credential back-end system environment by using a credential server and credential middleware, and physically isolating credential traffic and non-credential traffic; carrying out dynamic segmentation scheduling on the traffic of the credential and credential gateway in the credential and credential environment and the traffic of the non-credential and credential gateway in the non-credential and credential environment; deploying a domestic database, and finishing data migration from the original database to the domestic database through an automatic tool chain; segmenting the traffic to a credential and credential gateway in the credential and credential environment for gray verification, and switching a back-end service chain of the credential and credential environment to a domestic database after the verification is passed; and monitoring the operation performance data of the credential environment in real time, and switching the traffic to the non-credential environment and the original database through the global traffic management tool if an exception occurs. According to the method, the risk controllability and smooth transition of creative transformation are realized.

The invention relates to the field of data processing, and discloses a data dictionary table configuration method and device, equipment and a medium, and the method comprises the steps: obtaining an associated dictionary and table field configuration relation table in a dictionary table from a database according to the table name of an input data list, traversing the dictionary and table field configuration relation table, obtaining a dictionary code configured by each dictionary, and storing the dictionary code in the database; obtaining a dictionary value object list from a database according to the dictionary code, traversing each data item of the data list, extracting a field corresponding to the data item and an associated field value, traversing the dictionary value object list, determining a matched dictionary key according to the field value, mapping a dictionary value associated with the dictionary key, and putting the dictionary value into the data item; according to the data dictionary table management method and device, the list after dictionary escape is generated and output, and the technical problems that an existing data dictionary table management tool is often an independent product or tool and lacks deep integration with other data management, integration and development tools, so that the workload is huge, and the efficiency is low are solved.

A system for intelligent automation of project processes and prediction of project progress, including: (a) an AI-driven task automation module (101) for intelligent task allocation based on expertise and workload; b) a real-time monitoring and reporting module (102) to track project progress and provide real-time insights; (c) a predictive progress analysis module(103) that uses machine learning to predict project completion and identify risks; d) an adaptive workflow management engine (104) for dynamically reconfiguring workflows in response to project changes; (e) an integration and communication interface (105) for seamless interoperability with third-party project management tools.

The invention belongs to the technical field of cloud computing, and discloses a lightweight multi-domain hyper-fusion platform construction method compatible with heterogeneous hardware resources, which adopts a lightweight hyper-fusion platform architecture compatible with the heterogeneous hardware resources, converts physical resources into virtual resources on a virtual machine based on a KVM (Kernel-based Virtual Machine), shields the difference of underlying hardware, and improves the performance of the platform. According to the method, heterogeneous storage resources are integrated based on Ceph, and storage devices of different manufacturers are integrated into a unified storage pool through a distributed storage technology without being limited by types of bottom hardware devices. Through the SDN technology, the OpenStack network service can decouple a network control plane and hardware, and unified management of different network devices is realized. Through a virtualization technology and an automatic management tool, a simplified elastic expansion solution is provided, and changes of resource demands are easily coped with.

The invention provides an LLM command line generation tool based on OpenHarmony and a transplantation method, belongs to the technical field of industrial Internet of Things, and aims to solve the problems that a traditional TypeScript / Node. Js scheme has limitation, is insufficient in local reasoning performance, safety performance and reliability and is high in deployment cost. The tool comprises an ETS development environment module, a code migration module, a dependency library adaptation module, a security enhancement module certificate and a deployment and operation module, a zero-dependency transplantation scheme is designed for special environments of unpacked management tools such as OpenHarmony, the problem of strong coupling of a traditional Node.js application to the environment during operation is solved, and the safety of the Node. Js application is improved. And the deployment threshold of the heterogeneous system is further reduced.

A method, system, and program product includes an integrated artificial intelligent system for generating a work order by including processes configured to execute demand forecasting, tailored pricing, service provider matching, and dynamic job order management. An example leverages data analytics to forecast demand, determine tailored prices, match users with nearby service professionals, teams, and offers via a marketplace network, and manage job orders using a dynamic work order management tool. The system provides service delivery and enhances customer satisfaction. Generating the work order may include performing a task selected from a list that includes at least one of: demand forecasting, tailored pricing, service provider matching, and dynamic job order management. Based on the performed task, the system may match a user with nearby service professionals via a marketplace network to generate a job order and manage the job order.

The invention discloses an intelligent task management method based on OCR (optical character recognition) and AI (artificial intelligence) technologies, which comprises the following steps: S1, information source identification: a user intercepts a screenshot of a task message through a shortcut key, and analyzes the screenshot content through an image identification technology to judge which communication tool the message source is; s2, OCR (Optical Character Recognition) and task analysis: identifying text content in the screenshot through an OCR technology, and analyzing key information of a task in combination with an AI (Artificial Intelligence) natural language processing technology; s3, attachment identification and path positioning: automatically positioning a storage path of a task attachment in a user computer through an OCR (Optical Character Recognition) technology and a file type identification function; and S4, automatic task recording: associating the analyzed task information with the identified attachment, and then automatically inputting the task information into a task management system. Compared with the tedious process that a traditional task management tool needs to manually input, sort and manage task information, the operation steps of the user are greatly reduced, and the overall efficiency of task management is improved.

The invention discloses a zero-code heterogeneous data integration method. The method comprises the following steps: S1, configuring a multi-source data connector; s2, processing differences of different data formats and structures by adopting an adapter mode; s3, performing data cleaning; s4, performing data conversion; s5, loading the converted data into a target database or a data warehouse, and adopting a partition loading and incremental updating strategy; s6, providing a graphical user interface, and constructing a data integration workflow by dragging the component and connecting the data flow; s7, providing a visual workflow management tool, and supporting workflow version control, collaborative editing and parallel processing; s8, realizing an automatic workflow scheduling system, and supporting timed tasks and event driving; s9, performing data consistency check and integrity verification; and S10, a modular and extensible system architecture design is adopted. A zero code integration method is adopted, efficient integration of heterogeneous data sources is achieved, and the method has the advantages of being high in usability, high in processing efficiency and capable of guaranteeing data quality.

The invention relates to the technical field of medical health information services, in particular to a cerebral apoplexy knowledge question-answering system construction method and system based on a knowledge graph and a large language model. Natural language input of a user is analyzed through a query agent, a query intention and constraint conditions are recognized, and a structured execution plan is generated; a user state management tool is forcibly activated, a static clinical portrait and a dynamic rehabilitation log are loaded, and a personalized context is constructed; a plurality of tools such as knowledge graph query, authoritative literature retrieval and rehabilitation plan generation are scheduled, and accurate retrieval and reasoning of heterogeneous knowledge are completed; through double verification of fact consistency and clinical risks, error or high-risk suggestions are intercepted and replaced with risk early warning. The problems of'illusion 'risk, insufficient individuation, poor interpretability and the like of a traditional single model are solved to a large extent, high-credibility, individuation and traceable rehabilitation knowledge service can be provided for the stroke patient and a caregiver of the stroke patient, and rehabilitation safety and effect are guaranteed.

A method for the representation and operation of production resources, in particular for the plastics-processing industry, on one or several central operating units, preferably a computer or control station. Symbols for the production resource connected via a network or the management tools associated with the production resource, such as configuration systems or database systems, are present and selectable on the central operating unit, wherein after selection of one or several symbols on the screen of the operating unit, the visualization, in particular the visualization component from the associated production resources or management tool is started or executed on the operating unit. Subsequently, complete representation and operation is possible through the cooperation of the visualization software available at the operating unit and a control software running decentrally in the production resources.

Provided is an appointment adjustment system including at least one processor, the at least one processor being configured to: acquire communication data indicating content of a communication performed through use of a communication tool; acquire adjustment appointment data indicating an adjustment appointment which is an appointment to be adjusted through the communication based on the communication data and a language-analyzable machine learning model; and transmit the adjustment appointment data to an appointment management tool configured to work in cooperation with the communication tool.

This invention provides an automated system for managing cyber security and regulatory risks. It retrieves internal documents from platforms like Google Drive and OneDrive, and external documents from trusted sources via RSS feeds. Documents are stored in centralized locations with lifecycle management, enriched with contextual labels, and used to augment and fine-tune a language model. Real-time threat intelligence and news feeds are integrated, enabling the system to analyze security advisories and regulatory requirements, generating actionable insights and recommendations. The system integrates with workflow management tools like Jira for tracking work items and provides a natural language interface for ad-hoc user interaction. This comprehensive solution enhances operational efficiency, reduces manual efforts, and ensures timely responses to emerging threats and regulatory changes.

To provide a system for supporting easy setting of workflow.SOLUTION: A workflow management server 10 provides a workflow management tool to a user who operates a user terminal 30 communicably connected via a communication network 20. The workflow management server 10 acquires organizational position information in a company and rule information of approval workflow and automatically generates setup information of the approval workflow based on the two pieces of information, so that the user can set up the approval workflow by preparing the organizational position information and the rule information.SELECTED DRAWING: Figure 1

The invention relates to the technical field of container internal process detection, and discloses a container internal process anomaly detection and processing method and system.The container internal process anomaly detection and processing method comprises the steps that container process information of a target container is obtained, and operation key field features of each container process recorded in a container process list of the container process information are utilized; generating a container process feature set, calling a container process exception detection model to detect the exceptional container processes, extracting the exception type of each exceptional container process in a container process detection result, and considering the exception type of each exceptional container process, the operation cycles of a plurality of associated dependent processes and a to-be-executed container process list; according to the method, the container process exception handling strategy of the current detection round is generated, so that the abnormal container process is restarted, the accuracy of container process exception detection by the container management tool and the reasonability of abnormal container process handling are improved, and the user experience is ensured as much as possible on the premise of ensuring the health state of the container.

The invention provides a meta-application agent service system, which is used for micro-service packaging, publishing, containerization operation and maintenance, meta-application combination, business verification, credible traceability and application integration of an AI technology algorithm, and realizes cooperative work of an AI technology publishing party, an application integration party and a system platform operation and maintenance party through the service system. The platform end supports a technology supplier to publish AI technology algorithm data into micro-services with specific functions through technical model description specifications, application technology multilayer classification and visual interactive publishing service; the meta-application construction module supports an application integration party to upload application demand data and select micro-services to be combined for meta-application simulation evaluation, meta-applications with a large model agent as a carrier are generated, and the meta-applications can interact with the micro-services, the meta-applications, an application system, a cloud platform and an operating system; the operation and maintenance module comprises a containerized micro-service management tool and supports an operation and maintenance management party to distribute containers to micro-services and maintain operation of the containers.

The invention provides a code detection method and system, computing equipment, a storage medium and a program product, and is applied to a code detection system, the code detection system comprises a code management tool and a detection agent, and the method comprises the following steps: calling the code management tool to monitor a code submission event, and when the code submission event is monitored, sending the code submission event to a server; submission information of a to-be-detected code corresponding to the code submission event is obtained; identifying the event type of the code submission event, and determining a target prompt template based on the event type; and constructing a target prompt word based on the submission information and the target prompt template, calling a detection agent, executing a code detection task based on the target prompt word, and obtaining a detection result for the to-be-detected code. The problems that manual examination efficiency is low and research and development resources are occupied are effectively solved, the overall code examination efficiency and quality are improved, and the method can be widely applied to the fields of digital culture product manufacturing software, digital culture creative software and the like in a new generation of information technology.

The invention relates to a security protection module deployment method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps that when it is detected that an application program is started, compatibility checking, security testing and performance testing are conducted on the security protection module, and the running environment of the application program is checked; if the compatibility check, the security test and the performance test all pass, configuring the security protection module through an automatic deployment tool based on the operation environment, and deploying the configured security protection module to the application program; and monitoring and managing the security protection module deployed to the application program in real time through an automatic configuration management tool. By adopting the method, the security protection module can be deployed in a plurality of operation environments.

The present invention proposes a data-driven radar health management system modeling implementation method. Aiming at the needs of rapid customization and continuous upgrading of health management software for different models of radars, the present invention uses configuration files or visual logic modeling tools to create health management models based on radar system analysis data, sets radar fault diagnosis basis, trend prediction algorithm, functional status evaluation and maintenance guarantee measures, and generates model files; and uses database management tools to set collection points, configure data sources and associate BIT data, so as to achieve the association between logical data in the model file and actual collected physical data, and finally uses Web or desktop health management information display software to monitor the operating status of the radar system in real time, so as to achieve integrated management and control of the health status of the radar equipment system. The present invention significantly improves the development efficiency of the radar health management software system, and is of great significance to improving the comprehensive test and diagnosis capabilities of the radar system.

The invention discloses a self-adaptive computing system and method for power grid measurement, the system comprises an engine core service part and a matched storage computing part, the engine core service part comprises a service layer and an engine layer, and the matched storage computing part comprises a computing layer and a storage layer. The service layer provides a unified computing engine API, an SQL development terminal, a unified data directory view and a data directory management tool, so that a user can conveniently access and manage data; the engine layer optimizes data processing performance and management through a storage calculation acceleration module, a self-adaptive push-down module, a self-adaptive path planning module and a data standardization management module; the computing layer integrates and fuses a computing engine, an interactive query engine, a stream batch computing engine and a real-time computing engine, and provides efficient data computing and analysis services; the storage layer supports cross-source heterogeneous data storage and unified metadata storage, and ensures data storage and management. The power grid measurement data searching and using efficiency can be improved, and the application development cost is reduced.