35 results about "Continual improvement process" patented technology

The invention provides a multi-agent cooperation and dynamic feedback long text generation system and method, and the system comprises a performance agent which is used for receiving a theme or an outline inputted by a user, planning and scheduling a long text generation process, coordinating the interaction of an author agent and an evaluation agent, and dynamically adjusting a generation strategy based on an evaluation result; the author agent is used for generating text content according to an instruction of the director agent and correcting a generation result; the evaluation agent is used for performing multi-dimensional quality evaluation on the text content generated by the author agent and returning optimization suggestions; and the memory bank is used for storing the historical generation information and the global setting information and providing context support in subsequent text generation. Through the synergistic effect of the director agent, the author agent, the evaluation agent and the memory bank, a dynamic feedback closed-loop mechanism is established, so that the text generation process has continuous improvement capability, and the overall quality and stability of long text generation are improved.

The invention relates to the technical field of intelligent decision making, can be applied to business scenes such as financial science and technology and medical health, and discloses an action strategy optimization method and device based on an execution track, equipment and a medium, and the method comprises the steps: obtaining a task request, analyzing a task intention, and generating a preliminary execution plan based on the task intention and a preset action strategy; and recording the execution action and the state change in the execution process to form an execution track sequence, comparing the execution track sequence with a standard operation specification to identify an execution deviation, analyzing a deviation root cause of the execution deviation, generating a correction instruction based on the execution deviation and the deviation root cause, and updating the action strategy by applying the correction instruction to obtain an optimized action strategy. According to the method, a feedback strategy optimization mechanism is constructed through execution track recording, deviation recognition and root cause analysis, so that an action strategy can be continuously improved according to actual task performance, and the accuracy and consistency of task execution are improved.

The invention relates to the technical field of artificial intelligence education, in particular to a heuristic teacher observation and evaluation AI support system based on Prompt engineering and RAG collaborative enhancement. According to the system, infant observation records are subjected to standardization processing through a data processing module, then large language models are called in sequence through four professional Prompt templates, and heuristic thinking question generation, deep inquiry analysis, behavior disassembly and target matching based on the RAG technology and synchronous generation of educational strategies and parent suggestions are achieved respectively. And a complete observation-reflection-evaluation-strategy closed-loop processing flow is formed. Through cooperative enhancement of Prompt engineering and RAG technology, automatic analysis and deep professional reflection guidance of observation records are realized, system processing efficiency and analysis depth are improved, error propagation is avoided by adopting an independent parallel architecture, and continuous improvement of system performance is ensured through a continuous optimization mechanism.

This invention relates to the field of industrial Internet of Things (IoT) technology, and more particularly to an interactive system for tracking and providing feedback on maintenance strategies and tasks. The system includes a data hub and fusion module, a health assessment and early warning module, a maintenance strategy intelligent generation module, a task tracking and execution feedback module, a feedback optimization and knowledge base module, and a visualization push module. By constructing a multi-module collaborative intelligent maintenance platform, this invention achieves closed-loop management of the entire process, including equipment status monitoring, health assessment, maintenance decision-making, task execution, and knowledge optimization. It solves the problems of traditional systems relying on fixed rules and lacking continuous improvement; it also addresses the issues of low information transmission efficiency and insufficient decision support, ultimately achieving comprehensive optimization of equipment reliability, maintenance costs, and operational management efficiency.

The invention discloses a BIM-based maintenance file package full life cycle digital management system and method, and aims to solve the problems of insufficient maintenance task preparation and subjective quality evaluation. The core of the method is that a maturity gating model is introduced, indexes such as the data integrity, the risk reduction degree and the resource readiness of a file package are quantitatively evaluated before the file package is issued, and pre-quality control is carried out on task performability. And the system issues the qualified file package to the site, acquires execution data and writes the execution data back to the BIM base to form a closed loop of knowledge precipitation and continuous improvement. According to the invention, the reliability of maintenance work is effectively guaranteed, and the quality and intelligent level of facility operation and maintenance management are significantly improved.

A computer-implemented system and method for iterative compound value creation management is disclosed. The method includes receiving organizational input data, normalizing and embedding the data, and aggregating the embedded features using a neural model to generate a composite value index. Recommended actions are generated based on the index, and feedback is simulated or collected in response to these actions. The system updates the state of the input data based on feedback and repeats the process for a predetermined number of iterations or until a convergence criterion is satisfied. The invention enables adaptive, data-driven decision-making and continuous improvement in organizational value creation through a modular, scalable software architecture.

The invention relates to the technical field of geomembrane welding, and discloses an HDPE geomembrane welding process and a quality detection method, and the HDPE geomembrane welding process comprises a welding process dynamic control step, a multi-mode quality detection step and a quality judgment and positioning step. According to the method, through real-time monitoring and dynamic closed-loop control in the welding process, the uniformity and consistency of welding seam formation are improved from the source, and defects are effectively prevented; by synchronously executing air pressure detection and machine vision detection and performing intelligent judgment based on a fusion rule, comprehensive, efficient and objective evaluation on the airtightness and the appearance quality of the welding seam is realized, and the accuracy and the reliability of detection are improved; meanwhile, technological parameter self-optimization and defect accurate positioning promote continuous improvement of the welding technology, powerful support is provided for engineering quality tracing and efficient repairing, and the intelligent level of construction and the guarantee capacity of engineering quality are overall improved.

This invention discloses a data-driven method for quality management of formwork construction in high-speed railway bridge-building machines, belonging to the field of engineering construction management technology. It includes acquiring multi-source real-time status data, environmental monitoring data, and historical adjustment records; calculating based on an initial quality sensitivity model to generate a dynamic quality sensitivity graph and perform resource scheduling to obtain refined monitoring data; then performing digital twin simulation of the construction process to generate a simulation report and conduct multi-objective optimization decision analysis; generating adjustment instructions; controlling the execution mechanism to execute the adjustment instructions; collecting actual formwork response data and comparing it with the simulation report; generating model calibration data; and updating the initial quality sensitivity model. This invention employs a collaborative working mode of multi-source data perception, digital twin simulation, closed-loop feedback control, and model self-optimization, enabling predictive management of construction quality, scientific optimization of decisions, and continuous improvement of the process.

This invention discloses a pipe finishing system and method for each pipe. The pipe processing steps are sequential: warehousing, straightening, and flaw detection. The system includes a Manufacturing Execution System (MES), a Water Supply and Storage System (WCS / WMS), a Production Internet of Things (IoT) system, and a PLC control system. The PLC control system includes a straightening machine control system for controlling the straightening machine and a flaw detection control system for controlling the flaw detection equipment. The WCS / WMS system is connected to the automated steel pipe warehouse to control its operation. The MES system is connected to the production IoT system. The production IoT system is connected to the WCS / WMS system, the straightening machine control system, and the flaw detection control system. This system enables pipe-by-pipe tracking and data storage for each processing step in the finishing process, facilitating traceability, root cause analysis, and continuous improvement in steel pipe production.

The invention provides a method, a system, equipment and a medium for realizing unified process management, and the method comprises the steps: building an extensible access architecture based on a multi-protocol adaptation layer and a dual-interface separation mechanism, and achieving the standardized access and elastic data management of a heterogeneous system through the combination of dynamic business object modeling; a four-layer processing system with data full life cycle management as a core is constructed, and flow data standardization management is achieved through a dynamic rule engine and hierarchical storage; a full-link process intelligent sensing and self-adaptive early warning system is constructed, and a rule engine is used for driving multi-stage alarm closed-loop management, process abnormity active sensing and hierarchical management and control; an intelligent closed-loop optimization system based on holographic modeling and AI diagnosis is constructed, and end-to-end process efficiency quantitative evaluation and continuous improvement are realized through dynamic digital twinning, so that the problems of data real-time performance, system compatibility and implementation economy of a process management technology based on a data platform are solved.

The embodiment of the application provides a kind of intelligent auxiliary decision-making method and device based on multi-source data, realizes the accurate identification of information by semantic analysis and feature extraction.Construct decision mechanism, combine flow model and scheduling model, establish reliable task management strategy.Introduce efficiency optimization, through completion degree evaluation and model retraining, ensure the continuous improvement of decision-making.This method effectively solves the shortcomings of traditional technology in information extraction, task management and efficiency evaluation, etc., and provides technical support for intelligent auxiliary decision-making.

The invention relates to the technical field of work orders, in particular to a work order generation method and system based on autonomous matching. The method comprises the steps of pushing a work order to client equipment for processing, and generating execution evaluation for optimizing subsequent work order generation based on client processing feedback received from the client equipment. The execution evaluation is generated according to the client processing feedback, the optimized work order is generated based on the actual execution parameter value and the preset parameter value, self-adaptive learning and continuous improvement of the system are achieved, the actual execution result is quantized and fed back to the historical contract content, the work order generation system dynamically adjusts key parameters which the decision depends on, and the decision-making efficiency is improved. And the subsequently generated work order adapts to the change of the actual execution environment.

The embodiment of the application provides a modular function development method and device of a digital twin network management platform, accurate modeling of a network is realized through state analysis and topology mapping. A management mechanism is constructed, a reliable early warning strategy is established in combination with an intelligent model and fault correlation. Decision optimization is introduced, and continuous improvement of management is ensured through scheme verification and feedback adjustment. The method effectively solves the deficiencies of traditional technologies in network modeling, fault early warning and scheme decision, and provides technical support for network management.

The invention discloses a well cementation admixture-oriented whole production flow quality tracing method and system, and relates to the field of data tracing, and the method comprises the steps: carrying out the directional preprocessing of a raw material, and building a digital technology file; monitoring a key process in real time in production and deeply binding the key process with traceability information; a correlation tracing matrix is generated after a finished product is qualified through specific working condition inspection; precise inventory management and control are realized based on intelligent warehousing and RFID; recording transportation environment data by using a dynamic monitoring technology and incorporating the transportation environment data into a tracing chain; matching verification is completed on site through a compatibility test, and link data are used in a closed mode; finally, a full-life-cycle after-sales tracing mechanism is established, a problem source is reversely positioned through a tracing code, and continuous improvement is driven. The method has the advantages that a full-life-cycle digital closed-loop traceability system from raw material preprocessing to after-sale is constructed, data of all links are connected through multi-code association, quality traceability, problem checkability and production optimization are achieved, and admixture working condition adaptability and use stability are guaranteed.

The invention discloses a DCS hardware product release automation and intelligent examination method and device, and relates to the field of DCS product development, and the method comprises the steps: constructing a DCS hardware product research and development life cycle model; a forward design process from system requirements to design implementation is constructed based on an MBSE method, and a consistent and traceable structured product data model is formed; constructing a product release review rule base by setting review dimensions, defining review rules and digitally packaging the rules; an automatic execution, intelligent reasoning and intelligent review engine is constructed, and automatic and intelligent review of product release is achieved; and dynamically updating the rule base based on the review result and manual review suggestions, and realizing intelligence and continuous improvement of release review. According to the method, automation and intelligent management of the whole process of publishing and reviewing of DCS hardware products are realized, the efficiency and accuracy of publishing and reviewing are improved, the efficiency and quality of research and development iteration are improved, and data support is provided for quality management and control of the whole life cycle of product research and development.

A system and method for continuously improving the cybersecurity rating of a firm includes a feed processor configured to parse at least one cybersecurity report and to feed for building an instance graph. The system may identify, measure, and monitor portfolio dimensions of public-facing digital artifacts. In addition, the system and method may each include the instance graph being compared by a correlation engine with a reference graph to generate automated or semi-automated remediation recommendations that may lead to continuous improvement of a cybersecurity rating for an organization or firm.

The invention discloses a vPLC intelligent control strategy dynamic optimization method and system based on reinforcement learning, and the method comprises the following steps: carrying out the static analysis of a PLC program, and generating a resource demand report; generating an initial idle time table according to the resource demand report; receiving the initial idle time table at the PLC scheduling center, and generating a PLC scheduling task set; collecting CPU resource use data, and generating a corrected idle time table; the corrected idle time table and the CPU resource use data are input into the improved QR-DQN model, and an optimized idle time table is generated; executing a PLC scheduling task, and generating monitoring data; monitoring data is fed back to the improved QR-DQN model, and the idle time table is iteratively updated and optimized, so that dynamic optimization and continuous improvement of PLC scheduling are realized, and the CPU resource utilization rate is increased.

A production asset monitoring system, for monitoring current operating states of one or more production assets, associates a respective state machine which each asset in the one or more production assets, where each state machine implements a plurality of operating states, out of which operating states at least one state is categorized as a productive state and at least one state is categorized as an unproductive state. The production asset monitoring system further comprises a sensor system configured to detect a current operating state for each state machine, and the production asset monitoring system is configured to aggregate the states of a plurality of assets into a report indicative of production asset utilization.

The invention provides a cue word optimization method and system and electronic equipment, relates to the technical field of artificial intelligence, intelligently identifies a root cause of performance difference by utilizing collaborative annotation of different capability models and result comparison, and directionally triggers an optimization strategy according to the root cause. And the reasoning ability of the strong model possibly appearing in the reasoning process is precisely extracted and migrated to the cue word of the weak model. Compared with the prior art, the method has the advantages that dependence on manual debugging is remarkably reduced, autonomous and continuous improvement of the cue words is achieved, finally, the production model can achieve the performance close to a stronger model on a specific task through the optimized cue words, and therefore the model performance is remarkably improved while the cost is controlled.

The embodiment of the application provides a kind of based on the automatic test code generation method and device of large language model, by innovatively designing requirement processing system, by semantic analysis and scene construction, the effective generation of test case is realized.Code generation mechanism is constructed, in combination with quality evaluation and execution control, reliable test strategy is established.Result analysis is introduced, through experience accumulation and scheme optimization, ensure the continuous improvement of test.The method effectively solves the deficiency of traditional technology in requirement processing, code generation and result analysis etc., provides technical support for automated testing.

The invention discloses a photovoltaic glass production process optimization system based on detection data feedback, and relates to the technical field of photovoltaic production, and the system comprises a production process and data management end which is responsible for extracting predefined production process parameter groups from a database, and constructing an initial photovoltaic glass group set based on the parameter groups; afterwards, the light transmittance fluctuation characteristics of the photovoltaic glass are quantitatively evaluated through professional detection equipment in the photovoltaic glass intelligent detection end, and a comprehensive light transmittance fluctuation broken line is obtained through mathematical simulation; the collaborative optimization end combines the normalized light transmittance and the fluctuation characteristics to calculate the comprehensive index of each process group, so that the optimal process parameter group is accurately positioned; and finally, the iteration feedback end dynamically updates the optimal process parameters in a preset period to ensure that the production is always in an optimal state. The whole system remarkably improves the efficiency and quality of photovoltaic glass production through a closed-loop feedback mechanism, and meanwhile, the system adapts to the continuous improvement requirement of the performance of a photovoltaic material.

The present application relates to a method and system for automatic continuous improvement of a DevOps platform. The method comprises collecting defect data of a business, pushing a defect event to a relevant person, determining a root cause category of the defect event and an improvement measure or a preventive measure, judging the effectiveness of the measure, outputting the measure and automatically including the improvement measure in the daily work targets of each team on the DevOps platform, continuously implementing the improvement measure, tracking the implementation result feedback of the measure, quantifying the improvement quality, and further improving the key points in each step based on the improvement quality result. The method fully considers the mutual relationship among the three elements of people, processes and tools in the continuous improvement process of the DevOps platform, guides people to conduct lean root cause analysis of defect events, and on this basis, outputs effective improvement measures based on people, processes and tools on the DevOps platform, and dynamically tracks the implementation results, and dynamically establishes a self-feedback DevOps continuous improvement system according to the implementation results.

The invention discloses an electronic responsibility insurance service method. According to the invention, through accident prevention and risk assessment, an enterprise can identify and manage safety production risks more effectively, so that accidents are prevented. Meanwhile, the insurance company can better understand the risk condition of the enterprise by providing the services, and more suitable insurance products and services are provided for the enterprise. Through education and training, the safety consciousness and operation skills of employees are enhanced, and accidents caused by human errors are reduced. And by identifying and evaluating the risk, the enterprise can take measures in a targeted manner, so that accidents are prevented, and the overall safety management level is improved. Safety evaluation is helpful to discover defects of a safety management system and promote continuous improvement of the system, so that the implementation process of the method can help enterprises to better manage safety production risks, improve the ability to deal with accidents and reduce losses caused by the accidents.

The embodiment of the invention provides an automatic test code generation method and device based on a large language model, and effective generation of test cases is realized through innovative design of a demand processing system, semantic analysis and scene construction. And a code generation mechanism is constructed, and a reliable test strategy is established in combination with quality evaluation and execution control. Result analysis is introduced, and continuous improvement of the test is ensured through experience accumulation and scheme optimization. According to the method, the defects of the traditional technology in the aspects of demand processing, code generation, result analysis and the like are effectively overcome, and technical guarantee is provided for automatic testing.

The application provides a kind of based on wisdom beam factory's steaming process management and optimization method and system, it is related to steaming process management technical field, the method includes: step 1, constructs and trains an intelligent analysis model, obtains the intelligent analysis model after training;Real-time acquisition's steaming environment parameters and the temperature and humidity data of the internal multiple position points of precast beam body are input into the intelligent analysis model after training, obtain the temperature and humidity field distribution in space and time on the internal precast beam body;Step 2, extract the change characteristics of temperature and humidity field distribution in space and time dimension, determine a feature operating point according to change characteristics, and generate corresponding temperature and humidity gradient change trend line based on feature operating point.The application realizes the management and continuous improvement of steaming process, improves the steaming quality of precast beam body and production management efficiency by temperature and humidity field accurate prediction, structure state evaluation and individualized steaming regulation, combined with micro-crack risk control and model adaptive optimization.

The application discloses an intelligent decision-making method and system based on ecological-social double-network coupling and functional trait transmission, relates to the technical field of intelligent ecological management, and comprises the following steps: constructing ecological and social network modules, and superimposing outputs of the modules into an ecological-social double-network coupling model. Based on the model, ecological and social intelligent agents are created, and then the intelligent agents are used to form a double-intelligent-agent coordinator to optimize the model and generate a final decision scheme. The ecological function evaluation, social benefit accounting and dynamic optimization decision are split into independent intelligent agent modules, and cross-system collaboration is realized. The coordinator balances the states of ecological and social indexes, automatically triggers scheme re-optimization, and forms a continuous improvement mechanism of evaluation-decision-calibration. The ecological driving module and the social driving module output an optimal scheme that simultaneously satisfies ecological protection requirements and social feasibility constraints through an intelligent negotiation mechanism.

Computerized systems and methods for performing a personalized bundling process are described. The method includes receiving user input specifying product bundling preferences and retrieving related real-time data using a real-time data grid (RTDM). An Advanced Analysis and Machine Learning (AAML) module analyzes these inputs in conjunction with marketplace data to generate personalized bundled recommendations. The recommendation is then displayed to the user via a single view window user interface (SPoG UI) and transmitted as an order to the vendor system upon confirmation by the user. The verification step uses an algorithm within the AAML module to ensure the accuracy and correlation of the bundling. And generating a real-time report about the user participation degree and the bundling success rate. The system supports multi-device access, integrates an evolution-based data pattern with user feedback to continuously improve a machine learning model of a binding process, thereby enhancing personalization and efficiency.

To provide computerized systems and methods for automated, AI-driven customer and vendor segmentation and personalized insight delivery that facilitate continuous optimization and adaptation, improving relevance and accuracy. [Solution] The method collects real-time data including purchasing behavior and market trends, analyzes it using AI / ML algorithms for segmentation, provides personalized insights through a user-friendly single-pane-of-glass user interface (SPoG UI), records transaction details for continuous improvement, monitors and refines the effectiveness of segmentation based on user feedback and evolving market dynamics, and integrates multiple data sources and analysis tools for comprehensive analysis. Users can customize segmentation parameters.

The invention relates to the technical field of electric power system operation optimization, and particularly discloses a power transmission and distribution network annual operation evaluation and adjustment method based on an allowable income upper limit, and the method comprises the steps: integrating data of an electric power system, a financial system and an external environment through a multi-source data dynamic fusion technology, and generating a high-quality integrated data set; generating prediction cost and uncertainty measurement by using a dynamic cost prediction model in combination with cost trend and fluctuation characteristics, and feeding back optimization data collection; a closed loop is optimized through multi-target operation, various future scenes are simulated, an adjusted permitted income upper limit is generated by using a collaborative optimization algorithm, and technical efficiency improvement and external economic factors are balanced; and finally, through adaptive verification optimization, backtracking the upper limit of the permitted income after test adjustment, generating a traceable report and optimizing model parameters. According to the invention, continuous improvement of an evaluation system is realized, and prediction precision and robustness are improved.