28 results about "Process management system" patented technology

This application discloses a power training end-to-end management system and method based on digital threads, belonging to the field of power training information management. The system uses a digital thread engine as its core hub, connecting data and business flows across modules such as training resources, information, examinations, and points. It anchors the entire lifecycle behavior trajectory of trainees through unique identifiers and timestamps, achieving dynamic mapping and real-time traceability of all elements. The digital thread engine incorporates an AI-MLP neural network and a lightweight GNN module, automatically identifying inflection points in skill development and knowledge gaps, accurately attributing them to long-term skill evolution. The examination management module employs an AI anti-cheating mechanism, enabling seamless post-exam learning. The training resource module connects to a digital twin model, supporting AR immersive practical training. This invention constructs a closed loop of "learning-practice-examination-evaluation-application," improving the accuracy and practicality of training, addressing pain points such as low efficiency in new energy operation and maintenance talent training and fragmented training management, and providing solid talent support for the new energy industry.

Methods, systems, and computer-readable media are provided for asynchronous process automation that efficiently manages asynchronous processes based on process control metadata to keep data up-to-date at an adaptively variable type-specific cadence based on process control metadata, eliminating the need for manual engagement to maintain operational activities. An event-driven framework is provided for the asynchronous process automation that includes AI agent(s) for interactions, along with a process management system that tracks pending potential work items due to one or more changes in source data. The process management system activates an asynchronous process management process after a wait period. The process management system then determines which pending potential work items to perform in an iteration of evaluation of the pending potential work items. The process control metadata includes stored limitations on a timing for performing individual processes. The process management system prioritizes processes based on process control metadata, such as prioritizing specific ones over others, including throttling certain other processes during high impact time-sensitive activities.

The application provides an intestinal preparation quality real-time evaluation and whole-process management system based on artificial intelligence, which comprises the following functions: patient end provides intestinal preparation start time setting, laxative type selection, examination process and matters needing attention query and online consultation function; medical staff end provides patient management, progress and cleanliness score monitoring, early warning processing, data export and message interaction function, wherein the intelligent data acquisition module acquires multi-dimensional data; the artificial intelligence dynamic scoring module analyzes the data based on a deep learning model, generates a cleanliness pre-score and determines a quality grade; the active early warning and intervention module analyzes the data in real time, issues an early warning and provides intervention suggestions; the medical examination result closed-loop verification module receives the doctor's score after colonoscopy, compares and analyzes the pre-score, and optimizes the evaluation model. The application realizes the standardized guidance, active risk early warning and closed-loop optimization management of the intestinal preparation process, effectively improves the preparation quality, guarantees the safety of patients and optimizes the clinical decision-making.

This system reads the characters printed on the materials used in the work as images and recognizes the identification code. This eliminates the need for physical labeling such as paper or stickers on the materials, allowing for direct reading of the characters written on the materials, thereby improving the convenience of material management. The system provides a work process management management method and program. [Solution] The work process management system 1 includes a shooting unit 301 that photographs a string of characters 10 printed on a component 6 used in the work, and a display unit 302 that recognizes the string of characters 10 photographed by the shooting unit 301 and displays pre-stored string of character candidates 20 corresponding to the recognized string of characters 10. The display unit 302 displays one or more string of character candidates 20 for the worker 5 to select from and accepts the worker 5's selection.

This invention discloses a digital full-process SaaS system and method for the disposal of non-performing personal loans based on the S2B2C model, belonging to the fields of financial technology and non-performing asset disposal technology. The system uses artificial intelligence, big data, and blockchain as core technologies, constructing five modules: NPL intelligent assessment, NPL intelligent case allocation, diversified judicial mediation, grid worker collaboration, and value-added services. It pioneers an S2B2C case source supply chain architecture, connecting upstream asset providers, midstream service providers, downstream debtors, and grid workers across the entire chain. It achieves second-level asset package breakdown and valuation, multi-dimensional accurate case allocation, batch online judicial mediation (including AI emotion recognition, electronic service of process, and blockchain evidence storage), and offline grid worker services. It also integrates debt inquiry, employment-based debt resolution, and rapid lawyer order-grabbing functions, forming a complete "assessment-case allocation-disposal-resolution-closed loop" solution. It can be widely applied to large-scale disposal scenarios of non-performing personal loans for banks, consumer finance companies, asset management companies (AMCs), law firms, and other entities.

The application discloses a leather solid waste resourceization process management system based on big data analysis, comprising: a data acquisition module for collecting and preprocessing, constructing a basic data sequence; a feature extraction module for performing kernel regression operation according to the basic data sequence to form a solid waste feature sequence; a transformation prediction module for performing inference prediction operation on the solid waste feature sequence through a Fourier neural network; a path selection module for multi-objective sorting of the prediction result and screening of a target path; a parameter matching module for constructing a process parameter set according to the target path; a resource processing module for mapping the process parameter set into an instruction execution and recording feedback data; and a feedback updating module for updating parameters of the Fourier neural network. The application combines technologies such as feedforward neural network and Fourier neural network, has the advantages of high resource transformation efficiency, strong intelligent selection capability of processing path and sustainable optimization.

The application discloses a railway maintenance construction process management system, and relates to the technical field of railway maintenance construction management. The system comprises the following modules: a construction plan analysis module, a target section generation module, a construction mileage correction module, a polishing process data acquisition module, an empty load state benchmark generation module, a unit mileage polishing action calculation module, a rail profile change extraction module, a polishing action and profile change mutual verification module, an abnormal section identification module and a construction result verification module. A unified mileage corresponding relationship is established based on the mileage feature points in the polishing tool log, the detection data before and after construction and the re-measurement data. The empty load state benchmark is generated according to the state segment of the polishing head not contacting the rail. The unit mileage polishing action of the polishing target section is calculated. The rail profile change amount before and after construction is extracted and it is judged whether the two are matched. The abnormal section is identified and the pass, review or prohibition of record results are generated. Through the unified mileage alignment, the empty load benchmark deduction and the mutual verification of the action and the profile change, the accuracy and traceability of the rail polishing construction verification are improved.

This invention discloses a PCB board production process management system and method based on intelligent traceability, belonging to the field of production management technology. It includes constructing a board-level traceability knowledge graph from process event data during PCB board production based on board-level traceability primary keys and traceable process chains. When an anomaly is triggered in the process event data, the system expands the association starting from the abnormal PCB board graph node to determine candidate responsibility nodes and generate an anomaly responsibility chain. Then, based on the anomaly responsibility chain, it identifies suspected anomaly sources and generates process control status by combining the graph association relationships between other in-process PCB boards and suspected anomaly sources. Based on this, it executes review or rework insertion and writes the handling results. This upgrades PCB production traceability from linear history query to graph-based responsibility chain positioning, and enables precise release, review, and freeze control based on suspected anomaly sources, thereby improving anomaly traceability efficiency, reducing the risk of excessive interception, and enhancing the closed-loop nature of production process management.

Apparatus and associated methods relate to a claim adjustment process management system (CAPMS) including a coverage comparison engine configured to generate a coverage analysis object (CAO) and a reference model interpretation engine configured to generate an initial coverage determination (ICD). For example, the CAPMS may receive claim object, a reference model, and a claim adjustment input (CAI). The reference model interpretation engine may, for example, apply a natural language processing (NLP) model to process claim object and the reference model to generate the ICD. The coverage comparison engine may, for example, analyze the ICD and the CAI to identify variances between the two and generate the CAO. The CAO may, for example, include an error and omission signal. The CAO may, for example, include a resolution object. Various embodiments may advantageously automate claim adjustment processes and reduce human error and ensure consistent, fair coverage determinations.

The application discloses a geological survey project whole-process management system based on a workflow engine, relates to the field of automatic control of geological and mineral exploration, and comprises a central management control platform, a central workflow scheduling center, a cross-agency collaboration gateway and a distributed business database which are internally arranged, a field operation sensing subsystem, a portable geological survey terminal which is integrated with a double-frequency multi-constellation GNSS receiver board card, an intelligent checking calculation base which comprises a geological semantic checking engine and a multi-source spatial data fusion module, a resource and monitoring feedback subsystem which comprises a laboratory resource scheduler, and asynchronous communication between the central management control platform and the intelligent checking calculation base through a message queue bus.The application realizes real-time strong constraints of a process on quality through an evaluation system, solves the problem of inconsistent field positioning accuracy by using a BursaWolf model, and realizes optimal allocation of resources in the whole life cycle of a geological survey project through an incentive type scheduling index.

The invention relates to a process management system and method, in particular to a complex equipment-oriented intelligent management system and method capable of arranging processes and rules, and aims to solve the problems of high coupling of process control and business rules, coding solidification and the like in the prior art. The system comprises a core service component, the core service component comprises a process model library module and a rule model library module which are used for providing a visual arrangement interface, a process engine module for driving process execution and a rule engine module for executing a rule model, and all the modules are in decoupling connection through message middleware; the database component comprises a relational database and a memory database; and a service middleware component. The method comprises the steps of flow and rule visual modeling and association configuration; performing breakpoint debugging execution of the task process; and the rule engine dynamically generates data or instructions according to the model and interacts. According to the invention, decoupling of processes, rules and execution is realized, and the flexibility, maintainability and test intelligence level of the system are improved.

This application provides a method, apparatus, electronic device, and storage medium for marketing process control. By clearly defining the unique entry point (first node) and mandatory exit point (tail node) of the marketing process, it achieves precise and automated control of the process lifecycle. Specifically, the automatic trigger binding mechanism of the first node eliminates the process start delay caused by manual marking in traditional solutions, enabling real-time response to customer behavior. Meanwhile, the unconditional termination mechanism of the tail node, by skipping all intermediate task status checks, ensures that the computing resources occupied by the process instance are released immediately and completely, thus fundamentally solving the problem of long-term resource occupation caused by intermediate stagnation, and significantly improving the efficiency and resource utilization of the marketing process management system.

This invention provides a multi-source data fusion-based bronchoscope end-to-end management system, method, and chip, belonging to the field of data management technology. Specifically, it includes: a clinical diagnosis and treatment module responsible for patient information association, bronchoscope usage record registration, and post-use status registration; a processing module responsible for receiving and confirming the entire bronchoscope processing record, automatically collecting cleaning parameters, and using the fluctuations in the bronchoscope's lifespan in different disease types to determine the remaining lifespan assessment method for the bronchoscope in the given disease type; a linkage module responsible for receiving microbial monitoring requests, reporting monitoring results, and automatically triggering early warning and recall processes; and a central monitoring module responsible for displaying the real-time status of all bronchoscopes, graphically displaying the complete bronchoscope flow path and all operation records, and generating various reports, thereby improving the reliability of bronchoscope lifespan management.

The application provides a feed intelligent recommendation and precise feeding system based on pig multi-modal intelligent perception. The system includes a perception module, a data processing module, a decision module and an execution module. The perception module collects pig visual, sound, behavior and physiological data, the data processing module cleans, standardizes and fuses the data, the decision module generates feed feeding decisions through reinforcement learning algorithm, and the execution module automatically controls the feeding equipment according to the decisions. Different strategies are used for data cleaning to remove noise, standardization ensures compatibility of different modal data, and data fusion extracts features through convolutional neural network and LSTM and uses self-attention mechanism for weighted fusion. Reinforcement learning algorithm optimizes decision, balances health status, feed utilization and cost, and dynamically adjusts feeding strategy. Through automatic whole process management, the system can significantly improve feed utilization, reduce feed cost, improve pig health status, and maximize the economic benefit of the farm.

The application discloses a kind of individualized embroidery custom whole process management system, method, it is related to individualized customization management technical field, including the following steps: collecting order data in the whole process of individualized embroidery customization, pattern design, wire type information and process rhythm information, the data collected is analyzed, the production batch corresponding to metal wire is identified, and polarization sensitive list is generated.The application realizes the dynamic coordination of polarization angle, exposure timing and light switching by establishing polarization sensitive list, time scale table and brightness compensation control draft, eliminates brightness sudden drop and dark spot misjudgment, improves detection stability and imaging quality.Through light adjustment and gray scale compensation collaborative control, realize brightness balance and image consistency, enhance metal thread embroidery product detection precision and production collaborative efficiency.

The present application relates to the field of agricultural informatization and intelligent management, in particular to a full-process management system for selenium-rich bio-organic fertilizer research and development and demonstration and popularization, comprising: a raw material digital filing module for executing a sliding average filtering algorithm to clean data and generating a digital raw material fingerprint feature vector; a market demand reverse analysis module for extracting and calculating an environmental demand constraint matrix representing differentiated market demand based on preset element flow rules; a research and production intelligent decision-making module for starting an evolutionary solution model, mapping the raw material fingerprint to the environmental constraint matrix, and outputting a dynamic production management instruction sheet; a production execution supervision module for distributing instructions to terminals and real-time monitoring of process execution data; and a full-life-cycle closed-loop optimization module for obtaining feedback data to calculate deviations and generating a correction factor to update model parameters; the present application solves the contradiction between the heterogeneity of non-standard biomass raw materials and the precise nutritional requirements of functional agricultural products.

The application relates to the technical field of production line process management, and discloses an intelligent production line process management system based on artificial intelligence. The system comprises five modules, namely, a process data index module, a process knowledge graph construction module, a process path optimization module, a dynamic adjustment module and a process execution verification module. The process data index module acquires a target process procedure of a current production batch, collects real-time production environment parameters, and indexes a historical process data set; the process knowledge graph construction module fuses multi-source process parameter features, constructs a process element correlation graph, and extracts a process constraint condition and a quality index set; the process path optimization module generates a candidate path based on graph optimization, and calculates a process stability evaluation value; the dynamic adjustment module matches real-time parameters and constraint conditions, corrects the evaluation value, and outputs an optimized path; and the process execution verification module controls equipment operation, collects actual data to verify the quality index achievement degree, and realizes intelligent management of the whole process of a production line process.

This invention relates to the field of financial process management systems, specifically providing an adaptive processing method, system, device, and storage medium for refund processes. The method includes: acquiring business characteristic data in response to the creation of a receipt registration form; determining the business type based on the business characteristic data, where the business type includes partial refunds; establishing an association between the receipt registration form and the original payment slip that has been settled; and, based on the association, only canceling the portion of the amount corresponding to the receipt amount in the original payment slip, and simultaneously updating the integrated data of the business system associated with the original payment slip. This invention achieves accurate cancellation of partial amounts on completed payment slips by intelligently identifying partial refund transactions and establishing an association with the original payment slip. Simultaneously, it employs a transaction mode to ensure data consistency across multiple systems, thereby improving the efficiency of financial refund processing.

This invention relates to the field of automotive crash safety test management technology, and more particularly to a digital and intelligent full-process management system for crash safety laboratories. The system comprises a perception layer, a transmission layer, a core service layer, and an application layer. The perception layer collects sample, asset, and test information and presets constraints and filling rules. The transmission layer pushes data in real time. The core service layer achieves full-element management of tests, samples, assets, calibration, and data through five modules, integrating three types of intelligent models to realize intelligent asset scheduling, inventory early warning, and equipment maintenance. The application layer mines data value based on a joint probability density AI model, providing support for R&D decisions and design. This invention solves the problems of information silos, process disconnects, low data flow and application rates, and lack of full-process data quality control in traditional laboratory management, resulting in low laboratory management efficiency.

The present disclosure relates to a plug-in process management system, an electronic device, and a business process management method, which belongs to the field of computers. The plug-in process management system includes N storage spaces and N business plug-ins, wherein N is an integer greater than or equal to 2. Each of the business plug-ins corresponds to an independent storage space, and each of the business plug-ins is configured to receive business data to be processed and store the business data to be processed in a corresponding storage space, and to duplicate the business data to be processed and to process the duplicated business data to be processed. The problem of the existing plug-in process management system that when certain plug-in goes wrong the entire business process needs to re-execute from the beginning, resulting in consuming a lot of time, is solved.

The application relates to the technical field of intelligent data processing and office automation, and discloses a file process management system optimization method, which comprises the following steps: cleaning official documents, performing sliding window blocking, and generating a global semantic vector by naming entity density weighting; deciding a decoding path between differential extraction and full-amount generation modes based on a local semantic density index; generating an initial task by correcting an original logarithmic probability vector of a model by using a granularity mapping matrix; performing multi-dimensional redundancy checking, calculating an execution damping coefficient based on an official document reference map, and controlling a task state output final list; and calculating a granularity entropy change index according to an operation log to reversely update the granularity mapping matrix. The application utilizes a heterogeneous feedback closed loop to construct a dynamically updated granularity mapping matrix, realizes adaptive approximation of task generation granularity from extensive to accurate, and solves the problems that a model cannot continuously adapt to the evolution of specific department business habits and is difficult to solve model drift.

This invention discloses a gas well workover rig operation optimization management system based on multi-parameter automated adjustment, relating to the field of gas well workover rig operation optimization. It primarily targets the operation optimization management of gas well workover rigs, which commonly employ a single-objective control strategy of "maximum production priority." This strategy implicitly assumes a linear relationship between production and energy consumption and a perpetually positive marginal benefit. This invention achieves automated optimization and safety management of the entire gas well workover rig process across multiple dimensions. It abandons the pitfalls of traditional "maximum production priority" single-objective control by accurately identifying the dynamic relationship between production and energy consumption, analyzing hysteresis effects during operation in real time, and integrating environmental and equipment status to achieve dynamic safety early warning. It constructs a full-process management system of "data acquisition - analysis and identification - decision optimization - closed-loop update," effectively solving core problems such as low energy efficiency, execution deviations, and difficulty in predicting safety hazards during gas well workover rig operation.

The application discloses a kind of bag process single generation method and system, belong to industrial design and manufacturing technical field.The method includes: through client receiving bag type and process style selected by user, and obtain the component data input by user;Based on the combination of bag type, process style and component data, carry out graphic drawing through server end, generate corresponding cutting process sheet.The application adopts the flexible drawing mode of arbitrary combination of bag type and process type, realizes the rapid generation of bag process sheet, and the drawing speed is usually completed within 1-3 seconds, greatly improves process design efficiency.At the same time, the system supports local storage and query of process data, cost accounting, printing plate management, wire density calculation and other functions, and can convert internal process sheet into customer version process sheet without cutting details, effectively protect the core technical data of enterprise.The application is suitable for Windows 10 / 11 operating system, provides efficient, flexible and safe digital process design solution for bag production enterprises.

This invention belongs to the field of cleaning equipment technology and discloses a full-process management system for fibrous waste applicable to cleaning equipment. The system includes a cleaning equipment body and a compatible base. The cleaning equipment body integrates a flow guiding module, a centralized guiding module, a detection and analysis module, a processing and conversion module, a cleaning module, and a storage module. Both the cleaning equipment body and the base are electrically connected to a smart control module. This invention constructs a dedicated processing path for fibrous waste through the coordinated operation of six modules: flow guiding, centralized, detection, processing, cleaning, and storage. It achieves graded separation and storage of dust and fibers, converting long fibers into easily stored clumps / short filaments. Fiber clogging rate is reduced by 90%, and equipment operational stability is significantly improved. This invention completely solves the core problems of existing cleaning equipment, such as fiber entanglement in the roller brush, adhesion to channels, and mixed storage causing odors. It achieves fully automated management of the fiber process and solves the problem of entanglement and clogging.

The application belongs to the technical field of learning state monitoring, and particularly relates to a learning state monitoring method and device based on multi-modal perception. The method comprises the following steps: S1, acquiring multi-modal monitoring data of a monitored person in real time during learning state monitoring, and extracting feature information; S2, constructing a feature monitoring model, and calculating a feature index according to the feature information; S3, judging whether the feature index satisfies a feature time sequencing judgment condition; and S4, calculating a non-concentration degree according to the feature index satisfying the feature time sequencing judgment condition, and generating a reminding information and pushing the reminding information to the monitored person when the non-concentration degree is greater than a preset non-concentration degree threshold. The application forms a whole-process management system of non-sensing monitoring, intelligent identification and reminding intervention.