180 results about "Process management system" patented technology

The invention relates to the technical field of data management, in particular to a low-altitude economic flight data management method and system based on edge computing, and the system comprises edge computing nodes deployed on an aircraft and a ground base station, a sensor for collecting multi-source data, a module for preprocessing the data, and an annular data queue caching mechanism. The system comprises a composite biological feature code generation module, a distributed storage edge node, an abnormal fluctuation feature extraction module, an equipment health state and trend analysis and calculation module, a virtual power supply manager, a main power supply, a standby power supply, an abnormal behavior monitoring module and a defense strategy library. After preprocessing and caching, feature codes are generated and encrypted distributed storage is carried out, dynamic resource adjustment is realized through state evaluation and a digital twinning and optimization algorithm, a neural network is utilized to manage a power supply, data security is guaranteed in combination with abnormal monitoring, and a whole-process management system is formed.

The invention discloses a construction project whole-process management system and method with a BIM3D-6D technology as a base, and relates to the technical field of construction project management, and the method comprises the steps: loading a 3D building information model through a BIM lightweight calculation server, extracting the spatial topological relation and IFC semantic attributes of geometric components, and carrying out the calculation of the 3D building information model through a BIM lightweight calculation server; spatial topology analysis and semantic attribute mapping are carried out on the spatial topology relation of the geometric components and the IFC semantic attributes, and a three-dimensional data topology network packet based on a graph structure is generated; performing multi-dimensional deduction operation on the three-dimensional data topology network packet based on the graph structure to obtain a deviation compensation strategy containing an optimal cost control path; and carrying out dimension expansion and data fusion on the 3D building information model to generate a 5D cost dimension data set. Discrete decisions are converted into a continuous field model through mathematical transformation, so that a resource flow path has spatial relevance and time continuity, and the beneficial effect of dynamically optimizing a construction process is achieved.

The invention provides an intelligent feed recommendation and precise feeding system based on multi-mode intelligent perception of live pigs. The system comprises a sensing module, a data processing module, a decision module and an execution module. The sensing module collects visual, sound, behavior and physiological data of live pigs, the data processing module cleans, standardizes and fuses the data, the decision module generates a feed feeding decision through a reinforcement learning algorithm, and the execution module automatically controls feeding equipment according to the decision. Data cleaning adopts different strategies to remove noise, standardization ensures compatibility of different modal data, and data fusion extracts features through a convolutional neural network and LSTM and uses a self-attention mechanism to perform weighted fusion. A reinforcement learning algorithm optimizes decisions, balances the health state, the feed utilization rate and the cost, and dynamically adjusts the feeding strategy. Through automatic whole-process management, the system can significantly improve the feed utilization rate, reduce the feed cost, improve the health condition of live pigs, and maximize the economic benefits of a farm.

The invention relates to the technical field of building construction management, and discloses an assembly type building construction whole process management system based on artificial intelligence. The system comprises a construction element sensing layer, a construction state mapping layer, a strategy synthesis layer and an execution coordination layer which are connected in sequence. The construction element sensing layer continuously captures on-site multi-modal environment data and component state data; the construction state mapping layer fuses multi-source data into a construction scene digital image with space-time relevance, and key path nodes and potential conflict domains in a construction process are analyzed and identified through a depth mode; the strategy synthesis layer generates a dynamic optimization strategy set for hoisting, transportation and installation processes based on the identification result; and the execution coordination layer drives on-site construction equipment to execute the corresponding instruction sequence. By constructing the digital image and deeply analyzing the construction mode, accurate insight and dynamic optimization of the construction process are realized, the construction efficiency and the automation level are improved, and process conflicts and resource waste are reduced.

The invention relates to the technical field of building design, and discloses an Internet-based building design dynamic process management system, which comprises a multi-factor identity verification module, a distributed data storage module, a process dynamic optimization engine, a behavior evaluation and intervention module and an intelligent audit tracking system. According to the building design dynamic process management system based on the Internet, through cooperative work of the process dynamic optimization engine and the behavior evaluation and intervention module, the problem that task optimization of a traditional process management system depends on a preset rule is solved; the flow dynamic optimization engine dynamically adjusts the task priority and resource allocation by using an improved ant colony algorithm based on the real-time task progress and the executor behavior data; meanwhile, the behavior evaluation and intervention module quantitatively evaluates the behavior of the executor according to a weighted scoring model, triggers an automatic intervention process when necessary, and can quickly adapt to sudden changes, so that the resource allocation efficiency is improved, and human errors are reduced.

The invention discloses a project process management system fusing text analysis and progress tracking, and the system comprises the following modules: a dual-channel guide fusion module which is used for extracting a context semantic vector and a structure intention vector from a preprocessed text sequence, and generating a fusion representation for task element recognition; the self-adaptive weight module is used for carrying out adjustable aggregation and comprehensive association degree calculation on the multi-source semantic features; the edge weight self-adaptive sparsification processing module is used for performing edge weight screening and structure optimization on an edge set in the initial inter-task association graph and outputting a sparsified task dependency graph; the cross-layer state sensing module is used for adjusting a node style and an edge state to reflect a global execution trend; and the path rearrangement module is used for triggering key path analysis and structure reconstruction logic to form a new path structure. According to the invention, semantic analysis and graph modeling are fused, and an intelligent linkage project process management system is constructed.

The invention relates to the technical field of intelligent process management, and discloses a multi-node dynamic warning and compliance supervision authentication process management system, which comprises a dynamic rule generation module, a real-time data integration module, a predictive intervention module, a compliance verification module and a closed-loop feedback module. The efficiency and compliance weight are dynamically optimized through reinforcement learning and Monte Carlo tree search, a global task dependency relationship is constructed in combination with a distributed graph database, the time sequence convolutional network is used for predicting the overdue probability, the priority is adjusted based on the game theory, and dynamic resource allocation is achieved. Meanwhile, compliance verification logic is generated based on natural language analysis, cross-department knowledge migration and credible evidence storage are completed through federated learning and a block chain technology, and rule strategies and model parameters are optimized in a closed-loop mode through an incremental updating mechanism. According to the method, the problems of rule stiffness, data island and supervision lag of a traditional system are solved, the process efficiency can be improved, the compliance risk is reduced, and self-adaptive continuous optimization is realized.

The invention relates to the technical field of intelligent monitoring management, particularly discloses an intelligent assembly component whole-process management system, and aims to optimize production efficiency and product quality through real-time data acquisition and analysis. The system comprises a data acquisition module which monitors and records the switching time of each process and the quality inspection qualified rate of each component in real time through a sensor and automation equipment on an integrated production line, a production plan evaluation module which calculates a switching time abnormal coefficient, identifies a bottleneck or low-efficiency operation in the process switching time, and evaluates the quality inspection qualified rate of each component according to the bottleneck or low-efficiency operation. And the process switching time is dynamically adjusted based on the specific numerical values of the influence interference factors and the positive and negative characteristics of the influence interference factors, the production quality evaluation module calculates a quality abnormal coefficient according to the quality inspection qualified rate and judges the quality fluctuation condition of each component, and the influence analysis and adjustment module predicts the quality qualified rate by using a gradient enhanced regression model. The process switching time is dynamically adjusted to eliminate potential quality problems.

The invention discloses an AI-based rural educational resource intelligent matching and collaborative management system, and relates to the technical field of educational resource configuration and collaborative management. Comprising a user management module, a data acquisition module, a project generation module, an intelligent matching engine module, a project execution monitoring module, a fund supervision module, a certificate generation module and an incentive distribution module. The data acquisition module acquires service capability information and education demand information; the system has the beneficial effects that through multi-role authority management, multi-dimensional data acquisition, an intelligent matching engine and a dynamic monitoring mechanism, a full-process management system covering resource matching, project execution and fund supervision is constructed, and efficient integration and accurate matching of educational resources are realized; the method has the advantages that the resource matching accuracy is improved, the project implementation quality is guaranteed, the multi-party cooperation efficiency is enhanced, and the fund supervision transparency is improved.

The invention discloses a data process management system and method based on a block chain technology, and relates to the technical field of big data analysis. The change rule of the data reading request amount, the packet loss rate and the storage capacity of each block is accurately grasped based on historical data, the traditional limitation is broken through, and the data processing perspectiveness and accuracy are improved. And meanwhile, a comprehensive risk assessment and intelligent storage strategy system is constructed, a risk value is calculated in combination with multiple factors, the data backup fragmentation storage layout is optimized, dynamic monitoring and intelligent replacement are performed on storage blocks, the data loss risk is reduced, and the data security and availability are enhanced. From data prediction to dynamic migration, all links are in close cooperation, data security and consistency are ensured through transmission encryption and index real-time updating, the management process is continuously optimized, and a complete, efficient and intelligent solution is provided for block chain data process management.

The invention relates to the field of engineering project management, and discloses an engineering project full-process management system construction method, which comprises the following steps that project optimization objectives and constraint conditions are defined, the optimization objectives comprise project progress, cost and resource utilization rate, and the constraint conditions comprise task dependence, resource availability, construction period and budget limitation; based on the targets and the constraint conditions, a multi-target optimization model is constructed, and the multi-target optimization model adopts a weighted sum method to merge a plurality of target functions; and solving the optimization model by using a Lagrange multiplier method to obtain an optimal resource allocation and task scheduling scheme. By combining the dynamic control theory, the Internet of Things technology and the fluid mechanics model, real-time dynamic adjustment of project resource scheduling is achieved, resource allocation is optimized, project progress and cost control efficiency is improved, and it is ensured that the project efficiently and flexibly responds to changes in the execution process.

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.

An authorization flow management system provides multiple authorization options to a user when conducting an authorization event. The system provides options to a user during an authorization event to select a preferred interaction path without integrating the process into a merchant system. The system receives a request from a merchant computing device to authorize an interaction with a user and provides instructions to a user computing device to open a window on a user interface to receive a security code and to display options to select from one of at least two interaction paths, such as paying with rewards points or with an equal payment plan. When the user communicates a response to the opened window that selects one of the interaction paths and the communicated code, the system conducts the interaction utilizing the selected interaction path and communicates an authorization for the interaction to the merchant computing device.

The invention relates to the technical field of medicine management, and discloses a medicine informatization whole-process management system based on big data analysis. According to the system, a drug full-life-cycle multi-dimensional data stream is captured through a distributed sensing node network, and a drug state tensor is generated after noise filtering and missing value compensation are carried out through a real-time stream processing engine. The risk identification module scans and locates an abnormal data cluster through a multi-scale mode and constructs a drug quality hypersurface model; and the data aggregation module establishes a mapping relation between a state tensor and a quality model by using a space-time diagram attention network and calculates a global consistency metric value. The intelligent decision-making module generates an optimal drug management action sequence based on the measurement value under multiple constraint conditions, the closed-loop control module executes the action sequence and monitors system state deviation in real time, data acquisition parameters are dynamically calibrated, and autonomous optimization and closed-loop management of the whole process of drugs are achieved.

The invention relates to the technical field of process modeling, in particular to an intelligent process management system based on process parameter modeling, which comprises a parameter acquisition module, a nested analysis module, a path construction module, a coupling deconstruction module and a structure compression module. According to the method, the hierarchy and expression accuracy of parameter organization are improved by collecting structured interface information and constructing a parameter mapping relation, dominant and attached parameters are set to form a control logic hierarchy, the clarity and controllability of process parameter response are enhanced, and the stability and anomaly adaptability of path combination are improved; the node frequency and sequence difference are compared to form an interference disassembly fragment, structural anomaly perception and local reconstruction efficiency are enhanced, a path tree structure is constructed through compression and recombination of a repeated path segment, the reusability and organization compactness of path information are improved, parameter logic nesting, path change tracking and interference analysis are achieved under multi-process cooperation, and the reliability of the system is improved. And the logic coupling identification capability, the structural adaptability and the expression precision of process modeling are enhanced.

The invention discloses an intelligent monitoring system for operation construction of a chemical enterprise, and relates to the technical field of safety supervision of special operation, and the system comprises an equipment layer which comprises special operation intelligent guardian equipment and special operation personnel positioning equipment, and is used for providing data support for an algorithm layer and a platform layer; the algorithm layer is used for constructing a worker behavior intelligent analysis model and a worker real-time positioning and supervision model; and the platform layer comprises a video management and AI intelligent analysis system and a special operation permission and operation process management system. According to the invention, intelligent guardian equipment is taken as a core, technologies of artificial intelligence, personnel positioning and the like are fused, an intelligent supervision technology for special operation of a chemical enterprise is explored and researched, and meanwhile, a software platform is combined, so that whole-process licensing management of operation, video supervision of field operation and continuous sampling analysis of toxic and harmful gas are realized; and non-standard behaviors and unsafe scenes in the whole operation process are accurately identified and pre-warned by using technologies such as visual analysis.

The invention relates to the technical field of intelligent manufacturing and supply chain collaborative management of woven garments, in particular to a garment production process management system based on artificial intelligence, and the system comprises a production network management center which is used for obtaining a node execution entropy value; the demand dynamic analysis unit is used for carrying out feature extraction analysis on the collected unstructured design data to obtain an order complexity coefficient; the strategy game matching unit is used for obtaining a steady-state scheduling signal or a recovery scheduling signal; when a steady-state scheduling signal is generated, the collaborative scheduling execution unit is used for generating a global optimal scheduling instruction, and minimizing turnover time is taken as an optimization target; when a recovery scheduling signal is generated, the collaborative scheduling execution unit is used for generating a risk recovery scheduling instruction, taking a minimum node execution entropy value as an optimization target, and actively inhibiting pursuit of turnover time; according to the method, the problem that the hidden risk of the node cannot be sensed in the prior art is effectively solved, and an accurate quantitative basis is provided for subsequent preventive scheduling.

The invention provides a process digital collaborative management method and system based on a data unified exchange platform, and belongs to the technical field of industrial software and intelligent manufacturing, and the method comprises the steps: constructing the data unified exchange platform, so as to achieve the data exchange among a process management system, a PLM system and a production management system; in the process management system, based on the EBOM obtained from the PLM system, constructing and managing a multi-state process BOM including a basic PBOM, a trial-production PBOM and a production PBOM; and a collaboration mechanism is constructed through a data unified exchange platform, so that business collaboration based on the multi-state process BOM is realized. According to the method, the problems of data consistency and flow collaboration which troubles the manufacturing industry for a long time are solved, and key technical support is provided for enterprises to realize digital transformation and intelligent upgrading through efficiency improvement, quality guarantee, cost control and knowledge precipitation.

The invention provides an information technology consultation process management system based on the Internet, and relates to the technical field of information technology management. A client interaction port of the system collects consultation texts and operation events in real time to generate a behavior track data set; the semantic path tracking module analyzes the data set to construct a demand evolution diagram; the path response arrangement module generates a service sequence according to the demand evolution diagram and binds consultant resources; the service recording module monitors data such as satisfaction and time delay to trigger path optimization, analyzes the weight of an efficiency attenuation point reconstruction demand evolution diagram and inputs the weight into the information pushing module during path optimization; and the information pushing module actively pushes a solution entry according to the information. The problems that dynamic changes of client intentions are difficult to accurately capture and the service process cannot be optimized in real time are solved, the intelligent level and the execution efficiency of the consultation process are improved, the experience feeling and the satisfaction degree of clients are improved, and meanwhile the utilization efficiency of consultation resources is improved.

The invention discloses an upgrade task full-process management system and method based on a state machine, and relates to the technical field of task management, and the management method comprises the following steps: monitoring a terminal device in each task upgrade process, and generating a corresponding upgrade log; performing node division on the upgrade task in any upgrade log, and performing anomaly identification on each task node; randomly selecting one task node, extracting the abnormal features of the selected task node, and analyzing the matching degree of any abnormal feature and each task node; setting a state prediction rule to predict expected state conversion of any task node; monitoring the real-time upgrading process of the terminal equipment to obtain a real-time upgrading log, and performing feature extraction; performing abnormity judgment on the real-time upgrading log based on the matching condition of the extracted features and any node; the state of the next task node can be pre-judged in advance, and the upgrading fault risk is effectively reduced.

The invention provides a man-machine co-creation enterprise business process management system, belongs to the field of enterprise business process management, and is used for solving the problems of unreasonable task allocation, insufficient process monitoring and difficulty in knowledge reuse of a traditional system. The system comprises a task triggering and analyzing module, a disassembling and planning module, a workflow generating and optimizing module and the like. A task is obtained and analyzed through a task triggering and analyzing module, the task is disassembled by utilizing multiple strategies, workflow is determined through multi-objective optimization, man-machine execution subtasks are coordinated, the process is monitored in real time and dynamically adjusted, and meanwhile service data are collected to optimize AI capacity and accumulate enterprise knowledge. The system realizes full-life-cycle intelligent management of the business process from initiation, execution to optimization, improves the business execution efficiency and resource utilization rate of enterprises, and enhances the adaptability and core competitiveness of the enterprises to business changes.

The invention relates to the technical field of product traceability information management, in particular to a product traceability authentication full-process management system, which comprises the following steps of: determining equipment feature tags of different user types by analyzing equipment communication information in historical account login information, and matching current user equipment scanning information to identify the type of the current user equipment; historical browsing information is called based on the user type, the occurrence frequency of page browsing content and a node time coefficient are analyzed, a node content expression coefficient is calculated to generate information arrangement data, and finally the data are displayed on terminal equipment, so that a product full-process traceability data closed loop is realized, the authentication credibility is improved, and the user experience is improved. And meanwhile, the user type is accurately identified and the service is adapted, so that information redundancy is avoided, the terminal experience is optimized, the user acceptability is enhanced, and the problems of insufficient whole-process coverage and extensive service of a traditional system are further solved.

The invention provides a severely traumatic patient emergency treatment process management method, device and system, which are used for creating a severely traumatic patient emergency treatment process management system and configuring specific standardized emergency treatment process nodes and quality control indexes for the severely traumatic patient emergency treatment process management system. In this way, the accuracy and integrity of the whole process can be combined with important time nodes of trauma quality control, the current quality control management requirement can be well met, simple and perfect records can provide a good data basis for emergency treatment work of patients with serious trauma, the emergency treatment work can be promoted to be stably and efficiently promoted, and the emergency treatment efficiency is improved. And the personal safety of severely traumatic patients is ensured.

The invention relates to an AI-based full-process management system for a dried beancurd stick production line, which belongs to the technical field of intelligent production management and industrial process control, and comprises a real-time material characteristic sensing module used for detecting biophysical characteristics of soybean milk in the production process in real time and generating biophysical characteristic vectors; the quality evolution prediction module is used for calculating an expected quality score and a corresponding prediction confidence coefficient; the dynamic process optimization module is used for resolving dynamic compensation process parameters; the process instruction execution module is used for adjusting an execution unit on the production line; the finished product quality detection module is used for detecting the quality of a final finished product and generating the actual quality of the final product; and the data flywheel self-learning module is used for carrying out iterative training on the quality prediction model and the dynamic optimization model. According to the invention, a lagging management mode of postmortem detection in traditional production is converted into a prospective management mode of beforehand early warning and in-process regulation and control, so that potential quality reduction risks can be foreseen in advance and actively avoided.

The invention discloses a communication network operation state monitoring method and system, and belongs to the technical field of communication networks. The method comprises the steps of determining a supervision range and multi-source data acquisition, determining a monitoring boundary, collecting full-dimension original data, and obtaining online network data, local environment data and equipment maintenance data for constructing the original data; according to the method, through feature conversion and association rule analysis, the abnormal misjudgment rate is remarkably reduced, and the maintenance and inspection resource utilization efficiency is improved; the limitation of traditional passive monitoring is broken through, prediction analysis is taken as a core, maintenance and inspection linkage is taken as a support, risk disposal is upgraded from post-event response to pre-event prevention, the abnormal response time is effectively shortened, and the influence of disconnection on production efficiency and safety production is avoided; a full-process management system of accurate monitoring, early warning in advance and efficient disposal is constructed, the stability of a communication link of the intelligent equipment is practically guaranteed, and reliable communication support is provided for digital production of a factory area.

The invention discloses a smart agriculture whole-process management system based on environment detection and farmland inspection, and relates to the technical field of smart agriculture, and the system comprises a grid modeling module which is used for constructing a knowledge network, dividing a land block boundary into a plurality of management units, and creating scene grid nodes for each management unit in the knowledge network, the corresponding scene element information is written into scene grid nodes, and a scene knowledge grid basic graph is generated; and the patrol decision module is used for mapping patrol observation records obtained at preset patrol anchor points into patrol event nodes, associating the patrol event nodes with the corresponding periodic scene view sub-graphs and carrying out rule matching to generate a management task node set. According to the method, patrol event nodes and periodic scene view sub-graphs are associated according to scene grids and time proximity relationships, rule matching is performed through a preset scene rule template, and a trigger chain of environment state-patrol description-management tasks is explicitly described in the same knowledge network.

The invention relates to the technical field of agricultural informatization and intelligent management, in particular to a selenium-rich bio-organic fertilizer research and development and demonstration and popularization full-process management system, which comprises a raw material digital filing module, a raw material fingerprint feature vector generation module and a selenium-rich bio-organic fertilizer research and development and demonstration and popularization module, and is characterized in that the raw material digital filing module is used for executing a moving average filtering algorithm to clean data; the market demand reverse analysis module is used for extracting, calculating and generating an environment demand constraint matrix representing market differentiation demands based on a preset element circulation rule; the research and development and production scheduling intelligent decision-making module is used for starting an evolutionary resolving model, mapping raw material fingerprints to an environment constraint matrix and outputting a dynamic production management instruction list; the production execution supervision module is used for distributing an instruction to the terminal and monitoring process execution data in real time; the full-life-cycle closed-loop optimization module is used for obtaining feedback data calculation deviation and generating correction factors to update model parameters; the method solves the contradiction between the heterogeneity of the non-standard biomass raw materials and the precise nutritional requirements of functional agricultural products.

The invention belongs to the technical field of informatization management, and discloses a shared laboratory service full-process management system and method based on a micro-service architecture. Comprising the steps of performing user authentication on a laboratory user, and generating basic credit line data; the account age of the laboratory order is monitored in real time, and whether a user account is set to be in a frozen state is judged; receiving repayment state data, calculating an unfreezing waiting period, and recovering the user account to a normal state after the unfreezing waiting period is ended; when the user account is in a normal state, starting second-reading timing before the user places an order, and generating an anti-bypassing behavior log; after the second-reading timing is finished, generating comprehensive hash data, executing a digital signature, and generating signature chain data; generating an experiment detection report according to the signature chain data, and embedding a hidden watermark containing comprehensive hash data; the performance bottleneck of a traditional single framework in a high-concurrency scene can be effectively solved, and the management efficiency, the risk control capability and the service quality of the shared laboratory are improved.

This technology enables accurate detection of parent and child objects from images captured by cameras, such as wearable cameras, whose camera angle and position change. [Solution] The parent component detection unit 20 detects the parent component image region from the image captured by the wearable camera 40. The child component detection unit 30 detects the child component image region within the parent component image. The parent component detection unit 20 includes a parent component neural network 22 that has been trained on images including the parent component image region. The parent component neural network 22 sets up multiple candidate regions (bounding boxes) in the captured image and identifies the candidate region with the highest confidence score for the parent component image region. When the obtained confidence score is equal to or greater than the parent component threshold over a predetermined number of frames of the video, the parent component detection unit 20 confirms the candidate region that is equal to or greater than the parent component threshold, detected in the captured image after the predetermined number of frames, as the parent component image region.

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.