17 results about "Lifecycle model" patented technology

1. Name of the product in this design: Graphical User Interface for Generating Product Lifecycle Models for Electronic Devices. 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. Design 1 is designated as the basic design. 6. Purpose of the graphical user interface: It is used for intelligent modeling of products to achieve automatic generation and visualization of the product's entire lifecycle model. 7. Human-computer interaction method of graphical user interface: Design 1 main view displays the design interface of the product life cycle model of the selected product. The top left corner of the main view of Design 1 displays the product name of the selected product. The main view of Design 1 displays the "raw material acquisition" stage, "manufacturing" stage, "product transportation / delivery" stage, "product use" stage and "end of life" stage in the form of horizontal swimlanes from top to bottom. You can click / touch the "AI Intelligent Modeling" button in the upper left corner of the Design 1 main view to intelligently generate the modeling information of the selected product's full life cycle model and trigger the interface to change from the Design 1 main view to the Design 1 interface change state diagram 1. The left-hand list in Design 1 Interface Change Status Diagram 1 displays the generated modeling information for the selected product's entire lifecycle model. In Design 1 Interface Change State Diagram 1, you can select X target generation items by checking the boxes, and trigger the interface to change from Design 1 Interface Change State Diagram 1 to Design 1 Interface Change State Diagram 2, where X is a positive integer. You can click / touch the "Add X to Model" button in the lower left corner of Design 1 interface change state diagram 2 to generate a project to build the full life cycle model of the selected product using the selected X targets, and trigger the interface to change from Design 1 interface change state diagram 2 to Design 1 interface change state diagram 3. Design 1 Interface Change State Diagram 3 shows the full lifecycle model of the selected product. Design 2 main view displays the design interface for the product lifecycle model of the selected product. The top left corner of the main view of Design 2 displays the product name of the selected product. The main view of Design 2 displays the "Raw Material Acquisition", "Production and Manufacturing", "Product Transportation / Delivery", "Product Use" and "End of Life" stages of the product life cycle in the form of horizontal swimlanes from top to bottom. You can click / touch the "AI Intelligent Modeling" button in the upper left corner of the Design 2 main view to intelligently generate the modeling information of the selected product's full life cycle model, and trigger the interface to change from the Design 2 main view to the Design 2 interface change state diagram 1. The left-hand list in Design 2 Interface Change State Diagram 1 shows the generated modeling information of the selected product's full lifecycle model. In Design 2 Interface Change State Diagram 1, you can select n target generation items by checking the boxes, and trigger the interface to change from Design 2 Interface Change State Diagram 1 to Design 2 Interface Change State Diagram 2, where n is an integer. In Design 2 Interface Change State Diagram 2, you can click / touch the "Refresh" button in the left-hand list to regenerate the generated items other than the selected n target generated items, and trigger the interface to change from Design 2 Interface Change State Diagram 2 to Design 2 Interface Change State Diagram 3. The left-hand list in Design 2 Interface Change State Diagram 3 shows the regenerated results of the modeling information for the selected product's entire lifecycle model. In the interface change state diagram 3 of Design 2, the X target generation items can be selected by checking the boxes, and the interface will change from the interface change state diagram 3 of Design 2 to the interface change state diagram 4 of Design 2, where X is a positive integer. You can click / touch the "Add X to Model" button in the lower left corner of Design 2 interface change state diagram 4 to generate a project to build the full life cycle model of the selected product using the selected X targets, and trigger the interface to change from Design 2 interface change state diagram 4 to Design 2 interface change state diagram 5. Design 2 Interface Change State Diagram 5 shows the full lifecycle model of the selected product. Design 3's main view displays the design interface for the product's entire lifecycle model. The top left corner of the Design 3 main view displays the product name of the selected product. The Design 3 main view shows the "Raw Material Acquisition", "Production and Manufacturing", "Product Transportation / Delivery", "Product Use" and "End of Life" stages of the product life cycle from top to bottom in the form of horizontal swimlanes. You can click / touch the "AI Intelligent Modeling" button in the upper left corner of the Design 3 main view to intelligently generate the modeling information of the selected product's full life cycle model, and trigger the interface to change from the Design 3 main view to the Design 3 interface change state (Figure 1). The left-hand list in Design 3 Interface Change State Diagram 1 shows the generated modeling information for the selected product's entire lifecycle model. In Design 3 Interface Change State Diagram 1, you can select X target generation items by checking X target generation items, and trigger the interface to change from Design 3 Interface Change State Diagram 1 to Design 3 Interface Change State Diagram 2, where X is a positive integer. You can click / touch the "Add X to Model" button in the lower left corner of Design 3 interface change state diagram 2 to generate a project to build the full life cycle model of the selected product using the selected X targets, and trigger the interface to change from Design 3 interface change state diagram 2 to Design 3 interface change state diagram 3. Design 3 Interface Change State Diagram 3 shows the full lifecycle model of the selected product. You can click / touch the selected model node in Design 3 Interface Change State Diagram 3 to view the details of the selected model node and trigger the interface to change from Design 3 Interface Change State Diagram 3 to Design 3 Interface Change State Diagram 4. Design 4 main view displays the design interface for the product lifecycle model of the selected product. The top left corner of Design 4's main view displays the product name of the selected product. Design 4's main view shows the "Raw Material Acquisition," "Production and Manufacturing," "Product Transportation / Delivery," "Product Use," and "End of Life" stages of the product's entire life cycle in horizontal swimlanes from top to bottom. You can click / touch the "AI Intelligent Modeling" button in the upper left corner of the Design 4 main view to bring up the AI ​​tool floating window and trigger the interface to change from the Design 4 main view to the Design 4 interface change state (Figure 1). You can click / touch the "Start Generation" button in the AI ​​tool floating window in Design 4 interface change state diagram 1 to intelligently generate the modeling information of the selected product's full life cycle model, and trigger the interface to change from Design 4 interface change state diagram 1 to Design 4 interface change state diagram 2. The left-hand list in Design 4 Interface Change State Diagram 2 shows the generated modeling information for the selected product's entire lifecycle model. In Design 4 Interface Change State Diagram 2, you can view the details of the selected generated item by clicking / touching the list on the left, and trigger the interface to change from Design 4 Interface Change State Diagram 2 to Design 4 Interface Change State Diagram 3. 8. Other situations requiring explanation: Other explanation: "X" in the interface represents a text, number or symbol content area.

The present invention provides a navigation message lifecycle model learning method, GNSS signal processing method and electronic device, comprising: acquiring GNSS navigation data, and analyzing the GNSS navigation data to learn the lifecycle model, wherein the lifecycle model includes information indicating the lifecycle behavior of at least one GNSS navigation message dataset.

The application discloses a kind of air situation analysis methods based on track full life cycle, comprising the following steps: step 1, extraction typical track event;Step 2, establish track full life cycle model;Step 3, realize the quantitative analysis of air situation;Step 4, abnormal event identification and extraction.The air situation analysis method based on track full life cycle proposed in the application introduces track full life cycle model into the field of air situation estimation for the first time, and can deeply understand air situation on this basis.The application can make the process of situation analysis clear, the result of situation analysis more accurate and more intelligent, so as to provide more definite and accurate situation information for later decision makers.

The embodiment of the invention discloses a logistics management system multi-role permission adaptive configuration method and system. The method comprises the following steps: constructing a voyage number life cycle model used for describing an ocean logistics voyage number business process; determining the current basic stage state of the voyage number, and generating an initial permission set for a plurality of roles participating in the voyage number; when an anomaly is detected, reconstructing the voyage number state to a corresponding life cycle sub-state according to an anomaly type; based on the life cycle sub-state, constructing a multi-objective optimization model, generating multiple groups of candidate permission evolution schemes, and performing Pareto optimality analysis to obtain a non-dominated scheme on a Pareto front; taking a plurality of roles as game participants, carrying out Nash equilibrium screening on non-dominated schemes, selecting a scheme closest to a Nash equilibrium point as an optimal scheme, and carrying out dynamic adjustment on permissions of the roles; and after the abnormal event is finished, switching each role to a matched permission configuration state. According to the invention, the intelligence and safety of the logistics management system are improved.

The invention relates to a method and a system for evaluating a life cycle carbon footprint of a phenol preparation process. The method comprises the following steps: S1, determining a research purpose and a life cycle carbon footprint system boundary; s2, constructing a life cycle list: adopting industrial operation data for a cumene process; for a nitrous oxide and benzene oxide process, acquiring material and energy balance data under an industrial scale by adopting process simulation software; s3, modeling a life cycle model: modeling the two process scenes based on life cycle evaluation software and a background database; s4, carbon footprint quantification and comparison: quantifying the carbon footprint of the phenol with the set amount in the two process scenarios, and carrying out transverse comparison; and S5, result analysis and optimization suggestion. The method has the advantages that through integration of industrial operation data and process simulation, the bottleneck of newly-developing process list data missing is solved, the distribution problem of a traditional cumene method is avoided, and therefore a more scientific and comprehensive decision basis is provided for low-carbon process selection of phenol production.

This application relates to the field of product carbon footprint calculation, and discloses a method and system for constructing a product carbon footprint model based on intelligent agents. This method automatically collects and standardizes product information from multiple data sources using a large language model, generates product production process flows and identifies emission sources through a lifecycle model inference module, simulates activity data using deep neural networks, and matches emission factors in a background database using semantic similarity. Finally, a carbon footprint calculation model is constructed based on the ISO 14067 standard. This method overcomes the problems of difficult data acquisition and time-consuming processes in traditional carbon footprint calculation methods, achieving intelligent construction of product carbon footprint models and improving the efficiency and accuracy of carbon footprint calculation.

The application discloses a photovoltaic energy storage power station intelligent operation and maintenance and collaborative control system based on big data, and relates to the technical field of new energy power system automation.The system reconstructs the space-time characteristics of multi-source heterogeneous data of a photovoltaic energy storage power station through a graph attention network, calculates data confidence based on the reconstructed residual error, identifies battery physical parameters online using a recursive least square method with a forgetting factor, calculates the health degree, generates a dynamic current constraint boundary containing linear derating logic in combination with real-time temperature, adopts a model predictive control strategy, updates internal parameters of a prediction model in real time to match the aging state of equipment, and introduces data confidence as a penalty term into a control action smoothing term of an objective function.The application realizes flexible continuous control under the sub-health state of equipment, effectively solves problems such as fault hard shutdown impact, misoperation caused by data noise and model mismatch in the whole life cycle, and the like.

The invention discloses a digital twinning-based constructional engineering full-life-cycle intelligent collaborative management system, and relates to the technical field of constructional engineering management. The system comprises a model construction module, a data screening module, a model adjustment module and a collaborative management module, wherein the model construction module constructs a construction engineering full life cycle model based on engineering data of each stage of a construction engineering full life cycle; the data screening module is used for executing dynamic screening processing of engineering data detail levels and dynamic evaluation processing of engineering data complexity; the model adjustment module is used for executing stage self-adaptive adjustment processing and detail level self-adaptive adjustment processing of the building engineering full life cycle model; and the collaborative management module receives the constructional engineering full-life-cycle model adjusted by the model adjustment module, and when a model modification request is received, intelligent collaborative management of the constructional engineering full-life-cycle model is executed based on a collaborative management demand of the model modification request.

This invention belongs to the field of rock science and discloses a method, system, device, and medium for calculating the Helmholtz free energy storage of brittle rocks throughout their entire life cycle. The method includes: acquiring multimodal characteristic data of the brittle rock to be analyzed, including structural features, mineral composition, mineral grain size, stress-strain values ​​at different stages, and elastic energy, dissipated energy, and total energy corresponding to different stress-strain values; inputting the multimodal characteristic data of the brittle rock to be analyzed into a life-cycle model for classification and prediction to obtain the evolution law of the Helmholtz free energy of the brittle rock to be analyzed throughout its entire life cycle; wherein, the life-cycle model is constructed based on neural networks and physical methods, including an input layer, a physical constraint layer, and a prediction layer connected sequentially. This invention can quantify the coupled influence of different factors on the energy evolution of rocks and is applicable to rock mass engineering analysis under complex geological conditions.

The invention provides a spaceflight equipment test full life cycle data management method supporting cross-plant cooperation, and provides a cross-plant test input and output data cooperation management sub-method for solving the problem of insufficient cross-plant cooperation capability of existing spaceflight equipment tests, thereby realizing online technical state management and control of test data and models. Aiming at the problem that the test items are lack of digital standard implementation capability, a model test item standardization management sub-method is provided, and the problems of test item omission and redundancy, standard execution deviation and the like are effectively reduced; aiming at the problem of decentralized management of test-related models and data, a model test full-life-cycle model and a data unified management sub-method are provided, and centralized and standardized management of test data and models in the whole process is realized; in order to solve the problem that test data acquisition requirements and result data are insufficient in structured transmission interaction capability, a user-defined structured form configuration sub-method is provided, and structured storage of test data and models is realized.

The application discloses a kind of multi-source heterogeneous data integration tracing methods and systems for modeling simulation analysis in aviation field.The method comprises the following steps: step S1: constructing multi-source heterogeneous data specification system;Step S2: realizing the unified representation of SysML model based on aviation field special metamodel;Step S3: constructing the bidirectional traceable modeling of function-physical domain interface;Step S4: tool chain data transmission and tracing.This application realizes the standardized processing, unified representation, element-level tracing and global change synchronization of data, significantly improves the efficiency of modeling simulation analysis in aviation field and data consistency, full life cycle model integration and tracing capability, reduces the integration cost of tool chain, and eliminates the data barrier between tools.

The invention discloses a community model life cycle intelligent management system and method based on behavior driving, and relates to the related field of model management, and the method comprises the steps: registering and storing model metadata of a community model; deploying a lightweight monitoring agent to collect model interaction use data and user behavior index data in real time, and constructing a community model behavior dynamic portrait; according to the standardized life cycle model, constructing a community model state conversion logic, performing circulation state analysis on the dynamic behavior portrait of the community model, and determining a current circulation state of the model; operation strategy analysis is carried out based on the model metadata and the current circulation state of the model, community model operation strategy resources are configured, and model full-life-cycle management is carried out. The technical problems of lack of model behavior dynamic perception and inaccurate operation strategy adaptation in existing community model life cycle management are solved, and the technical effects of dynamically perceiving model behavior changes and improving the operation strategy adaptation accuracy are achieved.

The invention discloses a sewage treatment process based on aerobic granular sludge, which comprises the following steps: introducing sewage to be treated into a reaction tank, and enabling the sewage to be in contact with the aerobic granular sludge in the reaction tank; aerating the reaction tank; stopping aeration; discharging supernatant of the reaction tank and retaining granular sludge; a proper amount of aged sludge is discharged according to the system operation load, new living sludge is supplemented, and the water inflow is adjusted; the invention discloses intelligent cycle adjustment based on a granular sludge life cycle model. The method comprises the following specific steps: collecting related operation parameters; calculating the life cycle index LCI of the granular sludge; and dynamically adjusting the aeration time, the precipitation time and the sludge discharge proportion of the SBR according to the life cycle stage. According to the method, the life cycle model of the aerobic granular sludge is introduced, and an intelligent cycle adjustment algorithm is constructed in combination with multi-index data such as the particle size, the settling performance and the oxidation-reduction potential, so that accurate identification and dynamic regulation and control of the running state of the aerobic granular sludge are realized.

The invention discloses a TEE-based video monitoring fire reasoning model tamper-proofing method, and aims to solve the core problem that a fire reasoning model deployed by an edge camera is easily tampered to cause function failure, a three-layer protection system of'hardware-level trusted foundation + full life cycle model protection + output data security guarantee 'is constructed, and the tamper-proofing effect of a video monitoring fire reasoning model is improved. According to the method, the tampering risk of the fire inference model is accurately resisted, the credibility of model operation is remarkably improved, and the method is specifically embodied in the following three aspects: (1) depending on a static and dynamic combination integrity measurement mechanism, a model tampering path is blocked from the source, and the functional integrity of the fire inference model is guaranteed; and (2) through hierarchical data stream protection, the credibility of a fire reasoning result is guaranteed, and the decision value of the model is prevented from being damaged by tampering output data. And (3) safety and performance collaboration are optimized, the fire reasoning model is ensured to adapt to edge equipment, and credibility and real-time performance are both considered.

The invention provides a product full life cycle model construction method and system. The method comprises the following steps: obtaining a meta-meta model from a database; creating a meta-model oriented to the demand model based on the meta-meta-model combination; creating a product structured demand model based on the meta-model combination; searching related instances in a design demand instance library for the product structured demand model based on the meta-model, the attributes of the meta-meta-model and the relation characteristics; judging whether the instance is matched with the demand model or not through domain matching degree calculation, and if yes, incorporating the instance into a demand instance set to be pushed; outputting a demand instance set and a demand list; and if not, constructing a new product design demand instance until the new product design demand instance is matched with the demand model. According to the method, an efficient cross-unit, cross-stage and cross-level cooperation mechanism is met, system-level multi-scale definition of complex products is supported, visualization and traceability of the whole design process are achieved, and the product quality is improved.

The invention discloses a hierarchical management and control method and system for a full life cycle of data in the technical field of data management and control. The hierarchical management and control method comprises the steps of dividing security levels based on business data influence objects and degrees, and outputting a classification and classification result in combination with a classification tree structure; according to the historical stage change of the business data, constructing a full life cycle model to dynamically adjust a classification and grading result; outputting a data security label based on a classification and grading result, and determining a grading permission in combination with a role access control strategy; and building an exception identification model, identifying access exception and starting protection. According to the hierarchical management and control method, full-process closed-loop management and control of the data are realized, the flexibility and accuracy of data security protection are improved, abnormal risks in a data access process are effectively identified and prevented, and dynamic security hierarchical management and abnormal risk protection of enterprise business data in a full life cycle are realized.

The present application belongs to the technical field of power battery recycling evaluation, and particularly relates to a system and method for evaluating the synergistic effect of pollution reduction and carbon reduction in a power battery recycling process. A configuration module receives user input of process combination information to be compared, and generates baseline data by constructing a life cycle model with 1 kWh as a functional unit. A collection module retrieves basic emission data and material consumption and environmental emission data at each stage according to the process combination information to be compared, and generates a data set to be evaluated. An evaluation module calculates the primary energy consumption value and global warming potential value of two process combinations, and marks high-impact links. A calculation module determines a pollution reduction and carbon reduction synergy index. An output module generates comparison results of the two process combinations according to the index. The present application quantitatively evaluates the synergistic degree of different recycling processes in the two dimensions of pollution control and carbon emission reduction by calculating the pollution reduction and carbon reduction synergy index, and accordingly recommends a recycling process path with the optimal comprehensive environmental benefit to the user.