32 results about "Lifecycle model" patented technology

The invention discloses an MBSE model version pedigree iteration method and system based on a graph theory and a knowledge graph. The method comprises the following steps: constructing an MBSE model of an aerospace craft and mapping the MBSE model into an attribute graph; a directed acyclic graph is adopted to record a version iteration process, management is carried out through a version number system comprising a main version number, a combined version number and a revised version number, and a differential storage strategy is adopted; and performing change detection and merging based on a baseline, accurately identifying changes through a model comparison algorithm including internal attributes, internal relationships, overall relationships and difference analysis, and processing conflicts in collaborative design. According to the method, atomic-scale version control is realized, the problems of missing change traceability, low verification efficiency and multi-baseline conflict are solved, and the efficiency and quality of aerospace craft full-life-cycle model management are improved.

The invention provides a foundation pit supporting structure design method and system and a cloud platform, and belongs to the technical field of intelligent rock-soil control, and the method comprises the steps: building a coupling model, outputting a multi-field interaction result, carrying out intelligent partitioning, and generating a customized reinforcement scheme based on a risk working condition. A transition layer is designed according to the quantitative partition transfer relation, a fusion supporting system is formed, the fusion supporting system is combined with failure differences to construct a life cycle model and an early warning model, full-life-cycle dynamic monitoring is achieved, the monitoring efficiency is improved, environment monitoring data and supporting structure design are combined, and the reliability of the system is improved. Environmental factors are fully utilized, geotechnical engineering is promoted to be transformed to intelligence and greenization, functional safety and ecological protection are achieved, and meanwhile the service life of the foundation pit supporting structure is prolonged.

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 application provides a foundation pit supporting structure design method, system and cloud platform, and belongs to the technical field of intelligent geotechnical control. The method comprises the following steps: constructing a coupling model, outputting multi-field interaction results and intelligently partitioning, generating a customized reinforcement scheme based on a risk working condition. The transition layer is designed by quantifying the transmission relationship between the partitions, and a fusion supporting system is formed. The fusion supporting system combines the failure differences to construct a life cycle model and an early warning model, realizes dynamic monitoring throughout the life cycle, improves the monitoring efficiency, combines environmental monitoring data with supporting structure design, fully utilizes environmental factors, helps to promote the transformation of geotechnical engineering to intelligence and green, realizes functional safety and ecological protection, and prolongs the service life of the foundation pit supporting structure.

The invention belongs to the technical field of life cycle evaluation methods, relates to a method for quantifying full life cycle environmental influence of a plug-in hybrid electric vehicle, and is particularly suitable for accurately evaluating environmental benefits in a province popularization scene. According to the method, firstly, a target and a range are determined, wherein a function unit is set to enable the PHEV to run on a road under a comprehensive working condition for 150 thousand kilometers, and a research range covers a whole life cycle process; then collecting a material list and material energy input data of each component of the PHEV, carrying out list analysis, and constructing a full-life-cycle model; after classification, characterization and normalization processing, 11 kinds of midpoint environmental influence indexes are adopted for influence analysis, and meanwhile, probabilistic evaluation of key indexes (such as global warming potential (GWP)) is achieved through coupling of electric power structure clustering and Monte Carlo simulation; and finally, through result explanation, an accurate reference basis is provided for optimizing a PHEV manufacturing process, promoting technical innovation and reducing full-life-cycle environmental influence.

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.

This application relates to the field of data analysis technology, and discloses an operational analysis method, device, and electronic device applicable to different operational activities. Based on a lifecycle model, different operational activities are promoted using corresponding promotion methods, and feedback information under each promotion method is obtained. According to predefined raw indicator data, the raw indicator data corresponding to each operational activity is extracted from the feedback information. According to predefined operational performance indicator data, the raw indicator data of each operational activity is parsed to obtain the corresponding operational performance indicator data. In response to an operational performance viewing command, the operational performance indicator data of the operational activity is retrieved from the database and displayed in a visual form on a display interface. This solves the problem of the lack of unified and systematic operational data analysis in existing operational activities.

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.

To appropriately evaluate an environmental load regarding an evaluation target product to be taken in a biological system or an ecosystem.SOLUTION: An information processing device holds information for inferring dynamic changes in states of a biological system or an ecosystem that takes an evaluation target product in, a life cycle model that indicates the evaluation target product and a used item other than the evaluation target product used in each state, environmental load information that indicates an environmental load of each of the evaluation target product and the used item, and index information that indicates an index value related to quality of life of the biological system or the ecosystem affected by the evaluation target product in each state, infers the dynamic changes in the states based on the life cycle model, calculates an environmental load and an index value for the entire period of the inferred dynamic changes by referring to the environmental load information and the index information, and corrects the calculated environmental load based on the calculated index value.SELECTED DRAWING: Figure 1

The invention discloses an AI customer service hot problem dynamic recommendation method, system and device and a medium, and the method specifically comprises the steps: constructing a hot life cycle model fused with time sequence prediction based on user portrait features, and generating hot probability distribution in future preset time; based on user portrait features, dynamically calculating a user emotion weight coefficient through a multi-modal emotion perception algorithm, and associating an emergency degree label of a problem library solution; based on the popularity probability distribution and the urgency degree label, an antagonism sorting decision is executed, and a personalized recommendation problem queue is output; calling a dynamic verbal skill generation engine, and synthesizing interactive recommendation content adaptive to user portrait features in real time; and pushing the personalized recommendation problem queue and the interactive recommendation content to an adaptive layout container of a customer service interface. According to the method, accurate and personalized dynamic recommendation of AI customer service hot questions is realized, and the efficiency and experience of obtaining question answers by users are improved.

The invention discloses a product full-life-cycle unified modeling and management method based on a digital main line. The method is applied to efficient integration and unified management of multi-source heterogeneous data in each stage of a full life cycle of enterprise products. According to the method, a hierarchical modeling mode is adopted to establish a product full-life-cycle unified model; constructing a digital main line covering all stages of a full life cycle by fusing a meta-model theory and a hypergraph structure, and proposing a conflict resolution method based on an authority value; and establishing an event-driven unified model dynamic updating mechanism. According to the method, data of all stages of the full life cycle of the product are integrated through the digital main line, the unified model of the full life cycle of the product is established, cross-stage data association of the full life cycle of the product is realized, and data consistency and sustainability of the unified model in a product iteration process are ensured.

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 provides a hybrid energy storage configuration method, device and equipment for an optical storage integrated power station and a medium, and belongs to the technical field of energy storage. Establishing an operation layer optimization model of the optical storage integrated power station according to the income of exchanging unit energy of energy storage participating in primary frequency modulation and the actual electricity selling income of the optical storage integrated power station; and based on the energy storage full life cycle model and the light storage integrated operation layer optimization model, establishing a light storage integrated power station capacity configuration model balancing investment cost and peak regulation and frequency modulation operation requirements, and solving the light storage integrated power station capacity configuration model to obtain a peak regulation and frequency modulation energy storage configuration strategy. Therefore, the scheduling strategy and the optimal configuration which accord with multiple scenes can be formulated by combining the scenes of peak regulation and frequency modulation, and the earnings of the optical storage integrated power station are improved.

The invention discloses a client loyalty and right dynamic adjustment realization method based on a telecommunication BSS system, and relates to the technical field of resource evaluation. Comprising the following steps: step 1, based on a telecommunication BSS system, collecting customer behavior data, designing a general event model, defining a behavior event type according to the general event model, storing a behavior event, verifying the behavior event, storing the behavior event for future reference if the verification is passed, and rejecting duplicate events; 2, an integral calculation engine is used for configuring integral rules and dynamically calculating integral, 3, a grade layering model is constructed, a corresponding evaluation rule is defined for each grade, and grade rapid evaluation is triggered according to an evaluation triggering mechanism, and 4, a right and interest matching mechanism is defined; 5, multi-channel display and real-time linkage of related service systems are carried out; and step 6, creating a life cycle model, defining loyalty life cycles of the customer by using the life cycle model, including a new network access period, an active period, a stable period, a silence period, a loss period and a regression period, and matching different incentive measures according to the loyalty life cycles of the customer.

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

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 relates to the fields of virtualization and trusted computing technology, and discloses a static measurement method for virtual machine instances throughout their entire lifecycle based on TPCM. A novel virtual machine lifecycle model is defined, and based on this model, a request instance measurement module is added to the processing functions of each lifecycle stage of the virtual machine to implement the measurement request initiation function; a policy file located in the local protection component is constructed to specify how to organize the instance core data used for measurement, and the hardware trusted cryptography module located in the protection component is called to perform hash operations on the instance core data. This invention solves the following three problems existing in the static measurement schemes for virtual machine instances: (1) the static measurement operation of virtual machine instances fails to cover the entire lifecycle of the virtual machine; (2) the measurement overhead caused by the expansion of virtual machine instances is too large; (3) the trusted measurement service provided by TPM is at risk of being bypassed, making it difficult to guarantee the security of the measurement operation itself.

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.

An information processing device holds: a life cycle model indicating information for estimating a dynamic change of states of a biological system or an ecological system into which the target product is introduced, and the target product and a used item other than the target product used in each state; environmental load information indicating an environmental load of each of the target product and the used item; and index information indicating an index relating to, for each state, a quality of life of the biological system or the ecological system affected by the target product, and estimates the dynamic change of the states based on the life cycle model; calculates the environmental load and the index in an entire period of the estimated dynamic change with reference to the environmental load information and the index information; and corrects the calculated environmental load based on the calculated index.

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.

Methods, systems, and devices for wireless communications are described that provide techniques for associating groups of resources with artificial intelligence (AI) or machine learning (ML) life cycle model identifiers (LCM-IDs). A user equipment (UE) may receive, from a network entity, a message indicating a correspondence between two or more groups of resources and associated AI / ML LCM-IDs. The association between AI / ML LCM-IDs and groups of resources may be provided via CSI report setting (s) requesting measured L1 characteristics of set-A and set-B beams, via SSB / CSI-RS resource set (s) without associating to any CSI reports, or via cell-level system information or serving cell configuration information.

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 full-life-cycle BIM model ID mapping management system, and the system comprises an original BIM file component library which is used for analyzing an original BIM file of a project component by component, generating a full-life-cycle unique ID for each component, and recording the mapping relation between the original id1 of the component in the original BIM file and the full-life-cycle unique ID component by component; the derived file component library is used for analyzing the derived BIM file component by component and recording the mapping relation between the component id1 in the derived BIM file and the unique ID of the whole life cycle component by component; the derived system component library is used for analyzing the derived BIM system component by component and recording the mapping relation between the component id2 in the derived BIM system and the unique ID of the whole life cycle component by component; and the mapping relation storage module is used for storing a mapping relation between an original component id1 in the original BIM file and a full-life-cycle unique ID, a mapping relation between a component id2 in the derived BIM file and the full-life-cycle unique ID, and a mapping relation between a component id3 in the derived BIM system and the full-life-cycle unique ID.