137 results about "Life-cycle assessment" patented technology

The invention discloses a fatigue life simulation evaluation method for a lightweight aluminum alloy material of a new energy automobile, and relates to the technical field of material life evaluation. A microstructure image is collected, coupling features are extracted through machine learning, and heterogeneous data fusion and enhancement are completed; generating a topological optimization structure based on a GAN, introducing a VPSC model to describe anisotropy according to a stress gradient dynamic grid, and constructing a dynamic finite element model; fusing vehicle driving data, predicting a load by using LSTM, performing VMD decomposition and environment correction, and realizing space-time correlation load spectrum reconstruction; a phase field model is used in a microcosmic mode, cracks are tracked in a macroscopic mode through XFEM, damage parameters are transmitted in a bidirectional coupling mode, and multi-physics field coupling simulation is carried out; fusing simulation and test data by adopting Bayesian reasoning, calculating life probability distribution, and correcting parameters when errors exceed the limit; according to the method, the fatigue life prediction error is finally reduced, the time consumption of single simulation is reduced, full-life-cycle evaluation and visual early warning are realized, an efficient scheme is provided for lightweight design, and industrial technology upgrading is promoted.

The invention relates to an infrastructure carbon emission evaluation method and system based on dynamic and static fusion prediction. The method comprises the following steps: calculating carbon emission in a design stage and a construction stage through life cycle evaluation; obtaining carbon emission related data in the whole life cycle of the infrastructure, wherein the carbon emission related data comprises static data and dynamic data collected by a sensor; aiming at heterogeneous data of different data sources in carbon emission related data, screening data playing a key role through an attention mechanism, calculating a fusion weight of a sensor level based on reliability, information entropy and historical accuracy of a sensor, and realizing fusion of multi-source heterogeneous data through adaptive weight distribution; extracting features of the time sequence type data by utilizing Bi-GRU, and extracting features of the space correlation type data by utilizing GCN to obtain fused features; on the basis of the fused features, carbon emission in the operation and maintenance stage is predicted through a deep learning algorithm; and realizing infrastructure carbon emission evaluation by combining the carbon emission of each stage.

The invention relates to the technical field of sewage plant-oriented carbon neutralization regulation and control scheme design, in particular to a sewage plant-oriented carbon neutralization regulation and control method and a sewage plant-oriented carbon neutralization regulation and control system. The method comprises the steps of collecting water quality, energy consumption and gas emission data in real time through a multi-source sensor, inputting the data into a space-time diagram convolutional network to predict a carbon emission trend after edge computing node cleaning, and dynamically optimizing an aeration rate and a sludge reflux ratio based on a fuzzy PID controller. A whole-process carbon footprint is tracked by adopting a life cycle evaluation method, a multi-target compensation strategy is generated in combination with an improved NSGA-III algorithm, and photovoltaic, biogas and green power resources are scheduled preferentially. A digital twin platform and a block chain evidence storage module are innovatively integrated, the former calibrates model parameters in real time, and the latter generates a non-tampering carbon neutralization voucher. According to the method, the problems of one-sided data, regulation and control lag and insufficient credibility of a traditional method are solved, and minute-level sensing, prediction and offset closed-loop control of carbon emission in the sewage treatment process is realized.

The present invention relates to the technical field of carbon footprint accounting. Disclosed are a carbon footprint accounting method and system for a power grid material product based on life cycle assessment (LCA). The method comprises: on the basis of an LCA method, building a product carbon footprint model for a power grid material product; collecting power grid material product data, and on the basis of the collected data, accounting the carbon emission of the power grid material product from the raw material acquisition stage to the scrap recovery stage; during carbon footprint accounting, selecting power carbon emission factors of different grades in each stage, and accounting the power carbon emission; and on the basis of the accounted data, performing high-precision real-time carbon footprint accounting of the power grid material product in the whole life cycle. The environmental impacts of the product are assessed more accurately. Moreover, real-time accounting can reflect any change or improvement effect in time. By means of the comprehensive carbon footprint analysis, detailed carbon footprint accounting can reveal high-emission activities, provide data support for optimization of the product design and production process, and reduce the overall carbon emission.

The invention relates to a highway and waterway infrastructure whole-process carbon emission refined evaluation method and system. The method comprises the steps that carbon emission original data in the design, construction and operation and maintenance stages of highway and waterway infrastructures are acquired; carrying out preprocessing on the carbon emission original data; constructing a carbon emission factor library for carbon emission sources of highway and waterway infrastructures; evaluating the carbon emission of each stage based on the carbon emission factor library through life cycle evaluation; based on the original carbon emission data, obtaining a carbon emission prediction result under constraints through high-dimensional feature extraction, long-term and short-term feature modeling and inter-stage data interaction; and generating optimization suggestions based on carbon emission evaluation and carbon emission prediction results. Compared with the prior art, the method has the advantages of full-process coverage, high dynamic prediction capability and the like.

The invention discloses a digital twinning and AI analysis sewage treatment control system, and relates to the technical field of sewage equipment control, and the system comprises the steps: deploying a sensor to obtain a sewage treatment acoustic signal to form an acoustic energy matrix, and synchronously obtaining the operation data of a treatment plant to construct an operation data matrix; fusing the acoustic energy matrix and the operation data matrix to form a high-dimensional data matrix; mapping the high-dimensional data matrix into a low-dimensional point cloud, constructing a point cloud adjacent matrix, recognizing topological features according to the adjacent matrix, analyzing abnormal topological features by combining persistent coherence with topological entropy, and calculating an abnormal confidence coefficient; and establishing a hierarchical agent based on the abnormal confidence coefficient, defining a reward function through life cycle evaluation, adjusting a hierarchical agent optimization strategy by adopting DDPG reinforcement learning, predicting the performance of the optimization strategy in combination with a multi-step prediction loss function, and outputting the optimization strategy. According to the invention, the control strategy of the sewage treatment equipment is continuously optimized, and the strategy implementation efficiency is improved.

The invention discloses a life cycle evaluation missing data completion method, system and device and a medium, and the method comprises the steps: defining a data template, and determining a target range of life cycle evaluation in the data template; obtaining input data, and filling the data template with the input data; determining missing data in the data template; according to the method, the system, the equipment and the medium, the missing data in the data template is predicted by utilizing the large model, the predicted missing data is written into the data template, and the missing data in life cycle evaluation can be complemented based on the large model.

The invention relates to the technical field of carbon emission, in particular to an on-chain enterprise carbon emission accounting method and system. The method comprises the following steps: acquiring energy consumption, process flow and supply chain collaborative data of an enterprise in links of raw material acquisition, production processing, logistics transportation, equipment operation and recovery processing through Internet of Things equipment, and generating a carbon emission data set; based on a life cycle evaluation theory, boundaries of direct emission, indirect energy emission and supply chain associated emission are divided, and enterprise-level, product-level and supply chain-level carbon emission accounting models are constructed. A machine learning algorithm is utilized to analyze production fluctuation data in real time, carbon emission factor weights and accounting boundary parameters are dynamically adjusted, and an accounting result is optimized. And outputting a result to a green purchase decision-making system, a carbon asset management system and a supply chain collaboration platform. The system integrates industrial Internet of Things equipment, block chain nodes and enterprise resource planning system interfaces, collects and verifies full-link data in real time, solves the problem of data collection, and improves the accounting reliability.

The invention provides a lithium battery pollution reduction and carbon reduction collaborative optimization method, device and equipment based on LCA and a storage medium. Raw material composition, sintering temperature, energy consumption, tail gas pollutant concentration and key process parameters are collected in real time through an Internet of Things sensor network and fused with data of a life cycle evaluation database to generate standardized life cycle data so as to accurately reflect carbon emission and pollutant emission conditions. And calculating a carbon emission value and a pollution emission value in the production process based on the data to form a combined environment evaluation index, establishing an objective function according to the combined environment evaluation index, and solving by adopting a multi-objective optimization algorithm to obtain optimized process parameters meeting preset performance constraints. Optimized parameters are then transmitted to a production control system, real-time adjustment of key process parameters is achieved, production data are continuously monitored, closed-loop control is formed, collaborative optimization of pollution reduction and carbon reduction is ensured, and the problem that overall optimization is difficult to achieve in the prior art is solved.

The invention discloses a crane full life cycle evaluation method based on digital twinning, and relates to the technical field of cranes, the method comprises the following steps: determining a primary loss structural component set and a secondary loss structural component set; constructing an initial digital twin structure model; performing time sequence updating on the initial digital twinning structure model to obtain a digital twinning structure monitoring model sequence; performing multi-receptive-field space-time state interaction enhancement on the digital twin structure monitoring model sequence, and determining digital twin space-time state features of the target crane; and carrying out life evaluation based on the digital twin space-time state characteristics of the target crane, and determining a full life cycle stage and a life evaluation result of the target crane. The technical problems that in the prior art, crane operation data analysis depth is not enough, so that full life cycle evaluation is not comprehensive, and hysteresis exists are solved. The technical effects of carrying out multi-level component interaction analysis on the crane and improving the accuracy of full-life-cycle evaluation are achieved.

The present invention embodiments provide a forecasting system that derives carbon intensity, emissions, and environmental impact forecasts from a life cycle assessment (LCA). The forecasting system offers an intuitive, user-friendly interface that simplifies the complexity of forecasting methodologies, making generating forecasts of carbon intensity, emissions, and environmental impacts accessible to a broader audience. It incorporates advanced algorithms and real-time data integration, ensuring accurate and up-to-date forecasts. Additionally, the forecasting system is designed with a modular architecture and is highly configurable, allowing seamless integration with various industries and accommodating diverse products and processes.

The invention belongs to the related technical field of pressure tests, and particularly discloses a gas inlet pipeline test device for a gas generator, which comprises a first base and a second base, a round frame is fixedly mounted on one side of the upper end surface of the first base, an annular pipe is embedded in the round frame, and a connecting rod is mounted at one end in the annular pipe through an arranged telescopic mechanism. The outer side of the connecting rod is externally connected with a ring rod through an inner chuck fixedly connected in a sleeving mode, a sealing rotating ring is arranged on the outer wall of one side of the round frame, a test air inlet pipe movably penetrates through the sealing rotating ring in a penetrating mode, and a particle feeding simulation mechanism is arranged at the end, away from the sealing rotating ring, of the test air inlet pipe. By simulating multi-field coupling environments such as pressure fluctuation, particle scouring and mechanical vibration under real working conditions, the problem that data deviation is large due to the fact that complex working conditions cannot be reproduced in a traditional test is solved, and precise detection and full-life-cycle evaluation of the performance such as wear resistance, pressure resistance and blockage resistance of the pipeline are achieved.

The invention provides a carbon footprint tracing method and device suitable for a copper product, and relates to the technical field of carbon tracing. The method comprises the following steps: acquiring data of a copper product in a full life cycle, calling a preset emission factor library and an industry carbon intensity database, and calculating carbon emission of the copper product in the full life cycle by adopting a mixed life cycle evaluation method to obtain a carbon emission accounting result of each stage; based on the carbon emission accounting result of each stage, combining with a multi-region input-output model, constructing copper product supply chain carbon emission distribution throughout the country, and quantifying an inter-provincial implicit carbon flow path; using the structure decomposition analysis model to decompose the driving factors; and performing time sequence prediction and supply chain high-carbon node identification on the carbon emission accounting result and the trans-provincial carbon distribution data by using a deep learning model to obtain a carbon traceability result. The method is used in the carbon footprint tracing process of the copper product, and solves the technical problem that national scale copper product carbon flow tracking cannot be carried out in the prior art.

The invention relates to the technical field of livestock and poultry manure and crop straw resourceful treatment, and particularly provides a four-flow coupling full life cycle evaluation method for planting and breeding waste resource treatment based on a graph neural network, and the method comprises the steps: obtaining four-flow real-time working condition data representing a system state, and constructing a heterogeneous process map; constructing a topology generation network, and generating a group of candidate process topology adjacency matrixes; calculating to obtain a total topology expected value, a path robustness score and a reconstruction conversion cost corresponding to each candidate process topology adjacent matrix; and obtaining a final decision score, selecting the candidate process topology adjacency matrix with the highest final decision score as the optimal process topology, and outputting a self-adaptive execution strategy corresponding to the optimal process topology. According to the method, the limitation that only parameter optimization can be carried out in a traditional fixed technological process is broken through, and dynamic reconstruction of the whole technological topological structure is achieved.

The invention discloses a ceramic product carbon footprint accounting method and system, and relates to the technical field of carbon emission calculation. The method comprises the following steps: establishing a process flow framework of a target product based on input data and output data of each process device in a production flow and a sequence of each process device in the production flow; determining the data integrity of the input data and the output data of each process device, and dividing the process flow framework of the target product into a plurality of modules according to the data integrity; and for the plurality of modules, constructing a carbon footprint accounting model corresponding to each module by adopting a life cycle evaluation method, and carrying out carbon footprint accounting on the target product through the carbon footprint accounting model corresponding to each module. According to the scheme, accurate carbon footprint accounting can be carried out on specific types of ceramic products.

The invention belongs to the technical field of bridge engineering and rail transit, and particularly relates to a high-speed rail long-span cable-stayed bridge traffic safety analysis method and system. Comprising the following steps: establishing a physical constraint relation model between vehicle body vertical acceleration and secondary suspension vertical force resultant force; obtaining a vehicle dynamic response predicted value by using the German track irregularity spectrum, the track deformation quantity and a PI-LSTM model; obtaining a prediction interval of the vehicle dynamic response predicted value by using a cGANs model, the random noise variable, the real error value and the vehicle dynamic response predicted value; the maximum value between the peak value of the prediction interval and the vehicle dynamic response prediction value is obtained; comparing the maximum value with a standard limit value to obtain a comparison result; the comparison result is used for traffic safety evaluation. According to the method, high-precision and high-robustness full-life-cycle evaluation is provided for the traffic safety of the large-span cable-stayed bridge of the high-speed rail.

The invention discloses a life cycle evaluation system and method and a computer program product, and belongs to the technical field of life cycle evaluation, and the life cycle evaluation system comprises a data acquisition module, an artificial intelligence prediction module, an environmental influence calculation module and a feedback and visualization module. The data acquisition module acquires original data in equipment operation; the artificial intelligence prediction module receives the original data and performs prediction to form resource flow data; the environment influence calculation module receives the original data and the resource flow data and converts the original data and the resource flow data into standardized life cycle evaluation indexes; and the feedback and visualization module displays the life cycle evaluation index and generates a parameter adjustment instruction. According to the invention, real-time environmental impact assessment and sustainable optimization in the hybrid manufacturing process are realized.

The invention provides an underground complex low-carbon design and intelligent construction method, which comprises the following steps of: optimizing building material selection, a structure scheme and a construction process in combination with a life cycle assessment (LCA) method in a design stage, and reducing carbon emission in the design stage; environment data, material use data and equipment operation state data in the construction stage are collected through a sensor network and a monitoring system; a dynamic carbon emission quantitative model is constructed based on the carbon emission factor library, and the carbon emission level in the construction process is quantified in real time; generating a low-carbon construction strategy by utilizing an optimization algorithm, and globally optimizing a construction process, equipment scheduling and a material transportation path; and operation parameters of construction equipment, a material use plan and a construction process flow are dynamically adjusted in combination with an optimization strategy, and intelligent regulation and control of the construction process are ensured. Through combination of low-carbon design and intelligent construction, the carbon emission level of the whole life cycle of the underground complex is reduced.

The invention relates to the technical field of ecological influence evaluation, and discloses a reservoir dam ecological influence evaluation method based on life cycle analysis, and the method comprises the steps: carrying out the local similarity measurement of the index data of a target reservoir and the full life cycle index data of a reference reservoir, and obtaining a reference object of the target reservoir; and performing ecological influence evaluation on the reference object by using the reservoir dam ecological influence full life cycle evaluation model, and taking an ecological influence evaluation result of the reference object as an ecological influence evaluation result of the target reservoir in the full life cycle. Local similarity measurement is performed on the index data of the target reservoir and the full life cycle index data of the reference reservoir, the reference object similar to the target reservoir in feature and sequence form is selected, and then the ecological influence assessment values of the reference object in different reservoir operation stages are assessed. Reservoir dam full life cycle ecological impact assessment based on local data similarity is realized, and quantitative environmental impact data is provided for decision makers.

Provided are a product life cycle assessment apparatus, a product life cycle assessment system, a product life cycle assessment method, and a product life cycle assessment program that can evaluate the balance of environmental load, recycling rate, and cost in view of the entire product life cycle. 【Solution means】 The product life cycle assessment apparatus 20 includes a first processing unit 211 that performs simulation processing for calculating an environmental performance index including environmental load information and a recycling rate in the entire life cycle of a predetermined product and a total cost by changing conditions of influencing factors that affect the total cost, and an output unit 213 that outputs the simulation processing result of the first processing unit 211.

This application relates to the field of conveyor belt fire technology, and provides a method and system for assessing the fire hazard of conveyor belts based on surface elemental analysis. In this method, the degradation degree index of the target conveyor belt is calculated by analyzing the changes in the content of surface constituent elements over wear time. Once the degradation degree index passes reliability verification, a constructed degradation-combustion prediction model is used to calculate the fire risk index of the target conveyor belt over wear time. This avoids interference from environmental factors such as underground dust and humidity in traditional assessment methods, enabling full life-cycle assessment and diagnosis of conveyor belts under different wear states before a fire occurs, overcoming the lag in traditional early warning methods.

The invention relates to the technical field of carbon evaluation, and discloses an LCA-based power grid full-life-cycle carbon footprint evaluation method, system and device, and the key points of the technical scheme are that the method comprises the steps: obtaining all data of five stages of raw material mining, production and manufacturing, transportation and installation, operation and maintenance, and decommissioning and recovery covered by a target power grid system in the full life cycle; identifying typical equipment related to each stage and corresponding list data from all data of the five stages; on the basis of the LCA theory, carbon emission calculation sub-models of the typical equipment in the five stages of raw material exploitation, production and manufacturing, transportation and installation, operation maintenance and retirement recovery are constructed; according to all the carbon emission calculation sub-models, in combination with the life cycle evaluation database, establishing a full life cycle carbon footprint integrated evaluation model of the target power grid system; and operating the evaluation model, and calculating and outputting respective carbon emission, carbon emission paths and key emission reduction node information of the five stages.

The invention relates to the technical field of conveyor belt fire, and provides a conveyor belt fire risk assessment method and system based on surface element analysis. According to the method, the degradation degree index of the target conveying belt in the abrasion time is calculated according to the content change of the surface composition elements of the target conveying belt in the abrasion time, and when the degradation degree index of the target conveying belt in the abrasion time passes reliability verification, the target conveying belt is subjected to reliability verification according to the degradation degree index of the target conveying belt in the abrasion time. And calculating a fire risk index of the target conveyor belt in the wear time by using the constructed degradation combustion prediction model. Therefore, the situation that a traditional evaluation method is interfered by environmental factors such as underground dust and humidity is avoided, full-life-cycle evaluation and diagnosis of the conveying belt in different abrasion states are achieved before a fire disaster occurs, and the hysteresis quality of a traditional early warning mode is overcome.

The invention relates to the technical field of ecological restoration assessment, in particular to a restoration area full life cycle assessment method based on multi-source remote sensing and ground monitoring, and the method comprises the steps: firstly obtaining three types of multi-source data of satellite remote sensing, unmanned aerial vehicle / near-ground remote sensing and ground monitoring, and generating a unified reference data set through space-time alignment, quality control and restoration processing; multi-modal features are extracted and fused, and a spatial-temporal feature sequence representing the ecological state is formed; life cycle stages are automatically divided through the model, an evaluation index set is dynamically matched, and stage self-adaptive ecological health degree scores are calculated; and finally, predicting an ecological state change trend, judging a degradation risk and outputting spatialized early warning information. According to the invention, automatic and precise evaluation and degradation risk early warning of the life cycle of the ecological system in the restoration area can be realized.

The invention relates to a carbon footprint accounting method for a granulated blast furnace slag powder product, and the method comprises the following steps: determining a system boundary of carbon footprint accounting according to an international general life cycle evaluation standard; collecting primary data and secondary data according to the determined system boundary; integrating the primary data and the secondary data, and accounting the total carbon footprint in the boundary of the whole system; distributing the symbiotic products of the granulated blast-furnace slag powder by adopting a layered progressive distribution strategy to obtain a distribution result of the symbiotic products; and based on the total carbon footprint and the distribution result of the symbiotic products, obtaining the carbon footprint of the granulated blast furnace slag powder product. Compared with the prior art, the method has the advantages of scientificity, standardization and the like.

The present invention provides a cloud platform-based power asset lifecycle management method, which relates to the field of image recognition and is particularly suitable for the monitoring and management of early risks such as dynamic arcs and corona of power equipment. The cloud platform-based power asset lifecycle management method of the present invention specifically includes: collecting multi-source image information of power asset equipment; identifying multi-target positions in the power asset equipment based on the multi-source image information; identifying corona and arcs occurring in the power asset equipment based on the multi-source image information and the multi-target positions; performing lifecycle assessment on the power asset equipment based on the corona and arc identification results; through the above steps, the present invention realizes the efficient detection of hidden risks of power assets and realizes the safety management and maintenance of power assets.

This invention provides a multi-dimensional adsorbent performance evaluation method, belonging to the field of performance evaluation technology. The method includes: conducting a first experiment on the adsorbent, recording the mass change of the adsorbent after each cycle, evaluating the adsorption performance of the adsorbent, and obtaining the first experimental results; conducting a second experiment on the adsorbent to determine the adsorbent's anti-sintering ability and cycle stability, and obtaining the second experimental results. The microscopic morphological changes, influences, and crystal structure of the adsorbent before and after the experiment are observed to obtain the microscopic characterization of the adsorbent. Based on the microscopic characterization, multi-scale simulation of the adsorbent is performed, and the simulation results are used to conduct a life cycle assessment of the adsorbent, obtaining the life cycle assessment results. A comprehensive performance evaluation of the adsorbent is conducted to propose optimization directions. The aim is to achieve efficient and low-cost carbon capture technology, promote the effective utilization of solid waste resources, and provide a systematic evaluation method to promote the development of carbon capture technology.

The invention relates to the technical field of environmental information and industrial carbon emission, in particular to a cloud analysis and scheduling method for cement telegraph pole carbon footprint calculation, which comprises the following steps: constructing a cloud unified data access layer, and collecting raw material, energy and production logistics full-link data in real time; on the basis of life cycle evaluation, eight process stages are divided, and a high-precision carbon footprint accounting model is established; performing multi-dimensional sensitivity analysis on the key process parameters through Monte Carlo simulation to generate a carbon emission scene probability map; outputting a low-carbon scheduling instruction according to the optimal scene, and driving an advanced plan scheduling system to execute emission reduction and production scheduling; and a long-short-term memory neural network is introduced to realize model self-learning. According to the invention, real-time monitoring, accounting and active optimization of carbon emission in the whole life cycle of the cement telegraph pole are realized.

The present invention embodiments provide a forecasting system that derives carbon intensity, emissions, and environmental impact forecasts from a life cycle assessment (LCA). The forecasting system offers an intuitive, user-friendly interface that simplifies the complexity of forecasting methodologies, making generating forecasts of carbon intensity, emissions, and environmental impacts accessible to a broader audience. It incorporates advanced algorithms and real-time data integration, ensuring accurate and up-to-date forecasts. Additionally, the forecasting system is designed with a modular architecture and is highly configurable, allowing seamless integration with various industries and accommodating diverse products and processes.

The application relates to the technical field of booster station plant management, and discloses a booster station plant data acquisition self-control energy-saving management system. Through standardized collection and deep feature extraction of full-factor operation data of the booster station plant, an innovative combination of physical information neural networks and a Transformer architecture is adopted to establish a precise prediction system covering load changes, equipment efficiency attenuation and environmental coupling influence. Meanwhile, through data envelopment analysis and life cycle assessment methods, a dynamic evaluation mechanism in the double dimensions of energy efficiency and carbon emission is constructed. Based on the fusion of multi-objective optimization algorithms and model predictive control, a real-time control strategy considering economic operation and low-carbon constraints is formed, so that the dual goals of improving the overall energy efficiency level and the management accuracy of carbon emission of the system are achieved.