1615 results about "Full life cycle" patented technology

The invention discloses a smart park full life cycle management system and method based on digital twinning and Internet of Things, and relates to the technical field of smart park management, and the system comprises a sensing edge module, a data governance module, an intelligent analysis module, a life cycle module and a twinning modeling module. According to the invention, multi-protocol access and edge computing capability are supported, and the data transmission efficiency and stability are greatly improved; the intelligent analysis module outputs an accurate analysis result by constructing a multi-class feature matrix and deep multi-task joint modeling mechanism, and provides data support and model guidance for dynamic management and intelligent decision making of the park; the life cycle module integrates a Kepler optimization algorithm and a multi-agent reinforcement learning and simulated annealing algorithm, establishes a collaborative optimization mechanism, realizes combination of global search and local fine tuning of resource scheduling, and effectively optimizes energy consumption, response time, space utilization and safety risks; and the twin modeling module constructs a park three-dimensional model, so that the interactivity and operability of the system are improved.

The invention discloses a BIM-based building electromechanical full life cycle management platform and an optimization strategy thereof, relates to the technical field of building informatization management, and solves the defects of the existing BIM technology in the aspects of processing efficiency, model precision, data synchronization and change management. According to the invention, a data layer is used for data cleaning and standardization through a machine learning model, and distributed storage is realized through a cloud management system; the model layer constructs and updates a high-precision BIM model; the synchronization collaboration layer ensures the consistency and traceability of data by using a real-time synchronization mechanism and a block chain technology; the decision analysis layer performs data analysis and prediction through a deep learning algorithm; the full life cycle management layer integrates historical data and current data and automatically triggers a management process; according to the method, the data processing efficiency and the model precision are greatly improved, and the seamless sharing capability of information and the transparency of data synchronization and change management are remarkably improved, so that the overall collaborative efficiency and the management level of a project are enhanced.

The invention relates to the technical field of intelligent operation and maintenance of power equipment, in particular to a cable system full life cycle health management method based on digital twinning, which comprises the following steps: step 1, constructing a cable digital twinning body fusing electric-thermal-mechanical-chemical multi-physical fields; 2, dynamically fusing multi-source monitoring data, and collecting a cable skin continuous temperature sequence, a partial discharge pulse waveform and soil environment parameters; 3, solving a coupling equation of conductivity-thermal conductivity-stress tensor-ion diffusivity through iteration to realize multi-physical field linkage simulation; 4, generating a self-adaptive maintenance decision, inputting the insulation aging factor into an LSTM predictor to output a residual life prediction value, and generating a maintenance work order when the residual life is lower than a threshold value; and 5, carrying out closed-loop correction on the model, and updating the insulation aging factor calculation model and the physical attributes of the three-dimensional grid according to actual maintenance data. The method improves the safety, reliability and operation and maintenance efficiency of the cable system, and has important industrial application prospects.

The invention provides a valve actuator full life cycle intelligent test system and method. The system comprises a combined load generation module used for generating an initial dynamic proportion of axial thrust and radial disturbance force according to a test protocol instruction, simulating a comprehensive stress state of an actuator under the impact of a pipeline medium, and outputting mechanical data containing thrust fluctuation characteristics in real time; the environment coupling module is used for dynamically adjusting an environment stress condition according to the test protocol instruction; the digital twinborn analysis module is used for comparing the actual displacement track of the actuator with the theoretical model track through a space-time alignment algorithm according to the mechanical data and environmental stress conditions to obtain a deviation feature vector; and the intelligent diagnosis module is used for carrying out dynamic time warping matching on the basis of the deviation feature vector and a preset historical fault database, outputting a fault type identification code and confidence, and generating a test report. According to the scheme, the accuracy of accurate evaluation of the full life cycle state of the valve actuator is improved.

The invention provides a BIM intelligent management platform oriented to a whole life cycle of project construction. A building information model, Internet of Things sensing data and a block chain evidence storage mechanism are integrated through a multi-source data fusion technology, and a whole-process data chain of association planning, design, construction, operation and maintenance is associated. The platform adopts space optimization Huffman coding to realize model lightweight, combines a constraint genetic algorithm to optimize a construction path, and applies a bidirectional long-short-term memory network to analyze an equipment state. A three-chain block chain system is reconstructed on the architecture, intelligent association of engineering quantity and payment nodes is realized through cooperation of a main chain, a calculation quantity side chain and an auditing side chain, and mobile terminal offline interaction is supported based on a digital-analog separation technology. The platform covers an intelligent design management unit, a block chain investment management unit, a dynamic correction management unit, a quality safety responsibility tracing unit, an NLP risk management unit and a digital twin operation and maintenance unit. The units achieve cross-system cooperation through a unified data bus, and a closed-loop management architecture covering the whole life cycle of project construction is formed.

The invention discloses an electrical equipment operation state monitoring method, system and terminal, full life cycle health management of equipment is realized through multi-dimensional perception and intelligent analysis, a composite sensor network can be constructed from the level of the method, and high-frequency current, ultrahigh frequency, fiber grating temperature vibration, multi-parameter gas and acoustic sensors are integrated. Electromagnetic characteristics, mechanical states, environmental parameters and voiceprint characteristics are covered; the adaptive signal processing technology performs classification and noise reduction on multi-source data, and the three-dimensional digital twin model realizes time-space fusion of a temperature field, a vibration field, an electric field and a sound field; a lightweight space-time convolutional network is deployed to fuse a time domain waveform, a spectrogram and spatial distribution characteristics for diagnosis, and a hidden semi-Markov model and a particle filter algorithm are combined to dynamically predict the residual service life of equipment. The system architecture comprises a self-organizing sensor network with edge computing capability, a time-sensitive industrial communication network and a containerized analysis engine, and supports mixed reality visual interaction.

The invention relates to the technical field of engineering collaborative management, and discloses a BIM (Building Information Modeling)-based engineering full-life-cycle collaborative management method. The method comprises the following steps: acquiring building information model data and associated progress monitoring data of a full life cycle of a project, and providing a data basis for collaborative management. Based on a stage decomposition strategy, a single-stage dependency graph is constructed according to building information model data, and association logic of elements such as tasks and resources in each stage is clearly presented. Through a cross-stage dependency integration method, the single-stage dependency graph is integrated into a full-life-cycle dependency graph, and a cross-stage task connection and influence relationship is defined. In combination with data consistency verification and resource constraint review, conflict detection is performed according to a full life cycle dependency graph, and potential data conflicts and resource conflicts are identified in advance. And based on a progress fluctuation analysis mechanism, correcting the dynamic parameters according to a conflict detection result, and finally generating a full-life-cycle collaborative management report.

The invention relates to the technical field of grinding wheel monitoring, in particular to a centerless grinding machine full-life-cycle grinding wheel state monitoring system and method.According to the centerless grinding machine full-life-cycle grinding wheel state monitoring system and method, through combined difference extraction of a main shaft current mean value fluctuation ratio and a power gradient continuous variation value and assisted by transverse comparison of periodic response intervals, effective recognition of time period boundaries of working condition states is achieved; according to the method, operation stage division does not depend on static threshold judgment any more, a track trend is established through linkage judgment of an amplitude kick point and a slope extreme value, a dynamic evolution path of signal response is clearer, the identification resolution of abnormal changes and local deviation trends is improved, response synchronous identification of parameter state changes is achieved, and the identification accuracy is improved. In multi-signal collaborative analysis, directivity comparison between a temperature rise mutation fragment and a current residual error response is carried out, so that the consistency analysis capability of response logic between different monitoring paths is enhanced, and the real-time performance, stability and anti-interference capability of state identification are improved.

The invention relates to the technical field of data fusion, in particular to a power distribution equipment whole life cycle management system and method based on data fusion. The method comprises the following steps: acquiring multi-modal real-time sensing data and an equipment management log, and performing pre-semantic extraction and coding compression to obtain an edge semantic fragment; analyzing the topological structure of the power distribution equipment by using the equipment management log, and performing federal feature distributed modeling on the edge semantic fragment by using the topological structure of the power distribution equipment to obtain federal feature data of the power distribution equipment; state evolution causal inference is carried out based on the federation characteristic data of the power distribution equipment to obtain an equipment state evolution path; performing operation state dynamic simulation and prediction evaluation according to the equipment state evolution path to obtain a dynamic twin evolution body; and carrying out equipment operation parameter iterative optimization on the equipment management log according to the dynamic twin evolution body to obtain an optimized equipment operation parameter group. According to the invention, the use efficiency and the operation reliability of equipment are improved, and the maintenance cost is reduced.

The invention discloses a full-life-cycle carbon footprint intelligent detection system and method, belongs to the technical field of carbon emission monitoring, and aims to solve the problems of insufficient standardization of full-life-cycle carbon emission data, inaccurate abnormal link identification and delayed carbon footprint risk early warning. Carbon emission data of a production link in a full life cycle of a target product is collected and transmitted to a block chain, and standardized coding is realized through a hierarchical coding rule; detecting the collected data based on a multi-dimensional triggering rule to judge whether carbon footprint updating is triggered or not, if so, configuring an anchoring reference of a quantization range according to a production mode, and screening the data to construct a quantization data set; after the quantitative data set is corrected, the total carbon emission amount of each production link is calculated, and a carbon emission report is generated; the abnormal links are identified based on the carbon emission and proportion of the production links in the report, the abnormal source is positioned through standardized coding, historical carbon emission data are tracked to predict the emission, and carbon footprint grade change risk early warning is realized.

The invention discloses a BIM-driven tunnel construction full life cycle management method and system, and relates to the technical field of industrial data processing and analysis, and the method comprises the steps: building a BIM model covering the full life cycle of a tunnel and a data mapping linkage relation; collecting monitoring data, accessing the monitoring data to the BIM model, and performing dynamic linkage evaluation to obtain a construction state result; and constructing a safety management mechanism according to an evaluation result and feeding back field guidance. The technical problem that in tunnel construction full-life-cycle management, a traditional industrial data processing mode cannot achieve full-life-cycle integration and cross-stage linkage of multi-source industrial data in the stages of design, construction and operation and maintenance is solved. The technical effects of full-life-cycle integration and cross-stage linkage processing of the industrial data in tunnel design, construction and operation and maintenance stages, improvement of the comprehensiveness, collaboration and timeliness of industrial data processing, and accurate acquisition of the construction state to optimize on-site safety management are achieved.

The invention provides a block chain-based large model use traceability and access control method and system, and belongs to the technical field of block chains, and the method comprises the steps: extracting key version information of a large model, encrypting the key version information, storing the encrypted key version information to a block chain network, and generating a model evidence storage record; generating a dynamic watermark identifier based on the user identity identifier, the calling timestamp and the model evidence storage record, and embedding the dynamic watermark identifier into an output result of the large model; monitoring calling behavior data of the large model in real time through an intelligent contract in the block chain network; and when abnormal calling behavior data is detected, tracing the source of the abnormal calling behavior data based on the dynamic watermark identifier and the model evidence storage record, and executing permission limiting operation through the smart contract. According to the method, full life cycle protection and calling traceability management of the large model are realized based on the block chain evidence storage, the dynamic watermark identifier and the intelligent contract, so that effective traceability management and access control are realized in the use process of the current large model.

The invention discloses a railway station facility full life cycle information query method based on digital twinning. The method comprises the steps that a unified facility object with a unique logic identifier is generated based on multi-source heterogeneous data aggregation; constructing a facility tense version model containing time sequence nodes by using an event-driven rule; in combination with an engineering constraint knowledge base and statistical characteristics, evaluating physical feasibility and statistical association strength between events, introducing life cycle stage risk factors, and constructing a facility full life cycle causal chain model; and based on the model, responding to the explanatory query, executing a constrained path search in the facility full life cycle causal chain model, and generating a life cycle query result set containing a causal path. According to the method, the pseudo-correlation problem generated by pure data mining can be effectively solved, the cumulative influence of early-stage risks in the design and construction stage on later-stage operation is quantified, and accurate tracing and explanation of cross-stage fault causes are realized.

The invention discloses a shield tunnel full-life structure deformation monitoring and early warning method based on digital twinning, and the method comprises the following steps: S1, constructing a tunnel-geological complex model fusing BIM and three-dimensional point cloud technology according to the point cloud data of a tunnel completion section; s2, FBG and incremental sensing are adopted, and full-life-cycle deformation real-time monitoring is conducted on the tunnel; s3, constructing an LGBM proxy model, and updating the deformation prediction of the tunnel structure in the construction period; s4, constructing a GAT-LSTM model, and predicting structural deformation of the tunnel in the service period; s5, constructing a tunnel structure deformation grading early warning method based on a strength reduction and sharp point mutation theory; and S6, constructing a tunnel monitoring and early warning system based on digital twinning. The tunnel-geological complex structure reconstruction is realized by fusing stratum information and high-precision spatial data on the basis of a digital twinning technology, the tunnel construction and service period real-time deformation monitoring and early warning method and the twinning system are provided, and the service safety and failure risk pre-warning of the tunnel structure in the whole life cycle are guaranteed.

The invention relates to the technical field of building management, in particular to a BIM-based building construction management method and system and a medium. The method comprises the following steps: acquiring building structure data; a tower crane full-life-cycle BIM model is constructed based on the building structure data, wherein the tower crane full-life-cycle BIM model comprises three-dimensional parameterized information of a base embedded part, a tower body standard knot, a slewing mechanism and a suspension arm assembly; acquiring building construction environment data; carrying out tower crane installation optimization on the tower crane full-life-cycle BIM model by utilizing building construction environment data to obtain a tower crane installation scheme; and base stress distribution simulation is conducted on the tower crane full-life-cycle BIM model according to the tower crane installation scheme, and tower crane base stress distribution data are generated. According to the invention, by integrating the building structure and the construction environment data and combining real-time sensing monitoring and intelligent collaborative optimization, the accuracy and dispatchability of tower crane management in building construction are improved.

The invention belongs to the technical field of distribution boxes, and discloses an intelligent fault early warning maintenance management system for a distribution box. Comprising the steps of obtaining multi-dimensional monitoring data of a full life cycle of a distribution box, and comprehensively describing a health state of the distribution box; dividing the whole life cycle into a plurality of relevance operation stages, identifying a hidden fault mode from an equipment operation event sequence by using a time sequence relevance mining algorithm, and calculating an abnormal evolution coefficient of each stage; based on the abnormal evolution coefficient and the hidden fault mode, constructing a dynamic risk entropy value of the current stage, and dynamically evaluating the fault risk; obtaining a gradient change trend of a risk entropy value, positioning a key degradation component set, generating a priority diagnosis map containing a fault root path, adaptively adjusting a strategy decision tree, and outputting an optimization maintenance scheme set; the operation reliability and the maintenance efficiency of the distribution box are obviously improved, and stable operation of a power system is guaranteed.

The invention discloses a multi-sensor fusion heat pump full life cycle AI maintenance early warning system, and relates to the technical field of new energy utilization, and the early warning system comprises a data collection module which obtains operation parameters in a heat pump full life cycle based on a sensor array, and constructs a data set after preprocessing the parameters; the operation parameters comprise temperature, pressure, flow and micro vibration; the data fusion module is used for extracting trend correlation characteristics and parameter coupling characteristics from temperature, pressure and flow parameters by adopting a dynamic sliding window adaptive to a working condition, and preserving core nonlinear information through KPCA dimension reduction; the micro-vibration signal extraction comprises frequency domain and time domain features. According to the method, features are extracted through a working condition adaptive dynamic sliding window, then through cross-space mapping and a life cycle-working condition double-attention mechanism, the analysis and early warning module depends on a core feature mapping library and a two-dimensional dynamic baseline, through instantaneous and accumulated deviation double judgment, abnormal accurate recognition and stage division are achieved, and early warning perspectiveness is high.

The invention relates to the technical field of solid waste monitoring and management, in particular to a full-life-cycle management method and system for solid waste. The method comprises the following steps: collecting waste source monitoring data based on a sensor, performing distributed storage management according to the waste source monitoring data, and constructing a solid waste real-time management database; according to the solid waste real-time management database, waste recovery types are predicted one by one, dynamic sorting control treatment is carried out, and therefore a dynamic solid waste sorting strategy is generated; obtaining a solid waste recovery processing log; carrying out urban garbage disposal recycle bin node identification on the solid waste recycling log, and constructing an urban recycle bin distribution map; and identifying positioning information of each waste source according to the waste source monitoring data, and making a dynamic solid waste transportation decision based on the distribution map of the urban recycle bin to generate a dynamic solid waste transportation strategy. According to the invention, the solid waste recovery efficiency and real-time monitoring management are improved.

The invention discloses a method for enabling building supply chain information tracing and management based on a block chain, and relates to the technical field of block chains, and the method comprises the following steps: S1, constructing an information chaining platform for building supply chain management, enabling each participant node of a supply chain to access a block chain network, storing the full life cycle data of the supply chain through employing a distributed account book technology, and storing the whole life cycle data of the supply chain; the full life cycle data comprises data of decision making, design, construction, acceptance and operation and maintenance stages. According to the method for tracing and managing the full-life-cycle information on the building supply chain, the data is stored by adopting a distributed account book technology, the integrity and non-tampering property of the data are ensured, the accuracy and efficiency of data tracing are improved, the consistency and reliability of the data are further ensured through a Hash algorithm and a consensus mechanism, and the data tracing efficiency is improved. A credible data basis is provided for each participant of the supply chain, and the supply chain flow rule is automatically executed by deploying the intelligent contract.

The invention provides a fan full life cycle archive management system and a management method thereof, and the system comprises an Internet of Things data collection module which is installed on a key part of a wind turbine generator set and collects vibration, temperature and stress data; the full-life-cycle digital archive management module is used for recording full-process information of the fan from design to operation and maintenance based on a cloud database and a block chain technology, and ensuring data integrity and traceability; the AI predictive maintenance analysis module analyzes data and predicts faults by using deep learning and machine learning algorithms; the intelligent scheduling and maintenance decision support module is used for generating an optimal maintenance plan in combination with external factors; and the responsibility tracing and process optimization module is used for recording maintenance details to optimize the process and spare part management. The management method comprises the steps of data acquisition, storage, analysis, maintenance plan making, task execution and recording, effect evaluation, process optimization and the like. Through cooperative work of all the modules and implementation of the system method, accurate and efficient full-life-cycle management of the draught fan is achieved.

The invention discloses an archive digitization full-life-cycle encryption integrity verification method and system, relates to the field of digital archive safety management, and is used for solving the problem of integrity and safety guarantee in archive digitization management. Logic units are divided through a semantic analysis engine, non-uniform blocks are generated based on sensitivity dynamic partitioning, local hash values are calculated, and a multi-level verification tree is constructed to generate root hash and a hash path mapping table; acquiring visitor identities, terminal environments and timestamps in each stage of an archive life cycle, generating state snapshots in combination with a hash path, and constructing a life cycle state snapshot chain; reconstructing a target logic unit verification path according to the access request, analyzing the structural difference between a current path and a historical snapshot chain through a path reconstruction type difference verification algorithm, and identifying structural offset, node tampering or path loss; and response control is triggered for abnormal verification, including session key updating and permission freezing, so that the security integrity guarantee of the digital archive in the full life cycle is improved.

The invention provides a building full life cycle data run-through fusion method, and belongs to the technical field of building data fusion, and the method comprises the steps: constructing an innovative architecture comprising a data collection micro-service cluster, a service registration center, a service monitoring center and a resource scheduling center, and configuring structured and unstructured data collection agents for different data sources. The system comprehensively collects the full life cycle data of a building, and constructs a service dependence model through micro-service operation index monitoring and resource dynamic scheduling. The technologies of deep data preprocessing, standard conversion, feature mapping and the like are adopted, and a deep neural network and a spectral clustering algorithm are utilized to construct a data consanguinity map and perform coupling degree analysis. By configuring a data fusion service cluster, formulating a data fusion execution scheme and a fallback mechanism and establishing a fusion quality evaluation system, intelligent acquisition, processing and management of building full life cycle data are realized, and the problem that the accuracy and consistency of fusion results cannot be ensured due to lack of a data fusion mechanism at present is solved.

The invention discloses a bus duct full life cycle health management system based on digital twinning, and relates to the technical field of health management. The multi-source sensor collects operation parameters and static information, the digital twin modeling and mapping module constructs a physical model and associates real-time data, and a thermal-electric coupling equation is used for simulating temperature; the state monitoring and fault diagnosis module compares data to judge states and diagnoses faults by means of methods such as a fault tree, the health assessment and decision making module constructs an index system to assess health and makes a maintenance decision, the data management and interaction module stores data and realizes visual interaction and system integration, and the intelligent optimization module performs intelligent optimization based on operation and maintenance data. And optimizing model parameters and a decision strategy. According to the invention, intelligent health management of the bus duct is realized, multi-source data acquisition is accurate and comprehensive, fault diagnosis is more accurate and prediction is more timely through combination of digital twinning and an algorithm, and intelligent assessment assists scientific maintenance decision; and the operation and maintenance efficiency and the power transmission stability are improved through system integration and edge calculation.

A power transmission network carbon emission accounting method based on a full life cycle theory. The method comprises: acquiring a bill of materials required in a production stage of a power transmission network device and carbon emission factors of materials corresponding to the bill of materials, constructing a carbon emission accounting model and calculating carbon emissions; acquiring transportation modes and carbon emission coefficients of related materials in the transportation and construction stages, constructing a model and calculating carbon emissions; establishing a model, which takes into consideration indirect carbon emissions generated due to line losses and carbon emissions generated during a replacement process related to conventional consumables and device failure, and calculating carbon emissions; constructing a carbon emission accounting model in the decommissioning and recycling stages, and calculating carbon emissions and carbon emission reductions generated in the stages; and performing accounting on carbon emissions throughout the full life cycle of a power transmission network, and calculating the carbon emission intensity. A carbon emission reduction effect of device recycling in a decommissioning stage is taken into consideration, the carbon emission intensity throughout the full life cycle of the power transmission network is calculated by means of a net power transmission capacity, and practical significance is given to a carbon emission target.

The invention relates to the technical field of life cycle management, in particular to a catalyst industry chain-oriented full life cycle traceability method and system, and the method comprises the steps: obtaining the state attribute data of the same batch of recovered products, and carrying out the analysis to obtain an efficiency feature vector; inputting the efficiency feature vector into a preset quality evaluation model for matching, and identifying quality deviation data of the recycled product; the batch identity codes are analyzed, and resume data of the production full life cycle are obtained; a multi-dimensional data association model is constructed, quality deviation data is taken as a target variable, resume data is taken as an input feature, an influence weight coefficient and an attribution confidence index of each production control variable on the quality deviation are analyzed, and key control elements are screened out; and performing production regulation and control optimization operation based on the key control elements, calculating target parameter offset for correcting quality deviation, dynamically calculating a dynamic tolerance constraint range of a production control variable in combination with an attribution confidence index, and packaging into an execution management and control protocol.

The invention relates to the technical field of vehicle intelligent monitoring, and particularly discloses a vehicle full life cycle-oriented charging safety early warning method and system, and the method comprises the steps: collecting real-time data through an electric core level sensor, and calculating voltage, temperature and current fluctuation coefficients, thereby obtaining a safety coefficient in a charging process; comparing the real-time data with historical data, judging the attenuation degree of the health state of the battery cell, and calculating an attenuation coefficient; a comprehensive risk index is calculated by comprehensively analyzing the safety coefficient and the attenuation coefficient, and then the risk level in the charging process is judged and divided into a high-risk mode and a low-risk mode; for a high risk level, early warning measures are triggered in time, such as sound or visual alarm, automatic charging stopping and the like; and under the condition of low risk, the charging strategy is optimized, the charging current and voltage are adjusted, the charging efficiency is improved, and the service life of the battery is prolonged.

The invention relates to the technical field of equipment full-life-cycle management, in particular to an equipment full-life-cycle tracing and management and control system based on digital threads. Comprising a digital thread generation unit which is used for generating a dynamic digital thread penetrating through the whole life cycle of technical improvement purchasing, operation maintenance and decommissioning of equipment; a full life cycle data integration unit; and a business process management and control unit. According to the method, a dynamic digital thread which runs through the whole life cycle of technical improvement purchasing, operation overhaul and decommissioning of equipment is constructed, and a multi-dimensional data anchoring algorithm fused with weighted Bayesian estimation is combined, so that probabilistic association matching between heterogeneous physical data in each stage and a corresponding physical scene is realized; and a unique device identifier is bound to form dynamic association of physical state-data record-time node, so that the problems of fragmentation of data in the whole life cycle of the device and lack of association between the data and a physical scene in the prior art are solved.

The invention discloses a block chain-based support steel member full life cycle quality traceability system, and relates to the technical field of block chains, and the method comprises the steps: deploying sensors at each stage of a steel member, collecting data, and unifying the format; dynamically calculating a weight based on timeliness and confidence, and generating a comprehensive quality score; generating a block by hash encrypted data, and triggering an intelligent contract by a preset engineering rule; full life cycle data are retrieved through UID, and BIM model three-dimensional display is integrated; and analyzing a contract log to locate a responsible party, and optimizing parameters to form closed-loop management. Through a dynamic weight distribution algorithm, the system can dynamically adjust the weight of each data source according to the timeliness and confidence of the sensor data, and the objectivity and accuracy of quality evaluation are improved.

The invention discloses a copper product carbon footprint data collection and processing method based on a full life cycle, and the method comprises the steps: dividing the production and processing life cycle of a copper product into a plurality of stages, and collecting the carbon emission data of the current life cycle; constructing a multivariate time sequence matrix X (t) by using the carbon emission data of the plurality of stages according to a time sequence, decomposing the X (t) into a dynamic mode, and calculating mode energy to obtain a dominant mode subset; using the dominant mode subset to obtain a reconstructed data sequence, calculating with X (t) to obtain an abnormal score, and when the abnormal score is greater than a preset score threshold, inputting the carbon emission data and the carbon footprint network into a pre-trained graph convolutional neural network to obtain a data abnormal stage; and energy consumption data and operation data of the plurality of devices in the data exception stage are collected, and a plurality of abnormal devices are obtained through correlation analysis. The invention relates to the field of industrial carbon emission, and solves the technical problems of difficulty in abnormal detection of carbon emission data and complexity in data processing.

The invention belongs to the field of data processing, and provides a road engineering low-carbon data credible interaction method and system, and the method comprises the steps: constructing a full life cycle stage division model, generating corresponding low-carbon data according to the business activity of each stage, and adding a stage identifier, a timestamp and a spatial position label to the low-carbon data; based on a preset engineering logic, a data causal relationship and a carbon emission factor model, constructing a data context tree comprising a plurality of low-carbon data nodes, and organizing the low-carbon data nodes in different space-time ranges and life cycle stages into a life cycle data context tree with a staged conduction relationship; before data interaction is carried out, multi-dimensional verification operation is carried out on target data based on the data context tree; and calculating a credible score of the target data based on a structural path verification result, a space-time consistency verification result and a carbon factor mapping verification result, and controlling the interactive access permission of the low-carbon data according to a score result.