484results about "Technology management" patented technology

A system for data collection related to a fluid conveyance environment includes a data acquisition circuit comprising inputs and outputs; input sensors to provide sensor data values, coupled to a component in the fluid conveyance environment; and a processor comprising the data acquisition circuit. The processor is configured to determine a data storage profile; responsive to the data storage profile, configure the data acquisition circuit to selectively couple at least one of the inputs to at least one of the outputs; interpret the at least one of the sensor data values; store at least a portion of the at least one of the sensor data values in response to the data storage profile; analyze a set of the sensor data values and determine a data quality parameter; and adjust at least one of the data storage profile and a data collection routine in response to the data quality parameter.

Methods and systems for a monitoring system for data collection in an industrial environment including a data collector communicatively coupled to a plurality of input channels connected to data collection points operationally coupled to at least one industrial component in at least one of an industrial boiler system or industrial pipeline system; a data storage structured to store a library of stored noise patterns associated with operation of the at least one industrial component; a data acquisition circuit structured to interpret a plurality of detection values from the collected data; and a data analysis circuit structured to: analyze the collected data, determine a measured noise pattern for the at least one industrial component, and compare the measured noise pattern to the library of stored noise patterns to identify a changed condition of the at least one industrial component.

The invention provides a multi-source constraint gridding atmospheric pollutant and greenhouse gas emission estimation method, and relates to the technical field of environmental information processing, and the method comprises the steps: constructing a hierarchical grid structure of an urban core domain and a peripheral background domain based on a unified data basic set, forming a gridding baseline emission list through unified activity data and emission factors, establishing a multi-source scale coordination mechanism, fusing ground monitoring and satellite observation information, and generating an observation data set; according to the method, an SMOKE sparse matrix processing chain is embedded to construct a joint estimation model, parameter inversion and consistency constraint optimization are implemented, boundary flux coordination is carried out between a core domain and a background domain, and finally an emission list which has cross-atmospheric pollutant and greenhouse gas consistency and can be used for refined emission inversion is formed. According to the method, the problem of unstable data fusion caused by scale mismatching between the multi-source observation data and the emission model in the prior art is solved.

An information technology system for a distributed manufacturing network includes an additive manufacturing platform configured to manage workflows for a set of distributed manufacturing network entities associated with the distributed manufacturing network. The information technology system includes a set of digital twins generated by the additive manufacturing platform. The information technology system includes an artificial intelligence system configured to be executed by a data processing system in communication with the additive manufacturing platform. The artificial intelligence system is trained to generate process parameters for the workflows managed by the additive manufacturing platform using data collected from the set of distributed manufacturing network entities. The information technology system includes a control system configured to adjust the process parameters during an additive manufacturing process performed by at least one of the set of distributed manufacturing network entities.

The invention relates to the technical field of power consumption management, in particular to a pump station unit energy consumption real-time analysis system which comprises a load feature extraction module, a frequency stability recognition module, a start-stop window recognition module, a voltage response regulation and control module and a signal filtering module. According to the method, a sliding trend sequence is constructed through load state data and current increment information, slope abrupt change points are identified, trend direction classification is carried out, accurate identification of a frequency operation stability section is realized in combination with frequency variation amplitude, and a regulation and control time window is constructed through trend consistency judgment of energy consumption and flow in the same time domain. A voltage input strategy is set according to the trend change rate, a mapping table is formed, signal filtering and recovery are achieved through trend continuity judgment in confidence evaluation of energy consumption labels, and the behavior segmentation precision, the adaptability of electric energy input regulation and control and the continuity and controllability of overall operation state recognition in the energy consumption monitoring process are effectively enhanced.

The invention relates to the technical field of artificial intelligence and environmental information processing, discloses a carbon emission AI intelligent accounting method and system, and aims to solve the problems of weak data fusion capability, poor accounting real-time performance, insufficient model generalization and lack of self-evolution capability of the system in the prior art. The method comprises the steps that multi-source heterogeneous operation data are collected in real time, standardized management and spatial-temporal feature extraction are carried out, direct, indirect and implicit carbon emissions are calculated through a fusion graph neural network, a time sequence attention network and an accounting cluster of an integrated tree model, and online iterative optimization of model parameters is driven based on external calibration signals. According to the scheme, carbon emission accounting is converted into a high-precision minute-level response dynamic deduction mode, the transparency and traceability of accounting are remarkably improved, continuous self-adaptive optimization in a large-scale scene is supported, and reliable data support is provided for precise carbon reduction.

The invention relates to the technical field of energy management, in particular to a fuzzy logic-based multi-objective decision analysis method, which comprises the following steps of: firstly, dividing an evaluation criterion into quantitative and qualitative index sets; constructing an interval type-2 fuzzy set capable of describing data uncertainty according to quantitative indexes such as a full-life investment payback period; for qualitative indexes such as technology maturity, a spherical fuzzy set is introduced, and an intelligent operator for perceiving hesitance is used for aggregation; calculating a group utility value, an individual regret value and a comprehensive sorting index of each energy configuration scheme by applying a hybrid fuzzy VIKOR algorithm, outputting an optimal compromise sorting result, and identifying a reference optimal strategy and a risk avoidance strategy; according to the method, the explainable enhanced fuzzy rule is extracted, the robustness of the decision method is improved by analyzing different types of fuzzy logic, and the transparency, the credibility and the compliance of the energy configuration decision process are remarkably enhanced by the finally generated explainable decision basis, so that a feasible configuration decision reasoning model is provided for energy planning.

A system includes a fleet resources data store that maintains a fleet resource inventory indicating fleet resources that can be assigned to perform tasks. For each fleet resource, the inventory indicates features of each fleet resource and a respective status. A set of task definitions is accessible to an intelligence layer to facilitate improving task definition based on feedback from task-specific outcomes. The system receives a job request for a robotic fleet to perform a job and determines a job definition data structure indicating a set of tasks to be performed for the job. The system applies an outcome of performing a task by a resource assigned to perform the task to a machine learning system of the intelligence layer that facilitates improving, based on the outcome, the set of task definitions. The system updates the set of task definitions based on a result of applying the machine learning system.

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 illustrative embodiments provide a data platform, system and method for utilizing carbon credits through blockchain-based tokenization. The platform receives carbon utilization data corresponding to at least one carbon credit and executes a smart contract on a blockchain network based on the received data. Execution of the smart contract generates one or more blockchain tokens that embed metadata including a hash of the carbon utilization data and a unique security identifier. The platform performs electronic transactions for the tokens by propagating transactions for distributed consensus, verifying token authenticity through comparisons with distributed-ledger records, or generating immutable electronic records indicating tracking information for the carbon credit. Records of the electronic transactions are stored in a distributed ledger as immutable entries linking token activity to corresponding carbon utilization data.

The invention provides an automatic cutter handle production line based on an intelligent cutter system and a machining process. On the premise of guaranteeing the product machining efficiency and automatic intelligent production, in the whole intelligent cutter system-based cutter handle automatic production line operation and machining process implementation process, intelligent cutter wear state monitoring system software of a PC end upper computer monitors all intelligent cutters in a production line in real time through intelligent cutter embedded sensor data, and the intelligent cutter wear state monitoring system software is used for monitoring the intelligent cutter wear state in the production line. Comprise but are not limited to wear states and operation parameters of the intelligent external turning tool, the intelligent grooving turning tool and the intelligent milling cutter. The intelligent tool wear state monitoring system can give an early warning of tool breakage or end of service life in advance, and automatically calls a standby tool or prompts replacement, so that stable operation of a production line and high-precision consistency of tool holder products are ensured.

The invention relates to the technical field of fault diagnosis models, and discloses an adaptive AI fault diagnosis model fusing reinforcement learning and an industrial equipment predictive maintenance system. Comprising an industrial equipment data acquisition and preprocessing module, a reinforcement learning-fused adaptive fault diagnosis model module, an equipment health degree evaluation and residual life prediction module, a predictive maintenance decision and execution module and a system management and integration module, and the output end of the industrial equipment data acquisition and preprocessing module is electrically connected with the input end of a self-adaptive fault diagnosis model module fusing reinforcement learning. According to the system, data during equipment operation are effectively collected, the equipment health degree evaluation module is matched to convert the feature deviation degree into 0-100 visual scores, the health trend visualization unit displays changes through a broken line diagram and a thermodynamic diagram, fault risk points are marked, historical backtracking and multi-equipment comparison are supported, non-professional personnel can also rapidly judge the equipment state, and the equipment health degree evaluation module is matched to convert the feature deviation degree into 0-100 visual scores. And the equipment management efficiency is improved by 30%.

The invention belongs to the technical field of carbon accounting methods, and particularly relates to a carbon accounting method based on a green supply chain carbon management platform, and the method comprises the following steps: S1, constructing a whole industry chain product carbon footprint accounting model; s2, upstream product carbon footprint calculation; s3, performing factorization on the upstream product carbon footprint value; and S4, calculating the carbon footprint of the industrial product in each industry. According to the overall structure provided by the embodiment of the invention, in order to solve the problems that the current data islanding phenomenon is serious, and carbon emission data in each stage is lack of effective integration, an innovative mechanism of'upstream carbon footprint value factorization 'is put forward for the first time: actually measured emission in each stage of a supply chain is converted into a standardized carbon footprint factor and the standardized carbon footprint factor is incorporated into a shared library; downstream enterprises can call in real time according to batches and processes, and complete full-life-cycle accounting automatically after being coupled with production data of the enterprises; the factor supports version tracing, third-party verification and dynamic updating, data islands are thoroughly broken, and the carbon footprint precision, transparency and supply chain cooperation efficiency are remarkably improved.

A robot fleet management platform includes one or more processors configured to execute instructions. The instructions include receiving a job request comprising information descriptive of job deliverable and request-specific constraints for delivering the job deliverable. The instructions include applying content and structural filters to content received in association with a job request to identify portions thereof suitable for robot automation. The instructions include establishing a set of robot tasks, each defining at least a type of robot and a task objective, based on the portions of the job request that are suitable for robot automation and meet a first fleet objective. The instructions include applying fleet configuration services to the job content and the set of robot tasks to produce a fleet resource configuration data structure for the job request that associates at least one robot operating unit with each task in the set of tasks and robot adaptation instructions.

The invention relates to the technical field of intelligent operation and maintenance of industrial equipment, and discloses an industrial equipment predictive maintenance method based on an adaptive neural network. According to the method, equipment operation data and sensor ontology data are acquired through a multi-source sensor network, an equipment health index and sensor data confidence coefficient are calculated based on principal component analysis, and a comprehensive health feature vector is constructed in combination with change rate and correlation analysis; inputting the feature vector and the state identifier into a hybrid neural network model, extracting a time sequence feature through an attention mechanism, and outputting an equipment health state, a residual life prediction value and a prediction confidence coefficient in parallel; based on a multi-dimensional evaluation result, a hierarchical maintenance strategy is generated and executed through a decision of data confidence management; according to the invention, through dual health assessment of the equipment and the sensor, the attention mechanism enhanced prediction model and data-driven closed-loop optimization, the accuracy and reliability of fault prediction of the industrial equipment and the self-adaptive capability of the system are improved.

A system and method for trading of carbon credits is provided. The system comprises a server computing device comprising at least one hardware processor and memory; program instructions stored in the memory and executable in the server computing device that, when executed, direct the server computing device to: communicate with a plurality of vehicles over a communication network; receive distance information from a respective one of the vehicles detailing distance traveled by the respective one of the vehicles; generate a carbon credit based at least in part on the distance information received from the respective one of the vehicles; and associate the carbon credit with a user account associated with the respective one of the vehicles.

The invention provides an atmospheric pollutant and carbon dioxide cooperative control method and device, and relates to the field of atmospheric environmental science and urban low-carbon development decision support, and the method comprises the steps: constructing an atmospheric pollutant and carbon dioxide emission reduction technology library, and introducing a carbon pollution coupling emission accounting method to construct a carbon pollution emission accounting framework; performing technical emission reduction calculation by using a carbon emission accounting framework, and evaluating environmental benefits and health benefits of emission reduction measures according to the technical emission reduction to obtain benefit evaluation data; constructing an index system, combining the index system with the benefit evaluation data, and performing multi-dimensional quantitative evaluation on the emission reduction measures to obtain an evaluation result of the emission reduction measures; according to the overall scheme, an emission reduction path is designed and optimized on the basis of an emission reduction measure evaluation result, the emission reduction effect is predicted based on the optimized emission reduction path, and the emission reduction effect is obtained. According to the invention, through full-chain quantitative evaluation, scientific formulation and adjustment of a collaborative governance strategy are effectively supported, and environmental benefits and economic returns are improved.

Methods and systems for a data marketplace in a fluid conveyance device includes a self-organizing data marketplace. The self-organizing data marketplace includes at least one data collector and at least one corresponding fluid conveyance device in an industrial environment, wherein the at least one data collector is structured to collect detection values from the fluid conveyance device; a data storage structured to store a data pool comprising at least a portion of the detection values; a data marketplace structured to self-organize the data pool; and a transaction system structured to interpret a user data request, and to selectively provide a portion of the self-organized data pool to a user in response to the user data request.

A robot fleet platform for preparing a job request includes one or more processors configured to execute instructions. The instructions include a job request ingestion system configured to receive job content relating to at least one of picking, packing, moving, storing, warehousing, transporting or delivering of items in a supply chain. The job content includes an electronic job request and related data. The instructions include a job content parsing system configured to apply filters to the received job content to identify candidate portions thereof for robot automation. The instructions include a fleet intelligence layer that activates a set of intelligence services to process terms in the candidate portions of the job content and receive therefrom at least one recommended robot task and associated contextual information. The instructions include a demand intelligence layer that provides real time information relating to a parameter of demand for the items in the supply chain.

The illustrative embodiments provide a system, method, device, and platform for managing carbon credits. Information corresponding to carbon credits with carbon emissions are received. A smart contract is executed on a data platform of a blockchain network based at least on the received information. The smart contract specifies one or more parameters for transactions corresponding to the carbon credits. One or more tokens for performing transactions in accordance with the smart contract are generated utilizing the data platform. Transaction for the one or more tokens are performed in accordance with the smart contract. The transactions include at least verifying the authenticity of the one or more tokens. Records of the transactions for the tokens a stored in a distributed ledger associated with the blockchain network.

The invention provides an equipment energy consumption dynamic evaluation method based on multi-source data fusion, and relates to the field of energy consumption evaluation, and the method specifically comprises the steps: constructing an energy consumption evaluation data set, carrying out the statistics of the missing rate, jump rate and straightness rate of an energy consumption time sequence, and generating dimension-by-dimension credibility gating. Calculating the deviation degree of the multi-source data in the energy consumption time sequence based on the median vector to obtain the dynamic fluctuation intensity, differentiating the multi-source data and the median vector, normalizing the multi-source data and the median vector in combination with the robust scale vector and the dynamic fluctuation intensity, and performing element-by-element combination operation with dimension-by-dimension credibility gating to obtain normalized output, a soft division probability is obtained through a gating circulation unit, linear transformation and a Softmax activation function in sequence, weighted fusion is carried out on normalized output, global feature representation is formed through one-dimensional convolution and summary average, dynamic energy consumption evaluation values of the main equipment and the auxiliary equipment are obtained through mapping by combining fusion representation vectors, and the dynamic energy consumption evaluation values of the main equipment and the auxiliary equipment are fused to form a dynamic energy consumption evaluation value of the equipment. Accurate and dynamic evaluation of the energy consumption state of the equipment is realized.

The invention discloses a full-life-cycle carbon footprint dynamic accounting method and system and a medium. The method comprises the steps of S1, system boundary definition and data preparation; s2, dynamic datum line calculation: constructing a carbon emission calculation model under a datum line scene based on the current situation of the land parcel, and generating a datum line carbon emission curve changing along with time based on the carbon emission calculation model; s3, dynamically checking the carbon footprint of the project; and S4, quantifying the carbon assets, and calculating the net carbon emission reduction created by the updated project. According to the invention, carbon footprint dynamic accounting, evaluation and decision support are carried out in the whole life cycle of a city update project.

A work order management system automates the process of scheduling maintenance tasks and generating corresponding work orders via analysis of monitored data generated by the industrial assets. The work order management system can monitor control, status, or operational data from industrial devices on the plant floor, and initiate creation of work orders based on a determination that the monitored industrial data indicates a current or predicted performance risk requiring investigation or maintenance. The system can leverage generative artificial intelligence (AI) or other types of AI in connection with determining when and how to schedule a maintenance task intended to mitigate asset risk. The system can also factor contextual information when determining whether to create and schedule a work order, such as the cost of operator or maintenance time, scheduled plant downtimes, environmental factors (e.g., humidity), time of year, supplier issues, and other considerations.

The application belongs to the technical field of big data calculation, and particularly relates to a top-down urban carbon emission accounting method and system. The top-down urban carbon emission accounting method comprises the following steps: S1, gridizing map data to obtain carbon dioxide column concentration molar concentration data corresponding to each grid; S2, obtaining a carbon dioxide concentration difference between an accounting area and a surrounding area of the accounting area according to the carbon dioxide column concentration molar concentration data; S3, obtaining carbon dioxide concentration data of the accounting area according to the carbon dioxide concentration difference; S4, obtaining a carbon dioxide emission amount of the accounting area according to the carbon dioxide concentration data and a volume of the accounting area; and S5, obtaining a carbon emission amount of the accounting area according to the carbon dioxide emission amount. The top-down emission accounting method can avoid the problem of statistical inaccuracy in the traditional layer-by-layer reporting of carbon emissions by various industries.

The invention relates to the technical field of compressed air energy storage and underground engineering risk control, discloses a compressed air energy storage reservoir leakage risk assessment method and system, and aims to solve the problems that an existing assessment method does not cover exploration data, is insufficient in weight determination scientificity, cannot accurately recognize geological source leakage risks and the like. The method comprises the following steps: 1) collecting investigation data and structure, operation, environment and historical leakage data; 2) constructing a risk index system containing five-level first-level indexes of investigation, structure, operation, environment and history and corresponding second-level indexes; 3) determining an index weight by adopting expert scoring and analytic hierarchy process fusion; the system comprises a data acquisition module, an index system construction module, a weight determination module, a risk assessment module and an early warning suggestion generation module, and integrated assessments are achieved.By means of the method, assessment comprehensiveness and accuracy are improved, and the method is suitable for safety management and control of the underground energy storage library.

The invention discloses a compressed air system energy efficiency intelligent evaluation method based on environment compensation. The method comprises the following steps: firstly, acquiring operation performance parameters, air quality parameters and environment parameters, establishing thermophysical property mapping by using an enthalpy-humidity diagram, and forming a quality coupling characteristic data set; meanwhile, a theoretical energy efficiency reference surface and a thermodynamic constraint set are constructed based on an isentropic compression principle and a historical optimal sample. Then, a nonlinear machine learning model is trained, and a nonlinear mapping relation between environment parameter changes and performance and quality offsets is learned; the model converts operation parameters in a real-time non-standard environment into equivalent operation performance parameters under a standard environment condition. And during evaluation, mapping the equivalent operation parameters to a theoretical energy efficiency reference surface, and calculating a distance vector to obtain an evaluation result reflecting the energy efficiency deviation degree and the performance degradation degree of the system. According to the method, environmental interference is effectively eliminated, and the accuracy and comparability of energy efficiency evaluation of the compressed air system are remarkably improved.

The invention discloses an industrial Internet of Things intelligent predictive maintenance system based on edge computing, and belongs to the technical field of industrial Internet of Things and intelligent maintenance, and the system comprises a multi-source sensing data fusion module, an edge intelligent analysis module, a cloud health prediction module and a collaborative decision feedback module. A deep coupling closed loop optimization mechanism is formed through space-time registration fusion of multi-dimensional sensing data, edge end adaptive dimension reduction and feature extraction, cloud health situation modeling and residual life prediction, and cloud edge collaborative parameter adjustment feedback, the system predicts equipment faults 7-15 days in advance, the data transmission amount is reduced by 70-85%, the out-of-plan downtime is reduced by more than 40%, and the system performance is improved. The maintenance cost is reduced by about 35%, and precise and intelligent maintenance of industrial equipment is realized.

The invention discloses an urban rail cross-professional multi-mode production and maintenance plan and scheduling method, which comprises the following steps: a cross-professional production plan dynamic generation step: establishing a unified data interface and a data standard, converging and integrating data in different professional systems, analyzing the production and maintenance requirements of each professional system, and combining the existing plan and real-time construction information to generate a cross-professional production plan; generating a trans-professional preliminary production plan; a cross-professional production scheduling step: based on the preliminary production plan, performing preliminary scheduling on three resources of personnel, time and tools, and generating a globally optimized production plan and resource scheduling scheme based on an improved genetic algorithm; and a multi-mode production plan execution and tracking step: according to the work order type, performing execution management by respectively adopting a precise mode of a curing period and a rough mode of a non-curing period, and establishing a real-time monitoring and dynamic adjustment mechanism. The method is used for solving the problems that professional system data standards are different, sharing is difficult, and cross-professional conjoint analysis and collaborative planning cannot be supported.

The invention discloses an energy storage scheme evaluation method and system based on an entropy weight method, which is applied to the technical field of energy technology evaluation, and comprises the following steps: determining a plurality of dimension index values at least comprising a response dimension from each energy storage scheme, wherein the first index value of the response dimension is obtained by firstly extracting charging and discharging output and original load data in a scheme, determining a typical daily net load output curve through data processing, then extracting load characteristic data such as response time, power density and energy density from the curve, and further determining a response index value; the index values of all dimensions are processed through an entropy weight method, weighted data of each energy storage scheme are obtained, finally, comprehensive evaluation results are obtained based on the weighted data through an approximate ideal solution sorting method and sorted, and finally quantitative evaluation results of all the energy storage schemes are output. According to the energy storage scheme evaluation method based on the entropy weight method provided by the invention, a fair and credible quantitative basis can be provided for energy storage scheme evaluation.

The invention discloses a branch-level carbon management platform and method and a carbon management system. The method comprises the steps of collecting real-time carbon activity data of production measurement facilities in branch companies based on time granularity; on the basis of the accounting object granularity, respectively executing the following steps on each accounting object in the branch company: determining a dynamic carbon emission factor of the accounting object on the basis of the material flow data and the energy flow data of the accounting object, performing dynamic carbon emission accounting on the real-time carbon activity data of the accounting object based on the dynamic carbon emission factor to generate carbon emission accounting data of the accounting object; comparing the carbon emission accounting data with the real-time carbon intensity performance index of the corresponding accounting object; and if a target accounting object with a deviation between the carbon emission accounting data and the real-time carbon intensity performance index is obtained through comparison, a target emission reduction scheme is generated under the carbon management constraint condition of the branch company based on the deviation and feature data, so that the branch company can adjust the production plan and the carbon emission reduction measure of the accounting object related to the target emission reduction scheme.