1137results about "Technology management" patented technology

An information technology system for a distributed manufacturing network includes an additive manufacturing management platform configured to manage process and production workflows for a set of distributed manufacturing network entities through design, modeling, printing, and supply chain stages. The information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the set of distributed manufacturing network entities of the distributed manufacturing network to optimize digital production processes and workflows. The information technology system includes a distributed ledger system integrated with a digital thread configured to provide unified views of workflow and transaction information to entities in the distributed manufacturing network.

A robot fleet management platform includes a job configuration system that determines tasks to be performed by robots of a robot fleet based on a job request and a first fleet objective. A proxy service applies fleet configuration services to the tasks to produce a data structure. An intelligence layer activates intelligence services to produce a robot task and associated contextual information that facilitates robot selection and task ordering. A job workflow system generates a workflow defining a performance order of the tasks. A workflow simulation system simulates performance of the job request based on the workflow to recursively redefine the tasks, the data structure, or the workflow until the simulation result satisfies a second fleet objective. In response to the simulation result satisfying the set of fleet objectives, a plan generator generates a job execution plan based on the set of robot tasks, the data structure, and the workflow.

An industrial machine predictive maintenance system may include an industrial machine data analysis facility that generates streams of industrial machine health monitoring data by applying machine learning to data representative of conditions of portions of industrial machines received via a data collection network. The system may include an industrial machine predictive maintenance facility that produces industrial machine service recommendations responsive to the health monitoring data by applying machine fault detection and classification algorithms thereto. The system may perform a method of predicting a service event from vibration data captured data from at least one vibration sensor disposed to capture vibration of a portion of an industrial machine. A signal in a predictive maintenance circuit for executing a maintenance action on the portion of the industrial machine can be generated based on a severity unit calculated for the captured vibration.

The invention relates to the field of industrial production, and discloses a predictive maintenance system for an ultra-efficient explosion-proof motor based on digital twinning, which comprises the following steps: when key operation parameters of the motor deviate from a normal working condition interval, judging whether the key operation parameters reach a digital twinning model activation threshold value or not; based on historical operation data, sensor real-time data and environmental factors, a digital twin model is called to construct virtual mapping of motor operation, a current state evolution trajectory is generated, and whether the operation trend has a fault or not is predicted; the state track change mode is matched with a historical fault library, and whether a potential fault path exists or not is evaluated; according to the fault risk level and the predicted occurrence time, in combination with the current production plan and the equipment health tolerance, whether an advanced intervention measure needs to be taken is judged; and generating an intervention scheme according to state regulation and control parameters fed back by the digital twinning model in combination with specific working conditions. The method has the advantage that the operation safety and the maintenance efficiency are improved.

The invention relates to an intelligent maintenance decision method and system based on a knowledge graph, and relates to the technical field of intelligent operation and maintenance and decision support and information, and the method comprises the steps: obtaining the operation state data and historical maintenance data of industrial equipment; constructing an industrial knowledge graph and generating an incidence relation model; an optimal maintenance path is obtained based on the analysis of the relationship between the fault type and the maintenance path; optimizing a maintenance task allocation scheme through the optimal maintenance path, the incidence relation model and the maintenance resource constraint condition; carrying out maintenance time window dynamic adjustment analysis to obtain an optimal maintenance strategy; and finally, performing multi-objective optimization iterative analysis to obtain a final maintenance decision. According to the scheme, various factors can be comprehensively considered, and the scientificity and the accuracy of maintenance decision making of the industrial equipment are improved.

A dynamic vision system includes a variable focus liquid lens optical assembly. The dynamic vision system includes a variable lighting assembly. The dynamic vision system includes a control system configured to adjust one or more optical parameters and data collected from the variable focus liquid lens optical assembly in real time. The dynamic vision system includes a control system configured to adjust the variable lighting assembly. The dynamic vision system includes a processing system that dynamically learns on a training set of outcomes, parameters, and data collected from the variable focus liquid lens optical assembly to train a set of machine learning models to control the variable focus liquid lens optical assembly to optimize collection of data for processing by the set of machine learning models.

A system can include a backend system and a sensor kit configured to monitor an industrial setting. The sensor kit can include an edge device and a plurality of sensors that capture sensor data and transmit the sensor data via a self-configuring sensor kit network. At least one sensor can capture sensor measurements and output instances of sensor data, generate and output reporting packets, and transmit the reporting packets to the edge device via the self-configuring sensor kit network in accordance with a first communication protocol. The edge device receives reporting packets from the plurality of sensors via the self-configuring sensor kit network and transmits sensor kit packets to the backend system via a public network. The backend system can include a processing system and a storage system, where the processing system performs backend operations on the sensor data and the storage systems stores the sensor data.

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.

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.

A method of configuring a robot of a fleet of robots for use of an AI chipset includes receiving a request for a robotic fleet to perform a job. The method includes defining a set of tasks that are to be performed by the robotic fleet in performance of the job. The method includes assigning at least one task of the set of tasks to a robot. The method includes determining a configuration for the robot based on the assigned task and a components inventory that indicates different components that can be provisioned to the robot including at least one AI chipset, and for each component, a set of extended capabilities and a status of the component. The method includes configuring the robot based on the determined configuration to use the at least one AI chipset. The method includes deploying the robotic fleet to perform the job.

A new energy material safety evaluation method and system based on multi-agent reinforcement learning, and an electronic device, relating to the field of data mining and analysis for the safety of new energy materials against heavy object impact and thermal stimulation. The new energy material safety evaluation method based on multi-agent reinforcement learning comprises: using high-throughput computing to acquire data sets; providing a safety evaluation threshold on the basis of a hotspot formation and evolution process; labeling each sample in an initial data set with a safety label on the basis of the threshold to obtain a training data set; establishing a mapping relationship between the training data set and multi-agents; using an agent action selection policy to select an action having the maximum reward value to obtain a current cluster distribution result of the multi-agents; calculating a sample silhouette coefficient, a cluster silhouette coefficient, and an overall silhouette coefficient; using a reward function to calculate reward values of the multi-agents, and obtaining cluster centroids when the number of iterations satisfies a preset threshold; and determining a new energy material safety evaluation result on the basis of the cluster centroids.

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.

Systems for self-organizing data collection and storage in a power generation environment are disclosed. A system may include a data collector for handling a plurality of sensor inputs from sensors in the power generation system, wherein the plurality of sensor inputs is configured to sense at least one of: an operational mode, a fault mode, a maintenance mode, or a health status of at least one target system. The system may also include a self-organizing system for self-organizing a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs. The self-organizing system may organize a swarm of mobile data collectors to collect data from a plurality of target systems.

An automated carbon asset network system and a carbon asset management method are provided. The automated carbon asset network system includes a carbon asset management digital platform realizing a carbon inventory executed for an enterprise through a computer system, and the carbon asset management digital platform includes a carbon asset network establishing module, a carbon asset network connection module, and a carbon asset network update module. The carbon asset management method includes: receiving data for stakeholders, internal or external carbon asset management digital platform, carbon asset management report formats, and carbon inventory obtained through automated customized questionnaires, by using the application program interface; automatically integrating carbon inventory data for supply chains and performing version control; digitally managing a full scope of an organizational greenhouse gas inventory and product carbon footprint information throughout product life cycles for brands in the enterprise; and generating documents according to requirements.

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 provides a green low-carbon evaluation method, system and device for a supply chain and a medium. The method comprises the following steps: acquiring multi-source acquisition data of the supply chain; according to the multi-source acquisition data, updating the emission factor database by adopting a federated learning algorithm and an incremental updating mechanism to obtain updated carbon emission factors in the emission factor database; performing carbon emission accounting on the supply chain according to the updated carbon emission factors in the emission factor database to obtain carbon emission accounting data of the supply chain; based on the carbon emission accounting data and the multi-dimensional evaluation system, performing green low-carbon evaluation on the supply chain; according to the method, federal learning and an incremental updating mechanism are combined, the carbon emission factors are dynamically updated, and green and low-carbon evaluation of supply chain enterprises or industries is realized based on the dynamically updated carbon emission factors and the carbon emission accounting result, so that an objective, comprehensive and flexible green and low-carbon evaluation result can be obtained.

A system includes an expert graphical user interface configured to: present a list of reliability measures of an industrial machine, facilitate a selection by a user of a reliability measure from the list of reliability measures, present a representation of a smart band data collection template associated with the reliability measure selected by the user, and a data routing and collection system configured to, in response to a user indication of acceptance of the smart band data collection template, collect data from a plurality of sensors in an industrial environment in response to a data value from one of the plurality of sensors being detected outside of an acceptable range of data values.

The invention discloses an equipment health management and predictive maintenance method and system based on an industrial intelligent gateway, and relates to the technical field of industrial automation and predictive maintenance, and the method comprises the steps: collecting equipment data through a multi-source sensor, and carrying out the preprocessing of the equipment data through an intelligent gateway; mapping the high-quality data to a digital twinborn model for health state evaluation; based on an evaluation result, fault prediction is carried out by adopting knowledge-driven and data-driven fused intelligent diagnosis; generating and executing a personalized maintenance strategy; according to the system and the method, the problems of insufficient multi-source data fusion, insufficient health assessment accuracy and lack of individuation of a maintenance strategy are solved, and precision, self-adaption and resource optimization of equipment health management are realized.

The invention discloses a property energy consumption abnormity monitoring method and system based on big data analysis, and relates to the technical field of energy consumption abnormity management. According to the property energy consumption abnormity monitoring method based on big data analysis, in a specified energy consumption monitoring time period, an energy consumption power group of a specified property service area is firstly obtained, and the energy consumption power group is compared with an energy consumption prediction interval to screen energy consumption abnormity; according to the comparison result, energy consumption abnormal data in the abnormal energy consumption time period are repeatedly checked, transient normal fluctuation caused by normal working condition switching of equipment or sudden change of the external environment is eliminated, and repeated checking comprises peak value abnormal detection and recheck verification; and finally, after repeated inspection is completed, association verification is carried out in combination with the abnormal energy consumption feature set. According to the method, a scientific basis is provided for property energy consumption management, accurate positioning of energy consumption abnormal points is facilitated, the problem of abnormal identification misjudgment caused by transient change in energy consumption monitoring in the prior art is effectively solved, and energy conservation, consumption reduction and cost optimization are realized.

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 discloses a metering performance evaluation method and system for an intelligent electric energy meter, a medium and a product, and the method comprises the steps: employing a standard meter for synchronous operation for a to-be-evaluated intelligent electric energy meter, so as to obtain a field test value of a basic error of the intelligent electric energy meter, inputting the environment detection data and the operation time of the intelligent electric energy meter to be evaluated into the trained basic error prediction model corresponding to the model of the intelligent electric energy meter to be evaluated so as to obtain a model prediction value of a basic error; the basic error prediction model comprises an input layer, a one-dimensional CNN layer, an LSTM layer fused with a time attention mechanism, a full connection layer and a basic error output layer, and the metering performance level of the intelligent electric energy meter is determined according to a field test value and a model prediction value of a basic error. The invention aims to realize metering performance evaluation of the intelligent electric energy meter in a typical environment so as to realize higher operation efficiency and solve the bottleneck problem of long-term dependence on modeling and local feature extraction.

The invention discloses a carbon emission accounting system and method based on the whole process of a power system. A data acquisition module is used for historical activity data of various energy main bodies and desulfurizer consumption, historical operation data of a power system grid topological structure and a power system, power consumption data, load data and SF6 equipment parameters; the source side carbon emission conversion module is used for obtaining the total carbon emission of the source side; the electric power carbon emission flow calculation module is used for obtaining direct carbon emission and indirect carbon emission of a network side; and the load side power carbon emission calculation module is used for calculating the carbon emission of the load side according to the load side node carbon potential and the power consumption data. According to the method, the whole processes of the source side, the network side and the load side of the power system are comprehensively considered, the node carbon potential and the load side node carbon emission factor are dynamically calculated through the power system carbon emission flow method, and the problem that a traditional carbon emission accounting method cannot cover the whole process of source-network-load and cannot adapt to the dynamic power system is solved.

The invention relates to the technical field of carbon footprint management, in particular to a carbon footprint accounting method considering a whole life cycle, which comprises the following steps: analyzing life cycle path assessment influence and compressing to generate a path serial number sequence, reconstructing, assessing and transmitting identification abnormity according to serial numbers and reconstructing, identifying turning matching factors to obtain an adaptive set, and calculating a carbon footprint accounting result according to the adaptive set. And positioning heat loss inversion critical contrast monitoring to obtain accounting verification information, and collecting, layering and summarizing the carbon amount according to verification conversion. According to the method, through comprehensive utilization of flow path information, measurement of emission intensity and activeness and path screening are achieved, analysis of node carbon transfer characteristics is achieved, the accuracy of carbon flow judgment is improved, matching of operation sections and factor conditions is combined, identification and truncation of heat loss characteristics in an energy link are adopted, the reasonability of an inversion range is enhanced, and the accuracy of carbon flow judgment is improved. Through comparison of an emission monitoring result and energy input, verification of accounting data is enhanced, cross-level conversion and collection are combined, and consistency and integrity of carbon emission mapping are guaranteed.

A distributed manufacturing network information technology system includes a cloud-based additive manufacturing management platform with a user interface, connectivity facilities, data storage facilities, and monitoring facilities. The distributed manufacturing network information technology system includes a set of applications for enabling the additive manufacturing management platform to manage a set of distributed manufacturing network entities. The distributed manufacturing network information technology system includes an artificial intelligence system configured to learn on a training set of outcomes, parameters, and data collected from the distributed manufacturing network entities to optimize manufacturing and value chain workflows.

The invention relates to an energy storage converter performance evaluation method and device, equipment, a medium and a program product. The method comprises the following steps: acquiring performance index parameters of the energy storage converter under a plurality of evaluation dimensions; the evaluation dimensions at least comprise electrical performance, thermal performance, mechanical performance, environmental adaptability and control performance; performing correlation analysis processing on correlation degrees among the evaluation dimensions based on the performance index parameters to generate a correlation matrix; vectors in the incidence matrix are used for representing the correlation degree between every two evaluation dimensions; and performing statistical analysis and quantitative evaluation processing on the incidence matrix to obtain a performance evaluation result for the energy storage converter. By adopting the method, the accuracy of energy storage converter performance evaluation can be improved.

Digitally distributing media content using a distribution backbone system, including: receiving a request for media content from a client, the request including a client profile; performing inventory and analysis of source assets by iteratively progressing through the client profile to create output; performing a capability mapping in which a series of rules that allow the source assets to be mapped to the client profile; and planning a manufacturing process, which determines work items and execution steps from capabilities mapped in response to the request for media content from the client.

The invention provides a carbon emission accounting method, system, equipment and medium in a port and aviation container transportation process, and belongs to the technical field of logistics carbon management and intelligent algorithm crossing. The method comprises the following steps: collecting transportation and energy consumption data of roads, waterways, railways and logistics storage links in a classified manner, and preprocessing the transportation and energy consumption data to form a standardized data set; a carbon emission factor parameter library is created in combination with industrial standards, different transportation modes and cargo types, and dynamic updating is performed through a machine learning algorithm. And special accounting sub-models are constructed for different transportation modes, and carbon emission accounting is carried out by using corresponding formulas and parameters. And inputting the standardized data into the nuclear operator model, calculating the carbon emission and screening an optimal path. And model parameters are calibrated by using historical data to ensure accuracy. And finally, collecting data in real time, inputting the data into the accounting sub-model, generating a decision support report comprising implementation steps, resource demands and expected effects, and assisting in realizing low-carbon and efficient management of the transportation process.

The invention discloses an energy efficiency evaluation optimization system and method based on data analysis, and relates to the technical field of data analysis, and the method comprises the following steps: obtaining various types of equipment information and energy consumption and resource data contained in a catering service place; determining an energy efficiency evaluation quantitative index and a differentiation condition, comparing the equipment resource request with actual allocation, and calculating an evaluation total satisfaction degree; calculating the load balance degree of each type of equipment contained in the catering service place; acquiring request data of resources required for future operation of various devices included in the catering service place; and setting an optimization target for the total satisfaction degree and the total load balancing degree of each type of equipment included in the catering service place, and defining constraint conditions of resource allocation and equipment load balancing, thereby realizing energy efficiency evaluation optimization.