1543results about "Technology management" patented technology

The present application relates to systems and methods for creating Non-Fungible Digital Twins (NFDT) of objects. The objects may be digital objects or real work assets which change over time, or have properties (e.g. carbon emissions) that change over time. An initial or birth NFT smart contract is created and published which is a digital twin of the object, and as the object changes, the changes are published to subsequent NFT smart contracts with a link to the previous smart contract or the birth smart contract such that the birth smart contract and plurality of subsequent object contracts form a time series of object smart contracts which define a dynamic unique Non-Fungible Digital Twin (NFDT) representation of the object.

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.

The invention provides an electric energy meter state evaluation method based on a space-time attention mechanism. The method comprises the following steps: acquiring and preprocessing electric energy meter data; executing self-optimization variational mode decomposition to realize automatic extraction of multi-scale time domain features; constructing a power grid space topological relation; a dynamic attention modulation mechanism perceived by load characteristics is designed, and mode migration enhancement is achieved; constructing a hierarchical time domain characteristic spectrum to realize characteristic adaptive fusion; and generating an electric energy meter state evaluation result. Through an incremental decomposition strategy, a power grid topology association network and frequency domain attention modulation, the method achieves the precise evaluation of the states of the electric energy meters of different load types, remarkably improves the anomaly detection accuracy, and is especially suitable for the early recognition of a composite fault.

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.

Systems and methods for balancing remote oil and gas equipment are disclosed. An example system may include analog sensors coupled to a piece of equipment and an analog switch with a plurality of analog sensor channels, wherein a first analog sensor channel comprises a trigger channel coupled to a first of the analog sensors, and wherein a second one of the analog sensor channels comprises an input channel coupled to a second sensors. The analog switch may digitally derive a relative phase between the trigger channel and the input channel, utilize a PLL band-pass tracking filter to determine at least one of slow-speed RPMs or phase information for the piece of equipment, and a response circuit that provides a process change command to remotely balance at least one component of the piece of equipment based on the RPMs or the phase information.

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 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.

The invention provides an electric energy metering detection data quality evaluation method and system, and relates to the field of data processing. Data abnormity generated by a current transformer aiming at gradual change characteristics caused by contact oxidation is firstly eliminated through improved wavelet transform and feature extraction; the abnormal rate of the processed data is calculated according to a support vector machine model, the conditions of noise interference and abnormal fluctuation in actual current data are considered when a support vector machine is designed, and the contact resistance is slowly increased due to contact oxidation of the secondary winding of the current transformer; therefore, the problems that the gradual change abnormity is difficult to find in time and the electric charge metering deviation is easily caused by the existing fixed threshold value evaluation method are solved.

Changing environmental characteristics of an enclosure are controlled to promote thermal comfort, health, wellness, and / or performance for occupant(s) of the enclosure using sensor data, three-dimensional modeling, physical properties of the enclosure, and machine learning (e.g., Artificial Intelligence).

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.

Systems for self-organizing data collection and storage in a refining environment are disclosed. An example system may include a swarm of mobile data collectors structured to interpret a plurality of sensor inputs from sensors in the refining environment, 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 a plurality of refining system components disposed in the refining environment, and wherein the plurality of refining system components is structured to contribute, in part, to refining of a product. The self-organizing system organizes a swarm of mobile data collectors to collect data from the system components, and at least one of a storage operation of the data, a data collection operation of the sensors, or a selection operation of the plurality of sensor inputs.

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.

The invention discloses an AI large model driven full-process intelligent industrial equipment system and an application method thereof, which are characterized by being applied to industrial equipment. Comprising an integrated AI large model, a data acquisition and integration module, a real-time monitoring and analysis module, an automatic control module, an intelligent maintenance and predictive maintenance module, a supply chain optimization module and a user interaction module. The integrated AI large model performs self-learning and iteration according to new data and user feedback information acquired from the industrial equipment through the data acquisition and integration module, the real-time monitoring and analysis module, the automatic control module, the intelligent maintenance and predictive maintenance module and the supply chain optimization module; according to the integrated AI large model, the operation accuracy and efficiency of the integrated AI large model are improved, comprehensive intellectualization of the production process is achieved, the production efficiency and the product quality are improved, and the operation cost is reduced.

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.

An industrial information hub (IDH) serves as a single industrial ecosystem platform where multiple participants can deliver repeatable and standardized services relevant to their core competencies. The IIH system is centered around the development of an ecosystem that creates and delivers value to users—including industrial enterprises, OEMs, system integrators, vendors, etc.—through the aggregation of digital content and domain expertise. The IIH system serves as a trusted information broker between the ecosystem and the OT environments of plant facilities, and provides a platform for connecting assets, contextualizing asset data and providing secure access to the ecosystem. Additionally, the IIH system provides tools and support to OEMs and other subject matter experts, allowing those experts to enable their digital assets for use in the ecosystem. The IIH system reduces the cost and risks for digital transformation of industrial assets.

The invention discloses a digital operation management system and method, and relates to the technical field of digital operation management, and the system comprises an operation monitoring module which is used for carrying out the communication with a distributed industrial Internet of Things node through a network interface, collecting production environment parameters and process data, and forming equipment baseline parameters; the knowledge graph engine module is used for constructing an equipment relation network and process flow mapping based on the equipment baseline parameters; the predictive maintenance module is used for carrying out equipment life evaluation and fault early warning according to the relational data of the knowledge graph engine module; the decision support module is used for generating a resource scheduling scheme based on equipment life evaluation and fault early warning in combination with a historical decision effect, and transmitting the resource scheduling scheme to the digital twinning display module; and the digital twinning display module is used for displaying the real-time operation state of the factory by utilizing a three-dimensional visualization technology. The data processing efficiency, the resource utilization rate and the flexibility in the production process are improved, and global monitoring and intelligent optimization of the production process are achieved.

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.

The invention discloses a method and a device for evaluating the measurement uncertainty of an automatic verification line of an electric energy meter, and the method comprises the steps: analyzing each uncertainty source according to the structure of the to-be-evaluated automatic verification line of the electric energy meter and the environment condition of the to-be-evaluated automatic verification line of the electric energy meter, so as to determine each uncertainty component for evaluation; the same installation type standard electric energy meter is adopted to carry out a measurement experiment, data measured by the installation type standard electric energy meter is used as a reference value, and a relative error between the data measured by the installation type standard electric energy meter and data displayed by a working standard meter is used as a basic error of the automatic verification line of the electric energy meter. Calculating the value of each uncertainty component; taking the root mean square of the value of each uncertainty component as the synthesis standard uncertainty of the electric energy meter automatic verification line; and multiplying the synthesis standard uncertainty by a set confidence factor to obtain an evaluation result of the expansion uncertainty of the automatic verification line of the electric energy meter. According to the method, the measurement uncertainty can be evaluated more comprehensively and accurately.

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.