40 results about "Operational performance" patented technology

The invention relates to the technical field of digital twinning, and particularly discloses a historical building structure evaluation system based on digital twinning, which comprises a multi-source data acquisition module, a dynamic digital twinning modeling module, a structure health evaluation module, an intelligent maintenance decision module and a collaborative optimization analysis module. According to the scheme, geometric, material and environment information of the historical building is captured, omnibearing monitoring of the historical building is realized through a digital twin modeling technology, an intelligent evaluation model is constructed in combination with finite element analysis and an SVM algorithm, a high-risk area is rapidly identified, and authenticity and accuracy of an evaluation result are ensured; a reinforcement learning algorithm is used to adapt to complex and changeable environmental requirements of historical buildings, a maintenance strategy is automatically generated, cooperation efficiency between modules is continuously optimized through collaborative optimization analysis, the overall operation performance is improved, evaluation precision is guaranteed, and meanwhile system resource consumption is effectively controlled.

The invention relates to the technical field of performance evaluation, in particular to an MTO device operation performance evaluation method and system, and the method comprises the following steps: calling a sensor to collect particle density and flow data, recognizing structure abnormity through combination of pressure drop, obtaining a flow deviation rate, collecting energy consumption and yield to form an energy consumption response value, and calculating a hot pressure drift value through combination of temperature and pressure data; and analyzing the relationship between the pressure difference and the wind speed to obtain a feeding offset, summarizing offset parameters to evaluate the efficiency stability, and obtaining an operation efficiency analysis result. According to the method, early judgment of structural abnormity is achieved through combined recognition of the particle density and the flow change trend, response indexes are constructed through the energy consumption and yield difference value, the dynamic property of energy efficiency evaluation is enhanced, temperature and pressure migration path analysis is combined, the identification capacity of working condition fluctuation is improved, control signals and pressure difference response comparison are adopted, and the energy efficiency evaluation accuracy is improved. The matching performance of feeding regulation and control is optimized, multi-dimensional abnormal data are collected and evaluated, and the space stability quantification of the operation state is realized.

Systems and methods that support the creation and timely electronic scheduling and delivery of course materials for training of individuals in an organization, in which course recommendations, scheduling, and rollout are based upon a number of factors including, for example, specific individual and / or business day-to-day operational performance measures, sales performance, and seasonal weather conditions by geographical region.

This invention belongs to the field of elevator technology, specifically relating to a method, equipment, and medium for elevator fault injection based on multiphysics operating mechanisms. It establishes digital models of the main elevator platform system and the isolation door system and defines interfaces. The method includes configuring a data acquisition and control module; initializing the multiphysics parameter set; determining the fault injection point; sending a fault excitation signal to drive co-simulation; collecting state response data; evaluating the fault impact; and generating a report. This method, based on multiphysics coupling mechanisms, achieves accurate simulation of the fault evolution process, quantifies the impact of faults on elevator performance, and accurately locates fault propagation paths. Accurate modeling based on multiphysics mechanisms covers fault injection in core components, and co-simulation realistically reflects fault evolution. It quantitatively assesses the impact of faults on operational performance, clarifies propagation paths, and provides data support for elevator fault diagnosis, preventative maintenance, and control system optimization, thereby improving operational safety and reliability.

This application relates to the field of data analysis technology, and discloses an operational analysis method, device, and electronic device applicable to different operational activities. Based on a lifecycle model, different operational activities are promoted using corresponding promotion methods, and feedback information under each promotion method is obtained. According to predefined raw indicator data, the raw indicator data corresponding to each operational activity is extracted from the feedback information. According to predefined operational performance indicator data, the raw indicator data of each operational activity is parsed to obtain the corresponding operational performance indicator data. In response to an operational performance viewing command, the operational performance indicator data of the operational activity is retrieved from the database and displayed in a visual form on a display interface. This solves the problem of the lack of unified and systematic operational data analysis in existing operational activities.

A method includes generating a trading performance model for a trading activity involving a set of decisions by a set of expert traders. The trading performance model includes a set of input data sets, a set of data processing workflows operating on the input data sets, and a set of trading decision outputs resulting from interaction of the expert traders with a user interface representing the trading performance model. The method includes generating a brain state model representing a sequential set of brain states of the set of expert traders that characterize brain states measured during the interactions of the expert traders with the user interface representing the trading performance model, assessing the quality of the trading decisions, determining a preferred pattern of trader brain state sequences, and modifying a subsequent trading activity.

An AI-driven blockchain protocol provides systems / methods for managing tokenized assets via autonomous operational Risk-Aware Agents (RAAs). Distinct from prior art automating simple events, the protocol actively coordinates, incentivizes, and manages the RAAs' on-chain lifecycle and operational / financial performance metrics using an Agent Coordination Contract / Module. Verifiable on-chain incentives link RAA operational performance, tracked by said metrics, to risk / return outcomes, thereby underwriting operational execution. An AI engine analyzes aggregated data including RAA metrics, recommending adjustments to RAA incentives and / or metric targets managed by the protocol. RAAs execute operations driven by these incentives, using a protocol-native stablecoin for reward / penalty settlement. A continuous feedback loop enables optimization. This verifiable autonomous operational management provides a foundation for stablecoin-based leverage against the assets, addressing illiquidity. The system comprises interconnected contracts, processor, engine, and RAAs under protocol governance.

The invention discloses a double-layer ball heat preservation ball valve which comprises an active pressure relief intelligent control algorithm module, a double-layer ball heat preservation optimization algorithm module, an exhaust mechanism control algorithm module and a temperature field prediction algorithm module. A multi-objective collaborative optimization algorithm module; and the system performance evaluation algorithm module is used for realizing comprehensive evaluation, including pressure control performance evaluation, thermal insulation performance evaluation and operation performance evaluation, of the running state of the system through a multi-dimensional performance function, as well as a fault detection function based on residual analysis, and feeding back system performance indexes to the multi-target collaborative optimization algorithm module. The system has the following beneficial effects that through cooperative work of multiple algorithm modules, the problems of pressure accumulation, heat loss, operation safety and the like in the high-temperature medium conveying process are solved, and meanwhile the real-time performance, the accuracy, the safety and the energy efficiency performance of the system are considered.

This invention discloses a method for fault prediction and resource pre-allocation optimization in disaster recovery systems, relating to the field of disaster recovery systems. The method includes: real-time collection of operational performance data, historical fault sequences, and related business pressure indicators of primary and backup nodes in the disaster recovery system; simultaneously, collection of operation response logs and operation completion quality data from relevant operations and maintenance teams during historical disaster recovery switchover processes. By introducing an advanced prediction and flexible pre-allocation mechanism at the nascent stage of performance degradation, this invention can initiate precise resource preparation earlier than traditional fault thresholds, effectively extending the system intervention window and alleviating the pressure of insufficient switchover preparation time. Through a resource pre-collateralization mechanism, it dynamically allocates idle resources, significantly improving the overall resource pool utilization efficiency while ensuring the reliability of high-priority services, thus resolving the contradiction between resource idleness and temporary shortages.

Methods and systems for driving and evaluating operational performance for an organization, and an accompanying auditing system for creating audits, presenting the audits to auditor users, capturing data via a user interface, compiling results from captured data, and presenting the results, is disclosed.

This invention provides an elevator operation monitoring device and an elevator maintenance support method that can more reliably ensure the appropriate timing of maintenance and inspection work for elevators. [Solution] The operation information acquired by the operation monitoring device 61 from the elevator 2 includes cumulative values ​​accumulated during normal operation for one or more monitoring items related to the operation of the car 21 as operational performance values. When the operational performance value for at least one of the monitoring items exceeds a set threshold, the operation monitoring device 61 identifies the elevator 2 in which the operational performance value exceeds the set threshold as an elevator recommended for maintenance. When the operation monitoring device 61 identifies the elevator 2 as an elevator recommended for maintenance, it produces maintenance support information regarding the implementation of maintenance and inspection work for the elevator recommended for maintenance. The notification unit 62 notifies the maintenance support information in the control room 5 of the building 3 where the elevator 2 is installed.

This invention provides a control method for a thermal storage enclosure structure and the thermal storage enclosure structure itself. The control method first obtains initial control weight curves for multiple control objectives of the thermal storage enclosure structure. Then, according to a preset adjustment step size, the initial control weight curves are gradually corrected based on the operational performance of the thermal storage enclosure structure, resulting in corrected weight curves for the multiple control objectives. Next, based on the real-time operating state parameters of the thermal storage enclosure structure and a predictive model, the operational optimization function of the thermal storage enclosure structure is solved to obtain the control quantity of the thermal storage enclosure structure. Finally, the thermal storage enclosure structure is controlled to execute control actions corresponding to the control quantity. This control method for the thermal storage enclosure structure, through multi-objective weight adaptive correction and model predictive optimization, can closely meet the actual user needs, accurately and stably maintain indoor comfort, minimize room temperature fluctuations, provide more timely responses, and consistently remain within the comfortable range.

This invention discloses a method and system for generating and optimizing warehouse layouts in an automated warehousing system. The method includes: discretizing the warehouse space into a two-dimensional mesh diagram and defining a type for each mesh cell; generating an initial layout based on preset parameters, and storing it in an elite solution library based on a multi-dimensional feature space using the MAP-Elites algorithm after initial repair; selecting layouts from the elite solution library for mutation operations to generate candidate layouts under MAP-Elites driving; using a MILP model to perform constraint repair on the candidate layouts with the goal of minimizing the amount of modification; importing the repaired layouts into the MAPF simulation environment for performance evaluation, and updating the elite solution library based on the evaluation results. This invention constructs a complete closed loop for automated layout generation and optimization, capable of outputting multiple layout schemes with high throughput performance while meeting actual engineering constraints, significantly improving the design efficiency, operational performance, and scenario adaptability of the warehousing system.

A system, method, and computer program product for implementing deep learning software model modification is provided. The method includes monitoring operational performance of a software model. An expected confidence level associated with the operational performance is first determined and it is determined that an inference associated with the expected confidence level is below a selected range of inferences associated with assigning new feature data as candidate video data. A candidate sequence comprising video data associated with the candidate video data is received and a similarity between frames of the candidate sequence is determined. A frame comprising a highest similarity with respect to segments of candidate video data is selected and it is detected that the frame is not associated with additional frames stored within a full cache structure. The software model is retrained such that the operational performance is modified.

A method, system, device, and medium for monitoring power transmission and distribution equipment are disclosed. The method includes: acquiring operational data, historical fault data, real-time environmental data, electrical topology data of the power transmission and distribution network, and environmental prediction data of the area where the power transmission and distribution network is located for each equipment node in the power transmission and distribution network; constructing a set of operational performance indicators for the equipment nodes based on the operational data and real-time environmental data; calculating the fault probability of each equipment node based on the set of operational performance indicators and historical fault data; determining the fault impact level of each equipment node based on the electrical topology data; calculating a comprehensive risk index for each equipment node, and identifying equipment nodes in the power transmission and distribution network whose comprehensive risk index is greater than a preset risk threshold as target equipment nodes; and performing spatiotemporal extrapolation of the risk evolution process of the target equipment nodes based on environmental prediction data to generate a risk evolution extrapolation diagram. This application can improve the monitoring reliability of power transmission and distribution equipment.

The invention relates to a system and method for predicting manufacturing process risk. The present disclosure provides systems, methods, and computer program products for predicting and detecting anomalies in subsystems of a system. An example method may include (a) determining a first plurality of tags indicating operational performance of a subsystem. The tag may be obtained from (i) a plurality of sensors in the subsystem and (ii) a plurality of sensors in the system that are not located in the subsystem. The method may also include (b) processing the measurements of the first plurality of tags using an auto-encoder trained according to historical values of the first plurality of tags to generate estimates of the first plurality of tags; (c) determining whether a difference between the measured value and the estimated value satisfies a threshold; and (d) if the difference satisfies a threshold, transmitting an alert indicating that the subsystem is expected to experience an anomaly.

This invention relates to the field of hydraulic engineering technology and provides a long-term perception method for the operational performance of dams based on deformation-stress mapping analysis. The method includes: acquiring target environmental data of the dam in real-time monitoring; inputting the target environmental data into a deep learning model to obtain the three-dimensional stress field of the dam output by the deep learning model; the deep learning model serves as a student model, trained through knowledge transfer from a teacher model based on target environmental data samples and their corresponding three-dimensional stress field labels; the teacher model includes a first mapping model, a second mapping model, a third mapping model, and a fourth mapping model. This invention trains the deep learning model (serving as a student model) using a teacher model comprising four mapping models, enabling the deep learning model to not only accurately characterize the relationship between deformation and stress but also capture the nonlinear correlation between target environmental data, deformation, and the three-dimensional stress field, achieving online and accurate long-term perception of dam stress.

Disclosed in the embodiments of the present disclosure are a communication method and apparatus. The method may comprise: a first device sending a first command, wherein the first command is used for instructing a second device to enter a target state and / or indicating a first duration, which first duration may be a duration in the target state, and both the first device and the second device are devices in an Internet of Things (IoT) scenario. By means of implementing the embodiments of the present disclosure, the problem of how to instruct an IoT device to enter a specific power state in an IoT scenario can be solved, such that the power consumption of the device can be significantly reduced, the service life of the device can be prolonged, the availability of the device can be maintained, and the operational performance of the device can be improved.

The present disclosure provides system, methods, and computer program products for predicting and detecting anomalies in a subsystem of a system. An example method may comprise (a) determining a first plurality of tags that are indicative of an operational performance of the subsystem. The tags can be obtained from (i) a plurality of sensors in the subsystem and (ii) a plurality of sensors in the system that are not in the subsystem. The method may further comprise (b) processing measured values of the first plurality of tags using an autoencoder trained on historical values of the first plurality of tags to generate estimated values of the first plurality of tags; (c) determining whether a difference between the measured values and estimated values meets a threshold; and (d) transmitting an alert that indicates that the subsystem is predicted to experience an anomaly if the difference meets the threshold.

The present invention proposes a scene reproduction and augmented reality simulation training method for floating installation, which includes: real-time collection of external environment and equipment status; collection of trainees' operational behavior data during training, matching the operational behavior data with dynamic simulation scenarios by time stamp, building a synchronization model, and calculating key performance indicators based on the synchronized data to quantify the trainees' operational effects, thereby obtaining operational performance records; when a trainee's failed operation is detected, the trainee's failure scenario is reconstructed based on the currently collected operational behavior data and dynamic simulation scenarios, and the failure path is analyzed using a reinforcement learning model; an optimized floating trajectory and attitude adjustment path are generated based on the reconstructed trainee's failure scenario and optimization suggestions; the trainee completes the floating installation operation according to the optimized path displayed by the augmented reality device. The present invention can significantly improve the efficiency and effectiveness of floating installation training, providing a more efficient and intelligent solution for the field of marine engineering.

A method and system generate building operational performance analysis output. A synthetic dataset is generated and includes a set of 3D building conceptualized block geometry. The generating includes identifying geometry types, partitioning the geometry types into classes, and algorithmically generating the block geometry using a generative design using a separate workflow for each class. An analysis model is generated associated with each of the block geometry. Simulation results are generated for each of the analysis models. A surrogate model is trained using machine learning (ML) based on a set of features extracted from the simulation results. The ML iteratively determines the set of features based on a measured accuracy of the surrogate model. A geometry input is received and processed by the surrogate model to generate the building operational performance analysis output, which is then used to inform a designer of an approximate energy use intensity of the geometry input.

A method and system generate a building operational performance analysis output. A synthetic data set is generated and includes a set of 3D building conceptual mass geometries. The generating includes identifying geometry types, dividing the geometry types into categories, and algorithmically generating the mass geometries using a separate workflow for each category using generative design. Analytical models associated with each of the mass geometries are generated. Simulation results are generated for each of the analytical models. A surrogate model is trained based on a set of features extracted from the simulation results using machine learning (ML). The ML iteratively determines the set of features based on a measured accuracy of the surrogate model. Geometry input is received and processed through the surrogate model to generate the building operational performance analysis output which is then used to inform a designer of an approximate Energy Use Intensity of the geometry input.

This application provides a joint planning method for power systems, including establishing a shared energy storage joint planning model based on the power system's topology, and using a two-level optimization method to solve the shared energy storage joint planning model to obtain the energy storage system's operation plan. The optimization objective is to optimize the overall economic performance of the power system at the upper level, and the operational performance of the energy storage system in a typical scenario is used as the lower level optimization objective. The shared energy storage joint planning model uses a fuzzy chance-constrained programming method to describe the source-side model of wind turbine output, the load-side model describing the impact of price incentives on user demand load response, and the storage-side model considering the lifespan of the energy storage power station. This application considers the randomness and uncertainty of the power system's source-side and load-side operation, thereby achieving power system dispatch balance, improving energy utilization efficiency, and reducing overall system cost.

This invention relates to the field of hearing aids, specifically to a method and system for testing the operational performance of hearing aids. The system includes a processing module, a receiver module, a timed switching module, and a comparison module. The processing module comprises a processor module, a button module, a power supply module, a playback module, and a transmission module. By setting a timed self-test, the system collects sound from the inside of the device normally used for auxiliary receiver switching during the self-test. Then, the auxiliary receiver module collects the sound played by the playback module and compares it briefly with normal sounds in a normal sound database. If the playback is confirmed to be problem-free, it proves that neither the playback module nor the receiver module has a performance degradation issue. If the short comparison confirms a playback problem, a long-term comparison is performed, comparing the sound data played by the playback module with the sound data collected by the receiver module to determine whether either the playback module or the receiver module has a problem, or which module is malfunctioning.

The invention relates to the technical field of network operation and maintenance, in particular to a service fault processing method and device and related equipment. The method comprises the following steps: collecting business log data of each key business service of a system and operation performance data of each system middleware; detecting the business log data and the operation performance data based on a preset anomaly detection module to judge whether abnormal data for representing a service fault exists or not; when the abnormal data is judged to exist, determining a target fault type of a target service fault corresponding to the abnormal data, and determining a target fault processing strategy corresponding to the target fault type based on a preset decision model; and executing the target fault processing strategy.

The invention is suitable for the technical field of nuclear power human factor engineering, and provides an operation performance evaluation method, computer equipment and a computer program product, and the method comprises the steps: obtaining multi-dimensional data of a target operator in a process of achieving a task target in a target scene; generating an experimental sample set, and training the basic model based on the experimental sample set to obtain a target evaluation model; and inputting the multi-dimensional data into the target evaluation model to obtain a personnel performance score, wherein the personnel performance score represents the operation performance of the target operator for realizing the task target in the target scene. According to the method and the device, the operation performance of the task target under the target scene realized by the target operator in the digital main control room can be efficiently and accurately evaluated in real time.

This invention discloses an enterprise multi-business interconnection system for data processing, relating to the field of enterprise business process optimization technology. It includes a value stream definition module, a process analysis module, a requirement backpropagation module, a business interconnection module, a value stream processing module, and a value verification module. Based on enterprise business objectives, this invention determines the end-to-end business value stream and decomposes processes to identify business nodes, states, and collaborative relationships. Then, based on optimization objectives, it deduces the minimum necessary data interconnection scope. Subsequently, it implements data interaction between heterogeneous businesses according to this scope and integrates and processes multi-source data to construct a business interaction view, generating operational performance indicators. Finally, the value verification module quantifies and displays the optimization effect. This invention achieves value-driven interconnection and optimization, effectively improving the targeting and measurability of cross-system business operations.

This invention discloses a method and system for evaluating the operational performance of grid-connected wind farms based on correlation analysis. The method includes: collecting data sources for grid-connected performance evaluation indicators to form historical sampling data; preprocessing the data to obtain an experimental dataset; performing correlation analysis on the experimental dataset, selecting characteristic columns with strong correlations to form a new experimental dataset, and constructing a wind farm operational performance evaluation model; mining the correlation between preset grid-connected wind farm operational performance evaluation indicators and various influencing factors in the new experimental dataset, and converting the correlation into quantitative evaluation indicators; substituting actual wind farm operational data into the model to calculate the values ​​of various performance indicators of the wind farm, and combining the quantitative evaluation indicators to evaluate the performance of each performance indicator of the wind farm, thereby obtaining the operational performance evaluation results of the grid-connected wind farm. This invention overcomes the problem of limited indicator coverage in existing evaluation methods, enabling a more comprehensive and in-depth establishment of an operational performance evaluation system for wind turbine units.

This application discloses a method for predicting and analyzing the operational performance of metering equipment based on locally weighted partial least squares (LSS). The method includes: acquiring and preprocessing the historical operational dataset of the metering equipment to be predicted, dividing the dataset into training and testing sets; using the K-Means clustering algorithm to cluster the training set into several sub-training sets and calculating the centroid of each sub-training set; evaluating the prediction accuracy and performance of the model; acquiring operational data, combining the operational data with each sub-training set, using the locally weighted partial least squares modeling algorithm to build a sub-model for each sub-training set, and obtaining the predicted acquisition success rate value corresponding to the operational data under each sub-model; and calculating the final predicted acquisition success rate value corresponding to the operational data point by weighting the sub-model prediction values ​​based on the distance from the operational data point to the centroid of each sub-training set. This invention has high prediction accuracy and can be effectively applied to the prediction and analysis of the operational performance of metering equipment under climatic factors.

The present disclosure provides systems, methods, and computer program products for predicting and detecting anomalies in a subsystem of a system. An example method can include (a) determining a first plurality of tags indicative of operational performance of the subsystem. The tags can be obtained from (i) a plurality of sensors in the subsystem and (ii) a plurality of sensors in the system not located in the subsystem. The method can further include (b) processing measured values of the first plurality of tags using an autoencoder trained on historical values of the first plurality of tags to generate estimated values of the first plurality of tags; (c) determining whether a difference between the measured values and the estimated values satisfies a threshold; and (d) transmitting an alert indicating that the subsystem is predicted to experience an anomaly if the difference satisfies the threshold.