41 results about "Process analytics" patented technology

This invention discloses a multi-channel automated parking system and its application method for rapid storage and retrieval. The multi-channel automated parking system is housed within a dense, integrated automated racking system. Multiple shuttle cars are placed on staggered, non-interfering single-layer longitudinal aisles, along with multiple matching independent vertical shafts. This allows for simultaneous longitudinal and vertical transport of multiple layers and goods. Multiple entrances / exits and transverse aisles can be configured as needed, housing multiple continuous transition conveyors. Combined with corresponding warehouse management software and various sensors, the system performs tasks such as scanning and identifying goods upon entry and exit, data collection, goods location determination, handling process analysis, conveyor path optimization, and time allocation. It also provides electronic control for information and instruction transmission and reception for the various handling equipment, achieving multi-layered relay-style collaborative operations and thus realizing intelligent and rapid storage and retrieval of goods throughout the warehouse.

A method, apparatus, device, storage medium, and program product for information presentation are provided. The proposed method includes: receiving a generation request, the generation request instructing an intelligent system to generate at least one visualization component corresponding to tabular data; and presenting analytical information, the analytical information instructing the intelligent system to create at least one visualization component in a component page, the analytical information including first information of the at least one visualization component, the first information indicating the layout attributes of the at least one visualization component in the component page.

The invention provides an XCP calibration process data processing method, which comprises the following steps: S1, configuring a measurement variable and a calibration variable according to an A2L file, and configuring a sampling rate; s2, sampling a DAQ according to XCP protocol configuration, establishing connection between the DAQ and an ECU, and collecting real-time data of a measurement variable and a calibration variable; s3, initializing an MF4 file, and establishing a write-in index in the MF4 file according to the measurement variable and the calibration variable; s4, collecting real-time data in real time, judging whether the real-time data is changed or not, if not, making no response, and if not, writing the real-time data into the MF4 file according to the write-in index; and S5, after writing of the real-time data is completed, the MF4 file is saved and closed. The problem that in the XCP calibration process, based on an MF4 file, only measurement data can be recorded, and whole-process analysis cannot be conducted on the XCP calibration process is solved.

The invention discloses a coal washing process analysis and decision-making system and method under energy consumption constraint, and belongs to the technical field of energy consumption management and control, and the method comprises the steps: obtaining, standardizing and enhancing the processing of multi-source time sequence energy consumption and processing amount data, carrying out the clustering analysis of the processed data, recognizing an energy consumption interval and a state partition, and carrying out the analysis of the state partition. Based on the identification result, the deep learning network is used to analyze the association among the energy consumption, the power consumption and the equipment load, and energy consumption state discrimination and future trend prediction are obtained; the method comprises the following steps: acquiring multi-dimensional production data, performing segmentation alignment on the multi-dimensional production data, establishing multivariable time sequence mapping from production parameters to process effects, and performing production process intelligent management and control under energy consumption constraint based on the multivariable time sequence mapping and a future energy consumption prediction result. According to the method, process level energy consumption fine regulation and control are realized, the extreme working condition robustness and operation safety are improved, and accurate and intelligent technical support is provided for coal washing process optimization and production decision making.

Embodiments of the present disclosure include techniques for processing analytic data using delta processing. In one embodiment, result data is generated from source data using a data pipeline and calculation plans. The result data may be modified and the plan may be re-executed to produce the modified result data. A reduced calculation plan is generated including only a portion of the original calculation plan to reduce the computational resources needed to execute the data processing. In some embodiments, data cached by operations in the pipeline are reused so the entire pipeline does not require re-execution to produce the modified result data.

The system according to the embodiment aims to ensure that the created work does not use AI. [Solution] A system according to an embodiment includes a monitoring unit, an analysis unit, and a certification unit. The monitoring unit monitors the creation process in real time. The analysis unit analyzes the data collected by the monitoring unit and checks whether AI is involved. The certification unit issues a certificate based on the verification results obtained by the analysis unit.

Automatic detection and handling of security-related anomalies by utilizing machine learning and a large language model (LLM). A computerized method for detecting and handling security threats in an organizational network of an organization includes: (a) collecting event data that pertain to organizational users, organizational devices, and organizational resources of the organizational network; (b) constructing user profiles, device profiles, and resource profiles; (c) constructing Organizational Context information that pertains to organizational users, organizational devices, and organizational resources; (d) constructing a time-series of events, enriched with the Organizational Context information; (e) analyzing the time-series of events using a Machine Learning process that detects an anomalous event, and automatically generating an alert message pertaining to and describing the anomalous event.

This information processing device comprises a calculation unit that performs: satisfaction determination processing for analyzing an image in which a performer is captured as a subject to determine whether a content distribution condition is satisfied; distribution destination determination processing for determining, when the satisfaction determination of the content distribution condition is made, a distribution destination of the content on the basis of an action of the performer from which the satisfaction determination was made; and content processing for selecting or generating the content to be distributed to the set distribution destination.

The invention discloses an enterprise data quality evaluation and growth ability analysis method and system based on experience reuse. The method comprises the following steps: calculating a data quality evaluation score of enterprise operation data through a normalization method; performing clustering optimization on the score data by using a K-means + + algorithm, and dividing the data into three quality grades of priority, suboptimal and general; selecting preferential and suboptimal data, combining with Horkes process analysis, utilizing a random forest model to predict relevance, filtering low-predicted-value data, and forming a new data set; and based on the new data set, generating a centesimal system enterprise growth ability score by adopting a data quality evaluation index and a weight in combination with a Sigmoid operator. The system comprises a data quality evaluation module, a data clustering and grading module, a data association prediction module and an enterprise growth ability scoring module. According to the method, whole-process optimization of data quality from static evaluation to dynamic grading and from association mining to growth analysis is realized, and the accuracy and reliability of enterprise growth capability evaluation are improved.

The present invention discloses a method for realizing multi-column continuous chromatography design and analysis based on a chromatography model, and a method for realizing multi-column continuous chromatography design and analysis based on an artificial neural network. The method based on the chromatography model includes the following steps: step 101, experimental breakthrough curve fitting: performing fitting using a chromatography model to obtain model parameters; step 102: breakthrough curve prediction: substituting the model parameters into the chromatography model to obtain a breakthrough curve under different operation conditions; step 103, process analysis of continuous chromatography: substituting the predicted breakthrough curve and the continuous chromatography operation parameters into a continuous chromatography model to obtain performance indexes such as process productivity and resin capacity utilization; and step 104, operation space optimization of continuous chromatography: obtaining the operation space of the continuous chromatography design parameters based on a specific separation target. The method based on the artificial neutral network completes the respective steps above by replacing the chromatography model with artificial neural network.

The invention discloses a multivariable time point process analysis method based on two-dimensional local differential privacy, and relates to the technical field of federated learning and privacy protection. The method comprises the following steps: setting a total privacy budget on a client side and distributing the budget as a time and type dimension budget; a delta t-SAHRP-LDP order-preserving perturbation mechanism is adopted in the time dimension, interval scale adaptive noise adding, minimum right lower bound constraint and dynamic anchor point resetting are combined, and a time sequence after perturbation is generated; and generating a disturbed type sequence in the type dimension by adopting a random response mechanism. A client uploads an intermediate representation and an alignment scale based on disturbance data, and a server establishes a cross-end alignment and neighborhood relationship only according to the intermediate representation and the alignment scale, generates extension and neighborhood context features and returns the extension and neighborhood context features. And the client performs prediction and training in combination with the local real representation and the cross-end context. According to the method, on the premise of not exposing an original sequence, the leakage risk of sensitive time rhythm and type distribution is effectively reduced, and meanwhile, the accuracy of event prediction is improved by utilizing cross-client collaborative information.

The invention relates to a method, a device and equipment for positioning and tracing simulation alarm data. The method comprises the following steps: generating a digital machine room model and abnormal sensing tracing monitoring data; carrying out Horkes process analysis on management node sensing abnormal data in the abnormal sensing traceability monitoring data so as to generate a node with the maximum Horkes probability value; performing real-time federated learning on the node with the maximum Horkes probability value and node data sensed by the first abnormal sensing source to generate an abnormal data optimal solution and a to-be-confirmed abnormal data suboptimal solution; the optimal solution of the abnormal data and the suboptimal solution of the abnormal data to be confirmed cooperatively utilize distributed data to train a model deployed on a multi-level device in a privacy protection mode so as to generate a trained model; and performing anomaly detection on data needing to be calculated by the edge intelligent node in the anomaly sensing traceability monitoring data, and performing intelligent industrial control grid segmentation according to an anomaly detection result to generate a positioning and traceability result of the simulation alarm data.

1. The name of the design product: charge and discharge data acquisition and analysis instrument. 2. The use of the design product: for monitoring, recording electric vehicle AC and DC charge and discharge data and process analysis and diagnosis. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: design 1 perspective view 1. 5. Design 1 is designated as the basic design.

Embodiments of the present disclosure include techniques for processing analytic data using delta processing. In one embodiment, result data is generated from source data using a data pipeline and calculation plans. The result data may be modified and the plan may be re-executed to produce the modified result data. A reduced calculation plan is generated including only a portion of the original calculation plan to reduce the computational resources needed to execute the data processing. In some embodiments, data cached by operations in the pipeline are reused so the entire pipeline does not require re-execution to produce the modified result data.

The invention discloses a die cutting process analysis method based on LS-DYNA software, and relates to the technical field of analogue simulation methods, and the die cutting process analysis method comprises the following steps: step 1, establishing a two-dimensional simplified model of a cutter and a cut material by using HyperMesh software; step 2, dividing grids, encrypting a processing area, and prefabricating a node strain failure criterion for a material area which may fail; 3, defining a material; 4, setting the unit type as a plane stress shell unit; 5, setting boundary conditions, and constraining the gasket; step 6, defining a contact relationship, and adopting automatic surface-to-surface contact between the material layers; and 7, setting control parameters, and outputting k file analysis. According to the invention, the test cost is reduced, the test model and the test environment are more convenient to build, digital simulation and analysis of die cutting processing are realized, and the analysis result is more effective.

The invention discloses a cascading failure early warning method for a power transmission system, which belongs to the technical field of power system early warning, and comprises the following steps: S1, early warning data preparation: multi-source data acquisition and analysis, and power grid heterogeneous characteristic quantitative characterization; s2, early warning fault screening: simulation model building and fault searching based on an accident chain are carried out; s3, early warning grade judgment: carrying out risk value quantitative calculation and risk grade division; and S4, early warning execution: early warning information output and real-time dynamic updating are carried out. According to the cascading failure early warning method for the power transmission system, when the method works, the heterogeneous phenomenon of a complex power grid containing new energy grid connection in the aspects of the operation condition and the grid structure is studied, and characterization indexes reflecting the operation condition of the power grid and the heterogeneous degree of the grid structure are constructed; meanwhile, a cascading failure process analysis model which comprehensively considers new energy and load fluctuation and is suitable for a set power grid working condition is established, the influence mechanism of power grid heterogeneity on cascading failures is researched, and the problem brought by new energy grid connection is well solved.

The invention relates to a data acquisition process analysis method and device, a vehicle, a medium and a program product. According to the method, process information of a vehicle executing data acquisition process can be acquired, the data acquisition process is used for acquiring target data, the data acquisition process comprises a plurality of preset processing links which are executed in sequence, and the process information comprises processing process information of each processing link; for each processing link, obtaining state analysis information of the operation state of the processing link according to the processing process information of the processing link; according to the state analysis information of each processing link, process analysis information of the data acquisition process is obtained, and the process analysis information represents whether the data acquisition process meets a preset index or not.

A method for predicting the remaining life (RUL) of rolling bearings across multiple stages based on deep transfer learning is proposed. First, raw vibration signal data of the rolling bearing is collected and segmented using a sliding window method to obtain multi-stage vibration signal data. Next, the change in the health index curve is obtained by calculating the maximum mean difference between the self-health data and the multi-stage vibration signal data. Then, the bearing degradation stage is determined by applying statistical process analysis to the health index curve. The raw vibration signal data is preprocessed, partitioned, and labeled before being fed into a residual network and an Adaptive-Transformer network for training. Finally, the test set is fed into the Adaptive-Transformer network for prediction, outputting the final predicted RUL of the rolling bearing. This method considers the RUL of the bearing at each stage, reducing the prediction loss RMSE by approximately 0.1 and improving the prediction accuracy by 4%–6%.

The invention relates to the field of teaching analysis, and discloses a teaching process analysis method and system based on big data, and the method comprises the steps: collecting multi-modal data through a sensor group disposed at a teaching site, and processing and extracting time sequence parameters directly related to a learning cognitive state: obtaining a heart rate variability sequence based on visual data; obtaining pen point pressure and writing speed based on the intelligent writing tool; performing data preprocessing, including timestamp alignment, denoising and standardization processing, on the physiological state parameters and the writing kinetic parameters to form a regular time sequence data set; inputting the aligned heart rate variability sequence and the writing kinetic parameters into a preset fusion model for calculation, and outputting a cognitive input index of the student; and carrying out correlation comparison on the time sequence of the cognitive input degree index and the timeline of the teaching behavior of the teacher, and analyzing the average cognitive input degree change of the student corresponding to different teaching contents and activities, so as to evaluate the teaching effect.

The application discloses a self-adaptive improvement method and device for reading ability evaluation, which comprises the following steps: terminating judgment is performed based on the ability preset condition of the IRT self-adaptive algorithm; a reading path, information marking behavior, draft reasoning process and final score of a tested person are recorded in real time to generate an ability and strategy report and determine a reading process analysis and evaluation result; based on a reading evaluation index, necessary parameters, optional parameters and unlocking logic are determined; meanwhile, a NLP model and voice emotion analysis are used to collect voice interaction data in the reading process of a user; a reading task library covering different school stages and different reading targets is built, and a matching operation guide is formulated; a standardization intervention script library corresponding to a fixed intervention prompt is constructed, and the level and timing of reading intervention are determined; and a front-end chart engine is used in combination with a back-end graph to generate a visual ability report for reading result interpretation, so that the reading level and literacy of a reader are improved.

The invention relates to the technical field of human resource management, in particular to an expert talent performance management service system, which comprises a data acquisition module; a screening module which is connected with the data acquisition module and is used for determining the project contribution degree and the dynamic adaptation degree of the first-level target and calculating a first-level target evaluation value based on the project contribution degree and the dynamic adaptation degree so as to screen a second-level target based on the first-level target evaluation value; a depth screening module which is connected with the screening module and is used for carrying out three-dimensional depth portrait analysis on the screened second-level target so as to determine a third-level target; a target management library module which is connected with the deep screening module and is used for screening a fourth-level target based on the deviation between the development track of the third-level target and the business development track and the change rate of the third-level target, and constructing a target management library based on the fourth-level target; according to the method, the business management efficiency is improved by improving the analysis accuracy of the target management library establishment process.

The invention relates to a network terminal BOB calibration test method, system and device and a medium. The method comprises the steps that historical production data and a real-time welding temperature curve are obtained, a stress prediction model is trained, and a stress compensation matrix and a welding quality evaluation result are output; generating a target wavelength, optical power and extinction ratio reference value through a dynamic compensation algorithm in combination with real-time environment data; calculating a probe target coordinate based on the design drawing and the infrared thermal image; controlling the probe mechanism to move to the coordinate, realizing self-adaptive contact through the liquid metal micro contact, and outputting a stable electric contact signal after the contact impedance is continuously lower than a threshold value; cooperative calibration is executed through a parallel wavelength, optical power and temperature control loop, and calibrated optical parameters are output. And the welding quality evaluation result and the calibrated optical parameters are input into a manufacturing execution system, and a process optimization instruction is generated through statistical process analysis. According to the method, the calibration precision and efficiency are improved, and meanwhile continuous optimization of the process parameters is achieved.

The application relates to a network terminal BOB calibration test method, system, device and medium, the method comprising: obtaining historical production data and real-time welding temperature curve, training stress prediction model, outputting stress compensation matrix and welding quality evaluation result; combining real-time environment data, generating target wavelength, optical power and extinction ratio reference value through a dynamic compensation algorithm; based on the design drawing and the infrared thermal image, calculating the target coordinates of the probe; controlling the probe mechanism to move to the coordinates, realizing adaptive contact through the liquid metal micro contact point, and outputting a stable electrical contact signal after the contact impedance continuously falls below the threshold value; executing cooperative calibration through parallel wavelength, optical power and temperature control loops, and outputting calibrated optical parameters; inputting the welding quality evaluation result and the calibrated optical parameters into a manufacturing execution system, and generating process optimization instructions through statistical process analysis. The application improves the calibration accuracy and efficiency, and realizes continuous optimization of process parameters.

The embodiment of the invention discloses a large model analysis process generation method and device, equipment, a medium and a program product. Comprising the following steps: acquiring original process logic description information and a target user cognitive portrait, and performing structured analysis on the original process logic description information to determine structured skeleton information; according to the cognitive portrait of the target user, performing multi-level cognitive suitability evaluation on the structured skeleton information, and determining a multi-level cognitive evaluation result; wherein the hierarchy of the multi-hierarchy cognitive assessment is determined according to the hierarchy of the teaching plan structure; performing different levels of optimization on the original process logic description information according to the multi-level cognitive evaluation result and a pre-constructed optimization prompt information set, and determining a target process analysis file; wherein the optimization prompt information set comprises optimization prompt information corresponding to each level. The readability of the large model analysis process given externally is improved while the accuracy of the large model analysis process displayed externally is ensured.

The invention discloses a power transmission line operation and maintenance method and system based on digital twinning. The method comprises the following steps: collecting structure data and operation data of a power transmission line; extracting data features, and constructing a digital twinborn model; performing operation state evaluation on the digital twin model of the power transmission line, and analyzing the icing condition of the power transmission line through an evaluation result; and formulating an anti-icing operation and maintenance strategy of the power transmission line according to the icing condition, and managing and controlling the operation of the power transmission line. According to the invention, by using the existing acquisition equipment, power transmission line information platform data and system, the acquired data are integrated to establish a perfect power transmission line operation state monitoring and comprehensive management system through a digital twinning mode; the whole-process analysis and early warning of icing formation, development and ice melting of the overhead transmission line and the intelligent generation of an anti-icing operation and maintenance strategy of the transmission line are realized, and the operation and maintenance before, during and after icing of the transmission line are guided.

This application relates to a bidirectional feedback dynamic collaborative optimization system and method based on a large language model. The method includes: acquiring raw instruction data from multiple data sources; performing positive feedback optimization on the raw instruction data to form positively optimized instruction data; matching and sampling the positively optimized instruction data to generate training data; training a large language model using the training data and monitoring the training process to generate process analysis data; acquiring the training results of the large language model and generating result analysis data based on the training results; generating negative feedback instruction data through the process analysis data and the result analysis data; and applying the negative feedback instruction data to the positive feedback optimization process to achieve dynamic collaborative optimization of training data quality and large language model performance. This application can construct a closed-loop system for the co-evolution of data and model, achieving simultaneous improvement in data quality and model performance.

The invention provides a cooking process AI analysis and evaluation method and system, and relates to the technical field of analysis and evaluation.The cooking process AI analysis and evaluation method comprises the steps that a multi-view-angle sensor array is arranged, data collection and processing are conducted on the cooking process according to the multi-view-angle sensor array, and cooking collection and processing data are obtained; performing key feature extraction processing on the cooking acquisition processing data to obtain key feature extraction data, and performing cooking process analysis on the key feature extraction data to obtain cooking process analysis data; performing AI analysis operation process judgment on the cooking process analysis data to obtain operation process judgment information; and obtaining finished dish information, and performing AI analysis and evaluation on the finished dish information in combination with the operation process judgment information to obtain AI analysis and evaluation data. According to the method, operation and finished product quality correlation quantitative analysis can be realized, and a core influence link is accurately positioned.

Depending on embodiments, a system may efficiently recommend a book suitable for an emotion of a user.SOLUTION: A specific processing part 290 of a data processor 12 in the system executes user information analysis processing for analyzing user information, user information analysis processing for recommending an optimum book on the basis of the user information analyzed by the user information analysis processing, database management processing for managing a database of books, and user interface processing for providing the user with the book recommended by the recommendation processing.SELECTED DRAWING: Figure 2

The invention discloses a titanium dioxide production process analysis method, relates to the technical field of artificial intelligence, and aims to solve the problem of low credibility of cause and effect evaluation caused by data defects in a chlorination process titanium dioxide production process in the prior art. Forming a production process data set; s2, correcting or complementing abnormal features or missing samples in the production process data set by using a graph enhancement strategy; and S3, constructing a causal reasoning model based on the original data set and the graph-enhanced data set, performing multi-model fusion decision through a covariance cross fusion strategy, and evaluating the causal effect of the change of the control parameters in the reaction process on the product parameters. According to the method, the accuracy of causal effect evaluation is remarkably improved through a graph data enhancement and causal reasoning fusion framework.