71 results about "Process analytics" patented technology

The invention discloses a modular reconfigurable production line control system integration method, which relates to the technical field of industrial automation, and comprises the following steps: establishing virtual mapping based on physical attribute parameters to form a production line digital twin basic model framework; collecting data in real time based on an on-site sensor, and establishing a digital twinborn dynamic mapping mechanism synchronous with a physical production line state; a reconstruction scheme is imported into a virtual environment, and key performance indexes are analyzed through a production line digital twin model rehearsal module combination process. A virtual production line model is constructed through a digital twin technology, a rehearsal and verification reconstruction scheme in a virtual environment is supported, trial and error time and cost required by traditional physical debugging are remarkably reduced, a reconstruction strategy is further optimized through a cloud AI algorithm, closed-loop optimization from virtual verification to physical execution is achieved in combination with edge end real-time control, and the real-time performance of the virtual production line is improved. The production line can quickly complete local or overall reconstruction according to production requirements, and the response speed and flexibility of the production line are greatly improved.

The invention relates to the technical field of data processing, in particular to a mine slope intelligent early warning method and system based on dynamic threshold optimization and a medium. Comprising the following steps: acquiring a multi-modal heterogeneous data stream from a sensor network, generating a time sequence data matrix, and sending the time sequence data matrix into a parallel data processing model; the first data processing model executes statistical process analysis to generate a first data set; the second data processing model compares the data with the prediction data set to generate a second data set; fusing the data sets, verifying and judging, and generating a slope data record; when the conditions are met, an optimization process is started; and outputting a state list, storing the state list in a graph database, and performing parameter adjustment on the prediction model. According to the system, heterogeneous data streams are analyzed through a parallel data processing and data fusion method, comprehensive state evaluation is generated, data attribution and model closed-loop optimization are started when conditions are met, high-precision recognition of sudden change and gradual change modes in the data streams is achieved, and the diagnosis depth and the self-adaptive capacity of the analysis system are improved.

The invention discloses a real-time quantitative calibration spectrum on-line detection device and a use method thereof, a standard substance with a known concentration is accurately injected into a mixer and is mixed with a reaction liquid on line, and then a spectrum detector obtains spectrum absorption data of the mixed liquid; on the basis of the spectral absorption intensity linear superposition relation between the standard substance and the reaction liquid and the metering relation between the target product and the injected target product, online correction is carried out, and finally the concentration value of the target product is obtained. The online standard sample injection and dynamic online calibration technology is introduced, so that the problems of low quantitative precision, manual sampling lagging and the like in chemical reaction monitoring are solved, and the online standard sample injection and dynamic online calibration technology is particularly suitable for the fields of photochemical synthesis, continuous pharmacy and the like with strict process analysis timeliness requirements.

A system and method of for providing assistance to complete machine learning on workflow engines that deal with machine learning flows comprising operators configured in a coordinate grid. The process analyzes the positions and composition of operators, branches, inconsistencies, collisions and redundancy in the workflow in order to suggest to the user which changes should be made to the workflow.

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.

To provide a technique for easily and accurately registering information for identifying a program used for processing each set of analytic data in a device for processing analytic data acquired by multiple types of analysis devices.SOLUTION: Two or more types of processing modules and module information defining a character string corresponding to each of the two or more types of processing modules are stored in memory of a data management device 1. A processor of the data management device selects one processing module to be used for processing given analytic data from among the two or more types of processing modules on the basis of a character string included in a file name of the given analytic data and the module information for the given analytic data.SELECTED DRAWING: Figure 1

A method comprises the steps of: providing (S10; S60) digital signal data dependent on a temporal variation of at least one state variable of a process; and generating (S20; S70) at least one digital image containing an image of the provided signal data. In one embodiment, the method comprises the step of determining (S80, S90) an analysis result by means of image processing of the generated digital image. In one embodiment, the method comprises the steps of configuring (S30, S50) at least one parameter of image processing and / or identifying at least one event in a process and / or evaluating a process change and / or a process result for determining an analysis result by image processing based on the generated image. The invention also relates to a system or a computer program (product).

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.

The application discloses a kind of teaching management method and system for wisdom teaching platform, it is related to education big data technical field, including management center, the management center is connected with teaching acquisition module, course processing module, process analysis module and multi-source coordination module;Acquire courseware original text information and transform into courseware original text data chain;Courseware original text data chain is encrypted combination, obtains secret courseware data chain, sets up flow extraction function to courseware original text information and carries out feature conversion, obtains key data stream section, sets up and inputs the homogeneity conversion of reserve search feature word, obtains reserve search feature data stream section;According to the key data stream section of secret courseware data chain of reserve search feature data stream section is adapted comparison, obtains representative similarity node, according to representative similarity node carries out optimization learning to secret courseware data chain, obtains response learning record;Optimize teaching management process, protect courseware resources, improve search efficiency and accuracy.

The invention provides an event reconstruction method based on giant remote sensing star groups and time-space continuous multi-modal data, which comprises the following steps of: firstly, extracting key sensitive vocabularies, processing long-time-sequence multi-source remote sensing data and the like; then, target-time-position information pairs of the key target at all moments in the long-time-sequence multi-mode remote sensing image are obtained through a multi-source remote sensing image language description large model constrained by keywords, and finally, the obtained information pairs are processed through an existing language large model. Information such as the speed and the running direction of the key target at each moment is obtained, and the overall information is analyzed and processed to predict the future trend of the key target. According to the method, the problem of inaccurate event process reconstruction and understanding of multi-modal data in the aspects of over-the-ground real-time intelligent event process analysis and understanding is solved.

The invention relates to a tunnel excavation process analysis method based on three-dimensional point cloud data and a BIM model, and the method comprises the steps: collecting the three-dimensional point cloud data of a tunnel under different construction procedures through 360-degree panoramic scanning, building a first digital model, taking the BIM model as a second digital model for designing an excavation section, carrying out the comparative analysis of the two digital models, and carrying out the construction of a tunnel excavation process. According to the method, an inspection report of over-excavation and under-excavation conditions and surrounding rock full-section deformation can be quickly generated, guidance is provided for subsequent excavation control of tunnel engineering, the tunnel excavation quality and safety are ensured, reference is provided for analysis and application of multi-modal data such as point cloud data and model data of similar engineering in the future, and the engineering field digital intelligent management level is improved.

The invention provides a modular apparatus comprising at least one process analytical instrument and a controller adapted for fluid communication with a unit operation of a pharmaceutical or biopharmaceutical manufacturing system, systems comprising the modular apparatus, and related compositions and methods. The modular apparatuses described here and associated digital architecture provide a universal solution to fully integrate any pharmaceutical or biopharmaceutical manufacturing process using a wide range of process analytic tools.

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.

The application provides a laboratory multi-source data monitoring management method and system, and belongs to the technical field of laboratory data processing and state control. The method is applied to process monitoring of laboratory reaction equipment, and comprises the following steps: S100, acquiring a plurality of first monitoring data and a plurality of second monitoring data of a first type and a second type of monitoring sensor in a first time period; S200, determining a to-be-converted time domain data set and a non-converted time domain data set; S300, converting the to-be-converted time domain data set into a frequency domain data set; S400, constructing an N-order data analysis matrix; S500, performing stability analysis on the N-order data analysis matrix to obtain a process analysis result of the laboratory reaction equipment. The technical scheme can accurately judge whether the reaction process of the laboratory reaction equipment is stable based on multi-source multi-dimensional real-time sensing data of different domains, so as to provide a stable reaction environment for partial precision experiments, chemical test experiments and the like.

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 present invention discloses a computer vision-based concrete construction process and worker health monitoring system, which includes an input module, a computing module, and an output module. The input module collects construction site images in real time and uses embedded devices for real-time processing. The computing module receives data collected by the input module, including the posture of concrete workers, the operating status of equipment, and specific construction task requirements. The system uses improved YOLOv8-D and YOLOv8-P algorithms to accurately detect the posture characteristics of construction equipment and concrete workers. In addition, the system supports SVM to classify and identify construction operation categories, and combines ST-GCN to analyze continuous multi-frame image data to capture the dynamic changes of worker posture and equipment status at the construction site. The functions of the output module include construction process analysis and health monitoring. The system provides feedback on construction status and warning information to the operator in the form of voice broadcast through the human-computer interaction interface, helping them to adjust the operation mode in time to ensure the safety and efficiency of the construction process.

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.

The invention provides a modular apparatus comprising at least one process analytical instrument and a controller adapted for fluid communication with a unit operation of a pharmaceutical or biopharmaceutical manufacturing system, systems comprising the modular apparatus, and related compositions and methods. The modular apparatuses described here and associated digital architecture provide a universal solution to fully integrate any pharmaceutical or biopharmaceutical manufacturing process using a wide range of process analytic tools.

The application provides a kind of intelligent marking method for multi-process signal of milling machining of numerical control machine tool based on Transform. First, collect multi-modal time series signals (such as spindle vibration, cutting force, acoustic emission signal), denoise by wavelet transform and extract time-frequency domain features, classify signal samples according to process category, and construct high-quality training set combined with expert labeled samples. Second, design a multi-modal feature extraction framework based on Transform model, introduce multi-head attention mechanism, dynamically adjust modal weight to generate joint feature representation, determine key modal and feature through improved multi-modal feature sensitivity analysis, and combine self-supervised learning and physical constraint optimization model to accurately mark signals. Finally, use sequential labeling verification strategy to generate high-quality labeled dataset, improve the accuracy and consistency of process analysis. The application can be widely used in complex precision part numerical control machine tool machining quality analysis and intelligent optimization, and has high efficiency and reliability.

A process data store may contain a process model (e.g., a process graph, with process graph elements that include nodes and edges, as generated via process mining or a BPMN representation). A process server may retrieve information from the process data store and receive user feedback data. The server may determine if the information retrieved from the process data store is associated with a prior mapping of survey questions to the information retrieved from the process data store. If the information retrieved from the process data store is not associated with a prior mapping of survey questions, embodiments may utilize Machine Learning (“ML”) to automatically map the user feedback data to the information retrieved from the process data store. The server may then automatically assign, group, and analyze the user feedback data to generate a recommended alteration.

The invention discloses an AI-Agent-based PID control method, which combines the advantages of a PID algorithm and an AI technology, combines AI large models provided by suppliers such as DeepSeek, OpenAI and the like with a small amount of control data, designs an Agent suitable for PID control, utilizes the AI-Agent to self-drive a parameter setting process, analyzes system response characteristics, automatically adjusts PID control parameters according to results, and improves the PID control efficiency. And finally, better parameters are obtained through self-iteration, and the complex degree of parameter adjustment is greatly reduced.

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.

The application relates to system fault evolution process analysis in the safety field, and provides a system fault evolution sub-evolution process function analysis method, which is used for studying each sub-evolution process with different characteristics in a system fault evolution process, determining the function of each sub-evolution process on the total evolution process, and providing a sub-evolution process function analysis method based on spectral regression discriminant analysis; a basic data matrix and a normalized data matrix are constructed based on an object set measured and evolved at each moment; an object label set is generated by using uniformly distributed random numbers and is folded to form an object label matrix; the object label matrix is trained and predicted by using the spectral regression discriminant analysis method to obtain maximum accuracy, an optimal object label set and an optimal training object number; the function analysis of each sub-evolution process is realized by defining frequency and concentration; and the method is used for determining the function of each sub-evolution process in a system fault evolution process and clustering analysis of an object set.