95 results about "Process analytics" patented technology

The invention relates to the technical field of resource scheduling control, and discloses a network resource hierarchical coordination method and system based on multiple agents. The method comprises the following steps: monitoring a network node load to obtain a resource state parameter; virtually constructing a resource pool and dividing a regulation domain; deploying an intelligent agent to construct a self-adaptive control system; extracting load characteristics to generate an analysis report; executing a double-layer adjustment strategy recording execution process; and analyzing the energy consumption data to optimize control strategy parameters. According to the application, the efficient utilization of network resources and energy consumption optimization can be realized while the performance of a computer network system is guaranteed.

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.

An end-to-end enterprise computer vision system develops a software application performing one or more computer vision functions; and processes analytics from one or more computing vision functions. The analytics provide real-time insight associated with operations of an enterprise.

The invention provides a numerical control machine tool milling machining multi-process signal intelligent marking method based on Transform. Firstly, multi-mode time sequence signals (such as spindle vibration, cutting force and acoustic emission signals) are collected, denoising is performed through wavelet transform, time-frequency domain features are extracted, signal samples are classified according to process categories, and a high-quality training set is constructed by combining specialists to label the samples. Secondly, designing a multi-modal feature extraction framework based on a Transform model, introducing a multi-head attention mechanism, dynamically adjusting modal weights to generate joint feature representation, determining key modals and features through improved multi-modal feature sensitivity analysis, and accurately marking signals in combination with a self-supervised learning and physical constraint optimization model; finally, a sequential mark verification strategy is adopted, a high-quality mark data set is generated, and the accuracy and consistency of analysis in the machining process are improved. The method can be widely applied to machining quality analysis and intelligent optimization of the numerical control machine tool for complex precise parts, and has high efficiency and reliability.

An improved machine learning based method for authorizing the performance of a treatment, comprising the steps of: receiving a treatment authorization request, the treatment authorization request including a historical record of the person who will receive the treatment and treatment identifying information relating to the treatment; creating an extracted text of the historical record using optical character recognition on the historical record; determining whether to analyze authorization performance of the treatment using a machine learning authorization process, wherein the determination is based on treatment identifying information and whether treatment authorization guidelines exist for the identified treatment; in response to a determination to analyze authorization performance of the treatment using a machine learning authorization process: identifying authorization criteria for the treatment based on the treatment authorization guidelines, wherein the authorization criteria includes records data conditional to authorization of performance of the treatment; identifying a natural language record processing model corresponding to the expense authorization guidelines; and performing natural language processing on the extracted text of the record in accordance with the identified natural language record processing model to identify relevant record data in the record; determining whether the relevant record data meets the authorization criteria and in response to a determination that the relevant record data meets the authorization criteria, authorizing the performance of the treatment.

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.

The invention discloses an intelligent identification method and platform for tailings pond disaster hidden dangers, and relates to the technical field of intelligent monitoring, and the method comprises the steps: carrying out the automatic partitioning based on the global monitoring data of a tailings pond, and constructing a layered data collection system; establishing a physical and mechanical database, and combining consolidation and mechanical data to construct a mechanical parameter inversion model; identifying physical and mechanical characteristics of the dam body through the model, determining disaster types and performing grading; deducing a disaster evolution process, analyzing a disaster chain and constructing a structure tree; and judging a disaster occurrence rate according to the disaster chain, and if the disaster occurrence rate exceeds an early warning threshold, generating a risk early warning report. The technical problem that in the prior art, a tailing pond disaster hidden danger recognition system is insufficient in accuracy is solved, and the technical effect of improving the accuracy of tailing pond disaster hidden danger recognition is achieved through accurate recognition of tailing pond disaster types, dynamic adjustment of disaster grades and real-time deduction of the disaster evolution process.

The invention discloses a high-rise intensive storage shelf stability monitoring system based on stress data acquisition, which relates to the field of storage shelves and comprises a node load acquisition module, a node deformation quantity calculation module, a node deformation quantity prediction module and a deformation early warning module. The method comprises the following steps: acquiring a load value borne by a to-be-monitored node of a storage shelf and a deformation quantity data sequence changing along with time at the node when a loaded cargo load is G, constructing a grey prediction model according to the acquired data sequence, and calculating a deformation quantity prediction value of the load value borne by the to-be-monitored node; the allowable maximum deformation quantity of the to-be-monitored node of the storage shelf is set, the deformation quantity data sequence is updated in real time according to the obtained predicted value of the deformation quantity of the to-be-monitored node, the alarm strategy is formulated according to the sequence updating condition, and prediction can be carried out according to the change trend of the deformation quantity at the node. And formulating an early warning strategy in advance according to an accumulated action process analysis result of the goods shelf nodes under the load action.

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.

This application relates to the technical field of resource scheduling control, and discloses a multi-agent-based hierarchical coordination method and system for network resources. The method includes: monitoring the load of network nodes to obtain resource status parameters; virtually constructing a resource pool and dividing adjustment domains; deploying agents to construct an adaptive control system; extracting load characteristics to generate an analysis report; executing a two-layer adjustment strategy and recording the execution process; analyzing energy consumption data to optimize control strategy parameters. This application can achieve efficient utilization of network resources and energy consumption optimization while ensuring the performance of the computer network system.

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.

The invention relates to the technical field of agricultural automation, in particular to a fruit and vegetable factory production data multi-source acquisition and transmission integrated system, which comprises a multi-source data acquisition module, the multi-source data acquisition module is in network connection with a data preprocessing module, and the data preprocessing module is in network connection with a data transmission module. The data transmission module is in network connection with a data storage module, and the data storage module is in network connection with a data management control module. Equipment states are collected through current, voltage and pressure sensors, data support is provided for fruit and vegetable production in combination with crop growth data, and workers can monitor the production process in real time and analyze and adjust a production plan.

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.