109 results about "Multilevel data" patented technology

The invention relates to the technical field of block chains, and discloses a block chain and privacy computing collaborative verification system, which comprises a data fragmentation module, a privacy fusion module, a collaborative verification module, a consensus correction module and a fragmentation graph optimization module. The data fragmentation module constructs a multi-stage data collaborative topology through a preprocessing node and a dynamic fragmentation protocol; the privacy fusion module realizes privacy protection by using a ciphertext analysis unit to switch links in a multi-state manner and a zero-knowledge verification unit; a collaborative verification module generates a reference verification strategy through a consensus modeling node multi-dimensional fusion model, and a state optimization unit dynamically aggregates and sorts fragmentation states; the consensus correction module corrects the fragmentation state through an isolation verification unit and a delay compensation unit and calibrates ciphertext parameters; and the fragment atlas optimization module constructs a stability evaluation network monitoring synchronization rate and performs closed-loop adjustment on a verification strategy. The system realizes dynamic fragmentation, privacy fusion and intelligent verification, and improves the efficiency, security and stability of block chain data processing.

The invention relates to the technical field of intelligent control of automobile air conditioners, and discloses an intelligent regulation and control method of an automobile air conditioner controller based on a self-adaptive neural network, which realizes comprehensive perception of environment information inside and outside an automobile through distributed multi-source data intelligent fusion processing. Establishing a data quality evaluation model and an intelligent data interpolation algorithm to ensure data reliability; constructing a lightweight dual-path adaptive neural network model by adopting a knowledge distillation technology; establishing a hierarchical multi-objective optimization control architecture which comprises a three-layer optimization structure of a strategic layer, a tactical layer and an operation layer, and realizing organic combination of long-term planning, medium-term prediction and real-time control; the method further comprises key technologies such as multi-level data missing fault-tolerant processing, actuator self-adaptive compensation control, system-level fault self-healing and degradation control and the like, and stable and reliable intelligent regulation and control can be achieved under the extreme environment condition. According to the invention, the control precision, the energy consumption optimization and the user experience in extreme environments such as polar regions, plateaus and deserts are improved.

The invention provides an enterprise financial comprehensive management system based on an intelligent technology, and relates to the technical field of financial management. The accounting intelligent subsystem is used for acquiring financial data, performing semantic analysis and conversion on the financial data through a preset rule and a multi-stage data mapping mechanism, generating a standardized accounting service instruction and issuing the standardized accounting service instruction to an automatic process engine, thereby automatically generating a financial document meeting an accounting criterion; the financial management subsystem is used for integrally realizing execution of backlog management, compliance guidance manual query and financial data structured storage according to a preset standardized process; and the business service subsystem is used for providing tax-related business service, asset management service, fund management service and operation management service processing and guidance for enterprises through modular design. The invention aims to improve the operation efficiency, reduce the error rate, break information islands, realize effective integration and utilization of resources, and provide powerful support for decision making of enterprises.

The invention relates to a pumped storage and compressed air energy storage coupled scheduling method, which comprises the following steps of: establishing a multi-stage data system based on input data of multiple time scales; constructing a multi-time scale scheduling model; when the load of the pumped storage mixed compressed air energy storage power station is lower than a first preset threshold value, electricity is abandoned preferentially, water pumping and compression are conducted, and when the load is larger than a second preset threshold value, the pumped storage mixed compressed air energy storage power station is controlled to conduct combined energy release. The nonlinear relation between the pumped storage efficiency and the water level and the nonlinear relation between the compressed air energy storage power and the pressure are converted into linear constraints, solving is conducted by dividing the water level interval and the pressure level, and a scheduling strategy is obtained. Therefore, the problems of relatively low energy storage efficiency, insufficient scheduling refinement, relatively high system standby cost and the like caused by the problems that pumped storage water scheduling and CAES energy management are separated, a standby capacity coordination mechanism is lacked and the like in a hybrid system scheduling method in related technologies are solved.

The invention provides a low-power-consumption wireless ad hoc network dynamic configuration method and system based on adaptive routing, and relates to the technical field of network protocols, and the method comprises the steps: determining a data transmission path through obtaining node working state parameters and calculating an energy level value, and setting a multi-level data cache occupation threshold value; calculating a network load distribution state, and carrying out differentiated scheduling on nodes; selecting a low-power-consumption forwarding channel based on node routing adaptability; and monitoring the network state in real time and updating parameters. According to the invention, the network adaptability is improved, the communication power consumption is reduced, the network life cycle is prolonged, and the data transmission efficiency is improved.

The invention relates to the technical field of power equipment safety operation and maintenance, in particular to a power distribution network single-phase grounding positioning method and device based on multi-source information fusion. Heterogeneous sensing nodes deployed in a layered and distributed mode are adopted to synchronously collect steady-state and transient-state electrical quantity data of a power distribution network, and preprocessing is carried out to generate a standardized data set; multi-dimensional feature extraction is carried out; carrying out weighted fusion on the extracted multi-dimensional features based on a multi-feature fusion strategy of a dynamic weight adjustment mechanism and a D-S evidence theory, and calculating a line fault probability; when the calculated line fault probability meets a preset threshold value, the position of a fault point is locked through first-stage line screening, second-stage section division and third-stage accurate positioning in sequence. Therefore, through a multi-stage data fusion and hierarchical positioning algorithm, the fault positioning error is obviously improved from the line level in the prior art to the section level, and the fault positioning problem under the scenes of high-resistance grounding, intermittent faults and a complex power distribution network containing a distributed power supply is effectively solved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology and medical health, and discloses a demand-driven data processing method, device and equipment and a medium, and the method comprises the steps: obtaining demand information, and generating a structured demand analysis report through a demand analysis agent; generating an executable code by combining a strategy conversion agent with a zero code operation platform; generating a test case through the automatic test agent and executing an automatic test; when the test result is wrong, adjustment is carried out through the result feedback agent; when the test result is passed, executing the executable code to generate a processing result, and verifying the intelligent experience certificate by the accuracy; and storing the verified processing result, and performing multi-level data analysis through the data analysis agent to obtain an analysis result. By establishing a closed loop of demand analysis, rule conversion, test verification and data analysis, rapid conversion and accurate verification from the demand to the result are realized, the processing efficiency is improved, and the error risk is reduced.

The invention provides an encryption access control method and platform for industrial security data, and relates to the technical field of data encryption access, and the method comprises the steps: carrying out the attribute classification and code identification of an industrial system data set; performing security level division on the industrial system data set according to the data security quantitative index system; performing matching analysis on the multi-level industrial security data set based on the data encryption algorithm list; encrypting and storing the industrial system data set based on the industrial data identification code set by adopting a multi-stage data encryption algorithm to generate an industrial encrypted data warehouse; and establishing a fine-grained access mechanism and a decryption interaction mechanism according to the user role library, and performing access authority distribution and encryption access control on the industrial encrypted data warehouse based on the fine-grained access mechanism and the decryption interaction mechanism. According to the invention, the technical problem that the comprehensive requirements of high safety, high precision and high flexibility in a modern industrial system are difficult to meet in the prior art can be solved.

The invention relates to the technical field of Internet of Things, intelligent monitoring and sensor fusion, and discloses an intelligent multi-sensor data fusion processing system and method, and the system comprises a data collection preprocessing module, a feature and confidence evaluation module, a core data fusion module, a real-time decision early warning module and a performance feedback optimization module. The system preprocesses data through adaptive Kalman filtering, dynamically calculates the confidence coefficient of each sensor by using error entropy, further performs multi-stage data fusion by using weighted fusion and a D-S evidence theory, and finally outputs accurate state estimation and intelligent decision. Meanwhile, internal parameters are adaptively optimized through a closed-loop feedback mechanism. The system effectively solves the problems of data isolation, low precision, slow response and poor adaptability of a traditional sensor system, remarkably improves the precision, reliability, real-time performance and self-adaptability of Internet of Things monitoring, and is suitable for industrial equipment monitoring, smart cities and other scenes.

The invention discloses a metamorphic rock P-T-t trajectory reconstruction method and system based on multi-source data fusion, and relates to the field of metamorphic rock P-T-t trajectory reconstruction.The method comprises the steps that electronic probe microcell map data and internal standard sample data of a metamorphic rock sample are obtained, and multi-stage data correction is conducted; carrying out mineral phase identification and chemical component quantitative interpretation to generate a mineral classification map, an element concentration distribution map and a total rock main component data set; thermodynamic phase equilibrium simulation is carried out, a P-T view profile map is generated, and a P-T track is extracted; performing microcell in-situ chronological analysis on selected minerals in the mineral classification map to obtain chronological data; and carrying out space-time coupling on the P-T trajectory and chronology data, constructing a P-T-t three-dimensional trajectory model of the metamorphic rock, and outputting a visual reconstruction report. According to the method, automation and standardization of the whole process of metamorphic rock P-T-t trajectory from data acquisition and processing to model reconstruction are realized, and the precision, efficiency and reliability of trajectory analysis are improved.

The invention discloses an intelligent prediction method for the stirring power consumption of a reaction kettle, and the method comprises the steps: collecting and calibrating the fluid physical property and process operation parameters in the reaction kettle in real time through a high-precision sensor array and an automatic system, carrying out the multi-stage data cleaning, denoising, normalization and time alignment, generating standardized high-quality data, and carrying out the intelligent prediction of the stirring power consumption of the reaction kettle. Based on physical property mapping, dimension reduction and feature fusion, extracting a low-dimensional fluid physical property descriptor, inputting the low-dimensional fluid physical property descriptor into a deep regression network in combination with process parameters, carrying out stirring power consumption modeling and dynamic prediction, and carrying out dynamic prediction by introducing a self-adaptive physical property embedding mechanism and distribution alignment training. The self-adaption and generalization ability of the model to different and unseen fluid physical property distributions is improved, and the accuracy, the stability and the engineering decision support ability of energy consumption prediction under complex working conditions are remarkably enhanced.

According to the method, PCIe extended storage equipment is identified by responding to a system starting signal, and initialization and address mapping control logic is executed to construct a first address space addressable by a GPU (Graphics Processing Unit); then, loading model data according to an AI task instruction, and dynamically dividing hot, warm and cold multi-level data popularity levels by monitoring memory access frequency; a migration data block is generated based on the popularity level, a migration data flow is organized and verified through priority scheduling logic, and intelligent data scheduling between extended storage and a GPU video memory is achieved; finally, the resources are released after the task is completed. Therefore, dynamic expansion of the physical video memory capacity is effectively achieved, large-scale AI model localization training is met, dependence on a high-end GPU chip is remarkably reduced, and data safety and system economy are improved.

The invention discloses a railway sound barrier construction and operation period data information classification method and system, and the method comprises the following steps: S1, constructing a multi-stage data structure system which covers the whole flow information of a sound barrier project; s2, performing standardized classification on the data in the multi-level data structure system; and S3, constructing an association relationship among the hierarchical data in the multi-level data structure system to form a hierarchical data management framework. By constructing a three-level data structure system of projects, work points and components and defining four data types of character strings and numerical arrays, hierarchical management is achieved through unique codes, application characteristics of the sound barrier combination components in the stages of design, construction, operation and maintenance are considered, a classification algorithm of the sound barrier combination components and associated data of the sound barrier combination components is compiled, and the classification efficiency of the sound barrier combination components is improved. A classification method of various components and associated data of the sound barrier is defined, the problems of data fragmentation and poor full-life-cycle applicability of a traditional data structure and classification method are solved, the sound barrier design efficiency and full-life-cycle management capability are improved, and the multi-specialty cooperation efficiency is improved; and the method has important engineering application value.

The invention relates to a software system based on a self-adaptive decentration architecture and a communication method thereof, and belongs to the technical field of computer software and communication. According to the system, an ad hoc network is adopted as a communication means, a decentralized software architecture is adopted as a core, and the task processing efficiency and the system robustness in a distributed environment are improved. Through a self-adaptive decentralized task allocation mechanism, each node can make a decision autonomously according to a network state, computing power and task requirements, so that dynamic and balanced task scheduling is realized, and the problem of single-point failure is avoided. The system adopts a multi-stage data consistency management strategy to ensure data reliability and consistency in a distributed environment. The method is widely applied to the fields of unmanned systems, emergency communication, intelligent manufacturing and the like, and the software system stability and the collaborative operation capability in a network environment are improved.

The invention relates to the technical field of thermal power plant data management, in particular to a chemical supervision and management method and system based on a thermal power plant, and the system comprises a regular task management module, a chemical test report inputting and editing module, a data auditing module and an abnormity early warning and processing module which are in communication connection in sequence. The regular task management module creates a chemical supervision task according to a thermal power plant technical standard criterion; the chemical test report inputting and editing module adopts a standardized report template to input chemical test report data according to the executed chemical supervision task; the data auditing module is used for regularly auditing the chemical test data report according to a multi-stage data auditing mechanism; and when abnormal data occurs in the chemical test data report, the abnormal early warning and processing module timely gives out early warning, rechecks the abnormal data, carries out data analysis and generates an analysis report, so that the chemical supervision work of the thermal power plant can be efficiently and accurately carried out.

The invention relates to the technical field of industrial intelligent control and predictive maintenance, in particular to a multivariable time sequence prediction and closed-loop control method based on digital twinning and deep learning fusion, which comprises the following steps: constructing a multi-level data architecture based on a digital twinning five-dimensional model; data preprocessing and SMOTE resampling are carried out; time sequence feature extraction based on the 1D-CNN; virtual twin data generation based on Bi-LSTM (Bidirectional Long Short Term Memory) and an attention mechanism; performing multi-mode fault diagnosis based on the RNN-LSTM hybrid network; carrying out adaptive feedback control based on Lyapunov stability; a multi-task learning mechanism is adopted, and virtual twin data generation and fault diagnosis are jointly optimized. According to the method, excellent prediction precision and control performance are verified on a semiconductor etching process data set, and the fault diagnosis effect and the autonomous optimization capability of the system in the complex industrial process are remarkably improved.

This invention discloses a hierarchical public data visualization retrieval system and method based on crowdsourcing analysis and data standardization. The system includes: a crowdsourcing analysis platform for receiving and reviewing feature indicators extracted from public data by expert users; a data standardization module for performing unified exchange rate conversion and reporting period standardization on financial data to ensure data comparability; an indicator calculation module for calculating individual development indicators according to specific formulas; an index generation module for generating individual development indices, regional development indices, and global development indices based on benchmark values, and generating macro-trend charts; a multi-level index database for storing multi-level data and external links; an interactive response module for responding to chart click events and drilling down to obtain detailed data; a data display module for generating the next level of charts; and a context-aware navigation module for automatically navigating to preset external information sources based on user-selected function tags and entity clicks in the charts. This invention lowers the barrier to data utilization through crowdsourcing, ensures the comparability of global data through standardization, and achieves seamless and efficient retrieval and navigation from macro trends to micro information.

This invention relates to a data processing system for CTD (Conductivity, Temperature, Depth) instrument observations, comprising: building a fully domestically developed technology foundation, employing a combination of Spring Boot, MyBatis-Plus, DM database, and Vue3 to achieve domestic adaptation of the front-end, back-end, and database; incrementally accessing and storing raw CTD data files based on the technology foundation, generating dual traceability identifiers of FileInfoID and DataID; performing multi-step sequential processing on the stored data, including zero-error correction, outlier filtering, data smoothing, salt density and acoustic calculation, and standard file generation, with each step's algorithm optimized for the characteristics of domestically produced CTD data; storing raw data, intermediate data, and output data through a multi-level data table design, utilizing traceability identifiers to achieve full-process correlation and traceability, and providing visual display and standardized output. This invention achieves fully domestic processing from data access to output, effectively improving the accuracy and traceability of data processing and ensuring marine data security.

The invention discloses a data consanguinity and hotspot awareness driven global data table analysis method and system, and relates to the technical field of data processing, and the method comprises the steps: reading a data flow log, ETL job metadata and a query dependency relationship of a global data table, and carrying out the analysis to form a multi-level data consanguinity map; log features are extracted to construct a popularity time sequence and a popularity spectrum; fusing the sensitivity spectrum and the popularity spectrum to construct a double-spectrum hotspot sensing model; and inputting real-time data characteristics into the model to obtain a node comprehensive influence score, and performing node strategy correction management. According to the method, the technical problems that the data dependency relationship of a global data platform is difficult to accurately describe, data hotspots cannot be comprehensively sensed, data node influence evaluation is inaccurate and resource allocation and data value are not matched are solved, and the purposes of clear data dependency, comprehensive hotspot sensing, accurate node influence evaluation and reasonable resource allocation are achieved. Therefore, the technical effects of accurate evaluation and efficient management and control of the middle table are achieved.

The application discloses a device state analysis method based on a multi-level data architecture, and relates to the technical field of big data analysis.The method comprises the following steps: collecting device state data from a production control area of an electrochemical energy storage power station; performing protocol analysis, standardized conversion and measurement point mapping on the device state data in a management information area of the electrochemical energy storage power station to generate time sequence measurement point data; unidirectionally transmitting the time sequence measurement point data to a non-control area of the electrochemical energy storage power station through a forward isolation device; writing the time sequence measurement point data into a Kafka cluster for message buffering, and storing the time sequence measurement point data into a TDengine time sequence database by a consumer program; based on a configuration algorithm tool provided by an algorithm platform, performing algorithm modeling and instantiation on the time sequence measurement point data to generate a device state analysis algorithm; and executing an instance of the device state analysis algorithm through an algorithm center to output a device state analysis result.The application can accurately analyze the device state.

The invention provides an unmanned aerial vehicle obstacle avoidance method and system based on airborne sensor and lightning early warning fusion, and the method comprises the steps: obtaining airborne atmospheric electric field data and multi-source heterogeneous data, and carrying out the time-space alignment and multi-stage data fusion, and obtaining calculation parameters; based on the calculation parameters, combining a pre-constructed unmanned aerial vehicle lightning risk assessment model to obtain a lightning risk assessment value; constructing a three-dimensional risk field based on the thunder and lightning risk assessment value in combination with multi-source heterogeneous data; and obtaining an unmanned aerial vehicle route planning path based on the three-dimensional risk field in combination with a path planning algorithm, and completing obstacle avoidance. According to the constructed three-dimensional risk field, invisible thundercloud electric field intensity is quantified and mapped into a'dynamic obstacle 'in a three-dimensional space, so that the unmanned aerial vehicle has the capabilities of detecting and avoiding the thunder and lightning high-risk area, and the limitation that a traditional obstacle avoidance technology (such as a millimeter wave radar, a visual sensor and the like) only can identify an entity obstacle is broken through.

The images represented in the image diagrams are interactive graphical user interfaces (GUIs) that visually convey information to a user related to systems for (i) creating and / or modifying and / or analyzing unit operations and data generated therefrom in manufacturing processes, such as cell and gene therapy manufacturing processes, (ii) combining live data from databases with textual information, and (iii) interactive analysis of data (e.g., patient data visualization systems) and analysis of data related to manufacturing processes. (A. Graph-Based Visualization and Interactive Data Views for Pharmaceutical Manufacturing Process Data Reconciliation) For example, in certain embodiments, the images represented in the image diagrams are associated with graph-based visualization tools that enable a user to analyze data related to one or more experiments and / or manufacturing processes used to produce pharmaceutical products and / or variations thereof. In particular, the images for graph-based visualization and data analysis GUI tools represented in the image diagrams can be used in association with GUIs that enable a user to automatically and / or semi-automatically (e.g., in conjunction with user review and / or input) generate visualizations that facilitate the examination of experiments and / or manufacturing processes and reconcile data generated across multiple processes and / or process runs. For example, among other things, the interactive graph-based visualization tools and their images may be provided (e.g., rendered) individually or together via one or more GUIs or windows, sub-windows, panels, etc. (Process Graph) For example, the image diagram illustrates a GUI that provides a visualization of a process graph. In one embodiment, the graph-based visualization tool represented in the image diagram includes generating and / or rendering a process graph that displays an experimental and / or manufacturing process for the production of pharmaceutical products, including biologics such as cell-based therapeutics and biologic drugs. The process graph includes multiple nodes, each representing a data point corresponding to a unit operation in a particular experimental and / or manufacturing process where information is collected and / or an action is performed.The image diagrams further illustrate various approaches for capturing and / or displaying these data points and how users interact with them. (Comparing and Reconciling Multiple Processes) In certain embodiments, the images depicted in the image diagrams are used as GUIs to provide graph-based visualization tools that provide comparison and / or reconciliation of data from multiple experiments and / or manufacturing processes, e.g., in an automated and / or semi-automated manner (e.g., coupled with user interactions such as review and selection actions). Among other things, the images depicted in the image diagrams include designs that overlay multiple graph processes, structurally compare them, and reconcile them based on data points. For example, the images depicted in the image diagrams may provide rendering data created by process comparison and reconciliation tools that (e.g., automatically) identify and address discrepancies, missing data, or anomalies, which can be used to generate a harmonized data set for further analysis, such as mathematical modeling. Thus, among other things, the graph-based visualization tools provided in the images depicted in the image diagrams address the challenges presented by unreconciled data across multiple experiments and / or manufacturing processes and / or within a single process that may be performed under varying conditions. Achieving automated reconciliation of such data is a significant challenge that, if not addressed, hinders effective and accurate analysis, which, in turn, can dramatically impact a user's and / or organization's ability to optimize and / or maintain manufacturing process quality and / or develop new processes. In particular, the images presented in the graph-based visualizations and the graphical representations that present them aid in adjusting sampling points to maximize overlay for effective comparisons and / or, in certain embodiments, identify overlapping points in existing data for effective real-time analysis. For example, the graphical representations show examples of multiple levels of data views and clickable / expandable pop-ups. In particular, in certain embodiments, the graphical representations are associated with GUIs that integrate the ability to collect and link actual data with process diagrams.For example, multiple levels of data can be viewed in an interactive and dynamic manner. For example, images presented in pictorial diagrams provide a high-level view, and while not all data may be directly visible, they provide clickable / expandable, customized, dynamic nodes for each node type, allowing users to simultaneously inspect and analyze collected and / or input data. In this manner, images presented in pictorial diagrams facilitate the identification and analysis of differences between processes. In some embodiments, structural differences between different processes indicate variations in conditions (e.g., temperature, duration, chemical concentrations), as well as the sequence and / or presence of certain steps. While unit operations (basic steps or stages in a process) may be generally similar to other processes, unique conditions and / or sequences can significantly affect the outcome, or the nature of the product or result. Thus, images presented in pictorial diagrams that provide visual display tools can be extremely useful in process optimization, troubleshooting, and ensuring that a process meets desired specifications. For example, changing the device that performs a unit operation and / or certain parameter values ​​within the unit operation (e.g., rotation speed, total volume, duration, etc.) can have a significant impact on the quality, recovery, efficacy, etc. of the output of that unit operation, which in turn can affect characteristics such as the biological / potency of the product, and additionally or alternatively, factors such as the cost of production, the number of doses produced per manufacturing run, and the like. Thus, among other things, images depicted in pictorial diagrams can be associated with and provided with GUIs that present generated data, such as automatically generated data (e.g., using ontologies), which helps to identify commonalities and / or differences between studies and facilitates performing analyses to determine, for example, whether device or parameter changes have significant impacts.In particular, as described herein, a graph-based visualization tool represented in a pictorial diagram provides a user with a visual representation of one or more manufacturing processes via a graph-based approach that easily communicates and highlights differences in conditions and unit operations, as well as their nature. For example, an image represented in a pictorial diagram may include visual features that highlight unreconciled data points. This approach facilitates the identification and correction of data reconciliation issues, thereby streamlining data analysis for research and process development conducted in the creation and production of pharmaceuticals, such as cell-based therapeutics and biologic drugs. (i. Process Graph with Interactive Nodes) The pictorial diagram illustrates an example of a graph-based visualization of a manufacturing process. As shown in the pictorial diagram, a manufacturing process can be represented and displayed via a process graph. Each node in the process graph displays a data point corresponding to a unit operation in the particular manufacturing process that it (e.g., the process graph) represents. The node may include information about the current data state and content (e.g., in real time) of the data point it represents. Within a graph-based visualization, nodes can be dynamic, such that, for example, a user interaction with a particular node (e.g., a mouse hover, click, touchscreen tap or long press, etc.) triggers the display of a tooltip indicating the current data state for the displayed data point, including data collected at various stages of the manufacturing process. Process graphs can be rendered and used for experiment design and process execution to outline complex manufacturing experiments over multiple days of manufacturing procedures and to visualize and track different stages of a real-time experiment.Experimental Overview: In some embodiments, a graph is displayed as an experimental overview, providing a high-level overview of an experiment, including the process steps and (e.g., approximate) order in which they were performed, the dates particular process steps were performed, how the process steps relate to one another (e.g., the top half of FIG. 1A ), and an overview of the various states / arms evaluated during the experiment and how those states relate to one another (e.g., the bottom half of FIG. 1A ). Timeline: In some embodiments, a process graph may include a timeline, displaying multiple timepoints, such as the days, during which a particular experiment or manufacturing process represented by the graph was performed. As shown in the image depicted in the pictorial diagram, the timeline may be displayed along the horizontal axis, with labeled circular icons used to visually represent individual timepoints (e.g., days of execution from day 1 through day 21). Other ways of visually displaying the timeline may be used, for example, along the vertical axis and / or using other shaped icons, other units (e.g., hours, weeks, etc.). Process Unit Operations: In some embodiments, a process graph may visually identify individual process unit operations performed during a manufacturing process. Individual process unit operations may be displayed, for example, through a combination of text labels and icons or markings that convey the particular unit operations performed and, optionally, the time at which they are carried out and / or their (e.g., temporal) relationship with respect to other unit operations. For example, the graph-based visualization shown in the image depicted in the pictorial diagram includes a series of text labels along the top row (of nodes) identifying various unit operations such as "Material Preparation," "Start-Up," "Transduction," and "Compounding." In one embodiment, as shown in the image depicted in the pictorial diagram, for example, the text labels also include a numerical component and identify the particular day on which each unit operation is performed, and the text labels are arranged sequentially from left to right along the horizontal axis to mark the order in which the operations are performed over time.The vertical dotted lines extending downward from each text label provide a visual guide for the expected schedule for each unit operation and / or a temporal mapping of the manufacturing process. (Data Points (Nodes)) As shown in the images depicted in the pictorial diagrams, data points in the manufacturing process associated with a particular unit operation and from which pertinent information (measurements and / or recorded observations) is collected are represented via nodes. Nodes may be rendered as icons such as filled circles as shown in the images depicted in the pictorial diagrams. In the images depicted in the pictorial diagrams, each circle is a node and is positioned so as to be visually aligned with a particular unit operation. That is, in the images depicted in the pictorial diagrams, it is located on a vertical dotted line extending downward from a text label identifying a particular unit operation, thereby identifying the data point related to that particular unit operation. (Connectivity) In some embodiments, the process graph may display the dependency or sequence of material and / or data flow from one unit operation to another via rendered connections between the various nodes. For example, as shown in the image depicted in the pictorial diagram, node connectivity may be rendered as lines connecting nodes across a timeline. (Data Connection Points) Specific points along the process where data is collected are marked, such as important checkpoints for quality control or measurements required for process evaluation. (Arms) In some embodiments, a process graph may include and / or display one or more (e.g., separate) arms, each representing a different experimental condition and / or baseline process variation. For example, in the image depicted in the pictorial diagram, the arms are rendered as various smaller graphs positioned below the timeline. Each line in the arms section indicates a different arm / condition as defined by the operator.Labels are provided to help clarify the focus of these steps and / or to distinguish and identify nodes as belonging to one condition, so that corresponding data collected from the laboratory are placed in the correct node and we do not mix data across conditions. In this case, the base process may be the step following a standard or control process, while other processes are steps that make experimental changes to that process or supplement the base process (e.g., making media, preparing materials, etc.). (Baseline Process and Base Process Graph) A baseline process may be a control and / or standard procedure and may be rendered as a base process graph. Text labels, color schemes, icon styles, positioning, and the like may be used to visually identify the baseline process, such as in graph-based visualizations. For example, in images presented in pictorial diagrams, the baseline process is identified and displayed as a node line directly below the timeline. In process graphs of images presented in pictorial diagrams, the baseline process may be the standard process against which other variations are compared. Variations and Outputs / Endpoints In some embodiments, a graph-based visualization can include one or more auxiliary subgraphs, each corresponding to and representing variations to experimental conditions and / or baseline versions of a manufacturing process. For example, the image depicted in the image diagram shows auxiliary subgraphs displaying variations labeled as "Ver1.1," "Ver1.2," "Ver2.1," "Ver2.3," etc., and "Condition 1," "Condition 2," "Condition 3," etc., representing different experimental arms or conditions. In this manner, the graph-based visualization tool can be used to communicate variations in materials used, process conditions, or specific unit operations performed. Additionally or alternatively, endpoints and / or outputs of different processes can also be displayed (e.g., via endpoint graphs).For example, as shown in the image depicted in the pictorial diagram, a series of nodes labeled "Out.1" and "Out.2" represent different endpoints and / or outputs of a process, such as different ways of processing or storing a final product. For example, in the exemplary subgraph shown in the image depicted in the pictorial diagram, lines refer to experimental conditions or interrelated subsets of a process, such as preparing materials (e.g., media) to feed a cell-containing process. Arm / condition sections help orient operators to the specific task being performed, and by identifying various arms in a descriptive manner, data collected in the lab are entered into corresponding nodes, and numbers / quantities, etc., are not mixed between conditions. Process graphs therefore facilitate the visualization and management of complex manufacturing processes. Among other things, they allow researchers to track the progress of experiments, compare different conditions or variations side by side, and ensure that data is systematically collected at designated points throughout the process. The ability to visualize the entire process in this way helps identify bottlenecks, ensure consistency, and facilitate data-driven decision-making.

This invention relates to a method for precise localization of breast tumors based on multi-level double-layer coding, comprising the following steps: acquisition of scattered microwave signals; signal processing and feature construction; multi-level data label coding based on a breast model containing the tumor target: each level contains double-layer coding, the first layer of coding is coarse-grained localization, and the second layer of coding is fine-grained localization; the localization interval of the initial level is determined based on the breast model containing the tumor target; given the constraints of the termination coding, the localization interval of the next level is determined based on the current level's localization interval after data label double-layer coding; model training and coding prediction output.

The invention relates to a method and system for predicting the service life of a filling body based on energy constraint fusion deep learning, and belongs to the technical field of health monitoring and service life prediction of a filling body structure, and the method comprises the steps: collecting a transient elastic wave signal of a filling body sample; performing multi-stage data cleaning processing on the transient elastic wave signal; on the basis of the cleaned data, utilizing a physical damage evolution model based on accumulated energy normalization to calculate damage stage characteristics; the multi-scale feature construction module is used for constructing multi-scale features based on the cleaned data; the original waveform sequence features, the multi-scale features and the damage stage features in the sliding window are input into a Transform deep neural network model based on multi-source information fusion, model training is carried out, and a prediction model is obtained; and utilizing the prediction model to predict the residual life of the filling body. The method can accurately predict the residual service life of the filling body structure in real time, and has important theoretical significance and practical application value.

The invention belongs to the technical field of industrial internet and distributed time sequence data management, and discloses an industrial internet distributed power time sequence database. The database is composed of a time sequence data acquisition unit, a dynamic partition storage unit, a node load balance scheduling unit, a multi-stage data consistency maintenance unit, a time sequence perception index construction unit, a data compression and time sequence aggregation unit, an asynchronous data recovery and fault switching unit and a query optimization and result cache unit. According to the method, a dynamic sliding partition algorithm is designed, and real-time partition adjustment of the data in the time dimension is achieved according to the characteristic of continuous high-frequency writing of the power time sequence data; the system can dynamically slide the partition boundary according to the current data writing rate and the node load state, data is prevented from being continuously and intensively written into a certain single area, and node writing inclination and local hot spot phenomena are remarkably reduced.

The present disclosure relates to a molecular memory device comprising a molecular memory unit that includes a first conducting layer, a second conducting layer, and an organic layer interposed between the two conducting layers. The organic layer comprises a compound selected from Formula I, Formula II, their salts, stereoisomers, solvates, or combinations thereof. These compounds exhibit multiple redox states and voltage-dependent geometric isomerism, enabling enhanced electronic switching behavior and multilevel data storage capabilities. The organic materials are sensitive to external stimuli such as voltage, light irradiation, and pH variations, allowing multifunctional integration of sensing, memory, and computing functionalities within a single device architecture. This present disclosure provides a scalable, energy-efficient, and cost-effective platform for advanced memory and neuromorphic computing applications.

The invention discloses an equipment state analysis method based on a multistage data architecture, and relates to the technical field of big data analysis, and the method comprises the steps: collecting equipment state data from a production control region of an electrochemical energy storage power station; performing protocol analysis, standardized conversion and measuring point mapping on the equipment state data in the management information large area of the electrochemical energy storage power station to generate time sequence measuring point data; transmitting time sequence measuring point data to a non-control area of the electrochemical energy storage power station in a one-way manner through a forward isolation device; the time sequence measuring point data are written into a Kafka cluster for message buffering in the non-control area, and the time sequence measuring point data are stored in a TD engine time sequence database by a consumption end program; based on a configuration algorithm tool provided by an algorithm platform, algorithm modeling and instantiation are carried out on the time sequence measuring point data, and an equipment state analysis algorithm is generated; and scheduling and executing an instance of the equipment state analysis algorithm through the algorithm center, and outputting an equipment state analysis result. The equipment state can be accurately analyzed.

The invention discloses an intelligent X-ray defect detection system for automobile parts based on the 5G industrial internet, and belongs to the crossing field of intelligent manufacturing and the industrial internet. The hardware part comprises a six-axis collaborative robot, a sorting manipulator, an X-ray imaging module and a high-precision rotary conveying table. The software part comprises a microfocus X-ray imaging system, a 5G edge computing gateway and a cloud defect analysis platform; the six-axis robot cooperates with the high-precision rotary conveying table to achieve 360-degree dead-corner-free scanning, high-definition imaging of the internal structure of each casting is ensured, and real-time data is uploaded to the cloud defect analysis platform through the 5G edge computing gateway for defect judgment; and the automatic sorting manipulator performs efficient sorting according to an identification result. According to the invention, X-ray imaging, 5G communication, machine vision and robot control technologies are integrated, and multi-stage data processing and intelligent algorithm fusion are carried out, so that the system significantly improves the detection efficiency and precision, effectively reduces the labor cost, guarantees the product quality, and promotes the intelligent upgrading of the automobile manufacturing industry.

The invention relates to the technical field of data processing, and discloses a crowd nutrition data intelligent distribution processing method and system.The crowd nutrition data intelligent distribution processing method comprises the steps that original crowd nutrition data are processed through a multi-level data representation algorithm, the data are mapped in a layered mode, and an interlayer association mapping matrix is constructed; multi-level nutrition data representation is formed; constructing a directed acyclic graph model with a causal relationship by adopting a structured causal model construction algorithm; an anti-fact scene generation and simulation algorithm is utilized to generate and evaluate the effects of various possible nutrition intervention schemes, an intervention combination is constructed, and anti-fact reasoning is carried out; constructing and solving a differential equation system for describing the long-term influence of nutrition intervention; adopting a heterogeneity analysis and intervention optimization algorithm to identify heterogeneity characteristics in the crowd and optimize a personalized intervention strategy; through the structured causal model and strict confounding factor control, the accuracy of nutrition intervention effect evaluation is improved.

The invention belongs to the technical field of breast cancer molecular typing, and more specifically relates to a mammary gland ultrasound video HER2 expression state identification method based on spatial-temporal feature interaction. The method comprises the following steps: taking an original breast ultrasonic video as input data, performing multi-stage data preprocessing operation, and then performing data balance on training data to obtain a standardized training set; sequentially constructing a UniFormerV2 network-based feature module, a multi-stage feature fusion module and a classification module, and training to obtain a trained model; and inputting the test set into the model to obtain a final prediction result of the HER2 expression state. The problems that an existing HER2 detection method is invasive in operation and cannot perform dynamic monitoring, and an existing deep learning method based on static images neglects space-time dynamic information in an ultrasonic video, so that prediction accuracy of the HER2 expression state, especially HER2-Low subtype is insufficient are solved.