138 results about "Process map" patented technology

The invention relates to an AI-based automatic adaptation method for a work approval process, and the method comprises the steps: collecting multi-source approval data, and carrying out the preprocessing of the multi-source approval data, and forming standardized approval data; analyzing the system rule text by using the pre-trained AI large model, and extracting a structured approval rule comprising a trigger condition, an approval role and a process node sequence; matching a basic examination and approval template according to the form field and the user permission information, and dynamically generating an adaptive process comprising multiple stages of examination and approval nodes, aging parameters and an additional examination and approval link; and through resource scheduling optimization and time domain correlation analysis, establishing an optimized mapping relation based on flow execution characteristics such as approval timeliness deviation and node skipping frequency, and outputting a visual flow chart and execution parameters. A business logic writing mode is replaced by automatic analysis of an AI large model on an unstructured rule; the dynamic template matching and continuous iterative optimization mechanism can quickly respond to business changes, and resource scheduling optimization and automatic process generation shorten the implementation period and reduce the operation and maintenance cost.

Recorded sessions of skilled personnel at work are used to train a first machine learning (ML) tool to extract workflow maps. These maps are used to train a second ML tool to emulate at least one workflow. The first ML tool is trained to predict an annotator's output. Either ML tool can be a copilot having a microservice network architecture. Further, complex sets of workflows can be subdivided for efficient support with small ML tools for (1) workflow map extraction and (2) emulation. Based on segment demarcation accompanying a recording, segments are assigned to specialized ML extraction tools for workflow map extraction. Extracted workflow maps are used to train workflow emulator(s). Similarly, specialized ML emulation tools can emulate respective tasks. A distribution microservice can identify a task to be emulated and can invoke the appropriate specialized ML tool. Similar microservice network architectures support both applications.

The invention relates to the technical field of process parameter optimization, and discloses a multi-process collaborative aluminum processing process parameter tuning method and system, and the method comprises the steps: constructing a process map through analyzing the incidence relation among a plurality of processes of aluminum processing; according to the process map, dynamically updating the priority of each process through a preset process competition mechanism to obtain a priority sequence; based on the priority sequence, analyzing the influence weight of the process parameter corresponding to each process, and layering the process parameters to obtain multi-layer optimization grade process parameters; the process parameters of each process are subjected to layered optimization through a preset parameter optimization model in combination with the process atlas and the multi-layer optimization grade process parameters, optimized process parameters are obtained, and adjustment and optimization of the aluminum machining process parameters are achieved; according to the method, the pertinence and the efficiency of multi-process technological parameter optimization can be improved, and the quality and the stability of aluminum processing products are improved through accurate adjustment of the technological parameters.

The invention relates to the technical field of data processing, and discloses a full-life-cycle-oriented engineering technology data governance method and a full-life-cycle-oriented engineering technology data governance system. The method comprises the steps that project object attributes are divided into an anchor point attribute set and an extended attribute set, a locking mark is added to the anchor point attribute set, the extended attribute set is layered according to stages to obtain a data object meta-model, a dependency relationship mapping table is established according to the data object meta-model to achieve automatic verification when the attributes are changed, and the attribute change is completed. And establishing a spatial topological graph and a process graph to diagnose inconsistent defects, establishing a weighted graph to quantitatively analyze change influences, evaluating data quality according to scene types, and intelligently complementing missing attributes. According to the method, the integrity and consistency of cross-stage transmission of engineering data, the real-time performance and accuracy of multi-professional data collaboration, the depth and automation degree of data quality diagnosis, the scientificity and comprehensiveness of change impact analysis and the flexibility and efficiency of data utilization are improved.

The invention relates to the technical field of industrial automation control, in particular to an industrial signal stage matching method based on a process map, and aims to solve the problems that in the prior art, an accurate corresponding relation between signal characteristics and process stages is difficult to capture by manually set static weights; and the time sequence offset between the actual signal and the standard process map cannot be effectively processed. The method effectively solves the problems of subjectivity, static property and deviation non-traceability of manual weight setting by constructing a standardized process map and matching industrial signal features in real time and combining dynamic weight distribution and a self-adaptive adjustment mechanism, and has the advantages of improving control precision, enhancing dynamic adaptability and improving decision reliability.

The invention discloses a laboratory intelligent management and remote review system, and relates to the field of laboratory management, and the system comprises a standard atlas database construction module which is used for generating a standard process atlas of various sample detection tasks; the sample process map generation module is used for collecting actual operation processes of experimenters and generating map representation; the anomaly detection module is used for carrying out structure comparison on the actual process map and the standard process map and identifying an abnormal process path; the abnormal graph learning module is used for classifying the abnormal process graph into an abnormal graph library and carrying out clustering and labeling processing; the abnormal evolution prediction and early warning module is used for predicting a process evolution trend based on a graph neural network model and giving out an early warning when the process evolution trend deviates from a standard; and the remote review intelligent auxiliary module is used for generating a structured review report. According to the method, structured modeling of the laboratory process, inductive precipitation of the abnormal mode and prospective early warning of the process risk are realized.

Recorded sessions of skilled personnel at work are used to train a first machine learning (ML) tool to extract workflow maps. These maps are used to train a second ML tool to emulate at least one workflow. The first ML tool is trained to predict an annotator's output. Either ML tool can be a copilot having a microservice network architecture. Further, complex sets of workflows can be subdivided for efficient support with small ML tools for (1) workflow map extraction and (2) emulation. Based on segment demarcation accompanying a recording, segments are assigned to specialized ML extraction tools for workflow map extraction. Extracted workflow maps are used to train workflow emulator(s). Similarly, specialized ML emulation tools can emulate respective tasks. A distribution microservice can identify a task to be emulated and can invoke the appropriate specialized ML tool. Similar microservice network architectures support both applications.

Methods and apparatus customize machine learning (ML) tools including copilots. Document records related to tool objectives or tasks are used to identify corresponding data sources, which are integrated into data producers or data repositories accessible to a retrieval microservice. Data producers and other microservices are individually fine-tuned for the custom application and integrated into the tool. The integrated ML tool can be further refined. Training data combines expert-generated, developer-generated, and / or synthesized items. Some customized tools are trained and deployed to: conduct or monitor interviews; provide feedback; develop workflow maps, segmentation, or other annotation from interview scripts or recorded work sessions; or emulate workflows. Using workflow maps, other ML tools are trained to emulate associated workflows. Complex workflows can be subdivided for efficient support with small ML tools for workflow map extraction and also for emulation. Task microservices within a network of microservices handle respective subdivided work.

The invention relates to the technical field of model cross-scene application, and discloses a data model cross-scene management method and system.A judgment matrix is constructed through an analytic hierarchy process to quantify cross-scene demand importance, cross-scene demand priorities are sorted, a cross-scene flow chart is drawn, and a standardized data management system is constructed. A cross-scene data model is designed by adopting a layered architecture based on a standardized data management system, system docking and monitoring are realized through an AP I interface, service attribute characteristics and interface acquisition information of a scene are extracted, a scene data scale type and a real-time type are divided through a clustering algorithm, and a scene data real-time model is obtained; and distributing a basic computing power strategy for the scene according to the established computing power distribution strategy database, judging the data feature stability of the cross-scene data model in each scene by calculating a comprehensive drift index, and if the stability is low, triggering computing power distribution adjustment.

Intent-based automation that discovers automatable tasks and / or determines task variants in data is disclosed. Task capture data may be utilized to determine task variants in task mining data. Semantic understanding of user actions by artificial intelligence (AI) / machine learning (ML) model(s), for example, may be applied to determine the intent of the user rather than only focusing on what actions the user is performing on the computing system. Application logs and semantic understanding may be used to facilitate a more accurate determination of what the user actually intends to do. Task capture for individual user flows may be performed. Once these are captured, task capture algorithms and AI / ML models are used to determine which parts of the flows are similar and / or match and which parts are unique. The path through these flows can then be followed to build a process graph that includes decision points representing the unique flows.

The invention provides a test case sequence generation method and device, equipment and a storage medium. The method comprises the following steps: acquiring calling data of an interface in a business process in real time by adopting a probe; for each interface, multi-dimensional data features are extracted according to the corresponding calling data, and the data features comprise data fingerprints, semantic vectors and structural hash; performing feature matching according to the data feature of each interface to determine a dependency relationship between the interfaces; constructing an interface business flow chart according to the dependency relationship between the interfaces; and generating a test case sequence according to the interface business flow chart, associating the test case sequence with the static data template, and executing a test through dynamic injection parameters. According to the method, the interface dependency relationship can be accurately and automatically identified by extracting multi-dimensional features such as data fingerprints, semantic vectors and structural hash from the calling data and matching the multi-dimensional features, the interface business flow chart is constructed based on the dependency relationship, the test case sequence is generated according to the interface business flow chart, and the test efficiency and reliability are improved.

The invention discloses a business process processing method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence. The method comprises the following steps: converting target voice data of a to-be-handled service of a user into a voice text; identifying a target business intention corresponding to the voice text; determining a business flow chart corresponding to the target business intention; and in the process of handling the to-be-handled business by the user, generating operation guidance information based on the business flow chart, and displaying the operation guidance information. According to the technical scheme provided by the invention, the user can be guided to complete the business process operation, the use threshold of the digital service is reduced, and the business handling efficiency and the use experience feeling of various users are improved.

The invention discloses an inspection task configuration method and system based on a mandatory behavior interface and a structured scene file, and the method comprises the steps: providing functional module items which forcibly achieve a unified behavior interface in a graphical designer, and enabling a user to form an inspection flow chart through dragging; writing the flow chart into a structured scene file through a serialization method defined by an interface; the emulator and the deployment tool load the file respectively, function module items are reconstructed through deserialization, an execution method of the function module items is directly called for logic verification, or robot instructions are generated through analysis. According to the method, a unified interface is used for binding graphs and logics, a single scene file is used for connecting the whole process of design, simulation and deployment, the problems that in a traditional scheme, operation is complex, and data splitting and semantics are inconsistent are solved, the purpose that line is achieved through programming is achieved, the configuration threshold is remarkably lowered, and development efficiency and system reliability are improved.

The invention relates to the technical field of equipment fault root cause analysis and operation and maintenance management and control, and discloses an equipment operation and maintenance intelligent analysis and management and control method and system based on a fault root cause. Constructing a knowledge graph which contains causal association rules of all stages and is updated regularly; performing machine language processing, introducing a time dimension to form a time sequence fault mode knowledge graph, and designing an operation and maintenance large language model cue word containing a dynamic time window instruction; inputting processed map data, equipment full life cycle parameters and operation and maintenance demand natural language description, and outputting a fault root cause identification result considering full life cycle factors; and finally, automatically generating a preventive operation and maintenance scheme according to a result, updating atlas data, and dynamically adjusting the priority of the scheme in combination with a fault root cause weight and an operation and maintenance priority mapping model to form a closed-loop management and control mechanism so as to realize accurate analysis of equipment faults and beforehand efficient operation and maintenance.

The invention relates to the technical field of line identification of pixel-level process pipelines and instrument flow drawings, in particular to a line identification method and device of a process pipeline and instrument flow chart, and the method comprises the steps: identifying a target process pipeline and instrument flow chart, so as to search the initial line information of each component; performing line search and identification according to the initial line information of each component so as to obtain multiple pieces of line connection information; traversing each line connection information to determine the line type of each connection line and whether the line has an arrow and an arrow direction; searching connection information of each line by taking each component and each line as a main body to obtain component connection information and line comprehensive information; and carrying out comprehensive processing on the initial line information, the multiple pieces of line connection information, the component connection information and the line comprehensive information of each component so as to generate structured data. Therefore, the problem that accurate identification of line types and directions cannot be completely realized in the prior art is solved.

The invention discloses an aviation order analysis method and device, electronic equipment and a storage medium, and relates to the technical field of aviation information or other related fields, and the method comprises the steps: receiving to-be-analyzed target order data, and verifying the target order data based on a preset verification strategy; under the condition that verification is passed, service entity information is extracted from the target order data based on a preset extraction strategy, and the preset extraction strategy is used for specifying an extraction field and a field association relationship; a time sequence correlation model of the target order is constructed based on the business entity information by adopting a time sequence analysis algorithm, and the time sequence correlation model is used for recording all historical business operations of the target order according to a time sequence; and converting the time sequence correlation model into a business flow chart, and rendering the business flow chart to a display area. According to the invention, the technical problem of low data analysis precision caused by poor process data integration capability of an aviation system for complex orders in the prior art is solved.

The invention relates to the technical field of data processing. The natural resource right confirmation service intelligent collaboration method, device and equipment and the medium are provided, and the method comprises the following steps: analyzing a cross-department right confirmation service through a process modeling technology, and generating a visual flow chart comprising a responsibility boundary and a collaboration node; performing packaging processing on the right confirmation link through a micro-service packaging technology, generating an independent micro-service unit, and constructing a template library associated with resource types and policy labels; sorting the independent micro-service units to generate an optimized execution path; performing visual monitoring processing on the execution state through a workflow engine to generate a blocking point report; triggering resource allocation processing based on the blocking point report, and generating a dynamic resource allocation scheme; and updating the template library and the rule library to generate the adaptive collaborative system, so as to achieve the technical effects of improving the dynamic adaptive capacity of the process, reducing the rule conflict rate, optimizing the resource scheduling efficiency and enhancing the collaborative consistency of the template library and the rule library.

The model rendering method provided by the invention comprises the following steps: obtaining a three-dimensional model and a map corresponding to the three-dimensional model, creating a main body material and a shadow material, and carrying out stylization processing on the map to obtain a processed map; wherein the stylized texture comprises applying a drawing texture filter and / or adding a texture effect, distributing the processed chartlet to a corresponding material channel, performing rendering calculation based on the three-dimensional model and the material channel, and outputting a rendered image. According to the method provided by the embodiment of the invention, diversified artistic style rendering effects can be realized by performing stylization processing on the chartlet and creating the special material type, and compared with traditional single physical authenticity rendering, artistry and flexibility of visual performance are greatly improved; and meanwhile, through standardized material distribution and rendering processes, the automation degree and the processing efficiency of rendering operation are remarkably improved.

A method and apparatus for verifying updated software may be associated with an infotainment platform of a vehicle. The software verification method includes receiving data for software verification from an external server or external device, generating a flow map based on the received data, updating the flow map based on updated change specifications of software, reconfiguring the updated flow map based on estimated usage frequency of a display identifier (ID) added in the updated change specifications of the software, and verifying the changed specifications of the software by performing tests on the reconfigured flow map, where the display ID is a unique identifier assigned to each screen displayable during a software execution process.

This disclosure is directed to a method for modeling additive manufacturing of a part, including a number of steps. The steps include constructing a model for estimating output of a simulated additive manufacturing process, followed by entering process operating parameters for an additive manufacturing system into the model to produce an output. The output is compared to acceptance criteria to determine whether the output is acceptable or unacceptable. Next regions of operating parameters that support production of the part with acceptable quality characteristics are determined based upon the output. Regions of operating parameters that support production of the part with acceptable quality characteristics are added to a process map for additive manufacturing the part. The steps are repeated for different operating parameters until the process map is complete.

The invention discloses a method for converting a water treatment process flow diagram into a graph model, which comprises the following steps of: 1, identifying a graph and a character identifier in a water treatment process flow diagram to be converted by using an image and text identification algorithm; 2, extracting engineering project information, a technological process topological relation, technological unit information and technological unit part information in the water treatment technological process diagram; 3, dividing nodes of the graph model into three layers of mother nodes, child nodes and grandchild nodes which are connected with a topological relation step by step; 4, obtaining engineering project information; 5, converting into a child node and child node connection relationship in the graph model; step 6, acquiring information of each process unit in the process flow diagram; 7, converting the flow data and inlet and outlet water quality data of each process unit into attributes of a node connection relationship; and step 8, acquiring part information of each process unit. The method has the beneficial effect that the conversion rate of the water treatment process flow diagram to the digital diagram model can be improved.

The invention discloses a work order processing map interaction system and method for multi-dimensional screening and personnel positioning, and relates to the technical field of geographic information. Comprising the steps that 1, a work order processing map interaction system oriented to multi-dimensional screening and personnel positioning is created; 2, a front-end interaction module sets a screening control set in the area of a map interface; 3, the back-end data processing module responds to screening operation of a user on a map interface, obtains a first screening condition selected by the user, obtains corresponding in-transit work order data from the server and sends the in-transit work order data to the front-end interaction module, and the front-end interaction module sends the in-transit work order data to the back-end data processing module; 4, rendering the preset number of work order data or all the in-transit work order data on the map interface by the front-end interaction module, presenting the work order data or all the in-transit work order data in the form of markers, and responding to the click operation of a user on the markers on the map interface by the front-end interaction module. And executing operation according to the authority information of the user and the work order attribute corresponding to the marker.

The invention relates to the technical field of artificial intelligence, and discloses a conference summary automatic generation method and device, equipment and a medium, and the method comprises the steps: obtaining a conference image, a conference text and conference voice of a target conference, carrying out the text conversion of the conference voice, obtaining a converted text, and storing the converted text; performing cross-modal semantic alignment on the conference image, the conference text and the converted text sequence to obtain a semantic network, extracting structured information of the semantic network, and constructing a conference discussion process map based on the converted text and a timestamp of the structured information, generating a discussion content summary according to the conference discussion process graph, constructing a conference knowledge graph according to the semantic network, generating a conference hierarchical abstract according to the conference knowledge graph, and summarizing the discussion content summary and the conference hierarchical abstract to obtain the conference content summary, thereby improving the accuracy of the conference summary.

A method for visualizing a process map is executed by a process map server. The method includes receiving a flowchart and a step-by-step recording related to a process. Generating a process map by combining the flowchart and the step-by-step recording and displaying the process map. The process map displays a task, step, and action related to the process. A detail window shows information associated with the process, and portions of the process, in response to user input. The action is based on information from the step-by-step recording.

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.

The embodiment of the invention provides a map task processing method and device, equipment, a medium and a program product, and can relate to the field of maps, and the method comprises the following steps: creating task link configuration of a target map task; wherein the task link configuration comprises a production line code of a map production line to which the target map task belongs, and link codes and parameter configuration of each operation link of the target map task; when the target map task is created, reading task link configuration of the target map task; and after the target map task is created, if the historical task link flow chart comprises each operation link of the target map task, executing the target map task based on the historical task link flow chart and task link configuration of the target map task. When the historical task link flow chart comprises each operation link of the target map task, the historical task link flow chart does not need to be modified, and the historical task link flow chart is directly used, so that the execution efficiency of the map task can be improved.

The invention discloses a large model-based document handling flow knowledge base construction and multi-round memory question and answer method, which comprises the following steps of: respectively acquiring region information associated with document handling items and flow key information corresponding to various document handling items to form a target document; performing knowledge segmentation on data in the target document, performing vectorization processing on segmented contents by adopting an open source semantic vector and a reordering model, storing a result into a local vector database to construct a file handling flow knowledge base, and configuring a target function; an original retrieval question input by a user is processed in combination with the constructed multi-round memory question and answer intelligent model and the document handling flow knowledge base, reply content is output in a point division mode, and the reply content is automatically associated with a direct link corresponding to a document handling item and a document handling flow chart; and updating the file handling process knowledge base based on question feedback and executing a new round of memory questions and answers.

The present invention provides a method and apparatus for analyzing the security of an integrated circuit supply chain site. The method includes obtaining evaluation factors, where the evaluation factors include: service type factors, site entity factors, process factors, and process flow factors; extracting, from a control flow chart database based on a preset mapping relationship, a control flow chart of an integrated circuit supply chain site service that is adapted to the evaluation factors, where the preset mapping relationship stores the mapping relationship between the evaluation factors and the control flow chart of the integrated circuit supply chain site service; extracting asset elements included in the control flow chart of the integrated circuit supply chain site service; and retrieving preset threat analysis data corresponding to the asset elements from a preset database.

This invention discloses a rapid design tool and its application in process piping and instrumentation diagrams, relating to the field of engineering drawing technology. It includes: a data interaction module for exchanging data between drawing files loaded in the CAD platform and external data files, enabling the export of drawing data and / or drawing updates based on external data; an intelligent positioning module connected to the data interaction module for locating and highlighting corresponding graphic elements in the drawing file based on user-input search criteria; and an Excel integration module connected to the data interaction module for processing Excel data files associated with the drawing file, performing automatic generation of equipment tag numbers and / or merging of data from multiple tables. In summary, the advantages of this invention are: by integrating a lightweight plugin into a mature CAD platform, it achieves bidirectional intelligent interaction and efficient synchronization between drawing data and external table data.

The present invention discloses a method for implementing a process-driven dialogue system and a dialogue robot based on a workflow engine. Before designing a process-driven dialogue system, business requirement analysis should be carried out first, and at the same time, corresponding corpus information such as dialogue intents, corpus, slot values, etc. should be prepared. Secondly, break down the complex business into processes, and then create a BPMN file to draw a dialogue flow chart. After that, build a process engine, deploy the corresponding dialogue flow chart, upload the corresponding corpus to the database, and write the corresponding corpus into the natural language understanding configuration file of the traditional dialogue system. Train the traditional dialogue system while deploying. After training is completed, conduct tests. Finally, human-machine dialogue can be carried out, and then the dialogue system can be iteratively optimized. The present invention uses a process engine and a traditional dialogue system, and can quickly build a dialogue robot with low code, and ordinary users can also quickly build a dialogue system.