A data aggregation method and device based on a digitized flowchart

Abstract

The application discloses a data summarization method and device based on a digital flowchart, wherein the method comprises the following steps: step S1, designing a data interface suitable for various business systems, defining a unified data model for each data interface, and realizing standardized access of multi-source data; step S2, in the digital flowchart drawing stage, predefining a field for binding a data source based on the attribute of a graph element; establishing an association mapping between the graph element field and the data source to obtain a data mapping table, and binding the graph element field with the related data source based on the data mapping table; step S3, dynamically updating the related data into the corresponding graph element field based on the data interface of the related data source, combining all fields of each graph element to establish a data point bound with the graph element, and obtaining a flowchart after data summarization and binding.

Description

Technical Field

[0001] This invention relates to the field of nuclear power business data aggregation technology, and in particular to a data aggregation method and apparatus based on digital flowcharts. Background Technology

[0002] During the operation and maintenance of nuclear power plants, various business data are scattered and stored in multiple independent systems, such as the real-time database PI, the equipment management system EAM, the work order system, the isolation system, and the spare parts inventory system. Maintenance personnel need to frequently switch between multiple interfaces and manually link information to fully understand the equipment status, which is inefficient and prone to missing key information. The data source is unclear and the reliability is uncertain, making it difficult to support accurate decision-making. Anomaly detection relies on human experience and cannot achieve collaborative early warning of multi-source data. Summary of the Invention

[0003] In view of this, it is necessary to provide a data aggregation method and device based on digital flowcharts to effectively solve the technical problem of scattered storage and difficulty in linking various business data in nuclear power plants, which reduces the efficiency of data association applications.

[0004] This invention provides a data aggregation method based on digital flowcharts, comprising the following steps: Step S1: Design compatible data interfaces for various business systems, define a unified data model for each data interface, and realize standardized access to multi-source data; Step S2: In the digital flowchart drawing stage, predefine fields for binding data sources based on the attributes of graphic elements; establish the association mapping between graphic element fields and data sources to obtain a data mapping table; and bind graphic element fields to relevant data sources based on the data mapping table. Step S3: Based on the data interface of the relevant data source, dynamically update the relevant data to the corresponding graphic element fields, and combine all fields of each graphic element to establish data points bound to the graphic element, so as to obtain the flowchart after data summary and binding.

[0005] Preferably, step S1 specifically comprises: Design a real-time data interface compatible with the real-time database to acquire equipment measurement point data; design a compatible equipment master data interface for the equipment management system and document management system; design a compatible work order and defect interface for the work order system; design a compatible isolation information interface for the isolation system; design a compatible spare parts inventory interface for the ERP system and inventory system; design a compatible inspection data interface for the inspection system; and design a compatible document system interface for the document management system and document security management system. A unified data model is defined for each of the data interfaces, and the data model includes the data source identifier, data collection time, data validity period, and initial data confidence value.

[0006] Preferably, in step S2, establishing the association mapping between primitive fields and data sources to obtain a data mapping table, and binding primitive fields to relevant data sources based on the data mapping table, specifically: A data mapping table is obtained by establishing an association mapping between graphic element fields and data sources. The data mapping table is stored independently of the drawing data. Based on the device code of the graphic element, the corresponding data in each of the aforementioned business systems is automatically matched, and then bound to the corresponding graphic element field; If automatic matching fails, manually bind the graphic element field to the corresponding data source; If there is batch binding within the same system, then batch binding will be performed by device type or by system type.

[0007] Preferably, the method further includes establishing a data lineage chain for the data points on the flowchart and recording data lineage information; the data lineage information includes the data source system, original acquisition time, access time, data link, and data confidence level; the data confidence level includes source system confidence level, timeliness confidence level, matching confidence level, and historical stability, and a comprehensive confidence level is calculated based on all the confidence level information; changes to key selected data are recorded using a hash chain; and the data lineage chain is displayed on the data points.

[0008] Preferably, it also includes anomaly collaborative detection based on multi-source data associated with the data points; the anomaly collaborative detection includes consistency detection, logical verification, time-series anomaly detection, and multi-source fusion analysis; The consistency detection is as follows: set consistency detection rules for data points that need to maintain consistency, detect related data points based on the consistency detection rules, and issue a prompt when the deviation value is greater than a set threshold; The logical verification is as follows: based on the correlation between data points and the data characteristics of the data associated with the data points, corresponding logical verification rules are set, and logical verification of the corresponding data points is performed based on the logical verification rules. The time series anomaly detection is as follows: a time series algorithm is used to predict the normal value range of data points, and an alarm is triggered if the real-time data of the data points exceeds the normal value range. The multi-source fusion analysis involves: performing correlation analysis on multi-source data, setting fusion analysis rules, and implementing equipment fault early warning based on the fusion analysis rules; establishing an equipment lifespan model and predicting the remaining lifespan of the equipment by combining multi-source data.

[0009] Preferably, corresponding visualization methods are set for different data types contained in the data points to achieve diversified display of the data points at the corresponding graphic elements; The aggregated data is divided into multiple independent layers according to data type, and each layer is subject to independent display / hiding control. Users can customize data dashboards, set the associated data sources, display areas, display formats, and custom composite indicators for the data dashboards, and display the image and data points of the corresponding devices based on the data dashboards; save the configuration of the data dashboards as dashboard templates, classify the dashboard templates according to device types, and apply the corresponding dashboard templates to multiple similar devices to achieve batch application of the data dashboards; Save the current flowchart configuration information as a personal view, share the view with other users, manage different views for versions, backtrack historical configurations based on view versions, and compare different version views.

[0010] Preferably, the method also includes performing correlation analysis based on aggregated data from multiple sources to recommend data to users: recommending related devices to the user based on the device topology relationship in the flowchart; generating collaborative filtering recommendations for the user based on the historical operations of similar users; recommending relevant data to the user based on the current operating mode; identifying the context of the user's current operation based on the business scenario and recommending relevant data to the user based on the context; and recommending comparative data of other devices of the same model based on the user's currently viewed device. Record user feedback on recommendations and use the feedback to optimize the recommendation model; record the recommendation adoption rate to evaluate the effectiveness of the recommendation model; It also includes multi-dimensional global search based on aggregated data from multiple sources: automatically locating the corresponding device in the flowchart based on device code or device name; listing all relevant flowcharts based on system name and locating the corresponding system area in the flowchart; searching for all devices involved in the corresponding work order based on work order number and locating the corresponding flowchart; searching for all relevant flowcharts or devices based on fuzzy keywords; and performing searches based on combined conditions.

[0011] Preferably, it also includes saving snapshots of the current summary data of the flowchart; confirming the effect of the event based on the comparison of snapshots before and after the event; freezing the on-site state based on the snapshot after the accident for subsequent accident analysis; periodically and automatically saving snapshots to form a historical state archive; comparing any two snapshots and prompting the changed parts to generate a comparison report; setting a timeline control to associate the timeline with snapshot points to realize snapshot backtracking based on the timeline.

[0012] Preferably, it also includes setting differentiated refresh strategies for multi-source data: push refresh or short polling refresh for real-time data; event-triggered refresh or long polling refresh for work order and defect data and isolated data; local caching for equipment master data and reloading when the version changes or periodic synchronization; and on-demand refresh or timed synchronization for spare parts inventory data. A data pool collaboration mechanism is set up for multi-source data: a unified data pool is built and maintained, which is used to maintain the data list required by all current flowcharts; the latest data is periodically pulled from each of the business systems according to the configured data refresh strategy and stored in the data pool; each front-end instance pulls data from the data pool as needed, and only requests data once when the same device is used by multiple flowcharts; the data pool also records the number of subscribers for each data, and stops refreshing and releases resources when no one is subscribed.

[0013] The present invention also provides a data aggregation device based on a digital flowchart, including a memory and a processor. The memory stores a computer program, which, when executed by the processor, implements the data aggregation method based on the digital flowchart.

[0014] Compared with existing technologies, the advantages of this invention are as follows: This invention uses a flowchart as its core carrier to aggregate multi-source heterogeneous data into a unified view. Simultaneously, the multi-source data is aggregated around graphic elements as central points, achieving an organic combination of flowcharts and summarized data. Maintenance personnel do not need to switch between multiple systems; a single diagram is sufficient to grasp the overall picture of the equipment. Attached Figure Description

[0015] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a flowchart of an embodiment of a data aggregation method based on a digital flowchart provided by the present invention. Detailed Implementation

[0016] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0017] Example 1

[0018] Please see Figure 1 The data aggregation method based on digital flowcharts in this embodiment specifically includes the following steps: Step S1: Design compatible data interfaces for various business systems, define a unified data model for each data interface, and realize standardized access to multi-source data; Step S2: In the digital flowchart drawing stage, predefine fields for binding data sources based on the attributes of graphic elements; establish the association mapping between graphic element fields and data sources to obtain a data mapping table; and bind graphic element fields to relevant data sources based on the data mapping table. Step S3: Based on the data interface of the relevant data source, dynamically update the relevant data to the corresponding graphic element fields, and combine all fields of each graphic element to establish data points bound to the graphic element, so as to obtain the flowchart after data summary and binding.

[0019] The data aggregation method based on digital flowcharts provided in this embodiment uses flowcharts as the core carrier to aggregate multi-source heterogeneous data into a unified view, enabling data lineage traceability, anomaly collaboration detection, and intelligent information recommendation, thereby significantly improving the efficiency of operation and maintenance information retrieval and the accuracy of decision-making.

[0020] The following provides a detailed explanation of each step.

[0021] Step S1: Multi-source data interface adaptation A standardized data access framework is established to connect with various business systems, including real-time data interfaces, equipment master data interfaces, work order and defect interfaces, isolation information interfaces, spare parts inventory interfaces, inspection data interfaces, and document system interfaces. The real-time data interface connects to real-time databases such as PI and DCS to obtain equipment measurement point data, such as temperature, pressure, flow rate, valve opening, and pump start / stop status. It supports multiple access methods, including OPC UA, API, and direct database connection, and configures data refresh frequencies, such as 1 second for high-frequency data and 1 minute for low-frequency data. The equipment master data interface connects to EAM / ASP-1 to obtain information such as equipment code, equipment name, model specifications, installation location, equipment engineer, commissioning date, and design life. The work order and defect interface connects to the work order system to obtain ongoing work orders, historical work orders, defect records, and maintenance records, supporting queries by equipment code and time range. The isolation information interface connects to the isolation system to obtain the current isolation tag status of the equipment, such as tag placement / removal / temporary removal, isolation instructions, and isolation scheme number. The spare parts inventory interface connects to the ERP / inventory system to obtain spare parts codes, names, current inventory quantities, safety stock thresholds, quantities in transit, and storage locations. The inspection data interface connects to the inspection system to obtain the latest inspection values, historical trends, over-limit alarms, and inspection routes. The document system interface connects to ECM / DOCM to obtain equipment-related technical documents, drawings, experience feedback, and operating procedures.

[0022] Define a unified data model for each interface, including metadata fields such as data source identifier, data collection time, data validity period, and initial data confidence value, to shield the differences in the underlying system and achieve standardized data access.

[0023] Step S2, Dynamic Binding of Graphic Elements and Data During the drawing phase, predefined bindable data fields are defined for each graphic element, such as equipment codes corresponding to master data, measurement point IDs corresponding to real-time data, and material codes corresponding to spare parts data. An association mapping is established between graphic elements and data sources, i.e., data point matching, including three methods: automatic point matching, manual binding, and batch binding. Automatic point matching automatically matches measurement points in the real-time database, equipment records in EAM, and work orders in the work order system based on the graphic element's equipment code. The matching algorithm employs multi-level rules, such as exact matching, rule matching, fuzzy matching, and semantic matching, and outputs the matching confidence score. Manual binding provides a visual manual binding interface for cases where automatic point matching fails or special associations are required. Users can drag and drop graphic elements to the data source to complete the association; the system records manual binding operations for subsequent optimization. Batch binding supports batch binding by equipment type and system, such as binding all valves in the same system to the same pressure measurement point template.

[0024] The binding relationship is stored in a separate "data mapping table". Each mapping record contains element ID, data source ID, data field, binding time, binding method such as automatic / manual, confidence level, validity period, etc., which is decoupled from the drawing data and facilitates maintenance and updates.

[0025] Step S3: After setting up the data interface and binding the data source, dynamically update the relevant data to the corresponding graphic element fields. Combine the fields of all the data sources bound to the graphic elements to establish summary data points and obtain the digital flowchart after data summary and binding.

[0026] Furthermore, this includes data lineage tracing and reliability assessment, establishing a complete data lineage chain for each data point displayed on the drawing, recording the data source system, original acquisition time, access time, data link, and data confidence level. The data source system refers to the original source of the data, such as PI / EAM / work order / isolation / ERP systems, including system name, interface version, and data source health status. The original acquisition time is the timestamp of the data generation in the source system, such as the measurement point acquisition time. The access time is the timestamp of the data being acquired and processed by this system. The data link includes the middleware, interface services, and transformation rules the data passes through, such as unit conversions and calculation formulas. Data confidence level is dynamically calculated based on multiple dimensions, including source system confidence, timeliness confidence, matching confidence, and historical stability. Source system confidence score: the reliability score of the source system, such as 99.9% for the PI system and 90% for manual entry; Timeliness confidence score: the freshness of the data, calculated based on the difference between the current time and the collection time; Matching confidence score: the confidence score output by the matching algorithm when automatically matching points; Historical stability: the historical fluctuation of the data, with abnormal fluctuations reducing the confidence score; Overall confidence score = Source confidence score × Timeliness coefficient × Matching confidence score × Stability coefficient.

[0027] The flowchart visually presents data lineage and confidence levels. Confidence levels are indicated by color coding: green for high confidence (≥90%), yellow for medium confidence (60%-89%), and red for low confidence (<60%). Data lineage cards pop up when the mouse hovers over data, displaying the complete data lineage chain, including the source system, collection time, access time, and confidence level indicators. Data source status icons are displayed in the corner of the flowchart when the data source is abnormal, such as when the PI server is offline; clicking the icon displays detailed alarms. Hash chaining is used to record changes to critical data, ensuring that important operational data, such as isolation command execution and work order completion, is traceable and tamper-proof, meeting nuclear safety audit requirements.

[0028] Furthermore, it also includes multi-source data anomaly collaborative detection, which combines multiple data sources for cross-validation, executes detection logic, and discovers anomalies that cannot be identified by a single data source. Specifically, it includes consistency detection, logic verification, time-series anomaly detection, and multi-source fusion analysis.

[0029] Consistency verification: Compare upstream and downstream pressure measuring points. If the difference exceeds the threshold and there is no throttling element, it is judged as "possible measuring point failure or pipeline leakage". Compare redundant measuring points, such as triple redundant pressure switches. If two measuring points are consistent and the third deviates, it is judged as "the third measuring point may be faulty". Compare real-time values ​​with inspection values. If the deviation is too large, it is indicated that "the instrument may be drifting or there may be a manual input error".

[0030] Logic verification: If the pump is in "Running" status but the current is 0, it is determined that "the current measuring point may be faulty"; if the valve is in "Open" status but the downstream pressure is 0, it is determined that "the valve is not actually open or the measuring point is faulty"; if the equipment has an ongoing work order but the real-time data does not change for a long time, it is determined that "the work order may not have been actually executed".

[0031] Time series anomaly detection: Employ time series algorithms, such as ARIMA and exponential smoothing, to predict the normal range of values. An alarm is triggered if real-time data exceeds the range. Detect data mutations, drifts, and periodic anomalies, such as a sudden increase in vibration values. Detect frozen data, i.e., data that remains unchanged for an extended period.

[0032] Multi-source fusion analysis: Real-time data, inspection data, work order data, and historical fault data are correlated and analyzed. For example, if "vibration value increases + no recent maintenance record + increase in failure rate of similar equipment", an early warning will be pushed, indicating that "the equipment may be entering the early stage of failure". Combined with the equipment life model, the remaining life is predicted based on the cumulative operating time and historical maintenance records.

[0033] Anomaly detection results are also displayed visually on the flowchart. Anomaly devices are marked with exclamation mark icons, with colors representing severity levels: red = severe, yellow = warning, and blue = alert. Clicking the icon brings up an anomaly details card, including the anomaly type, detection basis, confidence level, and recommended troubleshooting steps. It supports one-click generation of work orders or notifications to relevant personnel, such as sending to the shift supervisor's console or pushing to mobile devices.

[0034] Furthermore, it includes the visualization of aggregated data points, employing diverse visualization methods based on data type. Status colors allow equipment elements to change color according to their real-time status, such as pump running = green, pump stopped = gray, pump fault = red, valve open = green, valve closed = gray, valve fault = red; Dynamic numbers display real-time values ​​directly next to instrument elements, supporting customizable decimal places, units, and color changes (normal black, over-limit red); Icons display different business icons at fixed positions in the four corners of the equipment: work order in the upper left, defect in the upper right, isolation in the lower left, and spare parts warning in the lower right; Mini trend charts pop up a small trend chart for the past 24 hours when the mouse hovers over the device, visually displaying the changing trends; Pop-up cards display information cards when the mouse clicks on the equipment, summarizing all related data for that equipment in tab format (overview, real-time, work order, spare parts, document, history); Heatmap overlay generates maintenance frequency heatmaps and anomaly distribution heatmaps on the drawing background; Hyperlinks allow clicking the document icon to directly jump to the document system to view the original file, and clicking the work order number to jump to the work order system.

[0035] Furthermore, it includes data layering and layer control, dividing the aggregated data into multiple independent layers according to type, with each layer's visibility controllable independently. Specifically, this includes: a real-time data layer displaying the device's current operating parameters, status color, and dynamic values; a work order / defect layer displaying in-progress work order icons (yellow wrench), defect icons (red triangle), and historical work order distribution; an isolation status layer displaying isolation sign icons, isolation border colors, and an isolation instruction list; a device information layer displaying device master data, technical parameters, and document links when the mouse hovers over or clicks; an inspection data layer displaying the latest inspection values, deviations from real-time values, and inspection over-limit alarms; a spare parts information layer displaying spare parts inventory status and inventory warning icons; a maintenance history layer displaying a heatmap of maintenance frequency and historical maintenance trajectories; and an anomaly alarm layer displaying anomaly icons detected by collaborative detection.

[0036] Provides a layer control panel that supports the following operations: check / uncheck layers to achieve "on-demand view"; adjust layer transparency for easy overlay viewing; save the current layer configuration as a personalized view, such as enabling a real-time layer + isolation layer + anomaly layer in the "Running Monitoring View", or enabling a work order layer + spare parts layer + maintenance history layer in the "Maintenance Preparation View"; and share view configurations with other users or teams.

[0037] Furthermore, it includes personalized data dashboard configuration, allowing users to customize the "data dashboard" for each device or the entire drawing. Drag-and-drop configuration allows users to drag measurement points, attributes, and documents from the "Available Data Sources" list to the device, automatically generating a data display area. Suitable locations include above, below, left, right, and inside the device. Display format settings allow users to customize the format of each display item, such as decimal places, units, font color, background color, and conditional formatting (e.g., turning red when the value is greater than a threshold). Composite index definitions allow users to define calculation formulas, such as "Efficiency = (Outlet Pressure - Inlet Pressure) / Current," with the calculation results displayed as virtual measurement points. Frequently used configurations can be saved as templates, such as a "Pump Monitoring Template" including current, vibration, temperature, and operating status, building a template library. Templates are categorized by device type, such as pumps, valves, and instruments, supporting preview, editing, and version management. Batch application to similar devices allows users to select multiple devices and apply the template with a single click. View saving and sharing: Save all configurations of the current drawing, including layer status, equipment dashboard, and zoom position, as a "personal view"; Views can be shared with the team, such as the shift leader configuring a "main control room monitoring view" and sharing it with all operators, and recipients can choose to adopt it or save it as a personal version; View version management is supported, allowing you to review historical configurations and compare the differences between different versions.

[0038] Furthermore, it includes an intelligent association recommendation engine that recommends relevant data that users might need based on multi-dimensional association analysis. This includes recommendations based on device topology (e.g., when viewing valves, it recommends pressure, flow, and status data of upstream and downstream equipment; when viewing pumps, it recommends their power switches and cooling water valves); recommendations based on historical operations (it analyzes other data viewed by similar users, such as operators and maintenance personnel, while viewing the same equipment, and generates collaborative filtering recommendations); recommendations based on time patterns (it automatically pushes relevant data based on the current time period, such as during major overhauls or routine operation; during major overhauls, it pushes isolation status and maintenance progress; during routine operation, it pushes real-time parameters and work order data); recommendations based on business scenarios (it identifies the user's current operation context; for example, if the user is viewing an isolation plan, it automatically pushes the real-time status of boundary devices; if the user is viewing a work order, it automatically pushes relevant spare parts inventory); and recommendations based on similar equipment (when a user views a certain piece of equipment, it recommends comparative data of other equipment of the same model, such as average vibration value and average maintenance interval).

[0039] The recommended display method involves showing a "Smart Recommendation" panel in the right sidebar of the drawing, listing recommended data items in card format. Each item includes a data name, current value, and reason for recommendation. Users can drag and drop recommended data directly onto the drawing to quickly add it to the current view. User feedback, such as "not interested," "useful," and "fixed display," is collected to continuously optimize the recommendation model. The recommendation adoption rate is recorded to evaluate the effectiveness of the recommendation algorithm.

[0040] Furthermore, it includes intelligent search and location, providing a global search box that supports multi-dimensional searches. For equipment codes / names, entering "RCV001VP" automatically locates the corresponding equipment in the drawing, highlighting and centering it. For system names, entering "RCV system" lists all RCV-related drawings; selecting one opens and locates the system area. For work order numbers, entering a work order number displays a list of all equipment related to that work order; clicking it locates the corresponding drawing. Keyword fuzzy search supports fuzzy searches of drawing titles, equipment descriptions, and remarks. Advanced search supports combined condition searches, such as "System=RCV AND Equipment Type=Pump AND Repair Count>3". Search results are sorted by relevance and display a preview of the relevant drawing, the number of devices, and other information. Search history records the user's search history and supports quick repeat searches.

[0041] Furthermore, it includes data snapshots and historical backtracking, supporting the saving of all current summary data for drawings in a snapshot format. Clicking "Save Snapshot" records the current status, values, alarms, layer configurations, and view positions of all devices. Snapshot metadata includes: snapshot name, creation time, creator, and associated events, such as work order number and isolation scheme number. Snapshots can be accompanied by text descriptions, recording the situation at the time, such as "status before overhaul." Snapshot application scenarios include event association, accident review, periodic archiving, snapshot comparison, and timeline backtracking. Event association: A snapshot is saved before a work order begins to compare changes before and after execution; a snapshot is saved before isolation execution to confirm the isolation effect. Accident review: A snapshot is saved after an accident occurs to freeze the on-site state for subsequent analysis. Periodic archiving: Snapshots are automatically saved daily or weekly to form a historical status archive. Snapshot comparison: Selecting two snapshots for comparison, the system highlights the changed parts: devices with changed values ​​are marked with a flashing yellow border; devices with changed status, such as changing from running to stopped, are marked with a color change; newly added / disappeared alarms are marked with icon changes; a comparison report is generated after the comparison is completed, listing all changes. The timeline backtracking feature provides a timeline control, allowing users to drag the slider to view the status of drawing data at any historical point in time. Historical data storage is required. It supports playback mode, automatically playing the status change process in chronological order. The timeline is associated with snapshot points, and clicking a snapshot point will directly jump to the corresponding moment.

[0042] Furthermore, this includes data refresh and caching mechanisms, with differentiated refresh strategies set for different types of data. Real-time data is pushed via WebSocket long-lived connections, pushing data as soon as it changes, or short polling (1-5 seconds) to ensure timeliness. Work order / isolation status is refreshed via event-triggered events, with the source system pushing a message when the status changes, or long polling (30 seconds). Device master data is cached locally and reloaded when the version changes, or synchronized daily. Spare parts inventory is refreshed on demand, triggered when users view it, or synchronized periodically, such as hourly.

[0043] The design incorporates a data pool collaboration mechanism to avoid duplicate requests: the backend maintains a unified data pool, containing a list of data required for all currently online drawings; the backend periodically retrieves the latest data from each business system according to the configured refresh strategy and stores it in the data pool; each frontend instance retrieves data from the data pool on demand via WebSocket, requesting only once when the same device is referenced by multiple drawings; the data pool records the number of subscribers for each data item, stopping refresh and releasing resources when no one is subscribing.

[0044] Compared with existing technologies, this embodiment has the following beneficial effects: one-stop information acquisition, eliminating the need for maintenance personnel to switch between multiple systems; a single diagram provides a comprehensive view of the equipment, improving information retrieval efficiency; reliable and traceable data, with data lineage tracing and confidence level identification, allowing users to clearly understand the source and reliability of each data point, avoiding decisions based on erroneous data; intelligent early warning, with multi-source data collaborative detection identifying anomalies that cannot be identified by a single data source, improving fault detection rates and reducing unplanned downtime through early warnings; personalized adaptation, allowing different roles, such as operation, maintenance, and isolation personnel, to configure exclusive views and dashboards, focusing on information of interest to them and improving work efficiency; knowledge accumulation, with data snapshots and historical backtracking functions providing complete data support for incident review and experience feedback; intelligent assistance, with an association recommendation engine helping users discover potential relationships between data, improving the depth and breadth of analysis; reduced system pressure, with a data pool collaboration mechanism avoiding duplicate requests and reducing concurrent pressure on business systems.

[0045] The following is a detailed explanation of the usage scenarios of three roles in a nuclear power plant's main control room: operator, maintenance engineer, and isolation manager.

[0046] Scenario 1: Operator operation monitoring, real-time data + anomaly detection The operator logs into the system and opens the RCV system diagram. The system automatically loads its saved "Operation Monitoring View": the real-time data layer, isolation layer, and anomaly layer are enabled, while the work order layer and spare parts layer are disabled. The diagram shows: valve V001 is green, indicating it is open, with the opening degree displayed as "100%"; pump P002 is red, indicating it is out of service due to a fault, with a red triangle icon in the upper left corner indicating a defect and a yellow wrench icon in the upper right corner indicating an ongoing work order; instrument PI003 displays a real-time pressure value of "2.5MPa", the value is green, indicating high confidence, and hovering the mouse over it pops up a data lineage card: source PI system, acquisition time 10:23:15, confidence level 98%.

[0047] The system anomaly detection module triggered an alarm: the current measuring point of pump P002 displayed "0A", but the running status was "Running", indicating a logical conflict. A red exclamation mark icon appeared next to pump P002. Clicking it brought up the anomaly details: "The current measuring point may be faulty; the running status is inconsistent with the current. It is recommended to check the measuring point or switch to the backup pump." The operator immediately notified the on-site personnel to check and switched to the backup pump P003. The work order icon for P002 on the drawing automatically updated to "Processing".

[0048] Scenario 2: Repair engineer prepares for repair, intelligent recommendation + spare parts inventory The maintenance engineer received a work order to "handle the current measuring point fault of pump P002". Upon opening the RCV system diagram and loading its "Maintenance View", the work order layer, spare parts layer, and maintenance history layer were enabled. P002 was highlighted on the diagram, and a smart recommendation panel was displayed next to it: Recommendation 1: Historical maintenance records for this pump, the last 3 maintenances: bearing replacement, seal replacement, current measuring point calibration; Recommendation 2: Related spare parts inventory, current transmitter model CT-202, 2 in stock, located in warehouse B-05; Recommendation 3: Experience feedback on similar equipment, "Note: Range calibration is required after calibration". The maintenance engineer clicked on the spare parts recommendation, and the system displayed the inventory details, generating a requisition form with one click. Viewing the maintenance history heatmap, it was found that the maintenance frequency of the RCV system pump area where P002 was located was significantly higher than other areas. The system prompted, "The equipment failure rate in this area is high; it is recommended to analyze the root cause."

[0049] Scenario 3: The isolation manager develops an isolation plan, using data snapshots and comparing them. The isolation manager needs to develop an isolation plan for the P002 repair. Open the drawings, and before starting the plan, click "Save Snapshot," name it "P002 Status Before Repair," and associate it with the work order number. The system automatically finds the isolation boundaries and generates a candidate boundary list. After the isolation manager confirms, the isolation plan is saved. After the repair is completed, the isolation manager reopens the drawings and saves a snapshot, "P002 Status After Repair." Click "Snapshot Comparison," select the two snapshots, and the system highlights the changes: P002 status changes from "Fault" to "Normal"; the isolation status of isolation boundary devices V001 and V002 changes from "Tagged" to "Released"; the work order status changes from "In Progress" to "Completed"; a comparison report is generated and archived as an attachment to the work order.

[0050] Scenario 4: Incident debriefing, timeline rewind Three days ago, a transient event occurred, requiring a review of the system state at that time. The administrator opened the RCV system diagram and entered timeline mode. Dragging the timeline to the event time of 10:23, the diagram automatically loaded the data status at that time: a sudden drop in pump P002 current, an increase in vibration value; the alarm list displayed "High Vibration Alarm"; the work order record showed no relevant work orders at that time. Clicking the play button, the system dynamically displayed the state changes in chronological order, helping to analyze the event's evolution path. The administrator saved this period's state as a snapshot "Event Review - 20250320", added analysis descriptions, and stored it in the experience feedback library.

[0051] Scenario 5: Personalized View Configuration Newly hired operator Xiao Wang wanted to customize his monitoring view. He opened the RCV system diagram and entered configuration mode. He dragged frequently used measurement points from the data source list on the left to the equipment: adding current, vibration, and temperature displays for all pumps; adding opening degree displays for all valves. He set conditional formatting: pressure >3MPa turned red, <2MPa turned yellow. He saved it as "Xiao Wang's Personal View" and shared it with his colleagues in the same shift. Colleagues could choose to adopt it or save it as a personal version for fine-tuning. Three months later, based on Xiao Wang's usage habits and frequent viewing of vibration data, the system automatically prioritized pushing vibration-related alarms and analyses in the intelligent recommendations.

[0052] Example 2

[0053] This embodiment provides a data aggregation device based on a digital flowchart, including a memory and a processor. The memory stores a computer program, and when the computer program is executed by the processor, it implements the data aggregation method based on a digital flowchart as described in Embodiment 1.

[0054] The data aggregation device based on digital flowcharts provided in this embodiment is used to implement the data aggregation method based on digital flowcharts. Therefore, the data aggregation device based on digital flowcharts also has the same technical effects as the data aggregation method based on digital flowcharts, which will not be described in detail here.

[0055] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of the present invention.

Claims

1. A data aggregation method based on digital flowcharts, characterized in that, Includes the following steps: Step S1: Design compatible data interfaces for various business systems, define a unified data model for each data interface, and realize standardized access to multi-source data; Step S2: In the digital flowchart drawing stage, predefine fields for binding data sources based on the attributes of graphic elements; establish the association mapping between graphic element fields and data sources to obtain a data mapping table; and bind graphic element fields to relevant data sources based on the data mapping table. Step S3: Based on the data interface of the relevant data source, dynamically update the relevant data to the corresponding graphic element fields, and combine all fields of each graphic element to establish data points bound to the graphic element, so as to obtain the flowchart after data summary and binding.

2. The data aggregation method based on digital flowcharts according to claim 1, characterized in that, Step S1 specifically involves: Design a real-time data interface adapted to the real-time database to acquire equipment measurement point data; design a device master data interface adapted to the equipment management system and document management system; and design a work order and defect interface adapted to the work order system. Design an isolation information interface that is compatible with the isolation system; Design compatible spare parts inventory interfaces for ERP and inventory systems; Design a compatible inspection data interface for the inspection system; design a compatible document system interface for the document management system and document security management system; A unified data model is defined for each of the data interfaces, and the data model includes the data source identifier, data collection time, data validity period, and initial data confidence value.

3. The data aggregation method based on digital flowcharts according to claim 1, characterized in that, In step S2, a data mapping table is obtained by establishing the association mapping between primitive fields and data sources. Based on the data mapping table, primitive fields are bound to relevant data sources, specifically as follows: A data mapping table is obtained by establishing an association mapping between graphic element fields and data sources. The data mapping table is stored independently of the drawing data. Based on the device code of the graphic element, the corresponding data in each of the aforementioned business systems is automatically matched, and then bound to the corresponding graphic element field; If automatic matching fails, manually bind the graphic element field to the corresponding data source; If there is batch binding within the same system, then batch binding will be performed by device type or by system type.

4. The data aggregation method based on digital flowcharts according to claim 1, characterized in that, It also includes establishing a data lineage chain for the data points on the flowchart, recording data lineage information and confidence information; the data lineage information includes the data source system, original collection time, access time, data link, and data confidence; the confidence information includes source system confidence, timeliness confidence, matching confidence, and historical stability, and calculating a comprehensive confidence based on all the confidence information; recording changes to key selected data using a hash chain; and displaying the data lineage chain on the data points.

5. The data aggregation method based on digital flowcharts according to claim 1, characterized in that, It also includes collaborative anomaly detection based on multi-source data associated with the data points; the collaborative anomaly detection includes consistency detection, logical verification, time-series anomaly detection, and multi-source fusion analysis. The consistency detection is as follows: set consistency detection rules for data points that need to maintain consistency, detect related data points based on the consistency detection rules, and issue a prompt when the deviation value is greater than a set threshold; The logical verification is as follows: based on the correlation between data points and the data characteristics of the data associated with the data points, corresponding logical verification rules are set, and logical verification of the corresponding data points is performed based on the logical verification rules. The time series anomaly detection is as follows: a time series algorithm is used to predict the normal value range of data points, and an alarm is triggered if the real-time data of the data points exceeds the normal value range. The multi-source fusion analysis involves: performing correlation analysis on multi-source data, setting fusion analysis rules, and implementing equipment fault early warning based on the fusion analysis rules; establishing an equipment lifespan model and predicting the remaining lifespan of the equipment by combining multi-source data.

6. The data aggregation method based on digital flowcharts according to claim 1, characterized in that, For different data types contained in the data points, corresponding visualization methods are set to achieve diversified display of the data points at the corresponding graphic elements; The aggregated data is divided into multiple independent layers according to data type, and each layer is subject to independent display / hiding control. Users can customize data dashboards, set the associated data sources, display areas, display formats, and custom composite indicators for the data dashboards, and display the image and data points of the corresponding devices based on the data dashboards; save the configuration of the data dashboards as dashboard templates, classify the dashboard templates according to device types, and apply the corresponding dashboard templates to multiple similar devices to achieve batch application of the data dashboards; Save the current flowchart configuration information as a personal view, share the view with other users, manage different views for versions, backtrack historical configurations based on view versions, and compare different version views.

7. The data aggregation method based on digital flowcharts according to claim 1, characterized in that, It also includes correlation analysis based on aggregated data from multiple sources to recommend data to users: based on the device topology relationship in the flowchart, it recommends related devices to the user for the current device; Based on the historical operations of similar users, generate collaborative filtering recommendations for the user's current device; Based on the current operating mode, relevant data is recommended to the user; Based on the business scenario, identify the context of the user's current operation and recommend relevant data to the user based on the context; Based on the user's current device, recommend comparative data for other devices of the same model; Record user feedback on recommendations and use the feedback to optimize the recommendation model; record the recommendation adoption rate to evaluate the effectiveness of the recommendation model; It also includes multi-dimensional global search based on aggregated data from multiple sources: automatically locating the corresponding device in the flowchart based on device code or device name; List all relevant flowcharts based on system name and locate the corresponding system area in the flowchart; search for all devices involved in the corresponding work order based on work order number and locate the corresponding flowchart; search for all relevant flowcharts or devices based on fuzzy keywords; perform a search based on combined conditions.

8. The data aggregation method based on digital flowcharts according to claim 1, characterized in that, It also includes saving snapshots of the current summary data of the flowchart; confirming the effect of an event based on a comparison of snapshots before and after the event; freezing the on-site state based on a snapshot after an accident for subsequent accident analysis; periodically and automatically saving snapshots to form a historical state archive; comparing any two snapshots and prompting for changes, generating a comparison report; and setting a timeline control to associate the timeline with snapshot points to achieve snapshot backtracking based on the timeline.

9. The data aggregation method based on digital flowcharts according to claim 1, characterized in that, It also includes setting differentiated refresh strategies for multi-source data: push refresh or short polling refresh for real-time data; event-triggered refresh or long polling refresh for work order and defect data and isolated data; local caching for equipment master data and reloading or periodic synchronization when the version changes; and on-demand refresh or timed synchronization for spare parts inventory data. A data pool collaboration mechanism is set up for multi-source data: a unified data pool is built and maintained, which is used to maintain the data list required by all current flowcharts; the latest data is periodically pulled from each of the business systems according to the configured data refresh strategy and stored in the data pool; Each front-end instance pulls data from the data pool on demand, and only requests data once when the same device is used by multiple flowcharts; the data pool also records the number of subscribers for each data item, and stops refreshing and releases resources when no one is subscribing.

10. A data aggregation device based on a digital flowchart, characterized in that, It includes a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, implements the data aggregation method based on a digital flowchart as described in any one of claims 1-9.

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