A method and apparatus for digitizing flowchart data to points

Abstract

The application discloses a kind of digital flow chart data to point method and device, wherein, method includes the following steps: step S1, set up the multiple matching rules between equipment code and measuring point ID, set priority for each matching rule, construct multi-level matching rule library;Step S2, scan all equipment graph elements in digital flow chart, extract equipment code and equipment type;Step S3, each matching rule in the matching rule library is sequentially applied to each equipment graph element to carry out measuring point matching, generate candidate measuring point list, and the candidate measuring point is sorted according to confidence degree, and the final matching measuring point is determined in combination with the candidate measuring point list and user selection.The application can realize the accurate, efficient, sustainable association of equipment graph element and measuring point.

Description

Technical Field

[0001] This invention relates to the field of nuclear power process flow diagram technology, and in particular to a digital process flow diagram data point-to-point method and apparatus. Background Technology

[0002] Currently, the association between equipment elements and real-time measurement points (PI points) in nuclear power digital flowcharts mainly relies on manual configuration, which is labor-intensive and prone to errors. Furthermore, the display of measurement point data on drawings is limited and cannot meet the personalized data display needs of different users; changes to measurement points, such as instrument replacement or measurement point decommissioning, are difficult to update in a timely manner, leading to invalid data displayed on the drawings; and the lack of effective health assessments after aligning a large number of measurement points makes it difficult to detect data quality issues. Summary of the Invention

[0003] In view of this, it is necessary to provide a digital flowchart data point-to-point method and apparatus to effectively solve the technical problems of low efficiency and easy error in existing flowchart data point-to-point matching.

[0004] This invention provides a method for point-to-point matching of digital flowchart data, comprising the following steps: Step S1: Set multiple matching rules between device codes and measurement point IDs, assign priorities to each matching rule, and build a multi-level matching rule library; Step S2: Scan all device elements in the digitized flowchart and extract the device code and device type; Step S3: Apply each matching rule in the matching rule library to perform measurement point matching for each device element in sequence, generate a candidate measurement point list, sort the candidate measurement points according to confidence level, and determine the final matched measurement point by combining the candidate measurement point list and user selection.

[0005] Preferably, step S1 specifically includes: The matching rules include an exact matching layer, a rule matching layer, a fuzzy matching layer, a semantic matching layer, a historical matching layer, and a multi-source fusion matching layer; The precise matching layer is as follows: the device code of the graphic element device is compared with the measurement point ID of each data measurement point, and the measurement point whose measurement point ID is completely consistent with the device code is taken as the matching measurement point of the device graphic element; The rule matching layer is as follows: based on naming rules, the measurement point ID of the corresponding measurement point is derived according to the device code, and the measurement point whose measurement point ID is the same as the derived measurement point ID is used as the matching measurement point of the corresponding device element. The fuzzy matching layer is as follows: based on the string similarity algorithm, the character similarity between the device code and the measurement point ID of each measurement point is calculated, and the measurement point with the highest character similarity is taken as the matching measurement point of the corresponding device element; The semantic matching layer is: based on the semantic association between device type and measurement point type, it matches measurement points with device primitives; The historical matching layer is: recording the history of device measurement point relationships manually bound by the user, and matching measurement points for device elements based on the history of device measurement point relationships; The multi-source fusion matching layer is as follows: when a measurement point exists in multiple data sources, the optimal measurement point is selected as the matching measurement point for the device element based on the data source priority and freshness. The priority of the precise matching layer, rule matching layer, semantic matching layer, fuzzy matching layer, and historical matching layer decreases in that order.

[0006] Preferably, in step S3, the final matching measurement point is determined by combining the candidate measurement point list and the user selection, specifically as follows: The measurement point with the highest confidence level is marked as the recommended measurement point, and the recommended measurement point is automatically bound to the device element. The measurement point with the confidence level within the set range is marked as the measurement point to be confirmed, and the measurement point with the confidence level below the corresponding threshold is marked as the prediction point to be affected. The user receives the recommended measurement point, modifies it to the measurement point to be confirmed, or modifies it to the prediction point to be affected as the final matching measurement point. The binding relationship selected by the user is recorded for subsequent optimization of the corresponding matching rules.

[0007] Preferably, it also includes setting up a multi-level display method for multiple measuring points of the same device: The multi-level display method includes a main display layer, a secondary display layer, a status color layer, and a dynamic annotation layer. The measurement points are divided into two categories: primary measurement points and secondary measurement points. The primary measurement points are displayed on the main display layer, which is directly displayed next to the corresponding device. The secondary measurement points are displayed on the secondary display layer, which is triggered by user settings. The status display layer is used to change the display status of the corresponding device elements according to the measurement point values. The dynamic annotation layer is used to annotate and display the dynamic status of the device according to the dynamic changes in the measurement point values.

[0008] Preferably, it also includes configuring batch display of equipment elements by setting a measurement point display template: The measurement point display template includes equipment templates, system templates, role templates, and working condition templates. The equipment template is used for display configuration of equipment of the same type, the system template is used for display configuration of all equipment in the same system, the role template is used for display configuration of equipment of the same type of personnel, and the working condition template is used to set the display configuration of equipment under working conditions. When multiple devices need to be configured, the corresponding measurement point display template can be selected to configure multiple devices in batches. Version management is implemented for the measurement point display template. After the version of the measurement point display template is updated, the user can choose whether to apply the new version of the measurement point display template to the configured device according to their needs.

[0009] Preferably, this also includes performing a health assessment on the bound monitoring points: The evaluation metrics include data freshness, data completeness, data volatility, confidence level change, and source system status. Data freshness is calculated based on the update time of the measurement point data and the current time; data completeness is calculated based on the proportion of missing data points per unit time; data volatility is calculated based on the current standard deviation and historical average standard deviation of the measurement point; confidence level change is calculated based on the initial and current confidence levels of the measurement point; and source system status is calculated based on the health status of the data source containing the measurement point. All evaluation metrics are normalized and then summed to obtain the overall health score. When the overall health score falls below a corresponding threshold, an early warning notification, recommended solutions, and a corresponding work order are automatically generated.

[0010] Preferably, this also includes periodically scanning the measurement point dictionary, detecting measurement point changes, and generating a pending list of measurement point changes: The system detects new measurement points and checks if any devices match them. If so, it generates a pairing suggestion for confirmation. It also checks if bound measurement points have been marked as decommissioned. If so, it marks the measurement points as invalid and prompts the user to rebind them. The system checks if the measurement point ID has changed. If so, it automatically updates the binding relationship or prompts the user for confirmation. It checks if the measurement point attributes have changed. If so, it automatically updates the measurement point's display configuration. If the same measurement point has multiple data sources, it automatically switches to the backup data source when the primary data source fails and marks any changes to the data source.

[0011] Preferably, it also includes generating point-to-point quality reports: The system statistically analyzes the point-to-point success rate, health distribution, and average health score of the matching rule base; it also statistically analyzes the health of points below the corresponding threshold to obtain a list of low-health points, a list of unmatched devices, a list of low-confidence matches with confidence levels below the corresponding threshold, and a list of frequently changed points with more than a set number of changes; and it statistically analyzes the trend of point health over time, the trend of new points, and the trend of point retirement. Based on the aforementioned point quality report, optimization suggestions are recommended to the user, and the overall health of each system is evaluated based on the statistical results of the measurement point health.

[0012] Preferably, it also includes saving the point-to-point configuration matching the equipment and the measuring point as auxiliary data of the flowchart, performing independent version management on the auxiliary data of the point, generating a new point-to-point version for each point-to-point modification, and decoupling the point-to-point version number from the drawing version number; Export the point-to-point configuration to a standard format for offline editing and backup, and then import the edited point-to-point configuration in batches to achieve batch point-to-point pairing of devices. Based on the binding relationship between devices and measurement points, a measurement point relationship map is constructed. Based on the measurement point relationship map, other matching measurement points of the device, related devices of the recommended measurement points, related measurement points of the recommended measurement points, and related measurement points of the faulty device are recommended to the user.

[0013] The present invention also provides a digital flowchart data matching device, including a memory and a processor, wherein the memory stores a computer program, and the computer program is executed by the processor to implement the digital flowchart data matching method.

[0014] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention realizes multi-dimensional intelligent matching between equipment and measuring points through a multi-level matching rule base, which greatly improves the matching efficiency between equipment and measuring points, realizes accurate, efficient and sustainable association matching, and improves data maintenance efficiency. 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 digital flowchart data point-to-point method 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 digital flowchart data point-to-point method in this embodiment specifically includes the following steps: Step S1: Set multiple matching rules between device codes and measurement point IDs, assign priorities to each matching rule, and build a multi-level matching rule library; Step S2: Scan all device elements in the digitized flowchart and extract the device code and device type; Step S3: Apply each matching rule in the matching rule library to perform measurement point matching for each device element in sequence, generate a candidate measurement point list, sort the candidate measurement points according to confidence level, and determine the final matched measurement point by combining the candidate measurement point list and user selection.

[0019] This embodiment achieves accurate, efficient, and sustainable association between devices and measurement points through multi-layer intelligent matching, dynamic display configuration, health assessment, automatic change detection, and version management, thereby improving data visualization quality and maintenance efficiency.

[0020] Each step will be explained in detail below.

[0021] Step S1: Establish a multi-layered matching rule base for equipment codes and measurement point IDs. The matching rule base includes a precise matching layer, a rule-based matching layer, a fuzzy matching layer, a semantic matching layer, a historical matching layer, and a multi-source fusion matching layer. The precise matching layer ensures that the equipment code and measurement point ID are completely identical, such as equipment code "RCV001VP" corresponding to measurement point "RCV001VP". The rule-based matching layer derives its rules based on power plant naming conventions, such as the status measurement point for equipment code "RCV001VP" being "RCV001VP_S", the opening measurement point being "RCV001VP_Z", the current measurement point being "RCV001VP_I", and the vibration measurement point being "RCV001VP_V". The fuzzy matching layer uses string similarity algorithms, such as edit distance and Jaccard similarity, to match the closest measurement point and provide a confidence level. The semantic matching layer uses semantic associations between equipment type and measurement point type, such as "pump" matching "current" and "vibration" measurement points, and "thermometer" matching temperature measurement points. The historical matching layer records the relationships between manually bound device measurement points in the past, and uses this information as a priority recommendation. The multi-source fusion matching layer automatically selects the optimal measurement point based on data source priority and freshness when measurement points exist in multiple data sources, such as PI, DCS, and third-party systems.

[0022] The execution flow of each matching rule in the matching rule base is as follows: The system scans all equipment elements in the drawing, extracts the equipment code and equipment type; it sequentially applies precise matching → rule matching → semantic matching → fuzzy matching to generate a candidate test point list, which is then sorted by confidence level; it automatically binds test points with high confidence (>90%), marks them as "pending confirmation" and prompts the user for test points with medium confidence (60%-90%), and requires manual intervention for test points with low confidence (<60%). After the user confirms or corrects the recommended test points, the system records the binding relationship and uses it for subsequent optimization of the matching algorithm.

[0023] Furthermore, it also includes multi-state display configuration for measurement points. It supports multi-level display modes for multiple measurement points on the same device, including a main display layer, secondary display layer, status color layer, and dynamic annotation layer. The main display layer displays values ​​directly next to the device and supports configuration of: display position (e.g., above, below, left, right, inside); display format (e.g., decimal places, unit display method, scientific notation); color rules (e.g., normal value color, alarm value color, trend color, e.g., rising green, falling red); font size and style. The secondary display layer displays detailed information when the mouse hovers over or clicks, which can include: a list of multiple related measurement points (e.g., pump display of current, voltage, power, frequency, temperature); recent trend changes (e.g., a mini trend chart that can be zoomed to view history); measurement point health status (e.g., confidence level, last update time, data source); associated alarm information (e.g., alarm level and description if any). The status color layer changes the device element color based on the measurement point value (e.g., pump running green, stopped gray, fault red). The dynamic labeling layer adds dynamic label icons next to the device, such as: trend arrows (upward ↑ / downward ↓ / stable →); alarm icons (red exclamation mark); and data quality icons (green dot for good data, yellow for outdated data, and red for invalid data).

[0024] It provides a visual configuration interface for multi-level display modes, supporting drag-and-drop operations. Drag measurement points from the "Available Measurement Points" list on the right to different areas of the device to automatically create display configurations. Double-clicking a configured display area brings up a property settings panel. It supports copying and pasting configurations to similar devices or applying them to the entire system.

[0025] Furthermore, it includes templated configurations for measurement point displays, saving commonly used measurement point display configurations as templates, including equipment templates, system templates, role templates, and operating condition templates. Equipment type templates, such as the "Pump Template," include display configurations for four measurement points: current, vibration, temperature, and status. System templates, such as the "RCV System Template," are applicable to all equipment within the system. Role templates, such as the "Operator Template," focus on real-time status; the "Maintenance Personnel Template" focuses on historical trends and alarms; and the "Isolation Manager Template" focuses on isolation status. Operating condition templates, such as the "Startup Condition" which displays startup current and vibration peak values, and the "Steady-State Condition" which displays stable operating parameters.

[0026] The application of measurement point display templates allows for one-click application of templates after point alignment on new drawings, enabling batch display configuration. Measurement point display templates support version management; after updates, users can choose whether to synchronize with already applied devices, and can choose incremental updates or forced overwrite. Exporting and importing measurement point display templates facilitates team sharing and standardized promotion.

[0027] Furthermore, it includes measurement point health assessment and early warning. A health assessment is performed on each bound measurement point, with assessment indicators including data freshness, data completeness, data volatility, confidence level change, and source system status. Data freshness is the interval between the last update time and the current time; exceeding a threshold (e.g., 30 minutes) reduces freshness. The formula is: Freshness = max(0, 1 - (current time - last update time) / threshold). Data completeness is measured per unit time, such as the proportion of missing data in the last 24 hours. Completeness = 1 - missing rate. Data volatility assesses whether the values ​​are abnormally stable (abnormal stability may indicate measurement point freezing) or abnormally volatile (abnormal volatility may indicate measurement point failure); this is calculated by comparing the standard deviation of the most recent N points with the historical average standard deviation; exceeding the range reduces the score. Confidence level change is the degree of agreement between the initial confidence level at the time of matching and the current actual situation; frequent deviations of measurement point values ​​beyond the range may indicate matching errors. Source system status refers to the data source where the measurement point is located, such as the health status of the PI server; if the data source is offline, the health level is 0. Health is represented on a scale of 0-100, and is indicated by different colors on the chart: green (90-100 points) indicates good health; yellow (60-89 points) indicates a warning and requires attention; and red (0-59 points) indicates poor health and requires action.

[0028] When the mouse hovers over a measurement point value, a health details card pops up, displaying scores for each indicator and suggested corrective actions. When the health level falls below a threshold, the system automatically generates an alert notification and recommends possible solutions, such as "The measurement point is offline; we suggest checking the network connection or contacting instrument personnel." The system can also automatically create a work order for the issue.

[0029] Furthermore, this includes automatic detection and synchronization of measurement point changes. It periodically scans measurement point dictionaries, such as PI and DCS point tables, to detect the following changes: new measurement points, measurement point decommissioning, measurement point name changes, measurement point attribute changes, and data source switching. When a new measurement point appears, it checks if there is matching equipment. If so, it generates a "Pending Confirmation" pairing suggestion and displays detailed information about the new measurement point. When a measurement point is decommissioned, the corresponding value on the drawing displays "Measurement Point Invalid," and the user is prompted to rebind it in red. When a measurement point name changes, if the measurement point ID changes, the binding relationship is automatically updated if the rules match, or the user is prompted for confirmation, while the original name is retained as an alias. When measurement point attributes change, such as engineering units, range, and alarm upper and lower limits, the display configuration is automatically updated. For example, the unit is automatically adjusted, and the alarm color threshold is updated. When a data source switches, if the same measurement point has multiple data sources, such as a primary PI and a backup PI, when the primary data source fails, it automatically switches to the backup data source and marks the data source change.

[0030] A "pending processing list" is generated based on the changed test results, which users can confirm item by item or accept in batches. Automatic processing rules can be set, such as "automatically bind new measuring points when their names conform to the specifications" and "automatically mark decommissioned measuring points as pending processing and notify the instrument engineer".

[0031] Furthermore, it includes point-to-point quality reports and optimization suggestions. The point-to-point quality report is automatically generated and includes overall statistics, a problem list, trend analysis, optimization suggestions, and a leaderboard. Overall statistics include the point-to-point success rate (the proportion of devices with bound measurement points), health distribution, percentage of each health segment, and average health score. The problem list includes measurement points with low health, unmatched devices, low-confidence matches, and frequently changing measurement points. Trend analysis shows the health trend over time, the trend of new measurement points, and the trend of measurement point decommissioning. Optimization suggestions include: "Five devices frequently use manual binding; it is recommended to add naming rules.", "Eight measurement points in the RCW system have consistently low health; it is recommended to check the network.", and "Three measurement points were found to have inconsistent units, such as pressure units MPa / bar; it is recommended to unify them." The leaderboard lists the 10 systems with the best and 10 systems with the worst health.

[0032] The report is presented in visual charts such as bar charts, pie charts, and line charts, and supports exporting to PDF / Excel. Based on the report analysis results, optimization processes can be triggered with one click: re-execute the matching algorithm for low-confidence matches; create new naming rules for frequently manually bound device types; and initiate work orders for measurement points with persistently low health, assigning them to instrumentation personnel.

[0033] Furthermore, it includes point-to-point version management and rollback, treating point-to-point configurations as supplementary data to drawings, supporting independent version management. Each point-to-point modification, automatically or manually, generates a new point-to-point version, with the version number decoupled from the drawing version, such as drawing v2.1 and point-to-point configuration v12. Version history includes: change time, person making the change, and a summary of the change content, such as "Add measurement point V001_Z", "Modify V002 confidence threshold", "Batch update RCW system points". Version comparison is supported, highlighting the differences between two versions, changes in the device-measurement point correspondence, and parameter changes. Point-to-point configurations can be backed up and restored separately, or exported along with the drawings. It supports rollback to view the point-to-point status at any point in history for problem tracing or auditing. If a problem is found to be caused by a new version of the point-to-point configuration, it can be rolled back to a stable historical version with one click.

[0034] Furthermore, it includes point configuration import / export and batch deployment, exporting point configurations to standard formats such as CSV and JSON for easy offline editing and backup. The exported file includes: device code, measurement point ID, matching confidence score, display configuration parameters, and health history. Batch import of point configurations is supported, suitable for: importing existing point templates when a new unit comes online; migrating point data from other systems; and batch updating after on-site modifications. During import, conflicts are automatically checked; if the device is already bound to other measurement points, a merging strategy is provided, such as overwrite / skip / create.

[0035] Furthermore, it includes a measurement point relationship map and intelligent recommendation. Based on the equipment-measurement point binding relationship, a plant-wide measurement point relationship map is constructed, displaying the associations between equipment and measurement points, and between measurement points themselves. The intelligent recommendation function based on the measurement point relationship map automatically recommends other measurement points that the equipment might need when a user views a certain piece of equipment, such as recommending vibration, temperature, and flow rate when viewing a pump; automatically recommending related equipment and other relevant measurement points when a user views a certain measurement point, such as recommending upstream and downstream pressure when viewing a pressure measurement point; and recommending related measurement points that need to be checked based on a fault mode library when a certain piece of equipment malfunctions, such as recommending bearing temperature and lubricating oil pressure when pump vibration is high.

[0036] Compared with existing technologies, this embodiment has the following beneficial effects: Significantly improved point matching efficiency; intelligent matching algorithms replace manual configuration line by line, reducing the point matching time for thousands of devices from several days to several hours, with high accuracy; Flexible and diverse display; multi-state display configuration and templated applications meet the personalized needs of different roles and scenarios, improving information acquisition efficiency; Visualized data quality; health assessment allows users to clearly understand the reliability of each measurement point, avoiding incorrect decisions based on faulty data; Dynamic adaptation; automatic detection of measurement point changes ensures that drawing data always remains consistent with actual measurement points, avoiding "zombie data" and reducing maintenance costs; Continuous optimization loop; quality reports and optimization suggestions drive continuous improvement of point matching configuration, forming a virtuous cycle; Version controllable; independent version management of point matching configuration, supporting retrospection and auditing, meeting nuclear safety level management requirements; Knowledge accumulation; measurement point relationship graphs and intelligent recommendations help users discover potential correlations between data, improving the depth of analysis.

[0037] The following uses the data pairing of a nuclear power plant's RCV system drawing as an example to further illustrate this embodiment in different scenarios.

[0038] Scenario 1: Initial Point-to-Point Dialogue The administrator completed the RCV system drawing, which includes 120 devices: 80 valves, 15 pumps, and 25 instruments. The "Intelligent Matching" function was executed: Precise matching (device code and measurement point ID match exactly), 45 devices were successfully matched; Rule matching (valve opening degree plus "_Z" according to naming conventions), 50 devices were successfully matched; Semantic matching (pumps should be matched with current measurement points based on device type), 15 devices were successfully matched; Fuzzy matching, of the remaining 10 devices, 8 matched with high confidence (confidence > 80%), and 2 matched with lower confidence. The system generated the point matching results: 118 high-confidence devices were automatically bound, and 2 low-confidence devices were marked as "Pending Confirmation". The administrator confirmed the 2 low-confidence devices: one was an old device with no corresponding measurement point, so it was selected "Ignore"; the other was a newly created device with an incorrect measurement point name, so the correct measurement point ID was manually entered, and a new naming rule was added. The first attempt at point-to-point connection was successful with a success rate of 119 / 119=100%, taking approximately 15 minutes.

[0039] Scenario 2: Display Configuration Operators require valves to display opening degree and status colors, and pumps to display current and vibration. The administrator opens the display configuration interface, drags the "Opening Measurement Point" from the "Available Measurement Points" list on the right to above valve V001, and sets the display format to "Integer + %" and the color rule to ">0% green, =0% gray." The administrator then drags the "Status Measurement Point" inside valve V001 and binds it to the device's fill color: Open = Green, Closed = Gray, Fault = Red. Pump P001 is selected, and the "Pump Standard Template" is applied to automatically generate display configurations for four measurement points: current, vibration, temperature, and status. The administrator saves this configuration as the "RCV System Operation View" template.

[0040] Scenario 3: Health Assessment During routine monitoring, operators noticed that the pressure display for valve V002 was yellow, indicating a warning. Hovering the mouse over the valve to view its health details revealed: data freshness 70 points (last update 30 minutes ago), data completeness 60 points, 40% of data missing in the last hour, and an overall health score of 65 points. The system message stated, "The measuring point may be intermittently offline; it is recommended to check the network connection." Operators contacted instrumentation personnel, who discovered a loose network cable. After repair, the health score returned to green.

[0041] Scenario 4: Measurement point change Three months later, the instrument engineer notified that the measuring point ID for valve V001 had changed from "RCV001VP_Z" to "RCV001VP_Z_NEW" due to instrument replacement. During a periodic scan of the measuring point dictionary, the system discovered that the original measuring point had been marked as decommissioned, and the new measuring point appeared with a name that matched the original equipment code. The system generated a change notification: "The original measuring point for valve V001 has been decommissioned. A new measuring point, RCV001VP_Z_NEW, has been found. Rebind?" The administrator logged into the system, viewed the change list, confirmed the new measuring point was correct, and clicked "Batch Update." The system automatically updated the measuring point binding for V001 to the new measuring point and recorded the change log. The valve's display on the drawings automatically recovered without any manual intervention.

[0042] Scenario 5: Quality Report At the end of each quarter, the administrator runs the point-to-point quality report to check the point-to-point health status of all plant drawings. The report shows: overall point-to-point success rate 98.5%; 5 devices in the RCW system have a yellow health rating, indicating low data freshness, requiring close monitoring; manual binding rate is 3%, mainly concentrated on older equipment without measurement points; recommendation: add naming rules for equipment types that are frequently manually bound. Based on the report, the administrator arranges the work for the next quarter: check the RCW system network, apply for additional measurement points for older equipment, and optimize naming rules.

[0043] Scenario 6: Version Backtracking After an upgrade, some measurement points displayed abnormalities. The administrator suspected a problem with the new version's point configuration. By reviewing the point version history and comparing the current version with last week's version, it was discovered that measurement points on five devices in the RCW system had been incorrectly updated. A one-click rollback to last week's version resolved the issue.

[0044] Example 2

[0045] This embodiment provides a digital flowchart data matching device, 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 digital flowchart data matching method described in Embodiment 1.

[0046] The digital flowchart data matching device provided in this embodiment is used to implement the digital flowchart data matching method. Therefore, the digital flowchart data matching device also has the same technical effects as the digital flowchart data matching method, which will not be described in detail here.

[0047] 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 method for point-to-point data matching in digital flowcharts, characterized in that, Includes the following steps: Step S1: Set multiple matching rules between device code and measurement point ID, assign priority to each matching rule, and build a multi-level matching rule library; Step S2: Scan all device elements in the digitized flowchart and extract the device code and device type; Step S3: Apply each matching rule in the matching rule library to perform measurement point matching for each device element in sequence, generate a candidate measurement point list, sort the candidate measurement points according to confidence level, and determine the final matched measurement point by combining the candidate measurement point list and user selection.

2. The digital flowchart data point-to-point method according to claim 1, characterized in that, Step S1 specifically involves: The matching rules include an exact matching layer, a rule matching layer, a fuzzy matching layer, a semantic matching layer, a historical matching layer, and a multi-source fusion matching layer; The precise matching layer is as follows: the device code of the graphic element device is compared with the measurement point ID of each data measurement point, and the measurement point whose measurement point ID is completely consistent with the device code is taken as the matching measurement point of the device graphic element; The rule matching layer is as follows: based on naming rules, the measurement point ID of the corresponding measurement point is derived according to the device code, and the measurement point whose measurement point ID is the same as the derived measurement point ID is used as the matching measurement point of the corresponding device element. The fuzzy matching layer is as follows: based on the string similarity algorithm, the character similarity between the device code and the measurement point ID of each measurement point is calculated, and the measurement point with the highest character similarity is taken as the matching measurement point of the corresponding device element; The semantic matching layer is: based on the semantic association between device type and measurement point type, it matches measurement points with device primitives; The historical matching layer is: recording the history of device measurement point relationships manually bound by the user, and matching measurement points for device elements based on the history of device measurement point relationships; The multi-source fusion matching layer is as follows: when a measurement point exists in multiple data sources, the optimal measurement point is selected as the matching measurement point for the device element based on the data source priority and freshness. The priority of the precise matching layer, rule matching layer, semantic matching layer, fuzzy matching layer, and historical matching layer decreases in that order.

3. The digital flowchart data point-to-point method according to claim 1, characterized in that, In step S3, the final matching measurement point is determined by combining the candidate measurement point list and the user's selection. Specifically: The measurement point with the highest confidence level is marked as the recommended measurement point, and the recommended measurement point is automatically bound to the device element. The measurement point with the confidence level within the set range is marked as the measurement point to be confirmed, and the measurement point with the confidence level below the corresponding threshold is marked as the prediction point to be affected. The user receives the recommended measurement point, modifies it to the measurement point to be confirmed, or modifies it to the prediction point to be affected as the final matching measurement point. The binding relationship selected by the user is recorded for subsequent optimization of the corresponding matching rules.

4. The digital flowchart data point-to-point method according to claim 1, characterized in that, This also includes setting up multi-level display modes for multiple measurement points of the same device: The multi-level display method includes a main display layer, a secondary display layer, a status color layer, and a dynamic annotation layer. The measurement points are divided into two categories: primary measurement points and secondary measurement points. The primary measurement points are displayed on the main display layer, which is directly displayed next to the corresponding device. The secondary measurement points are displayed on the secondary display layer, which is triggered by user settings. The status display layer is used to change the display status of the corresponding device elements according to the measurement point values. The dynamic annotation layer is used to annotate and display the dynamic status of the device according to the dynamic changes in the measurement point values.

5. The digital flowchart data point-to-point method according to claim 1, characterized in that, This also includes configuring the batch display of equipment elements using a measurement point display template: The measurement point display template includes equipment templates, system templates, role templates, and working condition templates. The equipment template is used for display configuration of equipment of the same type, the system template is used for display configuration of all equipment in the same system, the role template is used for display configuration of equipment of the same type of personnel, and the working condition template is used to set the display configuration of equipment under working conditions. When multiple devices need to be configured, the corresponding measurement point display template can be selected to configure multiple devices in batches. Version management is implemented for the measurement point display template. After the version of the measurement point display template is updated, the user can choose whether to apply the new version of the measurement point display template to the configured device according to their needs.

6. The digital flowchart data point-to-point method according to claim 1, characterized in that, This also includes performing health assessments on the already bound monitoring points: The evaluation metrics include data freshness, data completeness, data volatility, confidence level change, and source system status. Data freshness is calculated based on the update time of the measurement point data and the current time; data completeness is calculated based on the missing proportion of measurement point data per unit time; data volatility is calculated based on the current standard deviation and historical average standard deviation of the measurement point; confidence level change is calculated based on the initial confidence level and current confidence level of the measurement point; and source system status is calculated based on the health status of the data source where the measurement point is located. The total health score is obtained by summing the normalized and summed scores of all the evaluation indicators. When the overall health level is lower than the corresponding threshold, an early warning notification and recommended solutions will be automatically generated, and a corresponding work order will be automatically created.

7. The digital flowchart data point-to-point method according to claim 1, characterized in that, It also includes periodically scanning the measurement point dictionary, detecting measurement point changes, and generating a pending list of measurement point changes: Detect new measurement points and check if there is any equipment that matches the new measurement points. If so, generate a pairing suggestion to be confirmed. Check if the bound measurement points have been marked as decommissioned. If so, mark the measurement points as invalid and prompt the user to rebind them. The system detects whether the measurement point ID has changed, and if so, automatically updates the binding relationship or prompts the user for confirmation; it also detects whether the measurement point attributes have changed, and if so, automatically updates the measurement point's display configuration; if the same measurement point has multiple data sources, it automatically switches to the backup data source when the primary data source fails, and marks any changes to the data source.

8. The digital flowchart data point-to-point method according to claim 1, characterized in that, This also includes generating point-to-point quality reports: The matching rule base is statistically analyzed for the point-to-point success rate, the health distribution of the test points, and the average health score; test points with health below the corresponding threshold are statistically analyzed to obtain a list of low-health test points, a list of unmatched devices, a list of low-confidence matches with confidence below the corresponding threshold, and a list of frequently changed test points with more than a set number of changes. Statistical analysis of the health status of monitoring points over time, the trend of newly added monitoring points, and the trend of monitoring point decommissioning; Based on the aforementioned point quality report, optimization suggestions are recommended to the user, and the overall health of each system is evaluated based on the statistical results of the measurement point health.

9. The digital flowchart data point-to-point method according to claim 1, characterized in that, It also includes saving the point-to-point configuration matching the equipment and the measurement points as auxiliary data of the flowchart, managing the auxiliary data of the points independently, generating a new point-to-point version for each point-to-point modification, and decoupling the point-to-point version number from the drawing version number; Export the point-to-point configuration to a standard format for offline editing and backup, and then import the edited point-to-point configuration in batches to achieve batch point-to-point pairing of devices. Based on the binding relationship between devices and measurement points, a measurement point relationship map is constructed. Based on the measurement point relationship map, other matching measurement points of the device, related devices of the recommended measurement points, related measurement points of the recommended measurement points, and related measurement points of the faulty device are recommended to the user.

10. A digital flowchart data point-to-point device, 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 digital flowchart data pairing method as described in any one of claims 1-9.

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