A method and system for generating a continuous casting actual performance based on a production plan

Abstract

This invention discloses a method and system for generating continuous casting performance data based on production plans. The method includes the following steps: S1: Real-time acquisition and aggregation of multi-source data through a sensor network deployed at key nodes of the continuous casting production line; S2: Construction of a casting state machine and dynamic tracking of the casting process using the sensor network; S3: Intelligent anomaly diagnosis based on multi-threshold and timing logic based on the dynamic tracking results; S4: Dynamic association and binding of production plans and process data based on a data association engine; S5: Automatic calculation of casting completion determination and performance data. This invention completely replaces the traditional manual recording mode, reducing casting statistical errors from over 8% to below 0.5%, shortening the interruption response time from 20 minutes to less than 8 seconds, and compressing report generation time from several hours to 2 minutes. It achieves unmanned, intelligent, and high-precision full-process continuous casting production management, demonstrating significant industrial application value.

Description

Technical Field

[0001] This invention relates to a method and system for generating continuous casting results based on production plans. Background Technology

[0002] In traditional continuous casting production, the statistics and management of production performance rely heavily on manual operation, which has many drawbacks: 1) The number of pours is counted visually or by pressing buttons, which is easily affected by repeated pours and scrap removal, resulting in a high statistical error rate (5%-10%); 2) Key process parameters (such as crystallizer liquid level, casting speed, and secondary cooling water flow rate) rely on manual timed recording, which is inefficient and carries the risk of data tampering and loss; 3) Abnormal conditions such as interrupted casting and steel leakage rely on manual inspection for detection, resulting in a delayed response and an average processing time of more than 15 minutes, causing a large amount of molten steel waste (a single accident results in a loss of about 2-3 tons); 4) Production planning (MES system), process control data (L1 system), and quality inspection data (LIMS system) are independent of each other, forming "data silos". When conducting production traceability and quality analysis, manual cross-system queries and verifications are required, which takes up to 4-6 hours.

[0003] While existing technologies have made some automation improvements, such as using sensors for automatic counting or simple logic judgment based on a single signal (such as casting speed), there are still obvious defects: they cannot effectively identify non-standard working conditions such as "empty casting" and "re-casting"; the collected process data lacks dynamic correlation with the production plan (steel grade, furnace number), resulting in the data not being able to be effectively classified and analyzed by casting number or steel grade; the judgment of "interrupted casting" usually only relies on a fixed casting speed threshold, without considering normal casting speed fluctuations during production, resulting in a high false alarm rate and the inability to accurately record the start and end times of abnormalities.

[0004] Therefore, there is an urgent need for a statistical method and system for continuous casting production performance that can achieve full automation, high precision, real-time correlation and intelligent analysis. Summary of the Invention

[0005] To overcome the above-mentioned technical problems, the purpose of this invention is to provide a method and system for generating continuous casting performance based on production plans, which can realize fully automatic statistics of casting performance, real-time diagnosis of anomalies, seamless data association, and one-click generation of reports.

[0006] To achieve the above objectives, the method for generating continuous casting performance based on production plans according to the present invention includes the following steps: S1: Real-time acquisition and aggregation of multi-source data through a sensor network deployed at key nodes of the continuous casting production line; S2: Construct a casting state machine and use a sensor network to dynamically track the casting process of the state machine; S3: Perform intelligent anomaly diagnosis based on multiple thresholds and temporal logic according to the dynamic tracking results; S4: Dynamically links and binds production plans with process data based on a data association engine; S5: Determination of pouring completion and automatic calculation of actual results.

[0007] Furthermore, it also includes step S6: automatic generation and publication of intelligent reports.

[0008] Furthermore, the sensors include a tension speed sensor, a ladle and tundish weight sensor, and a temperature sensor. Each sensor collects process data in real time at a frequency of not less than 1Hz, and transmits the data uniformly through an industrial Internet of Things protocol and caches it in a high-speed memory database.

[0009] Furthermore, step S2 specifically involves: establishing a dynamic "pouring state machine" for each continuous casting machine in the memory database; the state transition of the state machine is triggered by multi-source data collaboration: when the ladle weight drops from above a set threshold to below the threshold, a "pouring start" event is triggered, and the system automatically captures the currently effective MES production plan information and binds it to the pouring; during the pouring process, the casting speed of each stream is continuously monitored to determine the continuity of pouring.

[0010] Furthermore, step S3 specifically includes: "Interruption of pouring" is defined as all pouring speeds simultaneously falling below the safety threshold and remaining below the stable time window for an extended period, in order to avoid misjudgments caused by short-term fluctuations.

[0011] Furthermore, step S4 specifically includes: Based on the system's built-in data association engine, when the casting start event occurs, the corresponding production plan entry is retrieved from the MES system to generate a unique traceability code; all process parameters collected during this time period are automatically associated with and stored with this traceability code, forming a complete data chain.

[0012] Furthermore, step S5 specifically includes: When the weight of the bound ladle remains low and the casting speed signal returns to zero, or when a new casting start event is triggered, the current casting cycle is determined to be over. The system automatically calculates key performance data such as the duration of this casting cycle, the total weight of the cast ladle, and the net steel volume, and automatically synchronizes them to the upstream refining system and the downstream quality management system through the interface.

[0013] Furthermore, step S6 specifically involves: based on the associated structured data, the system automatically generates a comprehensive report containing production plan information, process parameter curves, key performance data, and abnormal event records according to a preset template, and supports timed or event-triggered push to relevant production, technical, and management positions.

[0014] To achieve the above objectives, the system for generating continuous casting performance based on production plans according to the present invention includes: a sensing and acquisition layer, an edge computing layer, a data service layer, and an application layer; wherein, The sensing and acquisition layer consists of industrial sensors deployed at key nodes of the continuous casting production line to collect the working status and parameters of the continuous casting machine in real time. The edge computing layer is used to perform preliminary filtering, protocol conversion, and real-time caching of the collected data. The data service layer includes: a dynamic tracking and status management module, an anomaly diagnosis and alarm module, a data association and binding engine, and a performance calculation and reporting module; among which, The dynamic tracking and status management module establishes a dynamic "pouring state machine" for each continuous casting machine in the memory database. The state transition of the state machine is triggered by multi-source data collaboration: when the ladle weight drops from above a set threshold to below the threshold, a "pouring start" event is triggered, and the system automatically captures the currently effective MES production plan information and binds it to the pouring. During the pouring process, the casting speed of each stream is continuously monitored to determine the continuity of pouring. The anomaly diagnosis and alarm module defines "casting interruption" as all flow rates being simultaneously below the safety threshold and continuing for more than a stable time window. The data association and binding engine is used to retrieve the corresponding production plan entries from the MES system and generate a unique traceability code; all process parameters collected within this time period are automatically associated with and stored with this traceability code to form a complete data chain. The performance calculation and reporting module determines the end of the current pouring cycle when the bound ladle weight remains low and the pouring speed signal returns to zero, or when a new pouring start event is triggered. The system automatically calculates key performance data such as the duration of the current pouring cycle, the total weight of the poured ladle, and the net steel volume, and automatically synchronizes them to the upstream refining system and the downstream quality management system through the interface.

[0015] Furthermore, the sensors in the system include a tension speed sensor, a ladle and tundish weight sensor, and a temperature sensor. Each sensor collects process data in real time at a frequency of not less than 1Hz, and transmits the data uniformly through an industrial Internet of Things protocol and caches it in a high-speed memory database.

[0016] The present invention has the following advantages: Improved statistical accuracy: The counting is precisely triggered by weight changes, avoiding errors from manual visual counting or simple signal counting, reducing the error rate of casting quantity statistics from over 8% to below 0.5%.

[0017] Achieving a leap in abnormal response time: Based on real-time data streams and intelligent diagnostic algorithms, the identification and alarm response time for abnormal conditions such as pouring interruption has been reduced from 20 minutes to less than 8 seconds, greatly reducing accident losses.

[0018] Breaking down data silos and achieving full-chain traceability: Through dynamic binding technology, MES plans, L1 process data, and LIMS quality data are automatically linked, reducing traceability analysis time from 4-6 hours to within 2 minutes, achieving full-process quality traceability with "one code to the end".

[0019] Comprehensively improve automation: achieve unmanned operation of the entire process from data collection, anomaly detection, performance calculation to report generation, freeing up labor and eliminating human intervention and errors.

[0020] Flexible deployment and significant benefits: The system's modular design allows it to be adapted to different continuous casting machine models. Actual industrial applications show that it can help steel mills reduce casting interruption losses by more than 62%, demonstrating extremely high economic benefits and promotional value. Attached Figure Description

[0021] Figure 1 This is a flowchart of the present invention.

[0022] Figure 2 This is a system architecture diagram of the present invention.

[0023] Figure 3 This is a graph showing the variation trend of various parameters in continuous casting according to the present invention.

[0024] Figure 4 This is a real-time graph of the continuous casting ladle weight curve of the present invention. Detailed Implementation

[0025] The embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0026] In the description of this invention, it should be understood that the terms "center", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0027] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0028] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0029] like Figure 2 As shown, the system for generating continuous casting performance based on production plans according to the present invention includes: The system consists of a perception and acquisition layer, an edge computing layer, a data service layer, and an application layer.

[0030] The sensing and acquisition layer consists of various industrial sensors and instruments, including but not limited to speed sensors, ladle and tundish weight sensors, and temperature sensors. These sensors collect process data in real time at a frequency of no less than 1Hz and transmit it uniformly through industrial IoT protocols, caching it in a high-speed in-memory database (such as Redis).

[0031] The edge computing layer is responsible for initial data filtering, protocol conversion, and real-time caching.

[0032] The data service layer is the core, and includes: dynamic tracking and status management module, anomaly diagnosis and alarm module, data association and binding engine, and performance calculation and reporting module.

[0033] The application layer provides a visual monitoring interface, report generation tools, and system management functions.

[0034] The present invention provides a method for generating continuous casting performance based on production planning, such as... Figure 1 As shown, it includes the following steps: S1: Real-time acquisition and aggregation of multi-source data: Through a sensor network deployed at key nodes of the continuous casting production line, including but not limited to speed sensors, ladle and tundish weight sensors, and temperature sensors, process data is acquired in real time at a frequency of not less than 1Hz, and transmitted and cached in a high-speed memory database (such as Redis) through an industrial IoT protocol.

[0035] S2: Dynamic tracking of the casting process based on state machine: A dynamic "casting state machine" is established for each continuous casting machine in the memory database. The state transition of the state machine is triggered by multi-source data collaboration: When the weight of the ladle drops from above a set threshold (e.g., 220 tons) to below the threshold, a "casting start" event is triggered, and the system automatically captures the currently effective MES production plan information (e.g., heat number, steel grade code) and binds it to that casting; during the casting process, the casting speed of each stream is continuously monitored as the core basis for judging the continuity of casting.

[0036] S3: Intelligent anomaly diagnosis based on multi-threshold and timing logic: "Interruption of pouring" is defined as all pouring speeds simultaneously falling below a safety threshold (e.g., 0.5 m / min) and persisting for more than a stable time window (e.g., 10 seconds). This judgment logic avoids misjudgments caused by short-term fluctuations. Once an interruption of pouring is diagnosed, the system immediately triggers multi-level alarms (audio-visual, message push), automatically records the start time of the anomaly, and updates the pouring status to "interrupted".

[0037] S4: Dynamic Linkage and Binding of Production Plan and Process Data: The system has a built-in data association engine that retrieves the corresponding production plan entry from the MES system and generates a unique traceability code when the casting start event occurs. All process parameters (casting speed, temperature, weight change curves) collected during this time period are automatically associated with and stored with this traceability code, forming a complete data chain.

[0038] S5: Pouring End Judgment and Automatic Performance Calculation: When the bound ladle weight remains low and the casting speed signal returns to zero, or a new pouring start event is triggered, the current pouring cycle is determined to be over. The system automatically calculates key performance data such as the duration of this pouring cycle, total pouring weight, and net molten steel volume, and automatically synchronizes these data to the upstream refining system and downstream quality management system via an interface.

[0039] S6: Intelligent Report Automatic Generation and Publishing: Based on the associated structured data, the system automatically generates reports containing production plan information and process parameter curves (such as...) according to preset templates. Figure 3 and Figure 4 It provides comprehensive reports (such as Excel format) of key performance data and abnormal event records, and supports scheduled or event-triggered push to relevant production, technical and management positions.

[0040] Example 1 Taking the application of this system on the No. 1 slab continuous casting machine of a steel plant as an example.

[0041] 1. System Deployment and Data Acquisition: The following sensors are deployed on the No. 1 continuous casting machine: pull speed sensors for each flow (signal point such as st4_fircast_pulling_one_speed1), with a sampling frequency of 1Hz; ladle weighing sensors (signal point such as st4_fircast_bigbag1_weight), for real-time monitoring; and tundish thermocouples (signal point such as st4_fircast_midpack_temp_left). All sensor data is collected via the OPC UA protocol and written to a Redis database in real time, with key-value pairs organized by device, signal point, and timestamp.

[0042] 2. Casting tracking and result generation: After system initialization, the CastingTrackService service continues to run. When the value of st4_fircast_bigbag1_weight in Redis changes from ≥220 tons to <220 tons, the system triggers the castingTrigger1 method. This method executes the following logic: By tracing back from the time the event occurred, the currently executing SteelmakingPlan item is obtained from the MES system interface, and key information such as heatNo (heat number) and steelGrade (steel grade) is extracted.

[0043] Create and initialize a status tracking key in Redis, such as casting1Track:runStatus, set its value to 1 (running), and record the start time of each casting and the associated plan ID.

[0044] Call the checkMiddleStopCasting method to start continuous monitoring of the casting speed for this pour.

[0045] 3. Abnormal Diagnosis and Handling: The `checkMiddleStopCasting` method iteratively checks the casting speed of all streams during the current casting cycle. If the casting speed of all streams remains below 0.5 m / min for a 10-second time window, the casting is considered interrupted. The system executes: Update the status of casting1Track:runStatus to 0 (interrupted).

[0046] The alarm center is triggered, and SMS and platform messages are sent to notify the pouring worker and dispatcher.

[0047] Automatically invoke exception handling logic, update production performance record (CastingResult), record midpkgEndingTime (mid-package end time), and calculate the interrupted casting cycle.

[0048] 4. Data Linking and Report Generation: Throughout the entire casting cycle, all collected process parameters are marked with corresponding plan traceability codes. After each casting cycle is completed (determined by emptying the ladle or starting a new casting cycle), the system initiates a report generation task.

[0049] Retrieve all process data under this traceability code from Redis by time range.

[0050] Retrieve the planned information and actual performance calculation results (total weight, net weight, etc.) for this pouring from the database.

[0051] Fill the above data into the predefined Excel template to automatically generate a comprehensive report containing four worksheets: "Planning Information", "Process Curve", "Production Performance", and "Abnormal Records".

[0052] Report files are automatically saved to the file server and sent to designated personnel via email. Simultaneously, key performance data (such as net weight) is written back to the MES and refining systems via a WebService interface.

[0053] 5. Implementation Results: After the system went online, the No. 1 continuous casting machine achieved unmanned operation of the casting statistics position. After three months of data comparison: the number of casting furnaces count has basically achieved zero error; the average response time for casting interruption anomalies has been reduced to 5 seconds, and the steel loss caused by casting interruption has decreased by 65% ​​month-on-month; the time for production, quality and technical departments to obtain daily reports has been reduced from an average of 4 hours to after the start of the workday, greatly improving efficiency.

[0054] The present invention has been described in detail above with reference to the accompanying drawings. However, the present invention is not limited to the embodiments described above. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention. Many other changes and modifications made without departing from the concept and scope of the present invention should be considered within the scope of protection of the present invention.

[0055] In the description of this specification, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0056] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for generating continuous casting performance based on production plans, characterized in that, Includes the following steps: S1: Real-time acquisition and aggregation of multi-source data through a sensor network deployed at key nodes of the continuous casting production line; S2: Construct a casting state machine and use a sensor network to dynamically track the casting process of the state machine; S3: Perform intelligent anomaly diagnosis based on multiple thresholds and temporal logic according to the dynamic tracking results; S4: Dynamically links and binds production plans with process data based on a data association engine; S5: Determination of pouring completion and automatic calculation of actual results.

2. The method for generating continuous casting performance based on production plan according to claim 1, characterized in that, It also includes step S6: automatic generation and publication of intelligent reports.

3. The method for generating continuous casting performance based on production plan according to claim 1, characterized in that, The sensors include a tension speed sensor, a ladle and tundish weight sensor, and a temperature sensor. Each sensor collects process data in real time at a frequency of not less than 1Hz, and transmits the data uniformly through an industrial Internet of Things protocol and caches it in a high-speed memory database.

4. The method for generating continuous casting performance based on production plan according to claim 1, characterized in that, The specific steps of step S2 are as follows: a dynamic "pouring state machine" is established for each continuous casting machine in the memory database; the state transition of the state machine is triggered by multi-source data collaboration: when the weight of the ladle drops from above the set threshold to below the threshold, the "pouring start" event is triggered, and the system automatically captures the currently effective MES production plan information and binds it to the pouring. During the pouring process, the pouring speed of each stream is continuously monitored to determine the continuity of the pouring.

5. The method for generating continuous casting performance based on production plan according to claim 1, characterized in that, The specific steps of step S3 are as follows: "Stop pouring" is defined as all pouring speeds being simultaneously below the safety threshold and remaining below the stable time window for an extended period, in order to avoid misjudgments caused by short-term fluctuations.

6. The method for generating continuous casting performance based on production plan according to claim 1, characterized in that, The specific steps of S4 are as follows: Based on the system's built-in data association engine, when the casting start event occurs, the corresponding production plan entry is retrieved from the MES system to generate a unique traceability code; all process parameters collected during this time period are automatically associated with and stored with this traceability code, forming a complete data chain.

7. The method for generating continuous casting performance based on production plan according to claim 1, characterized in that, The specific steps of step S5 are as follows: When the weight of the bound ladle remains low and the casting speed signal returns to zero, or when a new casting start event is triggered, the current casting cycle is determined to be over. The system automatically calculates key performance data such as the duration of this casting cycle, the total weight of the cast ladle, and the net steel volume, and automatically synchronizes them to the upstream refining system and the downstream quality management system through the interface.

8. The method for generating continuous casting performance based on production plan according to claim 2, characterized in that, The specific steps of step S6 are as follows: Based on the associated structured data, the system automatically generates a comprehensive report containing production plan information, process parameter curves, key performance data, and abnormal event records according to a preset template, and supports timed or event-triggered push to relevant production, technical and management positions.

9. A system for generating continuous casting performance data based on production plans, characterized in that, The system comprises: a sensing and acquisition layer, an edge computing layer, a data service layer, and an application layer; wherein, The sensing and acquisition layer consists of industrial sensors deployed at key nodes of the continuous casting production line to collect the working status and parameters of the continuous casting machine in real time. The edge computing layer is used to perform preliminary filtering, protocol conversion, and real-time caching of the collected data. The data service layer includes: a dynamic tracking and status management module, an anomaly diagnosis and alarm module, a data association and binding engine, and a performance calculation and reporting module; among which, The dynamic tracking and status management module establishes a dynamic "pouring state machine" for each continuous casting machine in the memory database. The state transition of the state machine is triggered by multi-source data collaboration: when the ladle weight drops from above a set threshold to below the threshold, a "pouring start" event is triggered, and the system automatically captures the currently effective MES production plan information and binds it to the pouring. During the pouring process, the casting speed of each stream is continuously monitored to determine the continuity of pouring. The abnormal diagnosis and alarm module defines "casting interruption" as all flow rates being simultaneously below the safety threshold and lasting for more than a stable time window. The data association and binding engine is used to retrieve the corresponding production plan entries from the MES system and generate a unique traceability code; all process parameters collected within this time period are automatically associated with and stored with this traceability code to form a complete data chain. The performance calculation and reporting module determines the end of the current pouring cycle when the bound ladle weight remains low and the pouring speed signal returns to zero, or when a new pouring start event is triggered. The system automatically calculates key performance data such as the duration of the current pouring cycle, the total weight of the poured ladle, and the net steel volume, and automatically synchronizes them to the upstream refining system and the downstream quality management system through the interface.

10. The system for generating continuous casting performance based on production plans according to claim 9, characterized in that, The system includes sensors such as a tension speed sensor, a ladle and tundish weight sensor, and a temperature sensor. Each sensor collects process data in real time at a frequency of not less than 1Hz, and transmits the data uniformly through an industrial Internet of Things protocol and caches it in a high-speed memory database.

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