Multi-post collaborative molten iron dispatching workflow dynamic issuing method and system

Abstract

The invention provides a multi-post collaborative molten iron dispatching workflow dynamic issuing method and system. The method comprises the following steps: constructing a molten iron dispatching workflow system comprising a normal production process and an exception handling process; the normal production process is designed in a segmented mode according to production stages, dynamic connection is achieved through production state parameter threshold judgment, and the exception handling process is designed in a classified mode according to production exception types; manual filling data, sensor collection data and third-party system docking data in the molten iron dispatching process are obtained; in a normal production process, dynamic connection is realized based on logic association of each segment, and a to-be-handled task is generated and automatically issued to a corresponding post; analyzing the production data in real time to judge abnormity, and switching to a corresponding abnormity processing flow when abnormity occurs; recording the whole process to form a traceable log; the function of cooperation among multiple posts in the molten iron dispatching process is achieved, and the dispatching efficiency and the exception handling capacity are improved.

Description

Technical Field

[0001] This invention belongs to the field of hot metal dispatching technology in metallurgical production, specifically involving a method and system for dynamically issuing hot metal dispatching workflows with multi-position collaboration. Background Technology

[0002] With the development of the steel industry, molten iron dispatching, as a crucial link connecting ironmaking and steelmaking processes, directly impacts the stability and economic benefits of the entire steel production process. Currently, molten iron dispatching relies mainly on manual experience for coordination and arrangement, which suffers from problems such as untimely information transmission and low coordination efficiency.

[0003] In traditional molten iron scheduling management, steel companies typically employ a dynamic scheduling method at the ironmaking-steelmaking interface. This method involves collecting real-time status data during the production process to clarify the supply and demand relationship of molten iron in the ironmaking and steelmaking sections, and to formulate iron distribution plans, ladle allocation plans, and ladle transportation plans. When abnormal disturbances occur during production, adjustments are made according to the magnitude of the disturbance to reduce the impact of abnormal fluctuations on normal production. However, this method still has shortcomings in terms of multi-position collaboration and automation of anomaly handling, especially in terms of limitations in real-time data-driven dynamic scheduling.

[0004] In summary, the existing technology has the following shortcomings in molten iron dispatching and management: 1. The hot metal dispatching is out of sync with the production status, the dispatching rules are rigid and cannot be dynamically adjusted according to real-time production data, resulting in delayed dispatching instructions and an inability to respond to changes in the production site in a timely manner; 2. Information is isolated across multiple positions, resulting in low collaboration efficiency. Data interoperability between positions is poor, and task transfer relies on unsystematic methods, which are inefficient and prone to errors. 3. The abnormal response lacks an automated mechanism, requiring manual identification and initiation of the processing flow, resulting in slow response speed and a lack of an automatic identification mechanism based on comparing abnormal feature values ​​with preset thresholds; 4. The data verification mechanism is imperfect. There is a lack of linkage verification between manually entered data and automatically collected data, which can easily lead to data deviations and affect the accuracy of scheduling decisions.

[0005] Therefore, there is an urgent need for a method and system for molten iron scheduling that can dynamically distribute workflows based on real-time production data, support multi-position collaboration, and have an automated anomaly handling mechanism, so as to improve the efficiency and accuracy of molten iron scheduling, reduce production costs, and enhance the overall competitiveness of steel enterprises. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a method and system for dynamic distribution of molten iron scheduling workflow for multi-position collaboration, which is used for the collaborative cooperation among multiple positions in the molten iron scheduling process.

[0007] The technical solution adopted by this invention to solve the above-mentioned technical problems is as follows: a method for dynamically issuing multi-position collaborative molten iron scheduling workflow, comprising the following steps: S1: Construct a workflow system for molten iron dispatching that includes normal production processes and abnormal handling processes; S2: Nodes that are logically connected in the construction process; S3: Obtain production data during the molten iron dispatching process; S4: Dynamically connects the normal production process based on segmented logic association and generates the corresponding pending tasks for the positions; S5: Analyze production data in real time to identify anomalies, and switch to the corresponding anomaly handling process when an anomaly occurs; S6: After completing the pending task, verify the production data and generate the next pending task; S7: Records all pending tasks and creates a traceable log.

[0008] According to the above scheme, the specific steps in step S1 are as follows: The normal production process is designed in segments according to production stages, and dynamic connection is achieved by judging production status parameter thresholds; the abnormality handling process is designed according to the types of production abnormalities; the types of production abnormalities are determined by comparing the abnormal feature values ​​in the production data with preset thresholds.

[0009] According to the above scheme, in step S2, The nodes include task nodes, condition judgment nodes, jump nodes, and copy nodes; Task nodes are used to fill in production data in the form when pending tasks are being processed; Conditional decision nodes determine the subsequent execution branches of the process according to preset judgment conditions; Jump nodes are used to jump between any node based on production data and preset conditions; The copy node is used to inform relevant personnel of important information; The segmented connection of the normal production process is determined by comparing actual production data with preset thresholds; The generation of the exception handling process is determined by comparing production data with exception feature values ​​or preset thresholds; Node tasks can be associated with data entry forms. The forms can be pre-set with business-related fields according to requirements, and business data can be filled in when processing pending tasks.

[0010] According to the above scheme, in step S3, Production data includes manually entered data, sensor-collected data, and data from third-party system integrations; Manually entered data is collected via a mobile app to support real-time data entry and uploading by on-site personnel; The sensor collects data at a preset frequency to ensure the real-time nature of production data; Data from third-party systems is synchronized to this system in real time via a pre-defined interface.

[0011] According to the above scheme, the specific steps in step S4 are as follows: Production data is collected, processed, and organized. Based on the logical connections between different segments in the normal production process, dynamic connections are achieved. Based on the connection results, different types of workflows within the segments are initiated, and corresponding tasks are generated for the corresponding positions.

[0012] Furthermore, in step S4, The tasks to be completed include a task description, operation instructions, related production data display, and required data entry fields.

[0013] According to the above scheme, in step S5, When executing the exception handling process, the system automatically issues a pending task to the responsible position, which includes exception details, related production data and handling instructions. The system tracks the handling progress in real time. After the exception is completed, the system verifies the data to confirm that the exception has been eliminated, and then the workflow is transferred back to the corresponding segment of the normal production process and continues to be executed.

[0014] Furthermore, in step S5, the switching mechanism for the exception handling process includes: Anomaly detection: Identifying abnormal events through real-time data analysis or manual reporting; Process switching: Automatically match the corresponding abnormal process template based on the type of abnormality and pause the execution of the current normal process node; Abnormal closed loop: After the abnormal handling process is completed, the abnormal handling result is verified. If it meets the requirements, the normal production process is restored; otherwise, the abnormal handling process continues.

[0015] A dynamic workflow distribution system for molten iron dispatching with multi-position collaboration. The process design submodule is used to build a molten iron scheduling workflow system that includes normal production processes and abnormal handling processes. The node design submodule is used to construct logically connected nodes in the process; The data acquisition submodule is used to acquire production data during the molten iron scheduling process; The task generation submodule is used to dynamically connect the normal production process based on segmented logical associations and generate the pending tasks for the corresponding positions. The process switching submodule is used to analyze production data in real time to identify anomalies and switch to the corresponding anomaly handling process when an anomaly occurs. The data validation submodule is used to validate production data after completing the pending task and generate the next pending task. The logging submodule is used to record all pending tasks and create a traceable log.

[0016] A computer memory storing a computer program executable by a computer processor, the computer program executing a method for dynamically distributing a multi-position collaborative molten iron scheduling workflow.

[0017] The beneficial effects of this invention are as follows: 1. This invention provides a method and system for dynamically issuing multi-position collaborative hot metal dispatching workflows. It constructs a hot metal dispatching workflow system that includes normal production processes and exception handling processes. The normal production process is designed in segments according to production stages and dynamically connected using production status parameter thresholds. The exception handling process is designed according to the type of production exception. It acquires manually entered data, sensor-collected data, and data from third-party systems during the hot metal dispatching process. During normal production, it dynamically connects segments based on logical relationships, generates pending tasks, and automatically issues them to the corresponding positions. It analyzes production data in real time to identify exceptions and switches to the corresponding exception handling process when an exception occurs. The entire process is recorded to form a traceable log. This achieves collaborative cooperation among multiple positions during hot metal dispatching, improving dispatching efficiency and exception handling capabilities.

[0018] 2. This invention integrates manually entered data, real-time operational data, and related production data from various positions, incorporating multiple roles such as nickel-iron furnace operators, nickel-iron shift supervisors, steelmaking casting, dispatchers, train captains, and AOD assistant operators into a unified workflow platform. This enables real-time data sharing and automatic task transmission, allowing each position to obtain the necessary information in a timely manner and significantly improving collaboration efficiency. It solves technical problems such as the disconnect between molten iron scheduling and production status, isolated information from multiple positions, lack of automated mechanisms for abnormal response, and imperfect data verification mechanisms. It achieves multi-position collaboration and production status linkage during molten iron scheduling, breaking the information silos and enabling the scheduling process to be dynamically adjusted based on real-time production data. This effectively solves the problems of process connection and disconnect between pending task generation and production status in traditional scheduling methods.

[0019] 3. This invention constructs a dual-system workflow of "normal production process + abnormal handling process." The system can automatically select the appropriate process path based on real-time production data, closely integrating scheduling instructions with production status and improving the timeliness and accuracy of scheduling. Through real-time analysis of production data and automatic anomaly identification, it achieves the technical effects of automated anomaly response, improved data verification mechanism, and reduced molten iron temperature drop, shortening the anomaly handling time from minutes for manual identification to seconds for automatic system identification. Based on the comparison of preset anomaly characteristic values ​​and thresholds, it can automatically detect anomalies such as vehicle malfunctions and substandard molten iron parameters, and immediately initiate the corresponding anomaly handling process, greatly improving the anomaly response speed and processing efficiency.

[0020] 4. By automatically and logically verifying production data, the data verification mechanism has been improved, increasing the accuracy of production data to over 98%. The system compares and verifies manually entered data with automatically collected data, and performs logical verification based on production process rules, effectively reducing data deviation and providing reliable data support for scheduling decisions.

[0021] 5. Practical application shows that the method of this invention can effectively reduce the temperature drop of molten iron by 5°C compared to before using this method. This provides reliable data support for optimizing the scheduling process and makes the entire molten iron scheduling process traceable. By optimizing the scheduling process, the temperature loss of molten iron during transportation and waiting is reduced, improving the quality and utilization efficiency of molten iron and bringing significant economic benefits to enterprises.

[0022] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart of an embodiment of the present invention.

[0025] Figure 2 This is a structural diagram of the molten iron dispatching process according to an embodiment of the present invention.

[0026] Figure 3 This is a flowchart of the execution logic of an embodiment of the present invention.

[0027] Figure 4 This is a flowchart of the molten iron dispatching process according to an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0029] Example 1 See Figure 1 The specific steps of a dynamic workflow distribution method for multi-position collaborative molten iron scheduling are as follows: S1: Construct a workflow system for dispatching molten iron by road transport. The workflow system includes a normal production process and an anomaly handling process. The normal production process is designed to be segmented according to the production stage or process link of molten iron dispatching (empty package transport segment, iron receiving segment, loaded package transport segment, loaded package lifting segment, and iron exchange segment). Each segment is dynamically connected by threshold judgment of production status parameters (molten iron position, vehicle running status, iron output weight, iron exchange time, etc.). The anomaly handling process is designed to be classified according to the type of production anomaly (vehicle failure, equipment failure, molten iron parameters not meeting standards, road traffic abnormalities). The type of production anomaly is determined by comparing the abnormal feature value in the production data with a preset threshold. S2: Each constructed normal production process or abnormal handling process contains several logically connected nodes. Node types include task nodes, condition judgment nodes, jump nodes, and copy nodes. Among them, task nodes are used to fill in production data in the form when processing pending tasks; condition judgment nodes determine the subsequent execution branch of the process according to preset judgment conditions; jump nodes are used to jump to any node based on production data and preset conditions; and copy nodes are used to inform relevant personnel of important information. Preferably, each normal production process and abnormal process includes several task nodes, and the task nodes within the process are connected sequentially. The segmented connection of the normal production process needs to be determined by comparing actual production data with preset thresholds. For example, in the iron connection segment, the system dynamically adjusts the subsequent connected production process based on the actual iron output weight of nickel-iron and the actual blockage time. The generation of the abnormal handling process needs to be determined by comparing production data with abnormal feature values ​​or preset thresholds. For example, the abnormal feature value of vehicle abnormal on / off line entry is GPS speed = 0 and engine status "fault" lasts for 5 minutes.

[0030] Preferably, the node task supports association with a data entry form, and the form can be preset with business-related fields as needed so that business data can be filled in when processing pending tasks; S3: Obtain production data during the molten iron dispatching process. The sources of the production data include: manually entered data (ladle weight, tapping time, tapping weight, molten iron mixing time, remaining water weight, etc.) from various positions (nickel-iron furnace operator, nickel-iron shift supervisor, steelmaking casting, dispatcher, car team leader, AOD assistant operator) in the workflow pending tasks; real-time operation data (vehicle GPS positioning data, weighbridge weighing data) collected by sensors or intelligent equipment; and related production data (furnace plan data, molten iron composition data) obtained through docking with third-party systems (steelmaking MES system, LIMIS inspection and testing system). Preferably, the manually entered data is collected via a mobile APP, supporting real-time entry and uploading by on-site personnel; the sensor data is acquired at a preset frequency (e.g., GPS data every 10 seconds) to ensure the real-time nature of production data; data from third-party systems is synchronized to this system in real time through a preset interface (e.g., the furnace planning interface of the steelmaking MES system).

[0031] S4: During the normal operation of the molten iron dispatching process, the production data is collected, processed, and organized. Based on the logical connections between the segments in the normal production process (such as the empty ladle transport segment connecting to the molten iron receiving segment after arriving at the nickel-iron plant, and the heavy ladle transport segment connecting to the heavy ladle lifting segment after arriving at the steelmaking plant), dynamic connections are achieved. Based on the connection results, different types of workflows within the segments are initiated, and corresponding pending tasks are generated for the corresponding positions. The pending tasks include task descriptions, operation instructions, related production data displays, and required data entry items. S5: By analyzing the production data in real time, determine whether a production anomaly has occurred. If a production anomaly occurs (such as vehicle malfunction causing GPS speed to show 0 and engine status to be abnormal for 5 minutes, slag appearing at the tapping opening, actual tapping quantity deviating from the required tapping quantity by more than a preset range, or molten iron composition deviating from the required steel grade by more than a preset range), the workflow will be switched from the normal production process to the corresponding anomaly handling process. When the anomaly handling process is executed, a pending task containing anomaly details, related production data, and handling instructions will be automatically issued to the responsible position. The handling progress will be tracked in real time. After the handling is completed, the anomaly will be confirmed to be eliminated through data verification. Then, the workflow will be switched back to the corresponding segment of the normal production process and continue to be executed. Preferably, the switching mechanism for the exception handling process includes the following steps: Anomaly detection: Identifying anomalous events through real-time data analysis or manual reporting, such as: 1) The vehicle's GPS signal loss exceeds a preset threshold; 2) The manually reported iron tapping weight does not meet the minimum weight requirement for AOD smelting or the planned iron tapping demand for a single furnace. Process switching: The system automatically matches the corresponding abnormal process template according to the type of abnormality and pauses the execution of the current normal process node; Abnormal closed loop: After the abnormal process is completed, the system verifies the abnormal handling result. If it meets the requirements, the normal process node is restored to execution; otherwise, the abnormal process continues to be executed.

[0032] S6: When any position completes its corresponding pending task, the system verifies the collected production data (including automatic verification: comparing manually entered data with sensor-collected data or third-party system data; if the deviation exceeds the preset range, an anomaly alert is triggered; logical verification: judging the rationality of the data based on production process rules, such as the degree of matching between the time when molten iron arrives at the steelmaking plant and the time when the steelmaking plant adds iron). The system then dynamically generates the next pending task or starts the next stage of the workflow through preset logical judgment rules (based on at least one of the following: the division of responsibilities and operating permissions of each position, the sequence and time constraints of molten iron scheduling process, the deviation value between real-time production status parameters and target parameters, and the optimization model formed by historical scheduling data). The system also automatically sends the pending task to the corresponding position. S7: Record the entire process of each pending task, including generation time, recipient, completion time, personnel handling the task, data changes, and process switching nodes, to form a traceable full-process log for molten iron scheduling.

[0033] Example 2 The steps in this embodiment are the same as in Embodiment 1, except that each step is applied to a specific instance. Specifically, it includes the following steps: Taking the scenario of "No. 3 nickel-iron furnace → No. 2 AOD furnace" of a stainless steel company as an example, the implementation process is explained in detail with reference to the system modules.

[0034] like Figure 2 As shown, the multi-position collaborative hot metal scheduling workflow dynamic distribution method of the present invention includes two main parts: normal production process and abnormal handling process. The normal production process includes five segments: empty ladle transportation section, hot metal receiving section, loaded ladle transportation section, loaded ladle lifting section, and hot metal exchange section; the abnormal handling process includes five types: empty ladle recall, abnormal vehicle entry and exit, abnormal road reporting, hot metal casting, and furnace water treatment.

[0035] like Figure 3 As shown, the process execution logic of this invention includes modules such as basic design, data-driven, production task linkage and exception handling closed loop, as well as how to-do tasks are dynamically generated and issued based on data integration, rule base and exception handling mechanism.

[0036] like Figure 4 As shown, the process node connection relationship of the entire process of molten iron dispatching includes the empty package loading and dispatching process from empty package loading to empty package arrival, the receiving and transporting process from empty package unsealing to full package arrival at steelmaking, and the iron exchange process from full package unsealing to empty package weighing by overhead crane.

[0037] The specific implementation method is as follows: S1: Constructing a workflow system for dispatching road, rail, and water transport. A workflow system for dispatching molten iron by road was constructed, comprising normal production processes and exception handling processes. The normal production processes are segmented according to the production stages or technological steps of molten iron dispatching, including the empty ladle transportation segment (departure of empty steelmaking ladle → arrival of empty ladle), the molten iron receiving segment (opening of the taphole → sealing of the taphole), the loaded ladle transportation segment (departure of loaded ladle → arrival of loaded ladle), the loaded ladle lifting segment, and the molten iron exchange segment (molten iron exchanged for AOD → weighing of empty ladle by overhead crane). Each segment is dynamically connected through threshold judgments of production status parameters, including ladle position, vehicle operating status, tapped molten iron weight, and molten iron exchange time.

[0038] The segmented connection of the normal production process needs to be determined by comparing actual production data with preset thresholds. For example, in the iron connection segment, the system dynamically adjusts the subsequent production process based on the actual iron output weight of nickel-iron and the actual blockage time. The generation of the abnormal handling process needs to be determined by comparing production data with abnormal characteristic values ​​or preset thresholds.

[0039] The anomaly handling process is designed according to the type of production anomaly, including vehicle malfunction, equipment malfunction, substandard molten iron parameters, and road traffic anomalies. The type of production anomaly is determined by comparing the abnormal feature values ​​in the production data with preset thresholds. For example, the abnormal feature value for a vehicle going off-line is a GPS speed of 0 and an engine status of "fault" lasting for 5 minutes.

[0040] S2: Construct process nodes Each constructed normal production process or abnormal handling process contains several logically connected nodes. Node types include task nodes, condition judgment nodes, jump nodes, and copy nodes. Task nodes generate pending tasks that require production data to be filled in a form. Condition judgment nodes determine the subsequent execution branch of the process according to preset judgment conditions. Jump nodes are used to jump between any nodes based on production data and preset conditions. Copy nodes are used to notify relevant personnel of important information.

[0041] Node tasks can be associated with data entry forms. These forms can be pre-set with business-related fields to facilitate the entry of business data during task processing. Each normal production process and abnormal process contains several task nodes, which are sequentially linked within the process.

[0042] S3: Obtain production data during molten iron dispatching. The production data obtained during the molten iron scheduling process includes: manually entered data from each position in the workflow to-do tasks, real-time operation data collected by sensors or intelligent equipment, and related production data obtained through integration with third-party systems.

[0043] The various positions, including ferronickel furnace operators, ferronickel shift supervisors, steelmaking casting personnel, dispatchers, train captains, and AOD assistant operators, are responsible for filling in data such as ladle weight, tapping time, tapping weight, ferroalloy mixing time, and remaining water weight. Manual data entry is collected via a mobile app, allowing for real-time data entry and uploading by on-site personnel.

[0044] Real-time operational data collected by sensors or intelligent equipment includes vehicle GPS positioning data and weighbridge weighing data. Sensor data is acquired at a preset frequency (e.g., GPS data every 10 seconds) to ensure the real-time nature of production data.

[0045] Third-party systems include the steelmaking MES system and the LIMIS testing system. The associated production data obtained through these interfaces includes furnace schedule data and molten iron composition data. Data from these third-party systems is synchronized to this system in real time via a pre-defined interface (such as the furnace schedule interface of the steelmaking MES system).

[0046] S4: Dynamic connection of normal production process and generation of pending tasks During the normal operation of molten iron scheduling, production data is collected, processed, and organized. Based on the logical connections between different segments of the normal production process, dynamic connections are achieved, and different types of workflows within each segment are initiated according to the connection results, generating pending tasks for corresponding positions. Pending tasks include task descriptions, operation instructions, displays of related production data, and required data entry fields.

[0047] Taking the connection between the empty package transport section and the iron receiving section as an example: After the empty package transport process is started and the vehicle departs, an "empty package arrival" task is generated (including "reporting the empty package arrival time, associating vehicle 05, and associating package number C01"), and the task is pushed to the nickel-iron furnace operator (9:20). The nickel-iron furnace operator fills in the empty package arrival time as 10:00 through the APP, and after submission, the data acquisition module synchronizes it to the system. The data acquisition module obtains the GPS data of vehicle number "05" (longitude 122.175995, latitude -2.867308), showing that it has arrived at the iron receiving point of nickel-iron furnace #3, and the nickel-iron furnace operator confirms the vehicle's arrival on the mobile APP. The process design module determines that the "empty package → iron receiving" connection rule is met and sends the next stage process start signal to the task generation module.

[0048] S5: Switching and Execution of Exception Handling Procedures By analyzing production data in real time, it is determined whether any production anomalies have occurred. If an anomaly is found, the workflow is switched from the normal production process to the corresponding anomaly handling process. When the anomaly handling process is executed, a pending task containing anomaly details, related production data, and handling instructions is automatically issued to the responsible positions. The processing progress is tracked in real time. After the anomaly is cleared by data verification, the workflow is then switched back to the corresponding segment of the normal production process and continues to execute.

[0049] The switching mechanism for exception handling procedures includes the following steps: Anomaly detection: Identify abnormal events through real-time data analysis or manual reporting, such as: vehicle GPS signal loss exceeding a preset threshold; Process switching: The system automatically matches the corresponding abnormal process template according to the type of abnormality and pauses the execution of the current normal process node; Abnormal closed loop: After the abnormal process is completed, the system verifies the abnormal handling result. If it meets the requirements, the normal process node is restored to execution; otherwise, the abnormal process continues to be executed.

[0050] Taking a vehicle malfunction in the heavy-load transportation section as an example: After the heavy-load transportation section process is initiated, a "heavy-load arrival" task is generated and pushed to the steelmaking and casting department. The data acquisition module obtains the GPS data of vehicle number "05" and identifies abnormal characteristic values ​​(GPS speed = 0 and engine status "fault" lasting for 5 minutes), determining that the vehicle is abnormal (11:25). The module then sends a signal to the task generation module to initiate the vehicle abnormality on / offline process. The task generation module initiates the vehicle abnormality on / offline handling process, generates a "vehicle repair" task, sends it to the team leader, and suspends the heavy-load transportation process. The team leader reports vehicle repair completed in the mobile APP (12:10). The data acquisition module verifies the GPS data of vehicle "05," confirms that it meets the conditions for normal vehicle operation, determines that the abnormality has ended, triggers the process to revert to the "heavy-load transportation section," and activates the suspended "heavy-load transportation" process.

[0051] S6: Data validation upon completion of pending tasks and generation of the next task. Once any position completes its assigned task, the system verifies the collected production data and dynamically generates the next task or initiates the next stage of the workflow based on preset logical judgment rules, and automatically distributes the task to the corresponding position.

[0052] Data verification includes automatic verification and logical verification. Automatic verification compares manually entered data with data collected by sensors or third-party systems; if the deviation exceeds a preset range, an anomaly alert is triggered. Logical verification judges the rationality of data based on production process rules, such as the degree of matching between the time when molten iron arrives at the steelmaking plant and the time when iron is added during steelmaking.

[0053] The preset logical judgment rules are based on at least one of the following: the division of responsibilities and operating authority of each position, the sequence and time constraints of molten iron scheduling process, the deviation between real-time production status parameters and target parameters, and the optimization model formed by historical scheduling data.

[0054] Taking the ferroalloy connection process and branch selection as an example: The ferroalloy connection process starts, generating a "Temperature Opening" task (including "reporting opening time, whether slag is seen during opening, and uploading photos"), and pushing the task to the ferro-nickel furnace operator (10:00). The ferro-nickel furnace operator fills in the information via the APP: opening time 10:10, no slag seen during opening (upload photos), and after submission, the data acquisition module synchronizes it to the system. The process design module determines that the "slag seen during opening" rule is not met, proceeding along the normal tapping branch, without needing to enter the ferroalloy connection branch where slag is seen during opening. The task generation module generates a "Temperature Blocking" task (including "reporting blocking time and tapping weight"), and pushes the task to the ferro-nickel furnace operator (10:10), copying the blocking information to the dispatcher.

[0055] S7: Full-process logging The entire process of each pending task is recorded, including its generation time, recipient, completion time, personnel involved, data changes, and process switching nodes, forming a traceable log of the entire iron and steel dispatching process.

[0056] The collaborative module records logs for key nodes: 09:20: Issued the "empty package arrival" task to the nickel-iron furnace operator; completed at 10:00. 10:00: Collect GPS data for vehicle 05; 10:00: The iron receiving process is started, and the task of "opening the iron tap" is issued to the front-end workers of the nickel-iron furnace. It is completed at 10:10. 10:10: Issued the task of "blocking the iron tap" to the front-line workers of the nickel-iron furnace, completed at 11:10; 10:10: Send the blockage information to the dispatcher; 11:10: Collect vehicle scale data; 11:10: The heavy package transportation process is initiated, and the "heavy package dispatch" task is issued to the nickel-iron furnace front workers. It is completed at 11:15. 11:15: Issued the "re-package arrival" task to the steelmaking and casting department; 11:25: An abnormal signal was detected in vehicle 05, and the vehicle was determined to be abnormal; 11:25: The abnormal vehicle registration / departure handling process is initiated; 11:25: The heavy package transportation process is suspended, and the "heavy package arrival" task is temporarily halted; 11:25: Issued the "vehicle repair" task to the team leader, completed at 12:10; 12:10: GPS data for vehicle 05 is normal; 12:15: The "Re-package Arrival" task was reactivated and sent to the steel casting plant. It was completed at 12:45.

[0057] 12:45: Collect GPS data for vehicle 05; 12:45: Copy the arrival data of duplicate packets to the dispatcher; 12:45: The process of lifting the heavy ladle is initiated, and the task of "lifting the heavy ladle" is issued to the steelmaking and casting department. It is completed at 12:46. 12:46: The iron exchange process is initiated, and the task of "exchanging molten iron for AOD" is issued to the steelmaking and casting plant. It is completed at 12:53. 12:53: The empty package transportation process is initiated, and the "empty package dispatch" task is issued to the steelmaking and casting department. It is completed at 13:04.

[0058] By implementing the above seven steps, a dynamic workflow distribution method for molten iron scheduling with multi-position collaboration was realized, which effectively improved the efficiency and accuracy of molten iron scheduling, reduced human error, and enhanced the controllability and traceability of the production process.

[0059] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0060] Example 3 This embodiment is used to implement the principle of the above method embodiment to construct a dynamic distribution system for multi-position collaborative molten iron scheduling workflow, including sub-modules, sub-modules and sub-modules.

[0061] Process design module: Used to design molten iron dispatching processes, which are divided into normal production processes and abnormal handling processes, and support segmented design and classification design; Data acquisition module: used to collect manually entered data, sensor data, and data from third-party systems; Task generation module: Used to dynamically generate pending tasks based on verification results and logical judgments; Task distribution module: Used to automatically distribute tasks to users in the corresponding positions; Collaborative Module: Used to enable collaborative execution and status tracking of tasks across multiple roles.

[0062] Preferably, the process design module includes: Job binding engine: Binds process nodes to specific job positions; Form binding engine: binds process nodes to specific business data forms; Preferably, the task generation module includes: Rule base: Stores logical judgment rules for normal and abnormal processes; Task generation engine: Generates tasks to be done based on the rule base and real-time data.

[0063] Preferably, the collaborative linkage module includes: Exception handling engine: Supports automatic switching and closed-loop management of exception processes.

[0064] Each submodule is mainly used to implement the various steps of the method implementation, which will not be elaborated here.

[0065] It should be noted that, depending on the implementation needs, the various steps / components described in this application can be broken down into more steps / components, or two or more steps / components or parts of the operation of steps / components can be combined into new steps / components to achieve the purpose of this invention.

[0066] This embodiment also includes a processor, a communication interface, a memory, and a communication bus; wherein the processor, the communication interface, and the memory communicate with each other through the communication bus; the memory stores a computer program, and when the program is executed by the processor, the processor performs the steps of a method for dynamically issuing a multi-position collaborative molten iron scheduling workflow.

[0067] This embodiment also provides a computer-readable storage medium storing executable instructions, which, when executed by a processor, enable the processor to implement a dynamic distribution method for a multi-position collaborative molten iron scheduling workflow.

[0068] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects.

[0069] Furthermore, this application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0070] This application is described with reference to the flowchart of the method and computer program product according to Embodiment 1 and the block diagram of the device (system) according to Embodiment 3. It should be understood that each step or block in the flowchart or block diagram, as well as combinations of steps or blocks in the flowchart or block diagram, can be implemented by computer program instructions.

[0071] These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions, which are executable by the processor of the computer or other programmable data processing device, produce instructions for implementing the process. Figure 1 One or more processes or boxes Figure 1 A dynamic workflow distribution system for multi-position collaborative molten iron dispatching, specifying functions within one or more boxes.

[0072] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes or boxes Figure 1 The function specified in one or more boxes.

[0073] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes or boxes Figure 1 The steps of a dynamic distribution method for a multi-position collaborative iron and steel dispatching workflow are specified in one or more boxes.

[0074] The above embodiments are only used to illustrate the design concept and features of the present invention, and their purpose is to enable those skilled in the art to understand the content of the present invention and implement it accordingly. The protection scope of the present invention is not limited to the above embodiments. Therefore, all equivalent changes or modifications made based on the principles and design ideas disclosed in the present invention are within the protection scope of the present invention.

Claims

1. A method for dynamically issuing a multi-position collaborative iron and steel dispatching workflow, characterized in that: The method comprises the following steps: S1: constructing a molten iron scheduling workflow system comprising a normal production process and an abnormal treatment process; S2: constructing nodes logically connected in the process; S3: obtaining production data in the molten iron scheduling process; S4: dynamically connecting the normal production process based on segmented logic association, and generating to-be-completed tasks for corresponding posts; S5: analyzing production data in real time to determine abnormalities, and switching to the corresponding abnormal treatment process when abnormalities occur; S6: verifying production data after completing the to-be-completed tasks, and generating the next to-be-completed task; S7: recording to-be-completed tasks throughout the process to form a traceable log.

2. The method of claim 1, wherein the method further comprises: In step S1, the specific steps are as follows: The normal production process is segmented according to production stages, and is dynamically connected through production state parameter threshold judgment; the abnormal treatment process is designed according to production abnormality types; The production abnormality type is determined by comparing the abnormal characteristic value in the production data with the preset threshold value.

3. The method of claim 1, wherein the method further comprises: In step S2, The nodes include task nodes, condition judgment nodes, jump nodes, and copy nodes; The task node is used to fill in production data in the form when processing the to-be-completed task; The condition judgment node decides the subsequent execution branch of the process according to the preset judgment condition; The jump node is used to jump in any node according to production data and preset conditions; The copy node is used to inform relevant post personnel of important information; The segmented connection of the normal production process is determined by comparing actual production data with preset threshold values; The generation of the abnormal treatment process is determined by comparing production data with abnormal characteristic values or preset threshold values; The node task is associated with the data filling form, and the form fills in business data according to the demand and pre-set business-related fields when processing the to-be-completed task.

4. The method of claim 1, wherein the method further comprises: In step S3, The production data includes manually filled data, sensor collected data, and third party system interfaced data; The manually filled data is collected through a mobile APP, and is used to support real-time filling and uploading on site; The sensor collected data is obtained at a preset frequency to ensure the real-time nature of the production data; The third party system interfaced data is synchronized to the system in real time through a preset interface.

5. The method of Claim 1, wherein the method further comprises: In step S4, the specific steps are as follows: The production data is collected, processed and arranged, the dynamic connection is realized based on the logical association of each segment in the normal production process, and different types of workflows in the segment are started according to the connection result to generate to-be-completed tasks for corresponding posts.

6. The method of Claim 5, wherein the method further comprises: In step S4, The to-be-completed task includes task description, operation guide, associated production data display, and mandatory data filling item.

7. The method of Claim 1, wherein the method further comprises: In step S5, When executing the abnormal treatment process, the to-be-completed task including abnormal details, associated production data and processing guide is automatically issued to the responsible post, the processing progress is tracked in real time, the abnormality is confirmed to be eliminated through data verification after the processing is completed, and the workflow is transferred back to the corresponding segment of the normal production process and continues to be executed.

8. The method of Claim 7, wherein the method further comprises: In step S5, the switching mechanism of the abnormal treatment process includes: Abnormality detection: identifying abnormal events through real-time data analysis or manual reporting; Process switching: automatically matching the corresponding abnormal process template according to the abnormal type, and pausing the execution of the current normal process node; Abnormal loop: after the completion of the abnormal processing flow, the abnormal processing result is verified, if it meets the requirements, the normal production flow is resumed, otherwise the abnormal processing flow is continued to be executed.

9. A multi-post cooperative molten iron scheduling workflow dynamic issuing system, characterized in that: a flow design sub-module is used to build a molten iron scheduling workflow system including a normal production flow and an abnormal processing flow; a node design sub-module is used to build nodes logically connected in the flow; a data acquisition sub-module is used to acquire production data in the molten iron scheduling process; a task generation sub-module is used to dynamically link the normal production flow based on segmented logic association and generate to-be-done tasks for corresponding posts; a flow switching sub-module is used to analyze production data in real time to determine abnormalities and switch to corresponding abnormal processing flows when abnormalities occur; a data verification sub-module is used to verify production data after the to-be-done tasks are completed and generate the next to-be-done task; a log recording sub-module is used to record to-be-done tasks throughout the process to form traceable logs.

10. A computer memory, characterized by: The computer program stored therein can be executed by a computer processor, and the computer program executes a multi-post cooperative molten iron scheduling workflow dynamic issuing method according to any one of claims 1 to 8.

10. A computer readable storage medium storing a computer program, wherein the computer program can be executed by a computer processor, and the computer program executes a multi-post cooperative molten iron scheduling workflow dynamic issuing method according to any one of claims 1 to 8.

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