A real-time interface data exception feedback and dynamic re-push method of an aluminum electrolysis MES system

Abstract

A kind of aluminum electrolysis MES system real-time interface data exception feedback and dynamic re-push method, it is related to aluminum electrolysis production informatization management technical field, including steps S1, set interface data exception data rule, according to the abnormal data rule to the real-time monitoring and abnormal perception of multi-source interface data;Step S2, set exception data level, according to exception data level, MES system receives exception data and carries out hierarchical feedback;Step S3, according to the server state exception interface data dynamic re-push condition setting, MES system according to the server load state dynamically re-pushes exception data to external system etc..The present application makes up the problem that existing aluminum electrolysis MES system and external system exist in interface data interaction process in the production process, and exception feedback lag and re-push mechanism rigidification, ensure the accuracy and reliability of interface data.

Description

Technical Field

[0001] This invention relates to the field of information management technology for aluminum electrolysis production, and in particular to a method for real-time interface data anomaly feedback and dynamic re-pushing in an aluminum electrolysis MES system. Background Technology

[0002] In the aluminum electrolysis industry, the continuity of the production process, the real-time nature of data, and system collaboration have a decisive impact on production efficiency and product quality. The MES (Manufacturing Execution System) for aluminum electrolysis, as the core hub connecting enterprise resource planning (ERP) and the underlying automated control system, undertakes key functions such as production plan execution and quality traceability. However, in current practices within the aluminum electrolysis industry, the interface data interaction between the MES system and external systems generally faces problems such as delayed feedback of abnormal data and rigid re-push mechanisms.

[0003] Currently, when interface data transmission is interrupted or the transmitted data exhibits abnormal values ​​or formats, traditional MES systems cannot promptly detect and report any anomalies. Furthermore, when anomalies are detected, they typically rely on manual correction, resulting in alarm messages lagging significantly after the event occurs and excessively long processing times. Additionally, existing re-push mechanisms often employ fixed time intervals or manual triggering, failing to dynamically adjust re-push strategies based on data priority and system load, easily leading to data duplication or loss. Moreover, the lack of a closed-loop verification mechanism for re-push data means that erroneous data may continuously contaminate the production database, affecting quality traceability and decision-making accuracy.

[0004] Therefore, there is currently a lack of a real-time anomaly detection mechanism and dynamic re-pushing strategy for real-time interface data of aluminum electrolysis MES system, in order to solve the problem of abnormal data generation in the interface data interaction of aluminum electrolysis MES system and ensure production continuity and data reliability. Summary of the Invention

[0005] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a method for real-time interface data anomaly feedback and dynamic re-pushing in an aluminum electrolysis MES system, which makes up for the problems of delayed anomaly feedback and rigid re-pushing mechanism in the interface data interaction between the existing aluminum electrolysis MES system and external systems during the production process, and ensures the accuracy and reliability of interface data.

[0006] To achieve the above objectives, the main technical solutions adopted by the present invention include: A method for real-time interface data anomaly feedback and dynamic re-push in an aluminum electrolysis MES system, comprising: Step S1: Set abnormal data rules for interface data, and perform real-time monitoring and anomaly detection of multi-source interface data according to the abnormal data rules; Step S2: Set the abnormal data level. Based on the abnormal data level, the MES system receives abnormal data and provides graded feedback. Step S3: Set the conditions for dynamic re-pushing of abnormal interface data according to the server status. The MES system will dynamically re-push the abnormal data to the external system according to the server load status. Step S4: After the data is re-pushed, a closed-loop verification process is triggered. The system automatically performs rule verification on the re-pushed interface data. If the data meets the verification rules, the verification is deemed successful and the data update is allowed; otherwise, the data is deemed unsuccessful, the re-push fails, and the verification record result is returned.

[0007] Furthermore, the aluminum electrolysis MES system interacts with external systems, including a cell control system and an SAP system.

[0008] Further, step S1 includes setting the following as abnormal situations: when the electrolytic cell temperature value is empty, zero, or changes abruptly by more than 2°C / s, the aluminum level, quality level, molecular ratio, and other indices are not numerical values; when the Fe, Si, Cu, Ga, and Mg element content values ​​in the original aluminum composition are not numerical values, and the Fe content increases continuously for three days or the daily increase is greater than 0.02, it is an abnormal situation; when the date in the material number coding rule is not the current date, or the aluminum liquid tank number coding rule does not conform to the XXXX.XXXX.XXXX format, it is an abnormal situation; and when the missing aluminum in the aluminum ingot coding mark is greater than 5%, it is an abnormal situation.

[0009] Furthermore, the interface data includes aluminum electrolysis cell temperature, molecular ratio index, primary aluminum composition index, material number, aluminum liquid tank number code, and aluminum ingot marking.

[0010] Furthermore, step S1 specifically involves real-time monitoring of each transmitted data during the interface data transmission process, comparing the data with predefined anomaly rules, and determining the data as abnormal if the conditions are met.

[0011] Furthermore, step S1 also includes recording detailed information of the perceived abnormal interface data, including data source, collection time, abnormal type, and abnormal value, into an abnormal data table and storing it in a knowledge base.

[0012] Furthermore, the abnormal data table includes field unique identifiers, data source, collection time, abnormal type, abnormal value, and abnormal situation.

[0013] Further, step S2 specifically involves classifying the interface data into Level 1, Level 2, Level 3, and Warning Level anomalies. Level 1 anomalies include failures in transmitting important indicator data; Level 2 anomalies include format errors in data related to production materials during transmission, failing to meet the specified field requirements; Level 3 anomalies include situations where the data format or content does not affect business logic or data accuracy; and Warning Level anomalies involve issuing warnings for abnormal data conditions without modifying the data.

[0014] Furthermore, the MES system receives abnormal data and provides tiered feedback, including: For Level 1 abnormal data, the MES system immediately searches to find the location of the original data and determines whether the value exists. If it does, it sends it to the re-push mechanism. For Level 2 abnormal data, the MES system searches to find the location of the original data and determines whether the original data is correct. If it is correct, it sends it to the re-push mechanism; if it is incorrect, it searches for related data based on the code, modifies it, and then sends it to the re-push mechanism. For Level 3 abnormal data, the MES system corrects the original data value format and sends it to the re-push mechanism; For abnormal data at the warning level, the MES system searches for the current person in charge, issues a warning notification, and sends an anomaly report.

[0015] Further, step S3 specifically involves setting the server's CPU usage to <60% and memory usage to <70% as low load, setting the server's CPU usage to 60% ≤ CPU usage to <80% or 70% ≤ memory usage to <85% as medium load, and setting the server's CPU usage to ≥80% or memory usage to ≥85% or latency to >100ms as high load.

[0016] Furthermore, when the server is under low load, a data re-pull operation is triggered immediately; when the server is under medium load, a data re-pull operation is attempted, and if the transmission fails, a linearly increasing re-pull operation is performed; when the server is under low load, a data re-pull operation is attempted, and if the transmission fails, an exponential backoff re-pull operation is performed; high-priority abnormal data is processed first.

[0017] The beneficial effects of this invention are: 1. By establishing rules for abnormal interface data, it is possible to quickly detect abnormal situations in multi-source interface data, promptly identify potential problems, reduce the negative impact of abnormal data on production, and improve the stability and continuity of production.

[0018] 2. This invention can provide graded feedback on abnormal data and use the MES system's automatic processing module to handle different levels accordingly, thereby improving the efficiency and quality of problem solving and ensuring that abnormal situations can be handled in a timely and appropriate manner.

[0019] 3. The system dynamically adjusts the data re-push frequency and priority based on the server status, enabling the system to maintain efficient and stable data processing capabilities under different operating environments, thereby improving the system's reliability and availability.

[0020] 4. The closed-loop verification mechanism re-verifies the re-push data to ensure its accuracy and validity, providing a reliable basis for aluminum electrolysis production decisions and avoiding decision-making errors due to data errors. At the same time, abnormal situations and their handling are included in the knowledge base to provide reference for subsequent problem handling.

[0021] 5. This invention can automate the processes of anomaly detection, feedback, re-pushing, and verification, reducing the workload of manual investigation and processing, lowering labor costs, improving data accuracy, and ensuring the operational efficiency of enterprises. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the real-time interface data anomaly feedback and dynamic re-pushing method of the aluminum electrolysis MES system of the present invention. Figure 2 This is a schematic diagram of the interface anomaly data perception process; Figure 3 A schematic diagram of the hierarchical feedback process for abnormal data; Figure 4 This is a flowchart illustrating the dynamic re-push mechanism. Detailed Implementation

[0023] To better explain and facilitate understanding of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] This invention provides a method for real-time interface data anomaly feedback and dynamic re-push in an aluminum electrolysis MES system, such as... Figure 1 As shown, it includes: Step S1: Set abnormal data rules for interface data, and perform real-time monitoring and anomaly detection of multi-source interface data according to the abnormal data rules.

[0025] Interface data includes: aluminum electrolysis cell temperature and molecular ratio; primary aluminum composition (Fe, Si, Mg, etc. content); material number, aluminum liquid tank number code, and aluminum ingot marking. External systems include: cell control system and SAP system.

[0026] Specifically, such as Figure 2 As shown, step S1 includes the following steps: Step S101: Define data anomaly rules Develop exception rules based on data from multiple source interfaces. Here are a few examples: (1) Formulate rules for abnormal situations in the data transmission interface between the MES system and the cell control system. For example, set the abnormal situation when the electrolytic cell temperature value is empty, zero, or changes suddenly by more than 2℃ / s; or when the type of aluminum level, quality level, molecular ratio index, etc. is not a numerical value.

[0027] (2) Formulate rules for abnormal situations in the data transmission interface between the MES system and the SAP system. For example, when the content values ​​of elements such as Fe, Si, Cu, Ga, and Mg in the original aluminum composition are not numerical, an abnormal situation is defined when the Fe content increases for three consecutive days or the daily increment is greater than 0.02. When the coding rules of the material number contain dates that are not the current date, or when the coding rules of the aluminum liquid tank do not conform to the format XXXX.XXXX.XXXX, an abnormal situation is defined. When the missing aluminum in the aluminum ingot marking is greater than 5%, an abnormal situation is defined.

[0028] Step S102: Monitor the interface data and detect abnormal situations. During the interface data transmission process, each transmitted data is monitored in real time and compared with predefined anomaly rules. If the conditions are met, the data is determined to be abnormal data.

[0029] For example, see the following example: (1) When the transmitted electrolytic cell temperature is 0℃, the system immediately determines that the data is abnormal; (2) When the transmitted Fe content value is 0.03!, the system immediately determines that the data is abnormal; (3) When transmitting the Fe content value on the third day, it was found that the Fe content value had been rising for the past three days, and the system immediately determined that the data was abnormal. (4) If half of the aluminum ingot marking information transmitted on the same day is not marked, the system immediately determines that the data is abnormal.

[0030] Step S103: Record the abnormal data to the knowledge base. Detailed information about detected abnormal interface data, including data source, collection time, abnormal type, and abnormal value, is recorded in a dedicated abnormal data table and placed in a knowledge base. Each abnormal data entry is assigned a unique identifier for easy tracking and querying. For example, the abnormal data table includes fields such as "id" (unique identifier), "data_source" (data source), "collect_time" (collection time), "abnormal_type" (abnormal type), "abnormal_value" (abnormal value), and "abnormal_state" (abnormal state).

[0031] Step S2: Set the abnormal data level. Based on the abnormal data level, the MES system receives abnormal data and provides graded feedback.

[0032] like Figure 3 As shown, the specific steps include the following: Step S201: Define the levels of abnormal data First, a grading standard is established based on the characteristics of various interface data, the severity of abnormal data, and the scope of impact, classifying them into Level 1, Level 2, Level 3, and Warning Level. Level 1 anomalies are severe, mainly manifested as data transmission failures of important indicators, resulting in null values ​​or values ​​of 0, which will affect production monitoring. For example, an empty electrolytic cell temperature value belongs to Level 1. Level 2 anomalies are important, mainly manifested as format errors in data related to production materials during transmission, failing to meet the specified field requirements. For example, a material number encoding rule containing a date that is not the current date, or an aluminum liquid tank number encoding rule that does not conform to the XXXX.XXXX.XXXX format, belongs to Level 2. Level 3 anomalies are general, mainly manifested as data format or content that does not affect business logic or data accuracy. For example, Fe and Si content fields in the primary aluminum composition that are not numerical types belong to Level 3. Warning-level anomalies are special anomalies, mainly manifested as warnings about abnormal data conditions without modifying the data. For example, a sudden change in electrolytic cell temperature >2℃ / s, a continuous increase in Fe content for three days or a daily increase greater than 0.02, or a missing aluminum mark in the aluminum ingot coding system exceeding 5% are all warning-level anomalies.

[0033] S202. The MES system's automatic processing module receives abnormal data and provides tiered feedback. When the MES system detects that the transmitted interface data is abnormal, the automatic processing module receives the abnormal data, judges it according to the hierarchical standards dynamically loaded by the rule engine, and provides hierarchical feedback.

[0034] (1) For Level 1 abnormal data, the MES system automatic processing module performs an immediate search mode to find the location of the original data and determine whether the value exists. If it exists, it is sent to the re-push mechanism; if it does not exist, it returns a prompt and waits for the data to be retransmitted. Then it searches and judges the value in a loop.

[0035] (2) For Level 2 abnormal data, the MES system's automatic processing module performs a search mode to find the original data location and determine whether the original data is correct. If it is correct, it is sent to the re-push mechanism; if it is incorrect, it searches for related data according to the code and modifies it. For example, if the material number data has a format error during transmission, it is modified to the correct format according to the coding rules and then sent to the re-push mechanism.

[0036] (3) For Level 3 abnormal data, the MES system automatic processing module corrects the original data numerical format. For example, when the Fe and Si content fields in the original aluminum composition are not numerical, the original data is read, modified to numerical type, and the correct number of decimal places is saved. After modification, it is sent to the re-push mechanism.

[0037] (4) For warning-level abnormal data, the MES system's automatic processing module uses a knowledge base containing information on the person in charge to find the information of the current person in charge and immediately issues a warning notification via SMS, internal system platform push notifications, etc., and sends an abnormality report. For warning-level abnormal data, only a warning notification is issued, without modifying the data for a re-push mechanism.

[0038] Step S3: Set the conditions for dynamic re-pushing of abnormal interface data according to the server status. The MES system will dynamically re-push the abnormal data to the external system according to the server load status.

[0039] like Figure 4 As shown, the specific steps include the following: S301, Real-time Server Load Monitoring and Classification Server load status is determined and categorized by monitoring key indicators such as CPU utilization, memory usage, and network bandwidth utilization. The server load status and determination criteria are shown in Table 1. Table 1. Server Load Status and Judgment Criteria 1 low load CPU usage < 60% or memory usage < 70% 2 medium load 60% ≤ CPU < 80% or 70% ≤ Memory < 85% 3 High load CPU ≥ 80% or memory ≥ 85% or latency > 100ms S302: Setting of dynamic re-push mechanism When the server receives abnormal interface data, it immediately monitors the current server load status and sets dynamic retransmission conditions according to the categorized load levels. When the server is under low load, a data retransmission operation is triggered immediately; when the server is under medium load, a data retransmission operation is attempted, and if transmission fails, retransmission is performed linearly at preset time intervals (e.g., 5×n seconds); when the server is under low load, a data retransmission operation is attempted, and if transmission fails, retransmission is performed with a preset exponential backoff mechanism (e.g., at preset intervals). Redirecting immediately to avoid re-pushing. Simultaneously, based on the severity of the anomaly, higher-priority anomalies are processed first.

[0040] S303: Transmit the re-push interface data and record it. The re-push interface data is transmitted to the corresponding external system, and the time, result (success or failure) and related information of each re-push are recorded. The recorded information is then placed in the knowledge base.

[0041] Step S4: After data is re-pushed, a closed-loop verification process is triggered. The system automatically performs rule verification on the re-pushed interface data. If the data meets the verification rules, the verification is deemed successful, and data updates are allowed; otherwise, the data is deemed unsuccessful, the re-push fails, and the verification record result is returned. The closed-loop verification process involves re-verifying according to steps S1 to S3, such as verifying whether the data is null, whether the field type is correct, and whether the field format meets the specified requirements.

[0042] Simultaneously, the occurrence, handling process, and results of each anomaly are recorded in the knowledge base. A data entry interface has been developed for manual input by relevant personnel, while automated scripts automatically input verification failure information during closed-loop verification. Knowledge base data is regularly organized and analyzed, and handling solutions are updated to optimize content, such as summarizing and updating more effective methods and strategies based on historical handling experience. When handling new anomalies, the system automatically queries relevant information from the knowledge base to provide reference guidance, helping personnel quickly formulate handling solutions and improve the efficiency and accuracy of anomaly handling. If similar anomalies occur, the system directly recommends solutions for personnel to adjust and execute.

[0043] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Any modifications, alterations, substitutions, and variations made by those skilled in the art to the above embodiments are within the scope of the present invention.

Claims

1. A method for real-time interface data exception feedback and dynamic re-push of an aluminum electrolysis MES system, characterized in that, include: Step S1: Set abnormal data rules for interface data, and perform real-time monitoring and anomaly detection of multi-source interface data according to the abnormal data rules; Step S2: Set the abnormal data level. Based on the abnormal data level, the MES system receives abnormal data and provides graded feedback. Step S3: Set the conditions for dynamic re-pushing of abnormal interface data according to the server status. The MES system will dynamically re-push the abnormal data to the external system according to the server load status. Step S4: After the data is re-pushed, a closed-loop verification process is triggered. The system automatically performs rule verification on the re-pushed interface data. If the data meets the verification rules, the verification is deemed successful and the data update is allowed; otherwise, the data is deemed unsuccessful, the re-push fails, and the verification record result is returned.

2. The real-time interface data exception feedback and dynamic re-push method of an aluminum electrolysis MES system according to claim 1, characterized in that: Step S1 includes setting the following conditions as abnormal: when the electrolytic cell temperature value is empty, zero, or changes abruptly by more than 2°C / s, the aluminum level, quality level, molecular ratio, and other indices are not numerical values; when the Fe, Si, Cu, Ga, and Mg element content values ​​in the original aluminum composition are not numerical values, and the Fe content increases for three consecutive days or the daily increase is greater than 0.02, it is an abnormal condition; when the date in the material number coding rule is not the current date, or the aluminum liquid tank number coding rule does not conform to the XXXX.XXXX.XXXX format, it is an abnormal condition; and when the missing aluminum in the aluminum ingot coding mark is greater than 5%, it is an abnormal condition.

3. The real-time interface data exception feedback and dynamic re-push method of an aluminum electrolysis MES system according to claim 1, characterized in that: The interface data includes aluminum electrolysis cell temperature, molecular ratio index, primary aluminum composition index, material number, aluminum liquid tank number code, and aluminum ingot marking.

4. The real-time interface data exception feedback and dynamic re-push method of an aluminum electrolysis MES system according to claim 1, characterized in that: Specifically, step S1 involves real-time monitoring of each transmitted data during the interface data transmission process, comparing the data with predefined anomaly rules, and determining the data as abnormal if the conditions are met.

5. The real-time interface data exception feedback and dynamic re-push method of an aluminum electrolysis MES system according to claim 1, characterized in that: Step S1 also includes recording detailed information of the perceived abnormal interface data, including data source, collection time, abnormal type, and abnormal value, into an abnormal data table and storing it in a knowledge base.

6. The real-time interface data exception feedback and dynamic re-push method of an aluminum electrolysis MES system according to claim 5, characterized in that: The abnormal data table includes field unique identifiers, data source, collection time, abnormal type, abnormal value, and abnormal situation.

7. The method for real-time interface data anomaly feedback and dynamic re-pushing in an aluminum electrolysis MES system according to claim 1, characterized in that: Specifically, step S2 involves classifying interface data into Level 1, Level 2, Level 3, and early warning level anomalies. Level 1 anomalies include failures in the transmission of important indicator data; Level 2 anomalies include format errors in data related to production materials during transmission, failing to meet the specified field requirements; and Level 3 anomalies include situations where the data format or content does not affect business logic or data accuracy. Warning-level anomalies involve issuing warnings about abnormal data conditions without modifying the data.

8. The method for real-time interface data anomaly feedback and dynamic re-push of an aluminum electrolysis MES system according to claim 7, characterized in that, The MES system receives abnormal data and provides tiered feedback, including: For Level 1 abnormal data, the MES system immediately searches to find the location of the original data and determines whether the value exists. If it does, it sends it to the re-push mechanism. For Level 2 abnormal data, the MES system searches to find the location of the original data and determines whether the original data is correct. If it is correct, it sends it to the re-push mechanism; if it is incorrect, it searches for related data based on the code, modifies it, and then sends it to the re-push mechanism. For Level 3 abnormal data, the MES system corrects the original data value format and sends it to the re-push mechanism; For abnormal data at the warning level, the MES system searches for the current person in charge, issues a warning notification, and sends an anomaly report.

9. The method for real-time interface data anomaly feedback and dynamic re-push of an aluminum electrolysis MES system according to claim 1, characterized in that: Specifically, step S3 involves defining a server load as low when CPU usage is less than 60% and memory usage is less than 70%, a server load as medium when CPU usage is less than 80% or memory usage is less than 85%, and a server load as high when CPU usage is greater than 80% or memory usage is greater than 85% or latency is greater than 100ms.

10. The method for real-time interface data anomaly feedback and dynamic re-pushing in an aluminum electrolysis MES system according to claim 9, characterized in that: When the server is under low load, immediately trigger the data re-push operation; when the server is under medium load, attempt to trigger the data re-push operation, and if the transmission fails, re-push according to linear increment; when the server is under low load, attempt to trigger the data re-push operation, and if the transmission fails, re-push according to exponential backoff; prioritize handling high-priority abnormal data.

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