Data processing system and method for electrolytic bath leveling machine

Abstract

The invention relates to the technical field of industrial automation, in particular to a data processing system and method for an electrolytic cell leveling machine, and the method comprises the steps: a data analysis module recognizes disordered data collected by the leveling machine based on a pre-constructed key value-object mapping strategy, and extracts monitoring point height data of a cathode net and an anode net; and the space mapping module maps the height data into a physical monitoring grid of the electrolytic cell according to a structure-space mapping strategy constructed by a physical structure of the electrolytic cell, and structured flatness data is generated, so that a basis is provided for subsequent analysis and visualization. According to the method, the physical significance of the original data can be automatically analyzed, the original data is converted into parameter data with clear engineering semantics, the parameter data is accurately associated with the physical monitoring grid position of the electrolytic cell, positionable structural data is generated, whole-process tracing is supported through associated storage, and the data analysis efficiency, the positioning precision and the quality tracing capacity are improved.

Description

Technical Field

[0001] This invention relates to the field of industrial automation technology, and in particular to a data processing system and method for an electrolytic cell leveling machine. Background Technology

[0002] Currently, the operating data of electrolytic cell leveling machines is mainly obtained through local storage of PLC control devices or manual on-site recording. This data exists in isolated, raw key-value pairs, lacking a clear correspondence with the physical monitoring objects, resulting in several shortcomings. First, the data output by the leveling machine's PLC is usually just meaningless key-value identifiers, unable to directly identify the corresponding physical objects. Manual analysis based on experience and equipment drawings is required, which is inefficient and prone to errors. Second, the measurement data cannot be associated with specific numbers on the electrolytic cells, forming data silos. This makes it impossible to accurately locate the physical location of leveling anomalies, hindering subsequent quality traceability and process optimization. Finally, the scattered storage of key information without effective correlation with measurement data makes it difficult to trace the leveling process data of the same electrolytic cell completely in chronological order, failing to achieve transparency in the production process.

[0003] Therefore, there is an urgent need to provide a technical solution to address the above problems. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides a data processing system and method for an electrolytic cell leveling machine.

[0005] In a first aspect, the present invention provides a data processing system for an electrolytic cell leveling machine, the technical solution of which is as follows:

[0006] The electrolytic cell to be leveled includes: the cell body, the cell frame surrounding the cell body, the electrode mesh laid inside the cell frame, and multiple stiffening plates arranged at intervals along the transverse direction of the cell frame; the electrode mesh includes: an anode mesh and a cathode mesh.

[0007] The leveling machine includes: a PLC control device and multiple monitoring components corresponding to each rib plate arranged along the longitudinal direction of the slot frame, each monitoring component being electrically connected to the PLC control device; the leveling machine moves along the transverse direction of the slot frame to obtain flatness data of the electrode mesh on the rib plate.

[0008] The data processing system in the PLC control device includes:

[0009] The data parsing module is used to identify the disordered and chaotic data collected by the PLC control device of the leveling machine according to a pre-built key-value-object mapping strategy, and to obtain the identified parameter type data and reference information data; the parameter type data includes: cathode mesh monitoring point height data and anode mesh monitoring point height data;

[0010] The spatial mapping module is used to map the height data of the cathode grid monitoring points and the height data of the anode grid monitoring points to the physical monitoring grid of the electrolytic cell according to the pre-built structure-space mapping strategy, and generate structured flatness data.

[0011] The key-value-object mapping strategy is constructed based on disordered and chaotic data; the structure-space mapping strategy is constructed based on the physical structure information of the electrolytic cell.

[0012] The beneficial effects of the data processing system for an electrolytic cell leveling machine of the present invention are as follows:

[0013] The system of this invention can automatically parse the physical meaning of the raw data, transform it into parameter data with clear engineering semantics, and accurately associate it with the physical monitoring grid position of the electrolytic cell to generate locationable structured data. Through associated storage, it supports full-process traceability, improving data parsing efficiency, positioning accuracy and quality traceability capabilities.

[0014] Based on the above scheme, the data processing system for an electrolytic cell leveling machine of the present invention can be further improved as follows.

[0015] In one alternative approach, it also includes:

[0016] The data storage module is used to associate the structured flatness data and reference information data, generate index information, and synchronously store it in the database;

[0017] The reference information data includes: data key values, measurement values ​​distinguished by data key values, status signals, timestamp information, and a unique identifier for the electrolytic cell.

[0018] In the above-mentioned optional methods, by setting up a data storage module to associate and store structured flatness data with reference information data and synchronously store it in the database, the persistent storage and rapid retrieval of leveling data are realized. A complete quality file is established for each electrolytic cell, laying the foundation for subsequent quality traceability, production statistics and data analysis, and solving the problem that the original leveling machine data could not be accumulated and utilized because it was only used for a single leveling.

[0019] In one alternative approach, it also includes:

[0020] The first building module is used to extract all data key values ​​and their corresponding measurement values ​​from disordered and chaotic data;

[0021] Analyze the numerical distribution characteristics of the measured values ​​under each data key value, whereby the numerical distribution characteristics include the data value range and numerical precision;

[0022] Based on the numerical distribution characteristics, the physical object category corresponding to each data key value is identified, and key-value-object mapping configuration information containing data key values, physical object categories, and data value ranges is generated to construct the key-value-object mapping strategy; the physical object categories include cathode mesh monitoring point height, anode mesh monitoring point height, and tank frame thickness.

[0023] In the above-mentioned optional methods, the first construction module automatically extracts all key values ​​and their measured values ​​from the disordered and chaotic data, analyzes the numerical distribution characteristics of the data under each key value, and then identifies the physical object categories of the cathode mesh monitoring point height, anode mesh monitoring point height, and slot frame thickness, generating key value-object mapping configuration information. This realizes automatic semantic parsing from the original encoded data of the leveling machine to the physical meaning, avoiding the inefficiency and errors of manual identification and configuration, and significantly improving the automation and accuracy of data processing.

[0024] In an optional embodiment, the parameter type data further includes slot thickness data; the data parsing module is specifically used for:

[0025] Based on the key-value-object mapping strategy, obtain the physical object category and data value range corresponding to each data key in the key-value-object mapping configuration information;

[0026] Based on the physical object category, perform boundary checks on the measured values ​​under each data key value to remove abnormal data that exceeds the range of the data values;

[0027] Based on the numerical accuracy, the verified measurement values ​​are formatted and standardized in units, and parameter type data that characterizes the physical object category and conforms to standard measurement units, including cathode mesh monitoring point height data, anode mesh monitoring point height data, and slot frame thickness data, are output simultaneously, along with the reference information data.

[0028] In the above-mentioned optional methods, the data parsing module obtains the physical object category and value range corresponding to each data key value based on the key-value-object mapping strategy, performs boundary checks on the measured values ​​to remove abnormal data, and then performs format normalization and unit unification processing based on the numerical accuracy, outputting standardized parameter type data containing the height of the cathode / anode network monitoring points and the thickness of the slot frame. This effectively filters out erroneous data caused by communication interference and equipment malfunctions, ensuring data consistency and standardization, and providing high-quality, directly usable, and reliable data for subsequent analysis.

[0029] In one alternative approach, it also includes:

[0030] The second construction module is used to obtain the physical structure information of the electrolytic cell, which includes the number of stiffeners and the number of monitoring points set on each stiffener along its length.

[0031] Based on the physical structure information, a physical monitoring grid consisting of sequentially numbered stiffeners and monitoring points is determined.

[0032] Establish a correspondence between the set sequence of cathode mesh monitoring point height data and anode mesh monitoring point height data and the stiffener plate number and monitoring point number in the physical monitoring grid, generate structure-space mapping configuration information containing the correspondence, and construct the structure-space mapping strategy.

[0033] In the above-mentioned optional methods, the physical structure information such as the number of ribs of the electrolytic cell and the number of monitoring points on each rib is obtained through the second construction module. The physical monitoring grid formed by the sequentially numbered ribs and monitoring points is determined, and the correspondence between the data set sequence of the cathode / anode grid monitoring point height and the rib number and monitoring point number is established. The structure-space mapping configuration information is generated, so that the abstract data sequence has a clear spatial positioning capability, which solves the problem of lack of spatial context of the data and provides a spatial benchmark framework for accurate positioning and leveling of anomalies and regional quality assessment.

[0034] In an alternative embodiment, the space mapping module is specifically used for:

[0035] Based on the structure-space mapping strategy, the set sequence of cathode grid monitoring point height data and anode grid monitoring point height data is sequentially mapped to the physical monitoring grid; wherein, the set sequence includes multiple first-level subsets arranged in order, each first-level subset corresponding to a rib plate; each first-level subset includes multiple data elements arranged in order, each data element corresponding to a monitoring point on the corresponding rib plate;

[0036] The height data of the cathode mesh monitoring points and the height data of the anode mesh monitoring points are associated and bound with the corresponding stiffener plate numbers and monitoring point numbers, respectively, to generate the structured flatness data containing spatial location information.

[0037] In the above-mentioned optional methods, the spatial mapping module sequentially maps the set sequence of height data of the cathode / anode network monitoring points to the physical monitoring grid based on the structure-space mapping strategy. Each first-level subset corresponds to a rib plate, and the data elements in the subset correspond to each monitoring point on the rib plate. The data is associated and bound with the rib plate number and the monitoring point number to generate structured flatness data containing spatial location information. This achieves precise positioning of each rib plate and each monitoring point, enabling operators to clearly understand the actual flatness of each specific location in the electrolytic cell. It provides accurate coordinate data support for visualization and problem tracing.

[0038] In one alternative approach, it also includes:

[0039] The data acquisition module is used to convert the proprietary communication protocol data in the PLC control device of the leveling machine into standard industrial network protocol data, and to acquire the standard industrial network protocol data.

[0040] The standard industrial network protocol data is parsed to extract a structured data packet containing data key values, status signals, timestamp information, unique identifier of the electrolytic cell, and a set of measurement values ​​without physical meaning distinguished by data key values;

[0041] The structured data packet is subjected to integrity verification to obtain the disordered and chaotic data after the verification is passed.

[0042] In the above-mentioned optional methods, the private communication protocol of the leveling machine PLC control device is converted into a standard industrial network protocol through the data acquisition module to establish a continuous data transmission channel. The protocol data is parsed in frame structure and its integrity is verified, thereby breaking through the communication protocol barrier of the equipment and realizing fully automatic data acquisition. This avoids the problems of low efficiency and high error rate of traditional manual copying. At the same time, the verification mechanism ensures the integrity and reliability of the source data, providing high-quality data input guarantee for the entire data processing system.

[0043] In one alternative approach, it also includes:

[0044] The demonstration device is used to extract spatial location information from the structured flatness data based on the unique identifier of the electrolytic cell and the index of the status signal, and to retrieve the parameter type data and the reference information data;

[0045] The calibration duration is determined based on the timestamp information in the reference information data;

[0046] Based on the spatial location information, a matrix visualization chart is generated with the rib plate position as the horizontal axis and the monitoring point position as the vertical axis. Based on a preset height range, a preset color is used to mark whether the height data of each monitoring point is within the allowable range, so as to intuitively display the distribution of abnormal monitoring points in the leveling effect of a single electrolytic cell.

[0047] The slot thickness data in the parameter type data and the leveling length are used as leveling process status parameters and displayed in association with the matrix visualization chart to achieve remote comprehensive monitoring and quality traceability.

[0048] In the above-mentioned optional methods, the display equipment extracts spatial location information based on the unique identifier and status signal index of the electrolytic cell, retrieves parameter type data and reference information data, determines the leveling length based on the timestamp, and generates a matrix-style visualization chart with the rib plate position as the horizontal axis and the monitoring point position as the vertical axis. The distribution of abnormal points is intuitively displayed by using color to indicate whether the height of each monitoring point exceeds the tolerance. At the same time, the cell frame thickness and the leveling length are associated and displayed as process parameters, realizing remote visual monitoring and comprehensive quality assessment of the leveling effect. This allows managers to grasp the leveling quality and equipment operating status without being on-site, providing an intuitive and effective visualization tool for process optimization, quality traceability, and decision support.

[0049] Secondly, the present invention provides a data processing method for an electrolytic cell leveling machine, the technical solution of which is as follows:

[0050] Based on a pre-built key-value-object mapping strategy, the disordered and chaotic data collected by the PLC control device of the leveling machine is identified, and the identified parameter type data and reference information data are obtained; the parameter type data includes: cathode mesh monitoring point height data and anode mesh monitoring point height data;

[0051] Based on a pre-built structure-space mapping strategy, the height data of the cathode grid monitoring points and the height data of the anode grid monitoring points are mapped to the physical monitoring grid of the electrolytic cell to generate structured flatness data.

[0052] The key-value-object mapping strategy is constructed based on disordered and chaotic data; the structure-space mapping strategy is constructed based on the physical structure information of the electrolytic cell.

[0053] The beneficial effects of the data processing method for an electrolytic cell leveling machine of the present invention are as follows:

[0054] The method of this invention can automatically parse the physical meaning of the raw data, transform it into parameter data with clear engineering semantics, and accurately associate it with the physical monitoring grid position of the electrolytic cell to generate locationable structured data. Through associated storage, it supports full-process traceability, improving data parsing efficiency, positioning accuracy and quality traceability capabilities.

[0055] Based on the above scheme, the data processing method for an electrolytic cell leveling machine of the present invention can be further improved as follows.

[0056] In an alternative approach, the method further includes:

[0057] The structured flatness data and reference information data are associated to generate index information and synchronously stored in the database;

[0058] The reference information data includes: data key values, measurement values ​​distinguished by data key values, status signals, timestamp information, and a unique identifier for the electrolytic cell.

[0059] In the above-mentioned optional methods, by associating and storing structured flatness data with reference information data and synchronously storing it in the database, persistent storage and rapid retrieval of leveling data are achieved. A complete quality file is established for each electrolytic cell, laying the foundation for subsequent quality traceability, production statistics and data analysis. This solves the problem that the original leveling machine data could not be accumulated and utilized because it was only used for a single leveling.

[0060] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0061] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0062] Figure 1 This is a schematic diagram of the data processing system for an electrolytic cell leveling machine according to the present invention;

[0063] Figure 2 This is a schematic diagram of the electrolytic cell structure of the present invention;

[0064] Figure 3 This is a schematic diagram of the leveling machine monitoring component of the present invention;

[0065] Figure 4 This is a schematic diagram of the database table structure of the present invention;

[0066] Figure 5 This is a data schematic diagram using a cathode mesh measurement set as an example, as described in this invention.

[0067] Figure 6 This is a visual chart illustrating the measurement data of a cathode grid as an example in this invention;

[0068] Figure 7 This is a schematic flowchart of the data processing method for an electrolytic cell leveling machine according to the present invention;

[0069] Reference numerals: 1. Slot frame; 2. Rib plate; 3. Electrode mesh; 4. Monitoring component. Detailed Implementation

[0070] In the fabrication and assembly of electrolytic cells, the flatness of the electrode mesh (including anode and cathode meshes) is a key indicator affecting electrolysis efficiency, equipment lifespan, and operational safety. Local bulges or depressions in the electrode mesh can lead to uneven current distribution, causing localized overheating, accelerated corrosion, and even short circuits. Therefore, after the electrode mesh is laid, it must be leveled using a straightening process. Currently, the industry typically uses a vertical straightening machine for this process. This type of equipment has several monitoring points arranged longitudinally along the electrolytic cell. By moving laterally and scanning the height of the electrode mesh on each rib within the cell, it obtains the flatness data of the entire cell and mechanically corrects uneven areas based on the measurement results. The height status of each monitoring point is usually displayed visually in red and green on the software interface of the straightening machine, helping on-site operators to intuitively judge the flatness of the electrode mesh.

[0071] However, existing leveling machines primarily focus on the single step of "real-time measurement - on-site correction." The massive amounts of measurement data they generate are only used to drive the leveling action and for brief display on the local interface, without being effectively collected, structured, stored, or utilized in depth. Specifically, the raw data output by the leveling machine typically exists in the form of proprietary protocols and disorganized key values ​​(such as D2000, D2600, etc.) defined by the equipment manufacturer. Although the data contains height information for each monitoring point, it lacks a direct correspondence with the actual physical location of the electrolytic cell (such as which rib plate number or which monitoring point), resulting in a situation of "rich data but isolated information." When a large area of ​​color abnormality appears on the software interface, indicating poor overall flatness of the electrode mesh, it often means that there are process defects in the preceding processes (such as mesh laying, rib plate welding, etc.). Currently, the recording and analysis of such problems heavily rely on manual on-site observation and manual screenshot saving, which is inefficient and cannot achieve cross-time and cross-batch data correlation and backtracking. It is difficult to systematically trace the root cause of quality fluctuations in the production process, let alone provide data support for process optimization.

[0072] For example, in the production of a certain batch of electrolytic cells, large areas of the central region of the anode mesh remained red and triggered alarms multiple times after leveling. Due to a lack of historical data, technicians were unable to determine whether the problem was an occasional deviation or caused by a preceding welding process corresponding to the fixed stiffener number. Relying solely on on-site screenshots and manual records made it impossible to quickly pinpoint the anomaly pattern or formulate targeted process improvement suggestions, leading to recurring issues that impacted production efficiency and product quality consistency.

[0073] To address the aforementioned shortcomings, the core idea of ​​this invention lies in overcoming the limitations of the original single-process execution function of the leveling machine. By constructing a complete data processing system encompassing data acquisition, analysis, spatial mapping, structured storage, and visualization, the system transforms the disordered and unstructured measurement data generated during the leveling process into ordered, spatially labeled, and analytically correlated structured information. This system can be integrated as a software module into the existing control device of the leveling machine to enhance its local intelligence, or deployed as an independent external data processing platform. It can interface with leveling machines of different models or manufacturers through standard interfaces, thereby achieving in-depth data value mining and cross-process quality traceability while flexibly adapting to existing equipment. Using the above example, the system can automatically record detailed data for each leveling operation and, through data analysis, discover patterns such as "the central area of ​​the anode mesh (corresponding to a specific rib plate number range) consistently being higher before leveling." Furthermore, by tracing the data back to specific production batches and welding process parameters, the system can accurately locate defects in previous processes, achieving a shift from passive correction to proactive prevention in quality control.

[0074] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. Although exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention may be implemented in various forms and should not be limited to the embodiments set forth herein.

[0075] like Figure 2 As shown, the electrolytic cell to be leveled includes: a cell body, a cell frame arranged around the cell body, an electrode mesh laid inside the cell frame, and multiple stiffening plates arranged at intervals along the transverse direction of the cell frame; the electrode mesh includes: an anode mesh and a cathode mesh.

[0076] The leveling machine includes: a PLC control device, and components arranged along the longitudinal direction of the groove frame, such as... Figure 3 The multiple monitoring components corresponding to each stiffener plate are shown, and each monitoring component is electrically connected to a PLC control device; the leveling machine moves along the transverse direction of the groove frame to obtain the flatness data of the electrode mesh on the stiffener plate.

[0077] Figure 1 This diagram illustrates a structural schematic of an embodiment of a data processing system 200 for an electrolytic cell leveling machine provided by the present invention, as shown below. Figure 1 As shown, the system 200 includes:

[0078] The data parsing module 210 is used to identify the disordered and chaotic data collected by the PLC control device of the leveling machine according to the pre-built key-value-object mapping strategy, and obtain the identified parameter type data and reference information data; the parameter type data includes: cathode mesh monitoring point height data and anode mesh monitoring point height data;

[0079] The spatial mapping module 220 is used to map the height data of the cathode grid monitoring points and the height data of the anode grid monitoring points to the physical monitoring grid of the electrolytic cell according to a pre-built structure-space mapping strategy, thereby generating structured flatness data.

[0080] Among them, the key-value-object mapping strategy is constructed based on disordered and chaotic data; the structure-space mapping strategy is constructed based on the physical structure information of the electrolytic cell.

[0081] For example, in constructing the key-object mapping strategy: All data keys (such as D2000, D2600, D2500, etc.) and their corresponding measured values ​​are extracted from disordered and chaotic data; the numerical distribution characteristics of the measured values ​​under each data key are analyzed, such as the range of data values ​​(e.g., height values ​​are usually within a certain range of millimeters) and the numerical precision (e.g., retaining a certain number of decimal places); based on these characteristics, the physical object category corresponding to each data key is automatically identified, for example, D2000 corresponds to the height of the cathode mesh monitoring point, D2600 corresponds to the height of the anode mesh monitoring point, and D2500 corresponds to the thickness of the slot frame, and mapping configuration information containing key values, categories, and value ranges is generated. This process can be implemented through a separate first construction module, providing a configurable rule basis for data parsing.

[0082] Meanwhile, regarding the construction of the structure-space mapping strategy: The fixed physical structure information of the electrolytic cell is obtained, such as the number of ribs (e.g., 26) and the number of monitoring points arranged longitudinally on each rib (e.g., 11). Based on this, a two-dimensional physical monitoring grid consisting of sequentially numbered ribs and monitoring points is defined. Then, the correspondence between the data set sequence (e.g., an array under the D2000 key value) and the rib and monitoring point numbers in the grid is established. For example, the first subarray of the array is mapped to the first rib, and the first 11 elements in the subarray are mapped to the 11 monitoring points on that rib, generating mapping configuration information. This process can be implemented through a separate second building module to ensure that the data can be accurately associated with the physical location.

[0083] It is understood that the disordered and chaotic data in this invention specifically refers to the real-time data stream output from the PLC control device of the leveling machine through the original communication protocol. It is usually encapsulated in formats such as JSON, contains multiple data key values ​​without physical meaning and corresponding sets of measurement values, and lacks a direct correlation with the actual spatial location of the electrolytic cell. Therefore, it cannot be directly used for systematic analysis in its original state.

[0084] The physical monitoring grid is a two-dimensional coordinate system abstracted from the actual structure of the electrolytic cell (such as the cell frame and stiffener layout). Each coordinate point is uniquely determined by the stiffener number and the monitoring point number, representing a specific physical monitoring location on the electrolytic cell. It is used to convert height data from abstract numerical values ​​into information with spatial meaning.

[0085] Structured flatness data refers to a standard dataset generated after parsing and mapping. It not only contains the height value of the monitoring point, but also binds spatial location information such as the corresponding rib plate number and monitoring point number, as well as reference information such as timestamp, status signal, and unique identifier of the electrolytic cell, thus forming a complete, orderly, and queryable data record, providing a foundation for subsequent storage, visualization, and big data analysis.

[0086] The technical solution of this embodiment can automatically parse the physical meaning of the original data, transform it into parameter data with clear engineering semantics, and accurately associate it with the physical monitoring grid position of the electrolytic cell to generate locationable structured data. Through associated storage, it supports full-process traceability, improving data parsing efficiency, positioning accuracy and quality traceability capabilities.

[0087] In one alternative approach, it also includes:

[0088] The data storage module is used to associate structured flatness data and reference information data, generate index information, and synchronously store it in the database;

[0089] The reference information data includes: data key values, measurement values ​​distinguished by data key values, status signals, timestamp information, and a unique identifier for the electrolytic cell.

[0090] It should be noted that the data storage module refers to a software or hardware / software unit deployed in a data processing system, responsible for persistently storing the standardized data formed after parsing and mapping. This module does not simply write data into a database, but rather establishes an inherent relationship between structured flatness data and reference information data, and generates index information that facilitates rapid retrieval, thereby constructing a data storage system that supports efficient querying, is traceable, and is easily expandable.

[0091] Reference information data refers to a set of auxiliary data used to describe and trace the source, acquisition process, and context of structured flatness data. It includes, but is not limited to: data key values ​​without physical meaning in the original output of the leveling machine PLC (such as D2000, D2600, D2500), the array of measurement values ​​distinguished by the data key values, status signals automatically generated by the PLC to characterize the stage of the leveling process (such as CY to characterize the start of leveling, JPIN to characterize the middle of leveling, CYJS or JPJS to characterize the end of leveling), timestamp information marking the time of each data acquisition, and cell frame number to uniquely identify the identity of the electrolytic cell being leveled. This reference information data provides a complete contextual basis for data traceability, quality correlation analysis, and anomaly diagnosis.

[0092] Index information refers to the database retrieval identifier generated based on a combination field of unique identifier and status signal of electrolytic cell (such as sn_process). This index information forms a data mapping relationship with multi-dimensional query capabilities by associating and binding the spatial location field in the structured flatness data with the key value, timestamp, and status signal in the reference information data. This allows users to quickly locate and extract the corresponding flatness data and process parameters based on any dimension of electrolytic cell frame number, leveling status, or acquisition time.

[0093] It should be noted that the database table structure design used in this invention is as follows: Figure 4 As shown, the table contains core fields such as id, created_time, sn_process, sn, process, data, and device. The sn field stores the unique identifier of the electrolytic cell (workpiece number); the process field records the leveling status (e.g., CY indicates leveling start, JPJS or CYJS indicates leveling end); the data field stores the complete set of measurement data generated in a single leveling operation in JSON format; the device field identifies the equipment number performing the leveling; the sn_process field serves as a composite index for efficient retrieval; and the created_time field records the data row insertion time. This table structure, through the organic combination of fields, achieves orderly management and rapid querying of leveling data, providing a solid foundation for subsequent visualization and traceability.

[0094] In a preferred embodiment, the data storage module performs the following steps: First, it receives structured flatness data output from the spatial mapping module and reference information data output from the data parsing module. The structured flatness data includes at least the height data of the cathode network monitoring points and the height data of the anode network monitoring points bound to the electrolytic cell's physical monitoring grid, along with their corresponding spatial location information. The reference information data includes at least data key values, measurement values ​​distinguished by the data key values, state signals characterizing the leveling process stages, timestamps recording the data acquisition time, and a unique identifier for the electrolytic cell being leveled. Next, using the unique identifier of the electrolytic cell as the primary association key, the structured flatness data and reference information data corresponding to the same electrolytic cell are fused, and a time-series association is established based on the state signals and timestamps to form a complete data record. Then, a combined index field composed of the unique identifier of the electrolytic cell and the state signals is generated, and this index information, along with the fused data record, is submitted to the database. Finally, a synchronous storage operation is performed using a standard database write protocol to ensure data persistence.

[0095] This process ensures that all information, including raw measurements, processed height data, spatial location, process status, and time context, is completely and systematically preserved, providing a solid foundation for any subsequent data extraction, analysis, and visualization based on time, batch, equipment, or specific electrolyzer.

[0096] For ease of understanding, assume a certain type of electrolytic cell has 26 ribs, with 11 monitoring points on each rib. Then, the structured flatness data generated during a single leveling process for a single electrolytic cell contains 26 × 11 = 286 data nodes. The unique identifier for the electrolytic cell in the reference information data is the work number "ETK20241218001", the status signal sequence is "[CY,JPIN0141002,JPJS]", and the corresponding timestamps are "2024-12-18T08:00:00Z", "2024-12-18T08:00:05Z", and "2024-12-18T08:00:42Z". The data storage module will generate the index information "ETK20241218001_JPJS" and store this index along with 286 structured data records containing (rib number, monitoring point number, cathode mesh height, anode mesh height) and the reference information data in the database table. When it is necessary to trace the quality status of the electrolytic cell after leveling, the query condition "ETK20241218001" can be entered to quickly retrieve all relevant data. If it is necessary to further analyze the specific parameters at the end of leveling, the target record can be accurately located by adding the status signal "JPJS". This achieves the technical effect of extracting the complete leveling file of a specific electrolytic cell from massive leveling data in seconds.

[0097] In one alternative approach, it also includes:

[0098] The first building module is used to extract all data key values ​​and their corresponding measurement values ​​from disordered and chaotic data;

[0099] Analyze the numerical distribution characteristics of the measured values ​​under each data key value. The numerical distribution characteristics include the range of data values ​​and the precision of the values.

[0100] Based on the numerical distribution characteristics, the physical object category corresponding to each data key value is identified, and key-value-object mapping configuration information containing data key values, physical object categories and data value ranges is generated to construct a key-value-object mapping strategy; the physical object categories include cathode mesh monitoring point height, anode mesh monitoring point height and tank frame thickness.

[0101] It should be noted that data key values ​​refer to the string codes used by the PLC control device of the leveling machine in the original data frame to identify different measurement dataset types, such as "D2000", "D2600", "D2500", etc. These key values ​​themselves do not have physical meaning annotations, but are only unordered identifiers defined by the equipment manufacturer's private protocol. Each key value corresponds to one or more sets of measurement value sequences. The core task of the key value-object mapping strategy is to assign semantics of physical object categories through data analysis.

[0102] The measured value refers to the set of numerical values ​​that characterize the physical quantities of the electrode mesh or slot frame, collected by the leveling machine monitoring component during the scanning process under a specific data key value identifier. The numerical set is a flat array structure in its original state. For example, the D2000 key value contains the height numerical sequence of all monitoring points corresponding to all stiffeners. Its physical unit and range are determined by the characteristics of the equipment sensor, but are not explicitly marked before being identified by this system.

[0103] Numerical distribution characteristics refer to the set of numerical patterns obtained by statistical analysis of a large number of historically accumulated or real-time collected measurement values ​​under the same data key value. Numerical distribution characteristics include at least the range of data values ​​(i.e., the closed interval formed by the maximum and minimum values ​​of the measurement values ​​under normal operating conditions) and numerical precision (i.e., the number of significant digits or the smallest resolution unit of the measurement value). Optional statistical characteristics such as mean, standard deviation, and distribution concentration are also included. These characteristics constitute the core criteria for distinguishing different categories of physical objects.

[0104] Physical object category refers to the type of physical object that needs to be measured and monitored in the electrolytic cell leveling process. Specifically, physical object categories include: the height of the cathode mesh monitoring point, which represents the height of each rib monitoring point of the cathode mesh relative to the reference plane; the height of the anode mesh monitoring point, which represents the height of each rib monitoring point of the anode mesh relative to the reference plane; and the thickness of the cell frame, which represents the thickness of the upper and lower frame edges at multiple fixed positions. The above three categories of physical object cover all effective measurement contents of the leveling machine.

[0105] Key-value-object mapping configuration information refers to a structured configuration definition that uses data keys as indexes and includes the physical object category determination result and the corresponding data value range and numerical precision for that category. It is generated by the first construction module and called by the data parsing module. This configuration definition serves as a reference for parsing rules, guiding the data parsing module in interpreting and converting the raw data from the leveling machine. In practical applications, this mapping relationship can be implemented through various techniques, such as logical judgments embedded in the system code, predefined data structures, or stored in accessible configuration files (such as JSON files or database tables). Its content entries, such as {"key":"D2000","physical object category":"cathode mesh monitoring point height","data value range":[-5.0,5.0],"numerical precision":0.01}, realize the mapping from machine code to physical semantics.

[0106] In a preferred embodiment, the workflow of the first construction module is as follows: First, extract all data key values ​​(such as D2000, D2600, D2500) and their corresponding measurement value sets from the disordered and chaotic data provided by the data acquisition module; second, perform numerical distribution feature analysis on the measurement value set under each data key value, and calculate its data value range and numerical precision; then, based on predefined discrimination rules, match the numerical distribution features of each data key value with typical feature templates of three physical object categories: cathode mesh monitoring point height, anode mesh monitoring point height, and slot frame thickness, to identify the unique physical object category corresponding to each data key value; finally, generate key-object mapping configuration information containing data key values, physical object categories, data value ranges, and numerical precision, and store it as a key-object mapping strategy for subsequent parsing and calling.

[0107] To facilitate understanding, the following explanation uses an example with specific numerical values:

[0108] Suppose that the disordered and chaotic data obtained by the data acquisition module in a single leveling operation contains three core data key values: D2000, D2600, and D2500. When the first construction module is executed, it first extracts 286 measurement values ​​(26 ribs × 11 monitoring points) corresponding to the D2000 bond value from the batch of data. Statistical analysis shows that the data value range is [-4.85mm, 4.92mm], with a numerical precision of 0.01mm. The measurement value distribution is normal and the mean is close to zero, which is consistent with the characteristics of electrode mesh height deviation. Then, it extracts 286 measurement values ​​of the same scale under the D2600 bond value. The statistical analysis shows that the data value range is [-4.78mm, 4.88mm], with a numerical precision of 0.01mm as well. The distribution pattern is similar to that of D2000 but has a slight systematic shift, which is consistent with the physical difference between anode mesh and cathode mesh. Finally, it extracts 6 measurement values ​​under the D2500 bond value (corresponding to 3 at the top and 3 at the bottom of the slot frame, for a total of 6 fixed monitoring points). The statistical analysis shows that the data value range is [59.8mm, 60.5mm], with a numerical precision of 0.01mm. The distribution is concentrated and the mean is 60.15mm, which is consistent with the physical constant characteristics of slot frame thickness. Based on the above numerical distribution characteristics, the first construction module determines that D2000 corresponds to the height of the cathode mesh monitoring point, D2600 corresponds to the height of the anode mesh monitoring point, and D2500 corresponds to the thickness of the slot frame, generating the following key-value-object mapping configuration information:

[0109] {"D2000":{"Physical Object Category":"Cathode Mesh Monitoring Point Height","Data Value Range":[-5.0,5.0],"Numerical Precision":0.01,"Numerical Rib Plate Quantity":26,"Numerical Monitoring Points per Rib Plate":11},

[0110] "D2600":{"Physical Object Category":"Anode Mesh Monitoring Point Height","Data Value Range":[-5.0,5.0],"Numerical Precision":0.01,"Numerical Frame Quantity":26,"Numerical Frame Monitoring Points per Frame":11},

[0111] "D2500":{"Physical Object Category":"Slot Frame Thickness","Data Value Range":[59.5,60.8],"Numerical Precision":0.01,"Numerical Count":6}}.

[0112] After the configuration information is solidified into a key-value-object mapping strategy, the data parsing module can directly read the D2000 key value to find the corresponding cathode mesh when processing disordered and chaotic data collected subsequently from the same electrolytic cell. It also performs boundary verification using the range [-5.0, 5.0] and format normalization using 0.01 precision, thus realizing the automatic conversion from raw encoding to standard parameter type data.

[0113] In one alternative approach, the parameter type data also includes slot thickness data; the data parsing module is specifically used for:

[0114] Based on the key-value-object mapping strategy, obtain the physical object category and data value range corresponding to each data key in the key-value-object mapping configuration information;

[0115] Based on the physical object category, perform boundary checks on the measured values ​​under each data key value to remove abnormal data that exceeds the data value range;

[0116] Based on numerical accuracy, the measured values ​​that have passed the verification are formatted and their units are standardized. The output parameter type data, which represents the category of physical object and conforms to the standard measurement units, includes cathode mesh monitoring point height data, anode mesh monitoring point height data, and tank frame thickness data. Reference information data is also output simultaneously.

[0117] It should be noted that, in the physical object categories identified by the key-object mapping strategy, in addition to the height data of the cathode mesh monitoring points and the anode mesh monitoring points, the parameter type data further includes a dataset that quantitatively characterizes the thickness of the electrolytic cell frame. The frame thickness data is obtained through a set of measurement values ​​corresponding to specific key values ​​(such as D2500). This data reflects the thickness measured by the leveling machine during the leveling process, using the frame as the reference zero point. In practical applications, it is preferred to measure the thickness at six fixed monitoring points: the initial position, the middle position, the end position, and three positions at the upper and lower edges of the rectangular frame. This data is an important reference parameter for calculating the relative height benchmark of the electrode mesh and evaluating the deformation of the cell.

[0118] Boundary verification refers to the filtering operation performed by the data parsing module to determine the legality of the received measurement values ​​in real time, based on the predefined range of data values ​​for each data key in the key-value-object mapping configuration information. The operation includes comparing each measurement value to see if it falls within the closed interval. If the measurement value is greater than the maximum value or less than the minimum value, it is determined to be abnormal data caused by communication interference, sensor jumps, or equipment failure and is removed. Only valid data that passes the verification is retained to enter the subsequent processing flow, thereby ensuring the accuracy and reliability of parameter type data.

[0119] Format standardization and unit unification refers to the set of operations performed by the data parsing module to standardize measured values ​​that have passed boundary checks, based on the numerical precision defined in the key-value-object mapping configuration information. These operations include at least: retaining floating-point numbers to a specified number of decimal places (e.g., retaining two decimal places for 0.01mm precision), converting scientific notation to decimal, unifying the units of measurement from different data sources to standard units (e.g., unifying them to millimeters (mm), and formatting discrete numerical values ​​into fixed-length strings or numerical types. This ensures that all output parameter types conform to unified data specifications and measurement standards, facilitating direct calls by subsequent modules and database storage.

[0120] Understandably, in another scenario, considering that the data field output directly by the leveling machine already has a standardized format and uniform units, and can be directly stored and used, the aforementioned boundary verification and format normalization steps can be omitted. In this case, the data parsing module only performs data extraction and consistency checks. The consistency check refers to decimal point padding processing on the measured values ​​of the slot frame thickness data. That is, when a decimal point is detected missing in the measured value of the slot frame thickness data, the decimal point is added based on the number of digits to form a standard floating-point number format. If the cathode mesh monitoring point height data and the anode mesh monitoring point height data do not show any format abnormalities, they can also be directly extracted and output.

[0121] In a preferred embodiment, the data parsing module's specific workflow is as follows: First, it receives the key-value-object mapping configuration information generated by the first construction module, and obtains the physical object category, data value range, and numerical precision corresponding to each data key value. Second, for the set of measurement values ​​corresponding to each key value in the current batch of disordered and chaotic data, it calls the corresponding data value range for boundary verification based on its physical object category, iterates through the measurement values, and removes abnormal entries that exceed the range. Then, for the remaining valid measurement values ​​that pass the boundary verification, it performs rounding or truncation processing according to the numerical precision specified in the configuration information, and uniformly converts them into formatted values ​​under standard measurement units. Finally, it encapsulates the set of measurement values ​​that have been verified and normalized into cathode mesh monitoring point height data, anode mesh monitoring point height data, and cell frame thickness data according to the physical object category, and synchronously outputs reference information data containing the original data key values, status signals, timestamps, and unique identifiers of the electrolytic cell for use by the data storage module.

[0122] In another embodiment, if the raw data output by the leveling machine already meets the accuracy and format requirements, the workflow of the data parsing module can be simplified to improve processing efficiency as follows: First, receive key-value-object mapping configuration information and obtain the physical object category; second, directly extract the set of measurement values ​​corresponding to each data key value from the received disordered and chaotic data; then, perform decimal point completion processing on the measurement values ​​identified as tank frame thickness data, that is, check whether each measurement value contains a decimal point sign, and for values ​​missing decimal points, add decimal points at appropriate positions according to their fixed length or preset number of digits to form a standardized floating-point number format; finally, encapsulate the processed set of measurement values ​​into cathode mesh monitoring point height data, anode mesh monitoring point height data, and tank frame thickness data according to the physical object category, and simultaneously output reference information data containing raw data key values, status signals, timestamps, and unique identifiers of the electrolytic cell for direct storage and retrieval by the data storage module.

[0123] In one alternative approach, it also includes:

[0124] The second construction module is used to obtain the physical structure information of the electrolytic cell, including the number of stiffeners and the number of monitoring points set on each stiffener along its length.

[0125] Based on the physical structure information, determine the physical monitoring grid formed by the sequentially numbered stiffeners and monitoring points;

[0126] Establish a correspondence between the set sequence of cathode mesh monitoring point height data and anode mesh monitoring point height data and the stiffener plate number and monitoring point number in the physical monitoring grid, generate structure-space mapping configuration information containing this correspondence, and construct a structure-space mapping strategy.

[0127] It should be noted that physical structure information refers to the set of parameters characterizing the actual mechanical structure features of the electrolytic cell, including the total number of stiffeners arranged at intervals along the transverse direction of the cell frame and the number of monitoring points uniformly or non-uniformly set along the length of each stiffener. Physical structure information can be obtained from electrolytic cell design drawings, leveling machine configuration parameters or user input interface, and serves as the spatial reference input for constructing the physical monitoring grid.

[0128] The physical monitoring grid refers to the logical two-dimensional coordinate system generated by the second construction module based on the physical structure information. This system is orthogonally composed of sequentially numbered rib dimensions and sequentially numbered monitoring point dimensions. The rib numbers increase sequentially along the horizontal direction of the tank frame in the order of natural numbers (e.g., 1, 2, ..., N), and the monitoring point numbers increase sequentially along the vertical direction of the tank frame in the order of acquisition (e.g., 1, 2, ..., M). Each grid intersection point uniquely corresponds to a physical measurement position on the electrolytic cell.

[0129] The set sequence refers to the hierarchical data structure of the cathode mesh monitoring point height data or anode mesh monitoring point height data, which is output by the data parsing module and arranged in the scanning order of the leveling machine. The set sequence is represented by a list containing N first-level subsets at the outer layer, with each first-level subset corresponding to the data set of all monitoring points on a rib. At the inner layer, each first-level subset contains M sequentially arranged data elements, with each data element corresponding to the height value of a specific monitoring point on the rib. This structure maintains the temporal and spatial order characteristics of the original data collected by the leveling machine.

[0130] The structure-space mapping configuration information refers to the machine-readable configuration file generated by the second building module, which describes the mapping relationship between data positions in the set sequence and physical monitoring grid coordinates. The configuration information contains explicit mapping rules, that is, for any data element, the corresponding stiffener number is calculated based on its index position in the outer list, and the corresponding monitoring point number is calculated based on its index position in the inner subset. The configuration information is stored in the form of key-value pairs or relational tables for the space mapping module to retrieve and use.

[0131] The structure-space mapping strategy refers to a global rule system that automatically converts hierarchical data sequences into structured flatness data with spatial coordinates, based on physical structural information. The hierarchical data sequence consists of array structures output by the leveling machine. For example, data read from the data key D2000 contains 26 arrays, each with 14 elements: 11 elements are valid monitoring point data, and the remaining 3 elements are statistical data. The core of the structure-space mapping strategy lies in establishing a direct correspondence between array indices and rib plate numbers, and between element indices and monitoring point numbers. Specifically, rib plate number R = current array indices + 1, and monitoring point number C = current element indices + 1, thereby achieving a deterministic mapping from data hierarchical position to physical spatial position. The array indices represent the position of the target rib plate in the data sequence, starting from 0 and incrementing; the element indices represent the position of the target monitoring point within its respective rib plate array, starting from 0 and incrementing. Spatial mapping is only performed on the first 11 valid monitoring point elements; the last 3 statistical elements are processed according to independent rules.

[0132] In a preferred embodiment, the workflow of the second construction module is as follows: First, the physical structure information of the current leveling object is obtained through the system configuration interface or automatic identification process, including the total number of stiffeners N and the number of monitoring points M on each stiffener; second, a physical monitoring grid is generated based on N and M, and each intersection point in the grid is assigned a unique (stiffener number, monitoring point number) coordinate pair; then, the set sequence of cathode or anode grid monitoring point height data output by the data parsing module is parsed to determine whether the number of its outer subsets and the number of its inner elements match N and M+3, where M+3 indicates that each stiffener data contains M monitoring point elements and 3 statistical elements; next, a bidirectional mapping relationship between the index position of each data element in the set sequence and the coordinates of the physical monitoring grid is established to generate structure-space mapping configuration information; finally, the configuration information is solidified into a structure-space mapping strategy and stored in the system storage module for use by the space mapping module.

[0133] To facilitate understanding, the following explanation uses an example with specific numerical values:

[0134] Assume the physical structure of a certain type of electrolytic cell is as follows: N = 26 ribs, M = 11 monitoring points on each rib, and 14 data elements per rib (including 3 statistical values). The second construction module generates a physical monitoring grid based on this information. The rib numbers R ∈ {1, 2, ..., 26}, the monitoring point numbers C ∈ {1, 2, ..., 11}, and the total number of grid nodes is 26 × 11 = 286. The data parsing module outputs the cathode grid monitoring point height data as a sequence of 286 values. The outer structure of this sequence consists of 26 first-level subsets, each corresponding to one rib. Each inner subset contains 14 data elements with index k ∈ {0, 1, ..., 13}. The first 11 elements (k = 0~10) are valid monitoring point data, and the last 3 elements (k = 11~13) are statistical data. The mapping rules for the structure-space mapping configuration information are defined as follows: the outer array index i∈{0,1,...,25} corresponds to the rib plate number R=i+1; the inner element index k∈{0,1,...,10} corresponds to the monitoring point number C=k+1; statistical elements (k=11~13) do not participate in the spatial mapping and are treated as independent identifiers. For example, when the outer array index i=1 (the 2nd array) and the inner element index k=4, the corresponding rib plate number R=1+1=2 and the monitoring point number C=4+1=5, which means that the data element corresponds to the 5th monitoring point of the 2nd rib plate. After this structure-space mapping strategy is fixed into the configuration, the spatial mapping module can instantly calculate the precise physical coordinates of any monitoring point data element on the electrolytic cell using its array index and element index, realizing a lossless mapping from the data sequence to the spatial grid.

[0135] In one alternative approach, the space mapping module is specifically used for:

[0136] Based on the structure-space mapping strategy, the set sequence of cathode grid monitoring point height data and anode grid monitoring point height data is sequentially mapped to the physical monitoring grid; wherein, the set sequence includes multiple first-level subsets arranged in order, each first-level subset corresponding to a stiffener; each first-level subset includes multiple data elements arranged in order, each data element corresponding to a monitoring point on the corresponding stiffener;

[0137] The height data of the cathode mesh monitoring points and the height data of the anode mesh monitoring points are associated and bound with the corresponding stiffener plate numbers and monitoring point numbers, respectively, to generate structured flatness data containing spatial location information.

[0138] It should be noted that the set sequence refers to the hierarchical measurement data set output by the data parsing module that maintains the timing and logical order of the leveling machine's acquisition. The outer layer of this sequence consists of N first-level subsets arranged in order, where N corresponds to the total number of ribs in the electrolytic cell, and each first-level subset corresponds to all the monitoring data of one rib. The inner layer consists of M data elements arranged in order, where M corresponds to the number of monitoring points on a single rib, and each data element is the value of a single monitoring point. The structured organization of the set sequence is strictly consistent with the actual scanning path of the leveling machine.

[0139] The first-level subset is a mid-level data structure unit in the set sequence, used to encapsulate the height data of all monitoring points on a single stiffener. Its arrangement corresponds one-to-one with the actual physical order of the stiffeners as they are arranged laterally on the leveling machine; that is, the first first-level subset corresponds to the first stiffener, the second to the second, and so on, ensuring that the stiffener number can be directly located without additional lookup during spatial mapping. The data element refers to the basic data unit in the first-level subset. Each data element is a floating-point number, representing the electrode mesh height measurement value of a specific monitoring point on the corresponding stiffener. Its arrangement strictly matches the acquisition order of each monitoring point as the leveling machine moves longitudinally along the stiffener; that is, the first element in the subset corresponds to the monitoring point at the beginning of the stiffener, and the last element corresponds to the monitoring point at the end.

[0140] Association binding refers to the process by which the spatial mapping module combines and encapsulates the value of each data element with its unique spatial coordinates (stiffener number, monitoring point number) calculated through the structure-space mapping strategy. This operation generates a data record containing fields (stiffener number R, monitoring point number C, cathode mesh height value, anode mesh height value), enabling the original measurement value to obtain a clear physical location.

[0141] Structured flatness data refers to a set of height data indexed by physical monitoring grid coordinates, output after processing by the spatial mapping module. Its data structure is a two-dimensional table or a set of key-value pairs. Each record contains a spatial location field and a measurement value field. It supports random access and range query based on rib plate number and monitoring point number, providing a spatial data foundation for visualization and quality traceability.

[0142] In a preferred embodiment, the specific workflow of the spatial mapping module is as follows: First, it receives the structure-space mapping configuration information generated by the second construction module and obtains the number of ribs N and the number of single rib monitoring points M of the current electrolytic cell; second, it retrieves the cathode mesh monitoring point height data set sequence and the anode mesh monitoring point height data set sequence output by the data parsing module; then, it traverses the cathode mesh set sequence, and for each first-level subset index r (starting from 0) and each data element index c (starting from 0) within the subset, it calculates the rib number R=r+1 and the monitoring point number C=c+1 using a preset mapping formula, and binds the data element value with the (R, C) coordinate to generate a cathode mesh structured record; similarly, it traverses the anode mesh set sequence to generate an anode mesh structured record; finally, it merges the two types of structured records and outputs them as complete structured flatness data.

[0143] In one alternative approach, it also includes:

[0144] The data acquisition module is used to convert the proprietary communication protocol data in the PLC control device of the leveling machine into standard industrial network protocol data, and to acquire the standard industrial network protocol data.

[0145] Frame structure parsing is performed on standard industrial network protocol data to extract structured data packets containing data key values, status signals, timestamp information, unique identifiers of electrolytic cells, and sets of measurement values ​​without physical meaning, distinguished by data key values;

[0146] The integrity of the structured data packet is checked, resulting in disordered and chaotic data after the check passes.

[0147] It should be noted that proprietary communication protocols refer to unpublished, non-standard, dedicated communication protocols defined by the PLC control device manufacturer of the leveling machine for data transmission. These protocols typically employ specific message formats, data encoding methods, verification mechanisms, and transmission timings, making them incompatible with general industrial standards. This results in external systems being unable to directly parse and read the data, requiring a protocol conversion module for interoperability. Standard industrial network protocols, on the other hand, refer to communication protocols that conform to internationally or industry-recognized standards, including but not limited to Modbus TCP, OPC UA, EtherNet / IP, and Profinet. These protocols have publicly available message structures, unified data models, and robust error handling mechanisms, used to establish stable and reliable data transmission channels between industrial equipment and upper-level systems, enabling data sharing between heterogeneous systems.

[0148] Frame structure parsing refers to the process by which the data acquisition module disassembles, identifies fields, and extracts data from received standard industrial network protocol data packets layer by layer. The process includes identifying the packet header, data field, and check field, extracting the payload carried within, and converting the binary or hexadecimal data in the payload into a processable structured data packet. This data packet contains all the original measurement information and context parameters.

[0149] The set of measurement values ​​without physical meaning refers to the array of measurement data extracted from the PLC's raw data frame, which is uniquely identified by data key values ​​and contains only numerical values ​​but lacks descriptions of physical quantity types. In the initial stage of parsing, the data set does not distinguish between cathode mesh height, anode mesh height, or slot frame thickness. It needs to be identified by a subsequent key-object mapping strategy before it can be given physical meaning. Its essence is the raw digital output collected by the sensors inside the leveling machine.

[0150] Understandable, such as Figure 5 Although the original data may present a wide sequence range at the key value level (e.g., D2000-D2513 for the cathode mesh and D2600-D3113 for the anode mesh), in the actual disordered data, all key measurement information is aggregated under three outer primary key values. Specifically, the D2000 and D2600 primary key values ​​each correspond to a complete set of measurement values. This set contains, in a nested structure, a subset of data for all stiffeners and monitoring points of the corresponding electrode mesh (in a typical case, each subset consists of 11 monitoring point values ​​and 3 statistical calculation values, where the 11 monitoring point values ​​are the actual height sampling values ​​of each physical monitoring point on a single stiffener, and the 3 statistical calculation values ​​are the average, maximum, and minimum height values ​​of the stiffener segment automatically generated by the leveling machine PLC for rapid macroscopic quality judgment), while the D2500 primary key value independently corresponds to the set of measurement values ​​for the slot frame thickness. Therefore, when parsing the frame structure, the data acquisition module only needs to locate and extract the three primary key values ​​D2000, D2600, and D2500 to obtain all the valid monitoring data of the cathode mesh, anode mesh, and tank frame. It does not need to process the numerous subsequence key values ​​under each of them, thereby significantly reducing the parsing complexity and computational overhead while ensuring data integrity.

[0151] Integrity verification refers to the process by which the data acquisition module judges the legality of the parsed structured data packets based on preset verification algorithms (such as CRC check, parity check, data length verification, and numerical range reasonableness check). This process ensures the integrity and reliability of the data input to subsequent modules by recalculating the check value and comparing it with the check value carried in the message, or by checking whether the number of data entries meets the expected structure, and identifying data corruption entries caused by communication interference, transmission packet loss, or equipment failure.

[0152] Data corruption entries refer to data records containing illegal values, missing fields, abnormal lengths, or failed verifications that are generated during the communication process between the leveling machine PLC and the data acquisition module due to electromagnetic interference, network latency, buffer overflow, or protocol conversion errors. If such entries are not removed, they will lead to subsequent parsing errors, mapping failures, or storage anomalies. These are noisy data that must be filtered in industrial data acquisition.

[0153] In a preferred embodiment, the data acquisition module operates as follows: First, it unpacks the private communication protocol messages of the leveling machine PLC control device using a protocol conversion engine, repackages them according to standard industrial network protocol specifications, and establishes a bidirectional continuous communication connection. Second, it performs frame structure parsing on the received standard protocol messages, identifies the data key value field, status signal field, timestamp field, unique identifier field of the electrolytic cell, and measurement value array field distinguished by data key values ​​in the messages, and extracts the content of each field to construct a structured data packet. Then, it performs integrity verification on the structured data packet, including check code verification, data length consistency check, and outlier screening, marking entries that fail the verification as corrupted data entries and removing them from the data packet. Finally, it outputs the disordered and chaotic data after verification, i.e., a data packet containing valid data key values, status signals, timestamp information, unique identifier of the electrolytic cell, and a complete set of measurement values, for use by the first construction module and the data parsing module.

[0154] In one alternative approach, it also includes:

[0155] The demonstration device is used to extract spatial location information from structured flatness data based on the unique identifier and status signal index of the electrolytic cell, and to retrieve parameter type data and reference information data.

[0156] The calibration duration is determined based on the timestamp information in the reference data.

[0157] Based on spatial location information, a matrix-style visualization chart is generated with the position of the stiffener plate as the horizontal axis and the position of the monitoring point as the vertical axis. Based on a preset height range, a preset color is used to mark whether the height data of each monitoring point is within the allowable range, so as to intuitively display the distribution of abnormal monitoring points in the leveling effect of a single electrolytic cell.

[0158] The slot thickness data and the straightening length in the parameter type data are used as the straightening process status parameters and displayed in conjunction with a matrix-style visualization chart to achieve remote comprehensive monitoring and quality traceability.

[0159] It should be noted that the display device refers to a computing terminal with data retrieval, processing and graphics rendering capabilities, including but not limited to industrial touch screens, monitoring workstations, web servers or mobile terminals. The display device accesses structured flatness data, parameter type data and reference information data by calling the database query interface, uses a graphics rendering engine to convert spatial location information into visual charts, and integrates timestamp parsing and state parameter calculation functions to realize remote visualization and comprehensive monitoring of the leveling results.

[0160] The calibration duration refers to the time interval from the start to the end of the electrolytic cell calibration operation. Specifically, it is the time difference between the timestamp corresponding to the status signal CY (calibration start) and the timestamp corresponding to the status signal JPJS or CYJS (calibration end). The calculation formula is Δt = t JPJS -t CY Δt represents the time efficiency of the leveling process. It should be noted that the leveling process of an electrolytic cell typically involves multiple leveling iterations. The status signal JPING (leveling in progress) will be repeatedly output during the iterations until the required leveling accuracy is achieved, triggering the JPJS or CYJS signal. The above calculation formula calculates the cumulative time from the first CY signal to the final JPJS / CYJS signal. The leveling duration is directly calculated from the timestamp information in the reference data, without the need for additional sensor measurements.

[0161] Matrix-style visualization charts refer to two-dimensional heat maps or grid maps generated by displaying equipment based on spatial location information in structured flatness data. The horizontal axis corresponds to the rib plate number (1 to N), and the vertical axis corresponds to the monitoring point number (1 to M). Each grid cell is filled with color to indicate whether the electrode mesh height data at the corresponding location is within a preset height range. The chart presents a panoramic view of the planar distribution characteristics of the electrolytic cell leveling effect in a discrete matrix format. The charts of this invention adopt a single-page full data display mode, simultaneously rendering cathode and anode mesh height data in the same visualization interface. The two types of data are distinguished by color intensity, patterns, or row and column layouts, achieving a panoramic comparative display of the leveling effect. The specific presentation form of the matrix-style visualization charts of this invention is as follows: Figure 6 As shown in the figure, taking the cathode mesh measurement data as an example, the horizontal axis represents the number of stiffeners corresponding to the measurement point number (1-26), and the vertical axis represents the monitoring points on the stiffeners corresponding to the measurement positions marked by AK. Each cell displays the height measurement value of the corresponding position, and indicates whether it is within the allowable range (such as 0.400~1.200) by the numerical color or background color. At the same time, the statistical extreme values ​​and mean values ​​of each row are displayed, so as to intuitively visualize the spatial distribution of the leveling effect.

[0162] The preset height range refers to the continuous interval [H] formed by the upper and lower limits of the height deviation allowed by the electrolytic cell process standard. min Hmax ], where H min H is the minimum allowable height value (usually a negative tolerance). max The maximum allowable height value (usually a positive tolerance) serves as the criterion for determining whether a monitoring point is out of tolerance. This range is stored in the configuration file of the display device and can be dynamically adjusted according to the electrolytic cell model. In independent display mode, the cathode mesh and anode mesh can be configured with different preset height ranges to match the different process tolerance requirements of the two types of electrode meshes.

[0163] Leveling process status parameters refer to a set of key indicators used to describe the quality of the leveling operation and the operating status of the equipment. These parameters include at least the leveling length Δt, which characterizes time efficiency, and the slot thickness, which characterizes baseline stability. The parameters are displayed in conjunction with matrix-style visualization charts, providing managers with process-dimensional information beyond spatial distribution, supporting a comprehensive assessment of the leveling process stability. The associated display is based on logical data relationships rather than physical binding, allowing users to choose to hide or expand the status parameter panel on the remote monitoring interface. It also supports clicking on a monitoring point to display the leveling length or slot thickness value for that point, enabling interactive, multi-dimensional data exploration.

[0164] Remote integrated monitoring and quality traceability refers to the display equipment transmitting electrolytic cell leveling data from the on-site leveling machine to the remote monitoring center via network communication. This allows managers to view the leveling process status parameters, visualization charts, and anomaly distribution in real time on the terminal without being physically present at the site. Furthermore, they can use the unique identifier of the electrolytic cell to reverse-query historical leveling records, thus realizing a complete quality traceability chain from result to process.

[0165] In a preferred embodiment, the specific workflow of the demonstration device is as follows: First, the system receives the unique identifier of the electrolytic cell input by the user as an initial query index, retrieves and returns a list of all leveling records corresponding to the electrolytic cell from the database; second, it performs a secondary selection based on the status signals in the list, and after selecting the target leveling record, the system extracts all records (rib plate number, monitoring point number, cathode mesh height, anode mesh height) from the structured flatness data corresponding to the record, extracts the corresponding leveling start and end timestamps from the reference information data, and extracts the tank frame thickness data from the parameter type data; then, it iterates through the structured flatness data, compares the height values ​​with the preset range one by one, and assigns a color identifier to each grid cell; next, it generates a matrix chart with the rib plate number as the horizontal axis and the monitoring point number as the vertical axis, renders each cell according to the assigned color, and displays the specific leveling time (including start and end time) and tank frame thickness value next to the chart; finally, it outputs the rendered chart and status parameter panel to the display terminal to realize remote monitoring and traceability.

[0166] Figure 7A schematic flowchart of an embodiment of a data processing method for an electrolytic cell leveling machine provided by the present invention is shown. Figure 7 As shown, the method is executed by a data processing system for an electrolytic cell leveling machine and includes the following steps:

[0167] Based on a pre-built key-value-object mapping strategy, the disordered and chaotic data collected by the PLC control device of the leveling machine is identified, and the identified parameter type data and reference information data are obtained. The parameter type data includes: cathode mesh monitoring point height data and anode mesh monitoring point height data.

[0168] Based on a pre-built structure-space mapping strategy, the height data of the cathode grid monitoring points and the height data of the anode grid monitoring points are mapped to the physical monitoring grid of the electrolytic cell to generate structured flatness data.

[0169] Among them, the key-value-object mapping strategy is constructed based on disordered and chaotic data; the structure-space mapping strategy is constructed based on the physical structure information of the electrolytic cell.

[0170] The technical solution of this embodiment can automatically parse the physical meaning of the original data, transform it into parameter data with clear engineering semantics, and accurately associate it with the physical monitoring grid position of the electrolytic cell to generate locationable structured data. Through associated storage, it supports full-process traceability, improving data parsing efficiency, positioning accuracy and quality traceability capabilities.

[0171] In one alternative approach, the method further includes:

[0172] The structured flatness data and reference information data are associated to generate index information and synchronously stored in the database;

[0173] The reference information data includes: data key values, measurement values ​​distinguished by data key values, status signals, timestamp information, and a unique identifier for the electrolytic cell.

[0174] In one alternative approach, the method further includes:

[0175] Extract all data key values ​​and their corresponding measurements from disordered and chaotic data;

[0176] Analyze the numerical distribution characteristics of the measured values ​​under each data key value. The numerical distribution characteristics include the range of data values ​​and the precision of the values.

[0177] Based on the numerical distribution characteristics, the physical object category corresponding to each data key value is identified, and key-value-object mapping configuration information containing data key values, physical object categories and data value ranges is generated to construct a key-value-object mapping strategy; the physical object categories include cathode mesh monitoring point height, anode mesh monitoring point height and tank frame thickness.

[0178] In one alternative approach, the disordered and chaotic data collected by the leveling machine PLC control device is identified according to a pre-built key-value-object mapping strategy, and the identified parameter type data and reference information data are obtained, including:

[0179] Based on the key-value-object mapping strategy, obtain the physical object category and data value range corresponding to each data key in the key-value-object mapping configuration information;

[0180] Based on the physical object category, perform boundary checks on the measured values ​​under each data key value to remove abnormal data that exceeds the data value range;

[0181] Based on numerical accuracy, the measured values ​​that have passed the verification are formatted and their units are standardized. The output parameter type data, which represents the category of physical object and conforms to the standard measurement units, includes cathode mesh monitoring point height data, anode mesh monitoring point height data, and tank frame thickness data. Reference information data is also output simultaneously.

[0182] In one alternative approach, the method further includes:

[0183] Obtain the physical structure information of the electrolytic cell, including the number of ribs and the number of monitoring points set on each rib along its length.

[0184] Based on the physical structure information, determine the physical monitoring grid formed by the sequentially numbered stiffeners and monitoring points;

[0185] Establish a correspondence between the set sequence of cathode mesh monitoring point height data and anode mesh monitoring point height data and the stiffener plate number and monitoring point number in the physical monitoring grid, generate structure-space mapping configuration information containing this correspondence, and construct a structure-space mapping strategy.

[0186] In one alternative approach, based on a pre-built structure-space mapping strategy, the height data of the cathode mesh monitoring points and the height data of the anode mesh monitoring points are mapped to the physical monitoring grid of the electrolytic cell to generate structured flatness data, including:

[0187] Based on the structure-space mapping strategy, the set sequence of cathode grid monitoring point height data and anode grid monitoring point height data is sequentially mapped to the physical monitoring grid; wherein, the set sequence includes multiple first-level subsets arranged in order, each first-level subset corresponding to a stiffener; each first-level subset includes multiple data elements arranged in order, each data element corresponding to a monitoring point on the corresponding stiffener;

[0188] The height data of the cathode mesh monitoring points and the height data of the anode mesh monitoring points are associated and bound with the corresponding stiffener plate numbers and monitoring point numbers, respectively, to generate structured flatness data containing spatial location information.

[0189] In one alternative approach, the method further includes:

[0190] Convert the proprietary communication protocol data in the PLC control device of the leveling machine into standard industrial network protocol data, and collect the standard industrial network protocol data.

[0191] Frame structure parsing is performed on standard industrial network protocol data to extract structured data packets containing data key values, status signals, timestamp information, unique identifiers of electrolytic cells, and sets of measurement values ​​without physical meaning, distinguished by data key values;

[0192] The integrity of the structured data packet is checked, resulting in disordered and chaotic data after the check passes.

[0193] In one alternative approach, the method further includes:

[0194] Based on the unique identifier and status signal index of the electrolytic cell, spatial location information is extracted from the structured flatness data, and parameter type data and reference information data are retrieved.

[0195] The calibration duration is determined based on the timestamp information in the reference data.

[0196] Based on spatial location information, a matrix-style visualization chart is generated with the position of the stiffener plate as the horizontal axis and the position of the monitoring point as the vertical axis. Based on a preset height range, a preset color is used to mark whether the height data of each monitoring point is within the allowable range, so as to intuitively display the distribution of abnormal monitoring points in the leveling effect of a single electrolytic cell.

[0197] The slot thickness data and the straightening length in the parameter type data are used as the straightening process status parameters and displayed in conjunction with a matrix-style visualization chart to achieve remote comprehensive monitoring and quality traceability.

[0198] The parameters and steps in the data processing method for the electrolytic cell leveling machine described above, which are used to achieve the corresponding functions, can be referred to the parameters and modules in the above-described embodiment of the data processing system 200 for the electrolytic cell leveling machine, and will not be repeated here.

[0199] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and represent a limitation on a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0200] 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. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A data processing system for an electrolytic cell leveling machine, characterized in that, The electrolytic cell to be leveled includes: the cell body, the cell frame surrounding the cell body, the electrode mesh laid inside the cell frame, and multiple stiffening plates arranged at intervals along the transverse direction of the cell frame; the electrode mesh includes: an anode mesh and a cathode mesh. The leveling machine includes: a PLC control device and multiple monitoring components corresponding to each rib plate arranged along the longitudinal direction of the slot frame, each monitoring component being electrically connected to the PLC control device; the leveling machine moves along the transverse direction of the slot frame to obtain flatness data of the electrode mesh on the rib plate. The data processing system in the PLC control device includes: The data parsing module is used to identify the disordered and chaotic data collected by the PLC control device of the leveling machine according to a pre-built key-value-object mapping strategy, and to obtain the identified parameter type data and reference information data; the parameter type data includes: cathode mesh monitoring point height data and anode mesh monitoring point height data; The spatial mapping module is used to map the height data of the cathode grid monitoring points and the height data of the anode grid monitoring points to the physical monitoring grid of the electrolytic cell according to the pre-built structure-space mapping strategy, and generate structured flatness data. The key-value-object mapping strategy is constructed based on disordered and chaotic data; the structure-space mapping strategy is constructed based on the physical structure information of the electrolytic cell.

2. The data processing system according to claim 1, characterized in that, Also includes: The data storage module is used to associate the structured flatness data and reference information data, generate index information, and synchronously store it in the database; The reference information data includes: data key values, measurement values ​​distinguished by data key values, status signals, timestamp information, and a unique identifier for the electrolytic cell.

3. The data processing system according to claim 2, characterized in that, Also includes: The first building module is used to extract all data key values ​​and their corresponding measurement values ​​from disordered and chaotic data; Analyze the numerical distribution characteristics of the measured values ​​under each data key value, whereby the numerical distribution characteristics include the data value range and numerical precision; Based on the numerical distribution characteristics, the physical object category corresponding to each data key value is identified, and key-value-object mapping configuration information containing data key values, physical object categories, and data value ranges is generated to construct the key-value-object mapping strategy; the physical object categories include cathode mesh monitoring point height, anode mesh monitoring point height, and tank frame thickness.

4. The data processing system according to claim 3, characterized in that, The parameter type data also includes slot frame thickness data; the data parsing module is specifically used for: Based on the key-value-object mapping strategy, obtain the physical object category and data value range corresponding to each data key in the key-value-object mapping configuration information; Based on the physical object category, perform boundary checks on the measured values ​​under each data key value to remove abnormal data that exceeds the range of the data values; Based on the numerical accuracy, the verified measurement values ​​are formatted and standardized in units, and parameter type data that characterizes the physical object category and conforms to standard measurement units, including cathode mesh monitoring point height data, anode mesh monitoring point height data, and slot frame thickness data, are output simultaneously, along with the reference information data.

5. The data processing system according to claim 2, characterized in that, Also includes: The second construction module is used to obtain the physical structure information of the electrolytic cell, which includes the number of stiffeners and the number of monitoring points set on each stiffener along its length. Based on the physical structure information, a physical monitoring grid consisting of sequentially numbered stiffeners and monitoring points is determined. Establish a correspondence between the set sequence of cathode mesh monitoring point height data and anode mesh monitoring point height data and the stiffener plate number and monitoring point number in the physical monitoring grid, generate structure-space mapping configuration information containing the correspondence, and construct the structure-space mapping strategy.

6. The data processing system according to claim 5, characterized in that, The space mapping module is specifically used for: Based on the structure-space mapping strategy, the set sequence of cathode grid monitoring point height data and anode grid monitoring point height data is sequentially mapped to the physical monitoring grid; wherein, the set sequence includes multiple first-level subsets arranged in order, each first-level subset corresponding to a rib plate; each first-level subset includes multiple data elements arranged in order, each data element corresponding to a monitoring point on the corresponding rib plate; The height data of the cathode mesh monitoring points and the height data of the anode mesh monitoring points are associated and bound with the corresponding stiffener plate numbers and monitoring point numbers, respectively, to generate the structured flatness data containing spatial location information.

7. The data processing system according to claim 2, characterized in that, Also includes: The data acquisition module is used to convert the proprietary communication protocol data in the PLC control device of the leveling machine into standard industrial network protocol data, and to acquire the standard industrial network protocol data. The standard industrial network protocol data is parsed to extract a structured data packet containing data key values, status signals, timestamp information, unique identifier of the electrolytic cell, and a set of measurement values ​​without physical meaning distinguished by data key values; The structured data packet is subjected to integrity verification to obtain the disordered and chaotic data after the verification is passed.

8. The data processing system according to claim 4, characterized in that, Also includes: The demonstration device is used to extract spatial location information from the structured flatness data based on the unique identifier of the electrolytic cell and the index of the status signal, and to retrieve the parameter type data and the reference information data; The calibration duration is determined based on the timestamp information in the reference information data; Based on the spatial location information, a matrix visualization chart is generated with the rib plate position as the horizontal axis and the monitoring point position as the vertical axis. Based on a preset height range, a preset color is used to mark whether the height data of each monitoring point is within the allowable range, so as to intuitively display the distribution of abnormal monitoring points in the leveling effect of a single electrolytic cell. The slot thickness data in the parameter type data and the leveling length are used as leveling process status parameters and displayed in association with the matrix visualization chart to achieve remote comprehensive monitoring and quality traceability.

9. A data processing method for an electrolytic cell leveling machine, characterized in that, include: Based on a pre-built key-value-object mapping strategy, the disordered and chaotic data collected by the PLC control device of the leveling machine is identified, and the identified parameter type data and reference information data are obtained; the parameter type data includes: cathode mesh monitoring point height data and anode mesh monitoring point height data; Based on a pre-built structure-space mapping strategy, the height data of the cathode grid monitoring points and the height data of the anode grid monitoring points are mapped to the physical monitoring grid of the electrolytic cell to generate structured flatness data. The key-value-object mapping strategy is constructed based on disordered and chaotic data; the structure-space mapping strategy is constructed based on the physical structure information of the electrolytic cell.

10. The method according to claim 9, characterized in that, The method further includes: The structured flatness data and reference information data are associated to generate index information and synchronously stored in the database; The reference information data includes: data key values, measurement values ​​distinguished by data key values, status signals, timestamp information, and a unique identifier for the electrolytic cell.

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