Steel industry data management method, device, equipment and medium

Abstract

The application discloses a steel industry data management method, device, equipment and medium, and relates to the technical field of steel production. The method comprises the following steps: acquiring data element information of multiple production lines in a whole process of steel production; performing standardized definition on the data element information based on a preset data standard, and generating a unified data element set; constructing an equipment model based on the unified data element set; generating a data acquisition point dictionary based on the equipment model; collecting equipment operation data through a preset data acquisition interface based on the data acquisition point dictionary, performing format conversion processing on the equipment operation data, and generating equipment operation data after format conversion processing; storing the equipment operation data after format conversion processing into a database, analyzing the equipment operation data based on a preset data analysis rule, and generating a data analysis report; and performing access control and sharing management on the equipment operation data and the data analysis report based on a permission management strategy.

Description

Technical Field

[0001] This application relates to the field of steel production technology, and in particular to a data management method, apparatus, equipment and medium for the steel industry. Background Technology

[0002] The steel industry's production process encompasses multiple complex production lines, including ironmaking, steelmaking, and rolling, involving numerous physical equipment and heterogeneous data sources. Currently, operational data from these production lines is often scattered across different control systems and information platforms, lacking unified data definitions and format specifications. This leads to semantic ambiguity and inconsistent formats during data transfer across systems and processes, severely impacting data interoperability and overall utilization. Furthermore, traditional data management methods primarily focus on data collection and storage for local or independent systems, failing to systematically organize and standardize data elements from a holistic perspective. They also lack a unified digital model that integrates static equipment attributes, dynamic data, and business knowledge, hindering the full realization of data value and restricting the improvement of refined operations and intelligent decision-making within enterprises. Therefore, a data management method for the steel industry is urgently needed to address the aforementioned problems. Summary of the Invention

[0003] The summary section introduces a series of simplified concepts, which will be further explained in detail in the detailed description section. This summary section is not intended to limit the key and essential technical features of the claimed technical solutions, nor is it intended to determine the scope of protection of the claimed technical solutions.

[0004] Firstly, this application provides a data management method for the steel industry, including:

[0005] Acquire data element information from multiple production lines throughout the entire steel production process;

[0006] Based on preset data standards, the information of the data elements is standardized and defined to generate a unified set of data elements.

[0007] Based on the unified data element set, construct the device object model;

[0008] Based on the device model, a data acquisition point dictionary is generated;

[0009] Based on the data acquisition point dictionary, device operation data is acquired through a preset data acquisition interface, and the device operation data is converted according to the preset data standard to generate device operation data after format conversion.

[0010] The device operating data after format conversion is stored in the database, and the device operating data is analyzed based on preset data analysis rules to generate a data analysis report;

[0011] Based on the access control policy, access control and sharing management are implemented for the device operation data and the data analysis report.

[0012] In some implementations, the preset data standard includes business standards, technical standards, and management standards. The step of standardizing and defining the data element information based on the preset data standard to generate a unified data element set includes:

[0013] Based on the business standards, the data element information is classified to determine the name, dimension, classification and usage information related to production line parameters, which are used as the first type of attribute.

[0014] Based on the aforementioned technical standards, the data element information is classified to determine the data type, data calculation method, point category, data source, and database description information related to the described object, which are then used as the second type of attribute.

[0015] Based on the management standards, the data element information is classified, and the name information related to the enterprise's various levels of departments is determined as the third type of attribute;

[0016] The unified data element set is generated based on the first type of attribute, the second type of attribute, and the third type of attribute.

[0017] In some implementations, the equipment object model includes equipment attribute information, equipment dependency information, and equipment graphical information. The step of constructing the equipment object model based on the unified data element set includes:

[0018] Based on preset device coding rules, a unique device identifier code for the target device is determined;

[0019] Based on the unified data element set, at least one target data element associated with the target device is determined;

[0020] Based on the target data elements, determine the device attribute information of the target device;

[0021] Based on the preset equipment hierarchy, determine the parent equipment identifier and the production line identifier of the target equipment;

[0022] Based on the parent device identifier and the production line identifier, the device dependency information of the target device is determined;

[0023] Based on a preset graphics repository, determine the storage address of the two-dimensional drawings and the storage address of the three-dimensional model associated with the target device;

[0024] Based on the storage address of the two-dimensional drawing and the storage address of the three-dimensional model, the device graphic information of the target device is determined;

[0025] Based on the device attribute information, the device subordinate relationship information, and the device graphic information, a device object model of the target device is constructed.

[0026] In some implementations, the data acquisition point dictionary includes acquisition point codes, data types, sampling frequencies, and storage paths. Generating the data acquisition point dictionary based on the device object model includes:

[0027] Based on the device attribute information, determine the element identifier and element type of the target data element;

[0028] The collection point code is determined based on the unique device identifier, the element identifier, and the preset collection point coding rules;

[0029] Based on the element type, determine the data type of the target data element;

[0030] Based on the production line identifier in the equipment affiliation information, the sampling frequency of the target data element is determined;

[0031] Based on the unique device identifier, the element identifier, and the preset storage path rules, the storage path of the target data element is determined;

[0032] The data collection point dictionary is generated based on the collection point code, the data type, the sampling frequency, and the storage path.

[0033] In some implementations, the step of collecting device operation data through a preset data acquisition interface based on the data acquisition point dictionary, and performing format conversion processing on the device operation data according to the preset data standard to generate format-converted device operation data includes:

[0034] Based on the encoding of the acquisition points, the target acquisition protocol is determined;

[0035] Based on the target acquisition protocol, the preset data acquisition interface is controlled to acquire device operation data;

[0036] Based on the data type, determine the target transmission format;

[0037] Based on the target transmission format, the collected device operation data is encapsulated and processed to generate standardized data packets;

[0038] Based on the data format specifications corresponding to the data type in the preset data standard, the data in the standardized data packet is converted to generate device operation data after format conversion.

[0039] In some implementations, the device operating data after format conversion is stored in a database, and the device operating data is analyzed based on preset data analysis rules to generate a data analysis report, including:

[0040] Based on the storage path, determine the target database table for the device operation data after format conversion;

[0041] Based on the sampling frequency, determine the storage strategy for the device operation data;

[0042] Based on the storage strategy, the device operating data after the format conversion process is stored in the target database table;

[0043] Based on the preset data analysis rules and the device affiliation information, the set of associated devices to be analyzed is determined;

[0044] Based on the associated device set, obtain the target device operation dataset from the target database table;

[0045] Perform trend analysis, statistical analysis, or anomaly detection analysis on the target device's operational dataset to generate analysis result data;

[0046] Based on the analysis results, the data analysis report is generated.

[0047] In some implementations, the access control and sharing management of the device operation data and the data analysis report based on the permission management policy includes:

[0048] Based on the aforementioned access control strategy, the user role and department affiliation of the current user are determined;

[0049] Based on the user role and the department affiliation, determine the current user's data access permissions to the device operation data and the data analysis report;

[0050] Based on the data sensitivity levels of the equipment operation data and the data analysis report, the scope of data sharing between the equipment operation data and the data analysis report is determined;

[0051] Based on the data access permissions and the data sharing scope, control the current user's access to and sharing of the device operation data and the data analysis report;

[0052] Based on the access operation and the sharing operation, a data access log and a data sharing log are generated;

[0053] Based on the data access log and the data sharing log, traceability management of data operation behavior can be achieved.

[0054] Secondly, this application proposes a data management device for the steel industry, comprising:

[0055] The data acquisition unit is used to acquire data element information from multiple production lines throughout the entire steel production process.

[0056] The standard definition unit is used to standardize and define the information of the data elements based on a preset data standard, and generate a unified set of data elements;

[0057] The model building unit is used to build a device model based on the unified data element set;

[0058] The dictionary generation unit is used to generate a data acquisition point dictionary based on the device object model;

[0059] The data acquisition unit is used to acquire device operation data through a preset data acquisition interface based on the data acquisition point dictionary, and to perform format conversion processing on the device operation data according to the preset data standard to generate format-converted device operation data.

[0060] The data analysis unit is used to store the device operation data after format conversion into the database, and to analyze the device operation data based on preset data analysis rules to generate a data analysis report;

[0061] The access control unit is used to perform access control and sharing management on the device operation data and the data analysis report based on the access control policy.

[0062] Thirdly, an electronic device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program stored in the memory to implement the steps of the steel industry data management method of any one of the first aspects.

[0063] Fourthly, this application also proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the steel industry data management method of any one of the first aspects.

[0064] In summary, the steel industry data management method proposed in this application effectively solves the consistency and interoperability problems caused by inconsistent data definitions and formats by acquiring data element information from multiple production lines throughout the entire steel production process and standardizing its definition based on preset data standards to form a unified data element set. Furthermore, based on this unified set, an equipment object model is constructed, establishing a digital image of physical equipment containing attributes, hierarchical relationships, and graphical information, laying the foundation for accurate data association and integration. By generating a data collection point dictionary based on the object model, the standardization and automation of the collection process are achieved, ensuring the standardization and efficiency of data acquisition. Finally, through standardized processing, storage, analysis, and policy-based access control of the collected data, a systematic and standardized management of the entire lifecycle of steel industry data resources is achieved, improving data quality, availability, and security, and providing data support for production process monitoring, optimization, and intelligent decision-making. Attached Figure Description

[0065] Various other advantages and benefits will become apparent to those skilled in the art upon reading the following detailed description of preferred embodiments. The accompanying drawings are for illustrative purposes only and are not intended to limit this specification. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0066] Figure 1 A schematic diagram of a data management method for the steel industry provided in this application embodiment;

[0067] Figure 2 A schematic diagram of a data management device for the steel industry provided in this application embodiment;

[0068] Figure 3 This is a schematic diagram of the structure of an electronic data management device for the steel industry, provided as an embodiment of this application. Detailed Implementation

[0069] The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus. The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them.

[0070] Please see Figure 1 This is a schematic diagram of a data management method for the steel industry provided in an embodiment of this application, which may specifically include:

[0071] S110. Obtain data element information from multiple production lines throughout the entire steel production process;

[0072] For example, step S110 aims to comprehensively acquire data elements distributed across various production lines, such as ironmaking, steelmaking, and rolling, throughout the entire steel production process, from raw material handling to final product formation. This process gathers various parameters, status indicators, and business data that were originally scattered across different processes, equipment, and control systems to form an initial list of data elements.

[0073] S120. Based on preset data standards, standardize and define data element information to generate a unified set of data elements;

[0074] For example, step S120 processes and standardizes the raw data element information acquired in the previous stage based on predefined comprehensive data standards covering business, technology, and management dimensions. This process strictly defines and constrains the key attributes of each data element, such as its name, semantics, format, and attribution, eliminating ambiguity and ensuring that the data is understood and used in a unified and unambiguous manner throughout the enterprise, ultimately producing a standardized set of data elements.

[0075] S130. Construct a device model based on a unified set of data elements;

[0076] For example, step S130 aims to create a corresponding digital model for steel production equipment in the physical world. By associating and binding standardized data elements with specific equipment entities, a structured digital representation is constructed that can describe the equipment's static attributes, dynamic data parameters, subordinate relationships in the production line, and related graphical information, thereby laying the model foundation for the management, traceability, and application of equipment data.

[0077] S140. Generate a dictionary of data acquisition points based on the equipment object model;

[0078] For example, step S140 generates a data acquisition point dictionary to guide data acquisition work based on the constructed device object model. This dictionary acts as a bridge between the object model and specific data acquisition activities, transforming the device attributes, data elements and their relationships defined in the object model into data acquisition task instructions, which include a unique acquisition identifier for each data point, data type requirements, acquisition frequency settings and storage location guidance.

[0079] S150. Based on the data acquisition point dictionary, collect equipment operation data through a preset data acquisition interface, and perform format conversion processing on the equipment operation data according to the preset data standard to generate equipment operation data after format conversion.

[0080] For example, step S150 automatically collects raw equipment operation data from the corresponding physical devices or control systems through various preset data acquisition interfaces, based on the standardized acquisition requirements defined in the data acquisition point dictionary. The acquisition process follows constraints such as the identifier, protocol, and frequency specified for each data point in the dictionary. Subsequently, according to the data format, units, and precision specifications stipulated in the preset data standards, the collected raw operation data is cleaned, converted, and packaged, transforming it into standardized data with a unified format, clear semantics, and compliance with subsequent storage and analysis requirements.

[0081] S160. Store the converted device operating data in the database, and analyze the device operating data based on preset data analysis rules to generate a data analysis report.

[0082] For example, step S160 stores the standardized equipment operation data, after format conversion, into the corresponding database system according to its data characteristics and access requirements. Different storage strategies are adopted for high-frequency real-time data and long-term historical data to balance performance and cost. Then, based on various preset data analysis rules, the stored equipment operation data is automatically mined and calculated to identify its inherent trends, statistical regularities or abnormal states. Finally, the core results obtained from the analysis are output in the form of a structured report to provide direct information support for production monitoring and decision-making.

[0083] S170. Based on the access control policy, implement access control and sharing management for device operation data and data analysis reports.

[0084] For example, step S170 implements access control and sharing management for the stored device operation data and generated data analysis reports based on a pre-set permission management policy. This process identifies the current user's role and department to determine their specific operational permissions for each data resource and defines the scope of sharing based on the sensitivity of the data itself. Based on these permissions and scope controls, user data access and sharing behavior is managed, and all operations are logged to achieve a secure and controllable data environment and traceability of operational behavior.

[0085] In summary, this application's embodiments effectively solve the consistency and interoperability problems caused by inconsistent data definitions and formats by acquiring data element information from multiple production lines throughout the entire steel production process and standardizing and defining it based on preset data standards to form a unified data element set. Furthermore, based on this unified set, an equipment object model is constructed, laying the foundation for establishing a digital image of physical equipment containing attributes, hierarchical relationships, and graphical information. By generating a data collection point dictionary based on the object model, the standardization and automation of the data collection process are achieved, ensuring the standardization and efficiency of data acquisition. Finally, through standardized processing, storage, analysis, and policy-based access control of the collected data, a systematic and standardized management of the entire lifecycle of data resources in the steel industry is achieved, improving data quality, availability, and security.

[0086] In some instances, pre-defined data standards include business standards, technical standards, and management standards. Based on these pre-defined data standards, data element information is standardized and defined to generate a unified set of data elements, including:

[0087] Based on business standards, data element information is classified, and the name, dimension, classification and usage information related to production line parameters are determined as the first type of attribute;

[0088] Based on technical standards, data element information is classified to determine the data type, data calculation method, point category, data source, and database description information related to the described object, which are then used as the second type of attribute.

[0089] Based on management standards, data element information is classified, and name information related to the enterprise's various levels of departments is identified as the third type of attribute;

[0090] A unified set of data elements is generated based on the first type of attribute, the second type of attribute, and the third type of attribute.

[0091] For example, based on business standards, data element information is categorized to determine the name, dimension, classification, and usage information related to production line parameters, and these are classified as first-category attributes. Business standards, as pre-defined normative bases, primarily constrain and define data elements from a production business perspective. For instance, the standard name for the parameter "blast furnace hot blast temperature" is clearly defined, its dimension is specified as degrees Celsius (°C), it is classified as a process parameter, and its specific use for monitoring blast furnace combustion efficiency is defined. By performing this type of attribute assignment operation based on business standards on each data element, the semantic consistency and application standardization of the data at the business level are ensured.

[0092] Based on technical standards, data element information is further classified and processed to determine the data type, data calculation method, point category, data source, and database description information related to the described object, and categorize them into the second type of attribute. Technical standards, as another pre-set normative basis, focus on defining the data from the perspective of data technology implementation. For example, they specify that the data type of "blast furnace hot blast temperature" is floating-point, its data calculation must meet the accuracy requirement of ±1℃, its point category is clearly defined as analog input, its data source is identified as the temperature sensor at the hot blast stove outlet, and its storage table name in the time-series database is specified. This process ensures the uniformity and processability of the data at the technical implementation level.

[0093] Based on management standards, the data element information undergoes final classification processing to determine the name information related to various levels of organizational departments within the enterprise, as well as the production line affiliation and equipment association information of the data elements they manage, and classifies them into the third category of attributes. Management standards serve as pre-set management normative bases, aiming to locate data elements from an organizational management perspective. For example, it clarifies that the data element "blast furnace hot blast temperature" is managed by the "Ironmaking Plant - Blast Furnace Workshop - Blowing Section," and belongs to the "Ironmaking - Blast Furnace - Blowing System" production line process system, while also associating it with the specific equipment "No. 1 Blast Furnace Hot Blow Stove." Finally, by integrating the first, second, and third categories of attributes, a unified set of data elements is generated. Each data element in this set possesses complete and standardized business, technical, and management attribute definitions.

[0094] In summary, the embodiments of this application, through the standardized definition process of data element information based on the three preset standards of business, technology and management, generate a unified set of data elements that effectively eliminates semantic ambiguity and format conflicts in cross-system and cross-production line data interaction. This provides a solid and consistent data foundation for subsequent equipment model construction, data collection and integrated application, and improves the standardization and data quality of the entire process data management.

[0095] In some instances, the equipment object model includes equipment attribute information, equipment dependency information, and equipment graphical information. Based on a unified set of data elements, the equipment object model is constructed, including:

[0096] Based on preset device coding rules, a unique device identifier code for the target device is determined;

[0097] Based on a unified set of data elements, identify at least one target data element associated with the target device;

[0098] Based on the target data elements, determine the device attribute information of the target device;

[0099] Based on the preset equipment hierarchy, determine the parent equipment identifier and the production line identifier of the target equipment;

[0100] Based on the parent device identifier and the production line identifier, determine the device affiliation information of the target device;

[0101] Based on the preset graphics repository, determine the storage address of the two-dimensional drawings and the storage address of the three-dimensional model associated with the target device;

[0102] Based on the storage address of the two-dimensional drawing and the storage address of the three-dimensional model, the equipment graphic information of the target device is determined;

[0103] Based on equipment attribute information, equipment subordination information, and equipment graphic information, construct an equipment object model of the target equipment.

[0104] For example, a unique equipment identifier is determined for the target equipment based on a preset equipment coding rule. The preset equipment coding rule adopts a combined structure of "production line code - equipment type code - serial number." For instance, the production line code is represented by two uppercase letters (e.g., LT for ironmaking production line), the equipment type code consists of three letters and numbers (e.g., BLF for blast furnace), and the serial number is three digits (e.g., 001). According to this rule, a unique equipment identifier is generated for each physical piece of equipment across the entire enterprise. For example, "LT-BLF-001" represents blast furnace No. 1 in the ironmaking production line. This code serves as the unique identifier of the equipment in the digital space and is the foundation for constructing the equipment physical model.

[0105] Based on a unified data element set, at least one target data element associated with the target equipment is identified. The unified data element set is a standardized collection of data elements. By querying this set, all data elements related to the target equipment are filtered and bound according to equipment type, production line, and process. For example, for equipment "LT-BLF-001," its related data elements, such as "blast furnace hot blast temperature," "blast furnace top pressure," and "coal injection rate," are associated from the unified data element set, establishing a mapping relationship between the equipment and the data elements.

[0106] Based on the target data elements, the equipment attribute information of the target equipment is determined. Equipment attribute information describes the static parameters and dynamic data points of the equipment, and its content is directly derived from the standard definitions of the associated target data elements. Standardized attributes such as name, dimension, data type, value range, and unit defined in the target data elements are assigned to the target equipment model, thereby forming structured equipment attribute information. For example, the accuracy (±1℃), unit (℃), and value range (800-1300℃) of the data element "blast furnace hot blast temperature" are completely inherited as an attribute of the equipment "LT-BLF-001".

[0107] Based on the preset equipment hierarchy, the parent equipment identifier and the production line identifier of the target equipment are determined. The preset equipment hierarchy defines the organizational and process hierarchy among equipment in the enterprise, and is usually described using a tree or network structure. According to this preset relationship, the direct parent equipment (e.g., the parent of a blast furnace might be the ironmaking workshop) and the identifier of its production line (e.g., the ironmaking production line) are determined for the current target equipment. For example, the parent equipment identifier of equipment "LT-BLF-001" is determined to be "LT-Shop-03" (Ironmaking Workshop No. 3), and its production line identifier is "LT" (Ironmaking Production Line).

[0108] Based on the parent equipment identifier and its production line identifier, the equipment dependency information of the target equipment is determined. This dependency information clarifies the equipment's position within the overall production process and equipment organizational structure. By combining the parent equipment identifier and its production line identifier, and further expanding to include the identifiers of preceding and succeeding production line equipment (prevLine) and next line equipment (nextLine), a relationship expressing the process flow sequence is constructed, thus forming complete equipment dependency information. For example, the prevLine of equipment "LT-BLF-001" is recorded as "SJ-SIN-005" (Sintering Machine No. 5), and the nextLine is "LG-LD-002" (Converter No. 2).

[0109] Based on a pre-defined graphics repository, the storage addresses of the 2D drawings and 3D models associated with the target device are determined. The pre-defined graphics repository is a centralized file storage system pre-established and maintained by the enterprise, used to store and manage 2D engineering drawings (e.g., DWG format) and 3D digital models (e.g., FBX format) for all devices. Using the target device's unique device identifier, a search and matching process is performed within the pre-defined graphics repository to locate the graphics file associated with that device and obtain its exact storage address (e.g., URL or file path).

[0110] Based on the storage addresses of 2D drawings and 3D models, the equipment graphic information of the target equipment is determined. Equipment graphic information is a digital description of the equipment's visualization and spatial form. The obtained storage addresses of the 2D drawings and 3D models, along with any version information, coordinate annotations of key components (such as the coordinates of specific segments of the blast furnace body in the 3D model), and other metadata, are integrated to constitute the equipment graphic information of the target equipment.

[0111] Based on equipment attribute information, equipment dependency information, and equipment graphic information, a device object model of the target equipment is constructed. The three core types of information determined in the preceding steps—equipment attribute information (describing what the equipment is and what data it contains), equipment dependency information (describing where the equipment is located and with whom it is associated), and equipment graphic information (describing what the equipment looks like)—are integrated and encapsulated to form a complete, structured digital entity, namely, the device object model of the target equipment. This device object model serves as a comprehensive digital mapping of the physical equipment in the information space.

[0112] In summary, this application constructs an equipment physical model through the aforementioned steps, integrating and standardizing the previously isolated and heterogeneous static equipment information, dynamic data parameters, organizational hierarchical relationships, and visualization models. This effectively solves the model fragmentation problem caused by scattered equipment information and inconsistent descriptions. The constructed equipment physical model not only ensures the consistency and accuracy of equipment data during collection, transmission, and application, but also lays a data foundation for realizing full lifecycle management of equipment and optimization of production processes by establishing equipment-data-graphical relationships. This enhances the level of refinement in equipment management and the ability of data-driven decision-making in steel enterprises.

[0113] In some instances, the data acquisition point dictionary includes acquisition point codes, data types, sampling frequencies, and storage paths. Based on the device object model, the data acquisition point dictionary is generated, including:

[0114] Based on device attribute information, determine the element identifier and element type of the target data element;

[0115] The collection point code is determined based on the unique device identifier, element identifier, and preset collection point coding rules;

[0116] Based on the element type, determine the data type of the target data element;

[0117] Based on the production line identifier in the equipment dependency information, determine the sampling frequency of the target data element;

[0118] The storage path of the target data element is determined based on the unique device identifier, element identifier, and preset storage path rules.

[0119] A data collection point dictionary is generated based on the collection point code, data type, sampling frequency, and storage path.

[0120] For example, based on equipment attribute information, the element identifier and element type of the target data element are determined. Equipment attribute information is an important component of the equipment object model, derived from standardized data element definitions associated with the target equipment in a unified data element set. In this step, the equipment attribute information contained in the equipment object model is parsed, extracting the unique element identifier corresponding to each attribute (e.g., data element ID "DE-LT-001") and the element type to which the attribute belongs (e.g., physical quantity types such as "temperature" and "pressure", or enumeration types such as "equipment status"). This process ensures that subsequent dictionary generation can accurately associate with every data point defined in the equipment object model.

[0121] Based on the unique device identifier, element identifier, and preset acquisition point coding rules, the acquisition point code is determined. The preset acquisition point coding rules define the organization structure of the code, adopting the format "{device code}.{data element abbreviation}.{sensor number}". The unique device identifier of the target device (e.g., "LT-BLF-001"), the standard abbreviation corresponding to the element identifier extracted from the device attribute information (e.g., "HAT" for hot air temperature), and the sensor number used to distinguish multiple acquisition points of the same type of data on the same device (e.g., "01") are combined to generate a globally unique acquisition point code (e.g., "LT-BLF-001.HAT.01"). This code serves as an index for data acquisition, transmission, storage, and querying.

[0122] Based on the element type, the data type of the target data element is determined. The element type indicates the basic properties of the data. According to the preset mapping relationship from element type to data type, the data type that the target data element should adopt at the technical implementation level is determined. For example, if the element type is "temperature", the mapped data type is "floating-point (FLOAT)"; if the element type is "device status", the mapped data type may be "integer (INT)" or "character (STRING)", and associated with its preset encoding rules (such as R / S / M corresponding to operation / shutdown / maintenance). This determination process ensures the standardization and consistency of data format during subsequent data acquisition, encapsulation, and storage.

[0123] Based on the production line identifier in the equipment dependency information, determine the sampling frequency of the target data element. The equipment dependency information includes the identifier of the production line to which the equipment belongs (e.g., "LT" represents the ironmaking production line). Maintain a preset production line-sampling frequency mapping table, which defines the suggested or required sampling frequencies for various data elements under different production lines, different processes, or different equipment types. Based on the production line identifier of the target equipment, and combined with the importance or process requirements of the data element (e.g., the difference between high-frequency rolling force data and low-frequency temperature data), query and determine the sampling frequency of the target data element from this mapping table (e.g., the sampling frequency for blast furnace hot blast temperature is 10Hz, and the sampling frequency for furnace top pressure is 1Hz).

[0124] Based on the unique device identifier, element identifier, and preset storage path rules, the storage path of the target data element is determined. The preset storage path rules define the naming conventions and organization of data tables in the time-series database, typically using "{Device Model ID}_{Data Element Abbreviation}" or similar rules. The unique device identifier of the target device (formatted, such as converting hyphens to underscores: "lt_blf_001") and the standard abbreviation corresponding to the element identifier (such as "hat") are combined to generate the target database table name corresponding to the data element (such as "lt_blf_001_hat"). This table name constitutes the core part of its storage path, indicating the specific storage location of the data in the database.

[0125] Based on the collection point code, data type, sampling frequency, and storage path, a data collection point dictionary is generated. The four metadata items—collection point code, data type, sampling frequency, and storage path—determined in the previous steps are associated and integrated to form a complete, standardized record for each data point to be collected. The collection of records for all data points constitutes the data collection point dictionary. This dictionary serves as a globally unified metadata list, ensuring that the collection task can be executed accurately and efficiently.

[0126] In summary, this application's embodiments generate a data acquisition point dictionary through the above steps, transforming the static attribute information defined in the equipment physical model into executable data acquisition task instructions, thus achieving standardized, automated, and refined management of data acquisition requirements. This dictionary ensures the uniqueness and standardization of data acquisition point naming across the entire plant, clarifies the technical parameters and storage requirements of each data point, and provides a foundation for unified access, efficient storage, and consistent processing of heterogeneous data. It effectively avoids data errors, omissions, or redundancy caused by chaotic acquisition point information and unclear parameters, thereby improving the quality and efficiency of data acquisition work.

[0127] In some instances, based on a data acquisition point dictionary, device operation data is collected through a preset data acquisition interface, and the data is then format-converted according to a preset data standard to generate format-converted device operation data, including:

[0128] The target acquisition protocol is determined based on the acquisition point coding;

[0129] Based on the target acquisition protocol, control the preset data acquisition interface to acquire device operation data;

[0130] Determine the target transmission format based on the data type;

[0131] Based on the target transmission format, the collected device operation data is encapsulated and processed to generate standardized data packets;

[0132] Based on the data format specifications corresponding to the data type in the preset data standard, the data in the standardized data packet is converted to generate the device operation data after format conversion.

[0133] For example, based on the acquisition point codes defined in the data acquisition point dictionary, a target acquisition protocol matching the acquisition point is determined. The acquisition point code serves as a globally unique identifier for the data point, and its encoding structure implicitly contains or associates key information required for protocol selection. For instance, equipment type fragments or production line identifiers in the code can be mapped to a preset protocol rule table. A suitable acquisition protocol is assigned to each acquisition point. For example, for data points from high-temperature critical equipment such as blast furnaces, the highly reliable OPC UA protocol is selected based on rules; for data points from high-frequency acquisition equipment such as rolling mills, the lightweight MQTT protocol is selected.

[0134] Based on the determined target acquisition protocol, the corresponding preset data acquisition interface in the control system performs the acquisition operation of equipment operation data. The preset data acquisition interface is a pre-developed and integrated software component or driver that supports various industrial communication protocols (such as OPC UA, MQTT, Modbus, etc.). The corresponding acquisition interface is called according to the target acquisition protocol, and necessary parameters such as the acquisition point code are provided. The interface is then controlled to read the raw equipment operation data values ​​from the corresponding physical device, sensor, or control system (such as PLC) according to the technical requirements such as the sampling frequency defined in the data acquisition point dictionary.

[0135] Based on the data type defined in the data collection point dictionary, the appropriate target transmission format is determined. The data type (e.g., floating-point, integer, character) dictates the serialization and encapsulation format used during transmission to ensure clear structure and error-free parsing. Pre-defined format mappings are queried based on the data type. For example, lightweight and universal JSON is chosen as the target transmission format for most numerical and status data to meet the needs of structured data transmission and cross-platform parsing.

[0136] Based on the determined target transmission format, the raw device operation data acquired through the acquisition interface is standardized and encapsulated to generate standardized data packets. This encapsulation process organizes the acquired data values, their corresponding acquisition point codes, data quality stamps, acquisition timestamps, and other information into a structured data object according to the target transmission format (such as JSON). For example, the acquired value 1250.3 (°C), acquisition point code "LT-BLF-001.HAT.01", timestamp "2025-09-10 15:30:00.123", and quality stamp "Good" are encapsulated into a standardized data packet conforming to JSON format.

[0137] Based on the data format specifications corresponding to the data type in the preset data standard, the data in the standardized data packet undergoes final format conversion processing to generate format-converted equipment operation data. The technical standard section of the preset data standard clearly defines the data format for each data type (such as numerical precision, decimal places, units, encoding of enumerated values, etc.). The standardized data packet is parsed, and data values ​​are verified and converted according to these specifications. For example, it ensures that temperature values ​​retain one decimal place, pressure values ​​are converted to megapascals (MPa), and equipment status is converted from strings to preset codes (such as "running" to "R"). This process ensures that all data fully conforms to the enterprise's unified standard before entering storage and analysis, eliminating the problem of inconsistent source data formats.

[0138] In summary, this application's embodiments, through the aforementioned automated data acquisition and format conversion process driven by a data acquisition point dictionary and following preset data standards, realize the transformation of raw equipment operation data collected from heterogeneous data sources into standardized and normalized data. This process ensures the consistency of data flowing into subsequent stages, improves data quality, reliability, and interoperability, and effectively supports the needs of steel enterprises for management and intelligent decision-making based on a unified data model.

[0139] In some instances, the converted device operating data is stored in a database, and the data is analyzed based on preset data analysis rules to generate a data analysis report, including:

[0140] Based on the storage path, determine the target database table for the device operation data after format conversion;

[0141] Based on the sampling frequency, determine the storage strategy for device operation data;

[0142] Based on the storage strategy, the device operating data after format conversion is stored in the target database table;

[0143] Based on preset data analysis rules and equipment affiliation information, determine the set of related equipment to be analyzed;

[0144] Based on the associated device set, obtain the target device runtime dataset from the target database table;

[0145] Perform trend analysis, statistical analysis, or anomaly detection analysis on the target equipment's operational dataset, and generate analysis results data;

[0146] A data analysis report is generated based on the analysis results.

[0147] For example, based on the storage path, the target database table for the format-converted device operation data is determined. The storage path follows a preset naming rule in the data acquisition point dictionary (e.g., "{Device Model ID}_{Data Element Abbreviation}"), and this path directly maps to a specific data table in the time-series database. By parsing the metadata information carried by the format-converted device operation data, or directly based on its corresponding acquisition point code, the target database table to which the data should be written can be determined by querying the data acquisition point dictionary. For example, device operation data with acquisition point code "LT-BLF-001.HAT.01" points to the database table "lt_blf_001_hat" in its storage path. This process ensures that massive amounts of time-series data can be automatically and accurately routed to their predetermined storage locations.

[0148] Based on the sampling frequency, a storage strategy for equipment operation data is determined. The sampling frequency is defined as an inherent attribute of each data acquisition point in the data acquisition point dictionary; its value directly reflects the data generation rate and the requirements for storage and access performance. According to the preset sampling frequency-storage strategy mapping rules, appropriate storage strategies are dynamically allocated for data of different frequencies. For example, for high-frequency data with a sampling frequency higher than 1Hz (such as rolling mill rolling force data), a high-speed cache database (such as a Redis cluster) is used for short-term temporary storage to meet the requirements of real-time monitoring and millisecond-level response; for low-frequency data with a sampling frequency lower than or equal to 1Hz (such as blast furnace top temperature), it is determined to directly store it in a time-series database suitable for long-term storage (such as TDengine). This differentiated storage strategy achieves a balance between storage resource optimization and access performance assurance.

[0149] Based on the storage strategy, the format-converted device operating data is stored in the target database table. The database operation interface matching the storage strategy is invoked to perform data write operations. For data to be stored in the cache database, it is partitioned by device model ID and an automatic expiration time is set; for data to be stored in the time-series database, it is stored using a primary key composed of its timestamp and device model ID, and optimization techniques such as time window compression can be applied. This step ensures that all standardized device operating data can be persisted to a suitable database environment according to its characteristics, guaranteeing data integrity and availability.

[0150] Based on preset data analysis rules and equipment dependency information, the set of related equipment to be analyzed is determined. The preset data analysis rules define the objectives, scope, and logic of the analysis task. They may focus on the status assessment of a single piece of equipment, or involve the comprehensive analysis of multiple equipment groups with process connections. The analysis rules are parsed, and combined with the equipment dependency information defined in the equipment model (such as the process flow sequence relationship established through the `prevLine` and `nextLine` fields), all equipment related to the analysis objective is identified, thus determining a set of related equipment to be analyzed. For example, if the analysis rule requires analyzing "the impact of blast furnace hot blast temperature on sinter quality," then the associated blast furnace equipment and its corresponding preceding sintering machine equipment need to be determined based on the dependency information, forming a group of equipment to be analyzed.

[0151] Based on the associated device set, the target device operation dataset is obtained from the target database table. A data query statement is constructed using the unique identifier of each device in the associated device set, as well as the data elements to be analyzed and the time range (specified by the analysis rules). A query is then initiated against the corresponding target database table (determined by the storage path) to retrieve historical or real-time device operation data that meets the criteria, thereby aggregating the target device operation dataset required for this analysis.

[0152] Perform trend analysis, statistical analysis, or anomaly detection analysis on the target equipment's operational dataset to generate analysis results data. Invoke embedded or integrated analysis algorithm engines to process the acquired target equipment operational dataset according to the specific analysis type (such as trend analysis, statistical analysis, anomaly detection analysis, or a combination thereof) specified by preset data analysis rules. For example, perform trend analysis to plot curves of key parameters over time; perform statistical analysis to calculate statistics such as the mean and standard deviation of the data; perform threshold-based or machine learning algorithm-based anomaly detection to identify data points deviating from normal patterns. Finally, output the results of various analysis operations in a structured manner as analysis results data.

[0153] Based on the analysis results, a data analysis report is generated. Following a preset report template, key findings, calculated indicators, and chart visualizations from the analysis results are automatically integrated and rendered to generate a structured data analysis report. This report is presented in a readable format (such as PDF or HTML) and aims to support production decision-makers, process engineers, or equipment maintenance personnel. For example, the report may include conclusions and recommendations on the correlation between blast furnace hot blast temperature and sinter FeO content.

[0154] In summary, this application's embodiments achieve the mining of equipment operation data by combining standardized stored data with a rule-based automated analysis process. Differentiated storage strategies based on data characteristics optimize resource utilization and ensure access performance; determining the analysis set based on equipment relationships ensures the accuracy and process relevance of the analysis scope; and performing diverse data analyses and generating standardized reports transforms raw data into decision-making information, improving the level of data-driven intelligent operation.

[0155] In some instances, access control and sharing management are implemented for device operation data and data analysis reports based on access control policies, including:

[0156] Based on the access control strategy, determine the current user's user role and department affiliation;

[0157] Based on user roles and department affiliations, determine the current user's data access permissions for equipment operation data and data analysis reports;

[0158] Based on the data sensitivity level of equipment operation data and data analysis reports, determine the scope of data sharing for equipment operation data and data analysis reports;

[0159] Based on data access permissions and data sharing scope, control the current user's access to and sharing of device operation data and data analysis reports;

[0160] Based on access and sharing operations, generate data access logs and data sharing logs;

[0161] Based on data access logs and data sharing logs, traceability management of data operation behavior can be achieved.

[0162] For example, to implement access control and sharing management of equipment operation data and data analysis reports based on an access control policy, the first step is to rely on the enterprise's pre-defined and maintained access control policy, which defines the operational scope of different roles and departments. By identifying the identity credentials of the currently logged-in user, the system queries their corresponding user role (such as equipment operator, process engineer, system administrator) and their specific department (such as ironmaking plant, steel rolling mill), thereby determining the user's role and departmental affiliation. This step is the foundation for implementing access control and ensures the accuracy of subsequent access determinations.

[0163] After clarifying the current user's identity attributes, the user's role and department affiliation are matched with the preset permission entries in the policy based on the Access Control List (ACL) or Role-Based Access Control (RBAC) rules defined in the permission management policy. This process determines the specific data access permissions the current user has for the stored device operation data and generated data analysis reports, such as whether they have read-only, read-write, or no access permissions. This step maps the user identity to specific, executable data operation permissions.

[0164] Simultaneously, the data sharing scope of each piece of equipment operating data and data analysis report needs to be determined based on the data sensitivity level of the equipment operation data and data analysis reports themselves (this level is usually preset and assigned during the data standardization definition stage or the equipment physical model construction stage; for example, core process parameters of the blast furnace are marked as "high sensitivity," and general equipment status data are marked as "medium sensitivity"). The data sharing scope specifies which other departments or roles can access or use the data under what conditions; for example, "high sensitivity" data is limited to internal access within the production line, while "medium sensitivity" data can be shared between related production lines.

[0165] Based on the aforementioned data access permissions and data sharing scope, real-time control is implemented over current user access and sharing operations. When a user attempts to perform an operation, it is verified whether their operation permission is within the scope of their data access permissions, and whether the target of their requested sharing is within the permitted area of ​​the data sharing scope. For example, controlling whether an engineer in a steel rolling mill can access the real-time operating data of a blast furnace in an ironmaking plant (access control), and also controlling whether they can share an analytical report containing key indicators of the entire plant with unauthorized departments (sharing control).

[0166] The system records all successful and denied access and sharing operations by users. Each data query, download, and preview attempt generates a data access log, recording the operator, operation time, target, operation type, and result. Each data sharing initiation, approval, and execution generates a data sharing log, recording the sharing parties, shared content, sharing time, and operation result. Based on these data access and sharing logs, complete traceability and auditing of historical data operations can be achieved. When a data security incident occurs or an operation audit is required, administrators can query these logs to locate the source of the operation and reconstruct the process, thereby achieving effective security tracing and accountability.

[0167] In summary, this application's embodiments establish a multi-layered data security protection system that integrates with the enterprise's organizational structure and management needs. By combining user identity, preset permission policies, data sensitivity attributes, and operation behavior logs, it achieves control over access and sharing behavior throughout the entire lifecycle of steel production data. This not only effectively prevents unauthorized access and abuse of data, ensuring the security and confidentiality of core production process data, but also promotes the orderly flow and value utilization of data under the premise of security and compliance through a standardized sharing mechanism, providing support for the enterprise's data security governance.

[0168] Please see Figure 2 The diagram below illustrates the structure of a data management device for the steel industry, as provided in this application embodiment, and includes:

[0169] Data acquisition unit 21 is used to acquire data element information from multiple production lines throughout the entire steel production process;

[0170] Standard definition unit 22 is used to standardize the definition of data element information based on preset data standards and generate a unified set of data elements;

[0171] Model building unit 23 is used to build a device model based on a unified set of data elements;

[0172] The dictionary generation unit 24 is used to generate a dictionary of data acquisition points based on the device object model;

[0173] The data acquisition unit 25 is used to acquire equipment operation data through a preset data acquisition interface based on a data acquisition point dictionary, and to perform format conversion processing on the equipment operation data according to a preset data standard to generate equipment operation data after format conversion.

[0174] The data analysis unit 26 is used to store the device operation data after format conversion into the database, and to analyze the device operation data based on preset data analysis rules to generate a data analysis report;

[0175] The access control unit 27 is used to perform access control and sharing management of device operation data and data analysis reports based on access control policies.

[0176] Please see Figure 3 This application also provides an electronic device 300, including a memory 310, a processor 320, and a computer program 311 stored in the memory 310 and executable on the processor. When the processor 320 executes the computer program 311, it implements the steps of the data management method for the steel industry.

[0177] Since the electronic device described in this embodiment is the device used to implement a data management device for the steel industry in this application embodiment, those skilled in the art can understand the specific implementation method and various variations of the electronic device in this embodiment based on the method described in this application embodiment. Therefore, how the electronic device implements the method in this application embodiment will not be described in detail here. Any device used by those skilled in the art to implement the method in this application embodiment is within the scope of protection of this application.

[0178] In practice, when the computer program 311 is executed by the processor, it can implement any of the embodiments corresponding to the first aspect.

[0179] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0180] Those skilled in the art will understand that embodiments of this application can provide methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-readable storage media containing computer-readable program code.

[0181] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0182] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0183] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0184] This application also provides a computer program product, which includes computer software instructions that, when executed on a processing device, cause the processing device to perform... Figure 1 The flowchart of a data management method for the steel industry in the corresponding embodiment.

[0185] A computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the flow or function according to the embodiments of this application is generated. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transferred from one computer-readable storage medium to another. For example, computer instructions may be transferred from one website, computer, server, or data center to another website, computer, server, or data center via wired or wireless means. The computer-readable storage medium may be any usable medium that a computer can store or a data storage device such as a server or data center that integrates one or more usable media. The usable medium may be a magnetic medium, an optical medium, or a semiconductor medium, etc.

[0186] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0187] In the several embodiments provided in this application, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Furthermore, the mutual couplings or direct couplings or communication connections shown or discussed may be indirect couplings or communication connections through some interfaces, devices, or units, and may be electrical, mechanical, or other forms.

[0188] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0189] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in the form of hardware and / or software functional units.

[0190] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device to execute all or part of the steps of the methods in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory, magnetic disks, or optical disks.

[0191] The above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

[0192] Although preferred embodiments have been described in this specification, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications that fall outside the scope of this specification.

[0193] Obviously, those skilled in the art can make various modifications to this specification without departing from its spirit and scope. Therefore, this specification also intends to include any modifications that fall within the scope of the claims and their equivalents.

Claims

1. A data management method for the steel industry, characterized in that, include: Acquire data element information from multiple production lines throughout the entire steel production process; Based on preset data standards, the information of the data elements is standardized and defined to generate a unified set of data elements. Based on the unified data element set, a device object model is constructed, wherein the device object model includes device attribute information, device subordinate relationship information, and device graphic information; Based on the device model, a data acquisition point dictionary is generated, which includes acquisition point code, data type, sampling frequency and storage path; The step of generating a data acquisition point dictionary based on the device model includes: Based on preset device coding rules, a unique device identifier code for the target device is determined; Based on the device attribute information, determine the element identifier and element type of the target data element; The collection point code is determined based on the unique device identifier, the element identifier, and the preset collection point coding rules; Based on the element type, determine the data type of the target data element; Based on the production line identifier in the equipment affiliation information, the sampling frequency of the target data element is determined; Based on the unique device identifier, the element identifier, and the preset storage path rules, the storage path of the target data element is determined; The data collection point dictionary is generated based on the collection point code, the data type, the sampling frequency, and the storage path; Based on the data acquisition point dictionary, device operation data is acquired through a preset data acquisition interface, and the device operation data is converted according to the preset data standard to generate device operation data after format conversion. The device operating data after format conversion is stored in the database, and the device operating data is analyzed based on preset data analysis rules to generate a data analysis report; Based on the access control policy, access control and sharing management are implemented for the device operation data and the data analysis report.

2. The method according to claim 1, characterized in that, The preset data standards include business standards, technical standards, and management standards. The standardization and definition of the data element information based on these preset data standards to generate a unified data element set includes: Based on the business standards, the data element information is classified to determine the name, dimension, classification and usage information related to production line parameters, which are used as the first type of attribute. Based on the aforementioned technical standards, the data element information is classified to determine the data type, data calculation method, point category, data source, and database description information related to the described object, which are then used as the second type of attribute. Based on the management standards, the data element information is classified, and the name information related to the enterprise's various levels of departments is determined as the third type of attribute; The unified data element set is generated based on the first type of attribute, the second type of attribute, and the third type of attribute.

3. The method according to claim 1, characterized in that, The construction of the device object model based on the unified data element set includes: Based on the unified data element set, at least one target data element associated with the target device is determined; Based on the target data elements, determine the device attribute information of the target device; Based on the preset equipment hierarchy, determine the parent equipment identifier and the production line identifier of the target equipment; Based on the parent device identifier and the production line identifier, the device dependency information of the target device is determined; Based on a preset graphics repository, determine the storage address of the two-dimensional drawings and the storage address of the three-dimensional model associated with the target device; Based on the storage address of the two-dimensional drawing and the storage address of the three-dimensional model, the device graphic information of the target device is determined; Based on the device attribute information, the device subordinate relationship information, and the device graphic information, a device object model of the target device is constructed.

4. The method according to claim 1, characterized in that, The process of collecting device operation data through a preset data acquisition interface based on the data acquisition point dictionary, and converting the device operation data according to the preset data standard to generate format-converted device operation data includes: Based on the encoding of the acquisition points, the target acquisition protocol is determined; Based on the target acquisition protocol, the preset data acquisition interface is controlled to acquire device operation data; Based on the data type, determine the target transmission format; Based on the target transmission format, the collected device operation data is encapsulated and processed to generate standardized data packets; Based on the data format specifications corresponding to the data type in the preset data standard, the data in the standardized data packet is converted to generate device operation data after format conversion.

5. The method according to claim 1, characterized in that, The process of storing the format-converted device operating data in a database and analyzing the data based on preset data analysis rules to generate a data analysis report includes: Based on the storage path, determine the target database table for the device operation data after format conversion; Based on the sampling frequency, determine the storage strategy for the device operation data; Based on the storage strategy, the device operating data after the format conversion process is stored in the target database table; Based on the preset data analysis rules and the device affiliation information, the set of associated devices to be analyzed is determined; Based on the associated device set, obtain the target device operation dataset from the target database table; Perform trend analysis, statistical analysis, or anomaly detection analysis on the target device's operational dataset to generate analysis result data; Based on the analysis results, the data analysis report is generated.

6. The method according to claim 1, characterized in that, The access control and sharing management of the device operation data and the data analysis report based on the permission management policy includes: Based on the aforementioned access control strategy, the user role and department affiliation of the current user are determined; Based on the user role and the department affiliation, determine the current user's data access permissions to the device operation data and the data analysis report; Based on the data sensitivity levels of the equipment operation data and the data analysis report, the scope of data sharing between the equipment operation data and the data analysis report is determined; Based on the data access permissions and the data sharing scope, control the current user's access to and sharing of the device operation data and the data analysis report; Based on the access operation and the sharing operation, a data access log and a data sharing log are generated; Based on the data access log and the data sharing log, traceability management of data operation behavior can be achieved.

7. A data management device for the steel industry, used to implement the method according to any one of claims 1-6, characterized in that, include: The data acquisition unit is used to acquire data element information from multiple production lines throughout the entire steel production process. The standard definition unit is used to standardize and define the information of the data elements based on a preset data standard, and generate a unified set of data elements; The model building unit is used to build a device model based on the unified data element set; The dictionary generation unit is used to generate a data acquisition point dictionary based on the device object model; The data acquisition unit is used to acquire device operation data through a preset data acquisition interface based on the data acquisition point dictionary, and to perform format conversion processing on the device operation data according to the preset data standard to generate format-converted device operation data. The data analysis unit is used to store the device operation data after format conversion into the database, and to analyze the device operation data based on preset data analysis rules to generate a data analysis report; The access control unit is used to perform access control and sharing management on the device operation data and the data analysis report based on the access control policy.

8. An electronic device, comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that the processor, when executing the computer program stored in the memory, implements the steps of the steel industry data management method as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steel industry data management method as described in any one of claims 1 to 6.

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