A multi-element demand-oriented data processing method, system and electronic equipment

Abstract

The application provides a multi-element demand-oriented data processing method, system and electronic equipment, and belongs to the technical field of communication data processing. The method comprises the following steps: classifying and identifying original data in the field of communication to obtain a classification result of the original data; constructing a core data model according to the classification result; constructing a business domain data model corresponding to each business field according to the core data model and the business demand of each business field; constructing a unified data model based on the core data model and the business domain data model; establishing a mapping relationship between each business domain data model according to the unified data model; and sharing and exchanging data between different business fields based on the mapping relationship. The application enables all kinds of communication data to be interconnected without obstacles and improves the data interconnection capability.

Description

Technical Field

[0001] This invention relates to the field of communication data processing technology, and more specifically to a data processing method, system, and electronic device for diverse needs. Background Technology

[0002] Telecommunications data refers to the information transmitted by communication systems, as well as the data of related organizations and personnel. It encompasses application organizations, network planning and service maintenance personnel, equipment, platforms, and communication data. Telecommunications data modeling is the process of abstracting and describing the organization, structure, and behavior of communication data, with the aim of better understanding, analyzing, and utilizing this data. With the deepening development of information technology infrastructure, the system's demand for various types of data, such as network operation management and information service functions, is rapidly increasing, urgently requiring strengthened data modeling and standardization efforts. Data in the telecommunications field is characterized by its diversity, heterogeneity, and dynamic nature. This presents challenges for data modeling, including a wide range of data sources, diverse data types, and complex data relationships, making the unified organization and efficient utilization of communication data difficult.

[0003] Currently, in the field of communication data modeling, the DoD IEMA (Department of Defense Information Exchange Model Architecture) provides a framework for information exchange within the DoD and with other agencies, achieving information exchange goals by defining data standards, shared services, and security mechanisms. Furthermore, the NIEM (National Information Exchange Model), as a data modeling framework, abstracts common data components from data across multiple domains, describing various types of information in a componentized form. These components are used to construct information exchange models, aiming to improve the efficiency and accuracy of information sharing through unified information exchange standards.

[0004] However, the above technologies still have the following shortcomings when practically applied in the field of communications: First, data models are built independently for single systems or specific network systems, and there is a lack of unified data standards and specifications between systems. Communication systems such as shortwave, ultra-shortwave, and satellite have not carried out data construction from a global perspective of the entire communication network application, resulting in numerous business data silos and poor data interconnection and interoperability between systems and network systems, making it difficult to achieve efficient sharing and in-depth utilization. Second, different systems adopt different technical systems and development platforms, resulting in inconsistencies in the use of business domain terminology, inconsistent understanding of business concepts, non-standard data naming, and large differences in data structures, leading to poor consistency of multi-source heterogeneous data and making it difficult to understand and effectively integrate them. Third, data models are mostly static designs oriented towards single business scenarios. When faced with diversified data application needs such as task assurance, query statistics, correlation analysis, and prediction and early warning, it is difficult to quickly expand and adapt the models, resulting in weak service support capabilities and an inability to effectively meet the increasingly diversified data needs of the communications field. Summary of the Invention

[0005] In view of this, it is necessary to provide a data processing method, system and electronic device that meets diverse needs, in order to solve the technical problem of poor interoperability of communication data between cross systems and cross business domains in the existing technology.

[0006] To address the aforementioned technical problems, in a first aspect, the present invention provides a data processing method oriented towards diverse needs, comprising: The raw data in the field of communication is classified and identified to obtain the classification results of the raw data; Construct a core data model based on the classification results; Based on the core data model and the business requirements of each business area, construct the corresponding business domain data model for each business area. Based on the core data model and the business domain data model, a unified data model is constructed; Based on the unified data model, establish the mapping relationship between the data models of each business domain; Data sharing and exchange are conducted between different business areas based on the aforementioned mapping relationship.

[0007] In one possible implementation, constructing the core data model based on the classification results includes: The various basic resources, business processes, and business rules in the classification results are analyzed to extract common concepts. Based on the common concepts, core relationship classes, core attribute classes, and core relationship classes are defined, and a model framework is established based on the core relationship classes, the core attribute classes, and the core relationship classes. The corresponding data specifications are obtained based on the model framework; the data specifications include data type standards, data format standards, and data relationship representation standards. Based on the data type standard, the data format standard, and the data relationship representation standard, the model framework is configured with a data format to obtain the core data model.

[0008] In one possible implementation, constructing a business domain data model corresponding to each business domain based on the core data model and the business requirements of each business domain includes: For any given business domain, conduct a business requirements analysis to determine the specific entities, attributes, and relationships required for that business domain. Based on the core data model, the core relationship class, the core attribute class, and the core relationship class related to the business domain are inherited from the core data model; Based on the results of the business requirements analysis, and on the basis of the inherited core relationship class, core attribute class, and core relationship class, the dedicated entities, dedicated attributes, and dedicated relationships of the business domain are added to form the business domain data model corresponding to the business domain.

[0009] In one possible implementation, constructing a unified data model based on the core data model and the business domain data model includes: The core data model is used as the basic data layer, and the data models of each business domain are used as the extended data layer. A reference relationship is established between the basic data layer and the extended data layer, so that the data models of each business domain in the extended data layer inherit the core data model in the basic data layer; A unified data model is constructed based on the basic data layer, the extended data layer, and the reference relationships.

[0010] In one possible implementation, after forming the business domain data model corresponding to the business domain, the method further includes: The business domain data models corresponding to each of the aforementioned business areas are classified and integrated according to themes to generate theme data and model sets; The subject data and model sets are standardized, and the object class names are described using business terms. Object classes with the same attributes are merged.

[0011] In one possible implementation, establishing the mapping relationship between the various business domain data models based on the unified data model includes: Using the unified data model as a mapping benchmark, determine the correspondence between the core relationship classes, core attribute classes, and core relationship classes in each of the business domain data models; Based on the aforementioned correspondence, mapping rules are established between the data models of each business domain; The mapping relationship between the data models of each business domain is constructed according to the mapping rules.

[0012] In one possible implementation, the data sharing and exchange between different business domains through the mapping relationship includes: Based on the mapping relationship, determine the data mapping path between the source business domain and the target business domain; According to the data mapping path, the source data of the source business domain is converted into the target data format corresponding to the target business domain; The converted target data is then provided to the target business domain.

[0013] One possible implementation also includes: Version records are kept for every modification to the core data model and the data models of each business domain in the unified data model; Perform change management on the unified data model based on the change request; The unified data model is subjected to integrity checks, consistency checks, and accuracy checks at preset intervals, and the unified data model is optimized based on the check results.

[0014] Secondly, the present invention also provides a data processing system oriented towards diverse needs, comprising: The identification module is used to classify and identify raw data in the field of communication, and obtain the classification result of the raw data; A creation module is used to construct a core data model based on the classification results, construct a business domain data model corresponding to each business domain based on the core data model and the business requirements of each business domain, and construct a unified data model based on the core data model and the business domain data model. A module is established to establish mapping relationships between the data models of each business domain based on the unified data model. The processing module is used to share and exchange data between different business areas based on the mapping relationship.

[0015] Thirdly, the present invention also provides an electronic device, including a memory and a processor, wherein, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps in the data processing method for diverse needs described in any of the above implementations.

[0016] The beneficial effects of this invention are as follows: First, by classifying and identifying raw data in the communications field from multiple dimensions, heterogeneous data scattered across various business systems can be organized according to a unified classification system, providing a structured data foundation for subsequent modeling. Furthermore, by constructing a core data model and, based on the core data model and the business needs of each business domain, constructing corresponding business domain data models for each business domain, the problem of data heterogeneity is eliminated at the source. This allows for flexible adaptation to the personalized data needs of each business domain while maintaining the stability of the core model, achieving a balance between generality and specialization. Furthermore, by integrating the core data model and the business domain data model and establishing a unified data dictionary, a complete data model system for the communications field can be formed, improving the integrity and consistency of the data model. Furthermore, by establishing mapping relationships between the data models of each business domain and conducting continuous version and change management, data barriers between different business systems can be broken down, improving the efficiency and reliability of cross-business domain data sharing and interaction. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 A schematic flowchart of an embodiment of the data processing method for diverse needs provided by the present invention; Figure 2 For the present invention Figure 1 A schematic diagram of an embodiment of S102; Figure 3 For the present invention Figure 1 A schematic diagram of an embodiment of S103; Figure 4 For the present invention Figure 1 A schematic diagram of an embodiment of S104; Figure 5 This is a schematic flowchart of another embodiment of the data processing method for diverse needs provided by the present invention; Figure 6 For the present invention Figure 1 A schematic diagram of an embodiment of S105; Figure 7 For the present invention Figure 1 A schematic diagram of an embodiment of S106; Figure 8 This is a schematic flowchart of another embodiment of the data processing method for diverse needs provided by the present invention; Figure 9 A schematic diagram of an embodiment of the data processing system for diverse needs provided by the present invention; Figure 10 A schematic diagram of an embodiment of the electronic device provided by the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention 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 the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0020] In the description of the embodiments of the present invention, unless otherwise stated, "multiple" means two or more. "And / or" describes the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.

[0021] The terms "first," "second," etc., used in the embodiments of this invention are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a technical feature defined with "first" or "second" may explicitly or implicitly include at least one of that feature.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] This invention provides a data processing method, system, and electronic device for diverse needs, which will be described below.

[0024] Figure 1 A schematic flowchart of an embodiment of the data processing method for diverse needs provided by the present invention is shown below. Figure 1 As shown, data processing methods for addressing diverse needs include: S101. Classify and identify the raw data in the field of communication to obtain the classification result of the raw data.

[0025] It should be noted that the raw data includes, but is not limited to, communication equipment ledger data, network topology data, personnel organization data, operation and maintenance record data, frequency resource data, and link status data. A comprehensive review and classification of the above raw data is conducted. Based on data source, business affiliation, and usage characteristics, the raw data is divided into multiple business categories, such as UHF / UST communication business data, satellite communication business data, HF communication business data, communication operation and maintenance management business data, and communication organization and planning business data. During the classification and identification process, a classification label system is established based on the functional positioning and data characteristics of each business system to ensure that each type of business data has clear boundaries and explicit business semantics.

[0026] S102. Construct a core data model based on the classification results.

[0027] It should be noted that: based on the classification results of the raw data, semantic analysis and conceptual abstraction are performed on the business data of each category to construct a core data model independent of any specific business domain. The core data model is a set of data models built upon concepts and relationships independent of any specific business domain. It describes various basic resource data models and forms the cornerstone of the entire data system.

[0028] S103. Based on the core data model and the business requirements of each business area, construct the business domain data model corresponding to each business area.

[0029] It should be noted that, for different business domains, a thorough analysis of their business characteristics and data requirements is conducted first. For example, in the field of communication operation and maintenance management, attention needs to be paid to data such as equipment fault information, link status, and maintenance records; in the field of communication organization and application, attention needs to be paid to data such as task requirements, equipment status, and resource usage. Based on the business requirements analysis, and using the core data model described above as a foundation, business domain data models corresponding to each of the aforementioned business domains are constructed. The business domain data model consists of data types that reflect the unique business characteristics of each business domain.

[0030] S104. Based on the core data model and the business domain data model, construct a unified data model.

[0031] It should be noted that the core data model and the data models of various business domains are integrated to form a complete data model system for the communications field.

[0032] S105. Based on the unified data model, establish the mapping relationship between the data models of each business domain.

[0033] It should be noted that: based on the unified data model, semantically identical element pairs are identified in different business domain data models, and mapping relationships are established between these element pairs.

[0034] S106. Data sharing and exchange are carried out between different business areas based on the mapping relationship.

[0035] It should be noted that when the source business domain needs to provide data to the target business domain, the format and structure of the source business domain data are converted into the format and structure required by the target business domain data model according to a pre-established mapping relationship. This conversion includes data type conversion, value range mapping, and data structure reorganization.

[0036] In summary, the data processing method for diverse needs provided by this invention firstly classifies and identifies raw data in the communication field from multiple dimensions, organizing heterogeneous data scattered across various business systems according to a unified classification system, thus providing a structured data foundation for subsequent modeling. Furthermore, by constructing a core data model and, based on the core data model and the business needs of each business domain, constructing corresponding business domain data models for each business domain, the problem of data heterogeneity is eliminated at the source. This allows for flexible adaptation to the personalized data needs of each business domain while maintaining the stability of the core model, achieving a balance between generality and specialization. Furthermore, by integrating the core data model and the business domain data model and establishing a unified data dictionary, a complete data model system for the communication field can be formed, improving the integrity and consistency of the data model. Finally, by establishing mapping relationships between the data models of each business domain and conducting continuous version and change management, data barriers between different business systems can be broken down, improving the efficiency and reliability of cross-business domain data sharing and interaction.

[0037] In some embodiments of the present invention, such as Figure 2 As shown, step S102 includes: S201. Analyze the various basic resources, business processes and business rules in the classification results to extract common concepts.

[0038] It should be noted that in data modeling within the communications field, various basic resources include communication equipment such as antennas, radio frequency units, and baseband units, as well as physical resources such as mobile platforms, fixed platforms, and organizations; business processes include communication resource scheduling processes, communication service operation management processes, and communication organization planning processes; and business rules include communication protocol constraint rules, data transmission specification rules, and resource allocation management rules. A comprehensive review and analysis of these basic resources, business processes, and business rules identifies common, cross-business domain-wide concepts. For example, different business domains all involve basic concepts such as "personnel," "equipment," "organization," "link," and "protocol," which have the same essential meaning in different business scenarios. By analyzing the common requirements of various businesses in the communications field for basic resource data, universal and fundamental conceptual elements are extracted to provide a foundation for constructing the core data model.

[0039] S202. Based on the common concepts, set up core relationship classes, core attribute classes, and core relationship classes, and establish a model framework according to the core relationship classes, core attribute classes, and core relationship classes.

[0040] It should be noted that: concepts representing objective entities within the common concepts are designated as core object classes, such as communication equipment, mobile platforms, fixed platforms, and institutions; concepts describing entity characteristics or capabilities within the common concepts are designated as core attribute classes, such as communication protocols, communication links, and communication capabilities; and concepts describing relationships between entities within the common concepts are designated as core relationship classes, such as assembly hierarchical relationships and input-output relationships between devices. The core data model framework is thus composed of these core object classes, core attribute classes, and core relationship classes, along with their interrelationships. For example, communication equipment and communication links are interconnected through a "connection" relationship, and communication equipment and institutions are interconnected through a "hierarchical" relationship.

[0041] S203. Obtain the corresponding data specifications according to the model framework; the data specifications include data type standards, data format standards, and data relationship representation standards.

[0042] It should be noted that: data type standards define the types of various data, such as integer, string, and date types; data format standards define the specific formats of various data, such as the format of Internet Protocol Addresses (IP addresses), date and time formats, and the precision and units of numerical values; and data relationship representation standards define how relationships between entities are expressed, such as foreign key constraints and related tables. Based on the core data model framework, corresponding data type standards, data format standards, and data relationship representation standards are developed for each core object class, core attribute class, and core relationship class within the framework.

[0043] S204. Based on the data type standard, the data format standard, and the data relationship representation standard, the model framework is configured with a data format to obtain the core data model.

[0044] It should be noted that the established data type standards, data format standards, and data relationship representation standards are applied item by item to each core object class, core attribute class, and core relationship class in the core data model framework. This configures each data element in the framework with a data type, data format, and data relationship representation method that conforms to the standard requirements. For example, the "Device Name" attribute of the "Communication Equipment" object class is set to a string type, and its length and naming rules are specified; the "Link Status" attribute of the "Communication Link" object class is set to an enumeration type, and its value range is specified; the representation method of the "Connection" relationship is set to a foreign key association method, and the naming and referencing rules of the foreign key are specified.

[0045] In this embodiment, by analyzing various basic resources, business processes, and business rules in the communications field and extracting common concepts, it is possible to ensure that the core data model covers the common data needs of various business scenarios in the communications field, thereby improving the universality and applicability of the core data model. Furthermore, by setting core object classes, core attribute classes, and core relationship classes based on common concepts and establishing a core data model framework, a unified structured foundation can be provided for the organization and management of various types of data in the communications field, improving the consistency and standardization of data description across different business systems. In addition, by formulating data type standards, data format standards, and data relationship representation standards and applying these data specifications to the core data model framework for data format setting, ambiguities and conflicts in data definitions across different systems can be eliminated, enhancing the standardization and reusability of the core data model.

[0046] In some embodiments of the present invention, such as Figure 3 As shown, step S103 includes: S301. Conduct business requirements analysis for any business domain to determine the special entities, special attributes, and special relationships required for the business domain.

[0047] It should be noted that in actual communication systems, different business domains have significantly different data requirements. For example, in the field of communication operation and maintenance management, data such as user information, equipment fault information, link status, and maintenance records are needed; while in the field of communication organization and application, data such as task requirements, equipment status, and resource usage are needed. Therefore, for different business domains, it is necessary to conduct in-depth analysis of their business characteristics and data requirements. Through business requirement analysis, the data elements unique to that business domain and not yet included in the core data model should be identified, and these should be defined as dedicated entities (such as specific types of communication support entities), dedicated attributes (such as internal telephone lines, group affiliation, and other business-specific attributes), and dedicated relationships (such as the assignment relationship between tasks and personnel) required by that business domain.

[0048] S302. Based on the core data model, inherit the core relationship class, the core attribute class, and the core relationship class related to the business domain from the core data model.

[0049] It should be noted that when constructing the business domain data model, the initial business object classes are formed by first identifying business entities related to the business domain, such as organizational structures, equipment systems, communication resources, and support entities, as well as business concepts such as tasks, plans, and activities. Specifically, when constructing the business domain data model for communication operations and maintenance management, core object classes (such as "personnel," "equipment," and "organization"), core attribute classes (such as "equipment status" and "link bandwidth"), and core relationship classes (such as "affiliation" and "connection") related to the operations and maintenance domain are inherited from the core data model. Inheritance means that the business domain data model directly reuses the common data elements already defined in the core data model without redefining them. By inheriting relevant core elements from the core data model, the common data definitions and specifications already established in the core data model can be reused, avoiding redundant modeling and ensuring consistency in common data descriptions between different business domain data models.

[0050] S303. Based on the results of the business requirements analysis, and on the basis of the inherited core relationship class, core attribute class, and core relationship class, supplement the dedicated entity, dedicated attribute, and dedicated relationship of the business domain to form the business domain data model corresponding to the business domain.

[0051] It should be noted that: when the core attribute class contains the attributes required by the business object class, the core attribute class is directly referenced; when the core attribute class does not contain the attributes required by the business object class, the core attribute class is extended by adding new attributes or attribute groups, or a new attribute class is defined. For example, when constructing a business domain data model for communication operation and maintenance management, the core object class "Operation and Maintenance Personnel" in the core data model is used as the basis, inheriting its common attributes (such as name, date of birth, etc.), while supplementing the specific attributes unique to this business domain (such as team affiliation, service type, internal telephone number, etc.), and supplementing the business object classes (such as "maintenance work order," "fault record," etc.) and special relationship classes (such as "assignment relationship between work order and maintenance personnel," etc.) unique to this business domain, thus forming a complete business domain data model.

[0052] In this embodiment, by conducting business requirement analysis for any business domain and determining the specific entities, attributes, and relationships required for that business domain, it is possible to ensure that the business domain data model accurately reflects the actual data needs of each business domain, thereby improving the relevance and usability of the data model. Furthermore, by inheriting relevant core object classes, core attribute classes, and core relationship classes based on the core data model, the common data definitions and specifications established in the core data model can be reused, enhancing the consistency of common data descriptions between different business domain data models. In addition, by standardizing the inherited core object classes and using business terminology from the relevant business domain to describe the object class names, the readability and usability of the business domain data model in each business domain can be improved; by merging object classes with the same or similar attributes, the model structure can be simplified, reducing the redundancy of the data model. Simultaneously, by supplementing the inherited core elements with business domain-specific business object classes, attribute classes, and relationship classes, the business domain data model can fully reflect the personalized characteristics of each business domain while maintaining consistency with the core data model, enhancing the scalability and flexibility of the data model.

[0053] In some embodiments of the present invention, such as Figure 4 As shown, step S104 includes: S401. The core data model is used as the basic data layer, and the data models of each business domain are used as the extended data layer.

[0054] It should be noted that the business domain data model consists of data types reflecting the unique characteristics of each business domain. It is formed by inheriting and extending from the core data model, based on the integrated data service characteristics and information exchange needs of various network systems and applications. The core data model is positioned as the basic data layer, undertaking the most fundamental and universal data definition and standardization functions in the entire data system. The business domain data models are positioned as extended data layers, inheriting the universality of the core data model while also supporting the personalized data needs of each business domain. By positioning these two types of models in different data layers, the boundary between general and specialized data can be clearly delineated, providing a structured organizational framework for the unified management of basic and business data.

[0055] S402. Establish a reference relationship between the basic data layer and the extended data layer, so that each business domain data model in the extended data layer inherits the core data model in the basic data layer.

[0056] It should be noted that referencing relationships refer to the business domain data model in the extended data layer directly reusing the common data elements already established in the core data model by referencing the entity definitions, attribute definitions, and relationship definitions of the core data model in the basic data layer, without needing to redefine them. Specifically, when constructing a business domain data model for a certain business area, this business domain data model first obtains the core object classes, core attribute classes, and core relationship classes related to that business area from the core data model through referencing relationships, and then supplements them with business object classes, special attribute classes, and special relationship classes specific to that business area. By establishing referencing relationships between the basic data layer and the extended data layer, each business domain data model can maintain consistency with the core data model while fully reflecting the personalized data needs of each business area, achieving an organic unity of generality and specialization.

[0057] S403. Based on the basic data layer, the extended data layer, and the reference relationship, construct a unified data model.

[0058] It should be noted that the unified data model is a complete data model system formed by integrating the core data model and the data models of various business domains. When building the unified data model, the basic data layer provides the most fundamental and universal data definitions and specifications throughout the entire data system; the business domain data models in the extended data layer, based on the basic data layer, form thematic data and model sets adapted to different business scenarios through inheritance and extension; the reference relationships organically connect the basic data layer and the extended data layer, ensuring that each business domain data model can flexibly extend business-specific data elements while reusing the common elements of the core data model. By organically combining the basic data layer, the extended data layer, and the reference relationships, a unified data model with clear hierarchy, complete structure, and a balance of universality and flexibility is ultimately formed.

[0059] In this embodiment, by using the core data model as the basic data layer and the data models of each business domain as the extended data layer, the boundaries between general and specialized data can be clearly defined, improving the structured organization of the data model system. Furthermore, by establishing reference relationships between the basic and extended data layers, each business domain data model inherits the common elements of the core data model, avoiding redundant modeling of general data definitions across different business systems, reducing data redundancy, and enhancing consistency between data models from different business domains. In addition, by constructing a unified data model based on the basic data layer, extended data layer, and reference relationships, the personalized data needs of each business domain can be fully accommodated while maintaining the uniformity and standardization of the core data model, improving the overall scalability and flexibility of the data model system, and laying a solid model foundation for subsequent cross-business domain data sharing and exchange.

[0060] In some embodiments of the present invention, such as Figure 5 As shown, after forming the business domain data model corresponding to the business domain, the process further includes: S501. Classify and integrate the business domain data models corresponding to each business domain according to themes to generate theme data and model sets.

[0061] It's important to note that in the communications field, different business domain data models involve different types of data themes. For example, the operations and maintenance (O&M) domain involves themes like equipment O&M and link monitoring; the organizational planning domain involves themes like resource scheduling and task allocation; and the business development domain involves themes like service activation and service assurance. The completed business domain data models are categorized according to their respective business themes, and those belonging to the same theme are integrated together to form a set of data and models oriented towards a specific theme. For example, data models related to communication equipment O&M are integrated into "Equipment O&M Theme Data," data models related to communication link management are integrated into "Link Management Theme Data," and data models related to satellite communication services are integrated into "Satellite Service Theme Data." By classifying and integrating the business domain data models according to themes, data models scattered across different business domains can be reorganized according to thematic dimensions, providing a clearer data view for subsequent data analysis and applications.

[0062] S502. Standardize the subject data and model set, use business terms to describe the object class names, and merge object classes with the same attributes.

[0063] It should be noted that standardization refers to organizing and optimizing the object classes in the subject data and model sets according to a unified standard. Specifically, firstly, the names of each object class in the subject data and model sets are reviewed, using business terminology from the relevant business domain rather than technical terms to describe the object class names, ensuring that the names accurately reflect their actual meaning in the business context. For example, in the field of communication operation and maintenance management, the "personnel" object class in the core data model is standardized as "operation and maintenance personnel," and the "equipment" object class is standardized as "communication equipment" or "network element equipment"; in the field of communication organization and planning, the "personnel" object class is standardized as "planning personnel," and the "task" object class is standardized as "support task." Simultaneously, the attributes contained in each object class are analyzed, and object classes with the same or similar sets of attributes are merged. For example, if the "fixed platform" object class and the "mobile platform" object class have completely identical attribute definitions in a certain subject data, they are merged into a single "platform" object class to avoid redundant definitions. By using business terminology to describe object class names and standardizing them, the readability and usability of thematic data and model sets in the corresponding business domains can be improved. By merging object classes with the same or similar attributes, the model structure can be simplified, data redundancy can be reduced, and the simplicity and maintainability of the data model can be improved.

[0064] In this embodiment, by classifying and integrating data models from various business domains according to themes, data models scattered across different business domains can be reorganized according to thematic dimensions, improving the structure and searchability of the data model system. Furthermore, by standardizing thematic data and model sets and using business terminology from the respective business domains to describe object class names, the readability and comprehensibility of the data model across different business domains can be enhanced, reducing misunderstandings of the same data concepts among different business personnel. In addition, by merging object classes with the same or similar attributes, the model structure can be effectively simplified, reducing redundancy and complexity, improving the simplicity and maintainability of the data model, and laying a clearer and more efficient model foundation for subsequent cross-theme and cross-business domain data analysis and applications.

[0065] In some embodiments of the present invention, such as Figure 6 As shown, step S105 includes: S601. Using the unified data model as a mapping benchmark, determine the correspondence between the core relationship class, core attribute class and core relationship class in each of the business domain data models.

[0066] It should be noted that since all business domain data models inherit from the same core data model, the core object classes, core attribute classes, and core relationship classes in the core data model have the same semantic foundation in different business domain data models. Using the unified data model as the mapping benchmark means that when performing cross-business domain data mapping, the core data model in the unified data model is used as a reference standard to identify the correspondence between the core object classes, core attribute classes, and core relationship classes in each business domain data model. Specifically, for any core object class, its corresponding business object class in each business domain data model is determined; for any core attribute class, its corresponding attribute definition in each business domain data model is determined; for any core relationship class, its corresponding relationship definition in each business domain data model is determined. For example, the "Personnel" core object class in the core data model may correspond to the "Operations and Maintenance Personnel" object class in the business domain data model corresponding to the Operations and Maintenance Management domain, and to the "Planning Personnel" object class in the business domain data model corresponding to the Organizational Planning domain. Although the names are different, they both originate from the same core object class and therefore have a corresponding relationship. By using a unified data model as a mapping benchmark to determine the correspondence between data models of different business domains, an accurate matching basis can be provided for the subsequent establishment of mapping rules, ensuring that data models of different business domains can correspond correctly at the semantic level.

[0067] S602. Based on the correspondence, establish mapping rules between the data models of each business domain.

[0068] It should be noted that mapping rules refer to the specific specifications defining how data transformation occurs between the source and target business domain data models during data exchange. After determining the correspondence between core object classes, core attribute classes, and core relationship classes in each business domain data model, specific mapping rules need to be formulated for each type of correspondence. Specifically, for the correspondence between core object classes, entity mapping rules are formulated, specifying how business object classes in the source business domain are mapped to their corresponding business object classes in the target business domain; for the correspondence between core attribute classes, attribute mapping rules are formulated, specifying how attributes in the source business domain are converted to their corresponding attributes in the target business domain, including mapping attribute names, converting attribute types, and mapping attribute value ranges; for the correspondence between core relationship classes, relationship mapping rules are formulated, specifying how relationships in the source business domain are mapped to their corresponding relationships in the target business domain. The forms of mapping rules can include field-level correspondence definitions, data type conversion rule definitions, and data relationship representation conversion rule definitions. By establishing mapping rules between the data models of each business domain, clear conversion criteria can be provided for cross-business domain data exchange, ensuring that semantic consistency and integrity are maintained when data is converted between different business domains.

[0069] S603. Construct the mapping relationship between the data models of each business domain according to the mapping rules.

[0070] It should be noted that the mapping relationship is a correspondence between specific data in each business domain data model, formed based on mapping rules. After the mapping rules are established, they need to be instantiated into executable data mapping relationships between the business domain data models. Specifically, for each pair of business domain data models that need to exchange data, according to the established mapping rules, the mapping relationship between each data element in the source business domain data model and the corresponding data element in the target business domain data model is constructed item by item, forming a complete set of mapping relationships. The construction of mapping relationships includes: determining the corresponding path between source data elements and target data elements, clarifying the transformation operations to be performed during data transformation, and defining the data verification rules after transformation. By constructing the mapping relationships between the business domain data models according to the mapping rules, an executable cross-business domain data exchange scheme is finally formed, enabling different business domains to share and exchange data based on a unified mapping relationship.

[0071] In this embodiment, by using a unified data model as the mapping benchmark to determine the correspondence between data models of various business domains, the accuracy and consistency of cross-business domain data mapping at the semantic level can be ensured, improving the identifiability of the correspondence between data models of different business domains. Furthermore, by establishing mapping rules between data models of various business domains based on the correspondence, clear conversion criteria can be provided for cross-business domain data exchange, enhancing the standardization and operability of data conversion between different business domains. In addition, by constructing the mapping relationship between data models of various business domains based on the mapping rules, abstract mapping rules can be instantiated into an executable cross-business domain data exchange scheme, improving the efficiency and reliability of data sharing and exchange between different business domains, and providing solid data support for collaborative work between various business systems.

[0072] In some embodiments of the present invention, such as Figure 7 As shown, step S106 includes: S701. Based on the mapping relationship, determine the data mapping path between the source business domain and the target business domain.

[0073] It's important to note that data mapping refers to defining and establishing a complete set of configurations for the correspondence between fields, data format conversion rules, and data processing between source and target data during data integration or exchange. Specifically, when a source business domain needs to provide data to a target business domain, the transmission path from the source to the target is determined based on the mapping relationships between core object classes, core attribute classes, and core relationship classes in the data models of both business domains. For example, when exchanging data between the communication operations and maintenance management domain and the communication organization planning domain, the established mapping relationships determine which intermediate nodes the operations and maintenance data needs to pass through and what transformations it needs to undergo to reach the planning domain, thus forming a complete data mapping path. By determining the data mapping path, a clear execution route can be provided for subsequent data transformations, ensuring that data can flow accurately between different business domains.

[0074] S702. According to the data mapping path, convert the source data of the source business domain into the target data format corresponding to the target business domain.

[0075] It's important to note that data format conversion refers to transforming source data into the format required by the target data during data exchange, ensuring that the data can be correctly identified and used across different systems or business domains. Since different business domains may have different representations and format requirements for the same type of data, data format standardization is necessary based on the conversion rules defined in the mapping relationship. Specifically, different types of data use different conversion methods: for structured data, the field names, types, and values ​​of the source data are mapped one-to-one to the corresponding fields in the target data according to the field-level correspondence in the mapping relationship; for inconsistent data formats, conversion is performed according to the data type conversion rules defined in the mapping relationship, such as converting date formats from "YYYYMMDD" to "MM / DD / YYYY", or converting string values ​​to integers; for inconsistent data granularity, data aggregation or splitting rules defined in the mapping relationship are used. By performing data format conversion according to the mapping path, data from the source business domain can accurately adapt to the data format requirements of the target business domain, eliminating data format differences between different business domains.

[0076] S703. Provide the converted target data to the target business domain.

[0077] It should be noted that after the data format conversion is completed, the converted target data is transmitted to the data interface or data storage location of the target business domain according to the determined data mapping path, enabling the target business domain to directly access and use the data. By providing the converted data to the target business domain, a complete data exchange process from the source business domain to the target business domain is completed.

[0078] In this embodiment, by determining the data mapping path between the source and target business domains based on the mapping relationship, a clear execution route can be provided for data exchange, improving the accuracy and predictability of cross-business domain data flow. Furthermore, by converting the source data according to the data mapping path, data format differences between different business domains can be eliminated, improving the compatibility and usability of multi-source heterogeneous data. In addition, by providing the converted target data to the target business domain, data channels between different business domains can be opened, enhancing the smoothness of cross-business domain data sharing and exchange, and promoting collaborative work between various business systems.

[0079] In some embodiments of the present invention, such as Figure 8 As shown, it also includes: S801. Record the version of each modification to the core data model and the data models of each business domain in the unified data model.

[0080] It should be noted that every modification to the core data model and the data models of each business domain within the unified data model is version-recorded. This means that after each modification, a new version number is assigned to the modified data model, and the modified content, modification time, and the person who made the modification are recorded. By recording version information when the model is modified, the evolution history of the data model can be fully traced, providing a basis for subsequent change analysis and problem investigation.

[0081] S802. Perform change management on the unified data model based on the change request.

[0082] It's important to note that when business requirements change or new business scenarios emerge, a change management process must be implemented for the unified data model in response to change requests. Specifically, the requesting party first submits a change request, explaining the reasons, content, and scope of impact. Then, data model management personnel assess the change request, analyzing the degree and scope of its impact on existing business systems and data. After the assessment is approved, a detailed change plan is developed, including implementation steps, a timeline, and rollback contingency plans. Once the change plan is approved by relevant departments and personnel, it is implemented accordingly. After implementation, the modified data model is validated to confirm that the change's effects meet expectations and have not negatively impacted existing business systems. This rigorous change management process ensures that every model change undergoes thorough evaluation and validation, reducing change risks and guaranteeing the stability and reliability of the data model.

[0083] S803. Perform integrity checks, consistency checks, and accuracy checks on the unified data model according to a preset cycle, and optimize the unified data model based on the check results.

[0084] It should be noted that the unified data model undergoes integrity checks, consistency checks, and accuracy checks at preset intervals, and is optimized based on the check results. Specifically, integrity checks verify the absence of missing entity, attribute, or relationship definitions in the data model; consistency checks verify the existence of conflicting or contradictory definitions between data models from different business domains; and accuracy checks verify whether the definitions in the data model accurately reflect actual business needs. Regularly performing these three checks allows for the timely identification and resolution of problems in the data model, ensuring its continued normal operation. Simultaneously, the data model's structure is optimized and its performance improved according to business development needs, ensuring that the data model remains adaptable to the ever-changing business requirements of the communications field.

[0085] In this embodiment, by recording every modification to the unified data model through the aforementioned version management mechanism, the model's change history can be fully traced, enhancing the visibility and auditability of the model evolution process. Furthermore, by implementing a rigorous change management process that includes requirements assessment, solution development, approval, implementation, and effect verification for each model change, change risks can be effectively reduced, ensuring the stability and reliability of the data model during its evolution. In addition, by regularly conducting comprehensive checks on the completeness, consistency, and accuracy of the unified data model, potential defects in the model can be identified and corrected in a timely manner, improving the robustness and sustainable evolution capabilities of the data model.

[0086] In all specific embodiments of this application, when processing data related to user identity or characteristics, such as user information, user behavior data, user historical data, and user location information, user permission or consent is obtained first. Furthermore, the collection, use, and processing of this data comply with relevant laws, regulations, and standards. Additionally, when embodiments of this application require access to sensitive personal information, separate permission or consent from the user is obtained through pop-ups or redirects to confirmation pages. Only after obtaining the user's separate permission or consent is the necessary user-related data required for the proper functioning of these embodiments acquired.

[0087] To better implement the data processing method for diverse needs in the embodiments of the present invention, based on the data processing method for diverse needs, correspondingly, as follows: Figure 9 As shown, this embodiment of the invention also provides a data processing system 900 oriented towards diverse needs, the data processing system 900 oriented towards diverse needs includes: The identification module 901 is used to classify and identify raw data in the field of communication, and obtain the classification result of the raw data. A creation module 902 is used to construct a core data model based on the classification results, construct a business domain data model corresponding to each business domain based on the core data model and the business requirements of each business domain, and construct a unified data model based on the core data model and the business domain data model. Module 903 is used to establish a mapping relationship between the data models of each business domain based on the unified data model. The processing module 904 is used to share and exchange data between different business domains based on the mapping relationship.

[0088] The data processing system 900 for diverse needs provided in the above embodiments can implement the technical solutions described in the above embodiments of the data processing method for diverse needs. The specific implementation principles of each module or unit can be found in the corresponding content in the above embodiments of the data processing method for diverse needs, and will not be repeated here.

[0089] like Figure 10 As shown, the present invention also provides an electronic device 1000. The electronic device 1000 includes a processor 1001, a memory 1002, and a display 1003. Figure 10 Only some components of the electronic device 1000 are shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead.

[0090] In some embodiments, processor 1001 may be a central processing unit (CPU), microprocessor, or other data processing chip, used to run program code stored in memory 1002 or process data, such as the data processing method for diverse needs in this invention.

[0091] In some embodiments, processor 1001 may be a single server or a group of servers. The server group may be centralized or distributed. In some embodiments, processor 1001 may be local or remote. In some embodiments, processor 1001 may be implemented on a cloud platform. In one embodiment, the cloud platform may include a private cloud, public cloud, hybrid cloud, community cloud, distributed cloud, internal cloud, multi-cloud, etc., or any combination thereof.

[0092] In some embodiments, memory 1002 may be an internal storage unit of electronic device 1000, such as a hard disk or memory of electronic device 1000. In other embodiments, memory 1002 may also be an external storage device of electronic device 1000, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on electronic device 1000.

[0093] Furthermore, the memory 1002 may include both internal storage units of the electronic device 1000 and external storage devices. The memory 1002 is used to store application software and various types of data installed on the electronic device 1000.

[0094] In some embodiments, display 1003 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. Display 1003 is used to display information from electronic device 1000 and to display a visual user interface. Components 1001-1003 of electronic device 1000 communicate with each other via a system bus.

[0095] In one embodiment, when processor 1001 executes a data processing program in memory 1002 that caters to diverse needs, the following steps can be implemented: The raw data in the field of communication is classified and identified to obtain the classification results of the raw data; Construct a core data model based on the classification results; Based on the core data model and the business requirements of each business area, construct the corresponding business domain data model for each business area. Based on the core data model and the business domain data model, a unified data model is constructed; Based on the unified data model, establish the mapping relationship between the data models of each business domain; Data sharing and exchange are conducted between different business areas based on the aforementioned mapping relationship.

[0096] It should be understood that when the processor 1001 executes the data processing program for diverse needs in the memory 1002, in addition to the functions mentioned above, it can also perform other functions, as detailed in the description of the corresponding method embodiments above.

[0097] Furthermore, the embodiments of the present invention do not specifically limit the type of the electronic device 1000 mentioned. The electronic device 1000 can be a mobile phone, tablet computer, personal digital assistant (PDA), wearable device, laptop computer, or other portable electronic device. Exemplary embodiments of portable electronic devices include, but are not limited to, portable electronic devices running iOS, Android, Microsoft, or other operating systems. The aforementioned portable electronic device can also be other portable electronic devices, such as a laptop computer with a touch-sensitive surface (e.g., a touch panel). It should also be understood that in some other embodiments of the present invention, the electronic device 1000 may not be a portable electronic device, but rather a desktop computer with a touch-sensitive surface (e.g., a touch panel).

[0098] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware (such as a processor, controller, etc.), and the computer program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0099] The above provides a detailed description of the data processing method, system, and electronic device for diverse needs provided by the present invention. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. At the same time, those skilled in the art will recognize that there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

Claims

1. A data processing method oriented towards diverse needs, characterized in that, include: The raw data in the field of communication is classified and identified to obtain the classification results of the raw data; Construct a core data model based on the classification results; Based on the core data model and the business requirements of each business area, construct the corresponding business domain data model for each business area. Based on the core data model and the business domain data model, a unified data model is constructed; Based on the unified data model, establish the mapping relationship between the data models of each business domain; Data sharing and exchange are conducted between different business areas based on the aforementioned mapping relationship.

2. The data processing method for diverse needs according to claim 1, characterized in that, The construction of the core data model based on the classification results includes: The various basic resources, business processes, and business rules in the classification results are analyzed to extract common concepts. Based on the common concepts, core relationship classes, core attribute classes, and core relationship classes are defined, and a model framework is established based on the core relationship classes, the core attribute classes, and the core relationship classes. The corresponding data specifications are obtained based on the model framework; the data specifications include data type standards, data format standards, and data relationship representation standards. Based on the data type standard, the data format standard, and the data relationship representation standard, the model framework is configured with a data format to obtain the core data model.

3. The data processing method for diverse needs according to claim 2, characterized in that, The step of constructing a business domain data model corresponding to each business domain based on the core data model and the business requirements of each business domain includes: For any given business domain, conduct a business requirements analysis to determine the specific entities, attributes, and relationships required for that business domain. Based on the core data model, the core relationship class, the core attribute class, and the core relationship class related to the business domain are inherited from the core data model; Based on the results of the business requirements analysis, and on the basis of the inherited core relationship class, core attribute class, and core relationship class, the dedicated entities, dedicated attributes, and dedicated relationships of the business domain are added to form the business domain data model corresponding to the business domain.

4. The data processing method for diverse needs according to claim 3, characterized in that, The construction of a unified data model based on the core data model and the business domain data model includes: The core data model is used as the basic data layer, and the data models of each business domain are used as the extended data layer. A reference relationship is established between the basic data layer and the extended data layer, so that the data models of each business domain in the extended data layer inherit the core data model in the basic data layer; A unified data model is constructed based on the basic data layer, the extended data layer, and the reference relationships.

5. The method according to claim 3, characterized in that, After forming the business domain data model corresponding to the business domain, the method further includes: The business domain data models corresponding to each of the aforementioned business areas are classified and integrated according to themes to generate theme data and model sets; The subject data and model sets are standardized, and the object class names are described using business terms. Object classes with the same attributes are merged.

6. The data processing method for diverse needs according to claim 4, characterized in that, The step of establishing the mapping relationship between the data models of each business domain based on the unified data model includes: Using the unified data model as a mapping benchmark, determine the correspondence between the core relationship classes, core attribute classes, and core relationship classes in each of the business domain data models; Based on the aforementioned correspondence, mapping rules are established between the data models of each business domain; The mapping relationship between the data models of each business domain is constructed according to the mapping rules.

7. The method according to claim 1, characterized in that, The data sharing and exchange between different business domains through the mapping relationship includes: Based on the mapping relationship, determine the data mapping path between the source business domain and the target business domain; According to the data mapping path, the source data of the source business domain is converted into the target data format corresponding to the target business domain; The converted target data is then provided to the target business domain.

8. The method according to any one of claims 1 to 7, characterized in that, Also includes: Version records are kept for every modification to the core data model and the data models of each business domain in the unified data model; Perform change management on the unified data model based on the change request; The unified data model is subjected to integrity checks, consistency checks, and accuracy checks at preset intervals, and the unified data model is optimized based on the check results.

9. A data processing system oriented towards diverse needs, characterized in that, include: The identification module is used to classify and identify raw data in the field of communication, and obtain the classification result of the raw data; A creation module is used to construct a core data model based on the classification results, construct a business domain data model corresponding to each business domain based on the core data model and the business requirements of each business domain, and construct a unified data model based on the core data model and the business domain data model. A module is established to establish mapping relationships between the data models of each business domain based on the unified data model. The processing module is used to share and exchange data between different business areas based on the mapping relationship.

10. An electronic device, characterized in that, Including memory and processor, among which, The memory is used to store programs; The processor, coupled to the memory, is used to execute the program stored in the memory to implement the steps of the data processing method for diverse needs as described in any one of claims 1 to 8.

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