Enterprise private domain data and cloud tenant deep integration system oriented to digital operation

Abstract

The invention provides a digital operation-oriented enterprise private domain data and cloud tenant deep integration system, which belongs to the technical field of data processing, and comprises an acquisition module for acquiring enterprise private domain data to determine private domain data to be integrated, and acquiring cloud tenant data to determine cloud tenant data to be integrated; the determination module is used for determining a private domain field vector and a cloud tenant field vector; the mapping module is used for determining a direct mapping value, a logic mapping value and a semantic mapping value of each private domain field in the private domain field vector and each cloud tenant field in the cloud tenant field vector; the association module is used for determining an association relationship between the private domain field vector and the cloud tenant field vector; and the integration module is used for deeply integrating the to-be-integrated private domain data and the to-be-integrated cloud tenant data. According to the method, the accuracy of the mapping relation can be improved, the deep integration and matching capability among different data sources is enhanced, efficient and accurate deep integration of private domain data and cloud tenant data is realized, and powerful digital operation support is provided for enterprises.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to an enterprise private domain data and cloud tenant deep integration system for digital operation. Background Art

[0002] With the development of the digital age, enterprises have accumulated a large amount of private domain data, and at the same time, they increasingly rely on cloud platforms to carry out business, generating rich cloud tenant data. However, these two types of data are often stored separately, with differences in data format, semantics, and logical relationships, making it difficult to directly integrate and utilize. Traditional data integration methods rely on simple data matching or manual intervention, which are inefficient and inaccurate, and difficult to handle large-scale and high-complexity data streams.

[0003] Therefore, the present invention provides an enterprise private domain data and cloud tenant deep integration system for digital operation. Summary of the Invention

[0004] The present invention provides an enterprise private domain data and cloud tenant deep integration system for digital operation. By determining and analyzing the private domain data to be integrated, private domain field vectors, cloud tenant data to be integrated, and cloud tenant field vectors, determining the direct mapping values, logical mapping values, and semantic mapping values of each private domain field in the private domain field vector and each cloud tenant field in the cloud tenant field vector, determining the association relationship between the private domain field vector and the cloud tenant field vector, and deeply integrating the private domain data to be integrated and the cloud tenant data to be integrated. It can break through the limitations of data integration methods, improve the accuracy of mapping relationships, enhance the deep integration and matching capabilities between different data sources, realize the efficient and accurate deep integration of private domain data and cloud tenant data, improve data quality and decision support capabilities, and provide strong digital operation support for enterprises.

[0005] The present invention provides an enterprise private domain data and cloud tenant deep integration system for digital operation, including: An acquisition module: acquiring enterprise private domain data to determine the private domain data to be integrated, and acquiring cloud tenant data to determine the cloud tenant data to be integrated; A determination module: determining a private domain field vector based on the private domain data to be integrated, and determining a cloud tenant field vector based on the cloud tenant data to be integrated; A mapping module: analyzing the private domain data to be integrated, private domain field vectors, cloud tenant data to be integrated, and cloud tenant field vectors, and determining the direct mapping values, logical mapping values, and semantic mapping values of each private domain field in the private domain field vector and each cloud tenant field in the cloud tenant field vector; Association module: Determine the association relationship between the private domain field vector and the cloud tenant field vector based on the direct mapping values, logical mapping values, and semantic mapping values of each private domain field in the private domain field vector and each cloud tenant field in the cloud tenant field vector; Integration module: Based on the association relationship between the private domain field vector and the cloud tenant field vector, deeply integrate the private domain data to be integrated and the cloud tenant data to be integrated.

[0006] According to an enterprise private domain data and cloud tenant deep integration system for digital operation provided by the present invention, the acquisition module includes: Acquisition unit: Acquire the data integration requirements of the enterprise, the enterprise business scenario, and the data structure of the enterprise private domain data, and acquire the cloud platform service scenario and cloud platform data structure storing the enterprise tenant information; Private domain data unit to be integrated: Extract the enterprise private domain data from the enterprise internal database based on the data integration requirements, the enterprise business scenario, and the data structure of the enterprise private domain data, and preprocess the enterprise private domain data to determine the private domain data to be integrated; Cloud tenant data unit to be integrated: Extract the cloud tenant data from the cloud platform based on the data integration requirements, the cloud platform service scenario, and the cloud platform data structure, and preprocess the cloud tenant data to determine the cloud tenant data to be integrated.

[0007] According to an enterprise private domain data and cloud tenant deep integration system for digital operation provided by the present invention, the determination module includes: Private domain field vector unit: Extract the private domain fields in the private domain data to be integrated, and determine the private domain field vector based on all the extracted private domain fields; Cloud tenant field vector unit: Extract the cloud tenant fields in the cloud tenant data to be integrated, and determine the cloud tenant field vector based on all the extracted cloud tenant fields.

[0008] According to an enterprise private domain data and cloud tenant deep integration system for digital operation provided by the present invention, the mapping module includes: Extraction unit: Extract all the private domain field values of each private domain field in the private domain field vector in the private domain data to be integrated, and extract all the cloud tenant field values of each cloud tenant field in the cloud tenant field vector in the cloud tenant data to be integrated; First judgment unit: Judge whether the private domain field value of each private domain field in the private domain field vector is a numerical value. If so, determine the first value range of the private domain field based on all the private domain field values of the private domain field. Otherwise, extract the features of the private domain field to determine the first field vector of the private domain field, and extract the features of each private domain field value of the private domain field to determine the first field value vector of each private domain field value; Second judgment unit: Determine whether the cloud tenant field value of each cloud tenant field in the cloud tenant field vector is a numerical value. If so, determine the second value range of the cloud tenant field based on all cloud tenant field values of the cloud tenant field. Otherwise, extract features from the cloud tenant field to determine the second field vector of the cloud tenant field, and extract features from each cloud tenant field value of the cloud tenant field to determine the second field value vector of each cloud tenant field value. Calculation unit: Calculate the direct mapping value, logical mapping value, and semantic mapping value of each private domain field and each cloud tenant field based on each private domain field in the private domain field vector, the data type of the private domain field, the first value range or the first field vector, all the first field value vectors, and each cloud tenant field in the cloud tenant field vector, the data type of the cloud tenant field, the second value range or the second field vector, and all the second field value vectors.

[0009] According to an enterprise private domain data and cloud tenant deep integration system for digital operation provided by the present invention, the calculation unit includes: Direct mapping value calculation sub-unit: Calculate the direct mapping value of each private domain field and each cloud tenant field based on the field name of each private domain field in the private domain field vector and the field name of each cloud tenant field in the cloud tenant field vector. ; Wherein, represents the direct mapping value of the i-th private domain field in the private domain field vector and the j-th cloud tenant field in the cloud tenant field vector, represents the field name of the i-th private domain field in the private domain field vector, represents the field name of the j-th cloud tenant field in the cloud tenant field vector.

[0010] According to an enterprise private domain data and cloud tenant deep integration system for digital operation provided by the present invention, the calculation unit further includes: Logical mapping value calculation sub-unit: Calculate the logical mapping value of each private domain field and each cloud tenant field based on the data type of each private domain field in the private domain field vector, the first value range, the first field vector, and the data type of each cloud tenant field in the cloud tenant field vector, the second value range, and the second field vector. ; ; ; Wherein, represents the logical mapping value of the i-th private domain field in the private domain field vector and the j-th cloud tenant field in the cloud tenant field vector, Represents the logical numerical association value between the i-th private domain field in the private domain field vector and the j-th cloud tenant field in the cloud tenant field vector. Represents the data type matching value between the i-th private domain field in the private domain field vector and the j-th cloud tenant field in the cloud tenant field vector. Represents the data type of the i-th private domain field in the private domain field vector. Represents the data type of the j-th cloud tenant field in the cloud tenant field vector. Represents the upper limit in the first value range of the i-th private domain field in the private domain field vector. Represents the upper limit in the second value range of the j-th cloud tenant field in the cloud tenant field vector. Represents the lower limit in the first value range of the i-th private domain field in the private domain field vector. Represents the lower limit in the second value range of the j-th cloud tenant field in the cloud tenant field vector. Represents the logical numerical preset threshold. Represents the logical non-numerical preset threshold. Represents the first field vector of the a-th private domain field value of the i-th private domain field in the private domain field vector. Represents the second field vector of the j-th cloud tenant field in the cloud tenant field vector.

[0011] According to an enterprise private domain data and cloud tenant deep integration system for digital operation provided by the present invention, the calculation unit further includes: The third field value vector and the fourth field value vector sub-unit: Based on all the private domain field values of each private domain field in the private domain field vector and all the cloud tenant field values of each cloud tenant field in the cloud tenant field vector, determine the third field value vector of each private domain field based on each cloud tenant field and the fourth field vector of each cloud tenant field based on each private domain field. When = 1: ; ; Among them, Represents the number of private domain field values of the i-th private domain field in the private domain field vector. Represents the number of cloud tenant field values of the j-th cloud tenant field in the cloud tenant field vector. Represents The third field value vector of the i-th private domain field in the private domain field vector based on the j-th cloud tenant field in the cloud tenant field vector when = 1. Represents The fourth field vector of the j-th cloud tenant field in the cloud tenant field vector based on the i-th private domain field in the private domain field vector when = 1. respectively represent the first private field value, the a-th private field value, and the N1-th private field value of the i-th private field in the private field vector, respectively represent the th 0 supplemented to the third field value vector when ; respectively represent the first cloud tenant field value, the a-th cloud tenant field value, and the N1-th cloud tenant field value of the i-th cloud tenant field in the cloud tenant field vector, respectively represent the th 0 supplemented to the third field value vector when ; Semantic mapping value calculation subunit: Calculate the semantic mapping values of each private field and each cloud tenant field based on the data type matching values of each private field and each cloud tenant field, the first field vector of each private field in the private field vector, all first field value vectors, the third field vector, and the second field vector of each cloud tenant field in the cloud tenant field vector, all second field value vectors, and the fourth field vector; ; ; ; where represents the semantic mapping value between the i-th private field in the private field vector and the j-th cloud tenant field in the cloud tenant field vector, represents the semantic numerical association value between the i-th private field in the private field vector and the j-th cloud tenant field in the cloud tenant field vector, represents the semantic non-numerical association value between the i-th private field in the private field vector and the j-th cloud tenant field in the cloud tenant field vector, represents the first field value vector of the a-th private field value of the i-th private field in the private field vector, represents the second field value vector of the b-th cloud tenant field value of the j-th cloud tenant field in the cloud tenant field vector, 1 represents the numerical weight of the field value vector, 2 represents the non-numerical weight of the field value vector, represents the field vector weight, represents the semantic numerical preset threshold, represents the semantic non-numerical preset threshold, represents and divergence.

[0012] An enterprise private domain data and cloud tenant deep integration system for digital operation provided by the present invention, an association module, includes: Mapping relationship unit: Determine that the mapping priority order is direct mapping > logical mapping > semantic mapping. If there are multiple direct mapping values, logical mapping values, and semantic mapping values of each private domain field and each cloud tenant field that are 1, determine the mapping relationship between each private domain field in the private domain field vector and each cloud tenant field in the cloud tenant field vector based on the mapping priority order; Based on the mapping relationship between all private domain fields in the private domain field vector and all cloud tenant fields in the cloud tenant field vector, determine the association relationship between the private domain field vector and the cloud tenant field vector.

[0013] Compared with the prior art, the beneficial effects of the present application are as follows: By determining and analyzing the private domain data to be integrated, the private domain field vector, the cloud tenant data to be integrated, and the cloud tenant field vector, determine the direct mapping value, logical mapping value, and semantic mapping value of each private domain field in the private domain field vector and each cloud tenant field in the cloud tenant field vector, determine the association relationship between the private domain field vector and the cloud tenant field vector, and perform deep integration on the private domain data to be integrated and the cloud tenant data to be integrated. It can break through the limitations of data integration methods, improve the accuracy of mapping relationships, enhance the deep integration and matching capabilities between different data sources, achieve efficient and accurate deep integration of private domain data and cloud tenant data, improve data quality and decision support capabilities, and provide strong digital operation support for enterprises. Description of the Drawings

[0014] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0015] Figure 1 It is a schematic structural diagram of an enterprise private domain data and cloud tenant deep integration system for digital operation provided by an embodiment of the present invention. Detailed Embodiments

[0016] To make the objectives, technical solutions, and advantages of the present invention clearer, the following will clearly and completely describe the technical solutions in the present invention with reference to the drawings in the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.

[0017] Example 1: An embodiment of the present invention provides an enterprise private domain data and cloud tenant deep integration system for digital operation, as Figure 1 shown, including: Acquisition module: Acquire enterprise private domain data to determine the private domain data to be integrated, and acquire cloud tenant data to determine the cloud tenant data to be integrated; Determination module: Determine the private domain field vector based on the private domain data to be integrated, and determine the cloud tenant field vector based on the cloud tenant data to be integrated; Mapping module: Analyze the private domain data to be integrated, the private domain field vector, the cloud tenant data to be integrated, and the cloud tenant field vector, and determine the direct mapping value, logical mapping value, and semantic mapping value of each private domain field in the private domain field vector and each cloud tenant field in the cloud tenant field vector; Association module: Determine the association relationship between the private domain field vector and the cloud tenant field vector based on the direct mapping value, logical mapping value, and semantic mapping value of each private domain field in the private domain field vector and each cloud tenant field in the cloud tenant field vector; Integration module: Deeply integrate the private domain data to be integrated and the cloud tenant data to be integrated based on the association relationship between the private domain field vector and the cloud tenant field vector.

[0018] In this embodiment, it acquires private domain data from the enterprise's own data sources (such as internal databases, business systems, etc.), and determines the private domain data to be integrated after screening and preliminary processing; at the same time, it acquires cloud tenant data from the cloud platform and organizes it to determine the cloud tenant data to be integrated.

[0019] In this embodiment, field extraction is performed on the private domain data to be integrated and the cloud tenant data to be integrated respectively. The extracted private domain fields are combined into a private domain field vector, and the cloud tenant fields are combined into a cloud tenant field vector to prepare for subsequent data mapping and association analysis.

[0020] In this embodiment, by comprehensively analyzing the two types of data to be integrated and their corresponding field vectors, the direct mapping value, logical mapping value, and semantic mapping value between each private domain field in the private domain field vector and each cloud tenant field in the cloud tenant field vector are calculated.

[0021] In this embodiment, based on the various mapping values calculated by the mapping module, the association relationship between the private domain field vector and the cloud tenant field vector is determined. This association relationship describes the correspondence and connection between the two field vectors as a whole, providing a basis for data integration.

[0022] In this embodiment, according to the association relationship determined by the association module, the private domain data to be integrated and the cloud tenant data to be integrated are deeply fused to form a unified and available data resource to support the digital operation decision-making of the enterprise.

[0023] Beneficial effects of the above technical solution: By determining and analyzing the private domain data to be integrated, the private domain field vectors, the cloud tenant data to be integrated, and the cloud tenant field vectors, determining the direct mapping values, logical mapping values, and semantic mapping values of each private domain field in the private domain field vectors and each cloud tenant field in the cloud tenant field vectors, determining the association relationship between the private domain field vectors and the cloud tenant field vectors, and deeply integrating the private domain data to be integrated and the cloud tenant data to be integrated. It can break through the limitations of data integration methods, improve the accuracy of mapping relationships, enhance the deep integration and matching capabilities between different data sources, achieve efficient and accurate deep integration of private domain data and cloud tenant data, improve data quality and decision support capabilities, and provide strong digital operation support for enterprises.

[0024] Embodiment 2: The embodiment of the present invention provides an enterprise private domain data and cloud tenant deep integration system for digital operation. The acquisition module includes: Acquisition unit: Acquire the data integration requirements of the enterprise, the enterprise business scenarios, and the data structure of the enterprise private domain data, and acquire the cloud platform service scenarios and cloud platform data structures storing enterprise tenant information; Private domain data unit to be integrated: Extract enterprise private domain data from the enterprise internal database based on the data integration requirements, enterprise business scenarios, and the data structure of the enterprise private domain data, and preprocess the enterprise private domain data to determine the private domain data to be integrated; Cloud tenant data unit to be integrated: Extract cloud tenant data from the cloud platform based on the data integration requirements, cloud platform service scenarios, and cloud platform data structures, and preprocess the cloud tenant data to determine the cloud tenant data to be integrated.

[0025] In this embodiment, enterprises often perform data integration to support specific business goals, such as precision marketing, supply chain optimization, customer relationship management, etc. For example, if the enterprise's goal is to carry out precision marketing, then the data integration requirements may be to integrate data such as customer basic information, purchase history, browsing behavior, etc.

[0026] In this embodiment, the enterprise internal database is an important storage place for private domain data. Common ones include ERP (Enterprise Resource Planning) system databases, CRM (Customer Relationship Management) system databases, financial system databases, etc. Different databases store different types of business data. For example, the ERP system database contains core business process data such as procurement, production, and sales; the CRM system database stores customer information, sales opportunities, customer service records, etc.

[0027] In this embodiment, the business scenarios of the enterprise determine the direction and focus of data preprocessing. For example, in the e-commerce business scenario, more attention may be paid to customer purchase behavior data, such as purchase frequency, purchase amount, purchase categories, etc.; while in the manufacturing business scenario, more attention may be paid to production data, such as production efficiency, product quality, etc. According to the business scenario, determine the data fields that need to be extracted.

[0028] In this embodiment, analyze the data structure of the enterprise's private domain data: Understand the data structure of the enterprise's private domain data, including field types (such as integers, strings, dates, etc.), field lengths, relationships between fields, etc. For example, in the customer information table, the customer ID field may be of integer type, and the customer name field may be of string type, with certain length limitations.

[0029] In this embodiment, similar to the enterprise's private domain data, the extraction of cloud tenant data also needs to extract cloud tenant data from the cloud platform based on specific data integration requirements, cloud platform service scenarios, and cloud platform data structures. For example, an enterprise may need to integrate user behavior data (such as page view records, click events, etc.) on the cloud platform with the enterprise's private domain data to understand customers more comprehensively.

[0030] In this embodiment, the cloud platform usually provides various data storage methods, such as relational databases (such as AWS RDS, Alibaba Cloud RDS), non-relational databases (such as MongoDB, Redis), data warehouses (such as Snowflake, Google BigQuery), etc. Different storage methods have different data access methods and interfaces.

[0031] In this embodiment, extract cloud tenant data through the APIs (Application Programming Interfaces), SDKs (Software Development Kits), or data synchronization tools provided by the cloud platform. For example, using the AWS SDK can easily extract data from an AWS S3 bucket. When extracting data, attention needs to be paid to permission management to ensure legal access permissions.

[0032] In this embodiment, the service scenarios of the cloud platform are diverse, such as SaaS (Software as a Service), PaaS (Platform as a Service), IaaS (Infrastructure as a Service), etc. The data generated by different service scenarios has different characteristics and uses. For example, in the SaaS service scenario, a large amount of user operation log data may be generated; in the PaaS service scenario, there may be performance monitoring data of application programs. Combine the cloud platform service scenario to extract cloud tenant data.

[0033] In this embodiment, the preprocessing at least includes data cleaning and data standardization.

[0034] Beneficial effects of the above technical solution: Obtaining enterprise private domain data to determine the private domain data to be integrated, and obtaining cloud tenant data to determine the cloud tenant data to be integrated can provide a data basis for determining the private domain field vector and the cloud tenant field vector.

[0035] Embodiment 3: The embodiment of the present invention provides an enterprise private domain data and cloud tenant deep integration system for digital operation. The determination module includes: Private domain field vector unit: Extract the private domain fields in the private domain data to be integrated, and determine the private domain field vector based on all the extracted private domain fields; Cloud tenant field vector unit: Extract the cloud tenant fields in the cloud tenant data to be integrated, and determine the cloud tenant field vector based on all the extracted cloud tenant fields.

[0036] In this embodiment, all private domain fields are extracted from the private domain data to be integrated. Private domain fields refer to various data used to describe customers within an enterprise, such as customer names, contact information, purchase records, etc. After these fields are extracted, the system constructs a vector representation of the private domain fields based on the data type, characteristics, and relationships between each field. This vector transforms the private domain data into a multi-dimensional feature space.

[0037] In this embodiment, all cloud tenant fields are extracted from the cloud tenant data to be integrated. For example, in the user behavior data stored on the cloud platform, fields such as the user's login time, browsed pages, and stay duration may be extracted, and the extracted cloud tenant fields are combined into a vector.

[0038] Beneficial effects of the above technical solution: Determining the private domain field vector based on the private domain data to be integrated, and determining the cloud tenant field vector based on the cloud tenant data to be integrated can provide high-quality data support for determining the direct mapping value, logical mapping value, and semantic mapping value, and improve the data integration ability and analysis accuracy.

[0039] Embodiment 4: The embodiment of the present invention provides an enterprise private domain data and cloud tenant deep integration system for digital operation. The mapping module includes: Extraction unit: Extract all private domain field values of each private domain field in the private domain field vector in the private domain data to be integrated, and extract all cloud tenant field values of each cloud tenant field in the cloud tenant field vector in the cloud tenant data to be integrated; The first judgment unit: determines whether the private domain field value of each private domain field in the private domain field vector is a numerical value. If so, it determines the first value range of the private domain field based on all the private domain field values of the private domain field. Otherwise, it extracts features from the private domain field to determine the first field vector of the private domain field, and extracts features from each private domain field value of the private domain field to determine the first field value vector of each private domain field value; The second judgment unit: determines whether the cloud tenant field value of each cloud tenant field in the cloud tenant field vector is a numerical value. If so, it determines the second value range of the cloud tenant field based on all the cloud tenant field values of the cloud tenant field. Otherwise, it extracts features from the cloud tenant field to determine the second field vector of the cloud tenant field, and extracts features from each cloud tenant field value of the cloud tenant field to determine the second field value vector of each cloud tenant field value; The calculation unit: calculates the direct mapping value, logical mapping value, and semantic mapping value of each private domain field and each cloud tenant field based on each private domain field in the private domain field vector, the data type of the private domain field, the first value range or the first field vector, all the first field value vectors, each cloud tenant field in the cloud tenant field vector, the data type of the cloud tenant field, the second value range or the second field vector, and all the second field value vectors.

[0040] In this embodiment, specific field values are extracted from the data to be integrated. For each private domain field in the private domain field vector, it will find all the values corresponding to this field in the private domain data to be integrated; similarly, for each cloud tenant field in the cloud tenant field vector, all its field values will be extracted from the cloud tenant data to be integrated. For example, if there is a "customer age" field in the private domain field vector, the extraction unit will find all the age values of the customers from the private domain data to be integrated.

[0041] In this embodiment, for each private domain field in the private domain field vector, it is determined whether its field value is a numerical value. If the private domain field value is a numerical value, by analyzing all the numerical values of this private domain field, its value range is determined, that is, the first value range. For example, the field values of the "customer age" field are all numerical values. By statistics, the age range is 18 - 70 years old, which is the first value range of this field; if the private domain field value is not a numerical value, it is necessary to extract features from this private domain field to form the first field vector; at the same time, features are also extracted from each specific value of this field to obtain the first field value vector corresponding to each private domain field value. For example, the "customer occupation" field is non-numerical, and different occupation classifications can be characterized to form the first field vector, and each specific occupation (such as "teacher", "doctor", etc.) forms the first field value vector after feature extraction.

[0042] In this embodiment, for the cloud tenant fields in the cloud tenant field vector, it is determined whether the cloud tenant field value is a numerical value. If it is a numerical value, its second value range is determined; if it is not a numerical value, feature extraction is respectively performed on the cloud tenant field and each of its field values to obtain a second field vector and a second field value vector.

[0043] In this embodiment, by integrating the relevant information of the private domain fields and the cloud tenant fields, including the field names, data types, value ranges (the first value range or the second value range), or field vectors (the first field vector or the second field vector), and field value vectors (the first field value vector or the second field value vector), the direct mapping value, logical mapping value, and semantic mapping value between each private domain field and each cloud tenant field are calculated.

[0044] The beneficial effects of the above technical solution: By analyzing the private domain data to be integrated, the private domain field vector, the cloud tenant data to be integrated, and the cloud tenant field vector, and determining the direct mapping value, logical mapping value, and semantic mapping value between each private domain field in the private domain field vector and each cloud tenant field in the cloud tenant field vector, the mapping relationship between the fields of different data sources can be accurately determined, enhancing the deep integration and matching ability between different data sources, providing a scientific basis for the deep integration of enterprise data, and improving the accuracy and efficiency of data integration.

[0045] Embodiment 5: The embodiment of the present invention provides an enterprise private domain data and cloud tenant deep integration system for digital operation, and a calculation unit, including: Direct mapping value calculation sub-unit: Based on the field names of each private domain field in the private domain field vector and the field names of each cloud tenant field in the cloud tenant field vector, calculate the direct mapping value between each private domain field and each cloud tenant field; ; Among them, represents the direct mapping value between the i-th private domain field in the private domain field vector and the j-th cloud tenant field in the cloud tenant field vector, represents the field name of the i-th private domain field in the private domain field vector, represents the field name of the j-th cloud tenant field in the cloud tenant field vector.

[0046] In this embodiment, the direct mapping value reflects the numerical value of the direct correspondence relationship between the private domain field and the cloud tenant field.

[0047] In this embodiment, represents that the field name of the i-th private domain field in the private domain field vector is the same as the field name of the j-th cloud tenant field in the cloud tenant field vector.

[0048] Beneficial effects of the above technical solution: Based on the field names of each private domain field in the private domain field vector and the field names of each cloud tenant field in the cloud tenant field vector, calculate the direct mapping values of each private domain field and each cloud tenant field, which can accurately determine the direct mapping relationship between different data source fields and provide high-quality data support for determining the association relationship between the private domain field vector and the cloud tenant field vector.

[0049] Embodiment 6: The embodiment of the present invention provides an enterprise private domain data and cloud tenant deep integration system for digital operation. The calculation unit further includes: Logical mapping value calculation sub-unit: Based on the data type, first value range, and first field vector of each private domain field in the private domain field vector, and the data type, second value range, and second field vector of each cloud tenant field in the cloud tenant field vector, calculate the logical mapping values of each private domain field and each cloud tenant field; ; ; ; Wherein, represents the logical mapping value of the i-th private domain field in the private domain field vector and the j-th cloud tenant field in the cloud tenant field vector, represents the logical numerical association value of the i-th private domain field in the private domain field vector and the j-th cloud tenant field in the cloud tenant field vector, represents the data type matching value of the i-th private domain field in the private domain field vector and the j-th cloud tenant field in the cloud tenant field vector, represents the data type of the i-th private domain field in the private domain field vector, represents the data type of the j-th cloud tenant field in the cloud tenant field vector, represents the upper limit in the first value range of the i-th private domain field in the private domain field vector, represents the upper limit in the second value range of the j-th cloud tenant field in the cloud tenant field vector, represents the lower limit in the first value range of the i-th private domain field in the private domain field vector, represents the lower limit in the second value range of the j-th cloud tenant field in the cloud tenant field vector, represents the logical numerical preset threshold, represents the logical non-numerical preset threshold, represents the first field vector of the a-th private domain field value of the i-th private domain field in the private domain field vector, represents the second field vector of the j-th cloud tenant field in the cloud tenant field vector.

[0050] In this embodiment, the logical mapping value reflects the value of the private domain field and the cloud tenant field being associated through logical rules.

[0051] In this embodiment, represents the logical NOT numerical association value between the i-th private domain field in the private domain field vector and the j-th cloud tenant field in the cloud tenant field vector.

[0052] In this embodiment, represents the norm of the first field vector of the a-th private domain field value of the i-th private domain field in the private domain field vector.

[0053] In this embodiment, represents the norm of the second field vector of the j-th cloud tenant field in the cloud tenant field vector.

[0054] Beneficial effects of the above technical solution: Based on the data type, first value range, first field vector of each private domain field in the private domain field vector, and the data type, second value range, second field vector of each cloud tenant field in the cloud tenant field vector, calculate the logical mapping value of each private domain field and each cloud tenant field, which can accurately determine the logical mapping relationship between different data source fields, and further provide high-quality data support for determining the association relationship between the private domain field vector and the cloud tenant field vector.

[0055] Embodiment 7: The embodiment of the present invention provides an enterprise private domain data and cloud tenant deep integration system for digital operation. The calculation unit further includes: The third field value vector and the fourth field value vector sub-unit: Based on all the private domain field values of each private domain field in the private domain field vector and all the cloud tenant field values of each cloud tenant field in the cloud tenant field vector, determine the third field value vector of each private domain field based on each cloud tenant field and the fourth field vector of each cloud tenant field based on each private domain field; When = 1: ; ; Wherein, represents the number of private domain field values of the i-th private domain field in the private domain field vector, represents the number of cloud tenant field values of the j-th cloud tenant field in the cloud tenant field vector, represents the third field value vector of the i-th private domain field in the private domain field vector based on the j-th cloud tenant field in the cloud tenant field vector when = 1, represents When it is = 1, the j-th cloud tenant field in the cloud tenant field vector is the fourth field vector based on the i-th private domain field in the private domain field vector. respectively represent the first private domain field value, the a-th private domain field value, and the N1-th private domain field value of the i-th private domain field in the private domain field vector. respectively represent the th 0 supplemented to the third field value vector when it is ; respectively represent the first cloud tenant field value, the a-th cloud tenant field value, and the N1-th cloud tenant field value of the i-th cloud tenant field in the cloud tenant field vector. respectively represent the th 0 supplemented to the third field value vector when it is ; Semantic mapping value calculation subunit: Calculate the semantic mapping value of each private domain field and each cloud tenant field based on the data type matching value of each private domain field and each cloud tenant field, the first field vector of each private domain field in the private domain field vector, all first field value vectors, the third field vector, and the second field vector of each cloud tenant field in the cloud tenant field vector, all second field value vectors, and the fourth field vector. ; ; ; Among them, represents the semantic mapping value of the i-th private domain field in the private domain field vector and the j-th cloud tenant field in the cloud tenant field vector. represents the semantic numerical association value of the i-th private domain field in the private domain field vector and the j-th cloud tenant field in the cloud tenant field vector. represents the semantic non-numerical association value of the i-th private domain field in the private domain field vector and the j-th cloud tenant field in the cloud tenant field vector. represents the first field value vector of the a-th private domain field value of the i-th private domain field in the private domain field vector. represents the second field value vector of the b-th cloud tenant field value of the j-th cloud tenant field in the cloud tenant field vector. 1 represents the numerical weight of the field value vector. 2 represents the non-numerical weight of the field value vector. represents the field vector weight. represents the semantic numerical preset threshold. represents the semantic non-numerical preset threshold. represents and 's divergence.

[0056] In this embodiment, respectively represent the normalized values of the first private domain field value, the a-th private domain field value, and the N1-th private domain field value of the i-th private domain field in the private domain field vector.

[0057] In this embodiment, respectively represent the normalized values of the first cloud tenant field value, the a-th cloud tenant field value, and the N1-th cloud tenant field value of the i-th cloud tenant field in the cloud tenant field vector.

[0058] In this embodiment, represents When = 1, it is the KL divergence between the third field value vector of the i-th private domain field in the private domain field vector based on the j-th cloud tenant field in the cloud tenant field vector and the fourth field vector of the j-th cloud tenant field in the cloud tenant field vector based on the i-th private domain field in the private domain field vector.

[0059] In this embodiment, represents the norm of the first field value vector of the a-th private domain field value of the i-th private domain field in the private domain field vector.

[0060] In this embodiment, represents the second field value vector of the b-th cloud tenant field value of the j-th cloud tenant field in the cloud tenant field vector.

[0061] Beneficial effects of the above technical solution: By calculating the semantic mapping values of each private domain field and each cloud tenant field, the semantic mapping relationship between different data source fields can be accurately determined, and further provide high-quality data support for determining the association relationship between the private domain field vector and the cloud tenant field vector.

[0062] Embodiment 8: The embodiment of the present invention provides an enterprise private domain data and cloud tenant deep integration system for digital operation, and an association module, including: Mapping relationship unit: Determine that the mapping priority order is direct mapping > logical mapping > semantic mapping. If there are multiple direct mapping values, logical mapping values, and semantic mapping values of each private domain field and each cloud tenant field that are 1, determine the mapping relationship between each private domain field in the private domain field vector and each cloud tenant field in the cloud tenant field vector based on the mapping priority order; Based on the mapping relationship between all private domain fields in the private domain field vector and all cloud tenant fields in the cloud tenant field vector, determine the association relationship between the private domain field vector and the cloud tenant field vector.

[0063] In this embodiment, the priority order of different mapping types is clarified, that is, direct mapping > logical mapping > semantic mapping. This means that when determining the mapping relationship between private domain fields and cloud tenant fields, direct mapping has the highest priority, followed by logical mapping, and finally semantic mapping.

[0064] In this embodiment, it is possible that there are multiple mapping values of 1 in the direct mapping values, logical mapping values, and semantic mapping values of each private domain field and each cloud tenant field. A mapping value of 1 here indicates that the mapping relationship is established. When this situation occurs, the system will determine the final mapping relationship according to the previously set mapping priority order. For example, if the direct mapping value and the semantic mapping value of a certain private domain field and a certain cloud tenant field are both 1, since the direct mapping has a higher priority, the mapping relationship between them will be determined as a direct mapping.

[0065] In this embodiment, after determining the mapping relationship between each private domain field in the private domain field vector and each cloud tenant field in the cloud tenant field vector, the mapping relationship unit will further comprehensively consider the mapping situations of all private domain fields and cloud tenant fields to determine the association relationship between the private domain field vector and the cloud tenant field vector.

[0066] Beneficial effects of the above technical solution: By determining the association relationship between the private domain field vector and the cloud tenant field vector based on the direct mapping values, logical mapping values, and semantic mapping values of each private domain field in the private domain field vector and each cloud tenant field in the cloud tenant field vector, the accuracy of the mapping relationship can be improved, providing a clear association basis for the integration of enterprise private domain and cloud tenant data, and helping to complete data deep integration more efficiently and accurately.

[0067] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art can understand and implement it without creative labor.

[0068] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the essence of the above technical solution, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute the methods described in each embodiment or some parts of the embodiments.

[0069] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements 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 the present invention.

Claims

1. An enterprise private domain data and cloud tenant deep integration system for digital operation, characterized in that Including: Acquisition module: Acquire enterprise private domain data to determine the private domain data to be integrated, and acquire cloud tenant data to determine the cloud tenant data to be integrated; Determination module: Determine the private domain field vector based on the private domain data to be integrated, and determine the cloud tenant field vector based on the cloud tenant data to be integrated; Mapping module: Analyze the private domain data to be integrated, the private domain field vector, the cloud tenant data to be integrated, and the cloud tenant field vector to determine the direct mapping value, logical mapping value, and semantic mapping value of each private domain field in the private domain field vector and each cloud tenant field in the cloud tenant field vector; Association module: Determine the association relationship between the private domain field vector and the cloud tenant field vector based on the direct mapping value, logical mapping value, and semantic mapping value of each private domain field in the private domain field vector and each cloud tenant field in the cloud tenant field vector; Integration module: Deeply integrate the private domain data to be integrated and the cloud tenant data to be integrated based on the association relationship between the private domain field vector and the cloud tenant field vector.

2. The deep integration system of enterprise private domain data and cloud tenants for digital operation according to claim 1, characterized in that, The acquisition module includes: Acquisition unit: Acquire the data integration requirements of the enterprise, the enterprise business scenario, and the data structure of the enterprise private domain data, and acquire the cloud platform service scenario and cloud platform data structure storing the enterprise tenant information; Private domain data unit to be integrated: Extract the enterprise private domain data from the enterprise internal database based on the data integration requirements, the enterprise business scenario, and the data structure of the enterprise private domain data, and preprocess the enterprise private domain data to determine the private domain data to be integrated; Cloud tenant data unit to be integrated: Extract the cloud tenant data from the cloud platform based on the data integration requirements, the cloud platform service scenario, and the cloud platform data structure, and preprocess the cloud tenant data to determine the cloud tenant data to be integrated.

3. The enterprise private domain data and cloud tenant deep integration system for digital operation according to claim 1, characterized in that, The determination module includes: Private domain field vector unit: Extract the private domain fields in the private domain data to be integrated, and determine the private domain field vector based on all the extracted private domain fields; Cloud tenant field vector unit: Extract the cloud tenant fields in the cloud tenant data to be integrated, and determine the cloud tenant field vector based on all the extracted cloud tenant fields.

4. An enterprise private domain data and cloud tenant deep integration system for digital operation according to claim 1, characterized in that, The mapping module includes: Extraction unit: Extract all the private domain field values of each private domain field in the private domain field vector in the private domain data to be integrated, and extract all the cloud tenant field values of each cloud tenant field in the cloud tenant field vector in the cloud tenant data to be integrated; First judgment unit: Judge whether the private domain field value of each private domain field in the private domain field vector is a numerical value. If so, determine the first value range of the private domain field based on all the private domain field values of the private domain field. Otherwise, extract the features of the private domain field to determine the first field vector of the private domain field, and extract the features of each private domain field value of the private domain field to determine the first field value vector of each private domain field value; Second judgment unit: Judge whether the cloud tenant field value of each cloud tenant field in the cloud tenant field vector is a numerical value. If so, determine the second value range of the cloud tenant field based on all the cloud tenant field values of the cloud tenant field. Otherwise, extract the features of the cloud tenant field to determine the second field vector of the cloud tenant field, and extract the features of each cloud tenant field value of the cloud tenant field to determine the second field value vector of each cloud tenant field value; Computing unit: Calculate the direct mapping value, logical mapping value, and semantic mapping value of each private domain field and each cloud tenant field based on each private domain field in the private domain field vector, the data type of the private domain field, the first value range or the first field vector, all the first field value vectors, and each cloud tenant field in the cloud tenant field vector, the data type of the cloud tenant field, the second value range or the second field vector, and all the second field value vectors.

5. The deep integration system of enterprise private domain data and cloud tenants for digital operation according to claim 4, characterized in that The computing unit includes: Direct mapping value calculation sub-unit: Calculate the direct mapping value of each private domain field and each cloud tenant field based on the field names of each private domain field in the private domain field vector and the field names of each cloud tenant field in the cloud tenant field vector; ； Among them, represents the direct mapping value of the i-th private domain field in the private domain field vector and the j-th cloud tenant field in the cloud tenant field vector, represents the field name of the i-th private domain field in the private domain field vector, represents the field name of the j-th cloud tenant field in the cloud tenant field vector.

6. The deep integration system of enterprise private domain data and cloud tenants for digital operation according to claim 4, characterized in that The computing unit further includes: Logical mapping value calculation sub-unit: Calculate the logical mapping value of each private domain field and each cloud tenant field based on the data type of each private domain field in the private domain field vector, the first value range, the first field vector, and the data type of each cloud tenant field in the cloud tenant field vector, the second value range, and the second field vector; ； ； ； Among them, represents the logical mapping value between the i-th private domain field in the private domain field vector and the j-th cloud tenant field in the cloud tenant field vector, represents the logical numerical association value between the i-th private domain field in the private domain field vector and the j-th cloud tenant field in the cloud tenant field vector, represents the data type matching value between the i-th private domain field in the private domain field vector and the j-th cloud tenant field in the cloud tenant field vector, represents the data type of the i-th private domain field in the private domain field vector, represents the data type of the j-th cloud tenant field in the cloud tenant field vector, represents the upper limit in the first value range of the i-th private domain field in the private domain field vector, represents the upper limit in the second value range of the j-th cloud tenant field in the cloud tenant field vector, represents the lower limit in the first value range of the i-th private domain field in the private domain field vector, represents the lower limit in the second value range of the j-th cloud tenant field in the cloud tenant field vector, represents the preset threshold for logical numerical values, represents the preset threshold for logical non-numerical values, represents the first field vector of the a-th private domain field value of the i-th private domain field in the private domain field vector, represents the second field vector of the j-th cloud tenant field in the cloud tenant field vector.

7. An enterprise private domain data and cloud tenant deep integration system for digital operation according to claim 4, characterized in that The computing unit further includes: Third field value vector and fourth field value vector unit: Determine the third field value vector of each private domain field based on each cloud tenant field and the fourth field vector of each cloud tenant field based on each private domain field based on all the private domain field values of each private domain field in the private domain field vector and all the cloud tenant field values of each cloud tenant field in the cloud tenant field vector; When = 1: ； ； Among them, represents the number of private field values of the i-th private field in the private field vector, represents the number of cloud tenant field values of the j-th cloud tenant field in the cloud tenant field vector, represents When = 1, the third field value vector of the i-th private field in the private field vector based on the j-th cloud tenant field in the cloud tenant field vector, represents When = 1, the fourth field vector of the j-th cloud tenant field in the cloud tenant field vector based on the i-th private field in the private field vector, respectively represent the 1st private field value, the a-th private field value, and the N1-th private field value of the i-th private field in the private field vector, respectively represent When, the th 0 supplemented in the third field value vector, the th 0, respectively represent the 1st cloud tenant field value, the a-th cloud tenant field value, and the N1-th cloud tenant field value of the j-th cloud tenant field in the cloud tenant field vector, respectively represent When, the th 0 supplemented in the third field value vector, the th 0; Semantic mapping value calculation sub-unit: Calculate the semantic mapping value of each private domain field and each cloud tenant field based on the data type matching value of each private domain field and each cloud tenant field, the first field vector of each private domain field in the private domain field vector, all the first field value vectors, the third field vector, and the second field vector of each cloud tenant field in the cloud tenant field vector, all the second field value vectors, and the fourth field vector; ； ； ； Among them, represents the semantic mapping value of the $i$-th private domain field in the private domain field vector and the $j$-th cloud tenant field in the cloud tenant field vector, represents the semantic numerical association value of the $i$-th private domain field in the private domain field vector and the $j$-th cloud tenant field in the cloud tenant field vector, represents the semantic non-numerical association value of the $i$-th private domain field in the private domain field vector and the $j$-th cloud tenant field in the cloud tenant field vector, represents the first field value vector of the $a$-th private domain field value of the $i$-th private domain field in the private domain field vector, represents the second field value vector of the $b$-th cloud tenant field value of the $j$-th cloud tenant field in the cloud tenant field vector, 1 represents the numerical weight of the field value vector, 2 represents the non-numerical weight of the field value vector, represents the field vector weight, represents the preset threshold for semantic numerical values, represents the preset threshold for semantic non-numerical values, represents and the divergence of.

8. An enterprise private domain data and cloud tenant deep integration system for digital operation according to claim 1, characterized in that The association module includes: Mapping relationship unit: Determine that the mapping priority order is direct mapping > logical mapping > semantic mapping. If there are multiple 1s in the direct mapping value, logical mapping value, and semantic mapping value of each private domain field and each cloud tenant field, determine the mapping relationship between each private domain field in the private domain field vector and each cloud tenant field in the cloud tenant field vector based on the mapping priority order; Determine the association relationship between the private domain field vector and the cloud tenant field vector based on the mapping relationship between all the private domain fields in the private domain field vector and all the cloud tenant fields in the cloud tenant field vector.

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